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  • Published: 05 March 2021

Phenomes: the current frontier in animal breeding

  • Miguel Pérez-Enciso   ORCID: orcid.org/0000-0003-3524-995X 1 , 2 &
  • Juan P. Steibel 3 , 4  

Genetics Selection Evolution volume  53 , Article number:  22 ( 2021 ) Cite this article

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Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.

For the last two decades, genotyping and sequencing technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. Today, genomics is a mature technology but unfortunately the momentum may be fading away. Increasing marker density follows the law of diminishing returns, unless the causative mutations are identified. Simulation and empirical results have shown that only marginal gains in genetic prediction accuracy can be expected by genome sequencing data instead of high-density genotyping data [ 1 , 2 ]. This small advantage will probably vanish when the extra cost of computer storage and power that sequence analyses require compared to genotyping arrays are accounted for.

Looking forward, we argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’, as introduced in the premonitory words of Mike Coffey at the 2011 ICAR meeting, “In the age of the genotype, phenotype is king”. Phenomics, which is defined as ‘the acquisition of high-dimensional phenotypic data on an organism-wide scale’ [ 3 ], has flourished thanks to the development of all kinds of electronic devices and internet availability. Currently, sensors are able to inexpensively record images, videos, sounds, or a multitude of environmental parameters, making large-scale, continuous phenotyping possible. Improvements in this vast area occur at breath-taking pace.

However, there are some caveats in the ‘phenome’ concept [ 4 ]. While the genome, i.e., the DNA sequence, is finite and can in principle be fully characterized, the phenome is not a closed, fully-defined entity, and it will never be. An infinite number of phenotypes can be imagined: simply consider all the mathematical combinations of measured traits that can be defined. Furthermore, phenotypic measurements may involve several individuals simultaneously, as in many welfare and behavioral traits. Therefore, a phenome will always be a subset of an infinite number of possible measurements that may span several individuals. The difference with a ‘standard’ breeding setting is that, in the new paradigm, both the genome and the phenome are high-dimensional variables. Furthermore, whereas genome data are relatively homogeneous, phenome measurements can be highly heterogeneous and time-dependent. An example is the composition of microbiota, which change from birth to adult stages and may vary with health status. In this sense, it is important to realize that phenomics ‘big data’ problems arise because of the heterogeneity and rapid change over time of the data, not because of their size.

Compared to plant breeding or human genetics [ 3 ], animal phenomics has received somewhat less attention. This is surprising since new technologies allow the assessment of new phenotypes that are in high demand by the society, such as those related to animal welfare, resilience, disease incidence or resource use efficiency [ 5 , 6 , 7 , 8 ]. Fortunately, recent work indicates that animal phenomics is becoming popular in the animal sciences too, e.g., [ 9 , 10 , 11 , 12 ], and as reflected in public initiatives such as the USDA Agricultural Genomes to Phenomes Initiative AG2PI ( https://www.ag2pi.org/ ).

The novelty around high-throughput phenotyping in farm animal populations comes from two angles: (1) novel traits can be defined and measured that could not be recorded before, and (2) classical traits can now be observed on an almost continuous basis and in a non-invasive way on large numbers of animals under normal production environments. However, we can expect the datasets to be partially incomplete, noisy and partially redundant, especially when traits are recorded on a continuous basis.

With this opinion paper, our aim is to foster discussion and further research in the area. Towards this, we briefly recall some of the most relevant traits that can be captured by modern technologies, and then discuss the future needs in terms of methods and algorithms and the possible long-term impact of phenomics in breeding. The focus of this note is on the use of sensors to collect measures on animals themselves, but note that sensors can and are used to record all sorts of environmental variables (climate, pathogen exposure, etc.), which are also highly relevant for animal management and breeding.

Standard and new traits (re)visited

Behavior at the individual and group levels.

Behavior and social interactions between animals can greatly affect production and performance phenotypes and are a major component of animal welfare. Given the difficulty of measuring behavior before the ‘phenomics era’, in genetic evaluations the effect of behavior on production traits has been either ignored or accounted for indirectly, e.g., by using social genetic effects models [ 13 ]. However, selection to modify behavior is possible since some behaviors, especially those related to aggression, are partially heritable [ 14 ]. Understanding and modifying the genetic factors that induce fighting between pairs of animals is valuable not only for selection, but also for management purposes, as groups could be formed based on the genetic makeup of animals that are expected to fight less with each other. As a result, welfare and productivity should increase.

Today, behavioral traits can be measured with wearable sensors and computer vision techniques [ 15 , 16 , 17 ]. Behavior metrics can affect single individuals but often involve pairs or larger groups of animals. If behaviors are measured at the individual level, multi-trait direct genetic effects models can be used to obtain breeding values for the behaviors of interest together with production and welfare traits [ 18 ]. A less explored option is the modeling of behavior at the dyadic level (i.e., in individual pairs). This type of data has been used to parameterize social genetic effects models [ 19 ], but it could also be analyzed as a response matrix in a quantitative genetic study to determine, e.g., which genetic factors affect a group mate to be attacked. This type of analysis has not been performed yet, but dyadic behavioral data are being collected in livestock species and have been used to build social networks [ 20 ]. A typical interaction behavior is aggression (e.g., post-mixing attacks), where we distinguish between an individual measure and a group or dyadic measures. For instance, the total amount of time that each animal spends engaged in fighting represents an individual-level observation. But when the amount of time spent fighting is annotated for each pair of animals in a social group, a dyadic phenotypic dataset is generated. This type of datasets allows the development of specific, new modeling strategies (Fig.  1 ).

figure 1

Emerging behavioral data in the phenomics era and the need for new models. a Behavioral phenotyping for social interactions results in a matrix of dyadic interactions, Zs, that can be collapsed to individual behavioral data (w and y). b Existing genomics and phenomics data can be integrated with behavioral phenotypes. c Classic genomic evaluation models focus on multi-trait analyses of individual behaviors or on social genetic effects models where the interaction matrix is used as a predictor of existing phenotypes. d In novel models, multi-trait analyses have to include full behavioral matrices to be able to predict the dyadic interactions from rker data

Gas emissions

Genetic selection for reduced greenhouse gas emissions is possible, given that the trait is heritable and genetically correlated with other traits such as milk production and residual feed intake [ 21 ]. Traditional measuring methods that involve respiration chambers and analysis of tracer elements are not readily scalable for application in high-throughput phenotyping. However, recently developed spectroscopy technology for “breath analysis” can be applied on a large scale to measure methane emissions. This technology can be incorporated into on-farm devices such as feeding kiosks or milking robots and used to measure instantaneous methane emissions on thousands of animals as they voluntarily approach measuring stations several times per day [ 22 ].

Like other phenotyping data described in this paper, incorporating high-throughput measures of emissions into genetic evaluations will require the integration of heterogeneous data sources. In this case, the heterogeneity will arise not only from a variety of measuring devices, but also from the different sampling schemes and measures. Highly processed and distilled data such as estimated total emissions per day will be available for some individuals together with raw instantaneous measurements of emission for other animals.

Feed efficiency

Feed efficiency largely determines farm exploitation costs and has been measured on farms on a limited scale for many years now. As with behavior, measuring feed efficiency accurately and massively has only been possible due to sensor technology, via automatic feed recording devices [ 23 ]. An inherent characteristic of feed intake and feeding behavior data obtained with automatic feeders is its incompleteness and its heterogeneity [ 24 ]. Because different devices are used to record and pre-process feed intake data, the measured traits may be slightly different. Sometimes, due to malfunction, there may be partially missing data, for instance, meal is recorded but no animal identity (ID) is attached to it or an animal’s visit is recorded but the feed intake is clearly wrongly measured or not measured at all. These peculiarities of the data recording process need to be accounted for in the analysis pipelines, including raw data processing, cleaning, and imputation.

On the data modeling side, there are opportunities to extract extra information and novel traits from automatic feeding station data. The sequences of the visits to the feeders, the time between visits and the co-occurrence of animals in multi-space feeders can be mined to uncover interactions between animals. Also, meal characteristics extracted from automatic feeders have been used to parameterize social genetic effects models [ 25 ]. Combining automatic feeding data with computer vision algorithms will likely shed further light on these problems.

Traditional phenotypes revisited

Sensor technology allows the measurement of new phenotypes but is also disrupting how ‘standard’ traits, such as weight or conformation, are recorded. In the case of weight and condition score, traditional methods typically require moving the animals and are labor intensive; for that reason, only a limited number of measures can be taken in each production cycle. With the advent of phenomics, these traditional phenotypes can be collected automatically on a continuous basis on a massive number of animals, without the need to disturb them. For instance, weight can be accurately measured using 3D imaging [ 17 ], or conformation features can be measured from images [ 26 ]. These precision livestock farming devices record under normal production conditions of market animals, and not only on elite animals in nucleus farms. Together with these measures, environmental records of similar temporal and spatial resolution can be obtained from weather stations or from barn environment controlling devices.

Nevertheless, the incorporation of automatically-measured body condition score or weight gain into existing genomic evaluations imply unique challenges. Data streams from continuously-measured live-weight on millions of animals will have to be summarized and cleaned before feeding them into existing genetic evaluations. Cleaning data by detecting outliers before fitting growth curves to individual records may not be efficient in the phenomics era; instead, robust data analysis techniques such as non-linear quantile regression can be used for growth curves using all available data points but avoiding the effect of outlying observations. Finally, having full growth curves from each animal will allow the evaluation of new traits around these traditional phenotypes.

Traceability and identification

Environmental data collected by sensors allow the study of genotype-by-environment interactions at a higher resolution scale than has been so far possible and the incorporation of production farm records into the evaluation of elite animals. However, for such uses, the data need to be linked in some way: phenotypic records of production animals need to be linked to parental genotypes and to environmental and production records. In symmetry, continuous individual identification throughout the production cycle will open the opportunity to record many new traits. Thus, the needs for animal identification and tracking as well as the synchronization of real-time data streams will increase.

Currently, individual identification is attained through computer vision of marked or unmarked animals or using wearable radio frequency identification (RFID) devices that remain with each animal for the duration of their productive life. Computer vision algorithms for animal identification use different techniques, such as natural variation in appearance (conformation, coat color, etc.) of the whole animal, visual marking or tags that are permanently or temporarily attached to the animals [ 27 , 28 , 29 ]. Ideally, a computer vision algorithm will work using images of panoramic views of the animal space (pen or barn), which are taken with ceiling or high-wall mounted cameras that include images of several animals in the same picture frame.

Automatic, reliable animal identification is not a solved issue. Among the challenges that need to be addressed are: (1) improving current computer vision algorithms for animal ID and tracking using top-view cameras in normal production conditions, (2) the integration and synchronization of multiple data streams (e.g., RFID detection logs with video streams from more than one video camera), and (3) the ascertainment of animal identification or accounting for uncertain ID. For instance, what is to be done if the animal identification algorithm produces two likely ID with a roughly similar probability for a single animal image? Shall the ID be treated as missing data? Or should the uncertainty be propagated into the animal genetic evaluation by using methods similar to those proposed for dealing with uncertain paternity [ 30 , 31 , 32 ]? These questions will be revisited when these kinds of data streams become common.

High dimensionality

At the end of the day, phenomics technologies deliver highly-dimensional data that need to be processed and incorporated into breeding and management decisions. Two main, related statistical issues are relevant in this context: dimension reduction and penalization. Dimension reduction consists of obtaining new ‘synthetic’ variables that are combinations of the original dimensions. A usual justification of dimension reduction is that only a few dimensions are actually relevant, and that dimensionality is artificially high. Penalization refers to setting constraints of the parameter solutions in the predictive model.

In practice, dimension reduction techniques are mainly used for visualization. New variables in the reduced dimensional space are derived to retain the original data pattern as faithfully as possible. In principal component analysis (PCA), the new variables are the linear combinations of the original phenotypes that explain the maximum possible variance, with the additional constraint of being orthogonal between them. PCA, together with multidimensional scaling (MDS), are perhaps the most widely used dimension reduction tools, but interesting and less well-known options exist and can be good alternatives. Some of these provide nonlinear approximations, in contrast to linear PCA. An ‘autoencoder’ (AE) is one of such nonlinear alternatives [ 33 ]. Autoencoders are ‘deep learning’ (DL) algorithms, i.e., they are based on several stacked layers of neurons (Fig.  2 ). However, in contrast to a typical DL network, where output and input are different, both input and output are the same in AE. Thus, they are unsupervised techniques, as they are mainly dimensionality reduction methods. If no restriction is set, the optimum AE solution is the identity vector and reconstructed output is identical to input. It is then necessary to set some constraints, i.e., penalizations, to optimize the AE network. In the specific case of one single layer and a linear activation function, it has been shown that AE and PCA yield basically the same solution [ 34 ].

figure 2

Representation of an Autoencoder. Autoencoders (AE) are deep neural networks where the input and output are the same (in this case, multi-channel pixel intensity values from images of livestock). They consist of an encoder that codes the input in a low dimensional latent space and a decoder that transforms back the input into a regularized version. Variational autoencoders (VAE) generate a probability function instead of a point latent space. Then, random numbers are drawn and transformed into simulated images by the decoder. Applications of AE and VAE to phenomics remain to be explored, but they can be used for unsupervised learning and imputation. The figure of the cow is from www.dreamstime.com

Another interesting dimension reduction algorithm is t-distributed stochastic neighbor embedding (t-SNE, van der Maaten and Hinton [ 35 ]). The goal of t-SNE is to find a low dimensional representation whereby similar data-points in the original space are shown together and distant samples are shown far apart. The most important difference between PCA and t-SNE is that the former is a projection onto a lower dimensional space, whereas the latter is a representation strategy. Besides, by construction, PCA is aimed at maximizing distances when samples are plotted in the low dimensional space. MDS is a generalized approach for PCA and, similar to t-SNE, it is designed for preserving distances between samples. However, t-SNE offers a series of advantages over MDS; in particular, it reduces the tendency of samples to cluster together, which is caused by a large number of dimensions and results in increased resolution. It seems that neither t-SNE nor autoencoders are popular in animal phenomics, but they are techniques worth exploring since they offer complementary information to standard linear methods. Figure  3 illustrates how different algorithms can provide dramatically different representations in the low dimensional space.

figure 3

Comparison between PCA and t-SNE dimensional reduction methods using the 3D ‘S’ shape in left panel. Note that PCA is a projection that aims at maintaining the maximum variance, whereas t-SNE keeps local similarity in the low dimensional space. For instance, PCA projection clearly maintains the original ‘S’ shape, where the third dimension is lost. In contrast, the plot produced by t-SNE is better at representing local relative distances, where the first dimension reproduces the contour of the shape and the second dimension, the relative position within that part of the contour. As a result of different targets, very different pictures emerge. Implications for phenomics are to be explored. Plot using scikit [ 53 ], slightly modified from J. Vanderplas code ( https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html )

Penalization refers to setting constraints on variables to avoid collinearity and overfitting problems when the number of variables is large. As well known, penalization is needed to avoid the method ‘learning’ irreproducible noise, i.e., one of the ‘curses of dimensionality’ [ 36 ]. Two main types of regularization have been proposed: L1 and L2. L1 consists in setting a constraint on the sum of absolute values of the solutions, whereas L2 refers to the sum of squared solutions [ 37 ]. Concepts such as prior information in a Bayesian framework are equivalent to penalization. Although numerous methods with different names have been proposed in the Bayesian literature, most of them can be unified by realizing that they simply differ in the prior chosen [ 38 ]. In addition, deep learning technologies have developed specific additional regularization strategies. One of them is called ‘dropout’, which consists of randomly removing ‘neurons’ from the inner layers to force the system to use fewer parameters. In spite of its distinct definition, dropout can be interpreted from a Bayesian point of view, and thus can be viewed within the usual penalization framework [ 39 ]. Another approach used in some DL models is direct L1 or L2 penalization on the neuron weights, i.e., a constraint is added on the sum of the absolute or square value of the weights.

Complexity, heterogeneity, and especially data size will notably increase in the phenomics era, which will have repercussions in the modeling approaches. It is sometimes erroneously believed that the influence of the prior vanishes with large datasets, but this is not the case since the prior will always have an effect on the solution, regardless of the amount of data [ 38 ]. Thus, it is worth studying the impact of alternative regularization strategies, as we cannot expect that a single strategy will be optimum—from the predictive performance point of view—in all cases.

