NCDS Leadership Summit " The Friday Center" Chapel Hill, North Carolina" April 23 & 24, 2013!

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1 NCDS Leadership Summit " The Friday Center" Chapel Hill, North Carolina" April 23 & 24, 2013!

2 Data Collection Scale of Problem Challenges v Research versus clinical contexts v Science versus medicine v Genome may change over 8me v Need to store genomics data v Data repurposing and reuse v Data access/security/encryp8on v Incen8ve systems need to change 2

3 Data Collection Scale of Problem Recommenda8ons v Embrace the difference between medicine and science; data for clinicians versus research v Change incen8ve systems to promote data sharing- 5 v Streamline the processes involved with data access in data sharing ini8a8ves v Universal consent form v Combine public data with EMR data v Collect as much data as possible, but develop tools to structure data - 5 v NIH/NSF: recognize role in data collec8on - 5 3

4 Data Analysis - Variants Challenges v Accurate variant calling v High quality variant databases (most variants are not disease- causing and most databases are incomplete) v Rou8ne whole genome diagnos8cs v Storage of variant informa8on data standards (vcf) v Reprocessing and reuse; ethical issues v Clinical interpreta8on causa8on vs correla8on v Phenotypes problema8c v Epigenomic influences 4

5 Data Analysis Variants Recommenda8ons v Gene8cists need guidance rather than defini8ve results v Reports need to be structured for easy interpreta8on across special8es v Visual analy8cs for clinical decision- making; computable documenta8on - 5 v Data standards - 5 v NLP, but also accepted ontologies/standards/etc v Improve phenotyping v Integrated analyses; interac8ve/exploratory - 5 v Family- based sequencing; cancer panel sequencing 5

6 Data to Knowledge/Decision - Biostatistics Challenges v Need large data sets for risk assessment & diagnos8cs v Big data from mul8ple sources: transla8on must be understandable & interpretable v Privacy issues: informed consent, intellectual property, data sharing v Post hoc edi8ng of genomes for extremely rare muta8ons v Phenotypes problema8c v Bias mostly looking at coding sequences v Increasing Confidence Intervals on risk v Selec8vity in sample incorrect inference v Causality versus correla8on v Tracking/audi8ng of data flow 6

7 Data to Knowledge/Decision - Biostatistics Recommenda8ons v Preserve provenance - 5 v Medical gene8cists specialized in interpre8ng results v Iden8fy pathways/systems versus single genes v Test muta8ons in model organisms v New algorithms and compu8ng/so[ware methods (& funding for computa8onal research) - 5 v Federated, distributed data systems - 5 v Trust fabrics across federated networks v Cultural shi[ each individual is a unique resource v Crowdsourcing and Wikipedia- like system to guide algorithm development & clinical decision support, while preserving provenance - 5 v Open source clinical decision support 7

8 Data Dissemination - Bioethics Challenges v Legal dis8nc8on between privacy and genomics data at large (e.g., cigareae buas) v Legal dis8nc8on between real property versus intellectual property versus informa8onal property v Privacy versus security v Privacy versus confiden8ality v Define why it is problema8c to make our (each of us) genomic sequences public (hiding in private versus hiding in a crowd) v Reuse of genomic informa8on without informed consent 8

9 Data Dissemination - Bioethics Recommenda8ons v Broad dissemina8on about jus8ce and law/protec8on issues as related to genomics and big data in general v Define fair use, misuse, and personal insecuri8es related to the sharing of genomic data and other poten8ally sensi8ve data - 5 v Incidental findings learn how to deal with them - 5 v NCDS to serve as a forum for discussions related to this issue and to incorporate legal/policy issues as one of its 5 major goals moving forward - 5 9

10 Data to Knowledge/Decision - Phenotypes Challenges v Finding phenotype data in big data sets v Missing data/documenta8on; differences across studies v ID problems across studies; can t find same subject v Technical problems searching for phenotype data v Organiza8on of informa8on (lack of standardiza8on) v Phenotype vs. data elements - defini8ons v dbgap problema8c 10

11 Data to Knowledge/Decision Phenotypes Recommenda8ons v Harmonized data sets - 5 v Searchable phenotypes - 5 v Fix dbgap (Dataverse) v Incen8ves v Catalogues/libraries v Standardiza8on (e.g., PhenX or new system) v Documenta8on and audit trails - 5 v Data archivists

12 Data Dissemination - Privacy Challenges v Protect privacy? v Open access? v Difficult to share data v Secondary analysis not always possible v 10M genomes, which one? (sampling) v 5% false posi8ve rate of iden8fica8on; legal implica8ons v Cost- benefit analysis vs. harm reduc8on v Disentangle privacy (umbrella term) v IRB not uniform 12

13 Data Dissemination - Privacy Recommenda8ons v Uniform consent v Cost- benefit analysis of controlled vs open access scheme - 5 v Technical means of both schemes - 5 v Speed of science should be considered v Dissect privacy v Incen8ves for compliance/contractual agreement (bail bonds, audit trails)- 5 13

14 Overarching Recommendations q Consor8a of consor8a q Iden8fy small changes that will have immediate impact of any size 14

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