Meta-analysis of linkage studies for complex diseases: an overview of methods and a simulation study.
Linkage genome scans for complex diseases have low power with the usual sample sizes, and hence meta-analysis of several scans for the same disease might be a promising approach. Appropriate data are now becoming accessible. Here we give an overview of the available statistical methods and current applications. In a simulation study, we compare the power of different methods to combine multipoint linkage scores, namely Fisher's p-value combination, the truncated product method, the Genome Search Meta-Analysis (GSMA) method and our weighting methods. In particular, we investigate the effects of heterogeneity introduced by different genetic marker sets and sample sizes between genome scans. The weighting methods explicitly take those differences into account and have more power in the simulated scenarios than the other methods.
Dempfle
A
A
Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, 35037 Marburg, Germany. dempfle@med.uni-marburg.de
Loesgen
S
S
D030342:Genetic Diseases, Inborn / Q000235:genetics*; D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans
D005819:Genetic Markers