Meta-analysis of GWA datasets can increase the power to detect association signals by increasing sample size and by examining more variants throughout the genome than each dataset alone. Different datasets may have used different platforms and may have thus genotyped different variants. However, current approaches [9] allow imputing genotypes at untyped variants using a reference such as HapMap. Directly-typed or imputed genotype information can currently be combined across datasets on up to several millions of common variants. Meta-analysis can be conducted in a sequential, cumulative manner; as more datasets become available, these may be included in the calculations resulting in more discoveries of previously unrecognized variants. Cumulative meta-analysis thus potentially represents a “replication ad infinitum” process.
