1932

Abstract

Functional annotation of genomes is a prerequisite for contemporary basic and applied genomic research, yet farmed animal genomics is deficient in such annotation. To address this, the FAANG (Functional Annotation of Animal Genomes) Consortium is producing genome-wide data sets on RNA expression, DNA methylation, and chromatin modification, as well as chromatin accessibility and interactions. In addition to informing our understanding of genome function, including comparative approaches to elucidate constrained sequence or epigenetic elements, these annotation maps will improve the precision and sensitivity of genomic selection for animal improvement. A scientific community–driven effort has already created a coordinated data collection and analysis enterprise crucial for the success of this global effort. Although it is early in this continuing process, functional data have already been produced and application to genetic improvement reported. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-animal-020518-114913
2019-02-15
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/animal/7/1/annurev-animal-020518-114913.html?itemId=/content/journals/10.1146/annurev-animal-020518-114913&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Tuggle CK, Towfic F, Honavar V 2011. Introduction to systems biology for animal scientists. Systems Biology and Livestock Science MFW te Pas, H Woelders, A Bannick 1–30 Malden, MA: John Wiley & Sons
    [Google Scholar]
  2. 2.  Suravajhala P, Kogelman LJ, Kadarmideen HN 2016. Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare. Genet. Sel. Evol. 48:38
    [Google Scholar]
  3. 3.  Loor JJ, Vailati-Riboni M, McCann JC, Zhou Z, Bionaz M 2015. Triennial Lactation Symposium: nutrigenomics in livestock: systems biology meets nutrition. J. Anim. Sci. 93:5554–74
    [Google Scholar]
  4. 4.  Kell DB, Oliver SG 2004. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 26:99–105
    [Google Scholar]
  5. 5.  Meuwissen TH, Hayes BJ, Goddard ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–29
    [Google Scholar]
  6. 6.  Goddard ME, Kemper KE, MacLeod IM, Chamberlain AJ, Hayes BJ 2016. Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture. Proc. R. Soc. B Biol. Sci. 283:20160569
    [Google Scholar]
  7. 7.  Hayes BJ, Lewin HA, Goddard ME 2013. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet 29:206–14
    [Google Scholar]
  8. 8.  Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:433–43
    [Google Scholar]
  9. 9.  Lund MS, Su G, Janss L, Guldbrandtsen B, Brøndum RF 2014. Genomic evaluation of cattle in a multi-breed context. Livest. Sci. 166:101–10
    [Google Scholar]
  10. 10.  Wang M, Hancock TP, Chamberlain JA, Vander Jagt CJ, Pryce JE et al. 2018. Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits. BMC Genom 19:395
    [Google Scholar]
  11. 11.  Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A et al. 2014. Defining functional DNA elements in the human genome. PNAS 111:6131–38
    [Google Scholar]
  12. 12.  Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16:85–97
    [Google Scholar]
  13. 13.  Brookes AJ, Robinson PN 2015. Human genotype-phenotype databases: aims, challenges and opportunities. Nat. Rev. Genet. 16:702–15
    [Google Scholar]
  14. 14.  Chakravorty S, Hegde M 2017. Gene and variant annotation for Mendelian disorders in the era of advanced sequencing technologies. Annu. Rev. Genom. Hum. Genet. 18:229–56
    [Google Scholar]
  15. 15.  Meadows JRS, Lindblad-Toh K 2017. Dissecting evolution and disease using comparative vertebrate genomics. Nat. Rev. Genet. 18:624–36
    [Google Scholar]
  16. 16.  Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD et al. 2010. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328:1036–40
    [Google Scholar]
  17. 17.  Yue F, Cheng Y, Breschi A, Vierstra J, Wu W et al. 2014. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515:355–64