As just mentioned, the term ‘curse of dimensionality’ has become popular in statistics and by extension in breeding to mean that increasing ‘unnecessarily’ the complexity of a model leads to poor predictive performance [ 37 ]. The term ‘unnecessarily’ can be read as ‘without penalization’. According to Donoho [ 36 ], the term was initially coined to reflect the impossibility of enumerating all possible models as the number of variables grows. However, high dimensionality is a ‘blessing’ for many purposes, an aspect that is less widely acknowledged in our field. One reason is that having many highly correlated variables helps to smooth out noise. A further, more relevant advantage is that increasing the number of variables usually results in penalized models with improved predictive performance [ 40 ]. Finally, increasing dimensionality allows a gain in biological insight.

The way ahead

Strangely, one of the first obstacles that will need to be solved for routine phenome collection is access to broadband internet. Even in the USA, as much as 40% rural farms lack reliable access to broadband [ 9 ]. Aside from infrastructure issues, here we wish to focus on methodological issues.

Quality control and visualization techniques should be a first step in a phenomics pipeline. As phenotyping becomes a large-scale objective, reliability can be compromised and heterogeneity in environmental conditions may increase. However, bias can be a much more serious danger than error because phenome data will not be collected randomly. Likely, special attention will be paid to elite farms or breeding nucleus, and certain phenotypes will be collected preferentially on specific farms. We can also expect differences in accuracy for data from elite farms compared to data from commercial farms, which will have to be accounted for through proper modeling.

As a result, phenome data will also be highly unbalanced: the kind and amount of data available will vary largely across individuals, even if sensors are widely spread and collect information routinely. It is unlikely that identical phenotypes are recorded on different farms or in different periods, whether milk recording or behavior measurements. This can be an important obstacle, since it will require either removal of samples and/or imputation of missing values. We need to develop efficient and accurate imputation tools or use methods dealing with missing data directly. In this aspect, plant phenomics measurements can often be more systematically compared and measured on a larger scale than in livestock.

Given that missing data is unavoidable, imputation will be needed. This is a wide area and numerous approaches exist depending on the specific problem, e.g., [ 41 , 42 ]. However, it should be noted that most imputation techniques assume that missing data is at random, a condition that phenome data will unlikely fulfill, as discussed above. A further issue with phenomics data is their heterogeneity and thus no general imputation rule can be given. In addition to standard imputation techniques [ 42 ], alternative approaches exist based on deep learning that, to our knowledge, have not been used in this area and can be promising. For instance, autoencoders can be used to fill ‘holes’ in data, in particular those with a spatial pattern such as image and video. The standard autoencoders output is a regularized representation of the original input. This is accomplished via an ‘encoder’ that transforms the data into a ‘latent space’ and a ‘decoder’ that takes the latent space coordinates and outputs a regularized image. Instead of recoding the input into latent space coordinates, variational autoencoders (VAE) generate a probabilistic function to describe an observation in the latent space. As a result, realistic data-points can be generated. For instance, VAE have been used to increase the resolution of pictures or to restore damaged pictures, a problem that is conceptually identical to imputation. In the same vein, generational adversarial networks (GAN) are DL algorithms that can reproduce high-dimensional variables. So far, GAN have been mostly used to generate images, e.g., to generate very high-resolution pictures out of incomplete or low-resolution images, or even videos [ 43 , 44 ]. Figure  4 represents a scheme of a GAN for ‘inpainting’, i.e., completing missing parts in an image. Compared to VAE, GAN are much more flexible, yet they are more difficult and slower to train. They also require larger amounts of data. Applying GAN and VAE concepts to imputation in phenomics is a promising area of research, given their flexibility and lack of distributional assumptions. However, caution applies, since these methods have been tested mainly with image data, and performance in other scenarios has not been much explored.

figure 4

Representation of a generative adversarial network (GAN). This GAN aims at filling holes in an image (‘inpainting’, i.e., imputation). The generator network simulates a new image out of the input picture of the cow, which does not have front legs. The discriminator is trained with images that are true or fake and learns how a real cow looks like. The generator discriminates whether the generated image is true or fake, the result is passed on to the generator so that it can improve the quality of the output image. Each of the rectangles in the generator and the discriminator represent a group of neuron layers as in Fig.  3 , the shape is approximately proportional to its dimension. The figure of the cow is from www.dreamstime.com

However, even with complete data of reasonable size, there are important gaps in the current methodology and software that should be filled. There is an enormous range of analytical tools to be developed. Automatic individual identification allowing for free range, or at least movement in group-housed animals under confinement is needed. This can be accomplished, e.g., using continuous video recording and tracking. Algorithms that automatically extract phenotypes from image, video, sound records are a very active area of research. Standard metrics (e.g., Hausdorff, Euclidean, etc.) to measure similarities between images should be adapted to livestock, and new metrics for videos should be implemented as they are required to compare animal behaviors. Automatic conformation measures [ 26 ] must be improved to be performed in vivo with minimal human intervention. A further challenge is to incorporate all this information into genome predictions.

Not all species and breeding programs will benefit equally from phenomics. For instance, the aquaculture industry is highly advanced technologically and many measurements are difficult to obtain manually. Here, the use of phenomic technologies is far more widespread than in other species. Small ruminants in extensive farming may represent the opposite extreme. Yet, sensor technology can provide accurate animal tracking and remote measurement of physiological and environmental variables outdoors, which could boost productivity and health precisely in traditional, low-input agriculture conditions. In general, those production systems where precision agriculture and precision livestock management are being implemented will be better equipped to collect relevant phenomics data, and the limitation in those cases will be on data storage and transmission as well as data labeling and individual identification.

Although model interpretability is an issue [ 45 , 46 ], experience shows that opening the ‘black box’ is not needed for accurate prediction [ 40 ]. Nevertheless, phenomics data will definitely enlighten the biological basis of phenotypes and will complement genotype data. As geneticists, we are often in the quest of DNA causative polymorphisms. However, focusing on this search may often impede the detection of non-genetic factors that have a stronger effect on the phenotypic expression than the causative mutations. This has been observed, e.g., in expression quantitative trait loci (QTL) studies [ 47 ]. We contend that large-scale phenotyping is important per se, independently of whether genome data are available or not. In this context, structural equation modeling [ 48 ] but also unsupervised learning gains relevance. Influential DL pioneers, such as Yann LeCun, have actually argued that the future of artificial intelligence lies in unsupervised learning ( https://www.youtube.com/watch?v=Ount2Y4qxQo&t=1072s , NIPS conference, 2016). This is because unlabeled data (unsuited for supervised learning) are far more abundant than labeled data and, more importantly, because unsupervised learning resembles more precisely how the human brain actually works. As phenome data are collected over the years in the same or similar breeding schemes, by using unsupervised learning methods, we will gain invaluable knowledge on the effects of selection on the whole organism. For instance, unsupervised learning may uncover unsuspected relationships between traits or between traits and environmental variables. We could, e.g., discover how some selection barriers can be overcome or how to optimize economic weights dynamically.

Phenomics is a hot, promising area but is not exempt from risks and cannot be considered a panacea. As Cole et al. [ 12 ] warn us, ‘these new approaches have their own challenges, ranging from bias to interpretability and there is a temptation to oversell outcomes’. One serious issue is that, in contrast to genotypes, phenome data may not be easily transferable or comparable across farms. Often, measurement technology is proprietary and several systems, e.g., to measure methane emissions, coexist. Algorithms to transform raw sensor data into meaningful measurements and sensors themselves rapidly change over time, making it difficult to analyze data longitudinally. Standards and open-source algorithms in the sensor industry are necessary to fully unravel the potential of phenomics. A way to foster the development of novel algorithms in this field is the distribution of relevant datasets to the research community and the organization of activities (hackathons, competitions and journal special issues) around the analysis of such datasets. This is being done successfully in the fields of autonomous vehicles, computer vision [ 49 ] and in many other areas (e.g., https://www.kaggle.com/competitions ).

Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for highly dimensional phenomics. To begin with, do we need new definitions of breeding values? Genotype × environment (G × E) interactions can be an inspiring concept here. In essence, including G × E interactions in the model is equivalent to providing a function for the breeding value instead of a single value, a reaction norm. In the phenomics era, traditional point breeding values might be replaced by high-dimensional generative functions. It is not clear at this point how this will be accomplished. A natural approach is to use phenomics data to integrate mechanistic biological models into genetic evaluation. Examples are growth crop models in plants such as those developed by Totir et al. [ 50 , 51 ]. More generally, we hypothesize that phenomics-based genomic evaluations will likely be a combination of standard statistics methods with generative machine-learning and simulation tools. In a recent work, de los Campos et al. [ 52 ] applied large-scale simulation conditioned on genotype and environmental variables to predict future performance but, instead of a point prediction, a whole distribution over uncertain, future climate conditions was generated. We can imagine that future phenomics-assisted breeding schemes will be able to simulate expected complex phenotypes under a range of potential environmental conditions for each target genotype.

Conclusions

The influence of phenomics in livestock breeding has only begun and much work remains to be done. High dimensionality is a ‘blessing’ rather than a ‘curse’ to improve prediction [ 40 ] and we should not be afraid of it. High dimensionality should also help breeders to fine tune which are the most relevant phenotypes, and what are the expected constraints. Making progress in phenomics depends on the fast-developing field of sensor technology and machine learning. This reinforces the idea that the breeders of the future will require sound agronomic and biological backgrounds as well as a solid training in statistical and machine learning. This may seem paradoxical, but it will be the case because easy-to-use, powerful programming libraries and code will be widely available, whereas interpretation of results and application in breeding schemes require specific biological and agronomic knowledge. Last but not least, breeders trained in phenomics who can effectively collaborate with biologists, producers, engineers and computer scientists will have increased chances of succeeding in the job market.

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Acknowledgements

We are grateful for the comments and suggestions from L.M. Zingaretti, and GSE editors for endorsing this note. MPE is funded by MINECO Grants PID2019-108829RB-I00/AEI/10.13039/501100011033 and “Centro de Excelencia Severo Ochoa 2016-2019” award SEV-2015-0533 (Spain). JPS is funded by NIFA award 2017-67007-26176 (USA).

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Pérez-Enciso, M., Steibel, J.P. Phenomes: the current frontier in animal breeding. Genet Sel Evol 53 , 22 (2021). https://doi.org/10.1186/s12711-021-00618-1

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Animal Breeding and Genetics

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Table of contents (18 entries)

Front matter, animal breeding and genetics: introduction.

Matthew L. Spangler

Animal Breeding Methods and Sustainability

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Quantitative Methods Applied to Animal Breeding

  • Guilherme J. M. Rosa

Foundations of Molecular Genetics: From Major Genes to Genomics

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Building Genetic Models

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Genotype by Environment Interactions in Livestock Farming

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Socially Affected Traits, Inheritance and Genetic Improvement

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Pig Breeding for Increased Sustainability

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Poultry Breeding

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Beef Cattle Breeding

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Novel Trait Selection to Enhance Sustainability of Beef Production Systems

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Dairy Cattle Breeding

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Sustainable Genetic Improvement in Dairy Goats

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Horse Breeding

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Breeding in Developing Countries and Tropics

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Sustainability of Wild Populations: A Conservation Genetics Perspective

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Breeding in an Era of Genome Editing

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Long-term Challenges for Animal Breeding

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Back Matter

This newly updated and revised volume of the Encyclopedia of Sustainability Science and Technology (ESST) details the role of Animal Breeding and Genetics in the sustainability of animal agriculture. The volume covers scientific principles and applications includes the current science used to advance cattle, poultry, swine,sheep, and equine populations, as well as the future role of techniques such as gene editing. 

International leaders in the field explain foundational concepts such as heritability, the covariance between relatives, statistical approaches to predicting the genetic merit of individuals, and the development and advancement of molecular techniques to elucidate changes in the DNA sequence that underly phenotypic variation. The use of genetic-based tools to improve animal agriculture and meet consumer demands across species is treated in detail. 

Readers will gain an understanding of how global livestock producers have implemented advanced genetic selection tools and used them to improve reproduction, production, efficiency, health, and sustainability. The interactions of genetics and production environments, and the genetic components of the complex interactions among animals are also discussed. The future of Animal Breeding and Genetics, including the challenges and opportunities that exist in feeding a growing world population, are addressed.

  • Animal Genetics

Book Title : Animal Breeding and Genetics

Editors : Matthew L. Spangler

Series Title : Encyclopedia of Sustainability Science and Technology Series

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eBook ISBN : 978-1-0716-2460-9 Published: 01 November 2022

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Series E-ISSN : 2629-2386

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Number of Pages : XVIII, 417

Number of Illustrations : 6 b/w illustrations, 58 illustrations in colour

Topics : Veterinary Medicine/Veterinary Science , Biotechnology , Agriculture , Genetics and Genomics

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Genomics in animal breeding from the perspectives of matrices and molecules

  • Martin Johnsson   ORCID: orcid.org/0000-0003-1262-4585 1  

Hereditas volume  160 , Article number:  20 ( 2023 ) Cite this article

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This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules.

This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them.

The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research.

Genomics, in the sense of genetic analyses using markers spaced out along the whole genome, has become a mainstream part of animal breeding. In March 2021, the dairy cattle evaluation in the US run by the Council on Dairy Cattle Breeding had accumulated five million genotyped animals [ 1 ]. These data are gathered for the purpose genomic selection, that is, evaluation of animals based on genome-wide DNA-testing, which was implemented in the US in 2007 (reviewed by [ 2 ]). Genomic selection builds on the practice of genetic evaluation by estimating a breeding value — a prediction of the trait values of the offspring that an animal will have — based on measurements on the animal itself and its relatives. Genomic selection adds molecular information in the form of genome-wide DNA markers to the evaluation.

Animal breeding before genomics was already immensely effective in changing the traits of farm animals. Take for example broiler chicken breeding. Zuidhof et al. [ 3 ] compared commercial broilers from 2005 (Ross 308 from Aviagen) with populations where breeding stopped in 1957 or 1978, kept in the same environment and fed the same feed. At eight weeks of age, the average body mass was 0.9 kg for the population with genetics from 1957, 1.8 for the population with genetics from 1978, and 4.2 kg for the population with genetics from 2005. The first SNP chip for chickens was developed in 2005 [ 4 ], and Aviagen started using genomic selection in 2012 [ 5 ] and thus, this difference is due to breeding that occurred before genomics. Genomics, however, made selection even more effective, either by increasing accuracy of selection or reducing generation interval, depending on the species. Potentially, it can also tell us about the molecular nature of the variants under selection and lead to new biotechnology applications for livestock.

The term “genomics” is derived from “genome”, which was coined by Hans Winkler in 1920 [ 6 ] and refers to one haploid set of chromosomes [ 7 ], or —with some degree of slippage in meaning — the complete DNA of a species. According to Thomas Roderick [ 8 ] the extension to “genomics” was conceived in 1986, as founders of the journal Genomics were trying to find a name for it. From the start, they regarded genomics as the name of a new field — “an activity, a new way to think about biology”.

There are (at least) two ways to think of genomics in animal breeding: two perspectives on genomics that will, throughout this paper, be called the statistical and the sequence perspectives:

We may think of the genome as a big table of numbers, where each row is an individual and each column a genetic variant, and the numbers are ancestry indicators. These matrices lend themselves to statistical calculations such as estimation of genomic breeding values. This is the view from the statistical perspective.

Alternatively, we may think of the genome as a long string of A, C, G and T. They lend themselves to molecular biology operations like predicting the amino acid substitution from a base pair substitution, or identifying patterns of interest. This is the view from the sequence perspective.

The perspectives roughly map to two concepts of a so-called gene [ 9 ]: The statistical perspective relates to the instrumental gene, a calculating device used by classical geneticists to understand inheritance patterns. The instrumental gene is a particle of inheritance, observed indirectly through crosses and comparisons of traits between relatives. For an example, the textbook of classical genetics by Sturtevant and Beadle [ 10 ] is full of crossing schemes of fruit flies that allow modes of inheritance to be investigated. In the introduction, the authors describe their view of genetics as a science. They call it “a mathematically formulated subject that is logically complete and self-contained”, without the necessity of a physical or chemical account of how inheritance works. On the other hand, the molecular perspective aligns closer with the nominal gene concept, where a gene is a DNA sequence that has a name and (potentially) a function. As an example, we can look at a genome browser such as Ensembl [ 11 ], which shows a genome as a series of track, with colourful boxes denoting genes, regulatory DNA sequences, and other associated information.

