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Investigations on Genetic Architecture of Hairy Loci in Dairy Cattle by Using Single and Whole Genome Regression Approaches

  • Karacaoren, B. (Faculty of Agriculture, Akdeniz University)
  • Received : 2015.07.29
  • Accepted : 2015.10.25
  • Published : 2016.07.01

Abstract

Development of body hair is an important physiological and cellular process that leads to better adaption in tropical environments for dairy cattle. Various studies suggested a major gene and, more recently, associated genes for hairy locus in dairy cattle. Main aim of this study was to i) employ a variant of the discordant sib pair model, in which half sibs from the same sires are randomly sampled using their affection statues, ii) use various single marker regression approaches, and iii) use whole genome regression approaches to dissect genetic architecture of the hairy gene in the cattle. Whole and single genome regression approaches detected strong genomic signals from Chromosome 23. Although there is a major gene effect on hairy phenotype sourced from chromosome 23: whole genome regression approach also suggested polygenic component related with other parts of the genome. Such a result could not be obtained by any of the single marker approaches.

Keywords

References

  1. Aulchenko, Y. S., D. J. De Koning, and C. Haley. 2007. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177:577-585. https://doi.org/10.1534/genetics.107.075614
  2. Aulchenko, Y. S., S. Ripke, A. Isaacs, and C. M. van Dujin. 2007. GenABEL: An R library for genome-wide association analysis. Bioinformatics 23:1294-1296. https://doi.org/10.1093/bioinformatics/btm108
  3. Boehnke, M. and C. D. Langefeld. 1998. Genetic association mapping based on discordant sib pairs: the discordant-alleles test. Am. J. Hum. Genet. 62:950-961. https://doi.org/10.1086/301787
  4. de los Campos, G., D. Gianola, and D. B. Allison. 2010. Predicting genetic predisposition in human: the promise of whole-genome markers. Nat. Rev. Genet. 11:880-886. https://doi.org/10.1038/nrg2898
  5. de los Campos, G., D. Sorensen, and D. Gianola. 2015. Genomic heritability: what is it? PLoS Genet. 11:e1005048. https://doi.org/10.1371/journal.pgen.1005048
  6. Dikmen, S., J. B. Cole, D. J. Null, and P. J. Hansen. 2013. Genome wide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in holstein catlle. PLoS ONE 8:e69202. https://doi.org/10.1371/journal.pone.0069202
  7. Fernando, R. L. and D. Garrick. 2013. Bayesian methods applied to GWAS. In: Genome-Wide Association Studies and Genomic Prediction. (Eds. C. Gondro, J. van der Werf, B. Hayes). Humana Press, Clifton, NJ, USA. pp. 237-274.
  8. Karacaoren, B., D. J. de Koning, I. Velander, S. Petersen, C. S. Haley, and A. L. Archibald. 2010. Alternative association analyses on boar taint using discordant sib pairs experimental design. In 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany. 743 p.
  9. Karacaoren, B. 2012. Some observations for discordant sib pair design using QTL-MAS 2010 dataset. Kafkas. Univ. Vet. Fak. Derg. 18:857-860.
  10. Lee, T., D. H. Shin, S. Cho, H. S. Kang, S. H. Kim, H. K. Lee, H. Kem, and K. S. Seo. 2014. Genome-wide association study of integrated meat quality-related traits of the Duroc pig breed. Asian Australas. J. Anim. Sci. 27:303-309. https://doi.org/10.5713/ajas.2013.13385
  11. Littlejohn, M. D., K. M. Henty, K. Tiplady, T. Johnson, C. Harland, T. Lopdell, R. G. Sherlock, W. Li, S. D. Lukefahr, B. C. Shanks, D. J. Garrick, R. G. Snell, R. J. spelman, and S. R. Davis. 2014. Functionally reciprocal mutations of the prolactin signalling pathway define hairy and slick cattle. Nat. Commun. 5:5861. https://doi.org/10.1038/ncomms6861
  12. Meuwissen, T. H. E., B. J. Hayes, and M. E. Goddard. 2001. Prediction of total genetic value using genome wide dense marker maps. Genetics 157:1819-1829.
  13. Moser, G., H. S. Lee, B. J. Hayes, M. E. Goddard N. R. Wray, and P. M. Visscher. 2015. Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model. PLoS. Genet. 11:e1004969. https://doi.org/10.1371/journal.pgen.1004969
  14. Olson, T. A., C. Lucena, C. C Chase, and A. C. Hammond. 2003. Evidence of a major gene influencing hair length and heat tolarence in Bos taurus cattle. J. Anim. Sci. 81:80-90. https://doi.org/10.2527/2003.81180x
  15. 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 studies. Nat. Genet. 38:904-909. https://doi.org/10.1038/ng1847
  16. R Development Core Team. 2013. R: A language and environmental for statistical computing.R Foundation for Statistical Computing, Vienna, Austria.
  17. Svishcheva, G. R., T. I. Axenovich, N. M. Belonogova, C. M. van Duijn, and Y. S. Aulchenko. 2012. Rapid variance components-based method for whole-genome association analysis. Nat. Genet. 44:1166-1170. https://doi.org/10.1038/ng.2410
  18. Turkheimer, E. 2011. Still missing. Res. Hum. Dev. 8:227-241. https://doi.org/10.1080/15427609.2011.625321
  19. Visscher, P. M. 2008. Sizing up human height variation. Nat. Genet. 40:489-490. https://doi.org/10.1038/ng0508-489
  20. Yang J., Weedon M. N. Weedon, S. Purcell, G. Lettre, K. Estrada, WC. J. Willer, A. V. Smith, E. Ingelsson, J. R. O'Connell, Mangino M. Mangino, R. Magi, P. A. Madden, A. C. Heath, D. R. Nyholt, N. G. Martin, G. W. Montgomery, T. M. Frayling, J. N. Hirschhorn, M. I. McCarthy, M. E. Goddard, and P. M. Visscher. 2011. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19:807-812. https://doi.org/10.1038/ejhg.2011.39
  21. Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders, D. R. Nyholt, P. A. Madden, A. C. Heath, N. G. Martin, G.W. Montgomery, M. E. Goddard, and P. M. Visscher. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:565-569. https://doi.org/10.1038/ng.608
  22. Zhou, X. and M. Stephens. 2012. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44:821-824. https://doi.org/10.1038/ng.2310