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Вавиловский журнал генетики и селекции

Расширенный поиск

Использование геномных данных в селекции птицы

https://doi.org/10.18699/VJ17.298

Аннотация

Новые технологии определения последовательности нуклеотидов ДНК позволили открыть сотни тысяч мононуклеотидных поли морфных маркеров, часть из которых ассоциирована с племенны ми качествами животных. Разработанная на основе этих достижений геномная селекция произвела революционный сдвиг в птицеводстве. Система полиморфных маркеров  предоставляет уникальную возможность значительно повышать точность расчетных значений селекции, управлять генетической изменчивостью, сокращать интервалы между генерациями и ускорять генетический прогресс. Геномная селекция в птицеводстве имеет ряд отличий от подобной технологии, используемой на сельскохозяйственных видах млекопитающих. Наличие двух категорий хромосом (микро- и макрохромосомы) с разной скоростью рекомбинаций, включение в геномную оценку женских особей, а также быстрая смена поколений вносят свои особенности. Технология интенсивно внедряется в различные отрасли птицеводства, включая бройлерное производство, и используется основными птицеводческими компаниями. Совершенствованию отдельных этапов геномной селекции поможет улучшение регистрации количественных признаков, математической обработки молекулярной базы данных, импутации и оценки генетического неравновесия по сцеплению.

Об авторах

А. Ф. Яковлев
Всероссийский научно-исследовательский институт генетики и разведения сельскохозяйственных животных.
Россия
Санкт-Петербург, Пушкин.


Н. В. Дементьева
Всероссийский научно-исследовательский институт генетики и разведения сельскохозяйственных животных.
Россия
Санкт-Петербург, Пушкин.


Список литературы

1. Abdollahi-Arpanahi R., Morota G., Valente B.D., Kranis A., Rosa G., Gianola D. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genet. Sel. Evol. 2016;48:10. DOI 10.1186/s12711-016-0187-z.

2. Aerts J., Megens H.J., Veenendaal T., Ovcharenko I., Crooijmans R., Gordon L., Stubbs L., Groenen M. Extent of linkage disequilibrium in chicken. Cytogenet. Genome Res. 2007;117(1-4):338-345. DOI 10.1159/000103196.

3. Andreescu C., Avendano S., Brown S.R., Hassen A., Lamont S.J., Dekkers J.C. Linkage disequilibrium in related breeding lines of chickens. Genetics. 2007;177:2161-2169. DOI 10.1534/genetics. 107.082206.

4. Bai Y., Sun G., Kang X., Han R., Tian Y., Li H., Wei Y., Zhu S. Polymorphisms of the pro-opiomelanocortin and agouti-related protein genes and their association with chicken production traits. Mol. Biol. Rep. 2012;39(7):7533-7539. DOI 10.1007/s11033-012-1587-y.

5. Browning B.L., Browning S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 2009; 84(2):210-223. DOI 10.1016/j.ajhg.2009.01.005.

6. Brzyski D., Peterson Ch.B., Sobczyk P., Candes E.J., Bogdan M., Sa-batti C. Controlling the rate of GWAS false discoveries. Genetics. 2017;205(1):61-75. DOI 10.1534/genetics.116.193987.

7. Cosart T., Beja-Pereira A., Luikart G. EXONSAMPLER: a computer program for genome-wide and candidate gene exon sampling for targeted next generation sequencing. Mol. Ecol. Resour. 2014;14(6): 1296-1301. DOI 10.1111/1755-0998.12267.

8. Daetwyler H.D., Pong-Wong R., Villanueva B. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010; 185:1021-1031. DOI 10.1534/genetics.110.116855.

9. Daetwyler H.D., Villanueva B., Woolliams J.A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE. 2008;3(10):e3395. DOI 10.1371/journal.pone.0003395.

10. Dekkers J.C.M. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J. Anim. Sci. 2004; 82(E-Suppl.):E313-E328.

11. de Roos A.P.W., Hayes B.J., Spelman R.J., Goddard M.E. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics. 2008;179(3):1503-1512. DOI 10.1534/ genetics.107.084301.