To be clear, I am not suggesting that individual geneticists are so limited in their thinking as to use only one of these perspectives. Any one researcher probably has these and several other mental models of the genome for different tasks. In practice, geneticists seem to routinely switch between different perspectives and conceptions of central terms like “genome”, “gene” and “locus”, without much friction. Certainly, ambiguity may lead to “complexity and confusion” [ 12 ], but I would argue that the imprecision is also sometimes productive, as it avoids unnecessary debates about which of these concepts are “right”, when the real answer is that all of them are working models and all are useful in different contexts.

The two perspectives lead to different views about the importance of identifying sequence variants that cause trait differences between individuals (“causative variants”, for short). From the statistical perspective, genomic data are large sets of markers of ancestry; we can make use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; we need to identify and make use of them. To realise the future potential of the sequence perspective, geneticists need to identify causative variants, while the statistical perspective has been successful, precisely by ignoring causative variants. The power of markers [ 13 ] is what Sturtevant & Beadle described: The point is to make use of statistical regularities without getting bogged down in mechanistic detail. Conversely, the potential of the molecular perspective is in understanding mechanisms and learning to manipulate them in ways that would not be possible by traditional selection and crossing. Mostly, this potential of the sequence perspective has not been realised, but the search for molecular knowledge has made possible tools that underpin applications of the statistical perspective, especially genomic selection.

Tools of the statistical perspective

Genomic selection is the crowning achievement of the statistical perspective on genomics in animal breeding, building on a long line of research of mapping phenotypes to genotypes. Genetic mapping — the family of methods used for localising variants that affect traits, roughly at first — goes back to the early history of classical genetics. Once geneticists had discovered that genes were arranged linearly on chromosomes, they could build maps of where causative variants underlying visible phenotypes were located relative to each other, the first map being published by Sturtevant [ 14 ]. This map building activity, based on crossing and detecting recombinant individuals, is called linkage mapping. The extension to complex traits with many causative variants of small effects is traditionally called “quantitative trait locus mapping” [ 15 ]. The extension to large population samples of more distantly related individuals is called “genome-wide association” [ 16 ], and has become the dominant form of genetic mapping. Arguably, genetic mapping can be viewed both from the statistical and sequence perspectives. On one hand, these methods involve statistical genetical methods that are very similar to those used in genomic prediction, and involve representing genomic data statistically. On the other hand, the end goal is usually to identify causative variants.

Out of genetic mapping of traits relevant to breeding comes marker-assisted selection, an earlier paradigm for incorporating molecular information in breeding. In a way, marker-assisted selection is the most intuitive way to imagine molecular breeding: Imagine that we have identified some genetic variants that either cause a trait of interest, or are strongly associated with it. Then, we can genotype our selection candidates for the variant of interest, and incorporate those genotypes into selection decisions. For example, if we know about a strongly deleterious variant, we can exclude candidates that carry it. The proposition of a genetic test is especially attractive when the trait is otherwise hard to phenotype. This was precisely the situation with several large-effect deleterious alleles in pigs and cattle, where marker-assisted selection was successfully implemented against the problematic alleles: malignant hyperthermia and the RN gene in pigs (reviewed by [ 13 , 17 ]) and BLAD in cattle [ 18 ]. DNA tests for such large-effect damaging variants are now routinely included in many genomic breeding programs (e.g., [ 19 , 20 ]).

At some point during the late 1990 to early 2000s, animal breeding researchers shifted their thinking from marker-assisted selection to genomic selection, from thinking about mapping causative variants to treating the whole genome together. Arguably, the key paper, and the most cited, is the one by Meuwissen, Hayes and Goddard [ 21 ]. It presents the full case for genomic selection, including simulations and a few alternative estimation methods (leading to the so-called Bayesian alphabet family of methods). However, genomic selection did not appear fully formed at once. Other genomic selection precursor papers from the era include:

The 1990 paper by Lande & Thompson [ 22 ] that contains the key idea of covering the genome with markers and selecting on a total score based on all the markers.

The 1997 paper by Nejati-Javaremi, Smith & Gibson [ 23 ], the key idea of which is to create a relationship matrix based on variants that affect a trait, creating estimated breeding values based on what they call “total allelic relationship”.

The 1998 paper by Haley & Visscher [ 24 ] which uses the term “genomic selection” and clearly expresses the concept, including the interpretation of genetic markers as realised relatedness.

Exactly when and by whom (in conversation or in parallel) the shift happened is a topic of its own. It seems to have been a gradual process. Still, Meuwissen, Hayes and Goddard (2001) is a landmark in that it provided a full recipe for genomic selection, and ran the proof of concept in silico . Genomic selection worked well enough in theory that is provided the inspiration for creating the tools and the practical initiatives to make it reality.

We can think of genomic prediction it as refining the estimate of how closely related animals are to each other by observing how much DNA the animals share, as opposed to the average relatedness that can be predicted from a pedigree. Alternatively, we can think of it as simultaneously estimating the contribution of every part of the genome (that is, every marker we genotype), and adding them up to a genomic estimate for that animal (see [ 25 ] for a review of the statistical approaches used in animal breeding). Either way, the key insight in genomic selection is that one can accurately predict breeding values in the absence of information about the function of particular variants by combining all markers in one statistical model. As Lowe & Bruce point out [ 13 ], this black-boxing of genetic mechanisms is characteristic of the quantitative genetics tradition, here expressed by one of the pioneering applied quantitative geneticists, Lush [ 26 ]:

It is rarely possible to identify the pertinent genes in a Mendelian way or to map the chromosomal position of any of them. Fortunately this inability to identify and describe the genes individually is almost no handicap to the breeder of economic plants or animals. What he would actually do if he knew the details about all the genes which affect a quantitative character in that population differs little from what he will do if he merely knows how heritable it is and whether much of the hereditary variance comes from dominance or overdominance, and from epistatic interactions between the genes.

Lowe & Bruce argue that this attitude is key to the success of genomic selection: this strategy is the outcome of an alignment, but not a full integration of quantitative and molecular genetics, which allowed quantitative genetics to make use of molecular methods to generate ever denser marker maps, while sticking with the tradition of abstraction [ 13 ].

The effects of genomics have been dramatic. Genomic prediction allows selection to proceed more quickly, or more accurately, depending on the biology of the species and the design of the breeding program. In cattle, increased selection accuracy for young bulls without daughter records allow shorter generation times [ 2 , 27 , 28 ], and genotyping of heifers much improves selection accuracy of cows relative to pedigree-based evaluation [ 29 ]. In pigs, genomics have increased accuracy of selection in several traits by 50% [ 17 ]. In poultry, accuracy has also increased; a review of genomic selection in poultry gives accuracy increases ranging from 20% to over 50% in layers and broilers [ 5 ].

There are further statistical genetics tools, agnostic of marker function, that can be enriched by genomics. Optimal contributions selection (reviewed by [ 30 ]) is a family of methods to balance the genetic improvement and inbreeding or loss of diversity of a population. These methods work by finding less related individuals to pair, that still give a high expected genetic gain in the offspring. Like in genomic selection, pedigree relatedness can be substituted with genomic relatedness. Since genomic selection in practice tends to accelerate inbreeding, there may be greater need for optimal contributions selection in genomic breeding. Specifically, genomic selection can in principle differentiate between individuals that are identically related in terms of pedigree, and thus lead to less correlation between families, and a lower inbreeding rate, all else equal [ 31 ]. In practice, all else is not equal, because genomics leads to redesigns of breeding programs, which may in itself increase or decrease the inbreeding rate. In breeding programs where genomic selection helped reduce generation time, a low inbreeding rate per generation may translate to accelerating inbreeding per year. There are examples of both accelerated [ 32 ] and reduced inbreeding rates after genomic selection [ 33 ].

Furthermore, population genetic methods can find the similarity between populations and individuals, and classify individuals based on breed composition, geographic origin or assign offspring to parents. For example, DNA testing to confirm pedigree in cattle started with blood groups, moved on to genetic markers, and now use the genome-wide SNP chips that are used for genomic selection [ 34 ]. Genomics allows plentiful markers distributed throughout the genome, and so, methods can be more precise in pinpointing ancestry [ 35 ], and reconstruct pedigree information that is missing [ 36 ].

Tools of the sequence perspective

From the sequence perspective, the development of genomics in animal breeding can be seen as ongoing effort to build the tools for causative variant identification. In the process, it also gave rise to the enabling technology for genomic selection. This development includes reference genomes for farm animals, dense marker panels and affordable methods to type them (SNP chips, reduced representation sequencing), genome annotation and maps that localise causative variants in the genome (linkage mapping and genome-wide association).

The chicken genome sequence was published in 2004 [ 37 ], cattle in 2009 [ 38 ], and pig in 2012 [ 39 ]. The choice of any one publication and year as a milestone in a genome sequencing project is somewhat arbitrary, because the sequences reported in these papers were neither the first nor the last drafts. Genome assembly is an iterative process that combines different kinds of data, computational models, and human judgement to represent a genome. For a historical account of the diverse data and ways of reasoning used in the pig genome project, see Lowe [ 40 ]. Lowe points out that a genome project was not just about sequencing in the narrow sense of putting DNA base pairs in order, but “thick” sequencing, which also includes the creation of tools, annotation with additional data, and dissemination to a research community that makes reference genomes useful. Consequently, the development of farm animal reference sequences is still ongoing, with the pig, cattle and chicken genomes being updated [ 41 , 42 ] and followed by sheep, goat, ducks, turkeys and many other. There are now multiple high-quality genome assemblies, e.g. in cattle [ 43 , 44 ]. Inevitably, more are coming, as genome assembly becomes more affordable and streamlined.

The next layer atop the reference genome is annotation, here understood as any information that has a genomic coordinate, localising it in the genome. As Szymanski et al. [ 45 ] point out in a study of the yeast genome, one of the functions of a reference genome as a digital model of the genome is to allow researchers to organise and connect different sources of data. Researchers can put their data on the same coordinate system and create a coherent picture. In the yeast community, that coherence-building used to be achieved by sharing strains and standard protocols, before the reference genome. For logistical reasons, germplasm sharing is harder in farm animal genetics. But now, genome annotation is available in genome browsers such as the NCBI Genome Data Viewer and Ensembl, which contain comparative information [ 46 ], the location of genes, and non-genic elements of importance such as open chromatin (as it is becoming available). Projects like Functional Annotation of Animal Genomes [ 47 ] are producing detailed maps of gene-regulatory regions in farm animal genomes, with the express purpose that researchers are going to be able integrate their openly available data into their projects. Such functional genomic data might be useful both for annotating genetic variants as a part of fine-mapping and nominating potential causative variants, in genomic prediction with sequence data, and in molecular biology studies of gene-regulatory networks.

The key technology, however, enabling genomics in farm animals is affordable high throughput genotyping, in the form of SNP chip technology that allows the testing of thousands of single nucleotide variants (SNPs) at the same time. SNP chips are, generally, surfaces with known pieces of DNA them. The array captures fragments of DNA close to the markers we want to type, and a DNA polymerase enzyme that incorporates labelled nucleotides gives a fluorescence signal, where the relative signal intensity of the alleles will tell us the genotype [ 48 ]. A clustering algorithm will help turn the intensity values into genotypes — the numeric coding needed for all the statistical genomic methods.

Looking at the original three farm animal genome papers, they all mentioned genetic improvement of livestock, but in oblique terms. It is as if they either did not know precisely how a reference genome would improve breeding in these animals, or that the way forward now that the reference genome was in place was too obvious to even to mention:

The chicken genome sequence promotes both the development of more refined polymorphic maps (see the accompanying paper [ 49 ] ) and the framework for discovering the functional polymorphisms underlying interesting quantitative traits, thus fully exploiting the genetic potential of the chicken. [ 37 ]

The cattle genome and associated resources will facilitate the identification of novel functions and regulatory systems of general importance in mammals and may provide an enabling tool for genetic improvement within the beef and dairy industries. [ 38 ]

The pig genome sequence provides an important resource for further improvements of this important livestock species, and our identification of many putative disease-causing variants extends the potential of the pig as a biomedical model. [ 39 ]

However, when the first SNP chips were being published, the design of the SNP chips were explicitly motivated with the ability to perform genomic selection, in addition to the ability to improve genetic mapping:

The aim of this study was to develop and characterize a high-density, genome-wide SNP assay for cattle with the power to detect genomic segments harboring inter-individual DNA sequence variation affecting phenotypic traits and for application to GWS, in which an animal’s genetic merit is estimated solely from its multilocus genotype. [ 50 ]

The most efficient way to genotype large numbers of SNPs is to design a high-density assay that includes tens of thousands of SNPs distributed throughout the genome. These SNP “chips” are a valuable resource for genetic studies in livestock species, such as genomic selection, detection of [quantitative trait loci] or diversity studies. [ 51 ]

In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). [ 52 ]

These genomic tools — reference genomes, genome annotation, large-scale genotyping — build towards detecting causative variants that affect traits by allowing bigger and more marker-dense genome-wide association studies for localising causative variants, and the ability to look under the loci detected to find the underlying genes and important sequence elements, such as gene-regulatory sequences. It is striking to read the attitudes in commentaries on genomics in animal breeding from the early days of genomics. Here is Bulfield [ 53 ] in 2000 describing the isolation of causative variants:

Farm animal genomics is developing in four phases. (1) Constructing maps of highly informative markers and genes. (2) Using these maps to scan broadly across genomes of resource populations, segregating for commercially important traits, to locate quantitative trait loci (QTL) into 20–40 cM chromosomal segments. (3) Identifying the trait gene(s) themselves, within these regions. (4) Bridging the ‘phenotype gap’ between the gene(s) and the ultimate trait.

What implications would this have for animal breeding? Bulfield continues:

In animal breeding, a combination of genome analysis and cell culture-based transgenesis would permit a more controlled approach to animal breeding, especially for currently intractable traits such as fertility and disease resistance. In addition, cloning from adult cells (as with Dolly) would permit the replication of (for example) a proven high-yielding and productive dairy cow.

On the same theme, Goddard [ 54 ] wrote in 2003:

I believe animal breeding in the post-genomic era will be dramatically different to what it is today. There will be a massive research effort to discover the function of genes including the effect of DNA polymorphisms on phenotype. Breeding programmes will utilize a large number of DNA-based tests for specific genes combined with new reproductive techniques and transgenes to increase the rate of genetic improvement and to produce for, or allocate animals to, the product line to which they are best suited. However, this stage will not be reached for some years by which time many of the early investors will have given up, disappointed with the early benefits.

In retrospect, Bulfield was clearly too optimistic; Goddard’s more tempered optimism might still be right depending on how long time counts as “some years”. Also, the technologies listed by Bulfield [ 53 ] — linkage maps of 20 to 40 cM resolution, microsatellite and amplified fragment length markers, back-crosses and expressed sequence tag libraries — sound antique to students of animal breeding educated today. The low number of markers (e.g., 40 cM resolution would mean about 150 markers to cover the cattle genome), made sense for genetic mapping based on linkage within families, which was the state of the art at the time. The tools of the sequence perspective have moved far during 20 years, but the underlying problems of causative variant identification remain the same.

That is, despite the increasing development of molecular tools, statistical methods, and increasing dataset sizes, there are few known causative variants for economically important traits (see tables in [ 55 ]). None of them have yet led to transgenic animals that are used in farming. Why have we not found the causative variants? There are at least three problems:

It turns out that most traits of interest are massively polygenic. That is, they are affected by thousands of genetic variants, most of individually small effects. This has been a staple assumption of quantitative genetics since the early 20th century, and was further cemented by the failure of linkage mapping to explain large chunks of inheritance, and now there are methods (based on genomic selection models) to estimate polygenicity from data. The estimated number of variants for complex traits in humans are in the range of tens of thousands of causative variants [ 56 , 57 ].

Quantitative traits may have complex genetic architectures in other ways than polygenicity; they may be affected by rare variants whose effects are hard to estimate, and variants that act in non-additive ways (dominance or epistasis). This is less important for selection, as the response to selection depends on the additive genetic variance, and even non-additive effects at the variant level can result in substantial additive genetic variance [ 58 , 59 ]. However, when we go on to identify causative variants, it may matter, for example, if the apparently additive outcome depends on pairwise interactions between variants that are located close together.

Even when an association has been isolated (and there are thousands of them [ 60 ]), fine-mapping an association signal down to the causative variant or even gene is hard, because there are many variants, and they correlate (geneticists call this correlation, abstrusely, “linkage disequilibrium”), and interpreting them and testing their effects are hard work.

The Goddard [ 54 ] quote is particularly apt, because while the post-genomic future he envisaged, based on the sequence perspective, has not happened, at about the same time as that paper was published, he was involved in developing genomic selection, the statistical genomics future that happened instead.

