Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

中文翻译：

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评估数字表型以增强人类疾病的遗传研究。

人口规模的生物库结合了遗传数据和大量参与者的高维表型，为进行全基因组关联研究（GWAS）以确定与多种定量特征和疾病相关的遗传变异提供了令人兴奋的机会。GWAS在人口生物库中的主要挑战是从异构数据源（例如医院记录，数字问卷答复或访谈）中确定疾病病例。在这项研究中，我们使用遗传参数（包括遗传相关性）来评估GWAS是否使用从医院记录，问卷调查表和疾病家族史确定的UK Biobank中的病例进行，从而在一系列影响范围内暗示了相似的疾病遗传学。我们发现，医院记录和问卷GWAS在很大程度上识别了许多复杂表型的相似遗传效应，并且将两种表型方法结合在一起可以提高检测遗传关联的能力。我们还表明，使用根据疾病家族史确定的病例的家族史GWAS与合并的医院记录和问卷GWAS一致，家族史GWAS具有更好的检测某些表型遗传关联的能力。总的来说，这项工作表明，数字表型和非结构化表型数据可以与结构化数据（如医院记录）结合起来，以识别生物库中GWAS的病例，并提高此类研究识别遗传关联的能力。