Using mechanistic models for the clinical interpretation of complex genomic variation

María Peña-Chilet,Marina Esteban-Medina,Marta R Hidalgo,Kinza Rian,Matias M Falco,Carlos Loucera,Joaquín Dopazo

doi:10.1038/s41598-019-55454-7

Abstract

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

Highlights

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes
We demonstrate the possibilities that mechanistic models offer for the interpretation of genomic variability in two different scenarios
The first one is the case of Fanconi Anemia (ORPHA:84), a rare disease with well-known hallmarks which have been mapped in the Fanconi anemia (FA) pathway

Summary

Introduction

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. (e.g. PROVEAN19, PupaSNP20, CONDEL21, VAAST22, MutationTaster[23], etc.); (ii) variant population frequencies, given that variants with a relatively high frequency in the population are unlikely to be causative of many hereditary disorders (obtained from different repositories such as the 1000 genomes[3], the Exome Aggregation Consortium[24], the gnomAD25, or from local population repositories, which have demonstrated to be useful www.nature.com/scientificreports for this purpose26); (iii) evolutionary conservation (e.g. PhyloP27, GERP28, etc.); (iv) compendiums of different criteria, such as CADD29 or, more recently, based on artificial intelligence[30] These filters can be used in combination with knowledge on functional labels[31], syndromes and phenotypes[32], or diseases[33,34,35], previously associated to the most likely candidate genes, as implemented in tools such as Phen-Gen[36], eXtasy[37], PhenIX38, Exomiser[39], etc. Recent reports demonstrate that mechanistic models of the activity of metabolic or signaling pathways, render highly precise predictions of complex phenotypes, such as patient survival[65,66], drug response[67], etc

Results

Discussion

Conclusion