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Activity Number:
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415
- Recent advancements in the analysis of large-scale GWAS
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #318106
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Title:
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A Statistical Perspective on Baseline Adjustment in Pharmacogenomic Genome-Wide Association Studies of Quantitative Change
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Author(s):
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Judong Shen* and Hong Zhang and Aparna Chhibber and Peter M. Shaw and Devan V Mehrotra
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Companies:
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Merck & Co. Inc. and Merck & Co., Inc. and BMS and Merck & Co. Inc. and Merck & Co., Inc.
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Keywords:
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pharmacogenetics;
Genome Wide Association Studies;
change from baseline;
baseline (un-)adjustment;
type I error;
power
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Abstract:
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There are some debates in recent literatures on whether baseline should be adjusted in statistical models when one conducts quantitative change analyses in pharmacogenetic (PGx) genome-wide association studies (GWASs). Here, we provide a clear statistical perspective on baseline adjustment issue by running extensive simulations based on nine statistical models to evaluate the influence of baseline adjustment/un-adjustment on type I error and power performance. We applied these models to analyzing the IMPROVE-IT PGx GWAS to validate the conclusions drawn from our simulations. Both simulations and GWAS analyses consistently show that: 1) baseline unadjusted models inflate type I error for the variants associated with the baseline if the baseline is also associated with the change from baseline; 2) baseline adjustment models can control the type I errors in various scenarios and 3) baseline adjusted models provide larger power than unadjusted models under certain scenarios. We recommend performing baseline-adjusted analyses on change from baseline response as the primary analysis and baseline-unadjusted analyses as a sensitivity analysis in PGx GWASs of quantitative change.
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Authors who are presenting talks have a * after their name.
