期刊
BIOINFORMATICS
卷 37, 期 -, 页码 I161-I168出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab305
关键词
-
类别
资金
- National Institute of Health [U01EB023685, R01HG010798]
- National Science Foundation [CNS1838083]
- Indiana University (IU) Precision Health Initiative (PHI)
This article demonstrates the inference of haplotypes from genomic data summaries without the need for the target's genome. Novel haplotypes can be reconstructed from allele frequencies in genomic datasets, leading to a haplotype-based membership inference algorithm for identifying target subjects in a case group.
Motivation: The availability of human genomic data, together with the enhanced capacity to process them, is leading to transformative technological advances in biomedical science and engineering. However, the public dissemination of such data has been difficult due to privacy concerns. Specifically, it has been shown that the presence of a human subject in a case group can be inferred from the shared summary statistics of the group, e.g. the allele frequencies, or even the presence/absence of genetic variants (e.g. shared by the Beacon project) in the group. These methods rely on the availability of the target's genome, i.e. the DNA profile of a target human subject, and thus are often referred to as the membership inference method. Results: In this article, we demonstrate the haplotypes, i.e. the sequence of single nucleotide variations (SNVs) showing strong genetic linkages in human genome databases, may be inferred from the summary of genomic data without using a target's genome. Furthermore, novel haplotypes that did not appear in the database may be reconstructed solely from the allele frequencies from genomic datasets. These reconstructed haplotypes can be used for a haplotype-based membership inference algorithm to identify target subjects in a case group with greater power than existing methods based on SNVs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据