Identifying cross-disease components of genetic risk across hospital data in the UK Biobank.
Nat Genet
; 52(1): 126-134, 2020 01.
Article
em En
| MEDLINE
| ID: mdl-31873298
ABSTRACT
Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. Here, we develop a disease-agnostic approach to cluster the genetic risk profiles for 3,025 genome-wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify 339 distinct disease association profiles and use multiple approaches to link clusters to the underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and their potential in informing therapeutic strategies.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Bancos de Espécimes Biológicos
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Característica Quantitativa Herdável
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Predisposição Genética para Doença
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Polimorfismo de Nucleotídeo Único
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Estudo de Associação Genômica Ampla
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Loci Gênicos
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Doenças Genéticas Inatas
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article