FFselect

FFselect extends linear mixed models to improve genome-wide association studies (GWAS) by modeling large-effect loci and incorporating shared environmental effects to disentangle genetic and environmental contributions to phenotypic variation.

Key Features:

• Enhanced Modeling of Genetic Loci: Extends linear mixed models to more accurately model loci with large effects for improved estimation of genetic contributions to phenotypic variance.
• Shared Environmental Effects: Incorporates shared environmental effects into the model to separate environmental variance from genetic variance in populations with confounded environments.
• Improved Power and Control of False Discovery Rate (FDR): Increases statistical power in GWAS while maintaining control over the false discovery rate.
• Correction for Environmental Confounding: Simultaneously corrects for environmental confounding to reduce bias in association results.

Scientific Applications:

• Genome-wide association studies (GWAS): Identify loci associated with phenotypic traits in populations exhibiting shared environments or cryptic relatedness.
• Disentangling genetic and environmental contributions: Separate genetic effects from shared environmental effects when analyzing trait architecture.

Methodology:

FFselect employs a linear mixed model framework to handle population structure and relatedness, models large-effect loci within that framework, and integrates shared environmental effects.