Gibran Hemani
Abstract
The past 30 years of genetic analysis have helped to confirm theories, conceived at the beginning of the twentieth century, that complex diseases are heritable and likely to be influenced by many genetic factors, each exhibiting small effects. Detecting these genetic factors could be valuable for developing new drugs or predicting adverse outcomes before onset, but detection is generally only possible when the sample sizes are very large. We describe the failures in genetic analyses that have brought us to this understanding, and led to the development of the genome-wide association study (GWAS) framework. Meta-analysis has played a central role in GWAS and has helped to yield tens of thousands of genetic associations for a wide range of complex traits and diseases. We discuss how meta-analysis is implemented in the GWAS context, noting the technical challenges that arise and the various contexts in which it can be applied.
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Author affiliations
Gibran Hemani
Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
How to cite this chapter?
For the printed version of the book
Hemani, G. (2022). Chapter 20. Meta-analysis in genetic association studies. In: Systematic Reviews in Health Research: Meta-analysis in Context (eds M. Egger, J.P.T. Higgins and G. Davey Smith), pp 396-412. Hoboken, NJ : Wiley.
For the electronic version of the book
Hemani, G. (2022). Chapter 20. Meta-analysis in genetic association studies. In: Systematic Reviews in Health Research: Meta-analysis in Context (eds M. Egger, J.P.T. Higgins and G. Davey Smith). https://doi.org/10.1002/9781119099369.ch20