Learning directed acyclic graphical structures with genetical genomics data

Bioinformatics
Bin Gao, Yuehua Cui

Abstract

Large amount of research efforts have been focused on estimating gene networks based on gene expression data to understand the functional basis of a living organism. Such networks are often obtained by considering pairwise correlations between genes, thus may not reflect the true connectivity between genes. By treating gene expressions as quantitative traits while considering genetic markers, genetical genomics analysis has shown its power in enhancing the understanding of gene regulations. Previous works have shown the improved performance on estimating the undirected network graphical structure by incorporating genetic markers as covariates. Knowing that gene expressions are often due to directed regulations, it is more meaningful to estimate the directed graphical network. In this article, we introduce a covariate-adjusted Gaussian graphical model to estimate the Markov equivalence class of the directed acyclic graphs (DAGs) in a genetical genomics analysis framework. We develop a two-stage estimation procedure to first estimate the regression coefficient matrix by [Formula: see text] penalization. The estimated coefficient matrix is then used to estimate the mean values in our multi-response Gaussian model to estimate the r...Continue Reading

References

Nov 28, 2002·Nature Genetics·Vivian G Cheung, Richard S Spielman
Mar 21, 2003·Nature·Eric E SchadtStephen H Friend
Dec 15, 2007·Biostatistics·Jerome FriedmanRobert Tibshirani
Apr 14, 2009·American Journal of Human Genetics·Jennifer A WebsterAmanda J Myers
Aug 21, 2012·The Annals of Applied Statistics·Jianxin Yin, Hongzhe Li
May 21, 2013·Journal of Multivariate Analysis·Jianxin Yin, Hongzhe Li

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Citations

Apr 27, 2016·Current Opinion in Biotechnology·Deborah ChasmanSushmita Roy
Feb 6, 2020·Genetics·Willem KruijerFred A van Eeuwijk

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