12. Do D.N., Janss L.L., Jensen J., Kadarmideen H.N. SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs. J. Anim. Sci. 2015;93:2056-2063. DOI 10.2527/jas.2014-8640.

13. Elferink M.G., Megens H.J., Vereijken A., Hu X., Crooijmans R.P., Groenen M.A. Signatures of selection in the genomes of commercial and non-commercial chicken breeds. PloS ONE. 2012;7(2):e32720.

14. Ennis S. Linkage disequilibrium as a tool for detecting signatures of natural selection. Methods Mol. Biol. 2007;376:59-70. DOI 10.1007/978-1-59745-389-9_5.

15. Ewald S.J., Kapczynski D.R., Livant E.J., Suarez D.L., Ralph J., McLeod S., Miller C. Association of Mx1 Asn631 variant alleles with reductions in morbidity, early mortality, viral shedding, and cytokine responses in chickens infected with a highly pathogenic avian influenza virus. Immunogenetics. 2011;63:363-375. DOI 10.1007/ s00251-010-0509-1.

16. Fan W.L., Ng C.S., Chen C.F., Lu M.Y., Chen Y.H., Liu C.J., Wu S.M., Chen C.K., Chen J.J., Mao C.T., Lai Y.T., Lo W.S., Chang W.H., Li W.H. Genome-wide patterns of genetic variation in two domestic chickens. Genome Biol. Evol. 2013;5:1376-1392. DOI 10.1093/gbe/ evt097.

17. Fariello M.I., Boitard S., Naya H., SanCristobal M., Servin B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics. 2013;193:929-941. DOI 10.1534/genetics.112.147231.

18. Fu W., Dekkers J.C.M., Lee W.R., Abasht B. Linkage disequilibrium in crossbred and pure line chickens. Genet. Sel. Evol. 2015;47(1):11. DOI 10.1186/s12711-015-0098-4.

19. Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J., Blu-menstiel B., Higgins J., DeFelice M., Lochner A., Faggart M., Liu-Cordero S.N., Rotimi C., Adeyemo A., Cooper R., Ward R., Lander E.S., Daly M.J., Altshuler D. The structure of haplotype blocks in the human genome. Science. 2002;296(5576):2225-2229. DOI 10.1126/science.1069424.

20. Garcia-Gamez E., Sahana G., Gutierrez-Gil B. Linkage disequilibrium and inbreeding estimation in Spanish Churra sheep. BMC Genet. 2012;13:43. DOI 10.1186/1471-2156-13-43.

21. Gholami M., Reimer C., Erbe M., Preisinger R., Weigend A., Wei-gend S., Servin B., Simianer H. Genome scan for selection in structured layer chicken populations exploiting linkage disequilibrium information. PLoS ONE. 2015;10(7):e0130497. DOI 10.1371/journal.pone.0130497.

22. Groenen M., Megens H.J., Zare Y., Warren W.C., Hillier L.W., Crooij-mans R.P., Vereijken A., Okimoto R., Muir W.M., Cheng H.H. The development and characterization of a 60K SNP chip for chicken. BMC Genomics. 2011;12:274. DOI 10.1186/1471-2164-12-274.

23. Habier D., Fernando R.L., Dekkers J.C.M. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 2007;177(4):2389-2397. DOI 10.1534/genetics.107.081190.

24. Habier D., Tetens J., Seefried F.R., Lichtner P., Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet. Sel. Evol. 2010;42:5. DOI 10.1186/1297-9686-42-5.

25. Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. Invited review: genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 2009;92:433-443. DOI 10.3168/jds.2008-1646.

26. Heidaritabar M., Calus M., Vereijken A., Groenen M., Bastiaansen J. Accuracy of imputation using the most common sires as reference population in layer chickens. BMC Genetics. 2015;16:101. DOI 10.1186/s12863-015-0253-5.

27. Heidaritabar M., Vereijken A., Muir W.M., Meuwissen T., Cheng H., Megens H.J., Groenen M.A., Bastiaansen J.W. Systematic differences in the response of genetic variation to pedigree and genome-based selection methods. Heredity (Edinb.). 2014;13(6):503-513. DOI 10.1038/hdy.2014.55.