Statistical futures

What is the future of genomic breeding? From the statistical perspective, the immediate future seems to hold even more genomic selection — on more data, with new traits, spread to new species and breeding programs, and possibly enhanced with functional genomic data.

As data accumulate on more and more animals, larger datasets cause computational difficulties. Methods such as APY (the “algorithm of proven and young”), which splits a genomic selection dataset into a “core” group of animals and a “peripheral” group of animals and performs the most intense computations only on the core subset, allow one to use large numbers of genotyped animals and still be able to compute estimated breeding values in reasonable times [ 61 ]. There is a whole strand of genomics research in animal breeding that works on improving the way genomic selection models are used in practice, how to fit the models efficiently, how to re-fit them when new data arrives, and how to estimate their accuracy (see review by [ 62 ]).

Another ongoing strand of research is extending genomic selection to more complicated genetic scenarios like crossbred animals or generalisation between different populations. Standard genomic selection models work best for prediction within a single population. Thus, if crossbred animals are used for breeding, as is common for example in beef cattle, one would like to have genomic estimated breeding values for them. Even when the crossbred animals might not be used in breeding themselves, such as in pig or poultry breeding, there are traits that can only be measured on crossbred individuals and that information needs to be propagated back to the purebred nucleus animals. Similarly, small breeds might struggle to gather enough data, and the ability to borrow information from larger breeds is attractive.

However, genetic distance between animals quickly reduces the accuracy of genomic selection, complicating across-breed and multi-breed genomic prediction (see review by [ 63 ]). First, comparing distantly related breeds, the marker—trait associations in each breed could be very different, both because the breeds might carry different causative alleles and because the correlations (linkage disequilibrium) between causal variants and markers might be different. Second, non-additive genetic effects, which to a first approximation can be discounted as a nuisance factor within a population, can make a substantial difference as genetic differences accumulate. To accurately predict the outcome, a full model would have to consider both dominance and the genotypes at multiple interacting loci. However, without identifying the interactions and non-linearities, the correlation between marker effect estimates can be shown to decline with genetic differentiation [ 64 ].

Another avenue of development is to find a place for machine learning methods in genomics of animal breeding. Machine learning methods have been used in functional genomics to predict variant effects (reviewed by [ 65 ]), and in animal breeding applications for developing new phenotypes [ 66 , 67 ], but so far have not been widely used in genomic selection. This is not for lack of trying; early work included attempts at using kernel methods [ 68 , 69 ], tree regression [ 70 ] and neural networks [ 71 ], and later efforts have been made with deep learning [ 72 , 73 ]. However, unless we count linear mixed models as a machine learning application, these have not made much impact on applied genomic selection. Probably, this is because non-additive effects have hitherto not played a big role in selection, and these methods only outperform linear mixed models when predicting non-additive effects. This may change if genomic selection is extended to systems where non-additive effects are more important, and one has to design matings to produce offspring that deviate from the parent average in the right direction [ 74 ], or for applications where predicting individual phenotype rather than breeding value is the goal.

Finally, there is a strand of research that aims to improve genomic selection by adding more genomic information. For biological reasons, some variants are expected to contribute more — variants close to known associations from genome-wide association studies, variants predicted by bioinformatic means to be functional, variants associated with gene expression variation, variants located in open chromatin in a relevant tissue, and so on. Various statistical extensions to the genomic selection models allow groups of variants to be treated separately [ 75 , 76 ] and given different emphasis depending on their predicted function. Such methods would be important for performing genomic selection with whole-genome sequence data, that include millions rather than tens of thousands of variants. It seems clear that there is potential. A series of studies using gene expression quantitative trait locus data in combination with chromatin and evolutionary conservation suggest that one might be able to prioritise variants that are more likely to explain quantitative trait variation [ 77 , 78 ]. However, empirical results on whole-genome sequence data in genomic prediction [ 79 , 80 , 81 , 82 ] are inconsistent between methods, populations and traits about whether adding genomic information brings any benefit, or even degrades accuracy. Even in simulations where the causative variants are known [ 83 ], the increase in accuracy from including true causative variants is not great, unless the true effect sizes of the variants are known. Therefore, the potential gain from enhancing genomic selection is probably much less than from the improvement that came from starting genomic selection over traditional evaluation.

The statistical perspective also holds the opposite possibility for a turn away from the genome. Instead of pursuing more genomic data to possibly improve genomic prediction, one could invest in improving measurement technology or modelling to improve the measurement of traits. Because the task, from the statistical perspective, is not to understand the genome but to get a good enough estimate of ancestry, it might be that the best choice is to settle for a relatively crude genotyping strategy (like a medium density SNP chip) and instead focus on gathering more records on high-value but hard-to-measure traits [ 84 ].

Sequence futures

As we saw above, around the turn of the century there was optimism about identifying causative variants and exploiting them in animal breeding, which turned out to be mostly premature. Marker-assisted selection was successfully used on large-effect variants such as genetic defects, but less successful for quantitative traits. There are thousands of quantitative trait loci and genome-wide association hits published for economically relevant quantitative traits in farm animals, but only a handful that have been fine-mapped down to a causative variant [ 85 ]. However, molecular genetic techniques have moved rapidly over the last 20 years, not just adding new assays for gene-regulatory activity, but scaling them to the whole genome. With these new tools at hand, researchers are again optimistic that causative variants can be identified and exploited.

Several papers outline a vision of a future for the sequence perspective in animal [ 86 , 87 ] and plant breeding [ 88 ], using genome editing methods such as CRISPR/Cas9 to supplement classical breeding with causative variants of known function. They call future, causative-variant enabled breeding “Livestock 2.0” and “Breeding 4.0”. Beside the version number conflict the visions have a similar overall shape: the future of breeding lies in identifying genetic causative variants through large genomic datasets, and then introducing them into breeding individuals through gene editing. Clark et al. [ 86 ] also describe identifying functional variants and editing them as “a route to application” for functional genomic data in farm animals.

The first application along this route of gene editing would be the ongoing attempts at editing of monogenic high-value traits, such as hornlessness caused by polled alleles in cattle [ 89 ], or porcine reproductive and respiratory syndrome virus resistance in pigs conveyed by edits to the CD163 gene [ 90 ]. In the case of pigs, the causative variant does not occur naturally, and was designed based on molecular knowledge about the virus’ mode of infection. The hornless variant (“polled”) was identified by genome-wide association [ 91 ]. Conceptually, these proposed applications are somewhat different than the applications that have been proposed for transgenic animals before. Transgenic farm animals, such as the defunct “Enviropig” project [ 92 ] or the AquaAdvantage salmon [ 93 ], would have DNA introduced from different species, and can be thought of as examples of a genetic engineering approach. These modern proposals typically use less dramatic changes, alleles that exist in nature, or could relatively easily happen by natural mutation (e.g., partial deletion of a gene in the CD163 example, or producing a duplication similar to a naturally occurring duplication in the polled case).

Gene editing is like marker-assisted selection in the sense that the variants to be edited need to have large enough effects to be worthwhile, and editing must be more effective than conventional alternatives. Both resistance to porcine reproductive and respiratory syndrome and polledness are potentially traits of great value and connected to animal welfare. Outbreaks of porcine reproductive and respiratory syndrome has devastating consequences for pig health and farm profitability, and simulations suggest that gene editing in combination with partially protective vaccines could eliminate the disease [ 94 ]. Hornless cows are highly desirable by farmers and dehorning is a welfare issue. As for conventional alternative strategies, natural knockouts of the CD163 gene in pigs appear to be exceedingly rare [ 95 ]. Polled alleles, however, occur in many breeds, including dairy breeds conceived as targets of editing, and marker-assisted selection is already in use in breeding programs to promote it, as polled status can be predicted from SNP chips used for genomic selection. Simulation studies suggest that an editing-based strategy for promoting polled can have better consequences in terms of genetic gain and inbreeding than marker-assisted selection [ 96 , 97 , 98 ], but it remains to be seen whether the technological hurdles, regulations, acceptability and ethical issues will be resolved in time for polled gene editing to be successful.

However, going beyond monogenic traits to complex traits, the lack of other routes to application other than gene editing becomes a problem. If editing or marker-assisted selection are the only applications for knowledge of causative variants, and neither is likely to work well for complex traits, this limits the applied potential of the sequence perspective. Molecular insights about traits in farm animals are scientifically interesting, but currently have little other applied value. This is often not very clear from reading genomic studies, that often promise improvements to animal breeding without spelling out how they will come about. Allow me a personal and somewhat embarrassing example: In the introduction to my PhD thesis, which was defended in 2015, I wrote about the quantitative trait loci that I had identified, and speculated about what would be needed for them to be used in actual breeding. This discussion was completely misguided. It raised true concerns, such as whether the association would replicate in a different population, whether the underlying variant between shared associations in different populations are the same, and so on, but it missed the mark, because I was not aware that marker-assisted selection for quantitative traits was essentially dead at this point. The quantitative trait locus paradigm that I was operating within was dead and buried in animal breeding, and the first commercial genomic selection of poultry was already happening [ 5 ].

Most traits of economic relevance to animal breeding are affected by many variants of small effects. This polygenicity means that in order to know what sequences to edit and what to put instead one needs to solve the fine-mapping problem, to find ways to reliably identify causative variants, even if they are of moderate effect size. The situation is more challenging than with marker-assisted selection, where it may be enough to detect a variant in close linkage disequilibrium with the genuine causative variant. It is still an open question when and how we will get detailed enough knowledge of the genomic basis of complex traits to do this. It would require a workflow to identify causative variants reliably enough to edit them, in a very short time compared to current methods where thorough characterization of a causative variant takes years.

Furthermore, pleiotropy and non-additive effects might affect predictability of the outcomes of editing. Because the size of the genome and its repertoire of genes is limited, genes and pathways are recycled in a context-dependent manner for many biological functions. This suggests that many genetic variants will affect multiple traits, likely mediated by gene-regulatory relationships. This postulate of “universal pleiotropy” goes back to early quantitative genetics [ 99 ] and forms part of the more recent “omnigenic model” of complex traits [ 100 ]. This suggests that any use of gene editing needs to be vigilant against side-effects and consider the whole breeding goal in a balanced way, as argued by [ 101 ]. In the presence of non-additive effects, the statistical effect of an allele substitution depends on the frequency of the interaction partners. This means that the net effect of a gene edit might change as the population changes, as argued by [ 101 , 102 ]. However, one might argue that we already take genomic selection decisions, and thus shift the allele frequency of regions associated with large marker effects, on the basis of estimates that average over potential interactions and are liable to change over time.

The next problem to overcome is how to introduce many edits into a breeding program. The challenge has two parts: First, multiplex gene editing technically challenging on its own, given that the success rate of a biallelic homology-directed repair editing event with CRISPR/Cas9 is low. Even if it could be increase to double digits, the success rate for multilocus edits would scale poorly. Second, integrating gene editing into animal breeding programs would involve performing gene editing at the scale of many animals. Jenko et al. [ 103 ] suggested a strategy of promotion of alleles by gene editing, where the chosen sires of a breeding program would be edited to be homozygous for causative variants that they did not already carry. They assumed that causative variants were known and that sires could be selected before they were edited. This would require new reproductive technology integrated with genomic selection. Such in vitro breeding strategies have been proposed several times [ 24 , 104 , 105 ] as extensions of the already advanced reproductive technologies used in particular in cattle breeding. For example, if an embryo transfer is already in use to breed sires for a cattle breeding program, it might be possible in the future to use to introduce gene editing machinery into the embryo, then biopsy a small amount of DNA to both verify the integrity of the edits and perform genomic selection. It remains to be seen, if this strategy becomes technologically feasible, what numbers of edited embryos and what levels of failure of editing would be acceptable. The failure rate of gene editing technologies are currently high, and that may lead to high costs and loss of selection response [ 96 ].

Johnsson et al. proposed removal of deleterious alleles [ 106 ], reasoning that damaging variants might be easier to identify from sequence data than causative variants for quantitative traits, and that recessive deleterious alleles may be common in farm animal populations due to ineffective natural selection and the large impact of genetic drift. While that assumption may be true, there is currently no workflow for large-scale identification of deleterious variants in place, and when such variants are detected, marker-assisted selection is more attractive than gene editing.

In summary, the sequence perspective faces challenges, not just within genomics (the fine mapping problem) but also within reproductive technology and breeding program design (the problem of multiplex editing). Gene editing of very large-effect variants is somewhat akin to marker-assisted selection, where there are reliable workflows for causative variant identification, and individual effects may be dramatic enough to justify editing. However, gene editing of causative variants for complex traits appears to fraught with problems to be possible within the foreseeable future. Perhaps finding a promising route to application for the sequence perspective will require a shift in the thinking of the field that we are not yet seeing, similar to the shift from marker-assisted to genomic selection.

Conclusions

In conclusion, there are (at least) two ways to think of genomics in animal breeding, that are helpful in understanding how genomic technologies have changed and may continue to change animal breeding. Currently, tools derived from the statistical perspective are doing the heavy lifting in breeding practice, in the form of genomic selection. With the advent of new technologies, the sequence perspective could make an impact in the future, if it can overcome the twin problems of how to identify causative variants for complex traits and how to introduce them into animals, both at scale.

Data Availability

Not applicable.

Change history

18 may 2023.

A Correction to this paper has been published: https://doi.org/10.1186/s41065-023-00287-8

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Johnsson, M. Genomics in animal breeding from the perspectives of matrices and molecules. Hereditas 160 , 20 (2023). https://doi.org/10.1186/s41065-023-00285-w

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Genomics in animal breeding from the perspectives of matrices and molecules

Martin johnsson.

Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, Uppsala, 75007 Sweden

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This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules.

This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them.

The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research.

Genomics, in the sense of genetic analyses using markers spaced out along the whole genome, has become a mainstream part of animal breeding. In March 2021, the dairy cattle evaluation in the US run by the Council on Dairy Cattle Breeding had accumulated five million genotyped animals [ 1 ]. These data are gathered for the purpose genomic selection, that is, evaluation of animals based on genome-wide DNA-testing, which was implemented in the US in 2007 (reviewed by [ 2 ]). Genomic selection builds on the practice of genetic evaluation by estimating a breeding value — a prediction of the trait values of the offspring that an animal will have — based on measurements on the animal itself and its relatives. Genomic selection adds molecular information in the form of genome-wide DNA markers to the evaluation.

Animal breeding before genomics was already immensely effective in changing the traits of farm animals. Take for example broiler chicken breeding. Zuidhof et al. [ 3 ] compared commercial broilers from 2005 (Ross 308 from Aviagen) with populations where breeding stopped in 1957 or 1978, kept in the same environment and fed the same feed. At eight weeks of age, the average body mass was 0.9 kg for the population with genetics from 1957, 1.8 for the population with genetics from 1978, and 4.2 kg for the population with genetics from 2005. The first SNP chip for chickens was developed in 2005 [ 4 ], and Aviagen started using genomic selection in 2012 [ 5 ] and thus, this difference is due to breeding that occurred before genomics. Genomics, however, made selection even more effective, either by increasing accuracy of selection or reducing generation interval, depending on the species. Potentially, it can also tell us about the molecular nature of the variants under selection and lead to new biotechnology applications for livestock.

The term “genomics” is derived from “genome”, which was coined by Hans Winkler in 1920 [ 6 ] and refers to one haploid set of chromosomes [ 7 ], or —with some degree of slippage in meaning — the complete DNA of a species. According to Thomas Roderick [ 8 ] the extension to “genomics” was conceived in 1986, as founders of the journal Genomics were trying to find a name for it. From the start, they regarded genomics as the name of a new field — “an activity, a new way to think about biology”.

There are (at least) two ways to think of genomics in animal breeding: two perspectives on genomics that will, throughout this paper, be called the statistical and the sequence perspectives:

  • We may think of the genome as a big table of numbers, where each row is an individual and each column a genetic variant, and the numbers are ancestry indicators. These matrices lend themselves to statistical calculations such as estimation of genomic breeding values. This is the view from the statistical perspective.
  • Alternatively, we may think of the genome as a long string of A, C, G and T. They lend themselves to molecular biology operations like predicting the amino acid substitution from a base pair substitution, or identifying patterns of interest. This is the view from the sequence perspective.

