Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

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. Tissue specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. We propose an effective strategy to model tissue-specific functional relationship networks in the laboratory mouse. We integrated large scale genomics datasets as well as low-throughput tissue-specific expression profiles to estimate the probability that two proteins are co-functioning in the tissue under study. These networks can accurately reflect the diversity of protein functions across different organs and tissue compartments. By computationally exploring the tissue-specific networks, we can accurately predict novel phenotype-related gene candidates. We experimentally confirmed a top candidate gene, Mybl1, to affect several male fertility phenotypes, predicted based on male-reproductive system-specific networks and we predicted candidates related to a rare genetic disease ataxia, which are supported by experimental and literature evidence. The above results demonstrate the power of modeling tissue-specific dynamics of co-functionality through computational approaches.