Deterministic identification of specific individuals from GWAS results

Bioinformatics

Volumn 31, Issue 11, 2015, Pages 1701-1707

  Cai, Ruichu ^a,b   Hao, Zhifeng ^a   Winslett, Marianne ^b,c   Xiao, Xiaokui ^d   Yang, Yin ^e   Zhang, Zhenjie ^b   Zhou, Shuigeng ^f  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b Singapore Pte Ltd   (Singapore)

^c University of Illinois at Urbana Champaign   (United States)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^e HAMAD BIN KHALIFA UNIVERSITY   (Qatar)

^f FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; GENETIC ASSOCIATION; GENETIC PRIVACY; GENOTYPE; HUMAN; HUMAN GENOME; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; GENETIC PRIVACY; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84941628486     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv018     Document Type: Conference Paper

References (15)

1. Agrawal, R. et al. (1994) Fast algorithms for mining association rules. In: Bocca, J. B. et al. (eds) Proceedings of the 20th International Conference of Very Large Data Bases, VLDB, Springer, New York, NY, USA. Vol. 1215, pp. 487-499.
2. Fraser, J. A. et al. (2005) Same-sex mating and the origin of the Vancouver Island Cryptococcus gattii outbreak. Nature, 437, 1360-1364.
3. Haines, J. L. and Pericak-Vance, M. A. (2006) Genetic Analysis of Complex Disease. Hoboken, New Jersey, USA, John Wiley&Sons.
4. Hinney, A. et al. (2007) Genome wide association study for early onset extreme obesity supports the role of fat mass and obesity associated gene variants. PLoS One, 2, e1361.
5. Homer, N. et al. (2008) Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. , 4, e1000167.
6. Hunter, D. J. et al. (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat. Genet. , 39, 870-874.
7. Johnson, A. and Shmatikov, V. (2013) Privacy-preserving data exploration in genome-wide association studies. In: Dhillon, I. S. et al. (eds) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA, ACM, pp. 1079-1087.
8. McDonald, J. H. (2009) Handbook of Biological Statistics. Vol. 2. Sparky House Publishing, Baltimore, MD.
9. Ozeki, T. , et al. (2011) Genome-wide association study identifies HLA-A 3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum. Mol. Genet. , 20, 1034-1041.
10. Scott, L. J. et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science, 316, 1341-1345.
11. Sladek, R. et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature, 445, 881-885.
12. Wang, R. et al. (2009) Learning your identity and disease from research papers: information leaks in genome wide association study. In: Ahmad-Reza S. et al. (eds) Proceedings of the ACM Conference on Computer and Communications Security. New York, USA, ACM, pp. 534-544.
13. Yeager, M. et al. (2007) Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. , 39, 645-649.
14. Zeggini, E. et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science, 316, 1336-1341.
15. Zhou, X. et al. (2011) To release or not to release: evaluating information leaks in aggregate human-genome data. In: Proceedings of the ESORICS Conference, Springer, pp. 607-627.