Review Article | Published:

Harnessing genomic information for livestock improvement

Nature Reviews Geneticsvolume 20pages135156 (2019) | Download Citation


The world demand for animal-based food products is anticipated to increase by 70% by 2050. Meeting this demand in a way that has a minimal impact on the environment will require the implementation of advanced technologies, and methods to improve the genetic quality of livestock are expected to play a large part. Over the past 10 years, genomic selection has been introduced in several major livestock species and has more than doubled genetic progress in some. However, additional improvements are required. Genomic information of increasing complexity (including genomic, epigenomic, transcriptomic and microbiome data), combined with technological advances for its cost-effective collection and use, will make a major contribution.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


International Mouse Phenotype Consortium:

NCBI Genome database:

Online Mendelian Inheritance in Animals:

Online Mendelian Inheritance in Man:


  1. 1.

    Thornton, P. K. Livestock production: recent trends, future prospects. Phil. Trans. R. Soc. B 365, 2853–2867 (2010).

  2. 2.

    Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, Sunderland, MA, 1998).

  3. 3.

    Havenstein, G. B., Ferket, P. R. & Qureshi, M. A. Growth liability and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult. Sci. 82, 1500–1508 (2003).

  4. 4.

    Sigel, P. B. Evolution of the modern broiler and feed efficiency. Annu. Rev. Anim. Biosci. 2, 375–385 (2014).

  5. 5.

    Capper, J. L. & Bauman, D. E. The role of productivity in improving the environmental sustainability of ruminant production systems. Annu. Rev. Anim. Biosci. 1, 469–489 (2013).

  6. 6.

    Pryce, J. E., Royal, M. D., Garnsworthy, P. C. & Mao, I. L. Fertility in the high-producing dairy cow. Livestock Prod. Sci. 86, 125–135 (2004).

  7. 7.

    Goddard, M. E. & Hayes, B. J. Mapping genes for complex traits in domestic animals and their use in breeding programs. Nat. Rev. Genet. 10, 381–391 (2009).

  8. 8.

    Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001). This is a landmark paper that triggered the adoption of GS by the breeding industry.

  9. 9.

    Hill, W. G., Goddard, M. E. & Visscher, P. Data and theory point to mainly additive genetic variance for complex traits. PLOS Genet. 4, e1000008 (2008).

  10. 10.

    Wiggans, G. R., Cole, J. B., Hubbard, S. M. & Sonstegard, T. S. Genomic selection in dairy cattle: the USDA experience. Annu. Rev. Anim. Biosci. 5, 309–327 (2017).

  11. 11.

    Van Eenennaam, A. L., Weigel, K. A., Young, A. E., Cleveland, M. A. & Dekkers, J. C. Applied animal genomics: results from the field. Annu. Rev. Anim. Biosci. 2, 105–139 (2014).

  12. 12.

    Crossa, J. et al. Genomics selection in plant breeding: methods, models and perspectives. Trends Plant Sci. 22, 9661–9975 (2017).

  13. 13.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

  14. 14.

    Gusev, A. et al. Quantifying missing heritability at known GWAS loci. PLOS Genet. 9, e1003993 (2013).

  15. 15.

    Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

  16. 16.

    Herrero, M. et al. Livestock and the environment: what have we learned in the past decade? Annu. Rev. Environ. Resour. 40, 177–202 (2015).

  17. 17.

    International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716 (2004).

  18. 18.

    Bovine Genome Sequencing and Analysis Consortium. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science 324, 522–528 (2009).

  19. 19.

    Swine Genome Sequencing Consortium. Analysis of pig genomes provide insight into porcine demography and evolution. Nature 491, 393–398 (2012).

  20. 20.

    Dong, Y. et al. Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nat. Biotechnol. 31, 135–141 (2013).

  21. 21.

    Jiang, Y. et al. The sheep genome illuminates biology of the rumen and lipid metabolism. Science 344, 1168–1173 (2014).

  22. 22.

    Lien, S. et al. The Atlantic salmon genome provides insights into rediploidization. Nature 533, 200–205 (2016).

  23. 23.

    Green, E. D. Strategies for the systematic sequencing of complex genomes. Nat. Rev. Genet. 2, 573–583 (2001).

  24. 24.

    Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

  25. 25.

    Daetwyler, H. D. et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat. Genet. 8, 858–865 (2014). This is a report by the 1,000 Bull Genomes Project consortium that introduces a community resource to accelerate the detection and use of causative variants in cattle.

  26. 26.

    Worley, K. C. A golden goat genome. Nat. Genet. 49, 485–486 (2017).

  27. 27.

    Bickhart, D. M. et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat. Genet. 49, 643–650 (2017). This paper brilliantly illustrates the utility of novel scaffolding techniques in dramatically improving the quality of reference genome sequences in an affordable way.

  28. 28.

    International Chicken Polymorphism Map Consortium. A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms. Nature 432, 717–722 (2004).

  29. 29.

    Bovine HapMap Consortium. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528–532 (2009).

  30. 30.

    Charlier, C. et al. NGS-based reverse genetic screen for common embryonic lethal mutations compromising fertility in livestock. Genome Res. 26, 1–9 (2016).

  31. 31.

    The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  32. 32.

    MacLeod, I. M. et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics 17, 144 (2016).

  33. 33.

    Bourneuf, E. et al. Rapid discovery of de novo deleterious mutations in cattle enhances the value of livestock as model species. Sci. Rep. 7, 11466–11485 (2017).

  34. 34.

    Kadri, N. K. et al. Coding and non-coding variants in HFM1, MLH3, MSH4, MSH5, RNF212 and RNF212B affect recombination rate in cattle. Genome Res. 26, 1323–1332 (2016).

  35. 35.

    Schaub, M. A., Boyle, A. P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

  36. 36.

    Huang, H. et al. Association mapping of inflammatory bowel disease loci to single variant resolution. Nature 547, 173–178 (2017).

  37. 37.

    Andersson, L. et al. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol. 16, 57–63 (2015).

  38. 38.

    Tuggle, C. K. et al. GO-FAANG meeting: a gathering on functional annotation of animal genomes. Anim. Genet. 47, 528–533 (2016).

  39. 39.

    Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).

  40. 40.

    Zhou, Y. et al. Reduced representation bisulphite sequencing of ten bovine somatic tissues reveals DNA methylation patterns and their impacts on gene expression. BMC Genomics 17, 779 (2016).

  41. 41.

    Littlejohn, M. D. et al. Expression variants of the lipogenic AGPAT6 gene affect diverse milk composition phenotypes in Bos taurus. PLOS ONE 9, e85757 (2014).

  42. 42.

    Littlejohn, M. D. et al. Sequence-based association analysis reveals an MGST1 eQTL with pleiotropic effects on bovine milk composition. Sci. Rep. 6, 25376–25390 (2016).

  43. 43.

    Kemper, K. E. et al. Leveraging genetically simple traits to identify small-effect variants for complex phenotypes. BMC Genomics 17, 858–867 (2016).

  44. 44.

    Brand, B. et al. Adrenal cortex expression quantitative trait loci in a German Holstein x Charolais cross. BMC Genetics 17, 135–146 (2016).

  45. 45.