28. Heifetz E.M., Fulton J.E., O'Sullivan N., Zhao H., Dekkers J.C., Soller M. Extent and consistency across generations of linkage disequilibrium in commercial layer chicken breeding populations. Genetics. 2005;171(3):1173-1181. DOI 10.1534/genetics.105. 040782.

29. Hickey J.M., Kinghorn B.P., Tier B., Wilson J.F., Dunstan N., van der Werf J.H. A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes. Genet. Sel. Evol. 2011;43:12. DOI 10.1186/1297-9686-44-9.

30. Hillier L.W., Miller W., Birney E., Warren W., Hardison R.C., Ponting C.P., Bork P., Burt D.W., Groenen M.A., Delany M.E., Dodg-son J.B., Chinvalla A.T., Cliften P.F., Clifton S.W., Delehaunty K.D. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004;432:695-716. DOI 10.1038/nature03154.

31. Hindorff L.A., Sethupathy P., Junkins H.A., Ramos E.M., Mehta J.P., Collins F.S., Manolio T.A. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA. 2009;106:9362-9367. DOI 10.1073/pnas.0903103106.

32. Hormozdiari F., Kostem E., Kang Y., Pasaniuc B., Eskin E. Identifying causal variants at loci with multiple signals of association. Genetics. 2014;198:497-508. DOI 10.1534/genetics.114.167908.

33. International HapMap Consortium: A haplotype map of the human genome. Nature. 2005;437:1299-1320. DOI 10.1038/nature04226. International HapMap Consortium: A second generation human haplo-type map of over 3.1 million SNPs. Nature. 2007;449:851-861. DOI 10.1038/nature06258.

34. Johansson A.M., Pettersson M.E., Siegel P.B., Carlborg O. Genomewide effects of long-term divergent selection. PLoS Genet. 2010; 6(11):e1001188. DOI 10.1371/journal.pgen.1001188.

35. Kim Y., Nielsen R. Linkage disequilibrium as a signature of selective sweeps. Genetics. 2004;167(3):1513-1524. DOI 10.1534/genetics. 103.025387.

36. Kindt A.S., Navarro P., Semple C.A., Haley C.S. The genomic signature of trait-associated variants. BMC Genomics. 2013;14:108. DOI 10.1186/1471-2164-14-108.

37. Kizilkaya K., Fernando R.L., Garrick D.J. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J. Anim. Sci. 2010;88:544-551. DOI 10.2527/jas.2009-2064.

38. Koufariotis L., Chen Y.P., Bolormaa S., Hayes B.J. Regulatory and coding genome regions are enriched for trait associated variants in dairy and beef cattle. BMC Genomics. 2014;15:436. DOI 10.1186/1471-2164-15-436.

39. Kranis A., Gheyas A.A., Boschiero C., Turner F., Yu L., Smith S., Talbot R., Pirani A., Brew F., Kaiser P., Hocking P.M., Fife M., Salmon N., Fulton J., Strom T.M., Haberer G., Weigend S., Preis-inger R., Gholami M., Qanbari S., Simianer H., Watson K.A., Wool-liams J.A., Burt D.W. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics. 2013;14:59. DOI 10.1186/1471-2164-14-59.

40. Liu R., Lourenco D.A.L., Fragomeni B.O., Tsuruta S. Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken. Genet. Sel. Evol. 2015; 47:56. DOI 10.1186/s12711-015-0137-1.

41. Liu R., Wang Y.C., Sun D.X., Genomics and gene engineering Livant E.J., Avendano S., McLeod S., Ye X., Lamont S.J., Dekkers J.C., Ewald S.J. MX1 exon 13 polymorphisms in broiler breeder chickens and associations with commercial traits. Anim. Genet. 2007;38:177-179. DOI 10.1111/j.1365-2052.2007.01577.x.

42. Lourenco D.A.L., Misztal I., Tsuruta S., Aguilar I., Lawlor T.J., For-ni S., Weller J.I. Are evaluations on young genotyped animals benefiting from the past generations? J. Dairy Sci. 2014;97:3930-3942. DOI 10.3168/jds.2013-7769.

