Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method.
BACKGROUND
The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. RESULTS from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis.


OBJECTIVE
In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1).


RESULTS
Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings.


CONCLUSIONS
The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.
Forabosco
Paola
P
Department of Medical and Molecular Genetics, King's College London School of Medicine at Guy's, King's College and St. Thomas' Hospitals, London, UK.
Ng
Mandy Y M
MY
Bouzigon
Emmanuelle
E
Fisher
Sheila A
SA
Levinson
Douglas F
DF
Lewis
Cathryn M
CM
D002877:Chromosomes, Human; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D015233:Models, Statistical