A note on generalized Genome Scan Meta-Analysis statistics

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 asc...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics 2005-02, Vol.6 (1), p.32-32, Article 32
Main Authors: Koziol, James A, Feng, Anne C
Format: Article
Language:English
Subjects:
Chromosome Mapping
Chromosomes
Computational Biology - methods
Data Interpretation, Statistical
Genetic Linkage
Genetic Predisposition to Disease
Genome
Genome, Human
Humans
Meta-Analysis as Topic
Methodology
Models, Biological
Models, Statistical
Models, Theoretical
Probability
Research Design
Schizophrenia - genetics
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. 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. Theoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-6-32