Stratification-score matching improves correction for confounding by population stratification in case-control...

Area of Science:

• Population Genetics
• Statistical Genetics
• Genomic Epidemiology

Background:

• Controlling for population stratification is essential for accurate case-control association studies.
• Fine matching on genetic ancestry is a common strategy to address confounding in genome-wide association studies (GWASs) and sequencing studies.
• Current matching methods may improperly control confounding by combining non-confounding ancestry components.

Purpose of the Study:

• To propose a novel method for matching cases and controls based on the stratification score to improve control of population stratification.
• To address the limitations of existing methods that combine multiple ancestry components into a single measure.
• To enhance the accuracy of matches and the validity of genetic association analyses.

Main Methods:

• Developed a novel matching method using the stratification score, defined as the probability of disease given genomic variables.
• Applied the method to the African-American cohort of the GAIN GWAS of schizophrenia.
• Utilized simulated data to compare the performance of the novel method against existing approaches.

Main Results:

• The proposed stratification score matching method effectively resolved confounding due to population stratification in the schizophrenia GWAS data.
• Existing matching procedures failed to adequately control for confounding in the same dataset.
• Simulated data confirmed that the novel approach provides a more appropriate correction for population stratification than current methods.

Conclusions:

• Matching on the stratification score leads to more accurate case-control matches by aligning participants with similar disease risk given ancestry.
• This novel method offers a superior approach for controlling population stratification compared to existing techniques.
• The findings have significant implications for improving the reliability of genetic association studies, particularly in diverse populations.