Asian-Australasian Journal of Animal Sciences

  • 제31권4호
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  • Pages.473-479
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  • 2018
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  • 1011-2367(pISSN)
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  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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Whole-genome association and genome partitioning revealed variants and explained heritability for total number of teats in a Yorkshire pig population

  • Uzzaman, Md. Rasel (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA) ;
  • Park, Jong-Eun (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA) ;
  • Lee, Kyung-Tai (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA) ;
  • Cho, Eun-Seok (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA) ;
  • Choi, Bong-Hwan (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA) ;
  • Kim, Tae-Hun (Animal Genomics & Bioinformatics Division, National Institute of Animal Science, RDA)
  • 투고 : 2017.03.07
  • 심사 : 2017.10.09
  • 발행 : 2018.04.01
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초록

Objective: The study was designed to perform a genome-wide association (GWA) and partitioning of genome using Illumina's PorcineSNP60 Beadchip in order to identify variants and determine the explained heritability for the total number of teats in Yorkshire pig. Methods: After screening with the following criteria: minor allele frequency, $MAF{\leq}0.01$; Hardy-Weinberg equilibrium, $HWE{\leq}0.000001$, a pair-wise genomic relationship matrix was produced using 42,953 single nucleotide polymorphisms (SNPs). A genome-wide mixed linear model-based association analysis (MLMA) was conducted. And for estimating the explained heritability with genome- or chromosome-wide SNPs the genetic relatedness estimation through maximum likelihood approach was used in our study. Results: The MLMA analysis and false discovery rate p-values identified three significant SNPs on two different chromosomes (rs81476910 and rs81405825 on SSC8; rs81332615 on SSC13) for total number of teats. Besides, we estimated that 30% of variance could be explained by all of the common SNPs on the autosomal chromosomes for the trait. The maximum amount of heritability obtained by partitioning the genome were $0.22{\pm}0.05$, $0.16{\pm}0.05$, $0.10{\pm}0.03$ and $0.08{\pm}0.03$ on SSC7, SSC13, SSC1, and SSC8, respectively. Of them, SSC7 explained the amount of estimated heritability along with a SNP (rs80805264) identified by genome-wide association studies at the empirical p value significance level of 2.35E-05 in our study. Interestingly, rs80805264 was found in a nearby quantitative trait loci (QTL) on SSC7 for the teat number trait as identified in a recent study. Moreover, all other significant SNPs were found within and/or close to some QTLs related to ovary weight, total number of born alive and age at puberty in pigs. Conclusion: The SNPs we identified unquestionably represent some of the important QTL regions as well as genes of interest in the genome for various physiological functions responsible for reproduction in pigs.

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참고문헌

  1. Hirooka H, De Koning D, Harlizius B, et al. A whole-genome scan for quantitative trait loci affecting teat number in pigs. J Anim Sci 2001;79:2320-6. https://doi.org/10.2527/2001.7992320x
  2. Kim S, Easter R, Hurley W. The regression of unsuckled mammary glands during lactation in sows: the influence of lactation stage, dietary nutrients, and litter size. J Anim Sci 2001;79: 2659-68. https://doi.org/10.2527/2001.79102659x
  3. Clayton G, Powell J, Hiley P. inheritance of teat number and teat inversion in pigs. Anim Prod 1981;33:299-304.
  4. McKay R, Rahnefeld G. Heritability of teat number in swine. Can J Anim Sci 1990;70:425-30. https://doi.org/10.4141/cjas90-054
  5. Lundheim N, Chalkias H, Rydhmer L. Genetic analysis of teat number and litter traits in pigs. Acta Agric Scand A Anim Sci 2013;63:121-5.
  6. Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 1990;124: 743-56.
  7. Rothschild M, Jacobson C, Vaske D, et al. The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proc Natl Acad Sci USA 1996;93:201-5. https://doi.org/10.1073/pnas.93.1.201
  8. Kang HM, Sul JH, Service SK, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 2010;42:348-54. https://doi.org/10.1038/ng.548
  9. Kang HM, Zaitlen NA, Wade CM, et al. Efficient control of population structure in model organism association mapping. Genetics 2008;178:1709-23. https://doi.org/10.1534/genetics.107.080101
  10. Listgarten J, Kadie C, Schadt EE, Heckerman D. Correction for hidden confounders in the genetic analysis of gene expression. Proc Natl Acad Sci USA 2010;107:16465-70. https://doi.org/10.1073/pnas.1002425107
  11. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet 2010;11:459-63.
  12. Yu J, Pressoir G, Briggs WH, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 2006;38:203-8. https://doi.org/10.1038/ng1702
  13. Zhang Z, Ersoz E, Lai C-Q, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet 2010; 42:355-60. https://doi.org/10.1038/ng.546
  14. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88:76-82. https://doi.org/10.1016/j.ajhg.2010.11.011
  15. Burton PR, Clayton DG, Cardon LR, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78. https://doi.org/10.1038/nature05911
  16. William GH. Quantitative genetics in the genomics era. Curr Genomics 2012;13:196-206. https://doi.org/10.2174/138920212800543110
  17. Cardon LR, Palmer LJ. Population stratification and spurious allelic association. Lancet 2003;361:598-604.
  18. Chanock SJ, Manolio T, Boehnke M, et al. Replicating genotype-phenotype associations. Nature 2007;447:655-60. https://doi.org/10.1038/447655a
  19. Verardo LL, Silva FF, Lopes MS, et al. Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways. Genet Sel Evol 2016;48:9.
  20. Lopes MS, Bastiaansen JW, Harlizius B, Knol EF, Bovenhuis H. A genome-wide association study reveals dominance effects on number of teats in pigs. PloS One 2014;9:e105867. https://doi.org/10.1371/journal.pone.0105867
  21. Borchers N, Reinsch N, Kalm E. Teat number, hairiness and set of ears in a Pietrain cross: variation and effects on performance traits. Archiv Tierzucht 2002;45:465-80.
  22. Arakawa A, Okumura N, Taniguchi M, et al. Genome-wide association QTL mapping for teat number in a purebred population of Duroc pigs. Anim Genet 2015;46:571-5. https://doi.org/10.1111/age.12331
  23. Tomiyama M, Oikawa T, Hoque M, Kanetani T, Mori H. influence of early postweaning traits on genetic improvement of meat productivity in purebred Berkshire pigs. J Anim Sci 2009;87:1613-9. https://doi.org/10.2527/jas.2008-1214
  24. Speed D, Cai N, Johnson MR, et al. Re-evaluation of SNP heritability in complex human traits. Nat Genet 2017;49:986-92. https://doi.org/10.1038/ng.3865
  25. Kostem E, Eskin E. improving the accuracy and efficiency of partitioning heritability into the contributions of genomic regions. Am J Hum Genet 2013;92:558-64. https://doi.org/10.1016/j.ajhg.2013.03.010
  26. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci USA 2012;109:1193-8. https://doi.org/10.1073/pnas.1119675109

피인용 문헌

  1. A QTL for Number of Teats Shows Breed Specific Effects on Number of Vertebrae in Pigs: Bridging the Gap Between Molecular and Quantitative Genetics vol.10, pp.None, 2018, https://doi.org/10.3389/fgene.2019.00272