Identification of genes and variants associated with quantitative traits using Bayesian factor screening

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Identifying genes and single nucleotide polymorphism (SNP) variants associated with complex quantitative traits is crucial in genetic research.
• Bayesian model selection offers a robust framework for handling complex genetic data and uncertainty.

Purpose of the Study:

• To develop and apply a Bayesian factor-screening method for identifying genetic variants associated with the quantitative trait Q1.
• To evaluate the method's performance on simulated data from the Genetic Analysis Workshop 17 (GAW17).

Main Methods:

• A Bayesian model selection framework was employed.
• A Metropolis-Hastings algorithm was used to generate posterior distributions for variant association.
• The method allowed for inferences on individual variants or entire genes.

Main Results:

• The proposed method was applied to 10 simulated datasets.
• The genes FTP1 and KDR were identified as significantly associated with Q1 in a majority of the replicates.
• The factor-screening approach demonstrated effectiveness in identifying relevant genetic factors.

Conclusions:

• The Bayesian factor-screening method is a flexible and powerful tool for genetic association studies.
• The identified genes FTP1 and KDR warrant further investigation for their role in quantitative trait variation.
• This approach can aid in pinpointing specific genetic contributors to complex traits.