title: Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.
abstract: There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.
authors.affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.
authors.forename: Zheng-Zheng
authors.initials: ZZ
authors.lastname: Tang
authors.affiliation: Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, USA. Electronic address: lin@bios.unc.edu.
authors.forename: Dan-Yu
authors.initials: DY
authors.lastname: Lin
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056726:Genetic Association Studies / Q000379:methods* / Q000639:trends; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D035583:Rare Diseases / Q000235:genetics*; D012984:Software