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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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Second-order group knockoffs with applications to GWAS.

Benjamin B Chu, Jiaqi Gu, Zhaomeng Chen

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    This study introduces group knockoffs for enhanced genome-wide association studies (GWAS). These methods improve the identification of genetic variants influencing traits, even with highly correlated variables, offering better statistical power and precision.

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    Area of Science:

    • Genetics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Genome-wide association studies (GWAS) aim to find genetic variants linked to medical traits.
    • Traditional GWAS methods struggle with highly correlated explanatory variables.
    • The knockoff framework offers a way to control false discoveries in variable selection.

    Conclusions:

    • Group knockoffs provide a powerful and precise approach for GWAS, especially with correlated variables.
    • The expanded set of algorithms and software facilitates broader application of knockoff methods.
    • The Knockoffs.jl package offers accessible tools for researchers in statistical genetics.