43. Lu D., Sargolzaei M., Kelly M., Li C., Vander Voort G., Wang Z., Plastow G., Moore S., Miller S.P. Linkage disequilibrium in Angus, Charolais, and Crossbred beef cattle. Front. Genet. 2012;3:152. DOI 10.3389/fgene.2012.00152.

44. Megens H.J., Crooijmans R.P., Bastiaansen J.W., Kerstens H.H., Coster A., Jalving R., Vereijken A., Silva P., Muir W.M., Cheng H.H., Hanotte O., Groenen M.A. Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken. BMC Genet. 2009;10:86. DOI 10.1186/1471-2156-10-86.

45. Meuwissen T.H., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001; 157(4):1819-1829.

46. Morota G., Abdollahi-Arpanahi R., Kranis A. Genome-enabled prediction of quantitative traits in chickens using genomic annotation. BMC Genomics. 2014;15:109. DOI 10.1186/1471-2164-15-109.

47. Mugal C.F., Nabholz B., Ellegren H. Genome-wide analysis in chicken reveals that local levels of genetic diversity are mainly governed by the rate of recombination. BMC Genomics. 2013;14:86. DOI 10.1186/1471-2164-14-86.

48. Pertille F., Guerrero-Bosagna C., Henrique da Silva V., Boschiero C., da Silva Nunes J.R., Ledur M.C., Jensen P., Coutinho L.L. High-throughput and cost-effective chicken genotyping using next-generation sequencing. Sci. Rep. 2016;6:26929. DOI 10.1038/srep26929.

49. Peterson C.B., Bogomolov M., Benjamini Y. Many phenotypes without many false discoveries: error controlling strategies for vulti-trait association studies. Genet. Epidemiol. 2016;40(1):45-56. DOI 10.1002/gepi.21942.

50. Porto-Neto L.R., Kijas J.W., Reverter A. The extent of linkage disequilibrium in beef cattle breeds using high-density SNP genotypes. Genet. Sel. Evol. 2014;46:22. DOI 10.1186/1297-9686-46-22.

51. Qanbari S., Hansen M., Weigend S., Preisinger R., Simianer H. Linkage disequilibrium reveals different demographic history in egg laying chickens.BMCGenet.2010;11:103.DOI10.1186/1471-2156-11-103.

52. Rao Y., Sun L., Nie Q. The influence of recombination on SNP diversity in chickens. Hereditas. 2011;148(2):63-69. DOI 10.1111/j.1601-5223.2010.02210.x.

53. Resnyk C., Carre W., Wang X., Porter T.E., Simon J., Le Bihan-Duval E., Duclos M.J., Aggrey S.E., Cogburn L.A. Transcriptional analysis of abdominal fat in genetically fat and lean chickens reveals adipo-kines, lipogenic genes and a link between hemostasis and leanness. BMC Genomics. 2013;14:557. DOI 10.1186/1471-2164-14-557.

54. Sabeti P.C., Reich D.E., Higgins J.M., Levine H.Z., Richter D.J., Schaffner S.F., Gabriel S.B., Platko J.V., Patterson N.J., McDonald G.J., Ackerman H.C., Campbell S.J., Altshuler D., Cooper R., Kwiatkowski D., Ward R., Lander E.S. Detecting recent positive selection in the human genome from haplotype structure. Nature. 2002;419:832-837. DOI 10.1038/nature01140.

55. Sharma P., Bottje W., Okimoto R. Polymorphisms in uncoupling protein, melanocortin 3 receptor, melanocortin 4 receptor, and pro-opio-melanocortin genes and association with production traits in a commercial broiler line. Poult Sci. 2008;87:2073-2086. DOI 10.3382/ps.2008-00060.

56. Smaragdov M.G. Genomic selection as a possible accelerator of traditional selection. Russ. J. Genet. 2009;45(6):633-636. DOI 10.1134/S1022795409060015.

57. Smith J.M., Haigh J. The hitch-hiking effect of a favourable gene. Genet. Res. 1974;23(1):23-35. DOI 10.1017/S0016672308009579.

58. Solberg T.R., Sonesson A.K., Woolliams J.A., Odegard J., Meuwis-sen T.H. Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect. Genet. Sel. Evol. 2009;41:53. DOI 10.1186/1297-9686-41-53.

