Abstract
Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct genome-wide fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype data, we enable a linear search of causal variants instead of a combinatorial search of causal configurations used in most existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We show that, compared to other methods, the causal variants identified by SparsePro are highly enriched for expression quantitative trait loci and explain a larger proportion of trait heritability. We also identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs, and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.
A powerful fine-mapping method for transcriptome-wide association studies
