Literature DB >> 23548797

Efficient simulation of epistatic interactions in case-parent trios.

Qing Li1, Holger Schwender, Thomas A Louis, M Daniele Fallin, Ingo Ruczinski.   

Abstract

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.
Copyright © 2013 S. Karger AG, Basel.

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Year:  2013        PMID: 23548797      PMCID: PMC3800020          DOI: 10.1159/000348789

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


  70 in total

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2.  Backward simulation of ancestors of sampled individuals.

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Authors:  Clive J Hoggart; Marc Chadeau-Hyam; Taane G Clark; Riccardo Lampariello; John C Whittaker; Maria De Iorio; David J Balding
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4.  An ensemble learning approach jointly modeling main and interaction effects in genetic association studies.

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Journal:  Genet Epidemiol       Date:  2008-05       Impact factor: 2.135

5.  Linkage analysis of quantitative traits: increased power by using selected samples.

Authors:  G Carey; J Williamson
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6.  A log-linear approach to case-parent-triad data: assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting.

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Review 7.  The TDT and other family-based tests for linkage disequilibrium and association.

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8.  Forward-time simulations of human populations with complex diseases.

Authors:  Bo Peng; Christopher I Amos; Marek Kimmel
Journal:  PLoS Genet       Date:  2007-02-15       Impact factor: 5.917

9.  Screening large-scale association study data: exploiting interactions using random forests.

Authors:  Kathryn L Lunetta; L Brooke Hayward; Jonathan Segal; Paul Van Eerdewegh
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10.  Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

Authors:  Marylyn D Ritchie; Bill C White; Joel S Parker; Lance W Hahn; Jason H Moore
Journal:  BMC Bioinformatics       Date:  2003-07-07       Impact factor: 3.169

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  2 in total

1.  Detecting disease variants in case-parent trio studies using the bioconductor software package trio.

Authors:  Holger Schwender; Qing Li; Christoph Neumann; Margaret A Taub; Samuel G Younkin; Philipp Berger; Robert B Scharpf; Terri H Beaty; Ingo Ruczinski
Journal:  Genet Epidemiol       Date:  2014-07-21       Impact factor: 2.135

2.  Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect.

Authors:  M Shi; D M Umbach; A S Wise; C R Weinberg
Journal:  BMC Bioinformatics       Date:  2018-01-02       Impact factor: 3.169

  2 in total

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