Shuang Wang1, Yuchen Zhang2, Wenrui Dai2, Kristin Lauter3, Miran Kim4, Yuzhe Tang5, Hongkai Xiong6, Xiaoqian Jiang1. 1. Department of Biomedical Informatics, University of California, San Diego, CA 92093. 2. Department of Biomedical Informatics, University of California, San Diego, CA 92093, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. 3. Microsoft Research, San Diego, CA 92122, USA. 4. Seoul National University, Seoul, 151-742, Republic of Korea and. 5. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA. 6. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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