Original Paper

Meta-Analysis of 23 Type 2 Diabetes Linkage Studies from the International Type 2 Diabetes Linkage Analysis Consortium
Weihua Guan^a, Anna Pluzhnikov^b, Nancy J. Cox^b, Michael Boehnke^a, for the International Type 2 Diabetes Linkage Analysis Consortium

^aDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Mich.,
^bSection of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Ill., USA

Address of Corresponding Author

Hum Hered 2008;66:35-49 (DOI: 10.1159/000114164)

  Key Words

• Gene mapping
• Genetics
• GSMA
• Linkage analysis
• Meta-analysis
• Type 2 diabetes

  Abstract

Background: The International Type 2 Diabetes Linkage Analysis Consortium was formed to localize type 2 diabetes predisposing variants based on 23 autosomal linkage scans. Methods: We carried out meta-analysis using the genome scan meta-analysis (GSMA) method which divides the genome into bins of ~30 cM, ranks the best linkage results in each bin for each sample, and then sums the ranks across samples. We repeated the meta-analysis using 2 cM bins, and/or replacing bin ranks with measures of linkage evidence: bin maximum LOD score or bin minimum p value for bins with p value <0.05 (truncated p value). We also carried out computer simulations to assess the empirical type I error rates of these meta-analysis methods. Results: Our analyses provided modest evidence for type 2 diabetes-predisposing variants on chromosomes 4, 10, and 14 (using LOD scores or truncated p values), or chromosome 10 and 16 (using ranks). Our simulation results suggested that uneven marker density across studies results in substantial variation in empirical type I error rates for all meta-analysis methods, but that 2 cM bins and scores that make more explicit use of linkage evidence, especially the truncated p values, reduce this problem. Conclusion: We identified regions modestly linked with type 2 diabetes by summarizing results from 23 autosomal genome scans.

Copyright © 2008 S. Karger AG, Basel

  Author Contacts

Michael Boehnke, PhD
Department of Biostatistics, School of Public Health
University of Michigan, 1420 Washington Heights
Ann Arbor, MI 48109-2029 (USA)
Tel. +1 734 936 1001, Fax +1 734 615 8322, E-Mail boehnke@umich.edu

  Article Information

Received: June 4, 2007
Accepted after revision: September 5, 2007
Published online: January 28, 2007
Number of Print Pages : 15
Number of Figures : 2, Number of Tables : 7, Number of References : 56