The perspectives roughly map to two concepts of a so-called gene [ 9 ]: The statistical perspective relates to the instrumental gene, a calculating device used by classical geneticists to understand inheritance patterns. The instrumental gene is a particle of inheritance, observed indirectly through crosses and comparisons of traits between relatives. For an example, the textbook of classical genetics by Sturtevant and Beadle [ 10 ] is full of crossing schemes of fruit flies that allow modes of inheritance to be investigated. In the introduction, the authors describe their view of genetics as a science. They call it “a mathematically formulated subject that is logically complete and self-contained”, without the necessity of a physical or chemical account of how inheritance works. On the other hand, the molecular perspective aligns closer with the nominal gene concept, where a gene is a DNA sequence that has a name and (potentially) a function. As an example, we can look at a genome browser such as Ensembl [ 11 ], which shows a genome as a series of track, with colourful boxes denoting genes, regulatory DNA sequences, and other associated information.

To be clear, I am not suggesting that individual geneticists are so limited in their thinking as to use only one of these perspectives. Any one researcher probably has these and several other mental models of the genome for different tasks. In practice, geneticists seem to routinely switch between different perspectives and conceptions of central terms like “genome”, “gene” and “locus”, without much friction. Certainly, ambiguity may lead to “complexity and confusion” [ 12 ], but I would argue that the imprecision is also sometimes productive, as it avoids unnecessary debates about which of these concepts are “right”, when the real answer is that all of them are working models and all are useful in different contexts.

The two perspectives lead to different views about the importance of identifying sequence variants that cause trait differences between individuals (“causative variants”, for short). From the statistical perspective, genomic data are large sets of markers of ancestry; we can make use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; we need to identify and make use of them. To realise the future potential of the sequence perspective, geneticists need to identify causative variants, while the statistical perspective has been successful, precisely by ignoring causative variants. The power of markers [ 13 ] is what Sturtevant & Beadle described: The point is to make use of statistical regularities without getting bogged down in mechanistic detail. Conversely, the potential of the molecular perspective is in understanding mechanisms and learning to manipulate them in ways that would not be possible by traditional selection and crossing. Mostly, this potential of the sequence perspective has not been realised, but the search for molecular knowledge has made possible tools that underpin applications of the statistical perspective, especially genomic selection.

Tools of the statistical perspective

Genomic selection is the crowning achievement of the statistical perspective on genomics in animal breeding, building on a long line of research of mapping phenotypes to genotypes. Genetic mapping — the family of methods used for localising variants that affect traits, roughly at first — goes back to the early history of classical genetics. Once geneticists had discovered that genes were arranged linearly on chromosomes, they could build maps of where causative variants underlying visible phenotypes were located relative to each other, the first map being published by Sturtevant [ 14 ]. This map building activity, based on crossing and detecting recombinant individuals, is called linkage mapping. The extension to complex traits with many causative variants of small effects is traditionally called “quantitative trait locus mapping” [ 15 ]. The extension to large population samples of more distantly related individuals is called “genome-wide association” [ 16 ], and has become the dominant form of genetic mapping. Arguably, genetic mapping can be viewed both from the statistical and sequence perspectives. On one hand, these methods involve statistical genetical methods that are very similar to those used in genomic prediction, and involve representing genomic data statistically. On the other hand, the end goal is usually to identify causative variants.

Out of genetic mapping of traits relevant to breeding comes marker-assisted selection, an earlier paradigm for incorporating molecular information in breeding. In a way, marker-assisted selection is the most intuitive way to imagine molecular breeding: Imagine that we have identified some genetic variants that either cause a trait of interest, or are strongly associated with it. Then, we can genotype our selection candidates for the variant of interest, and incorporate those genotypes into selection decisions. For example, if we know about a strongly deleterious variant, we can exclude candidates that carry it. The proposition of a genetic test is especially attractive when the trait is otherwise hard to phenotype. This was precisely the situation with several large-effect deleterious alleles in pigs and cattle, where marker-assisted selection was successfully implemented against the problematic alleles: malignant hyperthermia and the RN gene in pigs (reviewed by [ 13 , 17 ]) and BLAD in cattle [ 18 ]. DNA tests for such large-effect damaging variants are now routinely included in many genomic breeding programs (e.g., [ 19 , 20 ]).

At some point during the late 1990 to early 2000s, animal breeding researchers shifted their thinking from marker-assisted selection to genomic selection, from thinking about mapping causative variants to treating the whole genome together. Arguably, the key paper, and the most cited, is the one by Meuwissen, Hayes and Goddard [ 21 ]. It presents the full case for genomic selection, including simulations and a few alternative estimation methods (leading to the so-called Bayesian alphabet family of methods). However, genomic selection did not appear fully formed at once. Other genomic selection precursor papers from the era include:

  • The 1990 paper by Lande & Thompson [ 22 ] that contains the key idea of covering the genome with markers and selecting on a total score based on all the markers.
  • The 1997 paper by Nejati-Javaremi, Smith & Gibson [ 23 ], the key idea of which is to create a relationship matrix based on variants that affect a trait, creating estimated breeding values based on what they call “total allelic relationship”.
  • The 1998 paper by Haley & Visscher [ 24 ] which uses the term “genomic selection” and clearly expresses the concept, including the interpretation of genetic markers as realised relatedness.

Exactly when and by whom (in conversation or in parallel) the shift happened is a topic of its own. It seems to have been a gradual process. Still, Meuwissen, Hayes and Goddard (2001) is a landmark in that it provided a full recipe for genomic selection, and ran the proof of concept in silico . Genomic selection worked well enough in theory that is provided the inspiration for creating the tools and the practical initiatives to make it reality.

We can think of genomic prediction it as refining the estimate of how closely related animals are to each other by observing how much DNA the animals share, as opposed to the average relatedness that can be predicted from a pedigree. Alternatively, we can think of it as simultaneously estimating the contribution of every part of the genome (that is, every marker we genotype), and adding them up to a genomic estimate for that animal (see [ 25 ] for a review of the statistical approaches used in animal breeding). Either way, the key insight in genomic selection is that one can accurately predict breeding values in the absence of information about the function of particular variants by combining all markers in one statistical model. As Lowe & Bruce point out [ 13 ], this black-boxing of genetic mechanisms is characteristic of the quantitative genetics tradition, here expressed by one of the pioneering applied quantitative geneticists, Lush [ 26 ]:

It is rarely possible to identify the pertinent genes in a Mendelian way or to map the chromosomal position of any of them. Fortunately this inability to identify and describe the genes individually is almost no handicap to the breeder of economic plants or animals. What he would actually do if he knew the details about all the genes which affect a quantitative character in that population differs little from what he will do if he merely knows how heritable it is and whether much of the hereditary variance comes from dominance or overdominance, and from epistatic interactions between the genes.

Lowe & Bruce argue that this attitude is key to the success of genomic selection: this strategy is the outcome of an alignment, but not a full integration of quantitative and molecular genetics, which allowed quantitative genetics to make use of molecular methods to generate ever denser marker maps, while sticking with the tradition of abstraction [ 13 ].

The effects of genomics have been dramatic. Genomic prediction allows selection to proceed more quickly, or more accurately, depending on the biology of the species and the design of the breeding program. In cattle, increased selection accuracy for young bulls without daughter records allow shorter generation times [ 2 , 27 , 28 ], and genotyping of heifers much improves selection accuracy of cows relative to pedigree-based evaluation [ 29 ]. In pigs, genomics have increased accuracy of selection in several traits by 50% [ 17 ]. In poultry, accuracy has also increased; a review of genomic selection in poultry gives accuracy increases ranging from 20% to over 50% in layers and broilers [ 5 ].

There are further statistical genetics tools, agnostic of marker function, that can be enriched by genomics. Optimal contributions selection (reviewed by [ 30 ]) is a family of methods to balance the genetic improvement and inbreeding or loss of diversity of a population. These methods work by finding less related individuals to pair, that still give a high expected genetic gain in the offspring. Like in genomic selection, pedigree relatedness can be substituted with genomic relatedness. Since genomic selection in practice tends to accelerate inbreeding, there may be greater need for optimal contributions selection in genomic breeding. Specifically, genomic selection can in principle differentiate between individuals that are identically related in terms of pedigree, and thus lead to less correlation between families, and a lower inbreeding rate, all else equal [ 31 ]. In practice, all else is not equal, because genomics leads to redesigns of breeding programs, which may in itself increase or decrease the inbreeding rate. In breeding programs where genomic selection helped reduce generation time, a low inbreeding rate per generation may translate to accelerating inbreeding per year. There are examples of both accelerated [ 32 ] and reduced inbreeding rates after genomic selection [ 33 ].

Furthermore, population genetic methods can find the similarity between populations and individuals, and classify individuals based on breed composition, geographic origin or assign offspring to parents. For example, DNA testing to confirm pedigree in cattle started with blood groups, moved on to genetic markers, and now use the genome-wide SNP chips that are used for genomic selection [ 34 ]. Genomics allows plentiful markers distributed throughout the genome, and so, methods can be more precise in pinpointing ancestry [ 35 ], and reconstruct pedigree information that is missing [ 36 ].

Tools of the sequence perspective

From the sequence perspective, the development of genomics in animal breeding can be seen as ongoing effort to build the tools for causative variant identification. In the process, it also gave rise to the enabling technology for genomic selection. This development includes reference genomes for farm animals, dense marker panels and affordable methods to type them (SNP chips, reduced representation sequencing), genome annotation and maps that localise causative variants in the genome (linkage mapping and genome-wide association).

The chicken genome sequence was published in 2004 [ 37 ], cattle in 2009 [ 38 ], and pig in 2012 [ 39 ]. The choice of any one publication and year as a milestone in a genome sequencing project is somewhat arbitrary, because the sequences reported in these papers were neither the first nor the last drafts. Genome assembly is an iterative process that combines different kinds of data, computational models, and human judgement to represent a genome. For a historical account of the diverse data and ways of reasoning used in the pig genome project, see Lowe [ 40 ]. Lowe points out that a genome project was not just about sequencing in the narrow sense of putting DNA base pairs in order, but “thick” sequencing, which also includes the creation of tools, annotation with additional data, and dissemination to a research community that makes reference genomes useful. Consequently, the development of farm animal reference sequences is still ongoing, with the pig, cattle and chicken genomes being updated [ 41 , 42 ] and followed by sheep, goat, ducks, turkeys and many other. There are now multiple high-quality genome assemblies, e.g. in cattle [ 43 , 44 ]. Inevitably, more are coming, as genome assembly becomes more affordable and streamlined.

The next layer atop the reference genome is annotation, here understood as any information that has a genomic coordinate, localising it in the genome. As Szymanski et al. [ 45 ] point out in a study of the yeast genome, one of the functions of a reference genome as a digital model of the genome is to allow researchers to organise and connect different sources of data. Researchers can put their data on the same coordinate system and create a coherent picture. In the yeast community, that coherence-building used to be achieved by sharing strains and standard protocols, before the reference genome. For logistical reasons, germplasm sharing is harder in farm animal genetics. But now, genome annotation is available in genome browsers such as the NCBI Genome Data Viewer and Ensembl, which contain comparative information [ 46 ], the location of genes, and non-genic elements of importance such as open chromatin (as it is becoming available). Projects like Functional Annotation of Animal Genomes [ 47 ] are producing detailed maps of gene-regulatory regions in farm animal genomes, with the express purpose that researchers are going to be able integrate their openly available data into their projects. Such functional genomic data might be useful both for annotating genetic variants as a part of fine-mapping and nominating potential causative variants, in genomic prediction with sequence data, and in molecular biology studies of gene-regulatory networks.

The key technology, however, enabling genomics in farm animals is affordable high throughput genotyping, in the form of SNP chip technology that allows the testing of thousands of single nucleotide variants (SNPs) at the same time. SNP chips are, generally, surfaces with known pieces of DNA them. The array captures fragments of DNA close to the markers we want to type, and a DNA polymerase enzyme that incorporates labelled nucleotides gives a fluorescence signal, where the relative signal intensity of the alleles will tell us the genotype [ 48 ]. A clustering algorithm will help turn the intensity values into genotypes — the numeric coding needed for all the statistical genomic methods.

Looking at the original three farm animal genome papers, they all mentioned genetic improvement of livestock, but in oblique terms. It is as if they either did not know precisely how a reference genome would improve breeding in these animals, or that the way forward now that the reference genome was in place was too obvious to even to mention:

  • The chicken genome sequence promotes both the development of more refined polymorphic maps (see the accompanying paper [ 49 ] ) and the framework for discovering the functional polymorphisms underlying interesting quantitative traits, thus fully exploiting the genetic potential of the chicken. [ 37 ]
  • The cattle genome and associated resources will facilitate the identification of novel functions and regulatory systems of general importance in mammals and may provide an enabling tool for genetic improvement within the beef and dairy industries. [ 38 ]
  • The pig genome sequence provides an important resource for further improvements of this important livestock species, and our identification of many putative disease-causing variants extends the potential of the pig as a biomedical model. [ 39 ]

However, when the first SNP chips were being published, the design of the SNP chips were explicitly motivated with the ability to perform genomic selection, in addition to the ability to improve genetic mapping:

  • The aim of this study was to develop and characterize a high-density, genome-wide SNP assay for cattle with the power to detect genomic segments harboring inter-individual DNA sequence variation affecting phenotypic traits and for application to GWS, in which an animal’s genetic merit is estimated solely from its multilocus genotype. [ 50 ]
  • The most efficient way to genotype large numbers of SNPs is to design a high-density assay that includes tens of thousands of SNPs distributed throughout the genome. These SNP “chips” are a valuable resource for genetic studies in livestock species, such as genomic selection, detection of [quantitative trait loci] or diversity studies. [ 51 ]
  • In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). [ 52 ]

These genomic tools — reference genomes, genome annotation, large-scale genotyping — build towards detecting causative variants that affect traits by allowing bigger and more marker-dense genome-wide association studies for localising causative variants, and the ability to look under the loci detected to find the underlying genes and important sequence elements, such as gene-regulatory sequences. It is striking to read the attitudes in commentaries on genomics in animal breeding from the early days of genomics. Here is Bulfield [ 53 ] in 2000 describing the isolation of causative variants:

Farm animal genomics is developing in four phases. (1) Constructing maps of highly informative markers and genes. (2) Using these maps to scan broadly across genomes of resource populations, segregating for commercially important traits, to locate quantitative trait loci (QTL) into 20–40 cM chromosomal segments. (3) Identifying the trait gene(s) themselves, within these regions. (4) Bridging the ‘phenotype gap’ between the gene(s) and the ultimate trait.

What implications would this have for animal breeding? Bulfield continues:

In animal breeding, a combination of genome analysis and cell culture-based transgenesis would permit a more controlled approach to animal breeding, especially for currently intractable traits such as fertility and disease resistance. In addition, cloning from adult cells (as with Dolly) would permit the replication of (for example) a proven high-yielding and productive dairy cow.

On the same theme, Goddard [ 54 ] wrote in 2003:

I believe animal breeding in the post-genomic era will be dramatically different to what it is today. There will be a massive research effort to discover the function of genes including the effect of DNA polymorphisms on phenotype. Breeding programmes will utilize a large number of DNA-based tests for specific genes combined with new reproductive techniques and transgenes to increase the rate of genetic improvement and to produce for, or allocate animals to, the product line to which they are best suited. However, this stage will not be reached for some years by which time many of the early investors will have given up, disappointed with the early benefits.

In retrospect, Bulfield was clearly too optimistic; Goddard’s more tempered optimism might still be right depending on how long time counts as “some years”. Also, the technologies listed by Bulfield [ 53 ] — linkage maps of 20 to 40 cM resolution, microsatellite and amplified fragment length markers, back-crosses and expressed sequence tag libraries — sound antique to students of animal breeding educated today. The low number of markers (e.g., 40 cM resolution would mean about 150 markers to cover the cattle genome), made sense for genetic mapping based on linkage within families, which was the state of the art at the time. The tools of the sequence perspective have moved far during 20 years, but the underlying problems of causative variant identification remain the same.

That is, despite the increasing development of molecular tools, statistical methods, and increasing dataset sizes, there are few known causative variants for economically important traits (see tables in [ 55 ]). None of them have yet led to transgenic animals that are used in farming. Why have we not found the causative variants? There are at least three problems:

  • It turns out that most traits of interest are massively polygenic. That is, they are affected by thousands of genetic variants, most of individually small effects. This has been a staple assumption of quantitative genetics since the early 20th century, and was further cemented by the failure of linkage mapping to explain large chunks of inheritance, and now there are methods (based on genomic selection models) to estimate polygenicity from data. The estimated number of variants for complex traits in humans are in the range of tens of thousands of causative variants [ 56 , 57 ].
  • Quantitative traits may have complex genetic architectures in other ways than polygenicity; they may be affected by rare variants whose effects are hard to estimate, and variants that act in non-additive ways (dominance or epistasis). This is less important for selection, as the response to selection depends on the additive genetic variance, and even non-additive effects at the variant level can result in substantial additive genetic variance [ 58 , 59 ]. However, when we go on to identify causative variants, it may matter, for example, if the apparently additive outcome depends on pairwise interactions between variants that are located close together.
  • Even when an association has been isolated (and there are thousands of them [ 60 ]), fine-mapping an association signal down to the causative variant or even gene is hard, because there are many variants, and they correlate (geneticists call this correlation, abstrusely, “linkage disequilibrium”), and interpreting them and testing their effects are hard work.

