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dc.contributor.author Crosslin, DR
dc.contributor.author Qin, X
dc.contributor.author Hauser, ER
dc.coverage.spatial Germany
dc.date.accessioned 2011-06-21T17:32:24Z
dc.date.issued 2010
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20887274
dc.identifier.citation Stat Appl Genet Mol Biol, 2010, 9 pp. Article35 - ?
dc.identifier.uri http://hdl.handle.net/10161/4611
dc.description.abstract Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.
dc.format.extent Article35 - ?
dc.language eng
dc.language.iso en_US en_US
dc.relation.ispartof Stat Appl Genet Mol Biol
dc.relation.isversionof 10.2202/1544-6115.1561
dc.subject African Americans
dc.subject Biosynthetic Pathways
dc.subject Case-Control Studies
dc.subject Computer Simulation
dc.subject Disease
dc.subject Epistasis, Genetic
dc.subject European Continental Ancestry Group
dc.subject Haplotypes
dc.subject Humans
dc.subject Leukotrienes
dc.subject Linkage Disequilibrium
dc.subject Models, Genetic
dc.subject Software
dc.title Assessment of LD matrix measures for the analysis of biological pathway association.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-00-00 en_US
duke.description.endpage 35 en_US
duke.description.issue 1 en_US
duke.description.startpage 35 en_US
duke.description.volume 9 en_US
dc.relation.journal Statistical Applications in Genetics and Molecular Biology en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20887274
pubs.organisational-group /Duke
pubs.organisational-group /Duke/School of Medicine
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments/Biostatistics & Bioinformatics
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers/Duke Molecular Physiology Institute
pubs.publication-status Published
pubs.volume 9
dc.identifier.eissn 1544-6115

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