Mapping eQTL networks with mixed graphical models
Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to the high-dimensional multivariate nature of continuous gene expression traits and discrete genotypes from genetical genomics experiments. Next to the expression heterogeneity produced by confounding factors an...
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| Tipo de documento: | tese |
| Estado: | Versão publicada |
| Data de publicação: | 2014 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositório: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/145479 |
| Acesso em linha: | http://hdl.handle.net/10803/145479 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Graphical Markov models Genetical genomics eQTL mapping eQTL network Yeast R/Bioconductor Qpgraph Models gr àfics de Markov Gen omica gen etica Cartografia gen ètica d'eQTLs Xarxa d'eQTLs Llevat 575 |
| Resumo: | Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to the high-dimensional multivariate nature of continuous gene expression traits and discrete genotypes from genetical genomics experiments. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect e ects spread throughout genes as a result of genetic, molecular and environmental perturbations. Disentangling direct from indirect e ects while adjusting for unwanted variability should help us moving from current parts list of molecular components to understanding how these components work together in networks of eQTL and gene to gene associations. There is a large body of statistical methodology to tackle this challenge within the context of linear models for continuous data. However, little has been investigated in using graphical Markov models (GMMs) and conditional independence on mixed continuous and discrete data from genetical genomics data sets, which are powerful tools for the analysis of complex associations. In this thesis we investigate the use of mixed GMMs to estimate eQTL networks from data. We develop procedures to simulate these models and data from them to gather insight into the propagation of additive e ects throughout the network. We derive the parameters for a likelihood ratio exact test that enables use of higher-order conditional independence with mixed GMMs. We exploit this test in the context of limited-order correlations and marginal distributions to obtain estimates of the underlying eQTL net- work. We show in the context of a yeast genetical genomics data set, that this estimate leads to a sparser network with more direct associations that provide valuable insight into the genetic control of gene expression in yeast. We develop an algorithm for accurate es- timation of the genetic e ects of eQTLs in the presence of missing data. All algorithms described in this thesis are implemented in the R/Bioconductor package qpgraph. |
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