Knockoff procedure improves susceptibility gene identifications in conditional transcriptome-wide association studies

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Transcriptome-wide association studies (TWASs) integrate genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) data to identify genes linked to complex traits.
• Existing TWAS methods often overlook gene-gene correlations within genomic regions and can yield false positives due to eQTL and genetic variant correlations.

Purpose of the Study:

• To introduce TWASKnockoff, a novel knockoff-based framework for robustly detecting susceptibility genes.
• To address limitations of traditional TWAS by evaluating conditional independence and controlling for complex correlation structures.

Main Methods:

• TWASKnockoff employs a knockoff-based inference framework using GWAS summary statistics and eQTL data.
• It assesses conditional independence between gene-trait pairs, considering cis-predicted expression correlations and genetic variant associations.
• A correlation matrix of genetic elements is estimated via parametric bootstrap, followed by knockoff inference to control the false discovery rate (FDR).

Main Results:

• TWASKnockoff demonstrates superior FDR control compared to traditional methods.
• The framework significantly enhances power in identifying relevant gene-trait associations at a fixed FDR level.
• Application to type 2 diabetes mellitus (T2D) data validated its effectiveness.

Conclusions:

• TWASKnockoff provides a more accurate and powerful approach for gene-trait association studies.
• It effectively mitigates false positives by accounting for complex correlation patterns in genetic and expression data.
• This method advances the identification of genetic underpinnings for complex diseases.