TY - JOUR
T1 - Quick assessment for systematic test statistic inflation/deflation due to null model misspecifications in genome-wide environment interaction studies
AU - for Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer’s Disease Metabolomics Consortium
AU - Ueki, Masao
AU - Fujii, Masahiro
AU - Tamiya, Gen
N1 - Funding Information:
This work was supported by Japan Society for the promotion of science (http://www. jsps.go.jp/english/), grant numbers JP16K00064 (received author is M.U.), JP16K08638 (received authors are M.U. and G.T.). Data collection and sharing for this project was funded by the Alzheimer?s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer?s Association; Alzheimer?s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; EliLilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer?s Disease Cooperative Study at the University of California San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data collection and sharing for the preparation of this article were obtained from the Alzheimer?s Disease Neuroimaging Initiative (ADNI) database (adni.Loni.usc.edu) led by Principal Investigator Michael W. Weiner, MD (Michael.Weiner@ucsf.edu), and generated by the Alzheimer?s Disease Metabolomics Consortium (ADMC), lead by Dr. Kaddurah-Daouk (kaddu001@mc.duke.edu). Data used in preparation of this article were obtained from the ADNI and the ADMC (adni.Loni.usc.edu). As such, the investigators within the ADNI and the ADMC contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: adni.Loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledge-ment_List.pdf, and a complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/. The authors thank Prof. Heather Cordell, Prof. Takashi Yanagawa, Prof. Tatsuyuki Kakuma, Prof. Satoshi Hattori, Dr. John Cologne, Prof. Saurabh Ghosh, an anonymous reviewer, and the associate editor for their insightful comments, and also sincerely thank Dr. Miriam Kesselmeier for careful review and for providing many incisive comments and concrete suggestions that led to significant improvement of the paper.
Publisher Copyright:
© 2019 Ueki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Gene-environment (GxE) interaction is one potential explanation for the missing heritability problem. A popular approach to genome-wide environment interaction studies (GWEIS) is based on regression models involving interactions between genetic variants and environment variables. Unfortunately, GWEIS encounters systematically inflated (or deflated) test statistics more frequently than a marginal association study. The problematic behavior may occur due to poor specification of the null model (i.e. the model without genetic effect) in GWEIS. Improved null model specification may resolve the problem, but the investigation requires many time-consuming analyses of genome-wide scans, e.g. by trying out several transformations of the phenotype. It is therefore helpful if we can predict such problematic behavior beforehand. We present a simple closed-form formula to assess problematic behavior of GWEIS under the null hypothesis of no genetic effects. It requires only phenotype, environment variables, and covariates, enabling quick identification of systematic test statistic inflation or deflation. Applied to real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), our formula identified problematic studies from among hundreds GWEIS considering each metabolite as the environment variable in GxE interaction. Our formula is useful to quickly identify problematic GWEIS without requiring a genome-wide scan.
AB - Gene-environment (GxE) interaction is one potential explanation for the missing heritability problem. A popular approach to genome-wide environment interaction studies (GWEIS) is based on regression models involving interactions between genetic variants and environment variables. Unfortunately, GWEIS encounters systematically inflated (or deflated) test statistics more frequently than a marginal association study. The problematic behavior may occur due to poor specification of the null model (i.e. the model without genetic effect) in GWEIS. Improved null model specification may resolve the problem, but the investigation requires many time-consuming analyses of genome-wide scans, e.g. by trying out several transformations of the phenotype. It is therefore helpful if we can predict such problematic behavior beforehand. We present a simple closed-form formula to assess problematic behavior of GWEIS under the null hypothesis of no genetic effects. It requires only phenotype, environment variables, and covariates, enabling quick identification of systematic test statistic inflation or deflation. Applied to real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), our formula identified problematic studies from among hundreds GWEIS considering each metabolite as the environment variable in GxE interaction. Our formula is useful to quickly identify problematic GWEIS without requiring a genome-wide scan.
UR - http://www.scopus.com/inward/record.url?scp=85069731830&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069731830&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0219825
DO - 10.1371/journal.pone.0219825
M3 - Article
C2 - 31318927
AN - SCOPUS:85069731830
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 7
M1 - e0219825
ER -