Literature DB >> 11108643

Ascertainment issues in variance components models.

M de Andrade1, C I Amos.   

Abstract

One of the main concerns in the family studies of complex diseases is the effect that ascertainment and correction for it may have on test procedures and estimators. Elston and Sobel [1979] and Hopper and Mathews [1982] proposed two ways to correct for ascertainment in the study of quantitative trait data. For single ascertainment, using a variance components approach, we present results of simulation studies comparing estimates from these two methods for different selection criteria. We also show results from simulations when ascertained families are analyzed either at random or by correcting for ascertainment. For discordant sibpairs, we compare a variance components model that incorporates ascertainment correction with the extreme discordant sib pairs (EDSP) design proposed by Risch and Zhang [1995]. Our results show that there is minimal difference between the two methods of ascertainment correction. In the presence of effects from a large genetic background and the segregation of a rare gene, both ascertainment affected the polygenic and environmental components of variance but had rather little impact on the estimate of the linked major gene component of variance. The results also show the EDSP is slightly more powerful the variance components procedures for common alleles, and the variance components procedure is much more powerful than using EDSP when there is a rare allele segregating in the population. Copyright 2000 Wiley-Liss, Inc.

Mesh:

Year:  2000        PMID: 11108643     DOI: 10.1002/1098-2272(200012)19:4<333::AID-GEPI5>3.0.CO;2-#

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  16 in total

1.  Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study.

Authors:  Mary F Feitosa; Ingrid B Borecki; Stephen S Rich; Donna K Arnett; Phyliss Sholinsky; Richard H Myers; Mark Leppert; Michael A Province
Journal:  Am J Hum Genet       Date:  2001-11-16       Impact factor: 11.025

2.  Ascertainment-adjusted parameter estimates revisited.

Authors:  Michael P Epstein; Xihong Lin; Michael Boehnke
Journal:  Am J Hum Genet       Date:  2002-03-05       Impact factor: 11.025

3.  A genomewide linkage scan for quantitative-trait loci for obesity phenotypes.

Authors:  Hong-Wen Deng; Hongyi Deng; Yong-Jun Liu; Yao-Zhong Liu; Fu-Hua Xu; Hui Shen; Theresa Conway; Jin-Long Li; Qing-Yang Huang; K M Davies; Robert R Recker
Journal:  Am J Hum Genet       Date:  2002-03-28       Impact factor: 11.025

4.  A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

Authors:  Xiaodong Wu; Richard S Cooper; Ingrid Borecki; Craig Hanis; Molly Bray; Cora E Lewis; Xiaofeng Zhu; Donghui Kan; Amy Luke; David Curb
Journal:  Am J Hum Genet       Date:  2002-03-28       Impact factor: 11.025

5.  Mapping quantitative traits with random and with ascertained sibships.

Authors:  Jie Peng; D Siegmund
Journal:  Proc Natl Acad Sci U S A       Date:  2004-04-14       Impact factor: 11.205

6.  A framework for structural equation models in general pedigrees.

Authors:  Nathan J Morris; Robert C Elston; Catherine M Stein
Journal:  Hum Hered       Date:  2011-01-06       Impact factor: 0.444

7.  A powerful and robust method for mapping quantitative trait loci in general pedigrees.

Authors:  G Diao; D Y Lin
Journal:  Am J Hum Genet       Date:  2005-05-25       Impact factor: 11.025

8.  A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes.

Authors:  Michael P Epstein; Jessica E Hunter; Emily G Allen; Stephanie L Sherman; Xihong Lin; Michael Boehnke
Journal:  Stat Biosci       Date:  2009-11

9.  Efficient semiparametric estimation of short-term and long-term hazard ratios with right-censored data.

Authors:  Guoqing Diao; Donglin Zeng; Song Yang
Journal:  Biometrics       Date:  2013-11-04       Impact factor: 2.571

10.  SNP set association analysis for familial data.

Authors:  Elizabeth D Schifano; Michael P Epstein; Lawrence F Bielak; Min A Jhun; Sharon L R Kardia; Patricia A Peyser; Xihong Lin
Journal:  Genet Epidemiol       Date:  2012-09-11       Impact factor: 2.135

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.