    [Google Scholar]
  18. 18.  Villar D, Berthelot C, Aldridge S, Rayner TF, Lukk M et al. 2015. Enhancer evolution across 20 mammalian species. Cell 160:554–66
    [Google Scholar]
  19. 19.  Elsik CG, Tellam RL, Worley KC, Gibbs RA, Muzny DM et al. 2009. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science 324:522–28
    [Google Scholar]
  20. 20.  Groenen MA, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y et al. 2012. Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–98
    [Google Scholar]
  21. 21.  Archibald AL, Flicek P, Birney E 2012. Enabling the reading of genome sequences for farmed and companion animals—a proposal for ENCODE consortia Presented at the 33rd Conference of the International Society for Animal Genetics, Cairns, Aust., July 15–20 https://www.isag.us/2012/docs/ISAG_2012_Abstracts.pdf
    [Google Scholar]
  22. 22.  Andersson L, Archibald AL, Bottema CD, Brauning R, Burgess SC et al. 2015. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol 16:57
    [Google Scholar]
  23. 23.  Tuggle CK, Giuffra E, White SN, Clarke L, Zhou H et al. 2016. GO-FAANG meeting: a Gathering On Functional Annotation of Animal Genomes. Anim. Genet. 47:528–33
    [Google Scholar]
  24. 24. Int. Chick. Genome Seq. Consort. 2004. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432:695–716
    [Google Scholar]
  25. 25.  Lindblad-Toh K, Wade CM, Mikkelsen TS, Karlsson EK, Jaffe DB et al. 2005. Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature 438:803–19
    [Google Scholar]
  26. 26.  Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S et al. 2009. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science 326:865–67
    [Google Scholar]
  27. 27.  Jiang Y, Xie M, Chen W, Talbot R, Maddox JF et al. 2014. The sheep genome illuminates biology of the rumen and lipid metabolism. Science 344:1168–73
    [Google Scholar]
  28. 28.  Dong Y, Xie M, Jiang Y, Xiao N, Du X et al. 2013. Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nat. Biotechnol. 31:135–41
    [Google Scholar]
  29. 29.  Berthelot C, Brunet F, Chalopin D, Juanchich A, Bernard M et al. 2014. The rainbow trout genome provides novel insights into evolution after whole-genome duplication in vertebrates. Nat. Commun. 5:3657
    [Google Scholar]
  30. 30.  Warr A, Robert C, Hume D, Archibald AL, Deeb N, Watson M 2015. Identification of low-confidence regions in the pig reference genome (Sscrofa10.2). Front. Genet. 6:338
    [Google Scholar]
  31. 31.  Bickhart DM, Rosen BD, Koren S, Sayre BL, Hastie AR et al. 2017. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat. Genet. 49:643–50
    [Google Scholar]
  32. 32.  Birney E, Hudson TJ, Green ED, Gunter C, Eddy S et al. 2009. Prepublication data sharing. Nature 461:168–70
    [Google Scholar]
  33. 33.  Macqueen DJ, Primmer CR, Houston RD, Nowak BF, Bernatchez L et al. 2017. Functional Annotation of All Salmonid Genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture. BMC Genom 18:484
    [Google Scholar]
  34. 34.  Soneson C, Love MI, Robinson MD 2015. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research 4:1521
    [Google Scholar]
  35. 35.  Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:621–28
    [Google Scholar]
  36. 36.  Wang Z, Gerstein M, Snyder M 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:57–63
    [Google Scholar]
  37. 37.  Robert C, Watson M 2015. Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol 16:177
    [Google Scholar]
  38. 38.  Clark EL, Bush SJ, McCulloch MEB, Farquhar IL, Young R et al. 2017. A high resolution atlas of gene expression in the domestic sheep (Ovis aries). PLOS Genet 13:e1006997
    [Google Scholar]
  39. 39.  Kuo RI, Tseng E, Eory L, Paton IR, Archibald AL, Burt DW 2017. Normalized long read RNA sequencing in chicken reveals transcriptome complexity similar to human. BMC Genom 18:323
    [Google Scholar]