    Lopdell, T. J. et al. DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content. BMC Genomics 18, 968 (2017).

  46. 46.

    Ponsuksili, S., Murani, E., Brand, B., Schwerin, M. & Wimmers, K. Integrating expression profiling and whole-genome association for dissection of fat traits in a porcine model. J. Lipid Res. 52, 6668–6678 (2011).

  47. 47.

    Liaubet, L. et al. Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism. BMC Genomics 12, 548–565 (2011).

  48. 48.

    Ernst, C. W. & Steibel, J. P. Molecular advances in QTL discovery and application in pig breeding. Trends Genet. 29, 215–224 (2013).

  49. 49.

    Heidt, H. et al. A genetical genomics approach reveals new candidates and confirms known candidate genes for drip loss in a porcine resource population. Mamm. Genome 24, 416–426 (2013).

  50. 50.

    Chen, C. et al. Genetic dissection of blood lipid traits by integrating genome-wide association study and gene expression profiling in a porcine model. BMC Genomics 14, 848–859 (2013).

  51. 51.

    Ponsuksili, S., Murani, E., Trakooljul, N., Schwerin, M. & Wimmers, K. Discovery of candidate genes for muscle traits based on GWAS supported by eQTL-analysis. Int. J. Biol. Sci. 10, 327–337 (2014).

  52. 52.

    Reiner, G. et al. Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine. Mamm. Genome 25, 600–617 (2014).

  53. 53.

    Ma, J. et al. A splice mutation in the PHKG1 gene causes high glycogen content and low meat quality in pig skeletal muscle. PLOS Genet. 10, e1004710 (2014).

  54. 54.

    Kogelman, L. J. et al. An integrative systems genetics approach reveals potential causal genes and pathways related to obesity. Genome Med. 7, 105–120 (2015).

  55. 55.

    Martinez-Montes, A. M. et al. Deciphering the regulation of porcine genes influencing growth, fatness and yield-related traits through genetical genomics. Mamm. Genome 28, 130–142 (2017).

  56. 56.

    Gonzalez-Prendes, R. et al. Joint QTL mapping and gene expression analysis identify positional candidate genes influencing pork quality traits. Sci. Rep. 7, 39830–39839 (2017).

  57. 57.

    Maroilley, T. et al. Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis. BMC Genomics 18, 967 (2017).

  58. 58.

    Blum, Y. et al. Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken. BMC Genomics 12, 567–575 (2011).

  59. 59.

    Johnsson, M., Jonsson, K. B., Andersson, L., Jensen, P. & Wright, D. Quantitative trait locus and genetical genomics analysis identifies putatively causal genes for fecundity and brooding in the chicken. G3 6, 311–319 (2015).

  60. 60.

    Johnsson, M., Williams, M. J., Jensen, P. & Wright, D. Genetical genomics of behavior: a novel chicken genomic model for anxiety behavior. Genetics 202, 327–340 (2016).

  61. 61.

    Fallahsharoudi, A. et al. QTL mapping of stress related gene expression in cross between domesticated chickens and ancestral red junglefowl. Mol. Cell. Endocrinol. 446, 52–58 (2017).

  62. 62.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  63. 63.

    Dunne, J. et al. First dairying in green Saharan Africa in the fifth millennium BC. Nature 486, 390–394 (2012).

  64. 64.

    Andersson, L. S. et al. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice. Nature 488, 642–646 (2012). This paper demonstrates the value of domestic animals in uncovering functions of mammalian genes by studying unique selected phenotypes, in this case ‘ambling’.

  65. 65.

    Andersson, L. Molecular consequences of animal breeding. Curr. Opin. Genet. Dev. 23, 295–301 (2013).

  66. 66.

    Durkin, K. et al. Serial translocations by means of circular intermediates underlies color sidedness in cattle. Nature 482, 81–84 (2012). This study identified a novel CNV-generating mechanism that may underlie exon shuffling by studying coat-colour variation in cattle.

  67. 67.

    Clop, A. et al. A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nat. Genet. 38, 813–818 (2006). This paper describes one of the most convincing examples in mammals of a phenotype resulting from perturbed microRNA-mediated gene regulation.

  68. 68.

    Georges, M. et al. in Epigenetics and Complex Traits (eds Naoumova, A. K. & Greenwood, C. M. T.) 89–106 (Springer, New York, NY, 2013). This paper reviews the current molecular understanding of the unique phenomenon of polar overdominance at the ovine callipyge locus.

  69. 69.

    MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

  70. 70.

    Bittles, A. H. & Neel, J. V. The costs of human inbreeding and their implications for variations at the DNA level. Nat. Genet. 8, 117–121 (1994).

  71. 71.

    Simmons, M. J. & Crow, J. F. Mutations affecting fitness in Drosophila populations. Annu. Rev. Genet. 11, 49–78 (1977).

  72. 72.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  73. 73.

    Baker, R. D., Snider, G. W., Leipold, H. E. & Johnson, J. L. Embryo transfer tests for bovine syndactyly. Theriogenology 13, 87 (1980).

  74. 74.

    Charlier, C. et al. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat. Genet. 40, 449–454 (2008). This study is one of the first illustrations of how genome-wide SNP arrays accelerated the identification of mutations causing genetic defects in domestic animals.

  75. 75.

    Littlejohn, M. D. et al. Functionally reciprocal mutations of the prolactin signalling pathway define hairy and slick cattle. Nat. Commun. 5, 5861–5869 (2014).

  76. 76.

    Agerholm, J. S. et al. A de novo missense mutation of FGFR2 causes facial dysplasia syndrome in Holstein cattle. BMC Genetics 18, 74–83 (2017).

  77. 77.

    Harland, C. et al. Frequency of mosaicism points towards mutation-prone early cleavage cell divisions. Preprint at bioRxiv (2016).

  78. 78.

    VanRaden, P. M., Olson, K. M., Null, D. J. & Hutchison, J. L. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 94, 6153–6161 (2011). This study is one of the first demonstrations of how the availability of genome-wide SNP genotypes for large populations can be used to effectively identify haplotypes harbouring EL mutations affecting fertility.

  79. 79.

    Adams, H. A. et al. Identification of a nonsense mutation in APAF1 that is likely causal for a decrease in reproductive efficiency in Holstein dairy cattle. J. Dairy Sci. 99, 6693–6701 (2016).

  80. 80.

    Kadri, N. K. et al. A 660-Kb deletion with antagonistic effects on fertility and milk production segregates at high frequency in Nordic Red cattle: additional evidence for the common occurrence of balancing selection in livestock. PLOS Genet. 10, e1004049 (2014).

  81. 81.

    Sahana, G., Nielsen, U. S., Aamand, G. P., Lund, M. P. & Guldbrandtsen, B. Novel harmful recessive haplotypes identified for fertility traits in Nordic Holstein cattle. PLOS ONE 8, e82909 (2013).

  82. 82.

    Fritz, S. et al. Detection of haplotypes associated with prenatal death in dairy cattle and identification of deleterious mutations in GART, SHBG and SLC37A2. PLOS ONE 8, e65550 (2013).

  83. 83.

    Pausch, H. et al. Homozygous haplotype deficiency reveals deleterious mutations compromising reproductive and rearing success in cattle. BMC Genomics 16, 312 (2015).

