Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity | Zendy

Benjamin B. Chu | Zendy; Kevin L. Keys | Zendy; Chris German | Zendy; Hua Zhou | Zendy; Jin Zhou | Zendy; Eric M. Sobel | Zendy; Janet S. Sinsheimer | Zendy; Kenneth Lange | Zendy

Open Access

Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity

Author(s) -

Benjamin B. Chu,

Kevin L. Keys,

Chris German,

Hua Zhou,

Jin Zhou,

Eric M. Sobel,

Janet S. Sinsheimer,

Kenneth Lange

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa044

Subject(s) - genome wide association study , lasso (programming language) , univariate , genetic association , biobank , regression , covariate , linear regression , computer science , regression analysis , logistic regression , statistics , single nucleotide polymorphism , computational biology , genetics , biology , mathematics , multivariate statistics , machine learning , genotype , world wide web , gene

Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression.

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