Literature DB >> 12037408

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

Peter Holmans1.   

Abstract

Interest has recently focussed on allowing for interactions between loci as a way to increase power to detect linkage. In this paper, a simplified logistic regression method was used to perform affected sib pair analyses allowing for the inclusion of data from other loci. A systematic search of two-locus disease models was carried out to determine the situations in which this was advantageous. If IBD information is available (e.g. from a genome scan), it is unlikely that allowing for interactions will give a large lod score in the absence of linkage evidence from sinlge-locus analysis. Furthermore, allowing for interactions rarely gave a significant increase in power to detect linkage over a single-locus analysis, except for heterogeneity models with low K(P). Conversely, the availability of disease-associated genotypes may greatly increase the power both to detect linkage to a second locus and interaction between the loci. These results indicate that when only IBD information is available, two-locus analysis of genome scan data should be restricted to regions giving peaks under single-locus analysis. If disease-associated genotypes are available, it may be worth re-analysing the whole genome. Copyright 2002 S. Karger AG, Basel

Mesh:

Year:  2002        PMID: 12037408     DOI: 10.1159/000057987

Source DB:  PubMed          Journal:  Hum Hered        ISSN: 0001-5652            Impact factor:   0.444


  13 in total

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

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

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Journal:  Am J Hum Genet       Date:  2003-06-26       Impact factor: 11.025

4.  Testing genetic linkage with relative pairs and covariates by quasi-likelihood score statistics.

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Journal:  Hum Hered       Date:  2007-06-12       Impact factor: 0.444

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6.  Linkage analysis of a derived glucose phenotype in the Genetic Analysis Workshop 13 simulated data using a variety of Haseman-Elston based regression methods.

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7.  Covariate linkage analysis of GAW14 simulated data incorporating subclinical phenotype, sex, population, parent-of-origin, and interaction.

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Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

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Journal:  Mol Psychiatry       Date:  2009-02-17       Impact factor: 15.992

9.  Genomewide significant linkage to recurrent, early-onset major depressive disorder on chromosome 15q.

Authors:  Peter Holmans; George S Zubenko; Raymond R Crowe; J Raymond DePaulo; William A Scheftner; Myrna M Weissman; Wendy N Zubenko; Sandra Boutelle; Kathleen Murphy-Eberenz; Dean MacKinnon; Melvin G McInnis; Diana H Marta; Philip Adams; James A Knowles; Madeleine Gladis; Jo Thomas; Jennifer Chellis; Erin Miller; Douglas F Levinson
Journal:  Am J Hum Genet       Date:  2004-04-22       Impact factor: 11.025

10.  Simple f test reveals gene-gene interactions in case-control studies.

Authors:  Guanjie Chen; Ao Yuan; Jie Zhou; Amy R Bentley; Adebowale Adeyemo; Charles N Rotimi
Journal:  Bioinform Biol Insights       Date:  2012-07-02
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