Literature DB >> 3435047

The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods.

E Boerwinkle1, R Chakraborty, C F Sing.   

Abstract

Improved laboratory methods allow one to investigate the contribution of measured allelic variability at a locus physiologically involved in determining the expression of a quantitative trait. We present statistical methods that incorporate measured genotype information into the analysis of a quantitative phenotype that allows one simultaneously to detect and estimate the effects of a measured single locus and residual polygenic effects. Likelihoods are presented for the joint distribution of the quantitative phenotype and a measured genotype that are appropriate when the data are collected as a sample of unrelated individuals or as a sample of nuclear families. Application of this method to the analysis of serum cholesterol levels and the concentration of the group specific component (Gc) are presented. The analysis of the contribution of the common Gc polymorphism to the determination of quantitative variability in Gc using samples of related and unrelated individuals presents, for the first time, the simultaneous estimation of the frequencies and the effects of the genotypes at a measured locus, and the contribution of residual unmeasured polygenes to phenotypic variability.

Entities:  

Mesh:

Substances:

Year:  1986        PMID: 3435047     DOI: 10.1111/j.1469-1809.1986.tb01037.x

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


  138 in total

1.  Population admixture: detection by Hardy-Weinberg test and its quantitative effects on linkage-disequilibrium methods for localizing genes underlying complex traits.

Authors:  H W Deng; W M Chen; R R Recker
Journal:  Genetics       Date:  2001-02       Impact factor: 4.562

2.  Identifying disease genes underlying complex traits.

Authors:  Mark L Johnson
Journal:  Clin Rev Allergy Immunol       Date:  2002-02       Impact factor: 8.667

3.  A perspective on epistasis: limits of models displaying no main effect.

Authors:  Robert Culverhouse; Brian K Suarez; Jennifer Lin; Theodore Reich
Journal:  Am J Hum Genet       Date:  2002-01-08       Impact factor: 11.025

4.  Generalized T2 test for genome association studies.

Authors:  Momiao Xiong; Jinying Zhao; Eric Boerwinkle
Journal:  Am J Hum Genet       Date:  2002-03-29       Impact factor: 11.025

5.  Genome-wide association of an integrated osteoporosis-related phenotype: is there evidence for pleiotropic genes?

Authors:  David Karasik; Ching Lung Cheung; Yanhua Zhou; L Adrienne Cupples; Douglas P Kiel; Serkalem Demissie
Journal:  J Bone Miner Res       Date:  2012-02       Impact factor: 6.741

6.  ABCB4 mediates diet-induced hypercholesterolemia in laboratory opossums.

Authors:  Jeannie Chan; Michael C Mahaney; Rampratap S Kushwaha; Jane F VandeBerg; John L VandeBerg
Journal:  J Lipid Res       Date:  2010-05-20       Impact factor: 5.922

7.  Use of the robust sib-pair method to screen for single-locus, multiple-locus, and pleiotropic effects: application to traits related to hypertension.

Authors:  A F Wilson; R C Elston; L D Tran; R M Siervogel
Journal:  Am J Hum Genet       Date:  1991-05       Impact factor: 11.025

8.  Genome-wide scan identifies a quantitative trait locus at 4p15.3 for serum urate.

Authors:  Nik Cummings; Thomas D Dyer; Navaratnam Kotea; Sudhir Kowlessur; Pierrot Chitson; Paul Zimmet; John Blangero; Jeremy B M Jowett
Journal:  Eur J Hum Genet       Date:  2010-06-30       Impact factor: 4.246

9.  A pleiotropic QTL on 2p influences serum Lp-PLA2 activity and LDL cholesterol concentration in a baboon model for the genetics of atherosclerosis risk factors.

Authors:  A Vinson; M C Mahaney; L A Cox; J Rogers; J L VandeBerg; D L Rainwater
Journal:  Atherosclerosis       Date:  2007-09-04       Impact factor: 5.162

10.  Contributions of 18 additional DNA sequence variations in the gene encoding apolipoprotein E to explaining variation in quantitative measures of lipid metabolism.

Authors:  Jari H Stengård; Andrew G Clark; Kenneth M Weiss; Sharon Kardia; Deborah A Nickerson; Veikko Salomaa; Christian Ehnholm; Eric Boerwinkle; Charles F Sing
Journal:  Am J Hum Genet       Date:  2002-08-05       Impact factor: 11.025

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.