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A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.
Chen, Ting-Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin.
Affiliation
  • Chen TH; Department of Mathematics and Statistics, Regular member, Cervo Brain Research Centre, University of Laval, 1045, av. of Medicine, Suite 1056, Quebec G1V 0A6, Canada.
  • Chatterjee N; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Baltimore, Maryland, United States of America, 615 N Wolfe Street Baltimore, MD 21205.
  • Landi MT; Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E106, Bethesda, MD, 20892.
  • Shi J; Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E122, Bethesda, MD, 20892.
J Am Stat Assoc ; 116(533): 133-143, 2021.
Article in En | MEDLINE | ID: mdl-34483403
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: J Am Stat Assoc Year: 2021 Type: Article Affiliation country: Canada
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: J Am Stat Assoc Year: 2021 Type: Article Affiliation country: Canada