4.7 Article Proceedings Paper

Haplotype-based membership inference from summary genomic data

Journal

BIOINFORMATICS
Volume 37, Issue -, Pages I161-I168

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab305

Keywords

-

Funding

  1. National Institute of Health [U01EB023685, R01HG010798]
  2. National Science Foundation [CNS1838083]
  3. Indiana University (IU) Precision Health Initiative (PHI)

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available