The genetic evaluation of quantitative traits based on genomic information has been increasingly practiced in beef cattle breeding. The quantitative traits are usually influenced by numerous genomic variants with small individual effects and remains a challenge to disclose the genetic basis of variation for those traits. Imputed sequence level SNP data (11,278,153 SNPs) were partitioned by functional annotation, chromosome and minor allele frequency (MAF) bin for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT) and marbling score (MS) traits. Genomic relationship matrices (GRM) were constructed for each classified region and fitted in the models both separately and simultaneously. Genome-wide association study (GWAS) was performed to identify genomic variants and their contribution on those traits using linear mixed model and Bayesian mixture model. Considering all SNPs together, the h2 estimates for the traits CWT, EMA, BFT and MS were found to be 0.57, 0.46, 0.45 and 0.49, respectively that reflected substantial genomic contribution. In joint analysis, the variance explained by each chromosome was found to be proportional against its physical length with weak linear relationships for all traits. Genomic variance attributed by functional classes varied largely between carcass and meat quality traits. Exon region contributed more genomic variances for BFT and MS (0.13 and 0.22) while intron and intergenic regions explained almost total genomic variances for CWT and EMA traits (0.22 to 0.32). More particularly, considering exon region variants and per SNP contribution analysis revealed that synonymous class explained the largest proportion of genetic variance. GWAS detected 27 exon variants those significantly associated with CWT and EMA traits, and were harboured by 14 candidate genes of which TOX, PPARGC1A, COL1A2, ZCCHC4, PRKDC, CRH, DNAJC5B and TRIM55 were noteworthy. Notably, SNPs with larger effects (10-3 × and 10-2 × ) varied only between 0.26 to 0.41% of the total numbers but explained 33.42 to 62.73% of the total genetic variance for the studied traits. Keyword: variance partitioning, genome level SNP, GWAS, Hanwoo cattle

Mohammad Bhuiyan, Dajeong Lim, Cedric Gondro, Jun Heon Lee, Seung Hwan Lee

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Methods and Tools - GWAS, , 88, 2018
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