Tracking updates in clinical databases increases efficiency for variant reanalysis.

Purpose: Variant interpretation, guided by American College of Medical Genetics and Genomics guidelines, can inform clinical decision-making. However, interpretations may change over time for a variety of reasons. Periodic reanalysis of previous variant interpretations is important to ensure that reported genetic findings remain accurate according to current knowledge.

Using multiplexed functional data to reduce variant classification inequities in underrepresented populations.

Background: Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).

Methods: We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database.

Frequency of pharmacogenomic variation and medication exposures among All of Us Participants.

Pharmacogenomics promises improved outcomes through individualized prescribing. However, the lack of diversity in studies impedes clinical translation and equitable application of precision medicine. We evaluated the frequencies of PGx variants, predicted phenotypes, and medication exposures using whole genome sequencing and EHR data from nearly 100k diverse All of Us Research Program participants.

Defining and Reducing Variant Classification Disparities.

Background: Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS).

Methods: We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from and the Genome Aggregation Database.

Empowering personalized pharmacogenomics with generative AI solutions.

Objective: This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics and to enhance patient care with equitable access.

Materials And Methods: The AI assistant employs retrieval-augmented generation (RAG), which combines retrieval and generative techniques, by harnessing a knowledge base (KB) that comprises data from the Clinical Pharmacogenetics Implementation Consortium (CPIC). It uses context-aware GPT-4 to generate tailored responses to user queries from this KB, further refined through prompt engineering and guardrails.

Genetic sex validation for sample tracking in next-generation sequencing clinical testing.

Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups.

Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories.

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities.

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants with diverse ancestry and access to healthcare, representing one of the largest biomedical research repositories of its kind. Here, we examine pathogenic and likely pathogenic variants that were identified in the All of Us cohort.

Genetic Sex Validation for Sample Tracking in Clinical Testing.

Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups.

Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

Background: The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network.

Methods: Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including , , and .

Best practices for the interpretation and reporting of clinical whole genome sequencing.

Whole genome sequencing (WGS) shows promise as a first-tier diagnostic test for patients with rare genetic disorders. However, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading health care and research organizations in the US and Canada, was formed to expand access to high quality clinical WGS by convening experts and publishing best practices.

Whole-genome sequencing as an investigational device for return of hereditary disease risk and pharmacogenomic results as part of the All of Us Research Program.

Background: The All of Us Research Program (AoURP, "the program") is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the USA. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants.

Artificial Intelligence and Cardiovascular Genetics.

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses.

Harmonizing variant classification for return of results in the All of Us Research Program.

The All of Us Research Program (AoURP) is a historic effort to accelerate research and improve healthcare by generating and collating data from one million people in the United States. Participants will have the option to receive results from their genome analysis, including actionable findings in 59 gene-disorder pairs for which disorder-associated variants are recommended for return by the American College of Medical Genetics and Genomics. To ensure consistent reporting across the AoURP, in a prelaunch study the four participating clinical laboratories shared all variant classifications in the 59 genes of interest from their internal databases.

Neptune: an environment for the delivery of genomic medicine.

Purpose: Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process.

Methods: We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration.

Exome variant discrepancies due to reference-genome differences.

Despite release of the GRCh38 human reference genome more than seven years ago, GRCh37 remains more widely used by most research and clinical laboratories. To date, no study has quantified the impact of utilizing different reference assemblies for the identification of variants associated with rare and common diseases from large-scale exome-sequencing data. By calling variants on both the GRCh37 and GRCh38 references, we identified single-nucleotide variants (SNVs) and insertion-deletions (indels) in 1,572 exomes from participants with Mendelian diseases and their family members.

The Implementation Science for Genomic Health Translation (INSIGHT) Study in Epilepsy: Protocol for a Learning Health Care System.

Background: Genomic medicine is poised to improve care for common complex diseases such as epilepsy, but additional clinical informatics and implementation science research is needed for it to become a part of the standard of care. Epilepsy is an exemplary complex neurological disorder for which DNA diagnostics have shown to be advantageous for patient care.

Objective: We designed the Implementation Science for Genomic Health Translation (INSIGHT) study to leverage the fact that both the clinic and testing laboratory control the development and customization of their respective electronic health records and clinical reporting platforms.

Improving reporting standards for polygenic scores in risk prediction studies.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field.

Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies.

Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.

Genome sequencing analysis of a family with a child displaying severe abdominal distention and recurrent hypoglycemia.

Background: Germline mutations in PTEN are associated with the PTEN hamartoma tumor syndrome (PHTS), an umbrella term used to describe a spectrum of autosomal-dominant disorders characterized by variable phenotypic manifestations associated with cell or tissue overgrowth. We report a boy who developed severe progressive abdominal distention due to a dramatic adipose mass from the age of 7 months and developed recurrent hypoinsulinemic hypoglycemia that led to seizures at the age of 4 years.

Methods: Trio-based whole-genome sequencing was performed by using blood DNA from the child and his parents.