Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange.

In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.

Analyzing and Reanalyzing the Genome: Findings from the MedSeq Project.

Although genome sequencing is increasingly available in clinical and research settings, many questions remain about the interpretation of sequencing data. In the MedSeq Project, we explored how much effort is required to evaluate and report on more than 4,500 genes reportedly associated with monogenic conditions, as well as pharmacogenomic (PGx) markers, blood antigen serotyping, and polygenic risk scores in 100 individuals (50 with cardiomyopathy and 50 healthy) randomized to the sequencing arm. We defined the quality thresholds for determining the need for Sanger confirmation. Finally, we examined the effort needed and new findings revealed by reanalyzing each genome (6-23 months after initial analysis; mean 13 months). Monogenic disease risk and carrier status were reported in 21% and 94% of participants, respectively. Only two participants had no monogenic disease risk or carrier status identified. For the PGx results (18 genotypes in six genes for five drugs), the identified diplotypes prompted recommendation for non-standard dosing of at least one of the analyzed drugs in 95% of participants. For blood antigen studies, we found that 31% of participants had a rare blood antigen genotype. In the cardiomyopathy cohort, an explanation for disease was identified in 48% of individuals. Over the course of the study, 14 variants were reclassified and, upon reanalysis, 18 new variants met criteria for reporting. These findings highlight the quantity of medically relevant findings from a broad analysis of genomic sequencing data as well as the need for periodic reinterpretation and reanalysis of data for both diagnostic indications and secondary findings.

Recontacting registry participants with genetic updates through GenomeConnect, the ClinGen patient registry.

PURPOSE: Variant classifications and gene-disease relationships may evolve. Professional societies have suggested patients share the responsibility to remain up-to-date on the implications genetic results have on their health, and that novel methods of recontact are needed. GenomeConnect, the ClinGen patient registry, has implemented a process to provide variant classification and gene-disease relationship updates to participants. Here, we report on our experience with this recontacting process. METHODS: GenomeConnect shares data with ClinVar and Matchmaker Exchange enabling the identification of updates to variant classifications and gene-disease relationships. For any updates identified, the reporting laboratory is contacted, and updates are shared with participants opting to receive them. RESULTS: Of 1,419 variants shared with ClinVar by GenomeConnect, 49 (3.4%) variant reclassifications were identified and 34 were shared with participants. Of 97 candidate genes submitted to Matchmaker Exchange, 10 (10.3%) gene-disease relationships have been confirmed and 9 were shared with participants. Details available from a subset of participants highlight that updated information is not always shared with the patient by testing laboratories. CONCLUSION: Patient registries can provide a mechanism for patients and their providers to remain informed about changes to the interpretation and clinical significance of their genetic results, leading to important implications for care.

Variant interpretation is a component of clinical practice among genetic counselors in multiple specialties.

PURPOSE: Genomic testing is routinely utilized across clinical settings and can have significant variant interpretation challenges. The extent of genetic counselor (GC) engagement in variant interpretation in clinical practice is unknown. This study aimed to explore clinical GCs' variant interpretation practice across specialties, understand outcomes of this practice, and identify resource and educational needs. METHODS: An online survey was administered to National Society of Genetic Counselors members providing clinical counseling. RESULTS: Respondents (n = 239) represented all major clinical specialties. The majority (68%) reported reviewing evidence documented by the laboratory for most (>60%) variants reported; 45.5% report seeking additional evidence. Prenatal GCs were less likely to independently assess reported evidence. Most respondents (67%) report having reached a different conclusion about a variant's classification than the testing laboratory, though infrequently. Time was the most commonly reported barrier (72%) to performing variant interpretation, though the majority (97%) indicated that this practice had an important impact on patient care. When presented with three hypothetical scenarios, evidence typically used for variant interpretation was generally applied correctly. CONCLUSION: This study is the first to document variant interpretation practice broadly across clinical GC specialties. Our results suggest that variant interpretation should be considered a practice-based competency for GCs.

Development of a consent resource for genomic data sharing in the clinical setting.

PURPOSE: Data sharing between clinicians, laboratories, and patients is essential for improvements in genomic medicine, but obtaining consent for individual-level data sharing is often hindered by a lack of time and resources. To address this issue, the Clinical Genome Resource (ClinGen) developed tools to facilitate consent, including a one-page consent form and online supplemental video with information on key topics, such as risks and benefits of data sharing. METHODS: To determine whether the consent form and video accurately conveyed key data sharing concepts, we surveyed 5,162 members of the general public. We measured comprehension at baseline, after reading the form and watching the video. Additionally, we assessed participants' attitudes toward genomic data sharing. RESULTS: Participants' performance on comprehension questions significantly improved over baseline after reading the form and continued to improve after watching the video. CONCLUSION: Results suggest reading the form alone provided participants with important knowledge regarding broad data sharing, and watching the video allowed for broader comprehension. These materials are now available at http://www.clinicalgenome.org/share . These resources will provide patients a straightforward way to share their genetic and health information, and improve the scientific community's access to data generated through routine healthcare.

