@article{c788fd325baf40ea919699f9c875928f,
title = "On meta- and mega-analyses for gene–environment interactions",
abstract = "Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a “mega” data set, which can be fit by a joint “mega-analysis.” Alternatively, analyses can be done at each site, and results across sites can be combined through a “meta-analysis” procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions.",
author = "Jing Huang and Yulun Liu and Steve Vitale and Penning, {Trevor M.} and Whitehead, {Alexander S.} and Blair, {Ian A.} and Anil Vachani and Clapper, {Margie L.} and Muscat, {Joshua E.} and Philip Lazarus and Paul Scheet and Moore, {Jason H.} and Yong Chen",
note = "Funding Information: Research reported in this publication was supported in part by the National Institutes of Health under award numbers R01 AI130460 (for JH and YC), R01 AI116794 (for JHM and YC), R01 LM009012 (for YC), R01 HG005859 (for PS and YL), P42 ES023720 (for IAB), and P30 ES013508 (for ASW, AV, IAB, JHM and TMP). This research was also supported by the Pennsylvania Department of Health under award number PA 4100038714 (for ASW, AV, IAB, JHM, MLC, PL and TMP). Funding Information: Jing Huang, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, PA 19104, United States of America. Email: jing14@mail.med.upenn.edu Paul Scheet, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, PA 19104, United States of America. Email: pscheet@alum.wustl.edu Jason H. Moore and Yong Chen, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, PA 19104, United States of America. Email: jhmoore@mail.med.upenn.edu; ychen123@mail.med.upenn.edu Grant sponsor: NIH; Grant numbers: R01 AI130460 (for JH and YC), R01 AI116794 (for JHM and YC), R01 LM009012 (for YC), R01 HG005859 (for PS and YL), P42 ES023720 (for IAB), P30 ES013508 (for ASW, AV, IAB, JHM and TMP). Grant sponsor: Pennsylvania Department of Health; Grant number: PA4100038714 (for ASW, AV, IAB, JHM, MLC, PL and TMP). Publisher Copyright: {\textcopyright} 2017 WILEY PERIODICALS, INC.",
year = "2017",
month = dec,
doi = "10.1002/gepi.22085",
language = "English (US)",
volume = "41",
pages = "876--886",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "8",
}