Testing for genetic heterogeneity in the genome search meta-analysis method.
The Genome Search Meta-Analysis (GSMA) method is widely used to detect linkage by pooling results of previously published genome-wide linkage studies. The GSMA uses a non-parametric summed rank statistic in 30 cM bins of the genome. Zintzaras and Ioannidis ([2005] Genet. Epidemiol. 28:123-137) developed a method of testing for heterogeneity of evidence for linkage in the GSMA, with three heterogeneity statistics (Q, Ha, B). They implement two testing procedures, restricted versus unrestricted for the summed rank within the bin. We show here that the rank-unrestricted test provides a conservative test for high heterogeneity and liberal test for low heterogeneity in linked regions. The rank-restricted test should therefore be used, despite the extensive simulations needed. In a simulation study, we show that the power to detect heterogeneity is low. For 20 studies of affected sib pairs, simulated assuming linkage in all studies to a gene with sibling relative risk of 1.3, the power to detect low heterogeneity using the Q statistic was 14%. With linkage present in 50% of the studies (to a gene with sibling relative risk of 1.4), the Q heterogeneity statistic had power of 29% to detect high heterogeneity. The power to detect linkage using the summed rank was high in both of these situations, at 98% and 79%, respectively. Although testing for heterogeneity in the GSMA is of interest, the currently available method provides little additional information to that provided by the summed rank statistic.
Lewis
Cathryn M
CM
Department of Medical and Molecular Genetics, King's College London School of Medicine at Guy's, King's College and St. Thomas' Hospitals, Guy's Hospital, London, UK. Cathryn.lewis@genetics.kcl.ac.uk
Levinson
Douglas F
DF
D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D015894:Genome, Human; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D015233:Models, Statistical