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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...

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Updated: Feb 18, 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

Direct Testing for Allele-Specific Expression Differences Between Conditions.

Luis León-Novelo¹, Alison R Gerken², Rita M Graze³

¹Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston-School of Public Health, Texas 77030.

G3 (Bethesda, Md.)

|November 24, 2017

Summary

This summary is machine-generated.

A new statistical model formally tests differences in allelic imbalance (AI) across conditions, accounting for gene expression and coverage. This method enables robust testing of gene-environment interactions (G×E) and genotype-genotype interactions (G×G).

Keywords:

allele-specific expression allelic imbalance gene regulatory networks robust regulation

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Last Updated: Feb 18, 2026

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Area of Science:

Genetics
Bioinformatics
Statistical Modeling

Background:

Allelic imbalance (AI) reveals functional variation in cis regulatory regions.
Current methods for detecting AI differences across conditions lack formal statistical testing.
A need exists for a robust model to compare AI across diverse biological contexts.

Purpose of the Study:

To develop and validate a novel statistical model for formally testing differences in AI across conditions.
To enable the testing of gene-environment (G×E) and genotype-genotype (G×G) interactions using AI.
To incorporate gene expression, coverage, and bias into AI modeling for increased generality and power.

Main Methods:

Development of a novel statistical model utilizing Bayesian credible intervals to test AI differences.
Incorporation of condition-specific bias and coverage into the model formulation.
Application of the model to reanalyze RNA-seq data from Drosophila melanogaster to compare AI in mated versus virgin females.

Main Results:

The proposed model demonstrates low type I and II error rates across various scenarios.
The model is robust to substantial differences in coverage between conditions.
Analysis of Drosophila data confirmed AI × genotype interactions and reaffirmed the robustness of cis regulation across environments.

Conclusions:

The novel Bayesian model provides a formal statistical framework for testing AI differences across conditions.
This approach facilitates the rigorous investigation of G×E and G×G interactions.
The model enhances the understanding of cis-regulatory variation and its stability across different biological contexts.