Comparison of linkage and association strategies for quantitative traits using the COGA dataset.
Genome scans using dense single-nucleotide polymorphism (SNP) data have recently become a reality. It is thought that the increase in information content for linkage analysis as a result of the denser scans will help refine previously identified linkage regions and possibly identify new regions not identifiable using the sparser, microsatellite scans. In the context of the dense SNP scans, it is also possible to consider association strategies to provide even more information about potential regions of interest. To circumvent the multiple-testing issues inherent in association analysis, we use a recently developed strategy, implemented in PBAT, which screens the data to identify the optimal SNPs for testing, without biasing the nominal significance level. We compare the results from the PBAT analysis to that of quantitative linkage analysis on chromosome 4 using the Collaborative Study on the Genetics of Alcoholism data, as released through Genetic Analysis Workshop 14.
McQueen
Matthew B
MB
Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. mmcqueen@hsph.harvard.edu
Murphy
Amy
A
Kraft
Peter
P
Su
Jessica
J
Lazarus
Ross
R
Laird
Nan M
NM
Lange
Christoph
C
Van Steen
Kristel
K
D000437:Alcoholism / Q000235:genetics*; D002894:Chromosomes, Human, Pair 4 / Q000235:genetics; D003299:Cooperative Behavior; D030541:Databases, Genetic; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable