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Updated: Jul 2, 2025
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
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Controlling for polygenic genetic confounding in epidemiologic association studies.
Zijie Zhao1, Xiaoyu Yang1, Jiacheng Miao1
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI.
Biorxiv : the Preprint Server for Biology
|February 26, 2024
Summary
Genetic confounding in observational studies is a major challenge. We introduce PENGUIN, a novel framework for polygenic genetic confounding control, demonstrating its superior performance over existing methods for accurate epidemiologic association studies.
Area of Science:
- Epidemiology
- Statistical Genetics
- Bioinformatics
Background:
- Observational studies often suffer from genetic confounding due to pleiotropy.
- Current methods, like polygenic scores (PGS), are insufficient for controlling genetic confounding.
- Measurement error and model misspecification limit the effectiveness of existing PGS approaches.
Conclusions:
- PENGUIN offers an effective solution for controlling genetic confounding in observational data.
- The framework has broad applications for future epidemiologic association studies.
- Genetically-unconfounded associations can be reliably estimated using PENGUIN.
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