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

Kirkpatrick, Robert M.; Pritikin, Joshua N.; Hunter, Michael D.; Neale, Michael C.

doi:10.1007/s10519-020-10037-5

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…For example, genome-wide complex trait analysis (GCTA; Yang et al 2011) was developed to incorporate a pairwise genetic relatedness matrix (GRM) between individuals using information from single nucleotide polymorphisms (SNPs). Twin studies have subsequently been adapted to incorporate empirical measures of genetic relatedness (Kirkpatrick et al 2021). However, incorporating a matrix of pairwise relatedness values for each set of participants leads to an increase in the computational time when estimating these model parameters.…”

Section: ‫ݕ‬ ൌ ߤ ‫ݔ‬ԣ ߚ ‫ܣ‬ ‫ܥ‬ ܵ ‫ܧ‬mentioning

confidence: 99%

Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models

Smith

Loughnan

Friedman

et al. 2022

Preprint

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Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive DevelopmentSM; Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924, 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability from 0.00 to 0.61), respectively. Incorporating measured genetic relatedness and using the full ABCD Study®sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study®sample.

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Section: ‫ݕ‬ ൌ ߤ ‫ݔ‬ԣ ߚ ‫ܣ‬ ‫ܥ‬ ܵ ‫ܧ‬mentioning

confidence: 99%

Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models

Smith

Loughnan

Friedman

et al. 2022

Preprint

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“…In this regard, methods that can simultaneously model the joint effect of genes and environment are likely to prove more informative. For instance, innovative approaches capable of applying genomic-relatedness based restricted maximum-likelihood [ 100 ] to structural equation modeling in the OpenMx [ 40 ] software package have the potential to analyze individual GWAS and phenotypic data and hold promise.…”

Section: Discussionmentioning

confidence: 99%

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

et al. 2022

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Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

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“…Despite their strengths, EasyMx and OpenMx have limitations when handling extended family data. Notably, they lack functions for handling modern molecular designs (Kirkpatrick, Pritikin, Hunter, & Neale, 2021), modeling complex genetic relationships, inferring relatedness, and simulating pedigrees.…”

Section: Statement Of Needmentioning

confidence: 99%

BGmisc: An R Package for Extended Behavior Genetics Analysis

Garrison,

Hunter,

Lyu

et al. 2024

JOSS

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Behavior genetics is a field that studies how our genes and environment contribute to differences in behavior and traits among individuals. Traditionally, twin studies have long been a cornerstone of this field, helping researchers understand how genetics influence behavior. Recently, the focus has expanded to include studies with more complex family structures, e.g., children of twins (CoT, D'Onofrio et al., 2003) and mother-daughter-aunt-niece (MDAN, Rodgers, Bard, Johnson, D 'Onofrio, & Miller, 2008). These broader studies offer more detailed insights into how our genes and environment shape us, but they also make analyzing and organizing the data more complex. BGmisc simplifies the analysis of these complex data structures by offering a comprehensive suite of functions for accommodating families of any size, from twins to extensive pedigrees.

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

Cited by 11 publications

References 40 publications

Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models

Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

BGmisc: An R Package for Extended Behavior Genetics Analysis

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

Cited by 11 publications

References 40 publications

Heritability estimation of cognitive phenotypes in the ABCD Study®using mixed models

Heritability estimation of cognitive phenotypes in the ABCD Study®using mixed models

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

BGmisc: An R Package for Extended Behavior Genetics Analysis

Contact Info

Product

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Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models

Heritability estimation of cognitive phenotypes in the ABCD Study^®using mixed models