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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Simultaneously correcting for population stratification and for genotyping error in case-control association studies.

K F Cheng¹, W J Lin

¹Biostatistics Center and Department of Public Health, China Medical University, Taiwan, China. kfcheng@mail.cmu.esu.tw

American Journal of Human Genetics

|September 12, 2007

Summary

This study introduces a new method to correct for biases in genetic association studies caused by population stratification and genotyping errors. The corrected test maintains accuracy and improves power, offering a valuable tool for analyzing complex traits.

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Area of Science:

Genetics
Biostatistics
Population Genetics

Background:

Case-control association studies commonly use the chi-squared test.
This test is susceptible to bias from population stratification and genotyping errors, which can inflate false-positive rates with larger sample sizes.
Family-based designs are robust to stratification but not genotyping errors.

Purpose of the Study:

To propose a novel method for simultaneously correcting biases from population stratification and genotyping errors in case-control studies.
To develop a simple, robust test applicable to genetic analysis of complex traits.

Main Methods:

A new bias-correction method is proposed, utilizing sample odds ratios from genotype-by-case/control tables.
The corrected test is robust against misspecification of the genetic model.
A simulation study was conducted to evaluate the method's performance.

Main Results:

The corrected test maintains the expected type I error rate under various conditions.
The method improves the power of association tests when population stratification and/or genotyping errors are present.
The power improvement is more significant as the bias magnitude increases.

Conclusions:

The proposed bias-correction method is effective for genetic analysis of complex traits.
This approach offers a valuable tool to mitigate common biases in case-control association studies.
The corrected test provides more reliable results and enhanced power in the presence of confounding factors.