Preview

Agrarian science

Advanced search

GWAS as a tool for detecting SNPs in cattle to study their relationship to reproduction, productivity, growth, behavior, diseases

https://doi.org/10.32634/0869-8155-2024-385-8-124-131

Abstract

The fundamental goal of animal husbandry is the cost-effective production of human food from healthy animals, which includes production, reproduction. The method of whole-genome association study (GWAS) is actively used in various fields, including agriculture. Genome-wide association analyzes were generated as an identifier for genomic variations associated with economically significant traits in different livestock species. This method of genomic selection provides new priorities for improving the productive and reproductive qualities of livestock.

The purpose of this review article is a comprehensive analysis of the current state of GWAS in cattle, focusing on the identification of SNPs associated with reproduction, productivity, growth, behavior and genetically determined diseases. The scope of the article covers the study of GWAS results worldwide, both in dairy and beef cattle, with special emphasis on the identification of candidate genes, QTL and genome regions related to the direction of productivity. Additionally, the organization of this review will include a classification of GWAS results based on the specific traits studied, providing a comprehensive overview of the genetic determinants of reproduction, growth, behavior, and disease traits in cattle.

About the Authors

T. A. Larkina
Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry
Russian Federation

Tatyana Aleksandrovna Larkina, Candidate of Biological Sciences, Junior Researcher 

55А Moskovskoe highway, Pushkin, St. Petersburg, 196601



G. V. Shiryaev
Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry
Russian Federation

Gennady Vladimirovich Shiryaev, Candidateof Agricultural Sciences, Senior Researcher

55А Moskovskoe highway, Pushkin, St. Petersburg, 196601



References

1. Mottet A., de Haan C., Falcucci A., Tempio G., Opio C., Gerber P. Livestock: On our plates or eating at our table? A new analysis of the feed/food debate. Global Food Security. 2017; 14: 1–8. https://doi.org/10.1016/j.gfs.2017.01.001

2. White R.R., Hall M.B. Nutritional and greenhouse gas impacts of removing animals from US agriculture. Proceedings of the National Academy of Sciences. 2017; 114(48): E10301–E10308. https://doi.org/10.1073/pnas.1707322114

3. Bögeholz A. et al. GWAS Hits for Bilateral Convergent Strabismus with Exophthalmos in Holstein Cattle Using Imputed Sequence Level Genotypes. Genes. 2021; 12(7): 1039. https://doi.org/10.3390/genes12071039

4. Araujo A.C. et al. Haplotype-Based Single-Step GWAS for Yearling Temperament in American Angus Cattle. Genes. 2022; 13(1): 17. https://doi.org/10.3390/genes13010017

5. Freebern E. et al. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics. 2020; 21: 41. https://doi.org/10.1186/s12864-020-6461-z

6. Abdellaoui A., Yengo L., Verweij K.J.H., Visscher P.M. 15 years of GWAS discovery: Realizing the promise. The American Journal of Human Genetics. 2023; 110(2): 179‒194. https://doi.org/10.1016/j.ajhg.2022.12.011

7. Wiggans G.R. et al. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. Journal of Dairy Science. 2009; 92(7): 3431‒3436. https://doi.org/10.3168/jds.2008-1758

8. Wiggans G.R. et al. Selection and management of DNA markers for use in genomic evaluation. Journal of Dairy Science. 2010; 93(5): 2287‒2292. https://doi.org/10.3168/jds.2009-2773

9. Van Tassell C.P. et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature Methods. 2008; 5(3): 247‒252. https://doi.org/10.1038/nmeth.1185

10. Matukumalli L.K. et al. Development and Characterization of a High Density SNP Genotyping Assay for Cattle. PLoS ONE. 2009; 4(4): e5350. https://doi.org/10.1371/journal.pone.0005350

11. Reis H.B.D. et al. Genome-Wide Association (GWAS) Applied to Carcass and Meat Traits of Nellore Cattle. Metabolites. 2023; 14(1): 6. https://doi.org/10.3390/metabo14010006

12. Fan H. et al. Pathway-Based Genome-Wide Association Studies for Two Meat Production Traits in Simmental Cattle. Scientific Reports. 2016; 5: 18389. https://doi.org/10.1038/srep18389

