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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...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

Updated: May 24, 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

Regularized regression method for genome-wide association studies.

Jin Liu¹, Kai Wang, Shuangge Ma

¹Department of Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, USA. jin-liu@uiowa.edu.

BMC Proceedings

|March 1, 2012

Summary

This study introduces a new penalized method for genome-wide association studies, improving genetic marker selection by accounting for linkage disequilibrium. The novel approach outperforms LASSO in identifying significant single-nucleotide polymorphisms.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

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Last Updated: May 24, 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

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
Linkage disequilibrium (LD) between adjacent markers can complicate association analysis.
Existing methods like LASSO may have limitations in bias and selection consistency.

Purpose of the Study:

To develop a novel penalized method for GWAS that explicitly accounts for LD.
To improve the accuracy and consistency of single-nucleotide polymorphism (SNP) selection in GWAS.
To compare the performance of the new method against the LASSO approach.

Main Methods:

A penalized approach incorporating a penalty on the difference of genetic effects at adjacent SNPs.
Integration of the minimax concave penalty (MCP) for enhanced performance.
Implementation via a coordinate descent algorithm.
Tuning parameter selection using extended Bayesian information criteria (EBIC).
Leave-one-out method for p-value computation of selected SNPs.

Main Results:

The novel method successfully identified three significant SNPs (C13S522, C13S523, C13S524) in simulated data.
The LASSO method identified only two SNPs (C13S522, C13S523).
The proposed method demonstrates superior performance in SNP selection compared to LASSO.

Conclusions:

The novel penalized GWAS method effectively handles LD between adjacent markers.
This approach offers improved estimator bias and selection consistency over LASSO.
The method provides a valuable tool for more accurate genetic association analysis.