Network Modules of the Cross-Species Genotype-Phenotype Map Reflect the Clinical Severity of Human Diseases

Abstract

Recent advances in genome sequencing techniques have improved our understanding of the genotype-phenotype relationship between genetic variants and human diseases. However, genetic variations uncovered from patient populations do not provide enough information to understand the mechanisms underlying the progression and clinical severity of human diseases. Moreover, building a high-resolution genotype-phenotype map is difficult due to the diverse genetic backgrounds of the human population. We built a cross-species genotype-phenotype map to explain the clinical severity of human genetic diseases. We developed a data-integrative framework to investigate network modules composed of human diseases mapped with gene essentiality measured from a model organism. Essential and nonessential genes connect diseases of different types which form clusters in the human disease network. In a large patient population study, we found that disease classes enriched with essential genes tended to show a higher mortality rate than disease classes enriched with nonessential genes. Moreover, high disease mortality rates are explained by the multiple comorbid relationships and the high pleiotropy of disease genes found in the essential gene-enriched diseases. Our results reveal that the genotype-phenotype map of a model organism can facilitate the identification of human disease-gene associations and predict human disease progression.

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

“…Moreover, Hofree et al found that subtyping cancer patients into groups based on the harboring of mutations in a similar region of a PPI network was predictive of overall survival 44 . In addition, we previously reported that PPI networks could be leveraged to identify various genotype–phenotype associations, including disease–gene relationships 45 – 49 , the clinical severity of human diseases 50 , gene essentiallity 51 , 52 , and phenotypic outcomes of chemical treatments 53 . Altogether, network analysis may help detect effective biological signals that can be manipulated for ML tasks, such as drug-response prediction.…”

Human and mouse essentiality screens as a resource for disease gene discovery

“…Essential genes have also been classified by whether they are known to be associated with human disease, with functional mutations in non-disease-associated genes and with a mouse orthologue that is LoF embryonic lethal suggested as likely to prevent pregnancy, lead to miscarriage or to early death 29 . Other research has reported that orthologues of embryonic lethal LoF mouse genes are shown to have an increased association to diseases with high mortality and neurodevelopmental disorders 23,30,31 .…”

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

“…First, we check if our false positive annotations become true annotations in later releases of the HPO annotations database. Second, we evaluate if our predicted genes are interacting with a phenotype-associated gene using the underlying assumption that phenotypes are determined by network modules of interacting genes and gene products [44][45][46]. Finally, we investigate if some of our false positive predictions were reported in GWAS studies.…”