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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.
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Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

Single Nucleotide Polymorphisms-SNPs

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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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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jul 16, 2025

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

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

Qingrun Zhang^1,2, Muskan Bhatia³, Taesung Park⁴

¹Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.

Frontiers in Genetics

|September 11, 2023

Summary

This summary is machine-generated.

This study introduces Vpairs and Gpairs, efficient machine learning methods for identifying pairs of DNA variants associated with complex diseases. These novel approaches overcome memory limitations, enabling analysis of larger datasets for improved disease gene mapping.

Keywords:

digenic trait genetic association genetic variant genotype pair single-nucleotide polymorphism

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Traditional genome-wide association studies (GWAS) analyze single DNA variants sequentially.
Digenic traits result from the combined effects of two variants, often missed by single-variant analyses.
Existing machine learning methods for digenic traits often have high memory requirements, limiting dataset size.

Purpose of the Study:

To develop and present novel, computationally efficient machine learning methods for identifying digenic traits.
To overcome the memory limitations of existing algorithms for analyzing large genetic datasets.
To evaluate the performance of the new methods against established algorithms.

Main Methods:

Development of a new approach evaluating all variant and genotype pairs for disease association.
Implementation of two components: Vpairs and Gpairs.
Comparison with established algorithms like Apriori and FP-growth using published case-control datasets.

Main Results:

The Vpairs and Gpairs methods demonstrate advantages over existing implementations.
Application to age-related macular degeneration and Parkinson disease datasets.
Construction of a receiver operating characteristic (ROC) curve for genotype patterns.

Conclusions:

The Vpairs and Gpairs methods offer a more efficient approach to identifying digenic traits.
These methods facilitate the analysis of larger datasets for comprehensive human gene mapping.
The approach enhances the discovery of complex genetic underpinnings of diseases.