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Introduction 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. Organization 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. Current progress 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. Future activities Plans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care. Summary 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.

Many modern human genomes retain DNA inherited from interbreeding with archaic hominins, such as Neandertals, yet the influence of this admixture on human traits is largely unknown. We analyzed the contribution of common Neandertal variants to over 1000 electronic health record (EHR)–derived phenotypes in ~28,000 adults of European ancestry. We discovered and replicated associations of Neandertal alleles with neurological, psychiatric, immunological, and dermatological phenotypes. Neandertal alleles together explained a significant fraction of the variation in risk for depression and skin lesions resulting from sun exposure (actinic keratosis), and individual Neandertal alleles were significantly associated with specific human phenotypes, including hypercoagulation and tobacco use. Our results establish that archaic admixture influences disease risk in modern humans, provide hypotheses about the effects of hundreds of Neandertal haplotypes, and demonstrate the utility of EHR data in evolutionary analyses.

The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute–funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype–phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine. Genet Med15 10, 761–771.

Advances in technologies for assessing genomic variation and an increasing understanding of the effects of genomic variants on health and disease are driving the transition of genomics from the research laboratory into clinical care. Genomic medicine, or the use of an individual's genomic information as part of their clinical care, is increasingly gaining acceptance in routine practice, including in assessing disease risk in individuals and their families, diagnosing rare and undiagnosed diseases, and improving drug safety and efficacy. We describe the major types and measurement tools of genomic variation that are currently of clinical importance, review approaches to interpreting genomic sequence variants, identify publicly available tools and resources for genomic test interpretation, and discuss several key barriers in using genomic information in routine clinical practice.

Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices. Genet Med 2013:15(4):258–267

Background The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. Results We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. Conclusion The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.

Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Dictyostelium discoideum , a unicellular organism capable of developing into a multicellular structure, is a powerful model system to study a variety of biological processes. Because it is inexpensive and relatively easy to grow, Dictyostelium is also frequently used in teaching laboratories. Here we describe conditions for successfully growing and developing Dictyostelium cells and methods for long-term storage of Dictyostelium amoebae and spores.

Background Genomes can be sequenced with relative ease, but ascribing gene function remains a major challenge. Genetically tractable model systems are crucial to meet this challenge. One powerful model is the social amoeba Dictyostelium discoideum , a eukaryotic microbe widely used to study diverse questions in the cell, developmental and evolutionary biology. Results We describe REMI-seq, an adaptation of Tn-seq, which allows high throughput, en masse , and quantitative identification of the genomic site of insertion of a drug resistance marker after restriction enzyme-mediated integration. We use REMI-seq to develop tools which greatly enhance the efficiency with which the sequence, transcriptome or proteome variation can be linked to phenotype in D. discoideum . These comprise (1) a near genome-wide resource of individual mutants and (2) a defined pool of ‘barcoded’ mutants to allow large-scale parallel phenotypic analyses. These resources are freely available and easily accessible through the REMI-seq website that also provides comprehensive guidance and pipelines for data analysis. We demonstrate that integrating these resources allows novel regulators of cell migration, phagocytosis and macropinocytosis to be rapidly identified. Conclusions We present methods and resources, generated using REMI-seq, for high throughput gene function analysis in a key model system.

Natural selection should favour generalist predators that outperform specialists across all prey types. Two genetic solutions could explain why intraspecific variation in predatory performance is, nonetheless, widespread: mutations beneficial on one prey type are costly on another (antagonistic pleiotropy), or mutational effects are prey-specific, which weakens selection, allowing variation to persist (relaxed selection). To understand the relative importance of these alternatives, we characterised natural variation in predatory performance in the microbial predator Dictyostelium discoideum . We found widespread nontransitive differences among strains in predatory success across different bacterial prey, which can facilitate stain coexistence in multi-prey environments. To understand the genetic basis, we developed methods for high throughput experimental evolution on different prey (REMI-seq). Most mutations (~77%) had prey-specific effects, with very few (~4%) showing antagonistic pleiotropy. This highlights the potential for prey-specific effects to dilute selection, which would inhibit the purging of variation and prevent the emergence of an optimal generalist predator. What prevents a generalist predator from evolving and outperforming specialist predators? By combing analyses of natural variation with experimental evolution, Stewart et al. suggest that predator variation persists because most mutations have prey-specific effects, which results in relaxed selection