  40. 40.  Imanishi T, Itoh T, Suzuki Y, O'Donovan C, Fukuchi S et al. 2004. Integrative annotation of 21,037 human genes validated by full-length cDNA clones. PLOS Biol 2:e162
    [Google Scholar]
  41. 41.  Chamberlain AJ, Vander Jagt CJ, Hayes BJ, Khansefid M, Marett LC et al. 2015. Extensive variation between tissues in allele specific expression in an outbred mammal. BMC Genom 16:993
    [Google Scholar]
  42. 42.  Engreitz JM, Haines JE, Perez EM, Munson G, Chen J et al. 2016. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539:452–55
    [Google Scholar]
  43. 43.  Hezroni H, Koppstein D, Schwartz MG, Avrutin A, Bartel DP, Ulitsky I 2015. Principles of long noncoding RNA evolution derived from direct comparison of transcriptomes in 17 species. Cell Rep 11:1110–22
    [Google Scholar]
  44. 44.  Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T et al. 2012. Landscape of transcription in human cells. Nature 489:101–8
    [Google Scholar]
  45. 45.  Anthon C, Tafer H, Havgaard JH, Thomsen B, Hedegaard J et al. 2014. Structured RNAs and synteny regions in the pig genome. BMC Genom 15:459
    [Google Scholar]
  46. 46.  Bush SJ, Muriuki C, McCulloch MEB, Farquhar IL, Clark EL, Hume DA 2018. Cross-species inference of long non-coding RNAs greatly expands the ruminant transcriptome. Genet. Sel. Evol. 50:20
    [Google Scholar]
  47. 47.  Bush SJ, Muriuki C, McCulloch MEB, Farquhar IL, Clark EL, Hume DA 2018. Cross-species inference of long non-coding RNAs greatly expands the ruminant transcriptome. Genet. Sel. Evol. 50:20
    [Google Scholar]
  48. 48.  Foissac S, Djebali S, Munyard K, Villa-Vialaneix N, Rau A et al. 2018. Livestock genome annotation: transcriptome and chromatin structure profiling in cattle, goat, chicken and pig. bioRxiv https://doi.org/10.1101/316091
    [Crossref] [Google Scholar]
  49. 49.  Muret K, Klopp C, Wucher V, Esquerré D, Legeai F et al. 2017. Long noncoding RNA repertoire in chicken liver and adipose tissue. Genet. Sel. Evol. 49:6
    [Google Scholar]
  50. 50.  Weikard R, Hadlich F, Hammon HM, Frieten D, Gerbert C et al. 2018. Long noncoding RNAs are associated with metabolic and cellular processes in the jejunum mucosa of pre-weaning calves in response to different diets. Oncotarget 9:21052–69
    [Google Scholar]
  51. 51.  Koufariotis LT, Chen YP, Chamberlain A, Vander Jagt C, Hayes BJ 2015. A catalogue of novel bovine long noncoding RNA across 18 tissues. PLOS ONE 10:e0141225
    [Google Scholar]
  52. 52.  Scott EY, Mansour T, Bellone RR, Brown CT, Mienaltowski MJ et al. 2017. Identification of long non-coding RNA in the horse transcriptome. BMC Genom 18:511
    [Google Scholar]
  53. 53.  Wang X, Zhang FX, Wang ZM, Wang Q, Wang HF et al. 2016. Histone H3K9 acetylation influences growth characteristics of goat adipose-derived stem cells in vitro. . Genet. Mol. Res 15:gmr15048954
    [Google Scholar]
  54. 54.  Kociucka B, Stachecka J, Szydlowski M, Szczerbal I 2017. Rapid communication: the correlation between histone modifications and expression of key genes involved in accumulation of adipose tissue in the pig. J. Anim. Sci. 95:4514–19
    [Google Scholar]
  55. 55.  Byrne K, McWilliam S, Vuocolo T, Gondro C, Cockett NE, Tellam RL 2014. Genomic architecture of histone 3 lysine 27 trimethylation during late ovine skeletal muscle development. Anim. Genet. 45:427–38
    [Google Scholar]
  56. 56.  Li C, Guo S, Zhang M, Gao J, Guo Y 2015. DNA methylation and histone modification patterns during the late embryonic and early postnatal development of chickens. Poult. Sci. 94:706–21
    [Google Scholar]
  57. 57.  He Y, Yu Y, Zhang Y, Song J, Mitra A et al. 2012. Genome-wide bovine H3K27me3 modifications and the regulatory effects on genes expressions in peripheral blood lymphocytes. PLOS ONE 7:e39094
    [Google Scholar]
  58. 58.  Xiao S, Xie D, Cao X, Yu P, Xing X et al. 2012. Comparative epigenomic annotation of regulatory DNA. Cell 149:1381–92
    [Google Scholar]
