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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