  84. 84.

    Häggman, J. & Uimari, P. Novel harmful recessive haplotypes for reproductive traits in pigs. J. Anim. Breed. Genet. 134, 129–135 (2017).

  85. 85.

    Derks, M. F. L. et al. A systematic survey to identify lethal recessive variation in highly managed pig populations. BMC Genomics 18, 858–870 (2017).

  86. 86.

    McClure, M. C. et al. Bovine exome sequence analysis and targeted SNP genotyping of recessive fertility defects BH1, HH2, and HH3 reveal a putative causative mutation in SMC2 for HH3. PLOS ONE 9, e92769 (2014).

  87. 87.

    Sonstegard, T. S. et al. Identification of a nonsense mutation in CWC15 associated with decreased reproductive efficiency in Jersey cattle. PLOS ONE 8, e54872 (2013).

  88. 88.

    Fritz, S. et al. An initiator codon mutation in SDE2 causes recessive embryonic lethality in Holstein cattle. J. Dairy Sci. 101, 6220–6231 (2018).

  89. 89.

    Martinez, V., Bünger, L. & Hill, W. G. Analysis of response to 20 generations of selection for body composition in mice: fit to infinitesimal model assumptions. Genet. Sel. Evol. 32, 3 (2000).

  90. 90.

    Henderson, C. R. Sire evaluation and genetic trends. J. Anim. Sci. 1973, 10–41 (1973).

  91. 91.

    Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).

  92. 92.

    Malecot, G. Les mathématiques de l’hérédité (Masson, Paris, 1948).

  93. 93.

    Ritland, K. Estimators of pairwise relatedness and individual inbreeding coefficients. Genet. Res. 67, 175–185 (1996).

  94. 94.

    Toro, M. et al. Estimation of coancestry in Iberian pigs using molecular markers. Conserv. Genet. 3, 309–320 (2002).

  95. 95.

    Garant, D. & Kruuk, L. E. B. How to use molecular marker data to measure evolutionary parameters in wild populations. Mol. Ecol. 14, 1843–1859 (2005).

  96. 96.

    Aguilar, I. et al. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93, 743–752 (2010). This study is one of the first demonstrations that pedigree, genomic and phenotypic information from millions of animals could be combined in a single-step approach to predict GEBVs.

  97. 97.

    Christensen, O. F. & Lund, M. S. Genomic prediction when some animals are not genotyped. Genet. Sel. Evol. 42, 2 (2010).

  98. 98.

    Liu, Z., Goddard, M. E., Reinhardt, F. & Reents, R. A single-step genomic model with direct estimation of marker effects. J. Dairy Sci. 97, 5833–5850 (2014).

  99. 99.

    Fernando, R. L., Dekkers, J. C. & Garrick, D. J. A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genet. Sel. Evol. 46, 50 (2014).

  100. 100.

    Fernando, R. L., Cheng, H., Golden, B. L. & Garrick, D. J. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet. Sel. Evol. 48, 96 (2016).

  101. 101.

    Misztal, I. & Legarra, A. Invited review: efficient computation strategies in genomic selection. Animal 11, 731–736 (2017).

  102. 102.

    García-Ruiz, A. et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc. Natl Acad. Sci. USA 113, 3995–4004 (2016). This study demonstrates the impact of GS on genetic gain in the US dairy industry.

  103. 103.

    Van Eenennaam, A. L., van der Werf, J. H. & Goddard, M. E. The value of using DNA markers for beef bull selection in the seedstock sector. J. Anim. Sci. 89, 307–320 (2011).

  104. 104.

    Lourenco, D. A. et al. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. J. Anim. Sci. 93, 2653–26662 (2015).

  105. 105.

    Abell, C. E., Dekkers, J. C. M., Rothschild, M. F., Mabry, J. W. & Stalder, K. J. Total cost estimation for implementing genome-enabled selection in a multi-level swine production system. Genet. Sel. Evol. 46, 32 (2014).

  106. 106.

    Shumbusho, F. et al. Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program. Animal 10, 1033–1041 (2016).

  107. 107.

    Brito, L. F. et al. Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip. BMC Genetics 18, 7 (2017).

  108. 108.

    Santos, B. F. S., van der Werf, J. H. J., Gibson, J. P., Byrne, T. J. & Amer, P. R. Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations. J. Anim. Breed. Genet. 135, 221–237 (2018).

  109. 109.

    Wolc, A. et al. Response and inbreeding from a genomic selection experiment in layer chicken. Genet. Sel. Evol. 47, 59 (2015).

  110. 110.

    Gonzalez-Recio, O., Pryce, J. E., Haile-Mariam, M. & Hayes, B. J. Incorporating heifer feed efficiency in the Australian selection index using genomic selection. J. Dairy Sci. 97, 3883 (2014).

  111. 111.

    Goddard, M. E. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257 (2009).

  112. 112.

    Grobet, L. et al. A deletion in the bovine myostatin gene causes the double-muscled phenotype in cattle. Nat. Genet. 17, 71–74 (1997).

  113. 113.

    Kambadur, R., Sharma, M., Smith, T. P. & Bass, J. J. Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Res. 7, 910–916 (1997).

  114. 114.

    McPherron, A. C. & Lee, S. J. Double muscling in cattle due to mutations in the myostatin gene. Proc. Natl Acad. Sci. USA 94, 12457–12461 (1997).

  115. 115.

    Grobet, L. et al. Molecular definition of an allelic series of mutations disrupting the myostatin function and causing double-muscling in cattle. Mamm. Genome 9, 210–213 (1998).

  116. 116.

    Fujii, J. et al. Identification of a mutation in the porcine ryanodine receptor associated with malignant hyperthermia. Science 253, 448–451 (1991).

  117. 117.

    Milan, D. et al. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 288, 1248–1251 (2000).

  118. 118.

    Van Laere, A. S. et al. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425, 832–836 (2003).

  119. 119.

    Grisart, B. et al. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 12, 222–231 (2002).

  120. 120.

    Blott, S. et al. Molecular dissection of a QTL: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics 1663, 253–2666 (2003).

  121. 121.

    Cohen-Zinder, M. et al. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 15, 936–944 (2005).

  122. 122.

    Karim, L. et al. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nat. Genet. 43, 405–413 (2011).

  123. 123.

    Bouwman, A. C. et al. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat. Genet. 50, 362–367 (2018).

  124. 124.

    Habier, D., Fernando, R. L., Kizilkaya, K. & Garrick, D. J. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186 (2011).

  125. 125.

    Erbe, M. et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J. Dairy Sci. 95, 4114–4129 (2012).

  126. 126.

    Druet, T. et al. Selection in action: dissecting the molecular underpinnings of the increasing muscle mass in Belgian Blue Cattle. BMC Genomics 15, 796 (2014).

  127. 127.

    Hedrick, P. W. Heterozygote advantage: the effect of artificial selection in livestock and pets. J. Hered. 106, 141–154 (2015).

  128. 128.

    Fasquelle, C. et al. Balancing selection of a frame-shift mutation in the MRC2 gene accounts for the outbreak of the Crooked Tail Syndrome in Belgian Blue Cattle. PLOS Genet. 5, e1000666 (2009).

