Sparse meta-analysis with high-dimensional data.

TitleSparse meta-analysis with high-dimensional data.
Publication TypeJournal Article
Year of Publication2016
AuthorsHe, Qianchuan, Hao Helen Zhang, Christy L. Avery, and D Y. Lin
Date Published2016 Apr
KeywordsComputer Simulation, Data Interpretation, Statistical, Genome-Wide Association Study, Genomics, Humans, Meta-Analysis as Topic, Models, Statistical

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach.

Alternate JournalBiostatistics
Original PublicationSparse meta-analysis with high-dimensional data.
PubMed ID26395907
PubMed Central IDPMC4834947
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
R37GM047845 / GM / NIGMS NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P2C HD050924 / HD / NICHD NIH HHS / United States