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Privacy-preserving approximate GWAS computation based on homomorphic encryption

机译:基于同态加密的隐私保护近似GWAS计算

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摘要

After the successful completion of the Human Genome Project in the early 21st century, high throughput technology on genetic variations has been rapidly developed and widely studied. In particular, through the development of microarray chip with rather small computational cost, it became possible to determine the genotype of millions of single nucleotide polymorphism (SNP), a variation in a single nucleotide that occurs at a specific position in the genome, for each individual. With those statistical data of genotypes, many researches are proposed that investigate associations between SNPs and phenotypes like major human disease, and especially Genome-wide association study (GWAS) aims to find top significant SNPs relevant to a certain phenotype.
机译:在21世纪初人类基因组计划成功完成之后,有关遗传变异的高通量技术得到了迅速发展和广泛研究。尤其是,通过以较低的计算成本开发微阵列芯片,就可以确定数百万个单核苷酸多态性(SNP)的基因型,即每个核苷酸在基因组中特定位置上发生的变异。个人。利用这些基因型的统计数据,提出了许多研究SNP和表型之间的关联的研究,例如主要的人类疾病,尤其是全基因组关联研究(GWAS)的目的是寻找与某种表型相关的最重要的SNP。

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