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Genome wide association test to identity QTL for dressing percentage in Hanwoo

전장 유전체 관련성 분석을 통한 한우 도체수율 관련 양적형질좌위 탐색

  • 이승환 (국립축산과학원 한우시험장) ;
  • 임다정 (국립축산과학원 동물유전체과) ;
  • 당창권 (국립축산과학원 한우시험장) ;
  • 장선식 (국립축산과학원 한우시험장) ;
  • 김형철 (국립축산과학원 한우시험장) ;
  • 전기준 (국립축산과학원 한우시험장) ;
  • 연성흠 (국립축산과학원 한우시험장) ;
  • 장길원 (국립축산과학원 동물유전체과) ;
  • 박응우 (국립축산과학원 동물유전체과) ;
  • 오재돈 (국립축산과학원 동물유전체과) ;
  • 이학교 (국립축산과학원 동물유전체과) ;
  • 이준헌 (한경대학교 생명공학과) ;
  • 강희설 (국립축산과학원 한우시험장) ;
  • 윤두학 (충남대학교 동물자원생명과학과)
  • Received : 2013.05.13
  • Accepted : 2013.06.12
  • Published : 2013.06.30

Abstract

Genome-wide association study was performed on data from 266 Hanwoo steers derived from 66 sire using bovine 10K mapping chip in Hanwoo (Korean Cattle). SNPs were excluded from the analysis if they failed in over 5% of the genotypes, had median GC scores below 0.6, had GC scores under 0.6 in less than 90% of the samples, deviated in heterozygosity more than 3 standard deviations from the other SNPs and were out of Hardy-Weinberg equilibrium for a cutoff p-value of $1^{-15}$. Unmapped and SNPs on sex chromosomes were also excluded. A total of 4,522 SNPs were included in the analysis. To test an association between SNP and QTL, GWAS for five genetic mode (additive, dominant, overdominant, recessive and codominant) was implemented in this study. Three SNPs (rs29018694, ss46526851 and rs29018222) at a threshold p< $1.11{\times}10^{-5}$ were detected on BTA12 and BTA21 for dressing percentages in codominant and recessive genetic mode. The G allele for rs29018694 has 4.9% higher dressing percentage than A allele, while the T allele for ss46526851 has 2.57 % higher dressing percentage than C allele. Therefore, rs29018694 SNP showed a bigger effect than the other two SNPs (ss46526851 and rs29018222) in this study. In conclusion, this study identifies three loci with moderate effects and many loci with infinitesimally small effect across genome in Hanwoo.

Keywords

References

  1. Aulchenko YS, de Koning DJ, Haley C. 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. Barendse W, Reverter A, Bunch RJ, Harrison BE, Barris W, Thomas MB. 2007. A Validated whole-genome association study of efficient food conversion in cattle. Genetics 176:1893-1905. https://doi.org/10.1534/genetics.107.072637
  3. Dekker JCM, Hospital F. 2002. The use of molecular genetics in the improvement of agricultural populations. Nature Review Genetics 3:22-32 https://doi.org/10.1038/nrg701
  4. Fernando RL, Grossman M. 1989. Marker assisted selection using best linear unbiased prediction. Genetic Selection Evolution 21:467-477. https://doi.org/10.1186/1297-9686-21-4-467
  5. Georges M. 1995. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139:907-920.
  6. Grapes L, Dekkers JC, Rothschild MF, Fernando RL. 2004. Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci. Genetics 166:1561-1570. https://doi.org/10.1534/genetics.166.3.1561
  7. Hayes BJ, Pryce J, Chamberlain AJ, Verbyla K, Goddard ME. 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetic Selection Evolution 41:1-9. https://doi.org/10.1186/1297-9686-41-1
  8. Hu ER, Reecy JM. 2007. AnimalQTLdb: A Livestock QTL Database Tool Set for Positional QTL Information Mining and Beyond. Nucleic Acids Research 35:D604-D609. https://doi.org/10.1093/nar/gkl946
  9. Karim L, et al 2011. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nature Genetics 43:405-413. https://doi.org/10.1038/ng.814
  10. Kim Y, Ryu JW, Kim JB, Kim CY, Lee C. 2010. Genome-wide association study reveals five nucleotide sequence variants for carcass traits in beef cattle. Animal Genetic doi:10.1111/ j.1365-2052.2010.02156.x
  11. Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, Marques E, Stothard P, Moore SS. 2008. A wholegenome scan to map quantitative trait loci for conformation and functional traits in Canadian Holstein Bulls. Journal of Dairy Science 91:2844-2856. https://doi.org/10.3168/jds.2007-0585
  12. Li A, Mo D, Zhao X, Jiang W, Cong P, He Z, Xiao S, Liu X, Chen Y. 2013. Comparison of the longissimus muscle proteome between obese and lean pigs at 180 days. Mammaliau Genome 24:72-79. https://doi.org/10.1007/s00335-012-9440-0
  13. Meuwissen THE, Goddard ME. (2001). Prediction of identity by descent probabilities from marker-haplotypes. Genetic Selection Evolution 33:605-634. https://doi.org/10.1186/1297-9686-33-6-605
  14. Miller SA, Dykes DD, Polesky HF. 1988. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Research 16(3):1215. https://doi.org/10.1093/nar/16.3.1215
  15. Muller M, Kersten S. 2003. Nutrigenomics: goals and strategies. Nature Review Genetics 4:315-322. https://doi.org/10.1038/nrg1047
  16. Nishmura S, Watanabe T, Mizoshita K, Tatsuda K, Fujita T, Watanabe N, Sugimoto Y, Takasuga A. 2012. Genome-wide association study indentified three major QTL for carcass weight including the PLAG1-CHCHD7 QTN for stature in Japanese Black cattle. BMC Genetics 13:40.

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  1. 한우 HGD 유전자내 변이지역과 경제형질간의 연관성 분석 vol.24, pp.11, 2013, https://doi.org/10.5352/jls.2014.24.11.1168