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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 LDLR , APOB , and PCSK9 . FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. Results: The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00–4.53]) and premature CHD (odds ratio, 3.68 [2.34–5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. Conclusions: In a multisite cohort of electronic health record–linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record–linked biobanks to detect FH.

Coronary artery disease (CAD) is a pandemic disease and the number one cause of death in the world. Predisposition to CAD is about 50% acquired and 50% genetic. CAD prevention has been proven in randomized clinical trials with statin therapy. However, primary prevention is limited by the lack of biomarkers to detect asymptomatic young individuals at risk. Traditional risk factors (TRFs) such as hypertension or Type 2 Diabetes are age-dependent and often not present until the sixth or seventh decade. In contrast, genetic risk determined at conception is potentially a biomarker to detect young individuals at risk for CAD. The first genetic risk variant for CAD (9p21) was discovered in 2007, and subsequently, over 200 risk variants for CAD were discovered. A genetic risk score (GRS) based on the genetic risk variants for CAD was evaluated in over one million individuals. Retrospective analysis of clinical trials assessing the effect of statin therapy showed that individuals with the highest GRS had the highest risk for cardiac events and also the most benefit from lowering cholesterol. In a recent study of 55,685 individuals, those with the highest GRS (20%) had a 91% higher risk for cardiac events. Furthermore, those with high genetic risk on a favorable lifestyle had 46% fewer cardiac events than those with an unfavorable lifestyle. The GRS is superior and independent of TRFs. Incorporation into clinical practice will be a paradigm shift in preventing this pandemic.

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. Of the 11,813 unique variants classified by at least two of the four laboratories, classifications were concordant with regard to reportability for 99.1% (11,711), with only 0.9% (102) having reportability differences. Through variant reassessment, data sharing, and discussion of rationale, participating laboratories resolved all 102 reportable differences. These approaches will be maintained during routine AoU reporting to ensure continuous classification harmonization and consistent reporting within AoURP.

Disparities in the data that underlies clinical genomic interpretation is an acknowledged problem but there is a paucity of data demonstrating it. The National Institutes of Health’sAll of UsResearch Program aims to collect whole genome sequences, electronic health record (EHR) data, surveys and physical measurements for over a million participants of diverse ancestry and varied access to healthcare resources. We grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups. The European subgroup showed the highest rate of pathogenic variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% (‘Other’). Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer, Hypercholesterolemia or Hemochromatosis. Variant frequencies were consistent with gnomAD and some notable exceptions were resolved using gnomAD subsets. We additionally use this data to enrich sets of participants for specific genetic findings and to calculate penetrance. Differences in the frequency of pathogenic variants observed between ancestral groups generally indicate biases of ascertainment, but some may indicate differences in disease prevalence. These analyses are available on theAll of UsResearcher Workbench.

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. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

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. We found that a total of 1.5% of SNVs and 2.0% of indels were discordant when different references were used. Notably, 76.6% of the discordant variants were clustered within discrete discordant reference patches (DISCREPs) comprising only 0.9% of loci targeted by exome sequencing. These DISCREPs were enriched for genomic elements including segmental duplications, fix patch sequences, and loci known to contain alternate haplotypes. We identified 206 genes significantly enriched for discordant variants, most of which were in DISCREPs and caused by multi-mapped reads on the reference assembly that lacked the variant call. Among these 206 genes, eight are implicated in known Mendelian diseases and 53 are associated with common phenotypes from genome-wide association studies. In addition, variant interpretations could also be influenced by the reference after lifting-over variant loci to another assembly. Overall, we identified genes and genomic loci affected by reference assembly choice, including genes associated with Mendelian disorders and complex human diseases that require careful evaluation in both research and clinical applications.

