The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies

<p>Abstract</p> <p>Introduction</p> <p>The eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR) systems for advancing discovery in genome science. eMERGE also includes a special emphasis on the ethical, legal and social issues related to these endeavors.</p> <p>Organization</p> <p>The five sites are supported by an Administrative Coordinating Center. Setting of network goals is initiated by working groups: (1) Genomics, (2) Informatics, and (3) Consent & Community Consultation, which also includes active participation by investigators outside the eMERGE funded sites, and (4) Return of Results Oversight Committee. The Steering Committee, comprised of site PIs and representatives and NHGRI staff, meet three times per year, once per year with the External Scientific Panel.</p> <p>Current progress</p> <p>The primary site-specific phenotypes for which samples have undergone genome-wide association study (GWAS) genotyping are cataract and HDL, dementia, electrocardiographic QRS duration, peripheral arterial disease, and type 2 diabetes. A GWAS is also being undertaken for resistant hypertension in ≈2,000 additional samples identified across the network sites, to be added to data available for samples already genotyped. Funded by ARRA supplements, secondary phenotypes have been added at all sites to leverage the genotyping data, and hypothyroidism is being analyzed as a cross-network phenotype. Results are being posted in dbGaP. Other key eMERGE activities include evaluation of the issues associated with cross-site deployment of common algorithms to identify cases and controls in EMRs, data privacy of genomic and clinically-derived data, developing approaches for large-scale meta-analysis of GWAS data across five sites, and a community consultation and consent initiative at each site.</p> <p>Future activities</p> <p>Plans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care.</p> <p>Summary</p> <p>By combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.</p

Accounting for quality improvement during the conduct of embedded pragmatic clinical trials within healthcare systems: NIH Collaboratory case studies

Embedded pragmatic clinical trials (ePCTs) and quality improvement (QI) activities often occur simultaneously within healthcare systems (HCSs). Embedded PCTs within HCSs are conducted to test interventions and provide evidence that may impact public health, health system operations, and quality of care. They are larger and more broadly generalizable than QI initiatives, and may generate what is considered high-quality evidence for potential use in care and clinical practice guidelines. QI initiatives often co-occur with ePCTs and address the same high-impact health questions, and this co-occurrence may dilute or confound the ability to detect change as a result of the ePCT intervention. During the design, pilot, and conduct phases of the large-scale NIH Collaboratory Demonstration ePCTs, many QI initiatives occurred at the same time within the HCSs. Although the challenges varied across the projects, some common, generalizable strategies and solutions emerged, and we share these as case studies. KEY LESSONS: Study teams often need to monitor, adapt, and respond to QI during design and the course of the trial. Routine collaboration between ePCT researchers and health systems stakeholders throughout the trial can help ensure research and QI are optimally aligned to support high-quality patient-centered care

Actionable, Pathogenic Incidental Findings in 1,000 Participants’ Exomes

The incorporation of genomics into medicine is stimulating interest on the return of incidental findings (IFs) from exome and genome sequencing. However, no large-scale study has yet estimated the number of expected actionable findings per individual; therefore, we classified actionable pathogenic single-nucleotide variants in 500 European- and 500 African-descent participants randomly selected from the National Heart, Lung, and Blood Institute Exome Sequencing Project. The 1,000 individuals were screened for variants in 114 genes selected by an expert panel for their association with medically actionable genetic conditions possibly undiagnosed in adults. Among the 1,000 participants, 585 instances of 239 unique variants were identified as disease causing in the Human Gene Mutation Database (HGMD). The primary literature supporting the variants’ pathogenicity was reviewed. Of the identified IFs, only 16 unique autosomal-dominant variants in 17 individuals were assessed to be pathogenic or likely pathogenic, and one participant had two pathogenic variants for an autosomal-recessive disease. Furthermore, one pathogenic and four likely pathogenic variants not listed as disease causing in HGMD were identified. These data can provide an estimate of the frequency (∼3.4% for European descent and ∼1.2% for African descent) of the high-penetrance actionable pathogenic or likely pathogenic variants in adults. The 23 participants with pathogenic or likely pathogenic variants were disproportionately of European (17) versus African (6) descent. The process of classifying these variants underscores the need for a more comprehensive and diverse centralized resource to provide curated information on pathogenicity for clinical use to minimize health disparities in genomic medicine

Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium

Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratory's own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease

Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain

Back pain is the #1 cause of years lived with disability worldwide, yet surprisingly little is known regarding the biology underlying this symptom. We conducted a genome-wide association study (GWAS) meta-analysis of ch

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS)

Loci influencing blood pressure identified using a cardiovascular gene-centric array

Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped 50 000 single-nucleotide polymorphisms (SNPs) that capture variation in 2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P 2.4 10(6)). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.</p

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice

Large-Scale Gene-Centric Meta-Analysis across 39 Studies Identifies Type 2 Diabetes Loci

To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom similar to 50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with similar to 2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 x 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p <2.4 x 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 x 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 x 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 x 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups