Integration of expression QTLs with fine mapping via SuSiE

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference. Genome-wide association studies (GWASs) have proven powerful in detecting genetic variants associated with complex traits. However, there are challenges in distinguishing the causal variants from other variants strongly correlated with them. To better identify causal SNPs, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants. We introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, which can improve the accuracy and efficiency of association studies by prioritizing functional variants within the risk genes before evaluating the causation. Our new fine-mapping framework, SuSiE2, connects two sum of single-effects (SuSiE) models, one for eQTL analysis and one for genetic fine-mapping. The posterior inclusion probabilities from an eQTL-based SuSiE model are utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. Through simulations and real data analyses focused on body mass index and Alzheimer's disease, we demonstrate that SuSiE2 improves fine-mapping results by increasing statistical power, having appropriate coverage, and reducing the average size of credible sets.