IMPORTANCE: Knowledge about the spectrum of diseases associated with hereditary cancer syndromes may improve disease diagnosis and management for patients and help to identify high-risk individuals. OBJECTIVE: To identify phenotypes associated with hereditary cancer genes through a phenome-wide association study. DESIGN, SETTING, AND PARTICIPANTS: This phenome-wide association study used health data from participants in 3 cohorts. The Electronic Medical Records and Genomics Sequencing (eMERGEseq) data set recruited predominantly healthy individuals from 10 US medical centers from July 16, 2016, through February 18, 2018, with a mean follow-up through electronic health records (EHRs) of 12.7 (7.4) years. The UK Biobank (UKB) cohort recruited participants from March 15, 2006, through August 1, 2010, with a mean (SD) follow-up of 12.4 (1.0) years. The Hereditary Cancer Registry (HCR) recruited patients undergoing clinical genetic testing at Vanderbilt University Medical Center from May 1, 2012, through December 31, 2019, with a mean (SD) follow-up through EHRs of 8.8 (6.5) years. EXPOSURES: Germline variants in 23 hereditary cancer genes. Pathogenic and likely pathogenic variants for each gene were aggregated for association analyses. MAIN OUTCOMES AND MEASURES: Phenotypes in the eMERGEseq and HCR cohorts were derived from the linked EHRs. Phenotypes in UKB were from multiple sources of health-related data. RESULTS: A total of 214 020 participants were identified, including 23 544 in eMERGEseq cohort (mean [SD] age, 47.8 [23.7] years; 12 611 women [53.6%]), 187 234 in the UKB cohort (mean [SD] age, 56.7 [8.1] years; 104 055 [55.6%] women), and 3242 in the HCR cohort (mean [SD] age, 52.5 [15.5] years; 2851 [87.9%] women). All 38 established gene-cancer associations were replicated, and 19 new associations were identified. These included the following 7 associations with neoplasms: CHEK2 with leukemia (odds ratio [OR], 3.81 [95% CI, 2.64-5.48]) and plasma cell neoplasms (OR, 3.12 [95% CI, 1.84-5.28]), ATM with gastric cancer (OR, 4.27 [95% CI, 2.35-7.44]) and pancreatic cancer (OR, 4.44 [95% CI, 2.66-7.40]), MUTYH (biallelic) with kidney cancer (OR, 32.28 [95% CI, 6.40-162.73]), MSH6 with bladder cancer (OR, 5.63 [95% CI, 2.75-11.49]), and APC with benign liver/intrahepatic bile duct tumors (OR, 52.01 [95% CI, 14.29-189.29]). The remaining 12 associations with nonneoplastic diseases included BRCA1/2 with ovarian cysts (OR, 3.15 [95% CI, 2.22-4.46] and 3.12 [95% CI, 2.36-4.12], respectively), MEN1 with acute pancreatitis (OR, 33.45 [95% CI, 9.25-121.02]), APC with gastritis and duodenitis (OR, 4.66 [95% CI, 2.61-8.33]), and PTEN with chronic gastritis (OR, 15.68 [95% CI, 6.01-40.92]). CONCLUSIONS AND RELEVANCE: The findings of this genetic association study analyzing the EHRs of 3 large cohorts suggest that these new phenotypes associated with hereditary cancer genes may facilitate early detection and better management of cancers. This study highlights the potential benefits of using EHR data in genomic medicine.

Discovering an incidental finding (IF) or secondary finding (SF) is a potential result of genomic testing, but few data exist describing types and frequencies of SFs likely to appear in broader clinical populations.The Electronic Medical Records and Genomics Network Phase III (eMERGE III) developed a CLIA-compliant sequencing panel of 109 genes and 1551 variants of clinical relevance or research interest and deployed this panel at ten clinical sites. We evaluated medically actionable SFs across 67 genes and 14 single-nucleotide variants (SNVs) in a diverse cohort of 21,915 participants drawn from a variety of settings (e.g., primary care, biobanks, specialty clinics).Correcting for testing indication, we found a 3.02% overall frequency of SFs; 2.54% from 59 genes the American College of Medical Genetics and Genomics recommends for SF return, and 0.48% in other genes, primarily HFE and PALB2. SFs associated with cancer susceptibility were most frequent (1.38%), followed by cardiovascular diseases (0.87%), and lipid disorders (0.50%). After removing HFE, the frequency of SFs and proportion of pathogenic versus likely pathogenic SFs did not differ in those self-identifying as White versus others.Here we present frequencies and types of medically actionable secondary findings to support informed decision making by patients, participants, and practitioners engaged in genomic medicine.

