Current Developments of Clinical Sequencing and the Clinical Utility of Polygenic Risk Scores in Inflammatory Diseases

In this mini-review, we highlight selected research by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence "Precision Medicine in Chronic Inflammation" focusing on clinical sequencing and the clinical utility of polygenic risk scores as well as its implication on precision medicine in the field of the inflammatory diseases inflammatory bowel disease, atopic dermatitis and coronary artery disease. Additionally, we highlight current developments and discuss challenges to be faced in the future. Exemplary, we point to residual challenges in detecting disease-relevant variants resulting from difficulties in the interpretation of candidate variants and their potential interactions. While polygenic risk scores represent promising tools for the stratification of patient groups, currently, polygenic risk scores are not accurate enough for clinical setting. Precision medicine, incorporating additional data from genomics, transcriptomics and proteomics experiments, may enable the identification of distinct disease pathogeneses. In the future, data-intensive biomedical innovation will hopefully lead to improved patient stratification for personalized medicine

Genetic Variation in ABCC4 and CFTR and Acute Pancreatitis during Treatment of Pediatric Acute Lymphoblastic Leukemia

Background: Acute pancreatitis (AP) is a serious, mechanistically not entirely resolved side effect of L-asparaginase-containing treatment for acute lymphoblastic leukemia (ALL). To find new candidate variations for AP, we conducted a genome-wide association study (GWAS). Methods: In all, 1,004,623 single-nucleotide variants (SNVs) were analyzed in 51 pediatric ALL patients with AP (cases) and 1388 patients without AP (controls). Replication used independent patients. Results: The top-ranked SNV (rs4148513) was located within the ABCC4 gene (odds ratio (OR) 84.1; p = 1.04 × 10−14). Independent replication of our 20 top SNVs was not supportive of initial results, partly because rare variants were neither present in cases nor present in controls. However, results of combined analysis (GWAS and replication cohorts) remained significant (e.g., rs4148513; OR = 47.2; p = 7.31 × 10−9). Subsequently, we sequenced the entire ABCC4 gene and its close relative, the cystic fibrosis associated CFTR gene, a strong AP candidate gene, in 48 cases and 47 controls. Six AP-associated variants in ABCC4 and one variant in CFTR were detected. Replication confirmed the six ABCC4 variants but not the CFTR variant. Conclusions: Genetic variation within the ABCC4 gene was associated with AP during the treatment of ALL. No association of AP with CFTR was observed. Larger international studies are necessary to more conclusively assess the risk of rare clinical phenotypes

Genetic Factors of the Disease Course After Sepsis: Rare Deleterious Variants Are Predictive

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. For its clinical course, host genetic factors are important and rare genomic variants are suspected to contribute. We sequenced the exomes of 59 Greek and 15 German patients with bacterial sepsis divided into two groups with extremely different disease courses. Variant analysis was focusing on rare deleterious single nucleotide variants (SNVs). We identified significant differences in the number of rare deleterious SNVs per patient between the ethnic groups. Classification experiments based on the data of the Greek patients allowed discrimination between the disease courses with estimated sensitivity and specificity > 75%. By application of the trained model to the German patients we observed comparable discriminatory properties despite lower population-specific rare SNV load. Furthermore, rare SNVs in genes of cell signaling and innate immunity related pathways were identified as classifiers discriminating between the sepsis courses. Sepsis patients with favorable disease course after sepsis, even in the case of unfavorable preconditions, seem to be affected more often by rare deleterious SNVs in cell signaling and innate immunity related pathways, suggesting a protective role of impairments in these processes against a poor disease course

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review

Loss-of-function mutations in HINT1 cause axonal neuropathy with neuromyotonia

Inherited peripheral neuropathies are frequent neuromuscular disorders known for their clinical and genetic heterogeneity. In 33 families, we identified 8 mutations in HINT1 (encoding histidine triad nucleotide-binding protein 1) by combining linkage analyses with next-generation sequencing and subsequent cohort screening of affected individuals. Our study provides evidence that loss of functional HINT1 protein results in a distinct phenotype of autosomal recessive axonal neuropathy with neuromyotonia