2012-8 Meta-analysis of a rare-variant association test

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