Improving estimates of genetic maps: a meta-analysis-based approach.
Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps.
Stewart
William C L
WC
Department of Biostatistics, Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. wstew@umich.edu
D000704:Analysis of Variance; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D005819:Genetic Markers; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D015233:Models, Statistical; D011995:Recombination, Genetic
D005819:Genetic Markers