High throughput sequencing technologies have revolutionized the identification of mutations responsible for a diverse set of Mendelian disorders, including inherited retinal disorders (IRDs). ...However, the causal mutations remain elusive for a significant proportion of patients. This may be partially due to pathogenic mutations located in non-coding regions, which are largely missed by capture sequencing targeting the coding regions. The advent of whole-genome sequencing (WGS) allows us to systematically detect non-coding variations. However, the interpretation of these variations remains a significant bottleneck. In this study, we investigated the contribution of deep-intronic splice variants to IRDs. WGS was performed for a cohort of 571 IRD patients who lack a confident molecular diagnosis, and potential deep intronic variants that affect proper splicing were identified using SpliceAI. A total of six deleterious deep intronic variants were identified in eight patients. An
minigene system was applied to further validate the effect of these variants on the splicing pattern of the associated genes. The prediction scores assigned to splice-site disruption positively correlated with the impact of mutations on splicing, as those with lower prediction scores demonstrated partial splicing. Through this study, we estimated the contribution of deep-intronic splice mutations to unassigned IRD patients and leveraged
and
methods to establish a framework for prioritizing deep intronic variant candidates for mechanistic and functional analyses.
Over 1,500 variants in the ABCA4 locus cause phenotypes ranging from severe, early-onset retinal degeneration to very late-onset maculopathies. The resulting ABCA4/Stargardt disease is the most ...prevalent Mendelian eye disorder, although its underlying clinical heterogeneity, including penetrance of many alleles, are not well-understood. We hypothesized that a share of this complexity is explained by trans-modifiers, i.e., variants in unlinked loci, which are currently unknown. We sought to identify these by performing exome sequencing in a large cohort for a rare disease of 622 cases and compared variation in seven genes known to clinically phenocopy ABCA4 disease to cohorts of ethnically matched controls. We identified a significant enrichment of variants in 2 out of the 7 genes. Moderately rare, likely functional, variants, at the minor allele frequency (MAF) <0.005 and CADD>25, were enriched in ROM1, where 1.3% of 622 patients harbored a ROM1 variant compared to 0.3% of 10,865 controls (p = 2.41E04; OR 3.81 95% CI 1.77; 8.22). More importantly, analysis of common variants (MAF>0.1) identified a frequent haplotype in PRPH2, tagged by the p.Asp338 variant with MAF = 0.21 in the matched general population that was significantly increased in the patient cohort, MAF 0.25, p = 0.0014. Significant differences were also observed between ABCA4 disease subgroups. In the late-onset subgroup, defined by the hypomorphic p.Asn1868Ile variant and including c.4253+43G>A, the allele frequency for the PRPH2 p.Asp338 variant was 0.15 vs 0.27 in the remaining cohort, p = 0.00057. Known functional data allowed suggesting a mechanism by which the PRPH2 haplotype influences the ABCA4 disease penetrance. These associations were replicated in an independent cohort of 408 patients. The association was highly statistically significant in the combined cohorts of 1,030 cases, p = 4.00E-05 for all patients and p = 0.00014 for the hypomorph subgroup, suggesting a substantial trans-modifying role in ABCA4 disease for both rare and common variants in two unlinked loci.
Molecular variant interpretation lacks disease gene‐specific cohorts for determining variant enrichment in disease versus healthy populations. To address the molecular etiology of retinal ...degeneration, specifically the PRPH2‐related retinopathies, we reviewed genotype and phenotype information obtained from 187 eyeGENE® participants from 161 families. Clinical details were provided by referring clinicians participating in the eyeGENE® Network. The cohort was sequenced for variants in PRPH2. Variant complementary DNA clusters and cohort frequency were compared to variants in public databases to help us to determine pathogenicity by current American College of Medical Genetics and Genomics/Association for Molecular Pathology interpretation criteria. The most frequent variant was c.828+3A>T, which affected 28 families (17.4%), and 25 of 79 (31.64%) variants were novel. The majority of missense variants clustered in the D2 intracellular loop of the peripherin‐2 protein, constituting a hotspot. Disease enrichment was noted for 23 (29.1%) of the variants. Hotspot and disease‐enrichment evidence modified variant classification for 16.5% of variants. The missense allele p.Arg172Trp was associated with a younger age of onset. To the best of our knowledge, this is the largest patient cohort review of PRPH2‐related retinopathy. Large disease gene‐specific cohorts permit gene modeling for hotspot and disease‐enrichment analysis, providing novel variant classification evidence, including for novel missense variants.
