Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock
Autoři:
Biaty Raymond aff001; Loic Yengo aff003; Roy Costilla aff004; Chris Schrooten aff005; Aniek C. Bouwman aff001; Ben J. Hayes aff004; Roel F. Veerkamp aff001; Peter M. Visscher aff003
Působiště autorů:
Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
aff001; Biometris, Wageningen University and Research, Wageningen, The Netherlands
aff002; Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
aff003; Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
aff004; CRV BV, Arnhem, The Netherlands
aff005; CRV BV, AL Arnhem, The Netherlands
aff005
Vyšlo v časopise:
Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock. PLoS Genet 16(9): e32767. doi:10.1371/journal.pgen.1008780
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008780
Souhrn
Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher’s exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
Klíčová slova:
Cattle – Complex traits – Genetics – Genome-wide association studies – Genomics – Human genomics – Livestock – Single nucleotide polymorphisms
Zdroje
1. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences. 2009;106(23):9362–7.
2. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856
3. Brachi B, Morris GP, Borevitz JO. Genome-wide association studies in plants: the missing heritability is in the field. Genome biology. 2011;12(10):232. doi: 10.1186/gb-2011-12-10-232 22035733
4. Sharma A, Lee JS, Dang CG, Sudrajad P, Kim HC, Yeon SH, et al. Stories and challenges of genome wide association studies in livestock—a review. Asian-Australasian journal of animal sciences. 2015;28(10):1371. doi: 10.5713/ajas.14.0715 26194229
5. Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, Zusmanovich P, et al. Many sequence variants affecting diversity of adult human height. Nature genetics. 2008;40(5):609. doi: 10.1038/ng.122 18391951
6. Lettre G, Jackson AU, Gieger C, Schumacher FR, Berndt SI, Sanna S, et al. Identification of ten loci associated with height highlights new biological pathways in human growth. Nature genetics. 2008;40(5):584. doi: 10.1038/ng.125 18391950
7. Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, Mangino M, et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nature genetics. 2008;40(5):575. doi: 10.1038/ng.121 18391952
8. Allen HL, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467(7317):832. doi: 10.1038/nature09410 20881960
9. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature genetics. 2014;46(11):1173. doi: 10.1038/ng.3097 25282103
10. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in∼ 700000 individuals of European ancestry. Human molecular genetics. 2018;27(20):3641–9. doi: 10.1093/hmg/ddy271 30124842
11. Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nature genetics. 2014;46(2):100. doi: 10.1038/ng.2876 24473328
12. Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M, Schenkel FS, et al. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature genetics. 2018;50(3):362. doi: 10.1038/s41588-018-0056-5 29459679
13. Jiang J, Ma L, Prakapenka D, VanRaden PM, Cole JB, Da Y. A large-scale genome-wide association study in US Holstein cattle. Frontiers in genetics. 2019;10:412. doi: 10.3389/fgene.2019.00412 31139206
14. Purfield D, Evans R, Berry D. Reaffirmation of known major genes and the identification of novel candidate genes associated with carcass-related metrics based on whole genome sequence within a large multi-breed cattle population. BMC genomics. 2019;20(1):720. doi: 10.1186/s12864-019-6071-9 31533623
15. Xiang R, Berg Ivd, MacLeod IM, Hayes BJ, Prowse-Wilkins CP, Wang M, et al. Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits. Proceedings of the National Academy of Sciences. 2019;116(39):19398–408. doi: 10.1073/pnas.1904159116 31501319
16. Dekkers JC. Commercial application of marker-and gene-assisted selection in livestock: strategies and lessons. Journal of animal science. 2004;82(suppl_13):E313–E28.
17. Kemper KE, Visscher PM, Goddard ME. Genetic architecture of body size in mammals. Genome biology. 2012;13(4):244. doi: 10.1186/gb4016 22546202
18. Pryce JE, Hayes BJ, Bolormaa S, Goddard ME. Polymorphic regions affecting human height also control stature in cattle. Genetics. 2011;187(3):981–4. doi: 10.1534/genetics.110.123943 21212230
19. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics. 2016;48(5):481. doi: 10.1038/ng.3538 27019110
20. Koonin EV. Orthologs, paralogs, and evolutionary genomics. Annu Rev Genet. 2005;39:309–38. doi: 10.1146/annurev.genet.39.073003.114725 16285863
21. Descorps-Declère S, Lemoine F, Sculo Q, Lespinet O, Labedan B. The multiple facets of homology and their use in comparative genomics to study the evolution of genes, genomes, and species. Biochimie. 2008;90(4):595–608. doi: 10.1016/j.biochi.2007.09.010 17961904
22. Gabaldón T, Koonin EV. Functional and evolutionary implications of gene orthology. Nature Reviews Genetics. 2013;14(5):360–6. doi: 10.1038/nrg3456 23552219
23. Dolinski K, Botstein D. Orthology and functional conservation in eukaryotes. Annu Rev Genet. 2007;41:465–507. doi: 10.1146/annurev.genet.40.110405.090439 17678444
24. Murphy WJ, Pevzner PA, O'Brien SJ. Mammalian phylogenomics comes of age. TRENDS in Genetics. 2004;20(12):631–9. doi: 10.1016/j.tig.2004.09.005 15522459
25. Carter-Su C, Schwartz J, Argetsinger LS. Growth hormone signaling pathways. Growth Hormone & IGF Research. 2016;28:11–5.
