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Genome-wide Association Studies-GWAS

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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: Jan 7, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Improved Identification of Large-effect Rare Genetic Variants using Haplotype Aggregated Allele-specific Expression

Kaushik Ram Ganapathy^1,2, Martin Broly^3,4, Sarah Silverstein^5,6,7

¹Dept. of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, USA.

Medrxiv : the Preprint Server for Health Sciences

|December 25, 2025

Summary

This summary is machine-generated.

ANEVA-h improves rare variant interpretation by leveraging haplotype-level allele-specific expression (ASE) data. This method enhances the detection of genetic regulatory variants, reducing noise and increasing gene discovery in diverse populations.

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Last Updated: Jan 7, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: November 3, 2010

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Published on: June 23, 2012

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

Background:

Allele-specific expression (ASE) outlier detection identifies regulatory variants but faces challenges with data sparsity and noise in low-count genes.
Genome phasing can aggregate ASE signals along haplotypes, mitigating sparsity and noise.
Existing statistical tools lack robust methods for utilizing haplotype-level ASE data in rare variant interpretation.

Purpose of the Study:

To introduce ANEVA-h, a novel statistical tool for quantifying genetic variation in gene expression from haplotype-level ASE data.
To enable more accurate and comprehensive detection of regulatory effects by analyzing population-level haplotype ASE data.
To facilitate the integration of haplotype-level ASE outlier testing into rare variant interpretation pipelines.

Main Methods:

Development and application of ANEVA-h for analyzing haplotype-level ASE data.
Integration with a compatible dosage outlier test.
Application to GTEx project data and clinical cohorts (neuromuscular and congenital heart disease).
Analysis of globally diverse populations to assess ancestry impact.

Main Results:

ANEVA-h demonstrated over a 2-fold increase in testable genes compared to existing methods.
Reduced spurious outlier calls and improved enrichment for rare, high-impact variants.
Enhanced gene prioritization in clinical cohorts, identifying candidate diagnoses missed by other tools.
Characterization of ancestry background effects on reference and test populations.

Conclusions:

ANEVA-h significantly improves the detection of genetic regulatory variants by utilizing haplotype-level ASE data.
The tool enhances gene prioritization and diagnostic capabilities in clinical settings.
ANEVA-h provides essential tools and data for advancing rare variant interpretation pipelines, especially in diverse populations.