American College of Medical Genetics and Genomics classification of variants, ClinVar concordance, Fisher's exact test of Genome Aggregation Database (gnomAD) allele frequency (AF) versus proband AF, and eyeGENE® proband allele frequencies versus gnomAD allele frequencies: (a) gnomAD AF of ClinVar variants according to ClinVar classification. There were 29 variants classified as pathogenic in ClinVar, of which two were also found in gnomAD: c.425G>A p.Arg142Gln and c.623G>A p.Gly208Asp. (b) eyeGENE® proband AF and gnomAD AF for the common variants between these two datasets. The variants with p < .00063 in the Fisher's exact test were annotated.
Abstract only
Objective
Diabetic Retinopathy is the leading cause of blindness, stimulating the rise in 20,000 cases in the United States per year, and leaving over 3 million individuals vulnerable ...at risk. During the early stages of Diabetic Retinopathy, inflammatory and innate immune responses are derivative from pattern‐recognition receptors (PRRs) expressed as Advanced Glycation End‐products (AGEs). It has been observed that AGEs increases the progression of retinopathy in patients enduring the early stages of retinopathy. The reason behind the complication progression induced by AGEs is the ligand ‐ receptor binding with the Receptor Advanced Glycation End‐products. Therefore, by instituting a drug to bind with RAGE, may prevent the ligand‐receptor binding between RAGEs and AGEs. The following study analyzed drug targets based on their hydrophobicity, as to whether they will be able to bind with RAGE.
Methods
A docking program, AutoDock Tools and AutoDock Vina, were both used to determine the binding affinity, rmsd values, and Log P values of the drug targets.
Results
It was found that Statin drugs and Fibric Acid drugs are the types of drugs that were able to bind with RAGE with a high hydrophobicity and affinity, while a very popularly marketed drug, Metformin, wasn't able to bind with RAGE.
Conclusion
RAGE may be of importance in the prevention and management of diabetes complications.
Support or Funding Information
none
There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both ...case counts and death counts.
This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000.
Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit.
Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
•Machine learning models predict risk for adverse events and likelihood of dropout.•Machine learning models predict disease progression risk and response to treatment.•Predictive models can be of ...great use in all stages of clinical trials.•Training data sets have to be diverse to maximize generalizability.•It is key to ensure patient safety and ethical use of the models.
Evaluate the degree of concept coverage of the general eye examination in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics ...Observational Medical Outcomes Partnership (OMOP) common data model (CDM).
Study of data elements.
Not applicable.
Data elements (field names and predefined entry values) from the general eye examination in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 equivalence designation–equal when the OMOP concept had the same meaning as the source EHR concept, wider when it was missing information, narrower when it was overly specific, and unmatched when there was no match. Initial mappings were reviewed by 2 graders. Intergrader agreement for equivalence designation was calculated using Cohen’s kappa. Agreement on the mapped OMOP concept was calculated as a percentage of total mappable concepts. Discrepancies were discussed and a final consensus created. Quantitative analysis was performed on wider and unmatched concepts.
Gaps in OMOP concept coverage of EHR elements and intergrader agreement of mapped OMOP concepts.
A total of 698 data elements (210 fields, 488 values) from the EHR were analyzed. The intergrader kappa on the equivalence designation was 0.88 (standard error 0.03, P < 0.001). There was a 96% agreement on the mapped OMOP concept. In the final consensus mapping, 25% (1% fields, 31% values) of the EHR to OMOP concept mappings were considered equal, 50% (27% fields, 60% values) wider, 4% (8% fields, 2% values) narrower, and 21% (52% fields, 8% values) unmatched. Of the wider mapped elements, 46% were missing the laterality specification, 24% had other missing attributes, and 30% had both issues. Wider and unmatched EHR elements could be found in all areas of the general eye examination.
Most data elements in the general eye examination could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the incorporation of important ophthalmology concepts in OMOP, including adding laterality to existing concepts. There exists a strong need to improve the coverage of ophthalmic concepts in source vocabularies so that the OMOP CDM can better accommodate vision research.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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