Discovery of shared genomic loci using the conditional false discovery rate approach

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) have identified thousands of trait-associated variants, but much of the genetic architecture of complex traits remains undiscovered.
• Existing GWAS methods face limitations in detecting the full spectrum of genetic influences on complex phenotypes.

Purpose of the Study:

• To review the conditional false discovery rate (condFDR) method for analyzing genome-wide association study (GWAS) summary data.
• To highlight the utility of condFDR in improving statistical power and uncovering novel genetic overlaps between diverse human phenotypes.

Main Methods:

• The review focuses on the model-free condFDR strategy, inspired by Empirical Bayes approaches.
• It details the cross-trait condFDR strategy for analyzing separate GWAS data and leveraging overlapping SNP associations.
• The conjunctional FDR (conjFDR) extension for identifying shared genomic loci between phenotypes is also discussed.

Main Results:

• The condFDR method has demonstrated improved yield in existing GWAS and identified novel genetic overlaps across psychiatric, cardiovascular, neurological, psychological, and cognitive traits.
• The cross-trait condFDR strategy increases the discovery of trait-associated single-nucleotide polymorphisms (SNPs) by analyzing cross-trait enrichment.
• The conjFDR approach effectively detects shared genomic associations, revealing complex directional effects between phenotypes.

Conclusions:

• The condFDR methodology offers a powerful, model-free approach to enhance GWAS discovery and understand the genetic relationships between complex traits.
• This method provides novel insights into the genetic architecture of human phenotypes, complementing existing analytical tools.