No abstract

No consensus yet exists on how to handle incidental fnd‐ings (IFs) in human subjects research. Yet empirical studies document IFs in a wide range of research studies, where IFs are fndings beyond the aims of the study that are of potential health or reproductive importance to the individual research participant. This paper reports recommendations of a two‐year project group funded by NIH to study how to manage IFs in genetic and genomic research, as well as imaging research. We conclude that researchers have an obligation to address the possibility of discovering IFs in their protocol and communications with the IRB, and in their consent forms and communications with research participants. Researchers should establish a pathway for handling IFs and communicate that to the IRB and research participants. We recommend a pathway and categorize IFs into those that must be disclosed to research participants, those that may be disclosed, and those that should not be disclosed.

Pseudoxanthoma elasticum (PXE) is a heritable disorder of the connective tissue. PXE patients frequently experience visual field loss and skin lesions, and occasionally cardiovascular complications. Histopathological findings reveal calcification of the elastic fibres and abnormalities of the collagen fibrils. Most PXE patients are sporadic, but autosomal recessive and dominant inheritance are also observed. We previously localized the PXE gene to chromosome 16p13.1 (refs 8,9) and constructed a physical map. Here we describe homozygosity mapping in five PXE families and the detection of deletions or mutations in ABCC6 (formerly MRP6) associated with all genetic forms of PXE in seven patients or families.

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.

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.