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

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.