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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 October 2021

Modeling Linkage Disequilibrium with Decomposable Graphical Models

Modeling Linkage Disequilibrium with Decomposable Graphical Models

(p.247) Chapter 10 Modeling Linkage Disequilibrium with Decomposable Graphical Models
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Haley J. Abel

Alun Thomas

Oxford University Press

This chapter describes the use of decomposable graphical models (DGMs) to represent the dependences within genetic data, or linkage disequilibrium (LD), prior to various downstream applications. First, general learning algorithms are reviewed: schemes based on Markov chains Monte Carlo and related simulated annealing strategies are described. However, for tractable processing of high-dimensional data, it is shown that sampling the space of DGMs is efficiently replaced with the sampling of representations of DGMs — the junction trees. Then, a first application is considered: the phase imputation for diploid data, which consists in inferring the latent genetical phased haplotypes underlying the observed genetical unphased genotypes. In particular, it is shown that in the case of diploid data, decoupling the model estimation step from the phasing step allows scalability of the whole learning process. The chapter ends with the illustration of the potentialities of DGMs through four applications.

Keywords:   linkage disequilibrium, phase imputation, decomposable graphical models

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