Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Statistical Genetics

Background:

• Quantitative trait loci (QTL) analysis is crucial for understanding genetic contributions to complex traits.
• Analyzing outbred populations presents challenges due to heterogeneity of variance, particularly between sexes.
• Existing methods may lack the power to detect QTLs when analyzing single environments separately.

Purpose of the Study:

• To develop a robust Bayesian method for QTL analysis in outbred populations with sex-specific variance heterogeneity.
• To improve the power of QTL detection by jointly analyzing data from multiple experimental environments.
• To illustrate the method's application using broiler growth efficiency and carcass trait data.

Main Methods:

• A scaled reduced animal model incorporating random polygenic and QTL allelic effects was employed.
• Parsimonious model specification and Markov chain Monte Carlo algorithms were used for parameter estimation and posterior density calculation.
• Joint analysis of multiple environments was compared to single-environment analyses for power.

Main Results:

• Joint analysis of multiple environments demonstrated greater power compared to single-trait analyses.
• A significant QTL was identified within marker bracket MCW0058-LEI0071 for both growth efficiency and carcass traits.
• This QTL explained substantial genetic variation: 34% in males and 24% in females for growth efficiency, and 19% in males and 6% in females for carcass traits.

Conclusions:

• The developed Bayesian method effectively handles complex genetic analyses in outbred populations with sex-specific variance.
• Jointly analyzing multiple environments is a more powerful strategy for QTL detection.
• The identified QTL significantly contributes to variation in key broiler production traits.