Evaluation of candidate gene effects for beef backfat via Bayesian model selection.

Candidate gene approaches provide tools for exploring and localizing causative genes affecting quantitative traits and the underlying variation may be better understood by determining the relative magnitudes of effects of their polymorphisms. Diacyglycerol O-acyltransferase 1 (DGAT1), fatty acid binding protein (heart) 3 (FABP3), growth hormone 1 (GH1), leptin (LEP) and thyroglobulin (TG) have been previously identified as genes contributing to genetic control of subcutaneous fat thickness (SFT) in beef cattle. In the present research, Bayesian model selection was used to evaluate effects of these five candidate genes by comparing competing non-nested models and treating candidate gene effects as either random or fixed.