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

Ronald De Vlaming, Eric A. W. Slob, Patrick J. F. Groenen, Cornelius A. Rietveld

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability (hSNP2) and genetic correlation (ρG) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity. Results: Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 hSNP2’s, 1225 ρG’s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/. Conclusion: MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.

Original languageEnglish
Article number305
JournalBMC Bioinformatics
Volume23
Issue number1
DOIs
Publication statusPublished - 27 Jul 2022

Bibliographical note

Funding Information:
This work was supported by the European Research Council (Starting Grant 946647 GEPSI to CAR). EAWS is funded by the NIHR Cambridge BRC. The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (Grant NIA U01AG009740) and is conducted by the University of Michigan. These funding bodies did not play any role in the design of the study, in the collection, analysis, and interpretation of data, and in writing the manuscript.

Funding Information:
The authors are grateful for computational resources provided by the Dutch national e-infrastructure with support of the SURF cooperative (Project EINF-607).

Publisher Copyright:
© 2022, The Author(s).

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