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Asian-Australas J Anim Sci > Accepted Articles
DOI: https://doi.org/10.5713/ajas.19.0699    [Accepted] Published online January 13, 2020.
Genomic partitioning of growth traits using a high-density SNP array in Hanwoo (Korean cattle)
Mi Na Park1,2  , Dongwon Seo1  , Ki-Yong Chung4  , Soo-Hyun Lee1  , Yoon-Ji Chung1  , Hyo-Jun Lee1  , Jun-Heon Lee1  , Byoungho Park3  , Tae-Jeong Choi2,*  , Seung-Hwan Lee1,* 
1Division of Animal & Dairy Science, Chungnam National University, Daejeon 34134, Korea
2Animal Genetic Improvement Division, National Institute of Animal Science, RDA, Seonghwan 31000, Korea
3Poultry Science Division, National Institute of Animal Science, RDA, PyeongChang, 25342, Korea
4Hanwoo Research Institute, National Institute of Animal Science, RDA, PyeongChang, 25340, Korea
Correspondence:  Tae-Jeong Choi,Email: choi6695@korea.kr
Seung-Hwan Lee, Tel: +82-42-821-5772, Fax: +82-42-825-9754, Email: slee46@cnu.ac.kr
Received: 5 September 2019   • Revised: 21 October 2019   • Accepted: 7 January 2020
Abstract
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 (GRM) was fitted as a random effect in this linear mixed model to correct the genetic structure of a sire family. Genomic restricted Maximum Likelihood (GREML) 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 0.04–0.5% 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.
Keywords: Genetic Architecture, Genome-wide Association Study, Hanwoo


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