Genome-wide association studies and QTL mapping for traits deviating from normal distribution

Area of Science:

• Genetics
• Statistical Genomics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping typically assume normally distributed traits.
• Many biological traits in crops, animals, and humans are non-normally distributed or not continuously distributed, limiting traditional statistical models.
• Existing linear mixed models (LMMs) are not suitable for analyzing these non-normal traits.

Purpose of the Study:

• To develop novel statistical models and software for QTL mapping and association studies of non-normally distributed traits.
• To extend the applicability of GWAS and QTL mapping to a wider range of biological data.
• To provide a flexible framework for analyzing various trait distributions beyond the normal assumption.

Main Methods:

• Development of the pseudo response generalized linear mixed model (PSR-GLMM) framework.
• Utilizing a pseudo response (PSR) method to estimate polygenic variance by creating a pseudo-response variable.
• Applying the PSR-GLMM to analyze binary, binomial, Poisson, and ordinal traits using a generalized linear mixed model (GLMM).

Main Results:

• The PSR-GLMM method successfully mapped QTLs and performed association studies for non-normal traits, including binary, binomial, Poisson, and ordinal data.
• The method was illustrated with a rice purple color trait and simulated non-normal traits.
• The approach was validated on diverse datasets from *Arabidopsis*, pig, and dog populations.

Conclusions:

• The PSR-GLMM provides a robust statistical framework for analyzing non-normally distributed traits in GWAS and QTL mapping.
• A user-friendly R software package (PSR-GLMM/R) has been developed, enabling broader application of these methods.
• This approach enhances the ability to identify genetic markers associated with complex traits across various species and data types.