  129. 129.

    Sartelet, A. et al. A splice site variant in the bovine RNF11 gene compromises growth and regulation of the inflammatory response. PLOS Genet. 8, e1002581 (2012).

  130. 130.

    Sartelet, A. et al. Allelic heterogeneity of Crooked Tail Syndrome fits the balancing selection hypothesis. Anim. Genet. 43, 591–594 (2012).

  131. 131.

    Galloway, S. M. et al. Mutations in an oocyte-derived growth factor gene (BMP15) cause increased ovulation rate and infertility in a dosage-sensitive manner. Nat. Genet. 25, 279–283 (2000).

  132. 132.

    Hanrahan, J. P. et al. Mutations in the genes for oocyte-derived growth factors GDF9 and BMP15 are associated with both increased ovulation rate and sterility in Cambridge and Belcaler sheep (Ovis aries). Biol. Reprod. 70, 900–909 (2004).

  133. 133.

    Rupp, R. et al. A point mutation in suppressor of cytokine signalling 2 (SOCS2) increases the susceptibility to inflammation of the mammary gland while associated with higher body weight and size and higher milk production in a sheep model. PLOS Genet. 11, e1005629 (2015).

  134. 134.

    Johnston, S. E. et al. Life history trade-offs at a single locus maintains sexually selected genetic variation. Nature 502, 93–95 (2013).

  135. 135.

    Cockett, N. E. et al. Polar overdominance at the ovine callipyge locus. Science 273, 236–238 (1996).

  136. 136.

    Freking, B. A. et al. Identification of the single base change causing the callipyge muscle hypertrophy phenotype, the only known example of polar overdominance in mammals. Genome Res. 12, 14966–11506 (2002).

  137. 137.

    Barson, N. J. et al. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528, 405–498 (2015). This paper describes a remarkable example of balancing selection occurring in a natural population as a result of antagonistic selection in males and females.

  138. 138.

    Ayllon, F. et al. The vgll3 locus controls age at maturity in wild and domesticated atlantic salmon (Salmo salar L.) males. PLOS Genet. 11, e1005628 (2015).

  139. 139.

    Utsunomiya, Y. T. et al. A PLAG1 mutation contributed to stature recovery of modern cattle. Sci. Rep. 7, 17140 (2017).

  140. 140.

    Fortes, M. R. S. et al. Evidence for pleiotropism and recent selection in the PLAG1 region in Australian Beef cattle. Anim. Genet. 44, 6636–6647 (2013).

  141. 141.

    Pszczola, M. & Calus, M. P. Updating the reference population to achieve constant genomic prediction reliability across generations. Animal 10, 1018–1024 (2016).

  142. 142.

    Calus, M. P. Right-hand-side updating for fast computing of genomic breeding values. Genet. Sel. Evol. 46, 24 (2014).

  143. 143.

    Calus, M. P., Bouwman, A. C., Schrooten, C. & Veerkamp, R. F. Efficient genomic prediction based on whole-genome sequence data using split-and-merge Bayesian variable selection. Genet. Sel. Evol. 48, 49 (2016).

  144. 144.

    Wang, T. et al. Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping. BMC Genomics 18, 618 (2017).

  145. 145.

    van den Berg, I. et al. Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect. Genet. Sel. Evol. 49, 70 (2017).

  146. 146.

    Brøndum, R. F. et al. Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction. J. Dairy Sci. 98, 4107–4116 (2015).

  147. 147.

    Veerkamp, R. F., Bouwman, A. C., Schrooten, C. & Calus, M. P. Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle. Genet. Sel. Evol. 48, 95 (2016).

  148. 148.

    VanRaden, P. M., Tooker, M. E., O’Connell, J. R., Cole, J. B. & Bickhart, D. M. Selecting sequence variants to improve genomic predictions for dairy cattle. Genet. Sel. Evol. 49, 32 (2017).

  149. 149.

    Long, H. K., Prescott, S. L. & Wysocka, J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell 167, 1170–1187 (2016).

  150. 150.

    Wragg, D. et al. Genome-wide analysis reveals the extent of EAV-HP integration in domestic chicken. BMC Genomics 16, 784–794 (2015).

  151. 151.

    Kemper, K. E., Jayes, B. J., Daetwyler, H. D. & Goddard, M. E. How old are quantitative trait loci and how widely do they segregate? J. Anim. Breed. Genet. 132, 121–134 (2015).

  152. 152.

    Yokota, S. et al. Contributions of FASN and SCD gene polymorphisms on fatty acid composition in muscle from Japanese Black cattle. Anim. Genet. 43, 790–792 (2012).

  153. 153.

    Zhang, W. et al. Genome-wide association studies for fatty acid metabolic traits in five divergent pig populations. Sci. Rep. 6, 24718 (2016).

  154. 154.

    Bolormaa, S. et al. Detailed phenotyping identifies genes with pleiotropic effects on body composition. BMC Genomics 17, 224 (2016).

  155. 155.

    Momozawa, Y. et al. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat. Commun. 9, 2427 (2018).

  156. 156.

    Fang, L. et al. Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds. BMC Genomics 18, 604 (2017).

  157. 157.

    Fragomeni, B. O., Lourenco, D. A. L., Masuda, Y., Legarra, A. & Misztal, I. Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genet. Sel. Evol. 49, 59 (2017).

  158. 158.

    Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. & Goddard, M. E. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41, 51 (2009).

  159. 159.

    Bolormaa, S. et al. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J. Anim. Sci. 91, 3088–3104 (2013).

  160. 160.

    Rolf, M. M. et al. Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genet. Sel. Evol. 47, 23 (2015).

  161. 161.

    Kemper, K. E. et al. Improved precision of QTL mapping using a nonlinear Bayesian method in multibreed population leads to greater accuracy of across-breed genomic predictions. Genet. Sel. Evol. 47, 29 (2015).

  162. 162.

    Lu, D. et al. Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes. J. Anim. Sci. 94, 1342–1353 (2016).

  163. 163.

    Hamidi Hay, E. & Roberts, A. Genomic prediction and genome-wide association analysis of female longevity in a composite beef cattle breed. J. Anim. Sci. 95, 1467–1471 (2017).

  164. 164.

    Pausch, H. et al. Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution. BMC Genomics 18, 853 (2017).

  165. 165.

    Bosse, M. et al. Artificial selection on introduced Asian haplotypes shaped the genetic architecture in European commercial pigs. Proc. Biol. Sci. 282, 20152019 (2015).

  166. 166.

    Sonesson, A. K., Woolliams, J. A. & Meuwissen, T. H. Genomic selection requires genomic control of inbreeding. Genet. Sel. Evol. 44, 27 (2012).

  167. 167.

    Sun, C., VanRaden, P. M., O’Connell, J. R., Weigel, K. A. & Gianola, D. Mating programs including genomic relationships and dominance effects. J. Dairy Sci. 96, 8014–8023 (2013).

  168. 168.

    Pryce, J. E., Hayes, B. J. & Goddard, M. E. Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information. J. Dairy Sci. 95, 377–388 (2012).

  169. 169.

