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Related Experiment Video

Updated: Nov 13, 2025

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Efficient verification for outsourced genome-wide association studies.

Xinyue Wang¹, Xiaoqian Jiang², Jaideep Vaidya¹

¹Rutgers University, Newark, NJ, USA.

Journal of Biomedical Informatics

|March 12, 2021

Summary

This summary is machine-generated.

This study introduces two algorithms for generating synthetic single nucleotide polymorphisms (SNPs) to ensure the integrity of cloud-based genome-wide association studies (GWAS). These methods create synthetic SNPs indistinguishable from real data, enabling trustworthy outsourced GWAS computations.

Keywords:

Computational integrity Efficient verification Genome wide association study

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Last Updated: Nov 13, 2025

Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Published on: August 21, 2016

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Cloud computing adoption for genome-wide association studies (GWAS) raises concerns about data integrity.
Verifying the accuracy of outsourced GWAS computations is a critical, yet unresolved, challenge.

Purpose of the Study:

To develop novel algorithms for generating synthetic single nucleotide polymorphisms (SNPs) that are indistinguishable from real SNPs.
To ensure the integrity and trustworthiness of outsourced GWAS computations in a cloud environment.

Main Methods:

Proposed two algorithms for synthetic SNP generation: one based on phenotype vectors, the other on similar real SNPs.
Employed game theoretic analysis to incentivize honest server behavior through randomized verification and appropriate payoffs.
Conducted extensive experiments to evaluate the indistinguishability of synthetic SNPs from real SNPs.

Main Results:

Generated synthetic SNPs that are computationally indistinguishable from real SNPs, even when mixed with real data.
Demonstrated that even machine learning models cannot differentiate synthetic SNPs from real SNPs.
Established the efficiency and trustworthiness of outsourcing logistic regression for GWAS.

Conclusions:

The proposed synthetic SNP generation methods enhance the security and reliability of cloud-based GWAS.
Randomized verification and game theoretic incentives ensure server honesty in outsourced computations.
This approach facilitates efficient and trustworthy execution of GWAS using cloud computing resources.