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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Mar 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data.

Hyoseok Ko¹, Kipoong Kim¹, Hokeun Sun¹

¹Department of Statistics, Pusan National University, Busan 46241, Korea.

Genomics & Informatics

|February 4, 2017

Summary

This summary is machine-generated.

This study compares group testing and group lasso methods for identifying disease-related genes in high-dimensional genomic data. Group lasso showed significant discrepancies in gene selection compared to traditional group testing procedures.

Keywords:

genetic association studies genetic selection genetic testing principal component analysis

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Last Updated: Mar 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

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

Area of Science:

Genomics
Statistical Genetics
Bioinformatics

Background:

High-dimensional genomic data analysis in genetic association studies presents challenges with multiple testing.
Identifying a few disease-associated genes among thousands is a key objective.
Regularization methods offer an alternative to traditional statistical testing for genomic data.

Purpose of the Study:

To compare the true positive selection performance of group testing procedures with group lasso.
To evaluate these methods in identifying genes associated with ovarian cancer using real genomic data.

Main Methods:

Extensive simulation studies were conducted.
Common group testing procedures (PCA, Hotelling's T2, permutation test) were compared with group lasso.
Methods were applied to identify genes associated with ovarian cancer from Illumina HumanMethylation27K Beadchip data.

Main Results:

Significant discrepancies were observed in the genes selected by group testing procedures versus group lasso.
Group lasso demonstrated different selection patterns compared to traditional group testing methods.

Conclusions:

The choice of method significantly impacts the identification of disease-associated genes.
Further research is needed to understand the implications of these discrepancies for genetic association studies.