Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total n ≈ 500,000) to impute exome-wide variants with accuracy R² > 0.5 down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P < 5 × 10⁻⁸) involving 675 distinct rare protein-altering variants (MAF < 0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct ‘likely-causal’ variants. Our results demonstrate the utility of within-cohort imputation in population-scale genome-wide association studies, provide a catalog of likely-causal, large-effect coding variant associations and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

迄今为止，外显子组关联研究通常不足以系统地评估非常罕见的编码变体的表型影响。我们利用 49,960 名外显子组测序的英国生物库参与者和队列的其余部分（总n  ≈ 500,000）之间的广泛单倍型共享来估算全外显子组变异，准确度R ²  > 0.5 下降到次要等位基因频率 (MAF) ~0.00005。54 个数量性状的关联和精细映射分析确定了 1,189 个显着关联 ( P  < 5 × 10 ^-8) 涉及 675 种不同的稀有蛋白质改变变体 (MAF < 0.01)，它们通过了可能的因果关系的严格过滤器。在所有性状中，49% 的关联 (578/1,189) 发生在具有两个或更多命中的基因中；对这些基因的后续分析确定了包含多达 45 个不同的“可能因果”变体的等位基因系列。我们的研究结果证明了队列内插补在人群规模的全基因组关联研究中的效用，提供了可能因果、大效应编码变异关联的目录，并预示了随着遗传生物库研究的不断发展将揭示的见解。