Literature DB >> 12830401

Two-locus heterogeneity cannot be distinguished from two-locus epistasis on the basis of affected-sib-pair data.

Veronica J Vieland1, Jian Huang.   

Abstract

The observation of multiple linkage signals in the course of conducting genome screens for complex disorders raises the question of whether distinct genes represent independent causes of disease (heterogeneity) or whether they interact to produce the phenotype of interest (epistasis); and there has been a corresponding interest in statistical methods for detecting and/or exploiting the distinction between these two possibilities. At the same time, researchers are increasingly relying on affected-sib-pair (ASP) data. Here, we demonstrate an apparently unrecognized fact about two-locus (2L) models and ASP data, namely, 2L heterogeneity and 2L epistasis cannot, in general, be distinguished from one another on the basis of ASP marker data, as a matter of mathematical principle and therefore regardless of sample size. By the same token, correlations across ASPs in single-locus LOD scores or other measures also cannot be used to distinguish 2L heterogeneity from 2L epistasis. This raises questions about the measurement of gene-gene interactions in terms of patterns of correlation in marker data. Portions of our results carry over to larger pedigree structures as well, as long as only affected individuals are included in analyses; the extent to which our overall findings apply to general pedigrees (including unaffected individuals) remains to be investigated.

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Year:  2003        PMID: 12830401      PMCID: PMC1180363          DOI: 10.1086/376563

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


  7 in total

1.  Detecting gene-gene interactions using affected sib pair analysis with covariates.

Authors:  Peter Holmans
Journal:  Hum Hered       Date:  2002       Impact factor: 0.444

2.  Inter- and intrafamilial heterogeneity: effective sampling strategies and comparison of analysis methods.

Authors:  M Durner; D A Greenberg; S E Hodge
Journal:  Am J Hum Genet       Date:  1992-10       Impact factor: 11.025

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Journal:  Am J Hum Genet       Date:  1990-02       Impact factor: 11.025

4.  Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans.

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Journal:  Nat Genet       Date:  1999-02       Impact factor: 38.330

5.  Joint linkage of multiple loci for a complex disorder.

Authors:  C J MacLean; P C Sham; K S Kendler
Journal:  Am J Hum Genet       Date:  1993-08       Impact factor: 11.025

6.  Some epistatic two-locus models of disease. I. Relative risks and identity-by-descent distributions in affected sib pairs.

Authors:  S E Hodge
Journal:  Am J Hum Genet       Date:  1981-05       Impact factor: 11.025

7.  The investigation of linkage between a quantitative trait and a marker locus.

Authors:  J K Haseman; R C Elston
Journal:  Behav Genet       Date:  1972-03       Impact factor: 2.805

  7 in total
  10 in total

1.  Reports of the death of the epistasis model are greatly exaggerated.

Authors:  Martin Farrall
Journal:  Am J Hum Genet       Date:  2003-12       Impact factor: 11.025

2.  Affected-sib-pair data can be used to distinguish two-locus heterogeneity from two-locus epistasis.

Authors:  Heather J Cordell
Journal:  Am J Hum Genet       Date:  2003-12       Impact factor: 11.025

3.  Mathematical assumptions versus biological reality: myths in affected sib pair linkage analysis.

Authors:  Robert C Elston; Danhong Song; Sudha K Iyengar
Journal:  Am J Hum Genet       Date:  2004-11-11       Impact factor: 11.025

4.  Causal models for investigating complex genetic disease: II. what causal models can tell us about penetrance for additive, heterogeneity, and multiplicative two-locus models.

Authors:  Ann M Madsen; Ruth Ottman; Susan E Hodge
Journal:  Hum Hered       Date:  2011-09-09       Impact factor: 0.444

5.  Causal models for investigating complex disease: I. A primer.

Authors:  Ann M Madsen; Susan E Hodge; Ruth Ottman
Journal:  Hum Hered       Date:  2011-09-09       Impact factor: 0.444

6.  Increasing genotype-phenotype model determinism: application to bivariate reading/language traits and epistatic interactions in language-impaired families.

Authors:  Tabatha R Simmons; Judy F Flax; Marco A Azaro; Jared E Hayter; Laura M Justice; Stephen A Petrill; Anne S Bassett; Paula Tallal; Linda M Brzustowicz; Christopher W Bartlett
Journal:  Hum Hered       Date:  2010-10-14       Impact factor: 0.444

7.  KELVIN: a software package for rigorous measurement of statistical evidence in human genetics.

Authors:  Veronica J Vieland; Yungui Huang; Sang-Cheol Seok; John Burian; Umit Catalyurek; Jeffrey O'Connell; Alberto Segre; William Valentine-Cooper
Journal:  Hum Hered       Date:  2011-12-23       Impact factor: 0.444

8.  Methods for detecting gene x gene interaction in multiplex extended pedigrees.

Authors:  Guy N Brock; Brion S Maher; Toby H Goldstein; Margaret E Cooper; Mary L Marazita
Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

9.  Exploiting gene x gene interaction in linkage analysis.

Authors:  Yungui Huang; Christopher W Bartlett; Alberto M Segre; Jeffrey R O'Connell; Lavonne Mangin; Veronica J Vieland
Journal:  BMC Proc       Date:  2007-12-18

10.  Comparison of affected sibling-pair linkage methods to identify gene x gene interaction in GAW15 simulated data.

Authors:  Emma K Larkin; Nathan J Morris; Yali Li; Nora L Nock; Catherine M Stein
Journal:  BMC Proc       Date:  2007-12-18
  10 in total

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