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Checking Genetic Homogeneity Between Two Samples Using Summary Statistics With Application to Mendelian

Kai Wang¹, Grace Z Wang²

¹Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA.

Statistics in Medicine

|July 8, 2026

Summary

Researchers developed a new method to check if single nucleotide polymorphisms (SNPs) have similar minor allele frequencies (MAFs) across two genome-wide association studies (GWASs). This quality control step is crucial for reliable two-sample Mendelian randomization (MR) studies when MAF data is missing.

Keywords:

GWAS Mendelian randomization genetic homogeneity linear regression summary statistics

Related Experiment Videos

Area of Science:

Genetics
Statistical Genetics
Epidemiology

Background:

Two-sample Mendelian randomization (MR) studies assume instrumental single nucleotide polymorphisms (SNPs) have consistent effects across exposure and outcome samples.
This consistency is often linked to similar minor allele frequencies (MAFs) between study samples.
Currently, no formal method exists to assess MAF similarity when this information is unavailable, hindering MR study quality.

Purpose of the Study:

To propose a novel method for assessing the equality of genetic variance, determined by MAF, across two genome-wide association studies (GWASs).
To provide a tool for quality control in two-sample summary-data MR studies, particularly when MAF data is missing.

Main Methods:

Leveraging the inference of relative SNP variance from GWAS summary statistics, even with nonlinear models.
Developing a V-V plot and a modified Bland-Altman plot to visually identify SNPs with differing genetic variances between GWAS datasets.

Main Results:

The proposed V-V and Bland-Altman plots can effectively identify SNPs with heterogeneous genetic variances across GWASs.
Identified instances where published two-sample MR studies may have included genetically non-homogeneous SNPs.

Conclusions:

The developed method enhances the quality control of two-sample summary-data MR studies.
Recommends checking for SNP MAF equality between exposure and outcome GWASs prior to analysis.
The proposed method is particularly valuable when MAF information is missing in one or both GWAS datasets.