Literature DB >> 15614722

Parental phenotypes in family-based association analysis.

Shaun Purcell1, Pak Sham, Mark J Daly.   

Abstract

Family-based association designs are popular, because they offer inherent control of population stratification based on age, sex, ethnicity, and environmental exposure. However, the efficiency of these designs is hampered by current analytic strategies that consider only offspring phenotypes. Here, we describe the incorporation of parental phenotypes and, specifically, the inclusion of parental genotype-phenotype correlation terms in association tests, providing a series of tests that effectively span an efficiency-robustness spectrum. The model is based on the between-within-sibship association model presented in 1999 by Fulker and colleagues for quantitative traits and extended here to nuclear families. By use of a liability-threshold-model approach, standard dichotomous and/or qualitative disease phenotypes can be analyzed (and can include appropriate corrections for phenotypically ascertained samples), which allows for the application of this model to analysis of the commonly used affected-proband trio design. We show that the incorporation of parental phenotypes can considerably increase power, as compared with the standard transmission/disequilibrium test and equivalent quantitative tests, while providing both significant protection against stratification and a means of evaluating the contribution of stratification to positive results. This methodology enables the extraction of more information from existing family-based collections that are currently being genotyped and analyzed by use of standard approaches.

Mesh:

Year:  2004        PMID: 15614722      PMCID: PMC1196370          DOI: 10.1086/427886

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  9 in total

1.  Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data.

Authors:  P C Sham; S S Cherny; S Purcell; J K Hewitt
Journal:  Am J Hum Genet       Date:  2000-04-12       Impact factor: 11.025

2.  Inference of population structure using multilocus genotype data.

Authors:  J K Pritchard; M Stephens; P Donnelly
Journal:  Genetics       Date:  2000-06       Impact factor: 4.562

3.  A general test of association for quantitative traits in nuclear families.

Authors:  G R Abecasis; L R Cardon; W O Cookson
Journal:  Am J Hum Genet       Date:  2000-01       Impact factor: 11.025

4.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits.

Authors:  S Purcell; S S Cherny; P C Sham
Journal:  Bioinformatics       Date:  2003-01       Impact factor: 6.937

5.  Properties of structured association approaches to detecting population stratification.

Authors:  Shaun Purcell; Pak Sham
Journal:  Hum Hered       Date:  2004       Impact factor: 0.444

6.  The effect of family structure on linkage tests using allelic association.

Authors:  J C Whittaker; C M Lewis
Journal:  Am J Hum Genet       Date:  1998-09       Impact factor: 11.025

7.  Combined linkage and association sib-pair analysis for quantitative traits.

Authors:  D W Fulker; S S Cherny; P C Sham; J K Hewitt
Journal:  Am J Hum Genet       Date:  1999-01       Impact factor: 11.025

8.  Pedigree tests of transmission disequilibrium.

Authors:  G R Abecasis; W O Cookson; L R Cardon
Journal:  Eur J Hum Genet       Date:  2000-07       Impact factor: 4.246

9.  Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).

Authors:  R S Spielman; R E McGinnis; W J Ewens
Journal:  Am J Hum Genet       Date:  1993-03       Impact factor: 11.025

  9 in total
  28 in total

Review 1.  Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses.

Authors:  M J Sillanpää
Journal:  Heredity (Edinb)       Date:  2010-07-14       Impact factor: 3.821

2.  An empirical comparison of case-control and trio based study designs in high throughput association mapping.

Authors:  P Hintsanen; P Sevon; P Onkamo; L Eronen; H Toivonen
Journal:  J Med Genet       Date:  2005-10-28       Impact factor: 6.318

3.  PLINK: a tool set for whole-genome association and population-based linkage analyses.

Authors:  Shaun Purcell; Benjamin Neale; Kathe Todd-Brown; Lori Thomas; Manuel A R Ferreira; David Bender; Julian Maller; Pamela Sklar; Paul I W de Bakker; Mark J Daly; Pak C Sham
Journal:  Am J Hum Genet       Date:  2007-07-25       Impact factor: 11.025

4.  A polymorphism of apolipoprotein E (APOE) gene is associated with age at natural menopause in Caucasian females.

Authors:  Li-Na He; Robert R Recker; Hong-Wen Deng; Volodymyr Dvornyk
Journal:  Maturitas       Date:  2008-12-05       Impact factor: 4.342

Review 5.  Clinical review: the genetics of type 2 diabetes: a realistic appraisal in 2008.

Authors:  Jose C Florez
Journal:  J Clin Endocrinol Metab       Date:  2008-09-09       Impact factor: 5.958

6.  Incorporating parental information into family-based association tests.

Authors:  Zhaoxia Yu; Daniel Gillen; Carey F Li; Michael Demetriou
Journal:  Biostatistics       Date:  2012-12-23       Impact factor: 5.899

7.  Association analysis of SNPs in the IL4R locus with type I diabetes.

Authors:  H A Erlich; K Lohman; S J Mack; A M Valdes; C Julier; D Mirel; J A Noble; G E Morahan; S S Rich
Journal:  Genes Immun       Date:  2009-12       Impact factor: 2.676

8.  Evidence for association of the TCF7 locus with type I diabetes.

Authors:  H A Erlich; A M Valdes; C Julier; D Mirel; J A Noble
Journal:  Genes Immun       Date:  2009-12       Impact factor: 2.676

9.  The impact of genetic relationship information on genomic breeding values in German Holstein cattle.

Authors:  David Habier; Jens Tetens; Franz-Reinhold Seefried; Peter Lichtner; Georg Thaller
Journal:  Genet Sel Evol       Date:  2010-02-19       Impact factor: 4.297

10.  Transcription factor SP4 is a susceptibility gene for bipolar disorder.

Authors:  Xianjin Zhou; Wei Tang; Tiffany A Greenwood; Shengzhen Guo; Lin He; Mark A Geyer; John R Kelsoe
Journal:  PLoS One       Date:  2009-04-09       Impact factor: 3.240

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