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. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and that of rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 “pharmacogenes” was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record (EHR). RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSIONS: Implementation of preemptive rather than reactive, sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to demonstrate more efficient use of health care resources.

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. Here, we present best practice recommendations for the interpretation and reporting of clinical diagnostic WGS, including discussion of challenges and emerging approaches that will be critical to harness the full potential of this comprehensive test.

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Purpose:To provide a validated method to confidently identify exon-containing copy number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.Methods:DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples (midpool), when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap).Results:Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multi-exon and 29 single-exon CNVs with high C-scores were assessed by MLPA.Conclusions:Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We proposed guidelines and criteria to identify high confidence single-exon CNVs.

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. Methods Whole-genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. Results The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. Conclusions We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Changes to the program in the future will be covered in supplementary submissions to the IDE and will support additional variant classes, sample types, and any expansion to the reportable regions.

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 United States. 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. To do so, whole genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Future supplements to the IDE will be submitted to support additional variant classes, sample types, and any expansion to the reportable regions.

Purpose Cardiovascular disease (CVD) is the leading cause of death in adults in the United States, yet the benefits of genetic testing are not universally accepted. Methods We developed the "HeartCare" panel of genes associated with CVD, evaluating high-penetrance Mendelian conditions, coronary artery disease (CAD) polygenic risk, LPA gene polymorphisms, and specific pharmacogenetic (PGx) variants. We enrolled 709 individuals from cardiology clinics at Baylor College of Medicine, and samples were analyzed in a CAP/CLIA-certified laboratory. Results were returned to the ordering physician and uploaded to the electronic medical record. Results Notably, 32% of patients had a genetic finding with clinical management implications, even after excluding PGx results, including 9% who were molecularly diagnosed with a Mendelian condition. Among surveyed physicians, 84% reported medical management changes based on these results, including specialist referrals, cardiac tests, and medication changes. LPA polymorphisms and high polygenic risk of CAD were found in 20% and 9% of patients, respectively, leading to diet, lifestyle, and other changes. Warfarin and simvastatin pharmacogenetic variants were present in roughly half of the cohort. Conclusion Our results support the use of genetic information in routine cardiovascular health management and provide a roadmap for accompanying research.

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network’s Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), to represent clinical genomics results. These new standards improve the utility of HL7 FHIR as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

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. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Introduction: Cardiovascular disease (CVD) is the leading cause of mortality in the United States, leading to one in four deaths. The role of inherited susceptibility to CVD is well established, from rare monogenic disorders to polygenic traits. For many of these conditions, guidelines exist for medical interventions and other preventative care that can improve outcomes and quality of life. Methods: We developed a comprehensive genetic screen, "HeartCare", consisting of a 158 gene panel evaluating 1) Mendelian conditions including cardiomyopathies, aortopathies, arrhythmias, and dyslipidemias, 2) a coronary artery disease polygenic risk score (PRS), 3) variants in the LPA gene encoding Lipoprotein(a) that are an independent risk factor for atherosclerotic CVD events, and 4) pharmacogenetic (PGx) variants contributing to simvastatin-induced myopathy and warfarin metabolism. After sequencing in a CAP/CLIA certified laboratory, results were returned to the ordering physician after a multi-disciplinary sign-out conference and uploaded to the EMR. Results: As of June 2020, 678 individuals had completed testing with a 31% overall positive rate for Mendelian genes, elevated polygenic risk, and LPA risk alleles (excluding PGx). Of these, 8.1% had a positive finding for a Mendelian condition, the majority (60%) being dyslipidemias (e.g., FH), followed by 25% cardiomyopathies (e.g., HCM, DCM, ARVC) and 6% aortopathies (e.g., Marfan, Loeys-Dietz). Approximately 20% of individuals carried an LPA risk allele, and 9.3% belonged to the high-risk group according to their PRS. Approximately half had a PGx finding related to simvastatin and/or warfarin metabolism. Nearly one in five individuals had a finding with direct clinical care impact, including referral to specialists, imaging, laboratory studies, therapies/procedures (e.g., PCSK9i, ICD). Conclusions: To our knowledge, this is the first test of its kind assaying four distinct categories of genetic variation related to cardiovascular health. Our results demonstrate that comprehensive testing can be routinely used to identify individuals who may benefit from interventions to improve survival, reduce morbidity, and enhance quality of life.

