Leveraging Auxiliary Data on Related Traits to Enhance GWAS Power

Music City Center 

Genome-wide association studies (GWAS) have been widely applied to identify genetic variants that are robustly associated with complex human traits and diseases. This has facilitated subsequent analyses, such as the calculation of polygenic risk scores and the performance of Mendelian randomization. Moreover, a variety of approaches have been developed to enhance GWAS power from different perspectives. Despite the considerable success of GWAS, many genetic variants linked to human traits remain undiscovered due to limited sample sizes. For instance, the UK Biobank Pharma Proteomics Project (UKB-PPP) recently highlighted that the number of identified protein quantitative trait loci (pQTL) has continued to increase steadily as sample size increased to its maximum of approximately 50,000. Motivated by the UKB-PPP proteomic data, and considering that (1) proteins are causal to many traits, and (2) genotype data and outcomes for various traits in the UKB encompass much larger sample sizes, we develop a novel method to leverage causal relationships and auxiliary data to enhance GWAS power and apply them to the UKB-PPP for pQTL discovery. The general framework of the proposed method make it broadly applicable to other biobank-scale data as well.

Protein Quantitative Trait Loci (pQTL)

Single Nucleotide Polymorphism (SNP)

Causal Inference