Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

By Yuanfang Guan, Dmitriy Gorenshteyn, Margit Burmeister, Aaron K. Wong, John C. Schimenti, Mary Ann Handel, Carol J. Bult, Matthew A. Hibbs, Olga G. Troyanskaya. 2012

154 citations recorded by the source corpus.

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

Subfields: Aging, Molecular Biology

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Citations in the corpus

Prediction of disease genes using tissue-specified gene-gene network (2014, unknown): “Some research has been carried out by constructing tissue specific networks to detect diseases through the Bayesian structure learning algorithms [21].”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, unknown): “…of disease association prediction mainly include matrix decomposition (Koren et al., 2009; Wang et al., 2017) network propagation (Lee et al., 2011; Guan et al., 2012; Li and Li, 2012; Sun et al., 2014; Zhou et al., 2015), and machine learning (Luo et al., 2016; Zhou and Skolnick, 2016; Frasca,…”
The BioGRID interaction database: 2015 update (2014, methodology): “For example, the integration of genetic interaction networks with other genome-wide data types has helped to explain how sets of genes function differently in specific cellular contexts, conditions or tissues (25–28).”