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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Scoring, Searching and Evaluating Bayesian Network Models of Gene-phenotype Association

Scoring, Searching and Evaluating Bayesian Network Models of Gene-phenotype Association

Chapter:
(p.269) Chapter 11 Scoring, Searching and Evaluating Bayesian Network Models of Gene-phenotype Association
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Xia Jiang

Shyam Visweswaran

Richard E. Neapolitan

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0011

The arrival of genome-wide association studies (GWASs) has opened the exciting possibility of identifying genetic variations (single nucleotide polymorphisms (SNPs)) that underlie common diseases. However, our knowledge of the genetic architecture of common diseases remains limited. One likely reason for this is the complex interactions between genes, the environment, and the studied disease. This chapter addresses three aspects which are expected to help make progress to reveal some of these complex interactions using GWAS data sets. First, results are shown that compare the performances of various Bayesian network scoring criteria. Second, developing heuristic search algorithms for learning complex interactions from high-dimensional data is a hot topic. Third, the hypothesis testing involved in genome-wide epistasis detection is substantially different from that involved in a standard GWAS analysis, where only a null hypothesis and an alternative are considered.

Keywords:   genome-wide association studies, epistasis, Bayesian network, scoring

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