Literature DB >> 15380462

A quantitative trait locus influences coordinated variation in measures of ApoB-containing lipoproteins.

David L Rainwater1, Michael C Mahaney, John L VandeBerg, Gerome Brush, Laura Almasy, John Blangero, Bennett Dyke, James E Hixson, Shelley A Cole, Jean W MacCluer.   

Abstract

Lipoprotein phenotypes are known to be strongly intercorrelated. These intercorrelations are due to genetic and environmental effects on common metabolic pathways. The purpose of this study was to determine if we could localize genes that exert pleiotropic effects on multiple related lipoprotein traits in humans. Using data from the San Antonio Family Heart Study, we extracted principal components from a set of 12 intercorrelated lipoprotein traits that included phenotypes reflecting lipid and protein concentrations and size distributions for LDLs and HDLs. Five principal components were extracted from the data and all were significantly heritable (h(2) = 0.41-0.57). When subjected to linkage analyses, only one, Component 5, returned a LOD score > or = 3 (LOD score was 3.0 at 38cM on chromosome 15; genome-wide P-value = 0.039). LDL median diameter (-0.529), non-HDLC (-0.422), and ApoB (-0.403) concentrations were the only traits with loadings (absolute value) >0.4, suggesting Component 5 is related to LDL size or perhaps more generally to beta-lipoprotein metabolism. Surprisingly, none of the 12 original lipoprotein traits had a LOD >1 in this region of chromosome 15. These data provide evidence for a novel gene, influencing beta-lipoprotein phenotypes, whose effect(s) is detected only when several lipoprotein traits are considered together.

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Year:  2004        PMID: 15380462     DOI: 10.1016/j.atherosclerosis.2004.06.004

Source DB:  PubMed          Journal:  Atherosclerosis        ISSN: 0021-9150            Impact factor:   5.162


  3 in total

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

Authors:  Hugues Aschard; Bjarni J Vilhjálmsson; Nicolas Greliche; Pierre-Emmanuel Morange; David-Alexandre Trégouët; Peter Kraft
Journal:  Am J Hum Genet       Date:  2014-04-17       Impact factor: 11.025

2.  Determinants of variation in human serum paraoxonase activity.

Authors:  D L Rainwater; S Rutherford; T D Dyer; E D Rainwater; S A Cole; J L Vandeberg; L Almasy; J Blangero; J W Maccluer; M C Mahaney
Journal:  Heredity (Edinb)       Date:  2008-10-29       Impact factor: 3.821

3.  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
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

  3 in total

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