Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data.

The causal genetic variants underlying more than 50% of single gene (monogenic) disorders are yet to be discovered. Many patients with conditions likely to have a monogenic basis do not receive a confirmed molecular diagnosis which has potential impacts on clinical management. We have developed a gene-specific score, essentiality-specific pathogenicity prioritization (ESPP), to guide the recognition of genes likely to underlie monogenic disease variation to assist in filtering of genome sequence data. When a patient genome is sequenced, there are frequently several plausibly pathogenic variants identified in different genes. Recognition of the single gene most likely to include pathogenic variation can guide the identification of a causal variant. The ESPP score integrates gene-level scores which are broadly related to gene essentiality. Previous work towards the recognition of monogenic disease genes proposed a model with increasing gene essentiality from 'non-essential' to 'essential' genes (for which pathogenic variation may be incompatible with survival) with genes liable to contain disease variation positioned between these two extremes. We demonstrate that the ESPP score is useful for recognizing genes with high potential for pathogenic disease-related variation. Genes classed as essential have particularly high scores, as do genes recently recognized as strong candidates for developmental disorders. Through the integration of individual gene-specific scores, which have different properties and assumptions, we demonstrate the utility of an essentiality-based gene score to improve sequence genome filtering.

Understanding the disease genome: gene essentiality and the interplay of selection, recombination and mutation.

Despite the identification of many genetic variants contributing to human disease (the 'disease genome'), establishing reliable molecular diagnoses remain challenging in many cases. The ability to sequence the genomes of patients has been transformative, but difficulty in interpretation of voluminous genetic variation often confounds recognition of underlying causal variants. There are numerous predictors of pathogenicity for individual DNA variants, but their utility is reduced because many plausibly pathogenic variants are probably neutral. The rapidly increasing quantity and quality of information on the properties of genes suggests that gene-specific information might be useful for prediction of causal variation when used alongside variant-specific predictors of pathogenicity. The key to understanding the role of genes in disease relates in part to gene essentiality, which has recently been approximated, for example, by quantifying the degree of intolerance of individual genes to loss-of-function variation. Increasing understanding of the interplay between genetic recombination, selection and mutation and their relationship to gene essentiality suggests that gene-specific information may be useful for the interpretation of sequenced genomes. Considered alongside additional distinctive properties of the disease genome, such as the timing of the evolutionary emergence of genes and the roles of their products in protein networks, the case for using gene-specific measures to guide filtering of sequenced genomes seems strong.

Association between vedolizumab and risk of clostridium difficile infection in patients with ulcerative colitis: A systematic review and meta-analysis.

BACKGROUND: The medical treatment of ulcerative colitis (UC) includes the use of biological agents such as vedolizumab, a gut-selective alpha4beta7 (ɑ4ß7) antagonist. The mechanism of action of vedolizumab involves interfering with leukocyte trafficking into the gut vasculature, which halts inflammation. Due to this mechanism of action, concerns have arisen regarding an increased risk of gut infections, specifically, clostridium difficile infection (CDI). The aim is to provide clarity regarding the association between the use of vedolizumab as a therapy for ulcerative colitis and the risk of developing CDI. METHODS: A systematic literature review was conducted, starting with the scoping search, followed by backward snowballing parallel with keyword-based search to identify related articles. A quality assessment was conducted on the initially selected articles and excluded low-quality papers. RESULTS: Pooled analyses indicated that there was no significant association between the use of vedolizumab and the risk of developing CDI (effect size = 0.03 [-0.02, 0.07]). CONCLUSIONS: Vedolizumab does not increase the risk of CDI in patients with UC. Further studies are needed to confirm these findings.

Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review.

The evolution of next-generation sequencing technologies has facilitated the detection of causal genetic variants in diseases previously undiagnosed at a molecular level. However, in genome sequencing studies, the identification of disease genes among a candidate gene list is often difficult because of the large number of apparently damaging (but usually neutral) variants. A number of variant prioritization tools have been developed to help detect disease-causal sites. However, the results may be misleading as many variants scored as damaging by these tools are often tolerated, and there are inconsistencies in prediction results among the different variant-level prediction tools. Recently, studies have indicated that understanding gene properties might improve detection of genes liable to have associated disease variation and that this information improves molecular diagnostics. The purpose of this systematic review is to evaluate how understanding gene-specific properties might improve filtering strategies in clinical sequence data to prioritize potential disease variants. Improved understanding of the 'disease genome', which includes coding, noncoding and regulatory variation, might help resolve difficult cases. This review provides a comprehensive assessment of existing gene-level approaches, the relationships between measures of gene-pathogenicity and how use of these prediction tools can be developed for molecular diagnostics.