Computationally efficient whole-genome regression for quantitative and binary traits

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine-learning method called REGENIE for fitting a whole-genome regression model for quantitat...

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Veröffentlicht in:	Nature genetics Jg. 53; H. 7; S. 1097 - 1103
Hauptverfasser:	Mbatchou, Joelle, Barnard, Leland, Backman, Joshua, Marcketta, Anthony, Kosmicki, Jack A., Ziyatdinov, Andrey, Benner, Christian, O’Dushlaine, Colm, Barber, Mathew, Boutkov, Boris, Habegger, Lukas, Ferreira, Manuel, Baras, Aris, Reid, Jeffrey, Abecasis, Goncalo, Maxwell, Evan, Marchini, Jonathan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.07.2021 Nature Publishing Group
Schlagworte:	45/43 631/114/794 631/208/205 Agriculture Animal Genetics and Genomics Association analysis Biobanks Biomedical and Life Sciences Biomedicine Cancer Research Case-Control Studies Chromosomes Computational biology Computational Biology - methods Computer applications Computer networks Datasets Distributed processing Gene Function Genome-Wide Association Study - methods Genomes Genomics Genomics - methods Genotype Genotypes Human Genetics Humans Learning algorithms Logistic Models Machine Learning Methods Phenotype Phenotypes Population structure Regression analysis Regression models Reproducibility of Results Statistical analysis technical-report United States United Kingdom > UK
ISSN:	1061-4036, 1546-1718, 1546-1718
Online-Zugang:	Volltext
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