Genetic data sharing and privacy.
Genetic data sharing and privacy.
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遗传数据共享和隐私。
DOI：
10.1007/s12021-014-9248-z
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发表时间：
2015
期刊：
影响因子：
3
通讯作者：
TRACKTBIInvestigators
中科院分区：
文献类型：
--
作者：
Sorani,MarcoD;Yue,JohnK;Sharma,Sourabh;Manley,GeoffreyT;Ferguson,AdamR;TRACKTBIInvestigators
遗传数据为神经科学中的疾病原因和风险以及药物发现和开发提供了有价值的见解。例如，人类遗传学研究提供了对认知（Glahn et al. 2013）和精神疾病（Kao et al. 2010）的见解。几种遗传性疾病的遗传基础，如唐氏综合征和泰-萨二氏病是众所周知的，其他相关性，如载脂蛋白E在阿尔茨海默病中的作用仍在广泛研究中。然而，尽管在了解人类基因组方面取得了进展，但人们对基因数据的隐私性和披露基因数据可能造成的歧视感到关切，对基因检测的监督也不完全（Scheuner等人，2008年）。与此同时，还加大了共享研究数据的力度，以促进科学发现并实现成本效益。很明显，没有一个科学家可以保证绝对的隐私，而且人们也越来越认识到，如果科学家有更多关于他们研究的人的信息，研究会更好地进行，并且可识别有一些好处（Angrist 2013）。在神经科学研究中有一些开创性的例子。fMRI数据中心是功能性神经成像领域开放获取数据共享的领导者，克服了后勤、文化和资金方面的障碍（Mennes et al. 2013）。同样，INCF神经影像数据共享工作组已开始开发工具，以简化和自动共享原始、处理和衍生的神经影像数据和元数据（Poline等人，2012）。在美国，《健康保险携带和责任法案》（HIPAA）等立法（Gostin 2001）和《遗传信息非歧视法》试图限制对敏感数据的访问以及与健康保险和就业有关的歧视，但十多年来，人们已经知道，看似匿名的数据可以与公开的信息相关联，以识别特定的个人（Braun等人，2009）使用诊断代码（Tamersoy等人，2010），罕见可见疾病（Eguale et al. 2005），等位基因频率（克雷格等人，2011年），出生地点和日期（Acquisti和Gross 2009），姓氏与年龄和状态的组合（Gymrek et al. 2013）和患者健康场所访视模式（Malin 2007）。重新鉴定方法包括基因型-表型推断、家族结构和字典攻击（Malin 2005）。总的来说，这些事实改变了许多研究组织的目标，从使数据重新识别变得不可能，使其变得非常不可能，并教育利益相关者了解问题和风险，同时通过共享数据加强研究合作。在这里，我们讨论数据隐私和共享方法，我们提供建议并描述我们在生物银行背景下的经验，我们展望未来
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)(Gostin 2001) and the Genetic Information 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, these facts have changed the goals of many research organizations from making data re-identification impossible to making it highly improbable and educating stakeholders about the issues and risks, while enhancing research collaborations by sharing data. Here, we discuss data privacy and sharing approaches, we provide recommendations and describe our own experiences in the context of biobanking, and we look ahead