Inference from genome-wide association studies using a novel Markov model

Hosking, FJ; Sterne, JAC; Smith, GD; Green, PJ

Inference from genome-wide association studies using a novel Markov model

Hosking, FJ Sterne, JAC Smith, GD Green, PJ

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Publication Type:: Journal Article
Citation:: Genetic Epidemiology, 2008, 32 (6), pp. 497 - 504
Issue Date:: 2008-09-01

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Field	Value	Language
dc.contributor.author	Hosking, FJ	en_US
dc.contributor.author	Sterne, JAC	en_US
dc.contributor.author	Smith, GD	en_US
dc.contributor.author	Green, PJ https://orcid.org/0000-0002-4367-4756	en_US
dc.date.issued	2008-09-01	en_US
dc.identifier.citation	Genetic Epidemiology, 2008, 32 (6), pp. 497 - 504	en_US
dc.identifier.issn	0741-0395	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15501
dc.description.abstract	In this paper we propose a Bayesian modeling approach to the analysis of genome-wide association studies based on single nucleotide polymorphism (SNP) data. Our latent seed model combines various aspects of k-means clustering, hidden Markov models (HMMs) and logistic regression into a fully Bayesian model. It is fitted using the Markov chain Monte Carlo stochastic simulation method, with Metropolis-Hastings update steps. The approach is flexible, both in allowing different types of genetic models, and because it can be easily extended while remaining computationally feasible due to the use of fast algorithms for HMMs. It allows for inference primarily on the location of the causal locus and also on other parameters of interest. The latent seed model is used here to analyze three data sets, using both synthetic and real disease phenotypes with real SNP data, and shows promising results. Our method is able to correctly identify the causal locus in examples where single SNP analysis is both successful and unsuccessful at identifying the causal SNP. © 2008 Wiley-Liss, Inc.	en_US
dc.relation.ispartof	Genetic Epidemiology	en_US
dc.relation.isbasedon	10.1002/gepi.20322	en_US
dc.subject.classification	Epidemiology	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Logistic Models	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Markov Chains	en_US
dc.subject.mesh	Polymorphism, Single Nucleotide	en_US
dc.subject.mesh	Genome, Human	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Genetic	en_US
dc.title	Inference from genome-wide association studies using a novel Markov model	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	32	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0604 Genetics	en_US
utslib.for	1117 Public Health and Health Services	en_US
dc.location.activity	ISI:000258871100001	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

In this paper we propose a Bayesian modeling approach to the analysis of genome-wide association studies based on single nucleotide polymorphism (SNP) data. Our latent seed model combines various aspects of k-means clustering, hidden Markov models (HMMs) and logistic regression into a fully Bayesian model. It is fitted using the Markov chain Monte Carlo stochastic simulation method, with Metropolis-Hastings update steps. The approach is flexible, both in allowing different types of genetic models, and because it can be easily extended while remaining computationally feasible due to the use of fast algorithms for HMMs. It allows for inference primarily on the location of the causal locus and also on other parameters of interest. The latent seed model is used here to analyze three data sets, using both synthetic and real disease phenotypes with real SNP data, and shows promising results. Our method is able to correctly identify the causal locus in examples where single SNP analysis is both successful and unsuccessful at identifying the causal SNP. © 2008 Wiley-Liss, Inc.

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http://hdl.handle.net/10453/15501