Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component model

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Variance component analysis is key for human complex trait genetic mapping.
• Existing methods are limited to normally distributed traits and single QTL models.
• Current approaches often require parental linkage phase information.

Purpose of the Study:

• To develop a Bayesian method for mapping multiple quantitative trait loci (QTL).
• To extend Bayesian mapping to complex binary diseases using a threshold model.
• To infer the number of QTL as a parameter.

Main Methods:

• Developed a Bayesian mapping procedure for multiple QTL.
• Extended the method to binary traits using a threshold model.
• Employed reversible jump Markov chain Monte Carlo (RJMCMC) for posterior inference.

Main Results:

• The Bayesian method successfully maps multiple QTL for complex traits.
• The procedure identifies QTL contributing to complex binary diseases.
• Inferred the joint posterior distribution of QTL number, locations, and variances.
• Demonstrated utility with simulated full-sib family data.

Conclusions:

• The developed Bayesian method offers a robust approach for complex trait genetic mapping.
• The method accommodates multiple QTL and binary disease models.
• It provides a comprehensive estimation of QTL parameters, including their number.