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Related Experiment Video

Updated: Jun 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Genome-wide association analysis by lasso penalized logistic regression.

Tong Tong Wu¹, Yi Fang Chen, Trevor Hastie

¹Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA.

Bioinformatics (Oxford, England)

|January 30, 2009

Summary

Lasso penalized logistic regression effectively identifies significant single nucleotide polymorphisms (SNPs) for disease gene mapping, even with numerous predictors. This method also reveals interactions among these genetic markers.

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Area of Science:

Genetics
Biostatistics
Computational Biology

Background:

Ordinary regression with lasso penalty simplifies continuous model selection.
Lasso penalized regression is effective when predictors outnumber observations.
This study focuses on disease gene mapping using genetic markers.

Purpose of the Study:

Evaluate lasso penalized logistic regression for case-control disease gene mapping.
Assess the performance with a large number of single nucleotide polymorphisms (SNPs) as predictors.
Investigate the identification of relevant SNPs and their interactions.

Main Methods:

Utilized lasso penalized logistic regression for SNP selection.
Tuned lasso penalty strength to predetermine the number of relevant SNPs.
Employed cyclic coordinate ascent for efficient penalized likelihood maximization.
Examined two-way and higher-order interactions among selected SNPs.

Main Results:

Demonstrated the effectiveness of the strategy on simulated and real data.
Replicated previous SNP findings for coeliac disease.
Provided insights into potential SNP interactions relevant to coeliac disease.

Conclusions:

Lasso penalized logistic regression is a powerful tool for disease gene mapping with high-dimensional SNP data.
The method facilitates the identification of key genetic markers and their complex interactions.
This approach aids in understanding the genetic architecture of diseases like coeliac disease.