Diagnosing rare diseases after the exome

Abstract

High-throughput sequencing has ushered in a diversity of approaches for identifying genetic variants and understanding genome structure and function. When applied to individuals with rare genetic diseases, these approaches have greatly accelerated gene discovery and patient diagnosis. Over the past decade, exome sequencing has emerged as a comprehensive and cost-effective approach to identify pathogenic variants in the protein-coding regions of the genome. However, for individuals in whom exome-sequencing fails to identify a pathogenic variant, we discuss recent advances that are helping to reduce the diagnostic gap.

Figure 1.

Challenges and approaches in “exome-negative” cases. Depending on the causal variant type, different options are available, influenced by both technical and biological diagnostic challenges. Without knowing the cause of the disease, it can be challenging to select a complementary approach in the postexome “experimental maze.”

Figure 2.

Using functional genomics to interpret rare diseases. (A) Power estimates for detecting a candidate regulatory region associated with a disease by the presence of recurrent de novo mutations. We compare power differences when there is a single candidate region (red) versus no candidate region (gray) using the model of Short et al. (2018). This demonstrates that additional biological knowledge of regions that are dysregulated in disease may significantly reduce the number of genomes required for their detection. (B) Ranked expression of OMIM genes across 53 tissues (GTEx v7). We used k-means to summarize expression data across all genes. Using three clusters, we show that we can separate our data as follow: high sharing, with high expression across all tissues (20% of OMIM genes); intermediate sharing, in which genes are expressed in a reasonable amount of tissues (28%); and low sharing, in which genes tend to be specifically expressed in a few tissues (52%). For 48% of OMIM genes, several tissues can be selected for RNA-seq analysis.