Estimation of population stratification in crossbred and inbred dairy cattle using genome wide association by simulation

پژوهشهای علوم دامی ایران

Article 8, Volume 7, Issue 2 - Serial Number 22, June 2015, Pages 191-198   

Document Type: Genetics & breeding

DOI: 10.22067/ijasr.v7i2.32597

Authors

Morteza Bitaraf sani¹; Mohammadreza Nassiri^{* 2}; Ali Asghar Aslaminejad²; Mohammad Mahdi Shariati³

¹Animal Science Research Department, Yazd Agricultural and Natural Resources Research and Education Center, AREEO, Yazd, Iran

²Department of Animal Sciences, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

³Department of Animal Sciences , Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Introduction: Domestic animals are invaluable resources to study the molecular architecture of complex traits. Although the mapping of quantitative trait loci (QTL) underlying economically important traits in domestic animals has achieved remarkable results in recent decades, not all of the genetic variation in the complex traits has been captured due to the low density of markers used in QTL mapping studies. The genome wide association study (GWAS) utilizing high-density single-nucleotide polymorphism (SNP), provides a new way to tackle this issue. Genetic association tests identify differences in allele frequency between cases and controls. Population stratification can be a problem in association studies, such as case-control studies, where the association found could be due to the underlying structure of the population.

Material and Methods: In current research, Genome wide association technique (Case-control design) was used to evaluate population stratification. Historical population and genome of 10000 cattle were simulated along 100 generations by Mutation-Drift Equilibrium (MDA) technique. By using historical population, 800 inbred and cross bred cattle with ~50000 SNPs on 30 chromosomes were simulated. Genomic control was performed to survey markers with a low prior probability of association with trait (“null markers”) and to estimate population stratification by Q-Q plot and lambda statistics.

Results and discussion: Deviation of cases/controls ratios between inbred subpopulations causes increasing lambda and population stratification; as lambda was estimated 0.42, 11.31 and 97.77 in additive genetic model with case/control ratios 1.00, 0.77 and 0.33, respectively and 0.47, 8.21 and 57.40 in co-dominant genetic model. Therefore, the more disparate composition cases/controls the more population stratification. When cases and controls were drawn from different randomly mating breeding populations, allele frequencies were different, but these differences may not be related to disease status or complex trait. This means that the assumption of independence of observations is violated. Often this will lead to an overestimation of the significance of an association but it depends on the way the sample is chosen. Population stratification was surveyed between two random groups of crossbred population (400 cases, 400 controls). There was no population stratification among subpopulations of crossbreds in current research; as lambda was estimated 0.55, 0.66, 0.89, 0.76 and 0.41 in co dominant, dominant, recessive, over dominant, additive genetic models, respectively.

Conclusion: The main GWAS problem in inbred cattle is population stratification. When cases and controls are drawn from different inbreeding populations, Population Stratification occurs. Lambda and PS is related to Cases/controls ratio among inbred lines, as more deviation ratio of one, more population stratification. It is suggested to control population stratification inbred cattle should not be used unless exactly equal ratio of cases/controls between inbred subpopulations can be achieved and it is better to use of crossbred cattle for genome wide association studies.

Keywords

Association Studies; Population Stratification; SNPs

References

1. Alkes, L., A. N. Price, D. Z. Reich, and N. Patterson. 2010. New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics, 11: 459–463.

2. Anthony H. L., K. E. Larkin and B. K. Suarez. 2009. Population Stratification and Patterns of Linkage Disequilibrium. Genetic Epidemiology, 33: S88–S92.

3. Devlin, B., and K. Roeder. 1999. Genomic control for association studies. Biometrics, 55:997–1004.

4. Freedman ML, Reich D, Penney KL, McDonald GJ, Mignault AA, Patterson N, Gabriel SB, Topol EJ, Smoller JW, Pato CN, Pato MT, Petryshen TL, Kolonel LN, Lander ES, Sklar P, Henderson B, Hirschhorn JN, Altshuler D. 2004. Assessing the impact of population stratification on genetic association studies. Nature Genetics, 36(4):388-93.

5. Lander, E. S., and N. J. Schork. 1994. Genetic dissection of complex traits. Science. 265:2037–2048.

6. Li l, Donghui Z, Hong L and Christopher A.2013. Robust methods for population stratification in genome wide association studies.BMC Bioinformatics, 14:132.

7. Li, M., G. R. Wiggans, Sh. Wang, T. S. Sonstegard, J. Yang, B. A. Crooker, J. B. Cole, C. P. Van Tasse, T. J. Lawlor, and Y. Da1. 2012. Effect of sample stratification on dairy GWAS results. BMC Genomics. 13:536.

8. Marchini J, Cardon LR, Phillips MS, Donnelly P .2004.The effects of human population structure on large genetic association studies. Nature Genetics May; 36(5): 512-7. Epub 2004 Mar 28.

9. Marchini, J., L. R. Cardon, M. S. Phillips, and P. Donnelly. 2004. The effects of human population structure on large genetic association studies. Nature Genetics, 36(5): 512–517.

10. Norrgard, K. 2008. Genetic variation and disease: GWAS. Nature Education: http://www.nature.com/scitable/topicpage/genetic-variation-and-disease-gwas-682.

11. Price, A. L., N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick, and D. Reich. 2006. Principal components analysis corrects for stratification in genome-wide association. Nature Genetics. 38:904-909.

12. Sargolzaei, M., and F. S. Schenkel. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680-681.

13. Sonstegard, T. S., M. Li, C. P. V. Tasse, E. S. Kim, J. B. Cole, G. R. Wiggans, B. A. Crooker, B. D. Mariani, L. K. Matukumalli, J. R. Garbe, S. C. Fahrenkrug, G. Liu, and Y. Da. 2010. Forty Years of Artificial Selection In U.S. Holstein Cattle Had Genome-wide Signatures. Proc. Of 9th world congress on genetics applied to livestock production.

14. Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J,Arnett DK, Ordovas JM, Buckler ES. 2010. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 42:355–360.

15. Zhang, H., Z. Wang, S. Wang, and H. Li. 2012. Progress of genome wide association study in domestic animals. Journal of Animal Science and Biotechnology, 3:26.

16. kushyar M M, nassiri M, Bitaraf sani M, Aslaminejad A A. 2013. Feasibility Study of the Detection of SNPs As-sociated with Breast Cancer by Genome-Wide Association Virtual Studies. Genetics In The 3rd Millennium.vol:3. 3190-3199. (In Persian).