59. Stainton J.J., Charlesworth B., Haley C.S., Kranis A., Watson K., Wiener P. Use of high-density SNP data to identify patterns of diversity and signatures of selection in broiler chickens. J. Anim. Breed. Genet. 2016;46(1):37-49. DOI 10.1111/jbg.12228.

60. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57-74. DOI 10.1038/nature11247.

61. Wang C.L., Ding X.D., Wang J.Y., Liu J.F., Fu W.X., Zhang Z., Yin Z.J., Zhang Q. Bayesian methods for estimating GEBVs of threshold traits. Heredity. 2013a;110(3):213-219. DOI 10.1038/hdy.2012.65.

62. Wang C., Habier D., Peiris B.L., Wolc A., Kranis A., Watson K.A., Avendano S., Garrick D.J., Fernando R.L., Lamont S.J., Dekkers J.C. Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens. Poultry Science. 2013b;92(7):1712-1723. DOI 10.3382/ps.2012-02941.

63. Wang J., Lin M., Crenshaw A., Hutchinson A., Hicks B., Yeager M., Berndt S., Huang W.Y., Hayes R.B., Chanock S.J., Jones R.C., Ra-makrishnan R. High-throughput single nucleotide polymorphism ge-notyping using nanofluidic Dynamic Arrays. BMC Genomics. 2009; 10:561. DOI 10.1186/1471-2164-10-561.

64. Weng Z., Wolc A., Shen X., Fernando R.L., Dekkers J.C., Arango J., Settar P., Fulton J.E., O'Sullivan N.P., Garrick D.J. Effects of number of training generations on genomic prediction for various traits in a layer chicken population. Genet. Sel. Evol. 2016;48:22. DOI 10.1186/s12711-016-0198-9.

65. Wolc A., Arango J., Settar P., Fulton J.E., O'Sullivan N.P., Preising-er R., Habier D., Fernando R., Garrick D.J., Dekkers J.C. Persistence of accuracy of genomic estimated breeding values over generations in layer chickens. Genet. Sel. Evol. 2011;43:23. DOI 10.1186/1297-9686-43-23.

66. Wragg D., Mwacharo J., Alcalde J., Hocking P.M., Hanotte O. Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens. Heredity. 2012;109:6-18. DOI 10.1038/hdy.2012.9.

67. Yakovlev A.F., Smaragdov M.G. A significant increase in the accuracy of estimation of the breeding value of animals in dairy cattle. Zoo-techniya = Zootechnics. 2011;5:2-4. (in Russian)

68. Yang J., Manolio T.A., Pasquale L.R., Boerwinkle E., Caporaso N., Cunningham J.M., de Andrade M., Feenstra B., Feingold E., Hayes M.G., Hill W.G., Landi M.T., Alonso A., Lettre G., Lin P., Ling H., Lowe W., Mathias R.A., Melbye M., Pugh E., Corne-lis M.C., Weir B.S., Goddard M.E., Visscher P.M. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 2011;43:519-525. DOI 10.1038/ng.823.

69. Zhang H., Hu X., Wang Z., Zhang Y., Wang S., Wang N., Ma L., Leng L., Wang S., Wang Q., Wang Y., Tang Z., Li N., Da Y., Li H. Selection signature analysis implicates the PC1/PCSK1 region for chicken abdominal fat content. PLoS ONE. 2012;7(7):e40736. DOI 10.1371/journal.pone.0040736.

70. Zhang Z., Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle. J. Dairy Sci. 2010;93:5487-5494. DOI 10.3168/jds.2010-3501.

71. Zhao H., Nettleton D., Dekkers J.C.M. Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms. Genet. Res. 2007;89:91-96. DOI 10.1017/S0016672307008634.

72. Zhou H., Mitchell A.D., McMurtry J.P., Ashwell C.M., Lamont S.J. Insulin-like growth factor-I gene polymorphism associations with growth, body composition, skeleton integrity, and metabolic traits in chickens. Poult. Sci. 2005;84(2):212-219. DOI 10.1093/ps/84.2.212.


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