HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.

TitleHEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.
Publication TypeJournal Article
Year of Publication2016
AuthorsWang, S, Zhang, Y, Dai, W, Lauter, K, Kim, M, Tang, Y, Xiong, H, Jiang, X
JournalBioinformatics
Volume32
Issue2
Pagination211-8
Date Published2016 Jan 15
ISSN1367-4811
iDASH CategoryPrivacy Technology
Abstract<p><b>MOTIVATION: </b>Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size.</p><p><b>RESULTS: </b>We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>
DOI10.1093/bioinformatics/btv563
Alternate JournalBioinformatics
PubMed ID26446135
PubMed Central IDPMC4739182
Grant ListK99 HG008175 / HG / NHGRI NIH HHS / United States