Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases
Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases
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DOI：
10.1016/j.ajhg.2020.03.007
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发表时间：
2020-05-07
影响因子：
9.8
通讯作者：
Rivas, Manuel A.
中科院分区：
文献类型：
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作者：
DeBoever, Christopher;Tanigawa, Yosuke;Rivas, Manuel A.
人口规模的生物库，结合联合收割机的遗传数据和高维表型为大量的参与者提供了一个令人兴奋的机会，进行全基因组关联研究（GWAS），以确定与各种数量性状和疾病相关的遗传变异。GWAS在人口生物库中的一个主要挑战是从异质数据源（如医院记录、数字问卷调查或访谈）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，以评估是否GWAS进行使用的情况下，在英国生物银行确定从医院记录，问卷调查和疾病家族史牵连相似的疾病遗传学在一系列的影响大小。我们发现，医院记录和问卷调查GWAS在很大程度上确定了许多复杂表型的相似遗传效应，并将两种表型分析方法结合在一起，提高了检测遗传关联的能力。我们还表明，家族史GWAS确定的疾病家族史的情况下，同意合并医院记录和问卷调查GWAS和家族史GWAS有更好的权力，以检测某些表型的遗传关联。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）相结合，以识别生物库中的GWAS病例，并提高此类研究识别遗传关联的能力。
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.