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Related Experiment Video

Updated: Feb 8, 2026

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

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Significance Testing for Allelic Heterogeneity.

Yangqing Deng¹, Wei Pan²

¹Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455.

|July 1, 2018

Summary

Allelic heterogeneity (AH), the presence of multiple causal single nucleotide polymorphisms (SNPs) in a genetic locus, is widespread in complex traits. A new intersection-union test (IUT) method offers a faster and more effective approach to detect AH than existing methods.

Keywords:

CAVIAR GWAS conditional/joint modeling intersection-union test statistical power

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Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Published on: September 20, 2020

Area of Science:

Genetics
Statistical Genetics
Genomics

Background:

Allelic heterogeneity (AH), the presence of multiple causal single nucleotide polymorphisms (SNPs) within a single locus, is crucial for understanding complex traits.
Previous methods like CAVIAR have limitations, including computational demands and assumptions about the number of causal SNPs, potentially affecting results.

Purpose of the Study:

To introduce a novel intersection-union test (IUT) for inferring AH in complex traits.
To develop sequential IUT-based procedures for estimating the number of causal SNPs.
To provide methods applicable to both individual-level data and genome-wide association study (GWAS) summary statistics.

Main Methods:

Developed an intersection-union test (IUT) utilizing a joint/conditional regression model incorporating all SNPs in a locus.
Proposed two sequential IUT-based testing strategies to estimate the count of causal SNPs.
Validated methods using simulated data and real-world GWAS summary statistics for schizophrenia (SCZ) and high-density lipoprotein (HDL) levels.

Main Results:

The IUT method demonstrated superior performance compared to CAVIAR in detecting AH loci.
The proposed IUT was computationally faster than CAVIAR.
The methods successfully identified more AH loci in both SCZ and HDL GWAS datasets.

Conclusions:

The novel IUT provides a more efficient and sensitive approach for detecting allelic heterogeneity in complex traits.
These methods enhance the ability to uncover the extent of AH, contributing to a deeper understanding of complex trait genetics.
The applicability to GWAS summary statistics broadens the potential for large-scale discovery of AH.