Literature DB >> 18261184

Genomewide linkage scan for combined obesity phenotypes using principal component analysis.

L-N He1, Y-J Liu, P Xiao, L Zhang, Y Guo, T-L Yang, L-J Zhao, B Drees, J Hamilton, H-Y Deng, R R Recker, H-W Deng.   

Abstract

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.

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Year:  2008        PMID: 18261184     DOI: 10.1111/j.1469-1809.2007.00423.x

Source DB:  PubMed          Journal:  Ann Hum Genet        ISSN: 0003-4800            Impact factor:   1.670


  13 in total

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2.  Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.

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Journal:  Sci Rep       Date:  2022-06-21       Impact factor: 4.996

4.  Genome wide association study: searching for genes underlying body mass index in the Chinese.

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Journal:  Biomed Environ Sci       Date:  2014-05       Impact factor: 3.118

5.  Principal component and linkage analysis of cardiovascular risk traits in the Norfolk isolate.

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6.  Genome-wide association study and follow-up analysis of adiposity traits in Hispanic Americans: the IRAS Family Study.

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Journal:  Obesity (Silver Spring)       Date:  2009-05-21       Impact factor: 5.002

7.  PCA-based GRS analysis enhances the effectiveness for genetic correlation detection.

Authors:  Yan Zhao; Yujie Ning; Feng Zhang; Miao Ding; Yan Wen; Liang Shi; Kunpeng Wang; Mengnan Lu; Jingyan Sun; Menglu Wu; Bolun Cheng; Mei Ma; Lu Zhang; Shiqiang Cheng; Hui Shen; Qing Tian; Xiong Guo; Hong-Wen Deng
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Review 8.  Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies.

Authors:  Chaoneng Wu; Yunguo Gong; Jie Yuan; Hui Gong; Yunzeng Zou; Junbo Ge
Journal:  Cardiovasc Diabetol       Date:  2012-06-14       Impact factor: 9.951

9.  An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

Authors:  James J Yang; Jia Li; L Keoki Williams; Anne Buu
Journal:  BMC Bioinformatics       Date:  2016-01-05       Impact factor: 3.169

10.  Genetics of Obesity Traits: A Bivariate Genome-Wide Association Analysis.

Authors:  Yili Wu; Haiping Duan; Xiaocao Tian; Chunsheng Xu; Weijing Wang; Wenjie Jiang; Zengchang Pang; Dongfeng Zhang; Qihua Tan
Journal:  Front Genet       Date:  2018-05-16       Impact factor: 4.599

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