    Palmiter, R. D. et al. Dramatic growth of mice that develop from eggs microinjected with metallothionein-growth hormone fusion genes. Nature 300, 611–615 (1982).

  170. 170.

    Hammer, R. E. et al. Production of transgenic rabbits, sheep and pigs by microinjection. Nature 315, 680–683 (1985).

  171. 171.

    Wilmut, I., Schnieke, A. E., McWhir, J., Kind, A. J. & Campbell, K. H. Viable offspring derived from fetal and adult mammalian cells. Nature 385, 810–813 (1997).

  172. 172.

    Tan, W., Proudfoot, C., Lillico, S. G. & Whitelaw, C. B. Gene targeting genome editing: from Dolly to editors. Transgenic Res. 25, 273–287 (2016).

  173. 173.

    Kim, H. & Kim, J.-S. A guide to genome engineering with programmable nucleases. Nat. Rev. Genet. 15, 321–334 (2014).

  174. 174.

    Komor, A. C., Badran, A. H. & Liu, D. R. CRISPR-based technologies for the manipulation of eukaryotic genomes. Cell 168, 1–17 (2017).

  175. 175.

    Van Eenennaam, A. L. Genetic modification of food animals. Curr. Opin. Biotechnol. 44, 27–34 (2017).

  176. 176.

    Sakuma, T., Nakade, S., Sakane, Y., Suzuki, K. T. & Yamamoto, T. MMEJ-assisted gene knock-in using TALENs and CRISP-Cas9 with the PITCh systems. Nat. Protoc. 11, 118–133 (2016).

  177. 177.

    Suzuki, K. et al. In vivo genome editing via CRISP/Cas9 mediated homology-independent targeted integration. Nature 540, 144–149 (2016).

  178. 178.

    Laible, G., Wei, J. & Wagner, S. Improving livestock for agriculture — technological progress from random transgenesis to precision genome editing heralds a new era. Biotechnol. J. 10, 109–120 (2015).

  179. 179.

    Lievens, A., Petrillo, M. & Querci Patak, M. A. Genetically modified animals: options and issues for traceability and enforcement. Trends Food Sci. Technol. 44, 159–176 (2015).

  180. 180.

    Pirottin, D. et al. Transgenic engineering of male-specific muscular hypertrophy. Proc. Natl Acad. Sci. USA 102, 6413–66418 (2005).

  181. 181.

    Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

  182. 182.

    Cong, L. et al. Multiplex genome engineering using CRISP/Cas systems. Science 339, 819–823 (2013).

  183. 183.

    Jenko, J. et al. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genet. Sel. Evol. 47, 55 (2015). This paper describes a strategy to combine genome editing and GS to accelerate genetic gains in livestock.

  184. 184.

    Kasinathan, P. et al. Acceleration of genetic gain in cattle by reduction of generation interval. Sci. Rep. 5, 8674–86766 (2015).

  185. 185.

    Blard, G., Zhang, Z., Coppieters, W. & Georges, M. Identifying cows with subclinical mastitis by bulk SNP genotyping of tank milk. J. Dairy Sci. 95, 4109–4113 (2012). This paper describes a method to identify cows with subclinical mastitis by SNP genotyping tank milk.

  186. 186.

    Hogeveen, H., Huijps, K. & Lam, T. J. Economic aspects of mastitis: new developments. NZ Vet. J. 59, 16–23 (2011).

  187. 187.

    Nicoloso, L., Crepaldi, P., Mazza, R., Ajmone-Marsan, P. & Negrini, R. Recent advance in DNA-based traceability and authentication of livestock meat PDO and PGI products. Recent Pat. Food Nutr. Agric. 5, 9–18 (2013).

  188. 188.

    Ross, E. M., Moate, P. J., Marett, L. C., Cocks, B. G. & Hayes, B. J. Metagenomic predictions: from microbiome to complex health and environmental phenotypes in human and cattle. PLOS ONE 8, e73056 (2013).

  189. 189.

    Kittelmann, S. et al. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLOS ONE 9, e103171 (2014).

  190. 190.

    Wang, M., Pryce, J. E., Savin, K. & Hayes, B. J. Prediction of residual feed intake from genome & metagenome profiles in first lactation Holstein-Friesian dairy cattle. Proc. Assoc. Adv. Breed. Genet. 21, 89–92 (2015).

  191. 191.

    Frantz, L. A. F. et al. Evidence of long-term gene flow and selection during domestication from analyses of Eurasian wild and domestic pig genomes. Nat. Genet. 47, 1141–1148 (2015). This paper describes a thorough analysis of more than 100 whole-genome sequences of pigs, which strongly suggests that long-term gene flow between wild and domestic pigs counteracted by recurrent selection for domestic traits creates ‘islands of domestication’ in the genome.

  192. 192.

    Wade, C. M. et al. The mosaic structure of variation in the laboratory mouse genome. Nature 420, 574–578 (2002).

  193. 193.

    Sankararaman, S. et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature 507, 354–357 (2014).

  194. 194.

    Berry, S. et al. A triad of highly divergent polymeric immunoglobulin receptor (PIGR) haplotytpes with major effect on IgA concentration in cow’s milk. PLOS ONE 8, e57219 (2013).

  195. 195.

    Ai, H. et al. Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat. Genet. 47, 217–225 (2015).

  196. 196.

    Rubin, C.-J. et al. Whole genome resequencing reveals loci under selection during chicken domestication. Nature 464, 587–591 (2010). This paper reports the identification of convincing signatures of selective sweeps following poultry domestication.

  197. 197.

    Carneiro, M. et al. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science 345, 1074–1079 (2014). This study provides strong evidence that polygenic adaptation plays a major role in shaping the phenotype of domestic animals in response to human needs.

  198. 198.

    Dong, Y. et al. Reference genome of wild goat (Capra aegagrus) and sequencing of goat breeds provide insight into genic basis of goat domestication. BMC Genomics 16, 431–442 (2015).

  199. 199.

    Park, S. D. E. et al. Genome sequencing of the extinct Eurasian wild aurochs, Bos primigenius, illuminates the phylogeography and evolution of cattle. Genome Biol. 16, 234–249 (2015).

  200. 200.

    Field, Y. et al. Detection of human adaptation during the past 2,000 years. Science 354, 760–764 (2016).

  201. 201.

    Nejati-Javaremi, A., Smith, C. & Gibson, J. Effect of total allelic relationship on accuracy of evaluation and response to selection. J. Anim. Sci. 75, 1738–1745 (1997).

  202. 202.

    VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).

  203. 203.

    Daetwyler, H. D., Villanueva, B. & Wooliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLOS ONE 3, e3395 (2008).

  204. 204.

    Hayes, B. J., Visscher, P. M. & Goddard, M. E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91, 47–60 (2009).

  205. 205.

    Pryce, J. E. et al. Genomic selection using a multi-breed, across-country reference population. J. Dairy Sci. 94, 2625–26630 (2011).

  206. 206.

    Kijas, J. W. et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLOS Biol. 10, e1001258 (2012).

  207. 207.

    Moghaddar, N., Gore, K. P., Daetwyler, H. D., Hayes, B. J. & van der Werf, J. H. J. Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction. Genet. Sel. Evol. 47, 97 (2015).

  208. 208.

