Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 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. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Community data-driven approach to identify pathogenic founder variants for pan-ethnic carrier screening panels.

BACKGROUND: The American College of Medical Genetics and Genomics (ACMG) recently published new tier-based carrier screening recommendations. While many pan-ethnic genetic disorders are well established, some genes carry pathogenic founder variants (PFVs) that are unique to specific ethnic groups. We aimed to demonstrate a community data-driven approach to creating a pan-ethnic carrier screening panel that meets the ACMG recommendations. METHODS: Exome sequencing data from 3061 Israeli individuals were analyzed. Machine learning determined ancestries. Frequencies of candidate pathogenic/likely pathogenic (P/LP) variants based on ClinVar and Franklin were calculated for each subpopulation based on the Franklin community platform and compared with existing screening panels. Candidate PFVs were manually curated through community members and the literature. RESULTS: The samples were automatically assigned to 13 ancestries. The largest number of samples was classified as Ashkenazi Jewish (n = 1011), followed by Muslim Arabs (n = 613). We detected one tier-2 and seven tier-3 variants that were not included in existing carrier screening panels for Ashkenazi Jewish or Muslim Arab ancestries. Five of these P/LP variants were supported by evidence from the Franklin community. Twenty additional variants were detected that are potentially pathogenic tier-2 or tier-3. CONCLUSIONS: The community data-driven and sharing approaches facilitate generating inclusive and equitable ethnically based carrier screening panels. This approach identified new PFVs missing from currently available panels and highlighted variants that may require reclassification.

The Gene Curation Coalition: A global effort to harmonize gene-disease evidence resources.

PURPOSE: Several 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. METHODS: The GenCC drafted harmonized definitions for differing levels of gene-disease validity on the basis of existing resources, and performed a modified Delphi survey with 3 rounds to narrow the list of terms. The GenCC also developed a unified database to display curated gene-disease validity assertions from its members. RESULTS: On the basis of 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 contained 15,241 gene-disease assertions on 4569 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%. CONCLUSION: Terminology 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.

Variant-level matching for diagnosis and discovery: Challenges and opportunities.

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).

Loss of Protocadherin-12 Leads to Diencephalic-Mesencephalic Junction Dysplasia Syndrome.

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.

Differential analysis of mutations in the Jewish population and their implications for diseases.

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.