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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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Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge

Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge

Chapter:
(p.165) Chapter 7 Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Jee Young Moon

Elias Chaibub Neto

Xinwei Deng

Brian S. Yandell

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0007

In a segregating population, quantitative trait loci (QTL) mapping can identify QTLs with a causal effect on a phenotype. A common feature of these methods is that QTL mapping and phenotype network reconstruction are conducted separately. As both tasks have to benefit from each other, this chapter presents an approach which jointly infers a causal phenotype network and causal QTLs. The joint network of causal phenotype relationships and causal QTLs is modeled as a Bayesian network. In addition, a prior distribution on phenotype network structures is adjusted by biological knowledge, thus extending the former framework, QTLnet, into QTLnet-prior. This integrative approach can incorporate several sources of biological knowledge such as protein-protein interactions, gene ontology annotations, and transcription factor and DNA binding information. A Metropolis-Hastings scheme is described that iterates between accepting a network structure and accepting k weights corresponding to the k types of biological knowledge.

Keywords:   QTL, Bayesian network, causal phenotype

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