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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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...
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Epistasis01:39

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Related Experiment Video

Updated: Apr 1, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

Jorge González-Domínguez, Lars Wienbrandt, Jan Christian Kässens

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study accelerates the detection of epistasis in Genome-Wide Association Studies by using parallel computing on FPGAs and GPUs. This approach dramatically reduces computation time for identifying genetic marker interactions.

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    Area of Science:

    • Genetics
    • Computational Biology
    • Bioinformatics

    Background:

    • High-throughput genotyping technologies generate millions of genetic markers per individual.
    • Detecting epistasis (2-SNP interactions) is crucial for Genome-Wide Association Studies (GWAS) but computationally intensive.
    • Current methods for epistasis detection are time-consuming, requiring days for moderately-sized datasets.

    Purpose of the Study:

    • To significantly accelerate the computational methods for detecting epistasis in GWAS.
    • To demonstrate the effectiveness of parallel computing architectures for speeding up epistasis detection.

    Main Methods:

    • Utilized a combination of fine-grained and coarse-grained parallelism.
    • Implemented the accelerated methods on two distinct computing systems: Field-Programmable Gate Arrays (FPGAs) and multiple Graphics Processing Units (GPUs).

    Main Results:

    • Achieved speedups of approximately four orders-of-magnitude compared to sequential implementations.
    • Reduced epistasis detection runtime to minutes for moderate datasets and hours for large datasets.

    Conclusions:

    • Parallel computing on FPGAs and GPUs offers a substantial acceleration for epistasis detection in GWAS.
    • This advancement makes large-scale genetic interaction analysis more feasible and efficient.