HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS
| Autor: | Yuchen Zhang, Miran Kim, Xiaoqian Jiang, Kristin E. Lauter, Hongkai Xiong, Shuang Wang, Yuzhe Richard Tang, Wenrui Dai |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Computer science Computation Correlation and dependence Genome-wide association study Mucocutaneous Lymph Node Syndrome Logistic regression computer.software_genre Biochemistry Rare Diseases Genetic variation Humans Genetic Privacy Molecular Biology Genetic association Genome Human Homomorphic encryption Genetic Variation Original Papers Computer Science Applications Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Algorithm design Data mining computer Algorithms Rare disease Genome-Wide Association Study |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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