Work

Two-Variance-Component Model Improves Genetic Prediction in Family Datasets

Year: 2015

Type: article

Source: The American Journal of Human Genetics

DOI: 10.1016/j.ajhg.2015.10.002

Authors: George Tucker, Po-Ru Loh, Iona M. MacLeod, Ben J. Hayes, Michael E. Goddard, Bonnie Berger, Alkes L. Price

Institutions: Broad Institute, Harvard University, Massachusetts Institute of Technology, The University of Melbourne, La Trobe University, Department of Environment, Land, Water and Planning, AgriBio, 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: bronze

Funders: National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences

Awards: Improving Polygenic Prediction using Next-Generation Data Sets, Leveraging functional data to predict disease risk in multi-ethnic populations, Compressive Genomics for Large Omics Data Sets: Algorithms, Applications and Tools, Fast and powerful extensions of mixed model methods for GWAS

OpenAlex ID: W2176623023

FWCI: 3.189

Cites: 65

Cited by: 26

Related to: 10