Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes

Abstract

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.

Keywords: Mendelian; disease–gene relationships; genetics; genomics; novel gene discovery; rare disease; rare genetic disorders.

FIGURE 1

The MME API and its connected nodes. MME uses a federated network of nine connected nodes. Image taken from https://www.matchmakerexchange.org/.

FIGURE 2

Comparison of the distribution of pLI and LOEUF. Panel (A) shows a histogram of human genes across the LOEUF spectrum displaying a continuous pattern. Lower scores represent higher gene constraint (for loss of function). The histogram is colored by the LOEUF decile. Panel (B) shows a histogram of human genes across the pLI spectrum. This spectrum is extremely dichotomous with the majority of genes skewed toward either 0 (not constrained for loss of function) or 1 (constrained for loss of function). This can help to discriminate genes that are likely to cause disease through haploinsufficiency (pLI > 0.9). The dichotomous nature of pLI is by design, as initially the reference databases were too small to have adequate power to discern depletion for loss-of-function variation in small- to medium-length genes. The pLI distribution is colored by the LOEUF decile to show the overlap between scores. Higher pLI scores correlate with lower LOEUF scores as expected. The continuous nature of the LOEUF score provides more granular detail than pLI across the middle of the spectrum and can better stratify genes with moderate levels of constraint that may be implicated in recessive disease.

FIGURE 3

Gene-to-patient approach for improved rare disease diagnostics. Scenario (1) shows a traditional patient-to-gene approach. Following variant analysis, rare disease patient A has several potential disease candidates, of which one (in black) is the disease-causing variant hidden within the sea of benign variation. Without prior knowledge that any of these variants are causative, the only way to test their pathogenicity is by expensive functional studies on genes of equally predicted causality. In scenario (2), the approach is reversed. High-confidence disease-causing variants in genes identified by constraint metrics and model organism data can be matched to patients and compared to clinical phenotypes, circumventing the analytical noise precluding variant interpretation. In turn, this identifies the best candidates for follow-up and for data sharing in the MME. Variants/genes that match to more than one patient with the same or overlapping phenotypes can add credence to the method. Figure adapted from Seaby and Ennis (2020).

FIGURE 4

A multi-omics approach to precision medicine. Schematic showing how the integration of multi-omics data is complementary and important for precision medicine.