Meta-analysis in genome-wide association studies

By Eleftheria Zeggini, John P. A. Ioannidis. Pharmacogenomics (London), 2009

632 citations recorded by the source corpus.

Summary The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate-to-small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/2dafe45a15f93956c7ecba39c6e039ac6ec45519

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Genotype Imputation with Thousands of Genomes (2011, background): “sets of variants (De Bakker et al. 2008; Zeggini and Ioannidis 2009).”
Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, methodology): “Fixed-effect meta-analysis combines estimated effect sizes and their standard errors from different studies to construct pooled estimates resulting in higher precision of effect-size estimates and hence superior statistical power [35, 36].”
A machine-learning heuristic to improve gene score prediction of polygenic traits (2017, neutral): “= y y R g X g X Cov( ( ), ) Var( ( ))Var( ) (9) 2 2”
Replication in genome-wide association studies (2009, background): “Prospective meta-analysis of multiple genomewide studies (conducted by different investigative teams, in different populations, using different technologies and different designs) can satisfy the requirement for replication in the context of gene discovery, without the need to genotype yet more samp”
Prioritizing GWAS results: A review of statistical methods and recommendations for their application. (2010, background): “The sheer number of interaction models is one of the most vexing problems plaguing GWAS.”
Meta-analysis methods for genome-wide association studies and beyond (2013, methodology): “With the inclusion of low-frequency and rare variants from the 1000 Genomes Project imputations and sequencing efforts, in which up to ~50 million markers may be typed, the number of the independent loci under study will be significantly larger than the 1 million markers estimated for GWASs.”