Genome-wide association study for feed efficiency traits using SNP and haplotype models.

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Bioinformatics

Background:

• Feed costs significantly impact beef cattle production economics, making feed efficiency a critical trait.
• Individual feed intake measurement is costly, necessitating genomic approaches for trait improvement.
• Genome-wide association studies (GWAS) are valuable tools for dissecting complex traits like feed efficiency.

Purpose of the Study:

• To conduct a genome-wide association study (GWAS) for average daily gain (ADG) and average daily feed intake (ADFI) in beef cattle.
• To compare the effectiveness of SNP-based (BayesC) and haplotype-based (BayesIM) models for identifying genetic variants influencing feed efficiency.
• To evaluate bivariate GWAS using BayesIM for a more comprehensive analysis of ADG and ADFI.

Main Methods:

• Utilized genotypic data from 748 crossbred steers and heifers genotyped with the BovineSNP50v2 BeadChip.
• Performed univariate and bivariate GWAS using SNP-based (BayesC) and haplotype-based (BayesIM) models, including hidden Markov models (HMM) for haplotype clustering.
• Analyzed phenotypes for ADG and ADFI, assessing genetic variation explained by 1-Mb windows and calculating molecular breeding values.

Main Results:

• Haplotype-based models (BayesIM) provided greater resolution for identifying potential quantitative trait loci (QTL) compared to SNP-based models.
• Both SNP and haplotype models yielded high correlations (>0.96) for molecular breeding values, indicating similar animal rankings.
• The top 1% of 1-Mb windows explained substantial genetic variation for ADG (24-40%) and ADFI (20-32%) across models.

Conclusions:

• Genomic-enabled approaches, particularly haplotype-based GWAS, are effective for improving feed efficiency traits in beef cattle.
• BayesIM offers enhanced resolution for QTL detection, aiding in the selection of more feed-efficient animals.
• The study supports the utility of genomic selection for economically relevant traits in beef production systems.