Genome-wide association studies using a penalized moving-window regression

Minli Bao; Kai Wang

doi:10.1093/bioinformatics/btx522

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Genome-wide association studies using a penalized moving-window regression

Journal article

Open access

Peer reviewed

Genome-wide association studies using a penalized moving-window regression

Minli Bao and Kai Wang

Bioinformatics (Oxford, England), Vol.33(24), pp.3887-3894

12/15/2017

DOI: 10.1093/bioinformatics/btx522

PMCID: PMC5860090

PMID: 28961706

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Abstract

Genome-wide association studies (GWAS) have played an important role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and presence of noise at non-causal variants. In an effort to overcome these difficulties, a previous study proposed a regularized regression method that penalizes on the difference of signal strength between two consecutive single-nucleotide polymorphisms (SNPs). We provide a generalization to the afore-mentioned method so that more adjacent SNPs can be incorporated. The choice of optimal number of SNPs is studied. Simulation studies indicate that when consecutive SNPs have similar absolute coefficients our method performs better than using LASSO penalty. In other situations, our method is still comparable to using LASSO penalty. The practical utility of the proposed method is demonstrated by applying it to Genetic Analysis Workshop 16 rheumatoid arthritis GWAS data. An implementation of the proposed method is provided in R package MWLasso. kai-wang@uiowa.edu.

Genome-Wide Association Study - methods

Humans

Polymorphism, Single Nucleotide

Regression Analysis

Details

Title: Subtitle: Genome-wide association studies using a penalized moving-window regression
Creators: Minli Bao - Interdisciplinary Graduate Program in Applied Mathematical and Computational Sciences
Kai Wang - Department of Biostatistics, University of Iowa, Iowa City, IA 52241, USA
Resource Type: Journal article
Publication Details: Bioinformatics (Oxford, England), Vol.33(24), pp.3887-3894
DOI: 10.1093/bioinformatics/btx522
PMID: 28961706
PMCID: PMC5860090
ISSN: 1367-4803
eISSN: 1367-4811
Grant note: P30 ES005605 / NIEHS NIH HHS R01 GM031575 / NIGMS NIH HHS
Language: English
Date published: 12/15/2017
Academic Unit: Biostatistics
Record Identifier: 9984214783002771

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