Original Article
Summary of Genetic Analysis Workshop 15: Group 9 linkage analysis of the CEPH expression data
Article first published online: 28 NOV 2007
DOI: 10.1002/gepi.20283
© 2007 Wiley-Liss, Inc.
Issue

Genetic Epidemiology
Supplement: Genetic Analysis Workshop 15: Summaries of the Design and Analysis of Genomic Data
Volume 31, Issue S1, pages S75–S85, 2007
Additional Information
How to Cite
Wijsman, E. M., Sung, Y. J. and Buil, A. (2007), Summary of Genetic Analysis Workshop 15: Group 9 linkage analysis of the CEPH expression data. Genet. Epidemiol., 31: S75–S85. doi: 10.1002/gepi.20283
Publication History
- Issue published online: 28 NOV 2007
- Article first published online: 28 NOV 2007
Funded by
- NIH. Grant Numbers: GM46255, GM28719, AG05136, AG2544, AG11762, HL30086, HD35465, HL070751
- FIS (Spain). Grant Number: RETIC-RECAVA-06/0014/0016RD
- Abstract
- References
- Cited By
Keywords:
- genetic heterogeneity;
- meiotic map;
- physical map;
- multipoint;
- SNP;
- data reduction
Abstract
Group 9 participants carried out linkage analysis of the Centre d'Etude de Polymorphism Humain (CEPH) expression data, using strategies that ranged from focused investigation of a small number of traits to full genome scans of all available traits. Results from five key areas encompass the most important results within and across the 17 participating groups. First, both extensive genetic heterogeneity and poor predictability of mapping results based on heritability have key implications for study design. Second, choice of the map used for linkage analysis is influential, with the implication that meiotic maps are preferable to physical maps. Third, performance of different analytic methods was in general fairly consistent, with the exception of one variance-component method that uses marker allele sharing as the dependent rather than independent variable. Fourth, multivariate analysis approaches did not generally appear to provide advantages over univariate approaches for linkage detection. Finally, there were computational and analytic challenges in working with a large public data set, along with need for more data documentation. Genet. Epidemiol. 31(Suppl. 1):S75–S85, 2007. © 2007 Wiley-Liss, Inc.

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