With the dramatic reduction in the cost of whole genome sequencing (WGS), genomic data are becoming increasingly available and have the potential to advance public health and promote personalized medicine. However, human genomic data usually carry sensitive personal information making data owners cautious about sharing it and genomic privacy is emerging as a big challenge for the entire biomedical community. In this proposal, we will develop novel methods for genomic privacy protection, which will facilitate genomic research.
Our first aim i s to develop privacy-preserving and efficiency-oriented computational models for processing, sharing, and storing genomic data in a cloud-based environment.
This aim relies on scalable cryptographic techniques, joint compression, and encryption schemes, as well as leverage of high-performance computing architecture to achieve privacy-preserving analysis and storage efficiency in the cloud.
The second aim i s to develop trustworthy computational models that enable researchers to analyze distributed genomic data without requiring patient-level data exchange.
These aims are devoted to the mission of the National Human Genome Research Institute (NHGRI) to develop resources and technology that will accelerate genome research and its application to human health. The NIH Pathway to Independence Award provides a great opportunity for the applicant to complement his computer engineering background with biomedical knowledge, and specialized training in genomic analysis, genomic privacy, as well as high-performance computing. It will also allow him to investigate new techniques to advance genomic privacy protection. The success of the proposed project will help his long-term career goal of obtaining a faculty position at a biomedical informatics program at a major US research university and conduct independently funded research in the field of genome privacy.

Public Health Relevance

The proposed research will develop practical methods to support privacy-preserving genomic data analysis, and leverage parallel computing for secure and efficient data access. The development of such privacy technology may increase public trust in research. The privacy technology we propose will also contribute to the sharing of genomic data in ways that meet the needs of those in biomedical research.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Career Transition Award (K99)
Project #
1K99HG008175-01
Application #
8804836
Study Section
(SEIR)
Program Officer
Kaufman, Dave J
Project Start
2014-09-01
Project End
2016-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Wang, Lichang; Fang, Yong; Aref, Dima et al. (2016) PALME: PAtients Like My gEnome. AMIA Jt Summits Transl Sci Proc 2016:219-24
Wang, Shuang; Zhang, Yuchen; Dai, Wenrui et al. (2016) HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS. Bioinformatics 32:211-8
Li, Yong; Jiang, Xiaoqian; Wang, Shuang et al. (2016) VERTIcal Grid lOgistic regression (VERTIGO). J Am Med Inform Assoc 23:570-9
Shi, Haoyi; Jiang, Chao; Dai, Wenrui et al. (2016) Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Med Inform Decis Mak 16 Suppl 3:89
Wu, Yuan; Jiang, Xiaoqian; Wang, Shuang et al. (2015) Grid multi-category response logistic models. BMC Med Inform Decis Mak 15:10
Han, Dong; Wang, Shuang; Jiang, Chao et al. (2015) Trends in biomedical informatics: automated topic analysis of JAMIA articles. J Am Med Inform Assoc 22:1153-63
Constable, Scott D; Tang, Yuzhe; Wang, Shuang et al. (2015) Privacy-preserving GWAS analysis on federated genomic datasets. BMC Med Inform Decis Mak 15 Suppl 5:S2
Lu, Chia-Lun; Wang, Shuang; Ji, Zhanglong et al. (2015) WebDISCO: a web service for distributed cox model learning without patient-level data sharing. J Am Med Inform Assoc 22:1212-9
Zhang, Yuchen; Dai, Wenrui; Jiang, Xiaoqian et al. (2015) FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption. BMC Med Inform Decis Mak 15 Suppl 5:S5
Wei, Wei; Demner-Fushman, Dina; Wang, Shuang et al. (2015) Ranking Medical Subject Headings using a factor graph model. AMIA Jt Summits Transl Sci Proc 2015:56-63

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