  59. 59.  Jahan S, Xu W, He S, Gonzalez C, Delcuve GP, Davie JR 2016. The chicken erythrocyte epigenome. Epigenet. Chromatin 9:19
    [Google Scholar]
  60. 60.  Mitra A, Luo J, He Y, Gu Y, Zhang H et al. 2015. Histone modifications induced by MDV infection at early cytolytic and latency phases. BMC Genom 16:311
    [Google Scholar]
  61. 61.  Messerschmidt DM, Knowles BB, Solter D 2014. DNA methylation dynamics during epigenetic reprogramming in the germline and preimplantation embryos. Genes Dev 28:812–28
    [Google Scholar]
  62. 62.  Jones PA 2012. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13:484–92
    [Google Scholar]
  63. 63.  Schübeler D 2015. Function and information content of DNA methylation. Nature 517:321–26
    [Google Scholar]
  64. 64.  Coleman-Derr D, Zilberman D 2012. DNA methylation, H2A.Z, and the regulation of constitutive expression. Cold Spring Harb. Symp. Quant. Biol. 77:147–54
    [Google Scholar]
  65. 65.  Suzuki MM, Bird A 2008. DNA methylation landscapes: provocative insights from epigenomics. Nat. Rev. Genet. 9:465–76
    [Google Scholar]
  66. 66.  Li M, Wu H, Luo Z, Xia Y, Guan J et al. 2012. An atlas of DNA methylomes in porcine adipose and muscle tissues. Nat. Commun. 3:850
    [Google Scholar]
  67. 67.  Bang WY, Kim SW, Kwon SG, Hwang JH, Kim TW et al. 2013. Swine liver methylomes of Berkshire, Duroc and Landrace breeds by MeDIPS. Anim. Genet. 44:463–66
    [Google Scholar]
  68. 68.  Ibeagha-Awemu EM, Zhao X 2015. Epigenetic marks: regulators of livestock phenotypes and conceivable sources of missing variation in livestock improvement programs. Front. Genet. 6:302
    [Google Scholar]
  69. 69.  Lan X, Cretney EC, Kropp J, Khateeb K, Berg MA et al. 2013. Maternal diet during pregnancy induces gene expression and DNA methylation changes in fetal tissues in sheep. Front. Genet. 4:49
    [Google Scholar]
  70. 70.  Namous H, Peñagaricano F, Del Corvo M, Capra E, Thomas DL et al. 2018. Integrative analysis of methylomic and transcriptomic data in fetal sheep muscle tissues in response to maternal diet during pregnancy. BMC Genom 19:123
    [Google Scholar]
  71. 71.  Jenkins TG, Carrell DT 2012. The sperm epigenome and potential implications for the developing embryo. Reproduction 143:727–34
    [Google Scholar]
  72. 72.  Kropp J, Carrillo JA, Namous H, Daniels A, Salih SM et al. 2017. Male fertility status is associated with DNA methylation signatures in sperm and transcriptomic profiles of bovine preimplantation embryos. BMC Genom 18:280
    [Google Scholar]
  73. 73.  Verma A, Rajput S, De S, Kumar R, Chakravarty AK, Datta TK 2014. Genome-wide profiling of sperm DNA methylation in relation to buffalo (Bubalus bubalis) bull fertility. Theriogenology 82:750–59.e1
    [Google Scholar]
  74. 74.  Lee JR, Hong CP, Moon JW, Jung YD, Kim DS et al. 2014. Genome-wide analysis of DNA methylation patterns in horse. BMC Genom 15:598
    [Google Scholar]
  75. 75.  Schachtschneider KM, Madsen O, Park C, Rund LA, Groenen MA, Schook LB 2015. Adult porcine genome-wide DNA methylation patterns support pigs as a biomedical model. BMC Genom 16:743
    [Google Scholar]
  76. 76.  Choi M, Lee J, Le MT, Nguyen DT, Park S et al. 2015. Genome-wide analysis of DNA methylation in pigs using reduced representation bisulfite sequencing. DNA Res 22:343–55
    [Google Scholar]
  77. 77.  Schachtschneider KM, Liu Y, Rund LA, Madsen O, Johnson RW et al. 2016. Impact of neonatal iron deficiency on hippocampal DNA methylation and gene transcription in a porcine biomedical model of cognitive development. BMC Genom 17:856
    [Google Scholar]
  78. 78.  Song L, Crawford GE 2010. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010:pdb.prot5384
    [Google Scholar]
  79. 79.  Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:1213–18
    [Google Scholar]
  80. 80.  Buenrostro JD, Wu B, Chang HY, Greenleaf WJ 2015. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109:21.9.1–9
    [Google Scholar]
