While writing my thesis I came across this nice review by Rita Cantor, Kenneth Lange, and Janet Sinsheimer at UCLA, "
Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application." Skip the introduction unless you're new to GWAS, in which case you'll probably want to start with
this more recent review by Teri Manolio. After skipping the intro you'll find succinct introduction to meta-analysis for GWAS with lots of very good references, including these among others:
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Yesupriya A., Yu W., Clyne M., Gwinn M., Khoury M.J. The continued need to synthesize the results of genetic associations across multiple studies. Genet. Med. 2008;10:633–635. [PubMed]
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Ioannidis J.P., Gwinn M., Little J., Higgins J.P., Bernstein J.L., Boffetta P., Bondy M., Bray M.S., Brenchley P.E., Buffler P.A., Human Genome Epidemiology Network and the Network of Investigator Networks A road map for efficient and reliable human genome epidemiology. Nat. Genet. 2006;38:3–5. [PubMed]
de Bakker P.I., Ferreira M.A., Jia X., Neale B.M., Raychaudhuri S., Voight B.F. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 2008;17(R2):R122–R128. [PMC free article] [PubMed]
Sagoo G.S., Little J., Higgins J.P., Human Genome Epidemiology Network Systematic reviews of genetic association studies. PLoS Med. 2009;6:e28. [PMC free article] [PubMed]
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This section covers using imputation in meta-analysis, fixed effects versus random effects meta-analysis, canned software for meta-analysis (such as
METAL), Bayesian hierarchical approaches, and references to many applications of meta-analysis in GWAS.
After the meta-analysis section there's a nice section on modeling epistasis, or gene-gene interactions, to prioritize associations with links to other reviews of statistical methods, and brief coverage of data mining procedures like CART, MDR, random forests, conditional entropy methods, neural networks, genetic programming, logic regression, pattern mining, Bayesian partitioning, and penalized regression approaches, again with lots of references. This section also covers parameterization of epistatic models, and covers some of the computation and statistical issues you'll face with the dimensionality problem.
Finally, the review concludes with a section on pathway analysis. As the review admits, pathway analysis in GWAS has no set of strict guidelines or best practices, and new approaches arise every day.
While this review is nearly a year old at this point, I think it's a real gem because of all the references it offers, especially in the meta-analysis and epistasis sections.
AJHG: Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application