The Goddard [ 54 ] quote is particularly apt, because while the post-genomic future he envisaged, based on the sequence perspective, has not happened, at about the same time as that paper was published, he was involved in developing genomic selection, the statistical genomics future that happened instead.

Statistical futures

What is the future of genomic breeding? From the statistical perspective, the immediate future seems to hold even more genomic selection — on more data, with new traits, spread to new species and breeding programs, and possibly enhanced with functional genomic data.

As data accumulate on more and more animals, larger datasets cause computational difficulties. Methods such as APY (the “algorithm of proven and young”), which splits a genomic selection dataset into a “core” group of animals and a “peripheral” group of animals and performs the most intense computations only on the core subset, allow one to use large numbers of genotyped animals and still be able to compute estimated breeding values in reasonable times [ 61 ]. There is a whole strand of genomics research in animal breeding that works on improving the way genomic selection models are used in practice, how to fit the models efficiently, how to re-fit them when new data arrives, and how to estimate their accuracy (see review by [ 62 ]).

Another ongoing strand of research is extending genomic selection to more complicated genetic scenarios like crossbred animals or generalisation between different populations. Standard genomic selection models work best for prediction within a single population. Thus, if crossbred animals are used for breeding, as is common for example in beef cattle, one would like to have genomic estimated breeding values for them. Even when the crossbred animals might not be used in breeding themselves, such as in pig or poultry breeding, there are traits that can only be measured on crossbred individuals and that information needs to be propagated back to the purebred nucleus animals. Similarly, small breeds might struggle to gather enough data, and the ability to borrow information from larger breeds is attractive.

However, genetic distance between animals quickly reduces the accuracy of genomic selection, complicating across-breed and multi-breed genomic prediction (see review by [ 63 ]). First, comparing distantly related breeds, the marker—trait associations in each breed could be very different, both because the breeds might carry different causative alleles and because the correlations (linkage disequilibrium) between causal variants and markers might be different. Second, non-additive genetic effects, which to a first approximation can be discounted as a nuisance factor within a population, can make a substantial difference as genetic differences accumulate. To accurately predict the outcome, a full model would have to consider both dominance and the genotypes at multiple interacting loci. However, without identifying the interactions and non-linearities, the correlation between marker effect estimates can be shown to decline with genetic differentiation [ 64 ].

Another avenue of development is to find a place for machine learning methods in genomics of animal breeding. Machine learning methods have been used in functional genomics to predict variant effects (reviewed by [ 65 ]), and in animal breeding applications for developing new phenotypes [ 66 , 67 ], but so far have not been widely used in genomic selection. This is not for lack of trying; early work included attempts at using kernel methods [ 68 , 69 ], tree regression [ 70 ] and neural networks [ 71 ], and later efforts have been made with deep learning [ 72 , 73 ]. However, unless we count linear mixed models as a machine learning application, these have not made much impact on applied genomic selection. Probably, this is because non-additive effects have hitherto not played a big role in selection, and these methods only outperform linear mixed models when predicting non-additive effects. This may change if genomic selection is extended to systems where non-additive effects are more important, and one has to design matings to produce offspring that deviate from the parent average in the right direction [ 74 ], or for applications where predicting individual phenotype rather than breeding value is the goal.

Finally, there is a strand of research that aims to improve genomic selection by adding more genomic information. For biological reasons, some variants are expected to contribute more — variants close to known associations from genome-wide association studies, variants predicted by bioinformatic means to be functional, variants associated with gene expression variation, variants located in open chromatin in a relevant tissue, and so on. Various statistical extensions to the genomic selection models allow groups of variants to be treated separately [ 75 , 76 ] and given different emphasis depending on their predicted function. Such methods would be important for performing genomic selection with whole-genome sequence data, that include millions rather than tens of thousands of variants. It seems clear that there is potential. A series of studies using gene expression quantitative trait locus data in combination with chromatin and evolutionary conservation suggest that one might be able to prioritise variants that are more likely to explain quantitative trait variation [ 77 , 78 ]. However, empirical results on whole-genome sequence data in genomic prediction [ 79 – 82 ] are inconsistent between methods, populations and traits about whether adding genomic information brings any benefit, or even degrades accuracy. Even in simulations where the causative variants are known [ 83 ], the increase in accuracy from including true causative variants is not great, unless the true effect sizes of the variants are known. Therefore, the potential gain from enhancing genomic selection is probably much less than from the improvement that came from starting genomic selection over traditional evaluation.

The statistical perspective also holds the opposite possibility for a turn away from the genome. Instead of pursuing more genomic data to possibly improve genomic prediction, one could invest in improving measurement technology or modelling to improve the measurement of traits. Because the task, from the statistical perspective, is not to understand the genome but to get a good enough estimate of ancestry, it might be that the best choice is to settle for a relatively crude genotyping strategy (like a medium density SNP chip) and instead focus on gathering more records on high-value but hard-to-measure traits [ 84 ].

Sequence futures

As we saw above, around the turn of the century there was optimism about identifying causative variants and exploiting them in animal breeding, which turned out to be mostly premature. Marker-assisted selection was successfully used on large-effect variants such as genetic defects, but less successful for quantitative traits. There are thousands of quantitative trait loci and genome-wide association hits published for economically relevant quantitative traits in farm animals, but only a handful that have been fine-mapped down to a causative variant [ 85 ]. However, molecular genetic techniques have moved rapidly over the last 20 years, not just adding new assays for gene-regulatory activity, but scaling them to the whole genome. With these new tools at hand, researchers are again optimistic that causative variants can be identified and exploited.

Several papers outline a vision of a future for the sequence perspective in animal [ 86 , 87 ] and plant breeding [ 88 ], using genome editing methods such as CRISPR/Cas9 to supplement classical breeding with causative variants of known function. They call future, causative-variant enabled breeding “Livestock 2.0” and “Breeding 4.0”. Beside the version number conflict the visions have a similar overall shape: the future of breeding lies in identifying genetic causative variants through large genomic datasets, and then introducing them into breeding individuals through gene editing. Clark et al. [ 86 ] also describe identifying functional variants and editing them as “a route to application” for functional genomic data in farm animals.

The first application along this route of gene editing would be the ongoing attempts at editing of monogenic high-value traits, such as hornlessness caused by polled alleles in cattle [ 89 ], or porcine reproductive and respiratory syndrome virus resistance in pigs conveyed by edits to the CD163 gene [ 90 ]. In the case of pigs, the causative variant does not occur naturally, and was designed based on molecular knowledge about the virus’ mode of infection. The hornless variant (“polled”) was identified by genome-wide association [ 91 ]. Conceptually, these proposed applications are somewhat different than the applications that have been proposed for transgenic animals before. Transgenic farm animals, such as the defunct “Enviropig” project [ 92 ] or the AquaAdvantage salmon [ 93 ], would have DNA introduced from different species, and can be thought of as examples of a genetic engineering approach. These modern proposals typically use less dramatic changes, alleles that exist in nature, or could relatively easily happen by natural mutation (e.g., partial deletion of a gene in the CD163 example, or producing a duplication similar to a naturally occurring duplication in the polled case).

Gene editing is like marker-assisted selection in the sense that the variants to be edited need to have large enough effects to be worthwhile, and editing must be more effective than conventional alternatives. Both resistance to porcine reproductive and respiratory syndrome and polledness are potentially traits of great value and connected to animal welfare. Outbreaks of porcine reproductive and respiratory syndrome has devastating consequences for pig health and farm profitability, and simulations suggest that gene editing in combination with partially protective vaccines could eliminate the disease [ 94 ]. Hornless cows are highly desirable by farmers and dehorning is a welfare issue. As for conventional alternative strategies, natural knockouts of the CD163 gene in pigs appear to be exceedingly rare [ 95 ]. Polled alleles, however, occur in many breeds, including dairy breeds conceived as targets of editing, and marker-assisted selection is already in use in breeding programs to promote it, as polled status can be predicted from SNP chips used for genomic selection. Simulation studies suggest that an editing-based strategy for promoting polled can have better consequences in terms of genetic gain and inbreeding than marker-assisted selection [ 96 – 98 ], but it remains to be seen whether the technological hurdles, regulations, acceptability and ethical issues will be resolved in time for polled gene editing to be successful.

However, going beyond monogenic traits to complex traits, the lack of other routes to application other than gene editing becomes a problem. If editing or marker-assisted selection are the only applications for knowledge of causative variants, and neither is likely to work well for complex traits, this limits the applied potential of the sequence perspective. Molecular insights about traits in farm animals are scientifically interesting, but currently have little other applied value. This is often not very clear from reading genomic studies, that often promise improvements to animal breeding without spelling out how they will come about. Allow me a personal and somewhat embarrassing example: In the introduction to my PhD thesis, which was defended in 2015, I wrote about the quantitative trait loci that I had identified, and speculated about what would be needed for them to be used in actual breeding. This discussion was completely misguided. It raised true concerns, such as whether the association would replicate in a different population, whether the underlying variant between shared associations in different populations are the same, and so on, but it missed the mark, because I was not aware that marker-assisted selection for quantitative traits was essentially dead at this point. The quantitative trait locus paradigm that I was operating within was dead and buried in animal breeding, and the first commercial genomic selection of poultry was already happening [ 5 ].

Most traits of economic relevance to animal breeding are affected by many variants of small effects. This polygenicity means that in order to know what sequences to edit and what to put instead one needs to solve the fine-mapping problem, to find ways to reliably identify causative variants, even if they are of moderate effect size. The situation is more challenging than with marker-assisted selection, where it may be enough to detect a variant in close linkage disequilibrium with the genuine causative variant. It is still an open question when and how we will get detailed enough knowledge of the genomic basis of complex traits to do this. It would require a workflow to identify causative variants reliably enough to edit them, in a very short time compared to current methods where thorough characterization of a causative variant takes years.

Furthermore, pleiotropy and non-additive effects might affect predictability of the outcomes of editing. Because the size of the genome and its repertoire of genes is limited, genes and pathways are recycled in a context-dependent manner for many biological functions. This suggests that many genetic variants will affect multiple traits, likely mediated by gene-regulatory relationships. This postulate of “universal pleiotropy” goes back to early quantitative genetics [ 99 ] and forms part of the more recent “omnigenic model” of complex traits [ 100 ]. This suggests that any use of gene editing needs to be vigilant against side-effects and consider the whole breeding goal in a balanced way, as argued by [ 101 ]. In the presence of non-additive effects, the statistical effect of an allele substitution depends on the frequency of the interaction partners. This means that the net effect of a gene edit might change as the population changes, as argued by [ 101 , 102 ]. However, one might argue that we already take genomic selection decisions, and thus shift the allele frequency of regions associated with large marker effects, on the basis of estimates that average over potential interactions and are liable to change over time.

The next problem to overcome is how to introduce many edits into a breeding program. The challenge has two parts: First, multiplex gene editing technically challenging on its own, given that the success rate of a biallelic homology-directed repair editing event with CRISPR/Cas9 is low. Even if it could be increase to double digits, the success rate for multilocus edits would scale poorly. Second, integrating gene editing into animal breeding programs would involve performing gene editing at the scale of many animals. Jenko et al. [ 103 ] suggested a strategy of promotion of alleles by gene editing, where the chosen sires of a breeding program would be edited to be homozygous for causative variants that they did not already carry. They assumed that causative variants were known and that sires could be selected before they were edited. This would require new reproductive technology integrated with genomic selection. Such in vitro breeding strategies have been proposed several times [ 24 , 104 , 105 ] as extensions of the already advanced reproductive technologies used in particular in cattle breeding. For example, if an embryo transfer is already in use to breed sires for a cattle breeding program, it might be possible in the future to use to introduce gene editing machinery into the embryo, then biopsy a small amount of DNA to both verify the integrity of the edits and perform genomic selection. It remains to be seen, if this strategy becomes technologically feasible, what numbers of edited embryos and what levels of failure of editing would be acceptable. The failure rate of gene editing technologies are currently high, and that may lead to high costs and loss of selection response [ 96 ].

Johnsson et al. proposed removal of deleterious alleles [ 106 ], reasoning that damaging variants might be easier to identify from sequence data than causative variants for quantitative traits, and that recessive deleterious alleles may be common in farm animal populations due to ineffective natural selection and the large impact of genetic drift. While that assumption may be true, there is currently no workflow for large-scale identification of deleterious variants in place, and when such variants are detected, marker-assisted selection is more attractive than gene editing.

In summary, the sequence perspective faces challenges, not just within genomics (the fine mapping problem) but also within reproductive technology and breeding program design (the problem of multiplex editing). Gene editing of very large-effect variants is somewhat akin to marker-assisted selection, where there are reliable workflows for causative variant identification, and individual effects may be dramatic enough to justify editing. However, gene editing of causative variants for complex traits appears to fraught with problems to be possible within the foreseeable future. Perhaps finding a promising route to application for the sequence perspective will require a shift in the thinking of the field that we are not yet seeing, similar to the shift from marker-assisted to genomic selection.

Conclusions

In conclusion, there are (at least) two ways to think of genomics in animal breeding, that are helpful in understanding how genomic technologies have changed and may continue to change animal breeding. Currently, tools derived from the statistical perspective are doing the heavy lifting in breeding practice, in the form of genomic selection. With the advent of new technologies, the sequence perspective could make an impact in the future, if it can overcome the twin problems of how to identify causative variants for complex traits and how to introduce them into animals, both at scale.

Authors’ contributions

MJ wrote the paper.

The author acknowledges the financial support from Formas—a Swedish Research Council for Sustainable Development Dnr 2020 − 01637.

Open access funding provided by Swedish University of Agricultural Sciences.

Data Availability

Declarations.

The author declares that he has no competing interests.

This paper is based on a presentation at “Approaches to genetics for livestock research” at IASH, University of Edinburgh, May 2019.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

A Correction to this paper has been published: 10.1186/s41065-023-00287-8

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Animal breeding in the age of biotechnology: the investigative pathway behind the cloning of Dolly the sheep

Affiliation.

  • 1 University of Edinburgh, Edinburgh, UK, [email protected].
  • PMID: 26205201
  • DOI: 10.1007/s40656-015-0078-6

This paper addresses the 1996 cloning of Dolly the sheep, locating it within a long-standing tradition of animal breeding research in Edinburgh. Far from being an end in itself, the cell-nuclear transfer experiment from which Dolly was born should be seen as a step in an investigative pathway that sought the production of medically relevant transgenic animals. By historicising Dolly, I illustrate how the birth of this sheep captures a dramatic redefinition of the life sciences, when in the 1970s and 1980s the rise of neo-liberal governments and the emergence of the biotechnology market pushed research institutions to show tangible applications of their work. Through this broader interpretative framework, the Dolly story emerges as a case study of the deep transformations of agricultural experimentation during the last third of the twentieth century. The reorganisation of laboratory practice, human resources and institutional settings required by the production of transgenic animals had unanticipated consequences. One of these unanticipated effects was that the boundaries between animal and human health became blurred. As a result of this, new professional spaces emerged and the identity of Dolly the sheep was reconfigured, from an instrument for livestock improvement in the farm to a more universal symbol of the new cloning age.

Publication types

  • Historical Article
  • Research Support, Non-U.S. Gov't
  • Animal Experimentation / ethics
  • Animal Experimentation / history*
  • Animal Experimentation / legislation & jurisprudence
  • Animals, Genetically Modified / genetics*
  • Biotechnology
  • Cloning, Organism / ethics
  • Cloning, Organism / history*
  • Cloning, Organism / legislation & jurisprudence
  • History, 20th Century
  • History, 21st Century
  • Sheep / genetics*

Grants and funding

  • Biotechnology and Biological Sciences Research Council/United Kingdom

Animal Breeding and Genetics Group

University of georgia.

