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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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Related Experiment Video

Updated: Aug 29, 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

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000

Remo Monti^1,2, Pia Rautenstrauch^2,3, Mahsa Ghanbari²

¹Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany.

Nature Communications

|September 10, 2022

Summary

This summary is machine-generated.

This study analyzed rare genetic variants in UK Biobank data to find gene-biomarker associations. Novel methods identified 193 significant links, revealing rare variants

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Last Updated: Aug 29, 2025

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

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Published on: August 21, 2016

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Genetics and Genomics
Biomarker Discovery
Computational Biology

Background:

Rare genetic variants significantly influence human traits and disease susceptibility.
Understanding the genetic architecture of blood biomarkers is crucial for personalized medicine.
Previous studies often aggregate variants, potentially masking functional effects.

Purpose of the Study:

To conduct an exome-wide rare genetic variant association study for 30 blood biomarkers.
To develop and compare novel statistical tests for rare variant association analysis.
To identify specific genes and functional mechanisms contributing to biomarker variability.

Main Methods:

Exome-wide association study in 191,971 UK Biobank participants.
Comparison of gene-based collapsing tests with variant-category-specific kernel-based tests.
Integration of functional variant effect predictions (missense, splicing, RNA-binding protein interactions).
Development of a computationally efficient likelihood-ratio and score test combination.
Introduction of local collapsing by amino acid position for missense variants.

Main Results:

Identified 193 significant gene-biomarker associations.
Genes linked to biomarkers were 4.5-fold enriched for Mendelian disorders.
Kernel-based tests identified 13% more associations than collapsing methods.
The novel test combination increased associations by 36% compared to score tests alone.
Identified potential novel gain-of-function variants in PIEZO1.

Conclusions:

Rare genetic variants substantially contribute to blood biomarker variability.
Variant-category-specific tests enhance the power and interpretability of association studies.
Investigating diverse functional mechanisms is essential for comprehensive rare-variant analysis.
The developed methods offer improved detection of genetic associations for complex traits.