Using linkage and association to identify and model genetic effects: summary of GAW15 Group 4

✍ Scribed by Qiong Yang; Joanna M. Biernacka; Ming-Huei Chen; Jeanine J. Houwing-Duistermaat; Tracy L. Bergemann; Saonli Basu; Ruzong Fan; Lian Liu; Mathieu Bourgey; Françoise Clerget-Darpoux; Wan-Yu Lin; Robert C. Elston; L. Adrienne Cupples

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 147 KB
• Volume: 31
• Category: Article
• ISSN: 0741-0395
• DOI: 10.1002/gepi.20278

✦ Synopsis

Group 4 at Genetic Analysis Workshop 15 focused on methods that exploited both linkage and association information to map disease loci. All contributions considered the dichotomous trait of rheumatoid arthritis, using either affected sibpairs and/or unrelated controls. While one contribution investigated linkage and association approaches separately in genome-wide analyses, the remaining others focused on joint linkage and association methods in specific genomic regions. The latter contributions proposed new methods and/or examined existing methods that addressed whether one or more polymorphisms partially or fully explained a linkage signal, particularly the methods proposed by Li et al. that are implemented in the computer program Linkage and Association Modeling in Pedigrees (LAMP). Using simulated SNP data under linkage peaks, several contributions found that existing family-based association approaches such as those of Martin et al. and Lake et al. had power similar to LAMP and to several methods proposed by the contributors for testing that a single nucleotide polymorphism partially explains a linkage peak. In evaluating methods for identifying if a polymorphism or a set of polymorphisms fully accounted for a linkage signal, several contributions found that it was important to understand that these methods may be subject to low power in some situations and thus, a non-significant result was not necessarily indicative of the polymorphism(s) being fully responsible for the linkage signal. Finally, modeling the disease using association evidence conditional on linkage may improve understanding of the etiology of disease.