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. Through INSIGHT, we can rapidly prototype and benchmark novel approaches to incorporating clinical genomics into patient care. Of particular interest are clinical decision support tools that take advantage of domain knowledge from clinical genomics and can be rapidly adjusted based on feedback from clinicians. METHODS Building on previously developed evidence and infrastructure components, our model includes the following: establishment of an intervention-ready genomic knowledge base for patient care, creation of a health informatics platform and linking it to a clinical genomics reporting system, and scaling and evaluation of INSIGHT following established implementation science principles. RESULTS INSIGHT was approved by the Institutional Review Board at the University of Texas Health Science Center at Houston on May 15, 2020, and is designed as a 2-year proof-of-concept study beginning in December 2021. By design, 120 patients from the Texas Comprehensive Epilepsy Program are to be enrolled to test the INSIGHT workflow. Initial results are expected in the first half of 2023. CONCLUSIONS INSIGHT’s domain-specific, practical but generalizable approach may help catalyze a pathway to accelerate translation of genomic knowledge into impactful interventions in patient care. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/25576

Polygenic risk scores (PRS), often aggregating the results from genome-wide association studies, can bridge the gap between the initial variant discovery efforts and disease risk estimation for clinical applications. However, there is remarkable heterogeneity in the reporting of these risk scores due to a lack of adherence to reporting standards and no accepted standards suited for the current state of PRS development and application. This lack of adherence and best practices hinders the translation of PRS into clinical care. The ClinGen Complex Disease Working Group, in a collaboration with the Polygenic Score (PGS) Catalog, have developed a novel PRS Reporting Statement (PRS-RS), updating previous standards to the current state of the field and to enable downstream utility. Drawing upon experts in epidemiology, statistics, disease-specific applications, implementation, and policy, this 23-item reporting framework defines the minimal information needed to interpret and evaluate a PRS, especially with respect to any downstream clinical applications. Items span detailed descriptions of the study population (recruitment method, key demographics, inclusion/exclusion criteria, and phenotype definition), statistical methods for both PRS development and validation, and considerations for potential limitations of the published risk score and downstream clinical utility. Additionally, emphasis has been placed on data availability and transparency to facilitate reproducibility and benchmarking against other PRS, such as deposition in the publicly available PGS Catalog (www.PGScatalog.org). By providing these criteria in a structured format that builds upon existing standards and ontologies, the use of this framework in publishing PRS will facilitate translation of PRS into clinical care and progress towards defining best practices.SummaryIn recent years, polygenic risk scores (PRS) have become an increasingly studied tool to capture the genome-wide liability underlying many human traits and diseases, hoping to better inform an individual’s genetic risk. However, a lack of tailored reporting standards has hindered the translation of this important tool into clinical and public health practice with the heterogeneous underreporting of details necessary for benchmarking and reproducibility. To address this gap, the ClinGen Complex Disease Working Group and Polygenic Score (PGS) Catalog have collaborated to develop the 23-item Polygenic Risk Score Reporting Statement (PRS-RS). This framework provides the minimal information expected of authors to promote the validity, transparency, and reproducibility of PRS by requiring authors to detail the study population, statistical methods, and potential clinical utility of a published score. The widespread adoption of this framework will encourage rigorous methodological consideration and facilitate benchmarking to ensure high quality scores are translated into the clinic.

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. The possible pathogenic variants were verified by Sanger sequencing. Functional characterization of the identified variant was completed by western blot. Results The child inherited a single‐nucleotide deletion NM_000314.6:c.849delA (p.Glu284Argfs) in the tumor suppressor gene PTEN from his father. The paternal family members have a history of cancer. It is conceivable that PTEN loss‐of‐function induced the adipose tumor growth and hypoglycemia, although the proband did not meet the usual diagnosis criteria of Cowden syndrome or Bannayan–Riley–Ruvalcaba syndrome that are characterized by germline mutations of PTEN. Conclusion This case underlines the variability of phenotypes associated with PTEN germline mutations and provides useful information for diagnosis and genetic counseling of PTEN‐related diseases for pediatric patients., We report a unique combination of drastic phenotypes from a pediatric patient who presented with massive abdominal distention and severe hypoglycemia that led to recurrent seizures. Trio‐based sequencing analysis identified a heterozygous germline single‐nucleotide deletion mutation in the coding region of the tumor suppressor gene PTEN.

