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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: Aug 1, 2025

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

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Bi-level structured functional analysis for genome-wide association studies.

Mengyun Wu¹, Fan Wang², Yeheng Ge¹

¹School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

|April 26, 2023

Summary

This study introduces a novel bi-level functional analysis method to simultaneously analyze genetic variants at both the single nucleotide polymorphism (SNP) and SNP group levels, improving complex disease association studies.

Keywords:

bi-level selection functional analysis genome-wide association study structured analysis

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Last Updated: Aug 1, 2025

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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Published on: July 27, 2021

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) identify genotype-phenotype links for complex diseases but face challenges with high-dimensional single nucleotide polymorphism (SNP) data.
Current functional analyses often treat SNPs discretely, failing to capture their inherent group structures, correlations, and network interactions.

Purpose of the Study:

To develop a novel bi-level structured functional analysis method for investigating disease-associated genetic variants.
To simultaneously analyze genetic variants at both the SNP and SNP group levels, accounting for group-level network structures.

Main Methods:

A novel bi-level structured functional analysis method was developed.
Penalization techniques were employed for bi-level selection and to incorporate group-level network structures.
Estimation and selection consistency properties were rigorously established.

Main Results:

The proposed method demonstrated superiority over existing alternatives in extensive simulation studies.
Biologically intriguing results were obtained from a type 2 diabetes SNP data application.
The method effectively analyzes genetic variants at both individual SNP and group levels.

Conclusions:

The developed bi-level structured functional analysis method offers a powerful approach to overcome dimensionality challenges in GWAS.
This method enhances the understanding of complex disease genetics by considering SNP group structures and network interactions.
The approach provides a robust framework for identifying disease-associated genetic variants with improved accuracy and biological insight.