The breeding and genetics group at UGA is active in cutting-edge research projects for the U.S. livestock industry. Our research combines quantitative genetics, genomics, programming, bioinformatics, and statistics. We collaborate with the largest U.S. breeding companies and associations in dairy, beef, swine, poultry, and fish. We have access to extensive data sets across species and premier computing facilities. We also have international contacts with leading research centers worldwide.

The following is a selected list of the current projects. Other projects can benefit from your collaboration. If you are looking for a graduate school or a place for a sabbatical, please consider the University of Georgia.

Joint analysis of phenotype, pedigree and genomic data

We focused on single-step genomic evaluation. It is BLUP, where the numerator relationship matrix  A  is replaced by matrix  H  that combines pedigree and genomic relationships; a lead paper by  Aguilar et al. (2010)  was chosen as 2013 most cited paper in Genetics and Breeding. The end result of single-step is a dramatic simplification of evaluation procedures combined with superior accuracy, ability to use any model and speed similar to that of BLUP. Single-step is already incorporated in routine evaluations by major genetics companies across species.

A problem in single-step GBLUP (ssGBLUP) was difficulty inverting  G  with a large number of genotyped animals; more than 1 million Holsteins are genotyped. We developed an algorithm called APY that makes getting the inverse for millions of animals simple, with a greater accuracy than a regular inverse. Read papers by the references on this exciting discovery. Results of APY put a new thinking on limits of genomic selection and on GWAS, with potential for many exciting papers.

Selected references for ssGBLUP

  • Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta, and T. J. Lawlor. 2010. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93:743:752.  https://doi.org/10.3168/jds.2009-2730
  • Legarra, A., I. Aguilar, and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663.  https://doi.org/10.3168/jds.2009-2061
  • Lourenco, D. A. L., I. Misztal, S. Tsuruta, I. Aguilar, E. Ezra, M. Ron, A. Shirak, J. I. Weller. 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. J. Dairy Sci. 97:1742–1752.  https://doi.org/10.3168/jds.2013-6916
  • Misztal, I., A. Legarra, and I. Aguilar. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree and genomic information. J. Dairy Sci. 92:4648-4655.  https://doi.org/10.3168/jds.2009-2064
  • Tsuruta, S., I. Aguilar, I. Misztal, and T. J. Lawlor. 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94:4198-4204.  https://doi.org/10.3168/jds.2011-4256

Selected references for ssGBLUP with APY

  • Fragomeni, B. O., D. A. L. Lourenco, S. Tsuruta, Y. Masuda, I. Aguilar, A. Legarra, T. J. Lawlor, and I. Misztal. 2015. Use of genomic recursions in single-step genomic BLUP with a large number of genotypes. J. Dairy Sci. 98:4090-4094. https://doi.org/10.3168/jds.2014-9125
  • Lourenco, D. A. L., S. Tsuruta, B. O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, J. K. Bertrand, T. S. Amen, L. Wang, D. W. Moser, and I. Misztal. 2015. Genetic evaluation using single-step genomic BLUP in American Angus. J. Anim. Sci. 93:2653-2662. https://doi.org/10.2527/jas.2014-8836
  • Masuda, Y., I. Misztal, S. Tsuruta, A. Legarra, I. Aguilar, D. Lourenco, B. Fragomeni and T. L. Lawlor. 2016. Implementation of genomic recursions in single-step genomic BLUP for US Holsteins with a large number of genotyped animals. J. Dairy Sci. 99:1968-1974. https://doi.org/10.3168/jds.2015-10540
  • Misztal, I. 2016. Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202:411-409. https://doi.org/10.1534/genetics.115.182089
  • Misztal, I., A. Legarra, and I. Aguilar. 2014. Using recursion to compute the inverse of the genomic relationship matrix. J. Dairy Sci. 97:3943-3952. https://doi.org/10.3168/jds.2013-7752
  • Pocrnic , I., D. A. L. Lourenco , Y. Masuda , A. Legarra , and I. Misztal . 2016. The dimensionality of genomic information and its effect on genomic prediction. Genetics 203:573-581. https://doi.org/10.1534/genetics.116.187013

See more references in the publications section.

Heat stress in dairy/beef cattle and pigs

Wherever heat stress affects livestock, for example in Southeastern USA, some animals perform satisfactorily but some perform poorly. We developed a methodology to study genetics of heat tolerance using easily available weather records. Our studies indicate that continued selection for performance in moderate climates makes cattle less heat tolerant for production and particularly reproduction. Analyzes of US data for Holsteins indicated negative trends for heat tolerance. Models from dairy are adopted in pigs and beef cattle for growth and for fertility.

Selected references

  • Aguilar, I., I. Misztal, S. Tsuruta. 2009. Genetic components of heat stress for dairy cattle with multiple lactations. J. Dairy Sci. 92:5702–5711. https://doi.org/10.3168/jds.2008-1928
  • Fragomeni, B. O., D. A. L. Lourenco, S. Tsuruta, S. Andonov, K. Gray, Y. Huang, and I. Misztal. 2016. Modeling response to heat stress in pigs from nucleus and commercial farms in different locations. J. Anim. Sci. 94:4789-4798.  https://doi.org/10.2527/jas.2016-0536
  • Bradford, H. L., B. O. Fragomeni, D. A. L. Lourenco, and I. Misztal. 2016. Genetic evaluations for growth heat tolerance in Angus beef cattle. J. Anim. Sci. 94: 4143–4150.  https://doi.org/10.2527/jas.2016-0707
  • Oseni, S., I. Misztal, S. Tsuruta, R. Rekaya. 2004. Genetic components of days open under heat stress. J. Dairy Sci. 87:4327–4333. https://doi.org/10.3168/jds.S0022-0302(04)73434-7
  • Ravagnolo, O., I. Misztal, G. Hoogenboom. 2000. Genetic component of heat stress in dairy cattle, development of heat index function. J. Dairy Sci. 83:2120–2125. https://doi.org/10.3168/jds.S0022-0302(00)75094-6

Efficient yet simple animal-breeding programming in modern Fortran

Use of object-oriented and matrix operations in   Fortran 90/95/2003/2008   can lead to programs that are almost as simple as in a matrix language but much more efficient. Read a paper titled:  Complex models, larger data, simpler computing?  This project has resulted in a large number of application programs. Why not C/C++/Java? We focus on algorithms t hat  can be programmed in any language. For numerical computations, modern  Fortran  seems to be the most efficient yet reasonably simple.

The programs now fully support genomic models and are capable of a large amount of data while remaining simple. The software become faster with optimized libraries replacing legacy subroutines. Conventional BLUP, REML and Gibbs sampling subroutines have been revised to be more efficient in computing time. Now the software are widely used throughout the world.

  • Aguilar, I., I. Misztal , A. Legarra , S.Tsuruta. 2011. Efficient computation of genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet. 128:422-428. https://doi.org/10.1111/j.1439-0388.2010.00912.x
  • Masuda, Y., S. Tsuruta, I. Aguilar, and I. Misztal. 2015. Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. J. Anim. Sci. 93:4670-4674. https://doi.org/10.2527/jas.2015-9395
  • Misztal, I. 1999. Complex models, larger data, simpler computing? Interbull Bulletin. 20:33-42. ( journal site )
  • Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet, D. H. Lee, 2002. BLUPF90 and related programs (BGF90). In: Proc. 7th World Cong. Genet. Appl. Livest. Prod., Communication 28-07. ( PDF )
  • Tsuruta, S., I. Misztal and I. Strandén. 2001. Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications. J. Anim. Sci. 79:1166-1172. https://doi.org/10.2527/2001.7951166x

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Animal Breeding And Genetics

Research papers/topics in animal breeding and genetics, genetic parameters of growth and reproduction in the west african dwarf goats reared in the humid tropics.

Abstract  Twenty-five intensively managed mature West African Dwarf goats were used for the experiment comprising 20 Does (dams) and 5 bucks (sires). The goats were classified into 5 mating pens of 4 Does (dams) and one buck (sire) randomly assigned per pen. Fresh water and forage were provided ad libitum in addition to 1kg concentrates Cajanus cajan to each animal per day. Data were collected on weights at birth and weaning; litter size and linear body measurements. Body weight gain was cal...

Effect of Ejaculation Frequency and Management Conditions on Semen Quality, Fertility and Hatchability of Local Turkeys in the Humid Tropics

ABSTRACT Two experiments were conducted to determine the effect of frequency of semen collection and management systems on semen quality, fertility and hatchability of local turkeys in the humid tropics. A total of 72 local Nigerian turkeys comprising 24 males and 48 females were used for the study at 36 weeks of age with average body weight of 9kg for the males and 4kg for the females. The males were randomly divided into two groups (1 and 2) with 12 males in each group. Group 1 males were i...

Genetic Study of Gudali and Wakwa Beef Cattle Breeds of Adamawa Region, Cameroon

ABSTRACT The present study was carried out to evaluate genetically the growth performance of the Gudali and Wakwa beef cattle. Data utilized for this study was obtained from the Institute of Agricultural Research for Development (IARD), Wakwa Station, Cameroon. The data used consisted of pedigree information of 3788 animals and 2276 performance records for the Gudali and Wakwa cattle respectively, ranging from birth to 36-months weight collected from 1968 and 1988. The data were collected fro...

Molecular Characterization And Some Environmental Factors Influencing Distribution Of The Endangered And Endemic Gulella Taitensis In Taita Hills, Kenya

ABSTRACT Gulella taitensis is a land snail of the family Streptaxidae and genus Gulella. It is endemic to Taita hills and categorized as endangered on the IUCN Red List. The species is threatened by habitat loss and disturbance due to human activities. Two people sampled snails at four sampling plots using standard timed direct search for one hour. Soil samples were collected from four different points within these sampling plots, and its pH, calcium and electrical conductivity obtained using...

Genetic Evaluation Of Net Feed Efficiency In Indigenous Chicken In Kenya

ABSTRACT Indigenous chicken are a valuable asset and form an integral part of many households in Kenya in terms of food security, economic and social roles. Given their value to the agricultural sector, various interventions are being developed and implemented to realize their potential in the sector. Such interventions involve genetic improvement of production traits which directly translate to increased revenues. However, this is likely to be accompanied with increased inputs, especially fe...

Bio-economic Modelling To Support Genetic Improvement Of Dairy Cattle In Kenya

A deterministic bio-economic model that incorporates risk for pasture-based dairy cattle production in the tropics was developed. Two production circumstances were considered: fixed pasture (FP) and fixed herd size (FH). In each circumstance, efficiencies (both economic and biological) and profit were calculated based on milk marketing on volume, and on volume and butter fat content. Additionally, a profit function was used to estimate risk-rated profit(s) where the intensity of the farm...

Management Of Genetic Diversity In Sahiwal Cattle Breed In Kenya

ABSTRACT The Sahiwal population in Kenya, which is bred under a closed nucleus, is faced with declining effective population size over the years and rate of inbreeding per generation >1% beyond which it should not exceed for a population to maintain its long-term fitness and viability. This study estimated gene origin statistics, Wright’s F-statistics, current and future rates of inbreeding, coancestry and effective population size and genetic gain in lactation milk yield at predetermined r...

Genetic Analysis Of Longevity And Performance Traits Of Sahiwal Cattle In Kenya

Genetic and phenotypic parameters for longevity, genetic relationship between longevity and growth, milk yield and fertility traits and rate of inbreeding were estimated for Sahiwal cattle in Kenya. The aim was to assess the genetic diversity and inbreeding depression for performance traits. Data utilized were for cows born between 1972 and 2004 and with milk production records between 1976 and 2008. Measures of longevity related to productive life were: time between birth (Long_1) or fi...

Optimising Dairy Cattle Breeding Systems By Incorporating Reproductive Technologies, Protein Yield And Resistance To Mastitis

The objective of this study was to contribute to dairy cattle improvement in Kenya through optimization of breeding systems that incorporate reproductive technologies and milk quality traits in the dairy cattle breeding programme. Specifically, the study: 1) compared response to selection realized in a closed two-tier nucleus breeding system utilizing different reproductive technologies, 2) estimated the economic values for milk protein yield and mastitis resistance, and 3) compared resp...

Characterisation Of Production Systems And Development Of Breeding Objectives For Indigenous Chicken In Rwanda

ABSTRACT Poultry production is one of the animal production enterprises with a promising future in Rwanda as 80.1% of all Rwandese raise chickens. Indigenous chickens (IC) are the most numerous and important species of poultry as they are found in most rural households in Rwanda. Currently, IC potential is underutilized due to the lack of well-defined production and breeding practices; the farmer, marketer and consumers’ breed preferences and traits of economic importance are unknown. The ...

ABSTRACT Genetic and phenotypic parameters for longevity, genetic relationship between longevity and growth, milk yield and fertility traits and rate of inbreeding were estimated for Sahiwal cattle in Kenya. The aim was to assess the genetic diversity and inbreeding depression for performance traits. Data utilized were for cows born between 1972 and 2004 and with milk production records between 1976 and 2008. Measures of longevity related to productive life were: time between birth (Long_1) o...

Breeding Goals For Production Systems Utilising Indigenous Chicken In Kenya

ABSTRACT Indigenous chicken are mainly kept in subsistence systems and constitute about 80% of Africa’s poultry flock. Currently, there are no well-defined breeding goals and genetic improvement programmes for the indigenous chicken are rare. The overall aim of this study was to develop breeding goals for use in production systems utilising the indigenous chicken. The specific objectives were to construct a deterministic bio-economic model for the economic evaluation of production systems ...

Genetic Diversity Of Aphid Species Attacking Amaranth And Nightshades In Different Agro-Ecological Zones Of Kenya And Tanzania

ABSTRACT Aphids are the major pests of vegetables leading to a significant yield loss in African indigenous vegetables including amaranth and nightshades. Information on the types of aphids that infest these vegetables and their genetic diversity in Kenya and Tanzania is scanty. This is an important diagnostic component in developing management strategies such as integrated pest management and early detection and control of invasive species. This study used a fragment of the mitochondrial cyt...

Spatial Distribution And Assessment Of Genetic Diversity Of The Kenyan Mount Ain Bongo (Tragelaphus Eurycerus Isaaciy Using Dna Based Molecular Markers

ABSTRACT The Kenyan mountaiu bongo (Fr(/ge/fI/J/lII,' curycerus is(1f1ci) is an endangered Tragclnphinc antelope sub-species endemic to Mt. Kenya- Aberdare ecosystems. This antelope has currently been reduced to pockets of small populations in forest patches (mainly in the Aberdares) exacerbated primarily by habitat fragmentation due to anthropogenic and disease factors. The conservation and survival of these species needs vital cmpiricul datil 011 critical luctors such as genetic processes t...

Phenotypic Characterization of Local Chickens (Gallus Gallus Domesticus) In Bekwarra Cross River State, Nigeria

This study was conducted in Bekwarra Local Government Area of Cross River State, Nigeria, to identify and determine some characteristics of local chickens. A total of 530 adult chickens of both sexes and 111 fresh eggs were carefully examined at seven administrative council wards of the local government. About 43.00% of the birds observed were male while 57.00% were females. Statistical Package for Social Sciences (SPSS) was employed to carry out descriptive statistics on qualitative and quan...

Animal breeding is a branch of animal science that addresses the evaluation of the genetic value (estimated breeding value, EBV) of livestock. Selecting for breeding animals with superior EBV in growth rate, egg, meat, milk, or wool production, or with other desirable traits has revolutionized livestock production throughout the world. Afribary curates list of academic papers and project topics in Animal Breeding And Genetics. You can browse through Animal Breeding And Genetics Project topics, Animal Breeding And Genetics thesis topics, Animal Breeding And Genetics seminar topics, Animal Breeding And Genetics research papers, termpapers topics in Animal breeding& Genetics. Animal breeding & genetics projects, thesis, seminars and termpapers topic and materials

Popular Papers/Topics

Effects of breed, sex and age on serum biochemistry in nigerian goats, genetic and non genetic factors affecting serum biochemical parameters in nigerian sheep, the effects of phenotypic and blup selection methods in livestock populations (swine) in tropical developing countries ., assessment of funaab alpha broiler chicken of genetic gain for breast girth, phenotypic characterization of chicken inbred lines that differ extremely in growth, body composition and egg production traits, healing effect of faldherbia albida stem bark extract (fasb) on burn wound regarding tissue regeneration in wistar albino rats, variation of meat-type chickens in relation to genotypes and age of slaughter on carcass indices., genotype and sex effects on the performance characteristics of pigs, effects of age and management system on egg quality traits of fulani ecotype hens, sex identification of nigerian indigenous frizzle feathered and naked neck chicks using vent sexing and molecular technique, quantitative trait loci segregating in crosses between new hampshire and white leghorn chicken lines: egg production traits, associaton of kappa-casein genotype and the linear parameter in two indigenious bos indicus and bos taurus cattle in nigeria, transcriptome profiling of four candidate milk genes in milk and tissue samples of temperate and tropical cattle, variants mining of kappa casein k-cn and prolactin prl genes among four indigenous cattle breeds in nigeria, comparison of conventional and automated freezing methods on pb2 rooster semen cryopreserved with glycerol and dimethylsulfoxide tris coconut-water extender.

