Identifying disease mutations in genomic medicine settings: current challenges and how to accelerate progress

Abstract

The pace of exome and genome sequencing is accelerating, with the identification of many new disease-causing mutations in research settings, and it is likely that whole exome or genome sequencing could have a major impact in the clinical arena in the relatively near future. However, the human genomics community is currently facing several challenges, including phenotyping, sample collection, sequencing strategies, bioinformatics analysis, biological validation of variant function, clinical interpretation and validity of variant data, and delivery of genomic information to various constituents. Here we review these challenges and summarize the bottlenecks for the clinical application of exome and genome sequencing, and we discuss ways for moving the field forward. In particular, we urge the need for clinical-grade sample collection, high-quality sequencing data acquisition, digitalized phenotyping, rigorous generation of variant calls, and comprehensive functional annotation of variants. Additionally, we suggest that a 'networking of science' model that encourages much more collaboration and online sharing of medical history, genomic data and biological knowledge, including among research participants and consumers/patients, will help establish causation and penetrance for disease causal variants and genes. As we enter this new era of genomic medicine, we envision that consumer-driven and consumer-oriented efforts will take center stage, thus allowing insights from the human genome project to translate directly back into individualized medicine.

Figure 1

Two approaches for prioritizing disease causal genes from whole-genome or exome sequencing data. (a) The probabilistic scoring approach collects relevant information from multiple data sources, and compiles a statistical model that ranks all genes in the genome by their likelihood of being disease causal. (b) The stepwise reduction approach removes variants that are unlikely to be disease causal based on a series of filtering criteria, until a small set of candidate genes is found. The first approach may be more effective and rigorous, yet the second approach may be easier for non-specialists to understand and interpret. GWAS, genome-wide association study; HGMD, Human Gene Mutation Database; I, indel; LSDB, locus-specific database; NS, non-synonymous; OMIM, Online Mendelian Inheritance in Man; SS, splice acceptor or donor site.