Work

Efficient Bayesian mixed-model analysis increases association power in large cohorts

Year: 2015

Type: article

Source: Nature Genetics

DOI: 10.1038/ng.3190

Authors: Po-Ru Loh, George Tucker, Brendan K Bulik-Sullivan, Bjarni J Vilhjálmsson, Hilary K Finucane, Rany M Salem, Daniel I Chasman, Paul M Ridker, Benjamin M Neale, Bonnie Berger, Nick Patterson, Alkes L Price

Institutions: Broad Institute, Harvard University, Massachusetts Institute of Technology, Massachusetts General Hospital, Boston Children's Hospital, Brigham and Women's Hospital, MIT Computer Science and Artificial Intelligence Laboratory

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Donald W. Reynolds Foundation, Amgen, Fondation Leducq, National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences

Awards: Women's Health Study: Continued Follow-Up, Improving Polygenic Prediction using Next-Generation Data Sets, G1214533002, G1528515809, G229684361, Methods for Genetic Association Analysis of Longitudinal and Multiple Phenotypes, G3219722387, Leveraging functional data to predict disease risk in multi-ethnic populations, G558986044, The Women's Health Study: Infrastructure Support for Continued Cohort Follow-up, G6008908520, TRIAL OF ASPIRIN AND VITAMIN E IN WOMEN, Women's Health Study: Continued Follow-up, G7962241407, Fast and powerful extensions of mixed model methods for GWAS, G9000058182

OpenAlex ID: W1966775465

FWCI: 51.93

Cites: 58

Cited by: 1,507

Related to: 10