    Lee, S. H., Weerasinghe, W. M., Wray, N. R., Goddard, M. E. & van der Werf, J. H. Using information of relatives in genomic prediction to apply effective stratified medicine. Sci. Rep. 7, 42091 (2017).

  209. 209.

    Hill, W. G. & Weir, B. S. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet. Res. 93, 47–64 (2011).

  210. 210.

    Pursel, V. G. et al. Genetic engineering of livestock. Science 24, 1281–1288 (1989).

  211. 211.

    Pursel, V. G., Hammer, R. E., Bolt, D. J., Palmiter, R. D. & Brinster, R. L. Integration, expression and germ-line transmission of growth-related genes in pigs. J. Reprod. Fertil. Suppl. 41, 77–87 (1990).

  212. 212.

    Rexroad, C. E. et al. Production of transgenic sheep with growth-regulating genes. Mol. Reprod. Dev. 1, 164–169 (1989).

  213. 213.

    Nottle, M. B. et al. in Transgenic Animals in Agriculture (eds Murray, J. D., Anderson, G. B., Oberbauer, A. M. & McGloughin, M. M.) 145–156 (CABI Publishing, Oxon, UK, 1999).

  214. 214.

    Adams, N. R., Briegel, J. R. & Ward, K. A. The impact of a transgene for ovine growth hormone on the performance of two breeds of sheep. J. Anim. Sci. 80, 2325–2333 (2002).

  215. 215.

    Pursel, V. G. et al. in Transgenic Animals in Agriculture (eds Murray, J. D., Anderson, G. B., Oberbauer, A. M. & McGloughin, M. M.) 131–144 (CABI Publishing, Oxon, UK, 1999).

  216. 216.

    Sutrave, P., Kelly, A. M. & Hughes, S. H. Ski can cause selective growth of skeletal muscle in transgenic mice. Genes Dev. 4, 1462–1472 (1990).

  217. 217.

    Grobet, L. et al. Modulating skeletal muscle mass by postnatal, muscle-specific inactivation of the myostatin gene. Genesis 35, 227–238 (2003).

  218. 218.

    Luo, J. et al. Efficient generation of myostatin (MSTN) biallelic mutations in cattle using zinc finger nucleases. PLOS ONE 9, e95225 (2014).

  219. 219.

    Proudfoot, C. et al. Genome edited sheep and cattle. Transgenic Res. 24, 147–153 (2015).

  220. 220.

    Ni, W. et al. Efficient gene knockout in goats using CRISPR/Cas9 system. PLOS ONE 9, e106718 (2014).

  221. 221.

    Han, H. et al. One-step generation of myostatin gene knockout sheep via the CRISPR/Cas9 system. Front. Agric. Sci. Eng. 1, 2–5 (2014).

  222. 222.

    Tessanne, K. et al. Production of transgenic calves expressing an shRNA targeting myostatin. Mol. Reprod. Dev. 79, 176–185 (2012).

  223. 223.

    Lee, S. J. Quadrupling muscle mass in mice by targeting TGF-beta signaling pathways. PLOS ONE 2, e789 (2007).

  224. 224.

    Saeki, K. et al. Functional expression of a delta 12 fatty acid desaturase transgene from spinach in transgenic pigs. Proc. Natl Acad. Sci. USA 101, 6361–6366 (2004).

  225. 225.

    Lai, L. et al. Generation of cloned transgenic pigs rich in omega-3 fatty acids. Nat. Biotechnol. 24, 345–436 (2006).

  226. 226.

    Wu, X. et al. Production of cloned transgenic cow expressing omega-3 fatty acids. Transgenic Res. 21, 537–543 (2012).

  227. 227.

    Zhang, P. et al. Handmade cloned transgenic sheep rich in omega-3 fatty acids. PLOS ONE 8, e55941 (2013).

  228. 228.

    Zheng, Q. et al. Reconstitution of UCP1 using CRISP/Cas9 in the white adipose tissue of pigs decreases fat deposition and improves thermogenic capacity. Proc. Natl Acad. Sci. USA 114, E9474–E9482 (2017).

  229. 229.

    Berg, F., Gustafson, U. & Andersson, L. The uncoupling protein 1 gene (UCP1) is disrupted in the pig lineage: a genetic explanation for poor thermoregulation in piglets. PLOS Genet. 2, e129 (2006).

  230. 230.

    Brophy, B. et al. Cloned transgenic cattle produce milk with higher levels of beta-casein and kappa-casein. Nat. Biotechnol. 21, 157–162 (2003).

  231. 231.

    Martin, P., Szymanowska, M., Zwierzchowski, L. & Leroux, C. The impact of genetic polymorphisms on the protein composition of ruminant milks. Reprod. Nutr. Dev. 42, 433–459 (2002).

  232. 232.

    Yu, S. et al. Highly efficient modification of beta-lactoglobulin (BLG) gene via zinc-finger nucleases in cattle. Cell Res. 21, 1638–1640 (2011).

  233. 233.

    Jabed, A., Wagner, S., McCracken, J., Wells, D. N. & Labile, G. Targeted microRNA expression in dairy cattle directs production of beta-lactoglobulin-free, high-casein milk. Proc. Natl Acad. Sci. USA 109, 16811–16816 (2012).

  234. 234.

    Cui, C. et al. Gene targeting by TALEN-induced homologous recombination in goats directs production of beta-lactoglobulin-free, high human lactoferrin milk. Sci. Rep. 5, 10482 (2015).

  235. 235.

    Zhu, H. et al. Generation of beta-lactoglobulin-modified transgenic goats by homologous recombination. FEBS J. 282, 4600–4613 (2016).

  236. 236.

    Wheeler, M. B., Bleck, G. T. & Donovan, S. M. Transgenic alteration of sow milk to improve piglet growth and health. Reprod. Suppl. 58, 313–324 (2001).

  237. 237.

    Wang, J. et al. Expression and characterization of bioactive recombinant human alpha-lactalbumin in the milk of transgenic cloned cows. J. Dairy Sci. 91, 4466–4476 (2008).

  238. 238.

    Jost, B., Vilotte, J. L., Duluc, I., Rodeau, J. L. & Freund, J. N. Production of low-lactose milk by ectopic expression of intestinal lactase in the mouse mammary gland. Nat. Biotechnol. 17, 160–164 (1999).

  239. 239.

    Reh, W. A. et al. Using a stearoyl-CoA desaturase transgene to alter milk fatty acid composition. J. Dairy Sci. 87, 3510–3514 (2004).

  240. 240.

    Damak, S., Su, H., Jay, N. P. & Bullock, D. W. Improved wool production in transgenic sheep expressing insulin-like growth factor 1. Biotechnology 14, 185–188 (1996).

  241. 241.

    Bawden, C. S., Powell, B. C., Walker, S. K. & Rogers, G. E. Expression of a wool intermediate filament keratin transgene in sheep fibre alters structure. Transgenic Res. 7, 273–287 (1998).

  242. 242.

    Bawden, C. S. et al. Expression of bacterial cysteine biosynthesis genes in transgenic mice and sheep: toward a new in vivo amino acid biosynthesis pathway and improved wool growth. Transgenic Res. 4, 87–104 (1995).

