Abstract: Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucialy, it requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationaly efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantialy outperform usual two-stage marginal association testing and fine-mapping procedure in precision and recal. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer’s disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively paralel reporter assays and CRISPR-Cas9 experiments. Additionaly, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses

Reading list: 

• Schaid, D.J., Chen, W. and Larson, N.B., 2018. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nature Reviews Genetics, 19(8), pp.491-504. 
• Burgess, D.J., 2022. Fine-mapping causal variants—why finding ‘the one’can be futile. Nature Reviews Genetics, 23(5), pp.261-261.