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Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.
DeBoever, Christopher; Tanigawa, Yosuke; Aguirre, Matthew; McInnes, Greg; Lavertu, Adam; Rivas, Manuel A.
Afiliação
  • DeBoever C; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Tanigawa Y; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Aguirre M; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • McInnes G; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Lavertu A; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Rivas MA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: mrivas@stanford.edu.
Am J Hum Genet ; 106(5): 611-622, 2020 05 07.
Article em En | MEDLINE | ID: mdl-32275883
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Doença / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Doença / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Ano de publicação: 2020 Tipo de documento: Article