McManus Justin N J, Lovelett Robert J, Lowengrub Daniel, Christensen Sarah
Kallyope, Inc., 430 East 29th Street, New York, NY 10016, USA.
Cell Genom. 2023 Mar 8;3(3):100264. doi: 10.1016/j.xgen.2023.100264.
Genome-wide association studies (GWASs) identify genomic loci associated with complex traits, but it remains a challenge to identify the genes affected by causal genetic variants in these loci. Attempts to solve this challenge are frustrated by a number of compounding problems. Here, we show how to combine solutions to these problems into a unified mathematical framework. From this synthesis, it becomes possible to compute the probability that each gene in the genome is affected by a causal variant, given a particular trait, without making assumptions about the relevant cell types or tissues. We validate each component of the framework individually and in combination. When applied to large GWASs of human disease, the resulting paradigm can rediscover the majority of well-known disease genes. Moreover, it establishes human genetics support for many genes previously implicated only by clinical or preclinical evidence, and it uncovers a plethora of novel disease genes with compelling biological rationale.
全基因组关联研究（GWAS）可识别与复杂性状相关的基因组位点，但要确定这些位点中受因果遗传变异影响的基因仍然是一项挑战。解决这一挑战的尝试因一系列复杂问题而受挫。在此，我们展示了如何将这些问题的解决方案整合到一个统一的数学框架中。通过这种综合，在不假设相关细胞类型或组织的情况下，就有可能计算出给定特定性状时基因组中每个基因受因果变异影响的概率。我们分别并综合验证了该框架的每个组成部分。当应用于人类疾病的大型GWAS时，由此产生的范式能够重新发现大多数知名的疾病基因。此外，它为许多先前仅由临床或临床前证据暗示的基因建立了人类遗传学支持，并且揭示了大量具有令人信服的生物学原理的新型疾病基因。
