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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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| Published in: | Behavior genetics 2021-05, Vol.51 (3), p.331-342 |
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| cites | cdi_FETCH-LOGICAL-c474t-7f6898a22614753ac8a4aecabf67b7ad5526f1f79dba90812f2361e9c16ba90f3 |
| container_end_page | 342 |
| container_issue | 3 |
| container_start_page | 331 |
| container_title | Behavior genetics |
| container_volume | 51 |
| creator | Kirkpatrick, Robert M. Pritikin, Joshua N. Hunter, Michael D. Neale, Michael C. |
| description | 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. |
| doi_str_mv | 10.1007/s10519-020-10037-5 |
| format | article |
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| 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 |
| title | Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx |
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