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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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...
Compacting Factor test01:22

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
Genomics02:02

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...

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Related Experiment Video

Updated: May 16, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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cuGWAM: Genome-wide association multifactor dimensionality reduction using CUDA-enabled high-performance graphics

Min-Seok Kwon1, Kyunga Kim, Sungyoung Lee

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-gu, Seoul 151-742, Korea. intellims@gmail.com

International Journal of Data Mining and Bioinformatics
|November 20, 2012
PubMed
Summary
This summary is machine-generated.

We developed new software, cuGWAM, to efficiently analyze gene-gene interactions for complex traits. This tool overcomes computational limits of traditional methods in genome-wide association studies.

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Multifactor dimensionality reduction (MDR) is crucial for detecting gene-gene interactions in complex traits.
  • Existing MDR software faces computational limitations for large-scale genome-wide association studies (GWAS) with millions of markers.

Purpose of the Study:

  • To develop efficient software for genome-wide association studies (GWAS) using Multifactor Dimensionality Reduction (MDR).
  • To overcome the computational challenges of traditional MDR methods in large genetic datasets.

Main Methods:

  • Developed CUDA (Compute Unified Device Architecture) based genome-wide association MDR (cuGWAM) software.
  • Utilized efficient hardware accelerators (GPUs) to enhance computational performance.

Main Results:

  • cuGWAM demonstrates superior performance compared to CPU-based MDR methods.
  • The developed software offers better performance than existing GPU-based MDR approaches.

Conclusions:

  • cuGWAM effectively addresses the computational limitations of MDR for GWAS.
  • The new software enables more efficient analysis of gene-gene interactions in large genetic datasets.