TY - JOUR
T1 - Exploiting pleiotropy to map genes for oligogenic phenotypes using extended pedigree data
AU - Comuzzie, Anthony G.
AU - Mahaney, Michael C.
AU - Almasy, Laura
AU - Dyer, Thomas D.
AU - Blangero, John
PY - 1997
Y1 - 1997
N2 - 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 of greater utility when the majority of pleiotropy is attributable to major loci.
AB - 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 of greater utility when the majority of pleiotropy is attributable to major loci.
KW - Linkage analysis
KW - Principal components
KW - Variance components
UR - http://www.scopus.com/inward/record.url?scp=0031443381&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031443381&partnerID=8YFLogxK
U2 - 10.1002/(SICI)1098-2272(1997)14:6<975::AID-GEPI69>3.0.CO;2-I
DO - 10.1002/(SICI)1098-2272(1997)14:6<975::AID-GEPI69>3.0.CO;2-I
M3 - Article
C2 - 9433610
AN - SCOPUS:0031443381
VL - 14
SP - 975
EP - 980
JO - Genetic Epidemiology
JF - Genetic Epidemiology
SN - 0741-0395
IS - 6
ER -