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Genomewide Linkage Scan for Combined Obesity Phenotypes using Principal Component Analysis
Author(s) -
He L.N.,
Liu Y.J.,
Xiao P.,
Zhang L.,
Guo Y.,
Yang T.L.,
Zhao L.J.,
Drees B.,
Hamilton J.,
Deng H.Y.,
Recker R. R.,
Deng H.W.
Publication year - 2008
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2007.00423.x
Subject(s) - principal component analysis , phenotype , linkage (software) , genetics , genome scan , obesity , biology , computational biology , medicine , evolutionary biology , computer science , gene , microsatellite , artificial intelligence , allele
Summary Traditional whole genome linkage scans for obesity were usually performed for a number of correlated obesity related phenotypes separately without considering their correlations. The purpose of this study was to identify quantitative trait loci (QTLs) underlying variations in multiple correlated obesity phenotypes. We performed principal component analysis (PCA) for four highly correlated obesity phenotypes (body mass index [BMI], fat mass, percentage of fat mass [PFM], and lean mass) in a sample of 427 pedigrees (comprising 3,273 individuals) and generated two independent principal components (PC1 and PC2). A whole genome linkage scan (WGS) was then conducted for PC1 and PC2. For PC1, the strongest linkage signal was identified on chromosome 20p12 (LOD = 2.67). For PC2, two suggestive linkages were found on 5q35 (LOD = 2.03) and 7p22 (LOD = 2.18). This study provided evidence supporting several previously identified linkage regions for obesity (e.g., 1p36, 6p23 and 7q34). In addition, our approach by linear combination of highly correlated obesity phenotypes identified several novel QTLs which were not found in genome linkage scans for individual phenotypes.