Group 6: Pleiotropy and multivariate analysis.
Analysis techniques using data on several traits simultaneously allow researchers to dissect the genetic architecture affecting correlated traits, and can increase the power to detect pleiotropic genes, i.e., genes that influence two or more traits. Several of the papers in this group from Genetic Analysis Workshop 13 presented promising univariate summaries of multiple traits that detected linkage signals that standard single-trait univariate methods did not. Other papers found linkage signals using multivariate techniques that univariate techniques missed, although this was not uniformly the case. Some papers also considered the correlation among measurements of a single trait taken at different ages to assess whether the genetic architecture of the trait changed over age. Applications of the Framingham Heart Study data identified major loci jointly influencing body mass index and high-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides, total cholesterol and triglycerides, and various combinations of four traits involved in metabolic syndrome.
Kraft
Peter
P
Department of Biostatistics, University of California at Los Angeles, 90095-1772, USA. pkraft@ucla.edu
de Andrade
Mariza
M
D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000235:genetics*; D003198:Computer Simulation / Q000706:statistics & numerical data; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D008126:Lod Score; D008137:Longitudinal Studies; D024821:Metabolic Syndrome / Q000453:epidemiology / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics; D019655:Quantitative Trait, Heritable; D012307:Risk Factors