  81. 81.  Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA et al. 2017. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14:959–62
    [Google Scholar]
  82. 82.  Lanctôt C, Cheutin T, Cremer M, Cavalli G, Cremer T 2007. Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat. Rev. Genet. 8:104–15
    [Google Scholar]
  83. 83.  Dekker J, Rippe K, Dekker M, Kleckner N 2002. Capturing chromosome conformation. Science 295:1306–11
    [Google Scholar]
  84. 84.  Fanucchi S, Shibayama Y, Burd S, Weinberg MS, Mhlanga MM 2013. Chromosomal contact permits transcription between coregulated genes. Cell 155:606–20
    [Google Scholar]
  85. 85.  Pombo A, Dillon N 2015. Three-dimensional genome architecture: players and mechanisms. Nat. Rev. Mol. Cell Biol. 16:245–57
    [Google Scholar]
  86. 86.  Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T et al. 2009. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:289–93
    [Google Scholar]
  87. 87.  Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I et al. 2012. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485:381–85
    [Google Scholar]
  88. 88.  Noordermeer D, Leleu M, Schorderet P, Joye E, Chabaud F, Duboule D 2014. Temporal dynamics and developmental memory of 3D chromatin architecture at Hox gene loci. eLife 3:e02557
    [Google Scholar]
  89. 89.  Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID et al. 2014. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159:1665–80
    [Google Scholar]
  90. 90.  Dixon JR, Selvaraj S, Yue F, Kim A, Li Y et al. 2012. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485:376–80
    [Google Scholar]
  91. 91.  Pope BD, Ryba T, Dileep V, Yue F, Wu W et al. 2014. Topologically associating domains are stable units of replication-timing regulation. Nature 515:402–5
    [Google Scholar]
  92. 92.  Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM et al. 2016. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167:1369–84.e19
    [Google Scholar]
  93. 93.  Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A et al. 2016. A survey of best practices for RNA-seq data analysis. Genome Biol 17:13
    [Google Scholar]
  94. 94.  Derrien T, Estellé J, Marco Sola S, Knowles DG, Raineri E et al. 2012. Fast computation and applications of genome mappability. PLOS ONE 7:e30377
    [Google Scholar]
  95. 95.  Johnson DS, Mortazavi A, Myers RM, Wold B 2007. Genome-wide mapping of in vivo protein-DNA interactions. Science 316:1497–502
    [Google Scholar]
  96. 96.  Krueger F, Kreck B, Franke A, Andrews SR 2012. DNA methylome analysis using short bisulfite sequencing data. Nat. Methods 9:145–51
    [Google Scholar]
  97. 97.  Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek PR 2015. The Ensembl regulatory build. Genome Biol 16:56
    [Google Scholar]
  98. 98.  Harrison PW, Fan J, Richardson D, Clarke L, Zerbino D et al. 2018. FAANG, establishing metadata standards, validation and best practice for the farmed and companion animal community. Anim. Genet. 49:520–26
    [Google Scholar]
  99. 99.  Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018
    [Google Scholar]
  100. 100.  Raney BJ, Dreszer TR, Barber GP, Clawson H, Fujita PA et al. 2014. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. Bioinformatics 30:1003–5
    [Google Scholar]
  101. 101.  Casper J, Zweig AS, Villarreal C, Tyner C, Speir ML et al. 2018. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res 46:D762–D69
    [Google Scholar]
  102. 102.  Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D et al. 2018. Ensembl 2018. Nucleic Acids Res 46:D754–D61
    [Google Scholar]
  103. 103.  Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS et al. 2016. ENCODE data at the ENCODE portal. Nucleic Acids Res 44:D726–32
    [Google Scholar]
  104. 104.  Bujold D, Morais DAL, Gauthier C, Côté C, Caron M et al. 2016. The International Human Epigenome Consortium Data Portal. Cell Syst 3:496–9.e2
    [Google Scholar]