Objectives: To identify clinically actionable genetic variants from targeted sequencing of 68 disease-related genes, estimate their penetrance, and assess the impact of disclosing results to participants and providers. Patients and Methods: The Return of Actionable Variants Empirical (RAVE) Study investigates outcomes following the return of pathogenic/likely pathogenic (P/LP) variants in 68 disease-related genes. The study was initiated in December 2016 and is ongoing. Targeted sequencing was performed in 2533 individuals with hyperlipidemia or colon polyps. The electronic health records (EHRs) of participants carrying P/LP variants in 36 cardiovascular disease (CVD) genes were manually reviewed to ascertain the presence of relevant traits. Clinical outcomes, health care utilization, family communication, and ethical and psychosocial implications of disclosure of genomic results are being assessed by surveys, telephone interviews, and EHR review. Results: Of 29,208 variants in the 68 genes, 1915 were rare (frequency

Motivation Clinical genome sequencing laboratories return reports containing clinical testing results, signed by a board-certified clinical geneticist, to the ordering physician. This report is often a PDF, but can also be a paper copy or a structured data file. The reports are frequently modified and reissued due to changes in variant interpretation or clinical attributes. Materials and Methods To precisely track report authenticity, we developed ARBoR (Authenticated Resources in a Hashed Block Registry), an application for tracking the authenticity and lineage of versioned clinical reports even when they are distributed as PDF or paper copies. ARBoR tracks clinical reports as cryptographically signed hash blocks in an electronic ledger file, which is then exactly replicated to many clients. Results ARBoR was implemented for clinical reporting in the Human Genome Sequencing Center Clinical Laboratory, initially as part of the National Institute of Health's Electronic Medical Record and Genomics (eMERGE) project. Conclusions To date, we have issued 15 205 versioned clinical reports tracked by ARBoR. This system has provided us with a simple and tamper-proof mechanism for tracking clinical reports with a complicated update history.

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. Through INSIGHT, we can rapidly prototype and benchmark novel approaches to incorporating clinical genomics into patient care. Of particular interest are clinical decision support tools that take advantage of domain knowledge from clinical genomics and can be rapidly adjusted based on feedback from clinicians. Methods Building on previously developed evidence and infrastructure components, our model includes the following: establishment of an intervention-ready genomic knowledge base for patient care, creation of a health informatics platform and linking it to a clinical genomics reporting system, and scaling and evaluation of INSIGHT following established implementation science principles. Results INSIGHT was approved by the Institutional Review Board at the University of Texas Health Science Center at Houston on May 15, 2020, and is designed as a 2-year proof-of-concept study beginning in December 2021. By design, 120 patients from the Texas Comprehensive Epilepsy Program are to be enrolled to test the INSIGHT workflow. Initial results are expected in the first half of 2023. Conclusions INSIGHT’s domain-specific, practical but generalizable approach may help catalyze a pathway to accelerate translation of genomic knowledge into impactful interventions in patient care. International Registered Report Identifier (IRRID) PRR1-10.2196/25576

Cardiovascular disease (CVD) is the leading cause of mortality in the United States, leading to 1 in every 4 deaths. The role of inherited susceptibility to CVD is well established, from rare monogenic disorders to pharmacogenetics and more recently studied polygenic traits. To advance precision

MotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.ResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30× short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.AvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/xatlas.Contactfarek@bcm.eduSupplementary informationSupplementary data are available at Bioinformatics online.

The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/.

