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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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Genomics

Genomics

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...

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,...

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Related Experiment Video

Updated: May 5, 2026

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

TorchGWAS : GPU-accelerated GWAS for thousands of quantitative phenotypes.

Xingzhong Zhao¹, Ziqian Xie¹, Islam¹

¹Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.

|May 4, 2026

Summary

TorchGWAS accelerates genome-wide association studies (GWAS) for large phenotype panels. This hardware-accelerated framework significantly increases throughput, making large-scale GWAS screening practical for thousands of quantitative traits.

Keywords:

GPU computing GWAS deep phenotyping imaging genetics

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Last Updated: May 5, 2026

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Published on: July 27, 2021

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Published on: July 1, 2020

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Published on: May 22, 2018

Area of Science:

Bioinformatics
Computational Biology
Genetics

Background:

Modern bioinformatics generates thousands of quantitative phenotypes, creating computational bottlenecks for traditional genome-wide association studies (GWAS).
Existing GWAS tools are inefficient for phenotype-rich screening workflows that reuse genotype data across numerous traits.

Purpose of the Study:

To develop TorchGWAS, a high-throughput framework for association testing in large phenotype panels using hardware acceleration.
To provide efficient GWAS screening for phenotype-rich datasets.

Main Methods:

Implemented in Python, TorchGWAS utilizes hardware acceleration (NVIDIA A100 GPU) for association testing.
Supports linear GWAS and multivariate phenotype screening with NumPy, PLINK, and BGEN genotype inputs.
Includes internal covariate adjustment and sample identifier alignment.

Main Results:

TorchGWAS achieved a 300- to 1700-fold increase in phenotype throughput compared to traditional methods.
Processed 20,480 phenotypes in 20 minutes on a single GPU, compared to fastGWA's ~100 seconds per phenotype on a multi-core CPU.
Demonstrated practicality for large-scale GWAS screening in phenotype-rich settings.

Conclusions:

TorchGWAS significantly enhances the efficiency of genome-wide association studies for large numbers of quantitative phenotypes.
Enables practical and rapid screening of thousands of traits, overcoming computational bottlenecks in modern bioinformatics.
Provides a valuable tool for genetic research in complex, phenotype-rich datasets.