Genome-wide assocation studies have often been carried out by meta-analysis rather than by pooling individual-level data. For one-dimensional parameter estimates and the corresponding tests of association these meta-analyses lead to essentially no loss of information relative to pooling individual data. The situation is different for multi-parameter tests, such as the omnidirectional rare-variant tests being used in resequencing studies. In this paper we consider one popular rare-variant test, a version of the sequence kernel association test. We show that meta-analyses based on the $p$-value or test statistic from each contributing study are importantly less efficient than an analysis pooling individual data, but that a more sophisticated meta-analysis retains full efficiency. The meta-analysis is based on a reformulation of the test that links it to tests used in survey analysis.
Thomas Lumley, Jennifer Brody, Josee Dupuis, Adrienne Cupples