The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

Background— Left ventricular noncompaction (LVNC) is a genetically and phenotypically heterogeneous disease and, although increasingly recognized in clinical practice, there is a lack of widely accepted diagnostic criteria. We sought to identify novel genetic causes of LVNC and describe genotype–phenotype correlations. Methods and Results— A total of 190 patients from 174 families with left ventricular hypertrabeculation (LVHT) or LVNC were referred for cardiac magnetic resonance and whole-exome sequencing. A total of 425 control individuals were included to identify variants of interest (VOIs). We found an excess of 138 VOIs in 102 (59%) unrelated patients in 54 previously identified LVNC or other known cardiomyopathy genes. VOIs were found in 68 of 90 probands with LVNC and 34 of 84 probands with LVHT (76% and 40%, respectively; P P P =0.01). The presence of sarcomeric gene mutations was associated with increased occurrence of late gadolinium enhancement ( P =0.004). Conclusions— LVHT and LVNC likely represent a continuum of genotypic disease with differences in severity and variable phenotype explained, in part, by the number of VOIs and whether mutations are present in sarcomeric or nonsarcomeric genes. Presence of VOIs is common in patients with LVHT. Our findings expand the current clinical and genetic diagnostic approaches for patients with LVHT and LVNC.

Motivation: The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure. Methods and Results: We find that when residue scores of evolutionary importance are distributed smoothly between nearby residues, functional site prediction accuracy improves. Accordingly, we designed a novel measure of evolutionary importance that focuses on the interaction between pairs of structurally neighboring residues. This measure that we term pair-interaction Evolutionary Trace yields greater functional site overlap and better structure-based proteome-wide functional predictions. Conclusions: Our data show that the structural smoothness of evolutionary importance is a fundamental feature of the coevolution of sequence, structure and function. Mutations operate on individual residues, but selective pressure depends in part on the extent to which a mutation perturbs interactions with neighboring residues. In practice, this principle led us to redefine the importance of a residue in terms of the importance of its epistatic interactions with neighbors, yielding better annotation of functional residues, motivating experimental validation of a novel functional site in LexA and refining protein function prediction. Contact: lichtarge@bcm.edu Supplementary information: Supplementary data are available at Bioinformatics online.

By design, structural genomics (SG) solves many structures that cannot be assigned function based on homology to known proteins. Alternative function annotation methods are therefore needed and this study focuses on function prediction with three-dimensional (3D) templates: small structural motifs built of just a few functionally critical residues. Although experimentally proven functional residues are scarce, we show here that Evolutionary Trace (ET) rankings of residue importance are sufficient to build 3D templates, match them, and then assign Gene Ontology (GO) functions in enzymes and non-enzymes alike. In a high-specificity mode, this Evolutionary Trace Annotation (ETA) method covered half (53%) of the 2384 annotated SG protein controls. Three-quarters (76%) of predictions were both correct and complete. The positive predictive value for all GO depths (all-depth PPV) was 84%, and it rose to 94% over GO depths 1-3 (depth 3 PPV). In a high-sensitivity mode, coverage rose significantly (84%), while accuracy fell moderately: 68% of predictions were both correct and complete, all-depth PPV was 75%, and depth 3 PPV was 86%. These data concur with prior mutational experiments showing that ET rank information identifies key functional determinants in proteins. In practice, ETA predicted functions in 42% of 3461 unannotated SG proteins. In 529 cases--including 280 non-enzymes and 21 for metal ion ligands--the expected accuracy is 84% at any GO depth and 94% down to GO depth 3, while for the remaining 931 the expected accuracies are 60% and 71%, respectively. Thus, local structural comparisons of evolutionarily important residues can help decipher protein functions to known reliability levels and without prior assumption on functional mechanisms. ETA is available at http://mammoth.bcm.tmc.edu/eta.

Summary: Most proteins lack experimentally validated functions. To address this problem, we implemented the Evolutionary Trace Annotation (ETA) method in the Cytoscape network visualization environment. The result is the ETAscape plugin, which builds a structural genomics network based on local structural and evolutionary similarities among proteins and then globally diffuses known annotations across the resulting network. The plugin displays these novel functional annotations, their confidence, the molecular basis for individual matches and the set of matches that lead to a prediction. Availability: The ETA Network Plugin is available publicly for download at http://mammoth.bcm.tmc.edu/networks/. Contact: lichtarge@bcm.edu

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.