Data Sharing as the New Norm: What About the People Part?

In January of 2017, the Board of Directors of the American College of Medical Genetics (ACMG) released a position statement calling for extensive sharing of laboratory and clinical data from individuals who have undergone genetic testing (ACMG Board of Directors, 2017). Sharing this information is not only important for individuals living with a genetic condition, the Board states, it is also important for individuals whose genetic variation increases the risk of developing a condition in the future. The call to share is an important next step if we were to continue the progress made in genetics since the sequencing of the human genome. That milestone revealed how little we actually know about our genetic makeup and its impact on disease: in 2010, some 10 years after the first regions of the human genome were published, the number of germline mutations reported to cause or be associated with inherited disease exceeded 100,000 across more than 3700 different genes, with an additional 300 new ‘‘inherited disease genes’’ reported annually (Cooper et al., 2010). By 2013, the Human Gene Mutation Database (a collection of human gene mutations that underlie or are associated with inherited disease) contained more than 141,000 mutations detected in more than 5700 different genes (Stenson et al., 2014). The number of variants associated with human disease continues to grow today—and the work required to identify and describe those variants does not take place in isolation. There are multiple examples demonstrating the effectiveness with which combined resources of genetic sequencing data can advance genetic discovery. Genome-wide association studies produce more findings every year—there are now more than 30,000 published findings listed in the NHGRI-EBI catalog of published genome-wide association studies, from 2700 different studies (www.ebi.ac.uk/gwas)—and these rely on larger and larger sets of data to identify rare variants (or more common variants with lesser effects; Chanock et al., 2007). Efforts within the cancer genetics community, such as the Cancer Genome Atlas (https://cancergenome.nih.gov) and the International Cancer Genome Consortium (http://icgc .org), also reveal the benefits of pooled data sets: for instance, the Cancer Genome Atlas (which consists of tumor profiles and matched normal tissues from 11,000 different patients) has contributed to more than a thousand different studies by independent cancer researchers (The National Cancer Institute and the National Human Genome Research Institute). Together, advances like those already described have helped to launch an age of ‘‘precision medicine’’: an approach for treatment and prevention that relies on knowledge of individuals’ genetic variation, and how that variation, together with other factors such as environment and lifestyle, can impact their health. Fully realizing the benefits of combined genetic resources can be a tricky business when it comes to precision medicine, however. The standards applied to genetic variant data in a research setting are different than those used in the clinic, and the former is not a substitute for the latter: importantly for the former, the level of certainty regarding the variant’s medical significance may not be as great. A growing number of initiatives have thus devoted themselves to aggregating variant data that can be used to support clinical decision making as well. For instance, the National Institutes of Health’s ClinVar (www.ncbi.nlm.nih.gov/clinvar) is an archive of medically relevant variants and their interpretations. The database pulls from multiple sources, and although some provide data obtained in a research context, many—like genetic testing laboratories—provide clinical-level data, with supporting information sometimes included. These data should not be used to make a diagnosis in isolation, but the existence of the database supports easier sharing of interpretation data overall, and can assist genetics professionals in resolving differences in opinion regarding interpretation. ClinVar also works with the Clinical Genome Resource (ClinGen), another initiative funded by the National Institutes of Health. ClinGen will form an authoritative central resource for defining the clinical relevance of genomic variants, and has already established a number of expert processes for the review and classification of the data in ClinVar (Rehm et al., 2015). Other projects are also emerging internationally. The Global Alliance for Genomics and Health has launched multiple demonstration projects for building combined resources of genetic variant data, and some of these, such as the Cancer Gene Trust (http://genomicsandhealth.org/workproducts-demonstration-projects/cancer-gene-trust) and the Matchmaker Exchange (www.matchmakerexchange.org/),