A note on generalized Genome Scan Meta-Analysis statistics.
BACKGROUND
Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.


RESULTS
We provide an Edgeworth approximation to the null distribution of the weighted GSMA statistic, and, we examine the limiting distribution of the GSMA statistics under the order statistic formulation, and quantify the relevance of the pairwise correlations of the GSMA statistics across different bins on this limiting distribution. We also remark on aggregate criteria and multiple testing for determining significance of GSMA results.


CONCLUSIONS
Theoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic.
Koziol
James A
JA
Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, MEM216, La Jolla, CA 92037, USA. koziol@scripps.edu
Feng
Anne C
AC
D002874:Chromosome Mapping; D002875:Chromosomes; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D016678:Genome; D015894:Genome, Human; D006801:Humans; D015201:Meta-Analysis as Topic; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D012107:Research Design; D012559:Schizophrenia / Q000235:genetics; D012984:Software
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