title: HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.
abstract: BACKGROUND
  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.
  
  
  METHODS
  Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biomedical Informatics, University of California, San Diego, CA 92093.
authors.forename: Shuang
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Biomedical Informatics, University of California, San Diego, CA 92093, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Informatics, University of California, San Diego, CA 92093, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Wenrui
authors.initials: W
authors.lastname: Dai
authors.affiliation: Microsoft Research, San Diego, CA 92122, USA.
authors.forename: Kristin
authors.initials: K
authors.lastname: Lauter
authors.affiliation: Seoul National University, Seoul, 151-742, Republic of Korea and.
authors.forename: Miran
authors.initials: M
authors.lastname: Kim
authors.affiliation: Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.
authors.forename: Yuzhe
authors.initials: Y
authors.lastname: Tang
authors.affiliation: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Xiong
authors.affiliation: Department of Biomedical Informatics, University of California, San Diego, CA 92093.
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D030661:Genetic Privacy; D014644:Genetic Variation; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D009080:Mucocutaneous Lymph Node Syndrome / Q000235:genetics; D035583:Rare Diseases / Q000235:genetics*