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Bayesian Models for Detecting Epistatic Interactions from Genetic Data

Bayesian Models for Detecting Epistatic Interactions from Genetic Data Current disease association studies are routinely conducted on a genome‐wide scale, testing hundreds of thousands or millions of genetic markers. Besides detecting marginal associations of individual markers with the disease, it is also of interest to identify gene–gene and gene–environment interactions, which confer susceptibility to the disease risk. The astronomical number of possible combinations of markers and environmental factors, however, makes interaction mapping a daunting task both computationally and statistically. In this paper, we review and discuss a set of Bayesian partition methods developed recently for mapping single‐nucleotide polymorphisms in case‐control studies, their extension to quantitative traits, and further generalization to multiple traits. We use simulation and real data sets to demonstrate the performance of these methods, and we compare them with some existing interaction mapping algorithms. With the recent advance in high‐throughput sequencing technologies, genome‐wide measurements of epigenetic factor enrichment, structural variations, and transcription activities become available at the individual level. The tsunami of data creates more challenges for gene–gene interaction mapping, but at the same time provides new opportunities that, if utilized properly through sophisticated statistical means, can improve the power of mapping interactions at the genome scale. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Human Genetics Wiley

Bayesian Models for Detecting Epistatic Interactions from Genetic Data

Zhang, Yu; Jiang, Bo; Zhu, Jun; Liu, Jun S.
Annals of Human Genetics , Volume 75 (1) – Jan 1, 2011
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References (43)

Publisher
Wiley
Copyright
Copyright © 2011 Wiley Subscription Services
ISSN
0003-4800
eISSN
1469-1809
DOI
10.1111/j.1469-1809.2010.00621.x
pmid
21091453
Publisher site
See Article on Publisher Site

Abstract

Current disease association studies are routinely conducted on a genome‐wide scale, testing hundreds of thousands or millions of genetic markers. Besides detecting marginal associations of individual markers with the disease, it is also of interest to identify gene–gene and gene–environment interactions, which confer susceptibility to the disease risk. The astronomical number of possible combinations of markers and environmental factors, however, makes interaction mapping a daunting task both computationally and statistically. In this paper, we review and discuss a set of Bayesian partition methods developed recently for mapping single‐nucleotide polymorphisms in case‐control studies, their extension to quantitative traits, and further generalization to multiple traits. We use simulation and real data sets to demonstrate the performance of these methods, and we compare them with some existing interaction mapping algorithms. With the recent advance in high‐throughput sequencing technologies, genome‐wide measurements of epigenetic factor enrichment, structural variations, and transcription activities become available at the individual level. The tsunami of data creates more challenges for gene–gene interaction mapping, but at the same time provides new opportunities that, if utilized properly through sophisticated statistical means, can improve the power of mapping interactions at the genome scale.

Journal

Annals of Human Genetics – Wiley

Published: Jan 1, 2011

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