Controlling for confounding factors and revealing their interactions in genetic association meta-analyses: a computing method and application for stratification analyses
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Shuhuang Lin1,2,*, Xu Liu1,2,*, Bin Yao1,2 and Zunnan Huang1,3
1Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University, Dongguan, Guangdong 523808, China
2The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, Guangdong 523808, China
3Institute of Marine Biomedical Research, Guangdong Medical University, Zhanjiang, Guangdong 524023, China
*These authors contributed equally to this work
Zunnan Huang, email: email@example.com
Keywords: meta-analysis; stratification analysis; subgroup analysis; confounding control; interacting effect
Received: September 04, 2017 Accepted: January 24, 2018 Published: January 29, 2018
Subgroup and stratification analyses have been widely applied in genetic association studies to compare the effects of different factors or control for the effects of the confounding variables associated with a disease. However, studies have not systematically provided application standards and computing methods for stratification analyses. Based on the Mantel-Haenszel and Inverse-Variant approaches and two practical computing methods described in previous studies, we propose a standard stratification method for meta-analyses that contains two sequential steps: factorial stratification analysis and confounder-controlling stratification analysis. Examples of genetic association meta-analyses are used to illustrate these points. The standard stratification analysis method identifies interacting effects on investigated factors and controls for confounding variables, and this method effectively reveals the real effects of these factors and confounding variables on a disease in an overall study population. We also discuss important issues concerning stratification for meta-analyses, such as conceptual confusion between subgroup and stratification analyses, and incorrect calculations previously used for factorial stratification analyses. This standard stratification method will have extensive applications in future research for increasing studies on the complicated relationships between genetics and disease.
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