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Perspective article, the new era of canine science: reshaping our relationships with dogs.

animal breeding research paper

  • 1 School of Anthropology, University of Arizona, Tucson, AZ, United States
  • 2 College of Veterinary Medicine, University of Arizona, Tucson, AZ, United States
  • 3 Cognitive Science, University of Arizona, Tucson, AZ, United States
  • 4 California State Polytechnic University, Pomona, CA, United States
  • 5 Department of Psychology, Western Carolina University, Cullowhee, NC, United States
  • 6 Center for Urban Resilience, Loyola Marymount University, Los Angeles, CA, United States
  • 7 Animal Welfare Science Centre, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, Australia

Canine science is rapidly maturing into an interdisciplinary and highly impactful field with great potential for both basic and translational research. The articles in this Frontiers Research Topic, Our Canine Connection: The History, Benefits and Future of Human-Dog Interactions , arise from two meetings sponsored by the Wallis Annenberg PetSpace Leadership Institute, which convened experts from diverse areas of canine science to assess the state of the field and challenges and opportunities for its future. In this final Perspective paper, we identify a set of overarching themes that will be critical for a productive and sustainable future in canine science. We explore the roles of dog welfare, science communication, and research funding, with an emphasis on developing approaches that benefit people and dogs, alike.

Dogs have played important roles in the lives of humans for millennia ( 1 , 2 ). However, throughout much of scientific history they have been dismissed as an artificial species with little to contribute to our understanding of the natural world, or our place within it. During the last two decades, this sentiment has changed dramatically; canine science is rapidly maturing into an established, impactful, and highly interdisciplinary field ( Figure 1 ). Canine scientists, who previously occupied relatively marginalized roles in academic research, are increasingly being hired at major research universities, and centers devoted to the study of dogs and their interactions with humans are proliferating around the world. The factors underlying dogs' newfound popularity in science are diverse and include (1) increased interest in understanding dog origins, behavior, and cognition; (2) diversification in our approaches to research with non-human animals; (3) recognition of dogs' value as a unique biological model with relevance for humans; and (4) growth in research on the nature and consequences of dog-human interactions, in their myriad forms, from working dog performance to displaced canines living in shelters.

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Figure 1 . Canine science is an interdisciplinary field with connections to other traditional and emerging areas of research. The specific fields shown overlap in ways not depicted here and are not an exhaustive list of disciplines contributing to canine science. Rather, they are included as examples of the diversity of scholarship in canine science.

This Perspective represents the final article in a collection of manuscripts arising from two workshops sponsored by the Wallis Annenberg PetSpace Leadership Institute. Leadership Fellows from around the world gathered in 2017 and 2020 to discuss the state of research and future directions in canine science. The individual articles in this collection provide a detailed treatment of key topics discussed at these events. In this final article, we identify a set of overarching challenges that emerge from this work and identify priorities and opportunities for the future of canine science.

The rise of canine science has benefited substantially from public interest and participation in the research process. Unlike many research studies, which unfold quietly in the ivory towers of research universities, the new era of canine science is intentionally public facing. The dogs being studied are not laboratory animals, bred and housed for research purposes, but rather are companions living in private homes, or assisting humans in capacities ranging from assistance for people with disabilities, to medical and explosives detection. Campus-based research laboratories have opened their doors to members of the public who bring their dogs to participate in problem-solving tasks, social interactions, and sometimes even non-invasive neuroimaging studies. Increasingly, dog owners themselves play a significant role in the scientific process, serving as community scientists who contribute to the systematic gathering of data from the convenience of their homes.

This new research model in conjunction with emerging technologies, makes canine science a highly visible field that engages public stakeholders in unprecedented ways. From a scientific perspective, society has become the new laboratory, and in doing so, has facilitated research with dogs of a scope and scale that was heretofore unthinkable. As tens of thousands of dogs contribute to research on topics ranging from cognition and genetics ( 3 , 4 ) to aging and human loneliness ( 5 ), canine science is entering the realm of “big data” and eclipsing many traditional research approaches. Importantly, these advances are occurring simultaneously across diverse fields of science, creating powerful new opportunities for consilience that will make canine science even more valuable in the years ahead. However, maturing this model toward a sustainable future that serves its diverse stakeholders—who include scientists, research funders, members of the public, and dogs themselves—will require careful navigation of key challenges related to dog welfare, science communication, and financial support ( Figure 2 ).

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Figure 2 . Visual summary of the key issues identified in this Perspective . A sustainable future in canine science will require (1) research approaches that prioritize and monitor the welfare of dogs, (2) improved science communication to avoid incorrect reporting of study results, and to translate research findings to meaningful change in practices relating to dogs, and (3) availability of research funding that is not tied exclusively to studying the possible benefits of dogs for humans.

Dog Welfare

Globally, animal welfare has been linked to the public acceptability that underpins sustainable animal interactions and partnerships ( 6 ). Where human-animal interactions have failed to meet community expectations, practices and in some case entire industries, have been disrupted or ceased. Recent examples include whaling for profit and greyhound racing ( 6 , 7 ). Science is not exempt from this necessity to meet with public expectations and the new era of canine science must place canine welfare at the forefront. Considering dogs as individuals and co-workers, rather than tools for work or subjects, reflects a community moral and ethical paradigm shift that is currently underway. Reimagining our relationship with domestic dogs in research will also help inform our treatment of other animals. In this way, studies of dogs and our interactions with them can serve as a pioneering new model for many areas of science.

As scientists advocate for the revision of community and industry practices with dogs in light of new evidence, we must apply the same criteria to the conduct of our research. This includes adjusting canine research and training methods to acknowledge the sentience of dogs, and the importance of the affective experience for dogs in both research and community settings ( 8 – 11 ). The discipline of animal welfare science has progressed rapidly over the last two decades, and we have many animal-based, welfare-outcome measures available to us ( 6 , 11 ). Ensuring the well-being of the dogs we study will be as critical to ongoing social license to operate (i.e., community approval) for canine science as it is for working dog interests ( 12 ). Being transparent about the issues of animal consent and vulnerability, as well as offering animals agency with regard to their participation in science are valuable suggestions offered within this special issue. We encourage our colleagues to not just consider this paradigm shift, but to effect it through prioritizing and representing the dog's perspective and welfare in their research.

Although increasingly, researchers may include a single or limited set of canine stress measures in studies exploring dogs' potential benefits to humans, this approach alone does not fill the need for studies that prioritize an understanding of canine welfare as their central focus. Canine welfare should be considered not just as an emergent population-level measure ( 13 ) but rather with respect to the way in which it is experienced: from the perspectives of individual dogs. Commonly used statistical methods from human research, such as group-based trajectory analysis ( 14 ) may offer proven techniques that allow meaningful reporting on populations while reflecting the nuance of shared, sub-group patterns. Such approaches will better reflect individual differences, for example variations in canine personality, social support and relationship styles, as well as other significant factors. One impediment to robust measurement of animal welfare in canine science has been limited funding.

We believe that all granting bodies who fund exploration of the possible benefits to people from dogs should also fund and require the canine perspective to be robustly monitored and reported. Impediments to this work arise not from lack of researcher interest or access to dogs, but rather from challenges to securing funding that is independent from a focus on human health outcomes, or other tangible outcomes of work that dogs perform. To be able to optimize canine welfare, there is an urgent need for increased funding specifically to study the welfare of dogs, in all their diversity. The new era of canine science will identify what dogs need to thrive, propelling us toward a mutually sustainable partnership between people and dogs.

Communication

One area that has not received much attention in relation to canine science is the way in which research findings are communicated outside the empirical literature. Fueled by media reports, interest in canine science and the impact of dogs on human health and well-being has grown substantially in the last 10 years. A survey by the Human-Animal Bond Research Institute found that 71% of pet owners were aware of studies demonstrating that pets improve mental and physical health. Some of these claims are justified. For example, many studies have found that interacting with therapy dogs reduces stress and anxiety and increases positive emotional states in a variety of settings including hospitals, schools and nursing homes ( 15 , 16 ). In other cases, high public expectations about the healing power of pets are not matched by the results of empirical studies. For instance, while the Human-Animal Bond Research Institute survey found that 86% of pet owners believe pets relieve depression, the majority of studies on pet-ownership and depression do not support these conclusions ( 17 ).

Because so many people have extensive personal experiences with dogs, investigators face unique challenges in sharing research results with the public. In their hearts, dog owners believe that their canine companions make them feel less depressed, or that dogs feel guilty when they've eliminated indoors or explored the kitchen garbage—even though research might suggest otherwise. In addition, when it comes to animal companions, people much prefer to read a news article in which visits with a therapy dog improved the well-being of a child undergoing chemotherapy than an article about a randomized clinical trial which found no differences between the well-being of children in a therapy dog group and a control group ( 18 ). Nor is there likely to be much press coverage devoted to methodological issues such as small effect sizes and inappropriate attributions of causality to the results of correlational studies.

Canine scientists and scholars of human-animal interactions (anthrozoologists) are fortunate that the public is intrinsically interested in our research. We feel that it is critical for investigators to make efforts to communicate the findings of important studies to the public. We caution however, that researchers should not overstate the implications of their findings in press releases and conversations with journalists, despite frequent pressure to do so. These distortions could have a negative impact on misleading the public and misrepresenting the actual findings, a problem that is particularly acute in canine science where well-intentioned pet owners may eagerly adopt practices based on media coverage of scientific studies. The now-established discipline of science communication offers guidance for how best to engage with community and research stakeholders in meaningful ways.

Traditionally, science communication has relied on the knowledge deficit model of communication ( 19 ). Directionally one-way, the deficit model operates on the assumption that ignorance is the reason for a lack of community support and application of scientific evidence. Examples where practices have not been updated in response to research findings include dog training methodology ( 9 ) and breeding selection for extreme body types, such as brachycephaly in pugs and bulldogs, even though the health and welfare impacts are scientifically well understood ( 20 ). Scientists who share their research results thinking that knowledge disseminated—to “educate” the public—is enough to result in different dog care decisions, industry practices or legislation, will generally find this to be ineffective ( 21 ). This is because the deficit model overlooks the underlying beliefs, existing attitudes and motivations for current practices. We now recognize that the deficit model is not the most effective way to communicate, engage stakeholders and effect change ( 22 , 23 ).

Further exploration of the effect of targeted and intentional science communication, informed by human behavior change research, will improve the translation of canine science to meaningful outcomes for dogs and people alike ( 12 ). This is important, as many studies in canine science have applied aims designed to inform global policies and the creation of best practices ( 24 , 25 ). Applied research from the livestock and farming sector suggests that coordinating human behavior change strategies from social and psychological sciences can influence beliefs and attitudes to motivate changes in the ways people behave toward animals, resulting in improved animal welfare ( 26 – 28 ). In the era of attention economics, where scientists are competing for public attention alongside other diverse media, it is vital that the communication of our work is honest, relevant, and effective, to ensure that our field stays on the radar of key stakeholders, funding bodies and change agents.

A third key challenge in the future of canine science concerns research funding and a careful balancing of the priorities of scientists and funding agencies. In the last decade, canine science has received considerable support from the pet care sector, as well as human health and defense agencies [e.g., ( 29 )]. Fine and Andersen ( 30 ) stress that although funding is still a challenge in human-animal interaction research, there are now more options to be found. In 2008, the Waltham Petcare Science Institute initiated a public-private partnership with the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Over the past decade, this partnership has provided funding for research aimed at measuring the impact of specific Animal-assisted interventions. Since 2014, the Human Animal Bond Research Institute has funded a total of 35 academic research grants investigating the health outcomes of pet ownership and/or human-animal interaction, both for the people and non-human animals involved. Despite clear benefits for enabling research, there remains a limited group of agencies responsible for funding this work. This has potential to constrain the range of topics being studied. In addition, scientists may feel compelled to support the agendas of industry groups, such as those in the pet sector, who often encourage research that will demonstrate the benefits of pets and human-animal interactions.

These constraints were recognized by Wallis Annenberg PetSpace in 2017 when they envisioned their Leadership Institute Program with a mission to promote interdisciplinary scholarship and convene meetings to accelerate research and policy development ( https://www.annenbergpetspace.org/about/leadership ). This model for engagement inspired the organization to offer two invited retreats (2017, 2020) for a total of 33 experts in the field that provided opportunities for open ended and frank discussion about the nature of human-animal interaction research, and the maturing field of canine science. By providing the space and financial support, plus the opportunity to work together and publish, Annenberg PetSpace provided a way to both illuminate current limitations, and to identify priorities for the future, free of constraints from outside interest groups. These intellectual salons have no specific agenda other than to consider the future of the field and what kinds of questions need to be asked based on what we already know. The results of these two retreats include 14 published refereed papers, plus a suite of collaborations that might otherwise not have happened. We hope that these fellowships and retreats continue and inspire others to support similar initiatives so that scholars across multiple disciplines have the opportunity to experience the transformational exchanges that occur during these programs. The new era of canine science will require diverse funding that is not limited to how dogs can benefit humans, from health, safety and economic perspectives. This change will enable researchers the freedom to further our understanding of dogs and their needs for optimized welfare. In turn, this will allow us to identify how dogs and people can thrive together.

Looking Ahead

We hope that the publications emerging from these retreats will reach a diverse community of stakeholders, including students, early career researchers, animal welfare and advocacy groups, legislators and policy makers, philanthropies, and traditional agency funders. The goal of these papers is to spark imagination for projects not yet engaged and to help set the agenda for future research that can enhance our understanding of human-dog interactions and identify paths to ensure a future of symbiotic relationships between these species.

The vision of this collective group of scholars includes the goal of establishing studies with dogs as a sustainable and broad-reaching research focus. Although dogs provide many advantages as a “model species” —including their phenotypic diversity, and shared environments and evolutionary history with humans—a research model centered around dogs has many additional benefits. Dogs provide a rich, interactive and sentient model with deep implications for the way scientists approach animal research, and animal welfare. Dogs also increase the accessibility of research, both literally, due to their ubiquity and opportunities for large-scale public participation in research ( 31 , 32 ), and figuratively, through a body of work with appeal to the broader public.

The field of canine science has much in common with a similar emerging science, that of urban ecology. Humans are historically at the core of the subject material, but non-human elements are often the focus of the study. As such, the work is always culturally embedded, relevant to a variety of stakeholders, and ultimately expected to improve quality of life. The urban ecologists coined a term Use-Inspired Research ( 33 ) from modifying the existing idea of Pasteur's Quadrant which organizes research questions across the axes of fundamental understanding and considerations of use ( 34 ). Both canine research and urban ecology seek fundamental understanding, but also expect to directly apply the knowledge gained to improve outcomes for their subjects and stakeholders.

By including the public in canine science we not only increase the quantity of the data that we can gather, we serve as ambassadors for a new model of responsible animal research. The result increases the value of human-animal interaction research and creates opportunities for the next generation of interdisciplinary scientists. The goal of this collection has been both to highlight specific recent advances in canine science as well as to identify emerging and overarching issues that will shape the future of this field. The multidisciplinary nature of our work with dogs allows scientists to contribute to a robust research agenda, enhancing our understanding of canines and their impact on society. Ultimately, the nexus of our discoveries should have profound effects on reshaping and enriching our relationships with dogs.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

We thank Wallis Annenberg PetSpace for supporting the open-access publishing fees associated with this manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: canine science, dog, animal welfare, human-animal interaction, science communication, funding, sustainability

Citation: MacLean EL, Fine A, Herzog H, Strauss E and Cobb ML (2021) The New Era of Canine Science: Reshaping Our Relationships With Dogs. Front. Vet. Sci. 8:675782. doi: 10.3389/fvets.2021.675782

Received: 03 March 2021; Accepted: 11 June 2021; Published: 15 July 2021.

Reviewed by:

Copyright © 2021 MacLean, Fine, Herzog, Strauss and Cobb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Evan L. MacLean, evanmaclean@arizona.edu

This article is part of the Research Topic

Our Canine Connection: The History, Benefits and Future of Human-Dog Interactions

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