Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Analyzing the link between genotype and phenotype is crucial for understanding heritability.
• Reconciling fixed genetic effects with variance components in family data presents analytical challenges.
• Existing methods may not adequately distinguish genetic effects from random variation in family-based studies.

Purpose of the Study:

• To develop an analytical tool for polygenic linear mixed models in family-based genome-wide association studies.
• To differentiate genetic predictor variables from random polygenic and residual effects.
• To identify influential families for specific genetic predictors.

Main Methods:

• Proposed a novel method utilizing added-variable plots for polygenic linear mixed models.
• Applied the method to family-based genome-wide association study designs.
• Developed an index to detect influential families for genetic predictor variables.

Main Results:

• The proposed method effectively discriminates genetic predictor variables.
• The developed index successfully identifies influential families.
• Performance was validated using simulated family data, including Genetic Analysis Workshop 17 data.

Conclusions:

• The added-variable plot method offers an effective approach for analyzing genetic data in family studies.
• This tool enhances the ability to identify genetic influences on phenotypic variability.
• The method contributes to more robust genome-wide association studies in family designs.