Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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The Prediction of the Expected Current Selection Coefficient of Single Nucleotide Polymorphism Associated with Holstein Milk Yield, Fat and Protein Contents

  • Lee, Young-Sup (Department of Natural Science, Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Shin, Donghyun (Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Lee, Wonseok (Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Taye, Mengistie (Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Cho, Kwanghyun (National Livestock Research Institute) ;
  • Park, Kyoung-Do (Genomic Informatics Center, Hankyong National University) ;
  • Kim, Heebal (Department of Natural Science, Interdisciplinary Program in Bioinformatics, Seoul National University)
  • Received : 2015.06.01
  • Accepted : 2015.09.18
  • Published : 2016.01.01
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Abstract

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.

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