The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease¹. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10⁻⁶), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into ‘gene burdens’ for identifying new gene–disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.

大多数基因的罕见功能丧失变异的临床影响尚未确定。将 DNA 测序数据与电子健康记录 (EHR) 相结合可以加强我们对罕见遗传变异对人类疾病的影响的理解¹。通过利用与 Penn Medicine Biobank 中的 EHR 数据相关联的 10,900 个全外显子组序列，我们解决了在全外显子组范围内人类疾病中每个单独基因的罕见预测功能丧失变异的累积效应的关联，如所评估的使用一组不同的 EHR 表型。在发现 97 个基因与外显子组-表型显着关联后 ( P  < 10 ^-6)，我们在 Penn Medicine Biobank 以及其他三个医学生物库和基于人群的 UK Biobank 中复制了其中的 26 个。在这 26 个基因中，有 5 个具有先前已报告的关联并代表阳性对照，而 21 个具有先前未报告的表型关联，其中包括与青光眼、主动脉扩张、糖尿病、肌营养不良和听力损失有关的基因。这些发现表明，在医学生物库中使用 EHR 表型将罕见的预测功能丧失变异聚集到“基因负担”中以识别新的基因-疾病关联的价值。我们建议将这种方法应用于更多的个体，将提供揭示罕见遗传变异与疾病表型之间未探索关系所需的统计能力。