|Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.
|Year of Publication
|Tang, Zheng-Zheng, and Dan-Yu Lin
|Am J Hum Genet
|2015 Jul 02
|Computer Simulation, Data Interpretation, Statistical, Genetic Association Studies, Genetic Variation, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Rare Diseases, Software
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.
|Am J Hum Genet
|Meta-analysis for discovering rare-variant associations: Statistical methods and software programs.
|PubMed Central ID
|R01 CA082659 / CA / NCI NIH HHS / United States
R01CA082659 / 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
P01CA142538 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.