13. Jiang L. et al. Genome Wide Association Studies for Milk Production Traits in Chinese Holstein Population. PLoS ONE. 2010; 5(10): e13661. https://doi.org/10.1371/journal.pone.0013661

14. Stegemiller M.R. et al. Genome-Wide Association Analyses of Fertility Traits in Beef Heifers. Genes. 2021; 12(2): 217. https://doi.org/10.3390/genes12020217

15. Melo T.P.d., de Camargo G.M.F., de Albuquerque L.G., Carvalheiro R. Genome-wide association study provides strong evidence of genes affecting the reproductive performance of Nellore beef cows. PLoS ONE. 2017; 12(5): e0178551. https://doi.org/10.1371/journal.pone.0178551

16. Zhuang Z. et al. Weighted Single-Step Genome-Wide Association Study for Growth Traits in Chinese Simmental Beef Cattle. Genes. 2020; 11(2): 189. https://doi.org/10.3390/genes11020189

17. Ma L., Cole J.B., Da Y., Van Raden P.M. Symposium review: Genetics, genome-wide association study, and genetic improvement of dairy fertility traits. Journal of Dairy Science. 2019; 102(4): 3735‒3743. https://doi.org/10.3168/jds.2018-15269

18. Teissier M. et al. Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds. Journal of Dairy Science. 2018; 101(4): 3126‒3139. https://doi.org/10.3168/jds.2017-13587

19. Kim H.J., de las Heras-Saldana S., Moghaddar N., Lee S.-H., Lim D., van der Werf J.H.J. Genome-wide association study for carcass traits in Hanwoo cattle using additional relatives’ information of non-genotyped animals. Animal Genetics. 2022; 53(6): 863‒866. https://doi.org/10.1111/age.13251

20. Raven L.-A., Cocks B.G., Hayes B.J. Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics. 2014; 15: 62. https://doi.org/10.1186/1471-2164-15-62

21. Cai Z., Christensen O.F., Lund M.S., Ostersen T., Sahana G. Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans. BMC Genomics. 2022; 23: 133. https://doi.org/10.1186/s12864-022-08373-3

22. Abanda B. et al. Genetic Analyses and Genome-Wide Association Studies on Pathogen Resistance of Bos taurus and Bos indicus Cattle Breeds in Cameroon. Genes. 2021; 12(7): 976. https://doi.org/10.3390/genes12070976

23. Dobson H., Smith R.F., Royal M.D., Knight C.H., Sheldon I.M. The Highproducing Dairy Cow and its Reproductive Performance. Reproduction in Domestic Animals. 2007; 42(S2): 17‒23. https://doi.org/10.1111/j.1439-0531.2007.00906.x

24. Liu A. et al. Variance components and correlations of female fertility traits in Chinese Holstein population. Journal of Animal Science and Biotechnology. 2017; 8: 56. https://doi.org/10.1186/s40104-017-0189-x

25. Rojas Canadas E. et al. Associations between postpartum phenotypes, cow factors, genetic traits, and reproductive performance in seasonal-calving, pasture-based lactating dairy cows. Journal of Dairy Science. 2020; 103(1): 1016‒1030. https://doi.org/10.3168/jds.2018-16001

26. Struck T.J., Mannakee B.K., Gutenkunst R.N. The impact of genome-wide association studies on biomedical research publications. Human Genomics. 2018; 12: 38. https://doi.org/10.1186/s40246-018-0172-4

27. Visscher P.M. et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics. 2017; 101(1): 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

28. Liu A. et al. Genome-Wide Association Studies for Female Fertility Traits in Chinese and Nordic Holsteins. Scientific Reports. 2017; 7: 8487. https://doi.org/10.1038/s41598-017-09170-9

29. Clapham D.E. Calcium Signaling. Cell. 2007; 131(6): 1047–1058. https://doi.org/10.1016/j.cell.2007.11.028

30. Parekh A.B. Decoding cytosolic Ca2+ oscillations. Trends in Biochemical Sciences. 2011; 36(2): 78–87. https://doi.org/10.1016/j.tibs.2010.07.013

31. Zhou L., Ding X., Zhang Q., Wang Y., Lund M.S., Su G. Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population. Genetics Selection Evolution. 2013; 45: 7. https://doi.org/10.1186/1297-9686-45-7