  105. 105.  Misztal I, Legarra A 2017. Invited review: efficient computation strategies in genomic selection. Animal 11:731–36
    [Google Scholar]
  106. 106.  Veerkamp RF, Bouwman AC, Schrooten C, Calus MP 2016. Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle. Genet. Sel. Evol. 48:95
    [Google Scholar]
  107. 107.  Brøndum RF, Su G, Janss L, Sahana G, Guldbrandtsen B et al. 2015. Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction. J. Dairy Sci. 98:4107–16
    [Google Scholar]
  108. 108.  MacLeod IM, Bowman PJ, Vander Jagt CJ, Haile-Mariam M, Kemper KE et al. 2016. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genom 17:144
    [Google Scholar]
  109. 109.  Pérez-Enciso M, Rincón JC, Legarra A 2015. Sequence- vs. chip-assisted genomic selection: Accurate biological information is advised. Genet. Sel. Evol. 47:43
    [Google Scholar]
  110. 110.  Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M 2012. Linking disease associations with regulatory information in the human genome. Genome Res 22:1748–59
    [Google Scholar]
  111. 111.  Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J 2014. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46:310–15
    [Google Scholar]
  112. 112.  Gulko B, Hubisz MJ, Gronau I, Siepel A 2015. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47:276–83
    [Google Scholar]
  113. 113.  Nguyen QH, Tellam RL, Naval-Sanchez M, Porto-Neto LR, Barendse W et al. 2018. Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data. Gigascience 7:1–17
    [Google Scholar]
  114. 114.  Wang M, Hancock TP, MacLeod IM, Pryce JE, Cocks BG, Hayes BJ 2017. Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits. Genet. Sel. Evol. 49:56
    [Google Scholar]
  115. 115.  Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M et al. 2018. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat. Genet. 50:362–67
    [Google Scholar]
  116. 116.  Koufariotis LT, Chen YP, Stothard P, Hayes BJ 2018. Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits. BMC Genom 19:237
    [Google Scholar]
  117. 117.  Klann TS, Black JB, Gersbach CA 2018. CRISPR-based methods for high-throughput annotation of regulatory DNA. Curr. Opin. Biotechnol. 52:32–41
    [Google Scholar]
  118. 118.  Lau CH, Suh Y 2018. CRISPR-based strategies for studying regulatory elements and chromatin structure in mammalian gene control. Mamm. Genome 29:205–28
    [Google Scholar]
  119. 119.  Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W et al. 2015. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373:895–907
    [Google Scholar]
  120. 120.  Hanssen LLP, Kassouf MT, Oudelaar AM, Biggs D, Preece C et al. 2017. Tissue-specific CTCF-cohesin-mediated chromatin architecture delimits enhancer interactions and function in vivo. Nat. . Cell Biol 19:952–61
    [Google Scholar]
  121. 121.  Wu Y, Zeng J, Zhang F, Zhu Z, Qi T et al. 2018. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9:918
    [Google Scholar]
  122. 122.  Kungulovski G, Jeltsch A 2016. Epigenome editing: state of the art, concepts, and perspectives. Trends Genet 32:101–13
    [Google Scholar]
  123. 123.  Hilton IB, D'Ippolito AM, Vockley CM, Thakore PI, Crawford GE et al. 2015. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33:510–17
    [Google Scholar]
  124. 124.  Morgan SL, Mariano NC, Bermudez A, Arruda NL, Wu F et al. 2017. Manipulation of nuclear architecture through CRISPR-mediated chromosomal looping. Nat. Commun. 8:15993
    [Google Scholar]
  125. 125.  Powell RH, Behnke MS 2017. WRN conditioned media is sufficient for in vitro propagation of intestinal organoids from large farm and small companion animals. Biol. Open 6:698–705
    [Google Scholar]
  126. 126.  Khalil HA, Lei NY, Brinkley G, Scott A, Wang J et al. 2016. A novel culture system for adult porcine intestinal crypts. Cell Tissue Res 365:123–34
    [Google Scholar]
