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.
journal_name
Genet Epidemioljournal_title
Genetic epidemiologyauthors
Comuzzie AG,Mahaney MC,Almasy L,Dyer TD,Blangero Jdoi
10.1002/(SICI)1098-2272(1997)14:6<975::AID-GEPI69>subject
Has Abstractpub_date
1997-01-01 00:00:00pages
975-80issue
6eissn
0741-0395issn
1098-2272pii
10.1002/(SICI)1098-2272(1997)14:6<975::AID-GEPI69>journal_volume
14pub_type
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