Asian-Australasian Journal of Animal Sciences (Oct 2020)

Genomic partitioning of growth traits using a high-density single nucleotide polymorphism array in Hanwoo (Korean cattle)

  • Mi Na Park,
  • Dongwon Seo,
  • Ki-Yong Chung,
  • Soo-Hyun Lee,
  • Yoon-Ji Chung,
  • Hyo-Jun Lee,
  • Jun-Heon Lee,
  • Byoungho Park,
  • Tae-Jeong Choi,
  • Seung-Hwan Lee

DOI
https://doi.org/10.5713/ajas.19.0699
Journal volume & issue
Vol. 33, no. 10
pp. 1558 – 1565

Abstract

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Objective The objective of this study was to characterize the number of loci affecting growth traits and the distribution of single nucleotide polymorphism (SNP) effects on growth traits, and to understand the genetic architecture for growth traits in Hanwoo (Korean cattle) using genome-wide association study (GWAS), genomic partitioning, and hierarchical Bayesian mixture models. Methods GWAS: A single-marker regression-based mixed model was used to test the association between SNPs and causal variants. A genotype relationship matrix was fitted as a random effect in this linear mixed model to correct the genetic structure of a sire family. Genomic restricted maximum likelihood and BayesR: A priori information included setting the fixed additive genetic variance to a pre-specified value; the first mixture component was set to zero, the second to 0.0001 × σ g 2, the third 0.001 × σ g 2, and the fourth to 0.01 × σ g 2. BayesR fixed a priori information was not more than 1% of the genetic variance for each of the SNPs affecting the mixed distribution. Results The GWAS revealed common genomic regions of 2 Mb on bovine chromosome 14 (BTA14) and 3 had a moderate effect that may contain causal variants for body weight at 6, 12, 18, and 24 months. This genomic region explained approximately 10% of the variance against total additive genetic variance and body weight heritability at 12, 18, and 24 months. BayesR identified the exact genomic region containing causal SNPs on BTA14, 3, and 22. However, the genetic variance explained by each chromosome or SNP was estimated to be very small compared to the total additive genetic variance. Causal SNPs for growth trait on BTA14 explained only 0.04% to 0.5% of the genetic variance Conclusion Segregating mutations have a moderate effect on BTA14, 3, and 19; many other loci with small effects on growth traits at different ages were also identified.

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