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Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks.
Dey, Rounak; Zhou, Wei; Kiiskinen, Tuomo; Havulinna, Aki; Elliott, Amanda; Karjalainen, Juha; Kurki, Mitja; Qin, Ashley; Lee, Seunggeun; Palotie, Aarno; Neale, Benjamin; Daly, Mark; Lin, Xihong.
Afiliação
  • Dey R; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
  • Zhou W; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Kiiskinen T; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Havulinna A; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Elliott A; Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
  • Karjalainen J; Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
  • Kurki M; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Qin A; Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
  • Lee S; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
  • Palotie A; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Neale B; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Daly M; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Lin X; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Nat Commun ; 13(1): 5437, 2022 09 16.
Article em En | MEDLINE | ID: mdl-36114182
With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article