Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 134, frequency range 0.0007−0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

全基因组谱系紧凑地代表了一组基因组的进化历史，从遗传数据中推断它们有可能促进广泛的分析。我们介绍了一种方法 ARG-Needle，用于从测序或基因分型阵列数据中准确推断生物样本库规模的家谱，以及利用家谱进行关联和其他复杂性状分析的策略。我们使用这些方法使用 337,464 个 UK Biobank 个体的基因分型数据构建全基因组谱系，并测试七个复杂性状之间的关联。基于谱系的关联检测到比使用约 65,000 个测序单倍型（N = 64). 在 138,039 个外显子组测序样本的一个子集中，这些关联强烈标记（平均r  = 0.72）潜在的测序变体富集（4.8×）功能丧失变异。这些结果表明，推断的全基因组谱系可用于复杂性状的分析，补充需要可用的大型群体特异性测序 panel 的方法。