Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies

Wang, Meng; Ji, Zhanglong; Wang, Shuang; Kim, Jihoon; Yang, Hai; Jiang, Xiaoqian; Ohno-Machado, Lucila

doi:10.1093/bioinformatics/btx470

Cited by 20 publications

(21 citation statements)

References 36 publications

(57 reference statements)

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“…According to our knowledge, no studies have been reported so far regarding the analysis of Kawasaki disease by means of ML analysis on time-series based gene expression data. So far, most of the works in the field have focused only on the analysis of the associated SNPs [19] , [20] , [21] . In addition, the reported biomarkers for KD diagnosis have been experimentally determined in laboratory studies [22] , [23] without the application of any data driven computational workflows.…”

Section: Introductionmentioning

confidence: 99%

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

Pezoulas

Papaloukas

Veyssiere

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

Pezoulas

Papaloukas

Veyssiere

et al. 2021

Computational and Structural Biotechnology Journal

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“…This makes it possible to apply the exponential mechanism under differential privacy. Subsequently, we evaluate the run time and accuracy of our methods through experiments and show that our exact method is 10, 000 times faster than Wang et al's method 22 for a small cohort with 5, 000 SNPs, and is the first globally to be applied to a large cohort with 10 6 SNPs. We also show that our approximation algorithm is much faster than the exact one, taking only about 4 seconds to complete the calculation even for the large cohort.…”

Section: Introductionmentioning

confidence: 99%

“…Since then, several studies 10,24,25 have proposed practical differential privacy mechanisms for case-control studies in GWAS. Other analyses in GWAS include family-based correlation analysis, and Wang et al 22 proposed differentially private mechanisms for TDTs in the case of trio families, that is, one affected child per family. 19 However, their exact algorithm requires solving the shortest path problems with a large number of nodes, which are computationally intensive, and takes a long time to run even for a relatively small dataset.…”

Section: Introductionmentioning

confidence: 99%

Efficient Differentially Private Methods for a Transmission Disequilibrium Test in Genome Wide Association Studies

Yamamoto

Shibuya

2021

Preprint

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To achieve the provision of personalized medicine, it is very important to investigate the relationship between diseases and human genomes. For this purpose, large-scale genetic studies such as genome-wide association studies are often conducted, but there is a risk of identifying individuals if the statistics are released as they are. In this study, we propose new efficient differentially private methods for a transmission disequilibrium test, which is a family-based association test. Existing methods are computationally intensive and take a long time even for a small cohort. Moreover, for approximation methods, sensitivity of the obtained values is not guaranteed. We present an exact algorithm with a time complexity of O(nm) for a dataset containing n families and m single nucleotide polymorphisms (SNPs). We also propose an approximation algorithm that is faster than the exact one and prove that the obtained scores' sensitivity is 1. From our experimental results, we demonstrate that our exact algorithm is 10,000 times faster than existing methods for a small cohort with 5,000 SNPs. The results also indicate that the proposed method is the first in the world that can be applied to a large cohort, such as those with 1,000,000 SNPs. In addition, we examine a suitable dataset to apply our approximation algorithm. Supplementary materials are available at https://github.com/ay0408/DP-trio-TDT.

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“… Wang et al (2017) proposed a solution based on differential privacy for TDT based on test statistics, P -values and the shortest Hamming distance scores. The privacy of an entire family participating in the study is considered as the target of the attacker.…”

Section: Introductionmentioning

confidence: 99%

Identifying disease-causing mutations with privacy protection

et al. 2020

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Motivation The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns, and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this paper, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. Results Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant, compound heterozygous) based privacy-preserving analyses of genomic data in order to find disease-causing mutations. Furthermore, we reimplement the privacy-preserving methods (MAX, SETDIFF, and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF, and INTERSECTION implementations are 2.5, 1122, and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. Availability https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis Supplementary information Supplementary data are available at Bioinformatics online.

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Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 20 publications

References 36 publications

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

Efficient Differentially Private Methods for a Transmission Disequilibrium Test in Genome Wide Association Studies

Identifying disease-causing mutations with privacy protection

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