32. Pausch H. et al. Genome-Wide Association Study Identifies Two Major Loci Affecting Calving Ease and Growth-Related Traits in Cattle. Genetics. 2011; 187(1): 289–297. https://doi.org/10.1534/genetics.110.124057

33. Schulman N.F. et al. Mapping of fertility traits in Finnish Ayrshire by genome-wide association analysis. Animal Genetics. 2011; 42(3): 263–269. https://doi.org/10.1111/j.1365-2052.2010.02149.x

34. Höglund J.K., Guldbrandtsen B., Lund M.S., Sahana G. Identification of genomic regions associated with female fertility in Danish Jersey using whole genome sequence data. BMC Genetics. 2015; 16: 60. https://doi.org/10.1186/s12863-015-0210-3

35. Minozzi G. et al. Genome Wide Analysis of Fertility and Production Traits in Italian Holstein Cattle. PLoS ONE. 2013; 8(11): e80219. https://doi.org/10.1371/journal.pone.0080219

36. Müller M.-P. et al. Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18. Journal of Dairy Science. 2017; 100(3): 1987–2006. https://doi.org/10.3168/jds.2016-11506

37. Pausch H., Emmerling R., Schwarzenbacher H., Fries R. A multi-trait metaanalysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle. Genetics Selection Evolution. 2016; 48: 14. https://doi.org/10.1186/s12711-016-0190-4

38. Sahana G., Höglund J.K., Guldbrandtsen B., Lund M.S. Loci associated with adult stature also affect calf birth survival in cattle. BMC Genetics. 2015; 16: 47. https://doi.org/10.1186/s12863-015-0202-3

39. Cole J.B. et al. Distribution and location of genetic effects for dairy traits. Journal of Dairy Science. 2009; 92(6): 2931–2946. https://doi.org/10.3168/jds.2008-1762

40. Sahana G., Guldbrandtsen B., Bendixen C., Lund M.S. Genome-wide association mapping for female fertility traits in Danish and Swedish Holstein cattle. Animal Genetics. 2010; 41(6): 579–588. https://doi.org/10.1111/j.1365-2052.2010.02064.x

41. Zhou J. et al. Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle. BMC Genomics. 2019; 20: 827. https://doi.org/10.1186/s12864-019-6224-x

42. Dong W. et al. Integrative analysis of genome-wide DNA methylation and gene expression profiles reveals important epigenetic genes related to milk production traits in dairy cattle. Journal of Animal Breeding and Genetics. 2021; 138(5): 562–573. https://doi.org/10.1111/jbg.12530

43. Cole J.B. et al. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics. 2011; 12: 408. https://doi.org/10.1186/1471-2164-12-408

44. Prakapenka D., Liang Z., Da Y. Genome-Wide Association Study of Age at First Calving in U.S. Holstein Cows. International Journal of Molecular Sciences. 2023; 24(8): 7109. https://doi.org/10.3390/ijms24087109

45. Li J., Wang Y., Mukiibi R., Karisa B., Plastow G.S., Li C. Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle. Scientific Reports. 2022; 12: 3389. https://doi.org/10.1038/s41598-022-06567-z

46. Niu Q. et al. Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle. Genomics. 2021; 113(5): 3325–3336. https://doi.org/10.1016/j.ygeno.2021.07.025

47. Liu J., Xu L., Ding X., Ma Y. Genome-Wide Association Analysis of Reproductive Traits in Chinese Holstein Cattle. Genes. 2024; 15(1): 12. https://doi.org/10.3390/genes15010012

48. Zepeda-Batista J.L., Núñez-Domínguez R., Ramírez-Valverde R., Jahuey-Martínez F.J., Herrera-Ojeda J.B., Parra-Bracamonte G.M. Discovering of Genomic Variations Associated to Growth Traits by GWAS in Braunvieh Cattle. Genes. 2021; 12(11): 1666. https://doi.org/10.3390/genes12111666


Review

For citations:


Larkina T.A., Shiryaev G.V. GWAS as a tool for detecting SNPs in cattle to study their relationship to reproduction, productivity, growth, behavior, diseases. Agrarian science. 2024;1(8):124-131. (In Russ.) https://doi.org/10.32634/0869-8155-2024-385-8-124-131

Views: 222


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0869-8155 (Print)
ISSN 2686-701X (Online)
X