Development of Clinical Domain Working Groups for the Clinical Genome Resource (ClinGen): lessons learned and plans for the future.

The Clinical Genome Resource (ClinGen) is supported by the National Institutes of Health (NIH) to develop expertly curated and freely accessible resources defining the clinical relevance of genes and variants for use in precision medicine and research. To facilitate expert input, ClinGen has formed Clinical Domain Working Groups (CDWGs) to leverage the collective knowledge of clinicians, laboratory diagnosticians, and researchers. In the initial phase of ClinGen, CDWGs were launched in the cardiovascular, hereditary cancer, and inborn errors of metabolism clinical fields. These early CDWGs established the infrastructure necessary to implement standardized processes developed or adopted by ClinGen working groups for the interpretation of gene-disease associations and variant pathogenicity, and provided a sustainable model for the formation of future disease-focused curation groups. The establishment of CDWGs requires recruitment of international experts to broadly represent the interests of their field and ensure that assertions made are reliable and widely accepted. Building on the successes, challenges, and trade-offs made in establishing the original CDWGs, ClinGen has developed standard operating procedures for the development of CDWGs in new clinical domains, while maximizing efforts to scale up curation and facilitate involvement of external groups who wish to utilize ClinGen methods and infrastructure for expert curation.

ClinGen's GenomeConnect registry enables patient-centered data sharing.

GenomeConnect, the NIH-funded Clinical Genome Resource (ClinGen) patient registry, engages patients in data sharing to support the goal of creating a genomic knowledge base to inform clinical care and research. Participant self-reported health information and genomic variants from genetic testing reports are curated and shared with public databases, such as ClinVar. There are four primary benefits of GenomeConnect: (1) sharing novel genomic data-47.9% of variants were new to ClinVar, highlighting patients as a genomic data source; (2) contributing additional phenotypic information-of the 52.1% of variants already in ClinVar, GenomeConnect provided enhanced case-level data; (3) providing a way for patients to receive variant classification updates if the reporting laboratory submits to ClinVar-97.3% of responding participants opted to receive such information and 13 updates have been identified; and (4) supporting connections with others, including other participants, clinicians, and researchers to enable the exchange of information and support-60.4% of participants have opted to partake in participant matching. Moving forward, ClinGen plans to increase patient-centric data sharing by partnering with other existing patient groups. By engaging patients, more information is contributed to the public knowledge base, benefiting both patients and the genomics community.

ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation.

Genome-scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high-quality interpretation requires multiple specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.

Short-term costs of integrating whole-genome sequencing into primary care and cardiology settings: a pilot randomized trial.

PURPOSE: Great uncertainty exists about the costs associated with whole-genome sequencing (WGS). METHODS: One hundred cardiology patients with cardiomyopathy diagnoses and 100 ostensibly healthy primary care patients were randomized to receive a family-history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months. RESULTS: The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared with family history alone. Mean 6-month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = -$1,560, 95% confidence interval -$7,558 to $3,866, p = 0.36; primary care: difference = $681, 95% confidence interval -$884 to $2,171, p = 0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively. CONCLUSION: Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream health care. Disclosing additional types of secondary findings has a limited cost impact following disclosure.

The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients: A Pilot Randomized Trial.