  243. 243.

    Maga, E. A. et al. Production and processing of milk from transgenic goats expressing human lysozyme in the mammary gland. J. Dairy Sci. 89, 518–524 (2006).

  244. 244.

    Liu, X. et al. Generation of mastitis resistance in cows by targeting human lysozyme gene to beta-casein locus using zinc-finger nucleases. Proc. R. Soc. B 281, 20133368 (2014).

  245. 245.

    Wall, R. J. et al. Genetically enhanced cows resist intramammary Staphylococcus aureus infection. Nat. Biotechnol. 23, 445–451 (2005).

  246. 246.

    Liu, X. et al. Zinc-finger nickase-mediated insertion of the lysostaphin gene into the beta-casein locus in cloned cows. Nat. Commun. 4, 2565 (2013).

  247. 247.

    Dunham, R. A. et al. Enhanced bacterial disease resistance of transgenic channel catfish Ictalurus punctatus possessing cecropin genes. Mar. Biotechnol. 4, 338–344 (2002).

  248. 248.

    Su, F. et al. Generation of transgenic cattle expressing human beta-defensin 3 as an approach to reducing susceptibility to Mycobacterium bovis infection. FEBS J. 283, 776–790 (2016).

  249. 249.

    Yang, X. et al. Overexpression of porcine beta-defensin 2 enhances resistance to Actinobacillus pleuropneumoniae infection in pigs. Infect. Immun. 83, 2836–2843 (2015).

  250. 250.

    Wu, H. et al. TALE nickase-mediated SP110 knockin endows cattle with increased resistance to tuberculosis. Proc. Natl Acad. Sci. USA 112, 1530–1539 (2015).

  251. 251.

    Pan, H. et al. Ipr1 gene mediates innate immunity to tuberculosis. Nature 434, 767–772 (2005).

  252. 252.

    Tosh, K. et al. Variants in the SP110 gene are associated with genetic susceptibility to tuberculosis in West Africa. Proc. Natl Acad. Sci. USA 103, 10364–10368 (2006).

  253. 253.

    Fox, G. J. et al. Polymorphisms of SP110 are associated with bothpulmonary and extra-pulmonary tuberculosis among the Vietnamese. PLOS ONE 9, e99496 (2014).

  254. 254.

    Hu, W. et al. Significant resistance to the infection of foot-and-mouth disease virus in shRNA transgenic pig. Transgenic Res. 21, 901–925 (2012).

  255. 255.

    Muller, M., Brenig, B., Winnacker, E. L. & Brem, G. Transgenic pigs carrying cDNA copies encoding the murine Mx1 protein which confers resistance to influenza virus infection. Gene 121, 263–270 (1992).

  256. 256.

    Yan, Q. et al. Production of transgenic pigs overexpressing the antiviral gene Mx1. Cell Regen. 3, 11–22 (2014).

  257. 257.

    Lyall, J. et al. Suppression of avian influenza transmission in genetically modified chickens. Science 331, 223–226 (2011).

  258. 258.

    Clements, J. E. et al. Development of transgenic sheep that express the visan virus envelope gene. Virology 200, 370–380 (1994).

  259. 259.

    Crittenden, L. B. & Salter, D. W. A transgene, alv6, that expresses the envelope of subgroup A avian leucosis virus reduces the rate of congenital transmission of a field strain of avian leucosis virus. Poult. Sci. 71, 799–806 (1992).

  260. 260.

    Whitworth, K. M. et al. Use of CRISPR/Cas9 system to produce genetically engineered pigs from in vitro derived oocytes and embryos. Biol. Reprod. 91, 1–13 (2014).

  261. 261.

    Burkard, C. et al. Precision engineering for PRRSV resistance in pigs: macrophages from genome edited pigs lacking CD163 SRCR5 domain are fully resistant to both PRRSV genotypes while maintaining biological function. PLOS Pathog. 13, e1006206 (2017).

  262. 262.

    Lillico, S. G. et al. Live pigs produced from genome edited zygotes. Sci. Rep. 3, 2847–2851 (2013).

  263. 263.

    Lillico, S. G. et al. Mammalian interspecies substitution of immune modulatory alleles by genome editing. Sci. Rep. 6, 21645–21650 (2016).

  264. 264.

    Richt, J. A. et al. Production of cattle lacking prion protein. Nat. Biotechnol. 25, 132–138 (2007).

  265. 265.

    Yu, G. et al. Functional disruption of the prion protein gene in cloned goats. J. Gen. Virol. 87, 1019–1027 (2006).

  266. 266.

    Denning, C. et al. Deletion of the alpha(1,3)galactosyl transferase (GGTA1) gene and the prion protein (PrP) gene in sheep. Nat. Biotechnol. 19, 559–562 (2001).

  267. 267.

    Golding, M. C., Long, C. R., Carmell, M. A., Hannon, G. J. & Westhusin, M. E. Suppression of prion protein in livestock by RNA interference. Proc. Natl Acad. Sci. USA 103, 5285–5290 (2006).

  268. 268.

    Wongsrikeao, P. et al. Combination of the somatic cell nuclear transfer method and RNAi technology for the production of a prion gene-knockdown calf using plasmid vectors harbouring the U6 or tRNA promoter. Prion 5, 39–46 (2011).

  269. 269.

    Benestad, S. L., Anstbø, L., Tranulis, M. A., Espenes, A. & Olsaker, I. Healthy goats naturally devoid of prion protein. Vet. Res. 43, 87–91 (2012).

  270. 270.

    Willyard, C. Putting sleeping sickness to bed. Nat. Med. 17, 14–17 (2011).

  271. 271.

    Genovese, G. et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329, 841–845 (2010).

  272. 272.

    Hew, C. L., Davies, P. L. & Fletcher, G. Antifreeze protein gene transfer in Atlantic salmon. Mol. Mar. Biol. Biotechnol. 1, 309–317 (1992).

  273. 273.

    Davies, P. L. & Hew, C. L. Biochemistry of fish antifreeze proteins. FASEB J. 4, 2460–2468 (1990).

  274. 274.

    Carlson, D. F. et al. Production of hornless dairy cattle from genome-edited cell lines. Nat. Biotechnol. 34, 479–481 (2016). This paper illustrates the engineering of a desirable phenotype in livestock by TALEN-mediated allele swapping and SCNT.

  275. 275.

    Medugorac, I. et al. Bovine polledness — an autosomal dominant trait with allelic heterogeneity. PLOS ONE 7, e39477 (2012).

  276. 276.

    Allais-Bonnet, A. et al. Novel insights into the bovine polled phenotype and horn ontogenesis in Bovidae. PLOS ONE 8, e63512 (2013).

  277. 277.

    Rothammer, S. et al. The 80-Kb DNA duplication on BTA1 is the only remaining candidate mutation for the polled phenotype of Friesian origin. Genet. Sel. Evol. 46, 44 (2014).

  278. 278.

    Golovan, S. P. et al. Pigs expressing salivary phytase produce low-phosphorus manure. Nat. Biotechnol. 19, 741–745 (2001).

