The Personal Data Is Political

The success of personalized medicine does not only rely on methodological advances but also on the availability of data to learn from. While the generation and sharing of large data sets is becoming increasingly easier, there is a remarkable lack of diversity within shared datasets, rendering any novel scientific findings directly applicable only to a small portion of the human population. Here, we are investigating two fields that have been majorly impacted by data sharing initiatives, neuroscience and genetics. Exploring the limitations that are a result of a lack of participant diversity, we propose that data sharing in itself is not enough to enable a global personalized medicine.

[1]  James J. Chen,et al.  Ensemble methods for classification of patients for personalized medicine with high-dimensional data , 2007, Artif. Intell. Medicine.

[2]  Megan E. Patrick,et al.  What is a representative brain? Neuroscience meets population science , 2013, Proceedings of the National Academy of Sciences.

[3]  Robert Oostenveld,et al.  MEG-BIDS, the brain imaging data structure extended to magnetoencephalography , 2018, Scientific Data.

[4]  Adrian Alexa,et al.  DNAdigest and Repositive: Connecting the World of Genomic Data , 2016, PLoS biology.

[5]  D. Bach,et al.  Blocking human fear memory with the matrix metalloproteinase inhibitor doxycycline , 2017, Molecular Psychiatry.

[6]  Alexander Wait Zaranek,et al.  The whole genome sequences and experimentally phased haplotypes of over 100 personal genomes , 2016, GigaScience.

[7]  S. Levinson,et al.  WEIRD languages have misled us, too , 2010, Behavioral and Brain Sciences.

[8]  Misha Angrist,et al.  Personalized medicine and human genetic diversity. , 2014, Cold Spring Harbor perspectives in medicine.

[9]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[10]  I. Gotlib,et al.  Time-varying effects of income on hippocampal volume trajectories in adolescent girls , 2017, Developmental Cognitive Neuroscience.

[11]  E. Siegel,et al.  Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment , 2013, Current Cardiology Reports.

[12]  J. Henrich,et al.  The weirdest people in the world? , 2010, Behavioral and Brain Sciences.

[13]  D. Bach.  Traumatische Erinnerungen medikamentös abschwächen , 2017 .

[14]  P. Bayer,et al.  openSNP–A Crowdsourced Web Resource for Personal Genomics , 2014, PloS one.

[15]  B. Liddell,et al.  The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder , 2016, European journal of psychotraumatology.

[16]  Euan A Ashley,et al.  Clinical interpretation and implications of whole-genome sequencing. , 2014, JAMA.

[17]  Daniel A. Hackman,et al.  Socioeconomic Status and the Developing Brain , 2022 .

[18]  Alessandro Blasimme,et al.  Open sharing of genomic data: Who does it and why? , 2017, PloS one.

[19]  Richard Ofori-Asenso,et al.  Perspective: Does personalized medicine hold the future for medicine? , 2015, Journal of pharmacy & bioallied sciences.

[20]  C. Sripada,et al.  Childhood poverty is associated with altered hippocampal function and visuospatial memory in adulthood , 2016, Developmental Cognitive Neuroscience.

[21]  David G. Weissman,et al.  Income change alters default mode network connectivity for adolescents in poverty , 2018, Developmental Cognitive Neuroscience.

[22]  Jason H. Moore,et al.  Chapter 11: Genome-Wide Association Studies , 2012, PLoS Comput. Biol..

[23]  Isaac S Kohane,et al.  Ten things we have to do to achieve precision medicine , 2015, Science.

[24]  Alan C. Evans,et al.  OMEGA: The Open MEG Archive , 2016, NeuroImage.

[25]  Martha J. Farah,et al.  Socioeconomic status and the brain: mechanistic insights from human and animal research , 2010, Nature Reviews Neuroscience.

[26]  Denise C. Park,et al.  Socioeconomic status moderates age-related differences in the brain’s functional network organization and anatomy across the adult lifespan , 2018, Proceedings of the National Academy of Sciences.

[27]  Christopher R. Madan,et al.  Advances in Studying Brain Morphology: The Benefits of Open-Access Data , 2017, Front. Hum. Neurosci..

[28]  Athina Tzovara,et al.  Human Pavlovian fear conditioning conforms to probabilistic learning , 2018, PLoS computational biology.

[29]  Daniel S. Margulies,et al.  NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain , 2014, bioRxiv.

[30]  Rebecca Smith-Bindman,et al.  Is computed tomography safe? , 2010, The New England journal of medicine.

[31]  S. Gilman,et al.  Race/ethnic differences in exposure to traumatic events, development of post-traumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States , 2010, Psychological Medicine.

[32]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[33]  Yingdong Zhao,et al.  Application of molecular profiling in clinical trials for advanced metastatic cancers. , 2015, Journal of the National Cancer Institute.

[34]  Satrajit S. Ghosh,et al.  Data sharing in neuroimaging research , 2012, Front. Neuroinform..

[35]  M. Craske,et al.  The moderating role of avoidance behavior on anxiety over time: Is there a difference between social anxiety disorder and specific phobia? , 2017, PloS one.

[36]  Xiang Du,et al.  KRAS mutation testing in metastatic colorectal cancer. , 2012, World journal of gastroenterology.

[37]  M. Otto,et al.  Assessment of skin conductance in African American and Non-African American participants in studies of conditioned fear. , 2017, Psychophysiology.

[38]  U. Habel,et al.  Culture but not gender modulates amygdala activation during explicit emotion recognition , 2012, BMC Neuroscience.

[39]  M. Thomason,et al.  Socioeconomic disadvantage and altered corticostriatal circuitry in urban youth , 2018, Human brain mapping.

[40]  Arcadi Navarro,et al.  High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants , 2013, PLoS genetics.

[41]  P. Fox,et al.  Mapping context and content: the BrainMap model , 2002, Nature Reviews Neuroscience.

[42]  Karen G. Martínez,et al.  Ethnic Differences in Physiological Responses to Fear Conditioned Stimuli , 2014, PloS one.

[43]  Angelita Pui-Yee Wong,et al.  Income inequality, gene expression, and brain maturation during adolescence , 2017, Scientific Reports.

[44]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..