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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

Updated: Dec 14, 2025

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

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

Published on: August 21, 2016

Semi-Parallel logistic regression for GWAS on encrypted data.

Miran Kim¹, Yongsoo Song², Baiyu Li³

¹School of Biomedical Informatics, University of Texas, Health Science Center at Houston, Houston, 77030, TX, USA.

BMC Medical Genomics

|July 23, 2020

Summary

This summary is machine-generated.

This study introduces a secure method for analyzing genetic data using homomorphic encryption, enabling privacy-preserving genome-wide association studies (GWAS). The approach efficiently identifies genetic variants linked to diseases without compromising sensitive patient information.

Keywords:

Genome-wide association studies Homomorphic encryption Logistic regression

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Last Updated: Dec 14, 2025

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

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Published on: July 27, 2021

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Bioinformatics
Computational Biology
Cryptography

Background:

Biomedical data sharing is vital for scientific discovery and healthcare improvement.
Privacy concerns hinder the use of large datasets like those in genome-wide association studies (GWAS).
Homomorphic encryption offers a solution by enabling computation on encrypted data, safeguarding sensitive information.

Purpose of the Study:

To develop a secure outsourcing solution for analyzing logistic regression models in the context of genotype-trait associations.
To adapt and improve existing methods for privacy-preserving GWAS.

Main Methods:

Adapted a semi-parallel training method for logistic regression on covariates and single nucleotide polymorphisms (SNPs).
Modified an approximate homomorphic encryption scheme to enhance computational performance.
Implemented a secure outsourcing solution for encrypted data analysis.

Main Results:

The solution was evaluated on a real-world dataset (245 samples, 10643 SNPs, 3 covariates).
Achieved state-of-the-art performance in homomorphic encryption for GWAS, balancing complexity and accuracy.
Completed logistic regression-based genome-wide association analysis over encryption in approximately 43 seconds.

Conclusions:

Demonstrated the feasibility and scalability of the proposed secure outsourcing solution.
The method enables privacy-preserving GWAS, facilitating broader data utilization.