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304168
Impact of the covariate measurement error on meta-analysis of genome wide association studies
Tuesday, November 18, 2014
Chao Xu
,
Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA
Huaizhen Qin
,
Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA
Jian Li
,
Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA
Hong-Wen Deng
,
Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA
Meta-analysis of genome wide association study (GWAS) is often used to increase the detection power of variants for complex disease. It benefits from the increased sample size by collecting studies of small size, while the covariate measurement error existed in single GWAS may also affect the performance of meta-analysis. In order to increase the power, researchers often adjust various covariates in single GWAS. However, errors and uncertainty may be introduced by the inclusion of covariates with measurement error. These issues may consequently affect the performance of meta-analysis of GWAS, which however has not been fully investigated. To better assess the usefulness and limitation of the meta-analysis of GWAS, we will perform comprehensive studies based on a covariate measurement error model specified in genotype imputation error, which can be generally extended to other error modes. Through the proposed model, the type I error and power as a function of imputation error rate and other common study conditions will be delicately investigated for meta-analysis of GWAS. Our study will aid in the planning, data analysis, and interpretation of meta-analysis of GWAS results when the possible measurement error in covariate is believed to be present. Further study will be conducted to correct the covariate measurement error.
Learning Areas:
Biostatistics, economics
Epidemiology
Learning Objectives:
Evaluate the impact of covariate measurement error on the meta-analysis of GWAS.
Assess the usefulness and limitation of the meta-analysis of GWAS.
Keyword(s): Statistics, Genetics
Presenting author's disclosure statement:Qualified on the content I am responsible for because: I am a PhD student in biostatistics. Under the supervision of my advisor, I have been involved in several research projects on statistical genetics. My research focuses on statistical genetics and systems genetics.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.
