Genetic Data Sharing and Privacy

Genetic data has provided valuable insights into disease cause and risk as well as drug discovery and development in neuroscience. For example, human genetics studies have provided insights into cognition (Glahn et al. 2013) and psychiatric disorders (Kao et al. 2010). The genetic basis of several inherited disorders such as Down's Syndrome and Tay-Sachs disease are well known, and other associations such as the role of APOE in Alzheimer's disease are still extensively studied. However, despite advances in understanding the human genome, there are concerns about the privacy of genetic data and potential discrimination resulting from its disclosure, and there has been incomplete oversight of genetic testing (Scheuner et al. 2008). At the same time, there have been increased efforts to share research data to enable scientific discovery and achieve cost efficiencies. It has become clear that no scientist can guarantee absolute privacy, and it is also increasingly recognized that research will work better if scientists have more information about the people they study and that being identifiable has some benefits (Angrist 2013). There are examples of pioneering efforts in neuroscience research. The fMRI Data Center is a leader in open-access data sharing in the functional neuroimaging community, overcoming logistical, cultural and funding barriers (Mennes et al. 2013). Similarly, the INCF Task Force on Neuroimaging Datasharing has started work on tools to ease and automate sharing of raw, processed, and derived neuroimaging data and metadata (Poline et al. 2012). In the United States, legislation such as the Health Insurance Portability and Accountability Act (HIPAA) (G o s t i n 2 0 0 1) a n d t h e G e n e t i c I n f o r m a t i o n Nondiscrimination Act have attempted to limit access to sensitive data and discrimination related to health insurance and employment, but it has been known for over a decade that seemingly anonymized data can be related to publicly available information to identify specific individuals (Braun et al. 2009) using diagnosis codes (Tamersoy et al. 2010), rare visible disorders (Eguale et al. 2005), allele frequencies (Craig et al. 2011), place and date of birth (Acquisti and Gross 2009), a combination of a surname with age and state (Gymrek et al. 2013), and patient health location visit patterns (Malin 2007). Re-identification methods have included genotype-phenotype inferences, family structures , and dictionary attacks (Malin 2005). In total, …