95% credible sets for eGFR from SparsePro+.

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.由于广泛存在的连锁不平衡（linkage disequilibrium, LD）以及同一基因组位点内可能存在多个因果变异，从全基因组关联研究（genome-wide association studies, GWAS）中识别因果变异极具挑战。基因组的功能注释可用于优先筛选具有生物学相关性的变异，从而优化GWAS结果的精细定位流程。传统精细定位方法若对变异层面的因果构型开展穷尽式搜索，往往计算成本高昂，尤其当潜在遗传架构与LD模式较为复杂时。SuSiE（Sum of Single Effects）提出了一种迭代贝叶斯逐步选择算法，用于开展高效精细定位。本研究通过稀疏投影的实现，建立了SuSiE与配对平均场变分推断算法之间的关联，并提出了超参数估计与后验概率总结的有效策略。此外，我们通过联合富集权重估计得到功能导向先验，将功能注释融入精细定位流程。本研究利用英国生物库（UK Biobank）的资源开展了大规模模拟实验，以此评估SparsePro的性能表现。与当前前沿方法相比，SparsePro在缩短计算耗时的同时，提升了精细定位的检验效能。我们通过对5种与临床表型相关的功能生物标志物开展精细定位，验证了SparsePro的应用价值。综上，本研究开发了一种可整合汇总统计量与功能注释的高效精细定位方法。该方法可广泛应用于解析复杂性状的遗传机制，并提升GWAS功能后续研究的产出效率。SparsePro软件已开源至GitHub，仓库地址为https://github.com/zhwm/SparsePro。