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Updated: Sep 3, 2025

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Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships.

Ronald De Vlaming1, Eric A W Slob2,3,4, Patrick J F Groenen5

  • 1Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. r.devlaming@vu.nl.

BMC Bioinformatics
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

The enhanced Multivariate Genomic-Relatedness-Based Restricted Maximum Likelihood (MGREML) method efficiently estimates genetic correlations and heritability from SNP data. It now supports user-specified factor models, enabling complex genetic architecture analysis with low computational cost.

Keywords:
GREMLGenetic correlationGenetic factor modelGenomic SEMSNP heritability

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Estimating heritability and genetic correlation from genome-wide single-nucleotide polymorphism (SNP) data is crucial in genetic studies.
  • Existing methods can be computationally intensive for large datasets and multiple traits.
  • Multivariate Genomic-Relatedness-Based Restricted Maximum Likelihood (MGREML) was previously developed for efficient estimation.

Purpose of the Study:

  • To extend MGREML to fit and test user-specified factor models.
  • To maintain computational efficiency while incorporating factor modeling capabilities.
  • To enable advanced genetic architecture analysis using SNP data.

Main Methods:

  • The extended MGREML method was applied to simulated datasets.
  • Factor models were fitted and compared to nested models using real data (height and BMI).
  • The method's performance was evaluated for statistical consistency and computational cost.

Main Results:

  • Simulations demonstrated that the extended MGREML provides consistent estimates and valid inferences for factor models.
  • The method achieves low computational cost, handling 50 traits and 20,000 individuals in under an hour on standard hardware.
  • Real data analysis successfully illustrated the estimation and testing of a factor model.

Conclusions:

  • The enhanced MGREML facilitates the estimation and inference of multivariate factor structures with high computational efficiency.
  • This advancement enables researchers to perform structural equation modeling on genetic data.
  • Researchers can now specify, estimate, and compare custom genetic factor models using SNP data via MGREML.