Download references


C.C. is Senior Research Associate of the Fonds de la Recherche Scientifique (FRS-FNRS). Research in animal genomics conducted by the authors is funded by the European Research Council (ERC) Advanced DAMONA, H2020 GpE and WALInnov CAUSEL grants to M.G. and the DGARNE Rilouke and ULiège RetroBlue grants to C.C. The authors are grateful to T. Druet for fruitful discussions and comments on the manuscript and to M. Goddard for excellent discussions over the years.

Reviewer information

Nature Reviews Genetics thanks D. Garrick and A. Legarra for their contribution to the peer review of this work.

Author information


  1. Unit of Animal Genomics, GIGA Institute, University of Liège, Liège, Belgium

    • Michel Georges
    •  & Carole Charlier
  2. Faculty of Veterinary Medicine, University of Liège, Liège, Belgium

    • Michel Georges
    •  & Carole Charlier
  3. Queensland Alliance for Agriculture and Food Innovation (QAAFI), Queensland Bioscience Precinct, The University of Queensland, Brisbane, Queensland, Australia

    • Ben Hayes


  1. Search for Michel Georges in:

  2. Search for Carole Charlier in:

  3. Search for Ben Hayes in:


All authors researched data for the article, made substantial contributions to discussions of the content and reviewed and/or edited the manuscript before submission. M.G. and B.H. wrote the article.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Michel Georges.

Supplementary information


Within-breed selection

A process by which sires and dams that have above average breeding values are selected as parents to produce the next generation of animals.

Genetic gains

Differences in the average breeding values of the population before and after selection. Genetic gain is a function of the amount of genetic variance, the accuracy of selection, the intensity of selection and the generation interval.

Quantitative trait loci

(QTL). Regions in the genome that encompass genetic variants with an effect on a quantitative trait of interest.

Genome-wide association studies

(GWAS). Scan of the entire genome to identify genetic variants for which variation in genotype is associated with variation for one or more phenotypes of interest.

Genomic selection

(GS). An ensemble of methods to estimate the breeding values of individual animals on the basis of genome-wide single-nucleotide polymorphism genotype information.

Single-nucleotide polymorphism arrays

(SNP arrays). Microarrays used to determine the genotype of individuals for hundreds to millions of SNPs at once.

Progeny testing

(PT). An approach by which the breeding value of an animal is estimated from phenotypic measures made on its progeny.

Genetic architecture

The description of the number, location and effects of the genetic variants that affect a phenotype of interest.

Genotype imputation

The in silico prediction of the genotype of an individual for ungenotyped variants on the basis of known genotypes at neighbouring variants and a reference population with genotype information for all variants. Imputation exploits the nonrandom association of alleles at neighbouring variants, referred to as linkage disequilibrium.

Soft sweeps

The process by which the frequency of a favourable old variant rapidly increases in the population by positive selection until eventual fixation. Soft sweeps are not associated with the concomitant fixation of one predominant haplotype, as the variant has been distributed over multiple haplotypes by recombination before selection. Old variants that are substrates for new selection constitute the standing variation in the population.


The combination of chemical modifications of the DNA sequence (such as cytosine methylation) or nucleosomes (such as methylation of Lys 27 of histone H3) that mark functionally distinct segments of the genome (such as active enhancers) and are inherited mitotically and/or meiotically.


A combination of chromatin immunoprecipitation and next-generation sequencing for genome-wide mapping of binding sites occupied by specific DNA-binding proteins or chromatin regions enriched in specific histone modifications.


A method based on next-generation sequencing for genome-wide detection of gene-switch components on the basis of their open chromatin conformation and resulting hypersensitivity to digestion by DNase I.


An assay based on next-generation sequencing for genome-wide detection of gene-switch components on the basis of their open chromatin conformation and resulting increased accessibility to transposase Tn5.

Expression quantitative trait loci

(eQTL). Quantitative trait loci that influence the transcript levels of specific genes. Cis-eQTL are due to regulatory variants that control the levels of RNA molecules transcribed from gene copies located on the same DNA molecule as the variant. Trans-eQTL are due to regulatory variants that can also control the levels of RNA molecules transcribed from gene copies located on different DNA molecules to the variant (homologous or other chromosomes).


The ability of a genetic variant to affect more than one phenotype.


Pertaining to an allele with partial loss of function when compared with the wild-type allele.


The phenotypic superiority (for example, on a quantitative scale) of heterozygotes (‘Aa’) over both homozygous classes (‘AA’ and ‘aa’).


Pertaining to genes for which one functional copy is sufficient to ensure normal development and function.

Compound heterozygosity

Pertaining to the inheritance of two distinct mutations in different alleles of the same gene, one from each parent.

Autozygosity mapping

Mapping of a recessive mutation on the basis that all affected individuals will be homozygous for the same (autozygous) haplotype. Typically applied in genetically isolated populations in which the hypothesis of allelic homogeneity is reasonable.

Modifier locus

A locus with variants that may (depending on the genotype of the individual) affect the phenotypic expression conferred by specific variants at another locus. The effects of modifier loci include suppression and epistasis.

Reverse genetic screens

Process aimed at completing the phenotype–genotype map by sorting individuals according to their genotype at a variant with unknown function and searching for shared phenotypes, as opposed to forward genetics, which consists of sorting individuals according to a phenotype and searching for shared variants.


A combination of alleles at multiple variant positions transmitted by a gamete. The term is often used to describe variants that are located close to each other in the genome.

Linkage disequilibrium

(LD). The nonrandom association of alleles at two or more loci, which is manifest by the over-representation of specific haplotypes and the concomitant under-representation of others.

Selection index

A weighted sum of breeding values for several traits, each weighted by economic or perceived relevance.

Kinship coefficient

A measure of genetic relatedness between two individuals. The kinship coefficient corresponds to the probability that two alleles (one from each individual) drawn at random from the two possible alleles (maternal and paternal) for each individual for a randomly selected locus in the genome are identical by descent. The kinship coefficient between two individuals corresponds to the expected inbreeding coefficient of their putative offspring.

Hard sweeps

The process by which the frequency of a favourable new variant rapidly increases in the population by positive selection until eventual fixation of the variant and the haplotype upon which it occurred.

Balancing selection

A selective force on a locus that leads to a steady state whereby multiple alleles are simultaneously maintained in the population, rather than one allele becoming fixed at the expense of the others.


Variants that cause a change in the amino acid sequence of a protein. By contrast, synonymous variants are variants in the open reading frame of a protein-coding gene that do not change the amino acid sequence. Most non-synonymous variants affect the first and second codon positions, while most synonymous variants affect the third codon position.

Intermediate phenotypes

Phenotypes that mediate the link between a causative variant and the end-point disease or agricultural phenotype of interest — includes transcript, protein and metabolite levels.

Gene flow

The passage of alleles between populations as a result of migration or interbreeding.

Polygenic adaptation

The process by which a phenotype caused by many genes evolves in a population under selection, not by massive changes in the frequency of a few variants with major effects on the phenotype (hard and soft sweeps) but by very small changes in the frequency of many variants with minor effects on the phenotype.


The occurrence of mutations in some but not all cells of an organism that is entirely derived from a single zygote.

Gartner hype cycle

A model first proposed by the Gartner firm to explain the phases of maturation, adoption and social application of new technologies.

About this article

Publication history