Meta-analysis methodology for combining non-parametric sibpair linkage results: genetic homogeneity and identical markers.
Meta-analysis methodology is developed for combining sibpair linkage results across multiple studies employing different study designs, some employing quantitative traits (e.g., blood pressure) and some employing qualitative traits (e.g., clinical hypertension), under the assumption that the underlying (disease) trait loci are the same. Pooling results based on three commonly used sibpair methods is considered: the affected sibpair method for dichotomous traits and, for quantitative traits, the Haseman-Elston regression method and the Risch-Zhang extremely discordant sibpair method. The proportion of genes shared identical by descent (IBD) by a sibpair of certain trait outcomes is chosen as a common effect to be pooled across studies. Variation in the observed IBD proportions among individual studies is modeled using a random effects model. A heterogeneity test is provided to assess the variability among individual studies. When results from all three types of studies are available, we derive pooled estimates of IBD proportions both for sibpairs with extremely concordant trait values and for sibpairs with extremely discordant trait values, and construct a combined test of linkage based on the difference of the two estimates. Simulation studies demonstrate the need for and the advantage of meta-analysis of linkage results. We also present some guidelines for reporting linkage studies bearing potential future meta-analysis in mind.
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Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA. gc@wubios.wustl.edu
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Todorov
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Rao
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D002874:Chromosome Mapping / Q000379:methods*; D015987:Effect Modifier, Epidemiologic; D018740:Genetic Heterogeneity; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D006973:Hypertension / Q000235:genetics; D016018:Least-Squares Analysis; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D015203:Reproducibility of Results; D012107:Research Design; D018709:Statistics, Nonparametric
D005819:Genetic Markers