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Introduction Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Methods Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a five-year period were obtained and variants were reinterpreted utilizing the Franklin© Artificial Intelligence (AI). Utilizing PowerBI©, the data were further matched to patients in the electronic healthcare record and matched to demographic data to generate a variant data table and map variants as a choropleth. Results Three thousand sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 27% of variants. A total of 723 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Discussion Utilizing readily available software we developed a database and Exploratory Data Analysis methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify patients amenable to new treatments, and localize geographies enriched for pathogenic variants.

Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that provide variant-level information and phenotypic features to researchers, clinicians, healthcare providers and patients. Following the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases have as one goal to facilitate connection to one another using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).

PURPOSESeveral groups and resources provide information that pertains to the validity of gene-disease relationships used in genomic medicine and research; however, universal standards and terminologies to define the evidence base for the role of a gene in disease, and a single harmonized resource were lacking. To tackle this issue, the Gene Curation Coalition (GenCC) was formed.METHODSThe GenCC drafted harmonized definitions for differing levels of gene-disease validity based on existing resources, and performed a modified Delphi survey with three rounds to narrow the list of terms. The GenCC also developed a unified database to display curated gene-disease validity assertions from its members.RESULTSBased on 241 survey responses from the genetics community, a consensus term set was chosen for grading gene-disease validity and database submissions. As of December 2021, the database contains 15,241 gene-disease assertions on 4,569 unique genes from 12 submitters. When comparing submissions to the database from distinct sources, conflicts in assertions of gene-disease validity ranged from 5.3% to 13.4%.CONCLUSIONTerminology standardization, sharing of gene-disease validity classifications, and resolution of curation conflicts will facilitate collaborations across international curation efforts and in turn, improve consistency in genetic testing and variant interpretation.

Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that have made variant-level information together with phenotype or phenotypic features available to researchers, clinicians, health care providers and patients. Following in the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases plan to connect to each other using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).

Objective To identify causes of the autosomal-recessive malformation, diencephalic-mesencephalic junction dysplasia (DMJD) syndrome. Methods Eight families with DMJD were studied by whole-exome or targeted sequencing, with detailed clinical and radiological characterization. Patient-derived induced pluripotent stem cells were derived into neural precursor and endothelial cells to study gene expression. Results All patients showed biallelic mutations in the nonclustered protocadherin-12 (PCDH12) gene. The characteristic clinical presentation included progressive microcephaly, craniofacial dysmorphism, psychomotor disability, epilepsy, and axial hypotonia with variable appendicular spasticity. Brain imaging showed brainstem malformations and with frequent thinned corpus callosum with punctate brain calcifications, reflecting expression of PCDH12 in neural and endothelial cells. These cells showed lack of PCDH12 expression and impaired neurite outgrowth. Interpretation DMJD patients have biallelic mutations in PCDH12 and lack of protein expression. These patients present with characteristic microcephaly and abnormalities of white matter tracts. Such pathogenic variants predict a poor outcome as a result of brainstem malformation and evidence of white matter tract defects, and should be added to the phenotypic spectrum associated with PCDH12-related conditions. Ann Neurol 2018;84:646-655.

Background: Interpretation of genetic variation remains an impediment to cost-effective application of genomics to medicine. An advanced artificial intelligence (AI)-based Variant Classification Engine (aiVCE), rooted in ACMG/AMP guidelines, employs data-driven methods to expedite gene-specific classification (franklin.genoox.com). In this blinded study, the aiVCE’s overall and rule-level performances were evaluated using ClinVar (v. 2018-10) variants with creation dates after 5/01/2017. By removing any prior knowledge of these variants from the aiVCE training data, they were treated as novel variants. Using a ‘Full’ dataset (75,801 variants with ≥1 star) and an ‘Increased-Certainty’ dataset (3,993 variants with ≥2 stars), the aiVCE classified variants as pathogenic (P), likely-pathogenic (LP), uncertain significance (VUS), likely-benign (LB), or benign (B). VUS with sufficient supporting data were subclassified as VUS-leaning benign or VUS-leaning pathogenic. aiVCE results were evaluated to determine concordance with final ClinVar classification and rule-level determinations. Results: The aiVCE demonstrated >97% concordance among Increased-Certainty variants. Concordance was >95% across variant effects (e.g., missense, null, splice region), and was >93.5% for the Full dataset. When assessing the aiVCE’s application of specific ACMG rules, significant differences were observed between ClinVar P/LP and B/LB variants rule-met proportions (all P

Sequencing large cohorts of ethnically homogeneous individuals yields genetic insights with implications for the entire population rather than a single individual. In order to evaluate the genetic basis of certain diseases encountered at high frequency in the Ashkenazi Jewish population (AJP), as well as to improve variant annotation among the AJP, we examined the entire exome, focusing on specific genes with known clinical implications in 128 Ashkenazi Jews and compared these data to other non-Jewish populations (European, African, South Asian and East Asian). We targeted American College of Medical Genetics incidental finding recommended genes and the Catalogue of Somatic Mutations in Cancer (COSMIC) germline cancer-related genes. We identified previously known disease-causing variants and discovered potentially deleterious variants in known disease-causing genes that are population specific or substantially more prevalent in the AJP, such as in the ATP and HGFAC genes associated with colorectal cancer and pancreatic cancer, respectively. Additionally, we tested the advantage of utilizing the database of the AJP when assigning pathogenicity to rare variants of independent whole-exome sequencing data of 49 Ashkenazi Jew early-onset breast cancer (BC) patients. Importantly, population-based filtering using our AJP database enabled a reduction in the number of potential causal variants in the BC cohort by 36%. Taken together, population-specific sequencing of the AJP offers valuable, clinically applicable information and improves AJP filter annotation.