Exploiting Pleiotropy to Map Genes for Oligogenic
Phenotypes Using Extended Pedigree Data
Comuzzie AG, Mahaney MC, Almasy L, Dyer TD, Blangero J
Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio,
Texas 78245-0549, USA.
Genetic Epidemiology, 14(6):975-980 (1997)
Abstract
We investigated the utility of two approaches for exploiting pleiotropy to search for
genes influencing related traits. To do this we first assessed the genetic correlations
among a set of five closely related quantitative traits (Q1, Q2, Q3, Q4, Q5). We then
used the genetic correlations among these five traits both to remove the common
genetic effects of the four remaining traits, thereby identifying the unique genetic
contribution to each trait, and to extract a synthetic phenotype which exploits the shared
genetic information (pleiotropy) among these five traits. After obtaining these
conditional traits, we then searched for evidence of quantitative trait loci (QTLs) (using
variance component linkage) influencing the unique residual genetic component for
each trait as well as those influencing the expression of the synthetic traits. From this
work, we conclude that the removal of the common genetic effects of other traits in a
group may be of greater utility when the majority of the pleiotropy initially detected
between traits is attributable to the shared additive effects of polygenes, rather than to
those of major loci. By contrast, decomposition of the genetic covariance matrix to its
principal components is a greater utility when the majority of pleiotropy is attributable to
major loci.
Variance component linkage analysis