Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios

Abstract

Purpose: Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene-disease associations.

Methods: We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients.

Results: We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10(-8)). This enrichment is only partially explained by mutations found in known disease-causing genes.

Conclusion: This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

Figure 1

Hot zone bioinformatic signatures of de novo mutations. Among (a) controls and (b) cases who had at least one protein-coding de novo mutation identified, the gene-level (Residual Variation Intolerance Score (RVIS) genic intolerance percentile) and variant-level (PolyPhen-2) scores are plotted in two-dimensional space. Black circles in (b) are used for trios achieving a genetic diagnosis via the plotted de novo mutation. Blue diamonds represent de novo mutations among trios that have at least one de novo mutation but are currently unresolved by a de novo or inherited mutation. In (a) and (b) the hot zone is the shaded region corresponding to PolyPhen-2 (x-axis) ≥0.95 and RVIS (y-axis) ≤0.25. Of the 337 control de novo mutations, 44 (13.1%) occur in the hot zone as compared with 29 (41.4%) of 70 de novo mutations observed among cases (Fisher's exact test, P = 2.3 × 10⁻⁷). This indicates that among the cases there is an excess of 20 (69%) de novo mutations in the hot zone. To further illustrate the difference between the two populations (cases = red; controls = blue), (c) a histogram shows the distribution of Euclidean distances to the most damaging coordinate (PolyPhen-2 = 1 and RVIS = 0) for de novo mutations plotted in (a) and (b). It is strikingly clear that (b) de novo mutations identified among patients ascertained for severe undiagnosed genetic conditions are drawn from a distribution that is significantly closer in Euclidean distance to the most damaging coordinate than are (a) de novo mutations ascertained from a control population (Mann–Whitney U-test, P = 6.3 × 10⁻⁷). Linear regression lines were generated for both populations. Population-level representations (d) and (e) of hot zone de novo mutation incidence among the two groups. Red silhouettes represent carriers of de novo hot zone mutations. For (e) cases, 29 of 103 (28.2%) patients ascertained for an undiagnosed genetic condition, without an inherited genetic diagnosis, had a hot zone de novo mutation as compared with (d) controls, for which only 44 of 728 (6.0%) sequenced control trios had a hot zone de novo mutation (Fisher's exact test, P = 3.0 × 10⁻¹⁰; 79% excess observations among cases). Moreover, adding in the layer of information regarding essential gene status (red silhouette with a blue asterisk) further pinpointed toward putative pathogenic mutations because among the control population only 1.9% had a hot zone de novo mutation occurring in an essential gene. This is compared with the 15.5% of cases (Fisher's exact test, P = 3.4 × 10⁻⁸; 88% excess observations among cases).