Abstract
The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a BayesCπ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions.
Highlights
Growth and carcass quality are very important traits, because of the relevance of these traits to economic profits for beef cattle farmers.Traditional breeding programs based on breeding values by best linear unbiased prediction (BLUP) using pedigree and phenotypic data has achieved significant genetic progress
The availability of genome-wide single nucleotide polymorphism (SNP) panels enables detection of any SNP that is associated with a trait by a genome-wide association study (GWAS), which allows mapping quantitative trait loci (QTL) across the genome
The GWAS profiles of all additive SNP effects for each trait are divided into the different statistical models (Figure 1)
Summary
Growth and carcass quality are very important traits, because of the relevance of these traits to economic profits for beef cattle farmers.Traditional breeding programs based on breeding values by best linear unbiased prediction (BLUP) using pedigree and phenotypic data has achieved significant genetic progress. Annual gains for carcass weight (CWT) and longissimus dorsi muscle area (LMA) in the 2000’s were 8 kg and 2.9 cm, respectively, in Hanwoo (NIAS, 2009). Genomic prediction at a young stage can further accelerate genetic gain, because prediction errors at. The availability of genome-wide single nucleotide polymorphism (SNP) panels enables detection of any SNP that is associated with a trait by a genome-wide association study (GWAS), which allows mapping QTL across the genome.
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