Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance–covariance components based not upon the expected degree of gene...

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Bibliographic Details
Published in:Behavior genetics 2021-05, Vol.51 (3), p.331-342
Main Authors: Kirkpatrick, Robert M., Pritikin, Joshua N., Hunter, Michael D., Neale, Michael C.
Format: Article
Language:English
Subjects:
Adoption studies
Analysis of Variance
Behavior
Behavioral Science and Psychology
Biometry - methods
Clinical Psychology
Design
Genetics
Genome-Wide Association Study - methods
Genomes
Genomics
Genotype
Genotypes
Health Psychology
Heritability
Likelihood Functions
Models, Genetic
Models, Theoretical
Original Research
Phenotype
Polymorphism, Single Nucleotide - genetics
Psychology
Public Health
Relatedness
Relatives
Software
Statistics as Topic - methods
Structural equation modeling
Twins - genetics
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Summary:There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance–covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available—genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, “mxGREML”, designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature’s current limitations, and our plans for its further development.
ISSN:0001-8244
1573-3297
DOI:10.1007/s10519-020-10037-5