A novel statistical method for interpreting the pathogenicity of rare variants

Genome sequencing data analysis for rare disease gene discovery

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Noncoding mutation in RPGRIP1 contributes to inherited retinal degenerations

Purpose

Despite the extensive use of next-generation sequencing (NGS) technology to identify disease-causing genomic variations, a major gap in our understanding of Mendelian diseases is the unidentified molecular lesion in a significant portion of patients. For inherited retinal degenerations (IRDs), although currently close to 300 disease-associated genes have been identified, the mutations in approximately one-third of patients remain unknown. With mounting evidence that noncoding mutations might contribute significantly to disease burden, we aimed to systematically investigate the contributions of noncoding regions in the genome to IRDs.

Methods

In this study, we focused on RPGRIP1, which has been linked to various IRD phenotypes, including Leber congenital amaurosis (LCA), retinitis pigmentosa (RP), and macular dystrophy (MD). As several noncoding mutant alleles have been reported in RPGRIP1, and we observed that the mutation carrier frequency of RPGRIP1 is higher in patient cohorts with unsolved IRDs, we hypothesized that mutations in the noncoding regions of RPGRIP1 might be a significant contributor to pathogenicity. To test this hypothesis, we performed whole-genome sequencing (WGS) for 25 patients with unassigned IRD who carry a single mutation in RPGRIP1.

Results

Three noncoding variants in RPGRIP1, including a 2,890 bp deletion and two deep-intronic variants (c.2710+233G>A and c.1468-263G>C), were identified as putative second hits of RPGRIP1 in three patients with LCA. The mutant alleles were validated with direct sequencing or in vitro assays.

Conclusions

The results highlight the significance of the contribution of noncoding pathogenic variants to unsolved IRD cases.