Predicting ExWAS findings from GWAS data: a shorter path to causal genes

Area of Science:

• Genetics
• Genomic Medicine
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify thousands of disease-associated loci, but causal genes remain largely unknown.
• Identifying causal genes is crucial for understanding disease mechanisms and developing genetics-based drugs.
• Exome-wide association studies (ExWAS) can pinpoint causal genes but are costly and have high false-negative rates.

Purpose of the Study:

• To evaluate the ability of existing gene prioritization algorithms to predict ExWAS findings from GWAS data.
• To determine if algorithms can identify causal genes within GWAS loci, enabling broader application of genetic discoveries.
• To assess the performance of Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) in predicting ExWAS significant genes.

Main Methods:

• Quantified the performance of four gene prioritization algorithms (Ei, L2G, PoPs, ABC).
• Evaluated algorithm ability to identify ExWAS significant genes across nine different human traits.
• Calculated areas under the precision-recall curve and odds ratios for gene significance based on algorithm scores.

Main Results:

• Ei, L2G, and PoPs demonstrated significant ability to identify ExWAS significant genes, with high areas under the precision-recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18).
• A unit increase in normalized scores for Ei, L2G, and PoPs correlated with a 4.6, 2.5, and 2.1-fold increase in the odds of a gene reaching exome-wide significance, respectively.
• ABC showed lower predictive performance (Area under curve: 0.14; Odds ratio: 1.3).

Conclusions:

• Ei, L2G, and PoPs algorithms can effectively anticipate ExWAS findings using readily available GWAS data.
• These methods offer a valuable approach for prioritizing candidate causal genes at GWAS loci when ExWAS data is limited.
• The findings support the use of these algorithms to accelerate gene discovery and aid in genetics-based drug development.