  127. 127.  van der Hee B, Loonen LMP, Taverne N, Taverne-Thiele JJ, Smidt H, Wells JM 2018. Optimized procedures for generating an enhanced, near physiological 2D culture system from porcine intestinal organoids. Stem Cell Res 28:165–71
    [Google Scholar]
  128. 128.  Meijerink E, Neuenschwander S, Fries R, Dinter A, Bertschinger HU et al. 2000. A DNA polymorphism influencing α(1,2)fucosyltransferase activity of the pig FUT1 enzyme determines susceptibility of small intestinal epithelium to Escherichia coli F18 adhesion. Immunogenetics 52:129–36
    [Google Scholar]
  129. 129.  Driehuis E, Clevers H 2017. CRISPR/Cas 9 genome editing and its applications in organoids. Am. J. Physiol. Gastrointest. Liver Physiol. 312:G257–G65
    [Google Scholar]
  130. 130.  MacHugh DE, Larson G, Orlando L 2017. Taming the past: ancient DNA and the study of animal domestication. Annu. Rev. Anim. Biosci. 5:329–51
    [Google Scholar]
  131. 131.  Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L et al. 2014. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. PNAS 111:E5661–69
    [Google Scholar]
  132. 132.  Gaunitz C, Fages A, Hanghøj K, Albrechtsen A, Khan N et al. 2018. Ancient genomes revisit the ancestry of domestic and Przewalski's horses. Science 360:111–14
    [Google Scholar]
  133. 133.  Librado P, Gamba C, Gaunitz C, Der Sarkissian C, Pruvost M et al. 2017. Ancient genomic changes associated with domestication of the horse. Science 356:442–45
    [Google Scholar]
  134. 134.  Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H et al. 2015. Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments. PNAS 112:E6889–97
    [Google Scholar]
  135. 135.  Tilgner H, Jahanbani F, Blauwkamp T, Moshrefi A, Jaeger E et al. 2015. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat. Biotechnol. 33:736–42
    [Google Scholar]
  136. 136.  Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C et al. 2011. Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat. Biotechnol. 30:99–104
    [Google Scholar]
  137. 137.  Lagarde J, Uszczynska-Ratajczak B, Carbonell S, Pérez-Lluch S, Abad A et al. 2017. High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing. Nat. Genet. 49:1731–40
    [Google Scholar]
  138. 138.  Jain M, Fiddes IT, Miga KH, Olsen HE, Paten B, Akeson M 2015. Improved data analysis for the MinION nanopore sequencer. Nat. Methods 12:351–56
    [Google Scholar]
  139. 139.  Takahashi H, Lassmann T, Murata M, Carninci P 2012. 5′ End-centered expression profiling using cap-analysis gene expression and next-generation sequencing. Nat. Protoc. 7:542–61
    [Google Scholar]
  140. 140.  Batut P, Gingeras TR 2013. RAMPAGE: promoter activity profiling by paired-end sequencing of 5′-complete cDNAs. Curr. Protoc. Mol. Biol. 104:25B.11.1–16
    [Google Scholar]
  141. 141.  Chang H, Lim J, Ha M, Kim VN 2014. TAIL-seq: genome-wide determination of poly(A) tail length and 3′ end modifications. Mol. Cell 53:1044–52
    [Google Scholar]
  142. 142.  Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S et al. 2015. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47:598–606
    [Google Scholar]
  143. 143.  Ramani V, Cusanovich DA, Hause RJ, Ma W, Qiu R et al. 2016. Mapping 3D genome architecture through in situ DNase Hi-C. Nat. Protoc. 11:2104–21
    [Google Scholar]
  144. 144.  Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA et al. 2016. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13:919–22
    [Google Scholar]
  145. 145.  Fang R, Yu M, Li G, Chee S, Liu T et al. 2016. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res 26:1345–48
    [Google Scholar]
  146. 146.  Mumbach MR, Satpathy AT, Boyle EA, Dai C, Gowen BG et al. 2017. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49:1602–12
    [Google Scholar]
/content/journals/10.1146/annurev-animal-020518-114913
Loading
/content/journals/10.1146/annurev-animal-020518-114913
Loading

Data & Media loading...

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error