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  3. Ultra-fast Implementation Of Multivariate Gwas In Genomic Sem Using Flexible Analytic Estimation.
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  3. Ultra-fast Implementation Of Multivariate Gwas In Genomic Sem Using Flexible Analytic Estimation.

Related Experiment Video

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Ultra-Fast Implementation of Multivariate GWAS in Genomic SEM Using Flexible Analytic Estimation.

Javier de la Fuente, Mijke Rhemtulla, Travis T Mallard

    Biorxiv : the Preprint Server for Biology
    |June 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Genomic Structural Equation Modelling (Genomic SEM) now offers a faster analytic solution for multivariate Genome-Wide Association Studies (GWAS). This advancement significantly reduces computation time for analyzing complex genetic architectures of various traits and disorders.

    More Related Videos

    Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
    05:53

    Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

    Published on: June 21, 2018

    Related Experiment Videos

    Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
    08:27

    Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

    Published on: July 27, 2021

    Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
    05:53

    Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

    Published on: June 21, 2018

    Area of Science:

    • Genetics
    • Biostatistics
    • Computational Biology

    Background:

    • Many traits and disorders have complex genetic underpinnings, involving shared and distinct genetic factors.
    • Genome-Wide Association Studies (GWAS) are crucial for understanding genetic influences on phenotypes.
    • Genomic Structural Equation Modelling (Genomic SEM) was previously developed to model multivariate genetic architectures.

    Purpose of the Study:

    • To introduce a novel closed-form analytic solution for estimating SNP effects in multivariate GWAS within the Genomic SEM framework.
    • To significantly enhance the speed and efficiency of multivariate GWAS analyses.
    • To reduce the computational burden and reliance on high-performance computing (HPC).

    Main Methods:

    • Implementation of a closed-form analytic solution for SNP effect estimation in Genomic SEM.
  • Comparison of the analytic estimator's speed against the existing iterative estimator.
  • Application of the new method to a multivariate GWAS of 13 phenotypes and 5 common factors.

    • Main Results:

    • The new analytic estimator is over 800 times faster than the previous iterative estimator.
    • A multivariate GWAS of 13 phenotypes was completed in approximately 2 minutes on a laptop.
    • The method drastically decreases the need for high-performance computing resources.

    Conclusions:

    • The analytic solution in Genomic SEM revolutionizes the speed of multivariate genetic architecture analysis.
    • This advancement makes complex genetic studies more accessible and efficient.
    • The updated GenomicSEM package offers a faster, more practical approach to genetic discovery.