Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants

Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of di...

Descripción completa

Detalles Bibliográficos
Autores principales: Visweswaran, Shyam, Wong, An-Kwok Ian
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2009
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041553/
https://www.ncbi.nlm.nih.gov/pubmed/21347185
_version_ 1782198445582319616
author Visweswaran, Shyam
Wong, An-Kwok Ian
author_facet Visweswaran, Shyam
Wong, An-Kwok Ian
author_sort Visweswaran, Shyam
collection PubMed
description Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of disease. When compared with multifactor dimensionality reduction (MDR), a widely used combinatorial partitioning method for detecting interactions, BCP has significantly greater power and is computationally more efficient.
format Text
id pubmed-3041553
institution National Center for Biotechnology Information
language English
publishDate 2009
publisher American Medical Informatics Association
record_format MEDLINE/PubMed
spelling pubmed-30415532011-02-23 Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants Visweswaran, Shyam Wong, An-Kwok Ian Summit on Translat Bioinforma Articles Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of disease. When compared with multifactor dimensionality reduction (MDR), a widely used combinatorial partitioning method for detecting interactions, BCP has significantly greater power and is computationally more efficient. American Medical Informatics Association 2009-03-01 /pmc/articles/PMC3041553/ /pubmed/21347185 Text en ©2009 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Visweswaran, Shyam
Wong, An-Kwok Ian
Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title_full Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title_fullStr Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title_full_unstemmed Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title_short Bayesian Combinatorial Partitioning For Detecting Interactions Among Genetic Variants
title_sort bayesian combinatorial partitioning for detecting interactions among genetic variants
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041553/
https://www.ncbi.nlm.nih.gov/pubmed/21347185
work_keys_str_mv AT visweswaranshyam bayesiancombinatorialpartitioningfordetectinginteractionsamonggeneticvariants
AT wongankwokian bayesiancombinatorialpartitioningfordetectinginteractionsamonggeneticvariants

Ejemplares similares