David Valle

A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.

Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web-based tool developed as part of the Baylor-Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, GeneMatcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Matchmaker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.

Acceleration in discovery of rare genetic variants possibly linked with disease may mean an increased risk of false-positive reports of causality; this Perspective proposes guidelines to distinguish disease-causing sequence variants from the many potentially functional variants in a human genome, and to assess confidence in their pathogenicity, and highlights priority areas for development. The wide-scale availability of high-throughput DNA sequencing technologies means that data on genetic variation in human diseases are accumulating rapidly. In this Perspective, Daniel MacArthur and colleagues sound a note of caution, pointing out that up to a quarter of reported disease-linked mutations have been found to either be common polymorphisms or have lacked sufficient evidence for pathogenicity. The authors discuss the key challenges associated with assessing sequence variants in human disease and propose guidelines for the robust differentiation between disease-causing genetic variants and other variants present in the human genome. They highlight several areas where research and resource development are urgently needed if genomic research findings are to be successfully translated into the clinical diagnostic setting. The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.