Background: Whole-genome sequencing (WGS) in asymptomatic adults might prevent disease but increase health care use without clinical value. Objective: To describe the effect on clinical care and outcomes of adding WGS to standardized family history assessment in primary care. Design: Pilot randomized trial. (ClinicalTrials.gov: NCT01736566). Setting: Academic primary care practices. Participants: 9 primary care physicians (PCPs) and 100 generally healthy patients recruited at ages 40 to 65 years. Intervention: Patients were randomly assigned to receive a family history report alone (FH group) or in combination with an interpreted WGS report (FH + WGS group), which included monogenic disease risk (MDR) results (associated with Mendelian disorders), carrier variants, pharmacogenomic associations, and polygenic risk estimates for cardiometabolic traits. Each patient met with his or her PCP to discuss the report. Measurements: Clinical outcomes and health care use through 6 months were obtained from medical records and audio-recorded discussions between PCPs and patients. Patients' health behavior changes were surveyed 6 months after receiving results. A panel of clinician-geneticists rated the appropriateness of how PCPs managed MDR results. Results: Mean age was 55 years; 58% of patients were female. Eleven FH + WGS patients (22% [95% CI, 12% to 36%]) had new MDR results. Only 2 (4% [CI, 0.01% to 15%]) had evidence of the phenotypes predicted by an MDR result (fundus albipunctatus due to RDH5 and variegate porphyria due to PPOX). Primary care physicians recommended new clinical actions for 16% (CI, 8% to 30%) of FH patients and 34% (CI, 22% to 49%) of FH + WGS patients. Thirty percent (CI, 17% to 45%) and 41% (CI, 27% to 56%) of FH and FH + WGS patients, respectively, reported making a health behavior change after 6 months. Geneticists rated PCP management of 8 MDR results (73% [CI, 39% to 99%]) as appropriate and 2 results (18% [CI, 3% to 52%]) as inappropriate. Limitation: Limited sample size and ancestral and socioeconomic diversity. Conclusion: Adding WGS to primary care reveals new molecular findings of uncertain clinical utility. Nongeneticist providers may be able to manage WGS results appropriately, but WGS may prompt additional clinical actions of unclear value. Primary Funding Source: National Institutes of Health.

Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar.

PURPOSE: Data sharing through ClinVar offers a unique opportunity to identify interpretation differences between laboratories. As part of a ClinGen initiative, four clinical laboratories (Ambry, GeneDx, Partners Healthcare Laboratory for Molecular Medicine, and University of Chicago Genetic Services Laboratory) collaborated to identify the basis of interpretation differences and to investigate if data sharing and reassessment resolve interpretation differences by analyzing a subset of variants. METHODS: ClinVar variants with submissions from at least two of the four participating laboratories were compared. For a subset of identified differences, laboratories documented the basis for discordance, shared internal data, independently reassessed with the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines, and then compared interpretations. RESULTS: At least two of the participating laboratories interpreted 6,169 variants in ClinVar, of which 88.3% were initially concordant. Laboratories reassessed 242/724 initially discordant variants, of which 87.2% (211) were resolved by reassessment with current criteria and/or internal data sharing; 12.8% (31) of reassessed variants remained discordant owing to differences in the application of the ACMG-AMP guidelines. CONCLUSION: Participating laboratories increased their overall concordance from 88.3 to 91.7%, indicating that sharing variant interpretations in ClinVar-thereby allowing identification of differences and motivation to resolve those differences-is critical to moving toward more consistent variant interpretations.Genet Med advance online publication 09 March 2017.

GenomeConnect: matchmaking between patients, clinical laboratories, and researchers to improve genomic knowledge.

As the utility of genetic and genomic testing in healthcare grows, there is need for a high-quality genomic knowledge base to improve the clinical interpretation of genomic variants. Active patient engagement can enhance communication between clinicians, patients, and researchers, contributing to knowledge building. It also encourages data sharing by patients and increases the data available for clinicians to incorporate into individualized patient care, clinical laboratories to utilize in test interpretation, and investigators to use for research. GenomeConnect is a patient portal supported by the Clinical Genome Resource (ClinGen), providing an opportunity for patients to add to the knowledge base by securely sharing their health history and genetic test results. Data can be matched with queries from clinicians, laboratory personnel, and researchers to better interpret the results of genetic testing and build a foundation to support genomic medicine. Participation is online, allowing patients to contribute regardless of location. GenomeConnect supports longitudinal, detailed clinical phenotyping and robust "matching" among research and clinical communities. Phenotype data are gathered using online health questionnaires; genotype data are obtained from genetic test reports uploaded by participants and curated by staff. GenomeConnect empowers patients to actively participate in the improvement of genomic test interpretation and clinical utility.

The Matchmaker Exchange: a platform for rare disease gene discovery.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

A systematic approach to the reporting of medically relevant findings from whole genome sequencing.

BACKGROUND: The MedSeq Project is a randomized clinical trial developing approaches to assess the impact of integrating genome sequencing into clinical medicine. To facilitate the return of results of potential medical relevance to physicians and patients participating in the MedSeq Project, we sought to develop a reporting approach for the effective communication of such findings. METHODS: Genome sequencing was performed on the Illumina HiSeq platform. Variants were filtered, interpreted, and validated according to methods developed by the Laboratory for Molecular Medicine and consistent with current professional guidelines. The GeneInsight software suite, which is integrated with the Partners HealthCare electronic health record, was used for variant curation, report drafting, and delivery. RESULTS: We developed a concise 5-6 page Genome Report (GR) featuring a single-page summary of results of potential medical relevance with additional pages containing structured variant, gene, and disease information along with supporting evidence for reported variants and brief descriptions of associated diseases and clinical implications. The GR is formatted to provide a succinct summary of genomic findings, enabling physicians to take appropriate steps for disease diagnosis, prevention, and management in their patients. CONCLUSIONS: Our experience highlights important considerations for the reporting of results of potential medical relevance and provides a framework for interpretation and reporting practices in clinical genome sequencing.

Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms.

Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.

Points to consider for sharing variant-level information from clinical genetic testing with ClinVar.

Data sharing between laboratories, clinicians, researchers, and patients is essential for improvements and standardization in genomic medicine; encouraging genomic data sharing (GDS) is a key activity of the National Institutes of Health (NIH)-funded Clinical Genome Resource (ClinGen). The ClinGen initiative is dedicated to evaluating the clinical relevance of genes and variants for use in precision medicine and research. Currently, data originating from each of the aforementioned stakeholder groups is represented in ClinVar, a publicly available repository of genomic variation, and its relationship to human health hosted by the National Center for Biotechnology Information at the NIH. Although policies such as the 2014 NIH GDS policy are clear regarding the mandate for informed consent for broad data sharing from research participants, no clear guidance exists on the level of consent appropriate for the sharing of information obtained through clinical testing to advance knowledge. ClinGen has collaborated with ClinVar and the National Human Genome Research Institute to develop points to consider for clinical laboratories on sharing de-identified variant-level data in light of both the NIH GDS policy and the recent updates to the Common Rule. We propose specific data elements from interpreted genomic variants that are appropriate for submission to ClinVar when direct patient consent was not sought and describe situations in which obtaining informed consent is recommended.

Approaches to carrier testing and results disclosure in translational genomics research: The clinical sequencing exploratory research consortium experience.

BACKGROUND: Clinical genome and exome sequencing (CGES) is primarily used to address specific clinical concerns by detecting risk of future disease, clarifying diagnosis, or directing treatment. Additionally, CGES makes possible the disclosure of autosomal recessive and X-linked carrier results as additional secondary findings, and research about the impact of carrier results disclosure in this context is needed. METHODS: Representatives from 11 projects in the clinical sequencing exploratory research (CSER) consortium collected data from their projects using a structured survey. The survey focused on project characteristics, which variants were offered and/or disclosed to participants as carrier results, methods for carrier results disclosure, and project-specific outcomes. We recorded quantitative responses and report descriptive statistics with the aim of describing the variability in approaches to disclosing carrier results in translational genomics research projects. RESULTS: The proportion of participants with carrier results was related to the number of genes included, ranging from 3% (three genes) to 92% (4,600 genes). Between one and seven results were disclosed to those participants who received any positive result. Most projects offered participants choices about whether to receive some or all of the carrier results. There were a range of approaches to communicate results, and many projects used separate approaches for disclosing positive and negative results. CONCLUSION: Future translational genomics research projects will need to make decisions regarding whether and how to disclose carrier results. The CSER consortium experience identifies approaches that balance potential participant interest while limiting impact on project resources.

Determinants of white matter hyperintensity burden in patients with Fabry disease.

OBJECTIVE: Using a semiautomated volumetric MRI assessment method, we aimed to identify determinants of white matter hyperintensity (WMH) burden in patients with Fabry disease (FD). METHODS: Patients with confirmed FD and brain MRI available for this analysis were eligible for this protocol after written consent. Clinical characteristics were abstracted from medical records. T2 fluid-attenuated inversion recovery MRI were transferred in electronic format and analyzed for WMH volume (WMHV) using a validated, computer-assisted method. WMHV was normalized for head size (nWMHV) and natural log-transformed (lnWMHV) for univariate and multivariate linear regression analyses. Level of significance was set at p < 0.05 for all analyses. RESULTS: Of 223 patients with FD and WMHV analyzed, 132 (59%) were female. Mean age at MRI was 39.2 ± 14.9 (range 9.6-72.7) years, and 136 (61%) patients received enzyme replacement therapy prior to enrollment. Median nWMHV was 2.7 cm(3) (interquartile range 1.8-4.0). Age (ß 0.02, p = 0.008) and history of stroke (ß 1.13, p = 0.02) were independently associated with lnWMHV. However, WMH burden-as well as WMHV predictors-varied by decade of life in this cohort of patients with FD (p < 0.0001). CONCLUSIONS: In this largest-to-date cohort of patients with FD who had volumetric analysis of MRI, age and prior stroke independently predicted the burden of WMH. The 4th decade of life appears to be critical in progression of WMH burden, as novel predictors of WMHV emerged in patients aged 31-40 years. Future studies to elucidate the biology of WMH in FD and its role as potential MRI marker of disease progression are needed.