Biobanks and archived datasets collecting samples and data have become crucial engines of genetic and genomic research. Unresolved, however, is what responsibilities biobanks should shoulder to manage incidental findings (IFs) and individual research results (IRRs) of potential health, reproductive, or personal importance to individual contributors (using “biobank” here to refer to both collections of samples and collections of data). This paper reports recommendations from a 2-year, NIH-funded project. The authors analyze responsibilities to manage return of IFs and IRRs in a biobank research system (primary research or collection sites, the biobank itself, and secondary research sites). They suggest that biobanks shoulder significant responsibility for seeing that the biobank research system addresses the return question explicitly. When re-identification of individual contributors is possible, the biobank should work to enable the biobank research system to discharge four core responsibilities: to (1) clarify the criteria for evaluating findings and roster of returnable findings, (2) analyze a particular finding in relation to this, (3) re-identify the individual contributor, and (4) recontact the contributor to offer the finding. The authors suggest that findings that are analytically valid, reveal an established and substantial risk of a serious health condition, and that are clinically actionable should generally be offered to consenting contributors. The paper specifies 10 concrete recommendations, addressing new biobanks and biobanks already in existence.

Objective

Genome-wide association studies (GWAS) require high specificity and large numbers of subjects to identify genotype–phenotype correlations accurately. The aim of this study was to identify type 2 diabetes (T2D) cases and controls for a GWAS, using data captured through routine clinical care across five institutions using different electronic medical record (EMR) systems.

Materials and Methods

An algorithm was developed to identify T2D cases and controls based on a combination of diagnoses, medications, and laboratory results. The performance of the algorithm was validated at three of the five participating institutions compared against clinician review. A GWAS was subsequently performed using cases and controls identified by the algorithm, with samples pooled across all five institutions.

Results

The algorithm achieved 98% and 100% positive predictive values for the identification of diabetic cases and controls, respectively, as compared against clinician review. By standardizing and applying the algorithm across institutions, 3353 cases and 3352 controls were identified. Subsequent GWAS using data from five institutions replicated the TCF7L2 gene variant (rs7903146) previously associated with T2D.

Discussion

By applying stringent criteria to EMR data collected through routine clinical care, cases and controls for a GWAS were identified that subsequently replicated a known genetic variant. The use of standard terminologies to define data elements enabled pooling of subjects and data across five different institutions to achieve the robust numbers required for GWAS.

Conclusions

An algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.

Recent studies have emphasized the ability to reconstruct genome sizes (C-values) of extinct organisms such as dinosaurs, using correlations between known genome sizes and bone cell (osteocyte lacunae) volumes. Because of the established positive relationship between cell size and genome size in extant vertebrates, osteocyte lacunae volume is a viable proxy for reconstructing C-values in the absence of any viable genetic material. However, intra-skeletal osteocyte lacunae size variation, which could cause error in genome size estimation, has remained unexplored. Here, 11 skeletal elements of one individual from each of four major clades (Mammalia, Amphibia, Aves, Reptilia) were examined histologically. Skeletal elements in all four clades exhibit significant differences in the average sizes of their lacunae. This variation, however, generally does not cause a significant difference in the estimated genome size when common phylogenetic estimation methods are employed. On the other hand, the spread of the estimations illustrates that this method may not be precise. High variance in genome size estimations remains an outstanding problem. Additionally, a suite of new methods is introduced to further automate the measurement of bone cells and other microstructural features on histological thin sections.

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.

Approaches with high spatial and temporal resolution are required to understand the regulation of nonmuscle myosin II in vivo. Using fluorescence resonance energy transfer we have produced a novel biosensor allowing simultaneous determination of myosin light chain kinase (MLCK) localization and its [Ca2+]4/calmodulin-binding state in living cells. We observe transient recruitment of diffuse MLCK to stress fibers and its in situ activation before contraction. MLCK is highly active in the lamella of migrating cells, but not at the retracting tail. This unexpected result highlights a potential role for MLCK-mediated myosin contractility in the lamella as a driving force for migration. During cytokinesis, MLCK was enriched at the spindle equator during late metaphase, and was maximally activated just before cleavage furrow constriction. As furrow contraction was completed, active MLCK was redistributed to the poles of the daughter cells. These results show MLCK is a myosin regulator in the lamella and contractile ring, and pinpoints sites where myosin function may be mediated by other kinases.