26. Heinonen TY, Mäki M. Peters’-plus syndrome is a congenital disorder of glycosylation caused by a defect in the β1, 3-glucosyltransferase that modifies thrombospondin type 1 repeats. Annals of medicine. 2009;41(1):2–10. doi: 10.1080/07853890802301975 18720094
27. Costilla R, Warburton C, Hayes B, editors. Genetic control of fertility traits across species: variance in tropical beef heifers’age at puberty explained by genes controling age at menarche in women. Proceeding, Association for the Advancement of Animal Breeding and Genetics. 2020
28. Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS genetics. 2011;7(2):e1002003. doi: 10.1371/journal.pgen.1002003 21304890
29. Consortium G. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204. doi: 10.1038/nature24277 29022597
30. Brøndum RF, Su G, Lund MS, Bowman PJ, Goddard ME, Hayes BJ. Genome position specific priors for genomic prediction. BMC genomics. 2012;13(1):543.
31. MacLeod I, Bowman P, Vander Jagt C, Haile-Mariam M, Kemper K, Chamberlain A, et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC genomics. 2016;17(1):144.
32. Su G, Christensen OF, Janss L, Lund MS. Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. Journal of dairy science. 2014;97(10):6547–59. doi: 10.3168/jds.2014-8210 25129495
33. Fragomeni BO, Lourenco DA, Masuda Y, Legarra A, Misztal I. Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genetics Selection Evolution. 2017;49(1):59.
34. Raymond B, Bouwman AC, Wientjes YC, Schrooten C, Houwing-Duistermaat J, Veerkamp RF. Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers. Genetics Selection Evolution. 2018;50(1):49.
35. Speed D, Balding DJ. MultiBLUP: improved SNP-based prediction for complex traits. Genome research. 2014;24(9):1550–7. doi: 10.1101/gr.169375.113 24963154
36. Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, 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. 2017;18(1):604. doi: 10.1186/s12864-017-4004-z 28797230
37. Elsik CG, Tellam RL, Worley KC. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science. 2009;324(5926):522–8. doi: 10.1126/science.1169588 19390049
38. Chen Y, Cunningham F, Rios D, McLaren WM, Smith J, Pritchard B, et al. Ensembl variation resources. BMC genomics. 2010;11(1):293.
39. consortium UK. The UK10K project identifies rare variants in health and disease. Nature. 2015;526(7571):82–90. doi: 10.1038/nature14962 26367797
40. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics. 2011;88(1):76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468
41. Daetwyler HD, Capitan A, Pausch H, Stothard P, Van Binsbergen R, Brøndum RF, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature genetics. 2014;46(8):858. doi: 10.1038/ng.3034 25017103
42. Bakshi A, Zhu Z, Vinkhuyzen AA, Hill WD, McRae AF, Visscher PM, et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Scientific reports. 2016;6:32894. doi: 10.1038/srep32894 27604177
43. Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR. Cohort profile: the health and retirement study (HRS). International journal of epidemiology. 2014;43(2):576–85. doi: 10.1093/ije/dyu067 24671021
44. Mirina A, Atzmon G, Ye K, Bergman A. Gene size matters. PloS one. 2012;7(11):e49093. doi: 10.1371/journal.pone.0049093 23152854
45. Cochran WG. Some methods for strengthening the common χ 2 tests. Biometrics. 1954;10(4):417–51.
46. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the national cancer institute. 1959;22(4):719–48. 13655060
47. Purcell S, Chang C. PLINK 1.9. URL https://www cog-genomics org/plink2. 2015.
48. Lee SH, Van der Werf JH. MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information. Bioinformatics. 2016;32(9):1420–2. doi: 10.1093/bioinformatics/btw012 26755623
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