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Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx.
Robert M Kirkpatrick1,2, Joshua N Pritikin3, Michael D Hunter4
1Virginia Commonwealth University, Richmond, USA. robert.kirkpatrick@vcuhealth.org.
Behavior Genetics
|January 13, 2021
Summary
This study introduces mxGREML, a new feature for OpenMx, enabling the fitting of traditional biometrical structural equation models (SEMs) using modern genomic data. This bridges pregenomic behavioral genetics with current genome-wide association studies.
Area of Science:
- Behavioral Genetics
- Quantitative Genetics
- Genomics
Background:
- Biometrical structural equation models (SEMs) have a long history in behavioral genetics using twin, family, and adoption studies.
- Genomic-relatedness-matrix restricted maximum-likelihood (GREML) estimates genetic components using observed genetic resemblance in unrelated individuals with genome-wide genotypes.
Purpose of the Study:
- To introduce a new feature, "mxGREML", within the OpenMx package.
- To enable the fitting of biometrical SEMs from pregenomic studies using contemporary genomic data.
- To extend the capabilities of GREML beyond heritability and genetic correlations to SEMs for multitrait genomic samples.
Main Methods:
- Integration of GREML methodology into the OpenMx package.
- Development of "mxGREML" to fit biometrical SEMs on genome-wide genotype data.
- Illustrative example provided to demonstrate the feature's application.
Main Results:
- The mxGREML feature in OpenMx now supports fitting biometrical SEMs in genomic study designs.
- The new functionality allows for the analysis of multitrait samples of genotyped participants using established SEM frameworks.
- The study details the functionality and provides a practical example of its use.
Conclusions:
- mxGREML bridges traditional biometrical SEMs with modern genomic data analysis.
- The feature enhances OpenMx's utility for researchers in behavioral and quantitative genetics.
- Future development plans are outlined, addressing current limitations and potential extensions.
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