Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Vlaming, Ronald de; Slob, Eric A W; Jansen, Philip R.; Koellinger, Philipp; Groenen, Patrick J. F.; Rietveld, Cornelius A.

doi:10.1038/s42003-021-02712-y

Cited by 9 publications

(20 citation statements)

References 63 publications

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“…More specifically, REML estimation is equivalent to maximum-likelihood estimation applied to , where the rows of matrix form a basis of the left null space of . However, once REML estimates of and are obtained, one can readily calculate the generalized least squares estimator of the fixed effects [ 1 , 9 ]. This option is implemented in both GCTA and MGREML.…”

Section: Methodsmentioning

confidence: 99%

“…The univariate LMM can be generalized to a multivariate LMM [ 17 , 18 ], which can be used to jointly estimate genetic covariance and environmental covariance between Traits and , denoted by and respectively. Using the same notation as seen in the original derivations of MGREML [ 9 ], this multivariate LMM can be written as follows: where ‘ ’ denotes the Kronecker product. In this model, is the genetic variance matrix and the environmental variance matrix.…”

Section: Methodsmentioning

confidence: 99%

“…The variance matrix of the multivariate model (i.e., ) is dense, rendering naïve REML estimation infeasible for large N and T , as mere evaluation of the log-likelihood function already requires time. However, the time complexity can be drastically reduced by transforming the data using the eigenvalue decomposition (EVD) of the GRM [ 4 , 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…We recently developed MGREML, a computationally and statistically efficient approach for multivariate GREML [ 9 ]. Importantly, MGREML resolves inconsistencies when combining bivariate estimates into a multivariate matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas Genomic SEM [ 11 ], another method to estimate genetic factor models, relies on preexisting summary statistics from large-scale genome-wide association studies (GWAS) for all traits of interest, MGREML uses individual-level data, giving users (1) more statistical power for a fixed sample size [ 9 ] and (2) more direct control over model specification and estimation (e.g., being able to control for an additional covariate in the MGREML analysis itself, rather than having to obtain a new set of GWAS results).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 ($$h^2_{\text{SNP}}$$ h SNP 2 ) and genetic correlation ($$\rho _G$$ ρ G ) 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 $$h^2_{\text{SNP}}$$ h SNP 2 ’s, 1225 $$\rho _G$$ ρ G ’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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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General dimensions of human brain morphometry inferred from genome‐wide association data

Fürtjes

Arathimos

Coleman

et al. 2023

Human Brain Mapping

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Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis ( Genomic PCA ) to model general dimensions of brain‐wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome‐wide association data for 83 brain‐wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain‐wide regions accounted for substantial genetic variance ( R 2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure ‐ specifically frontal and parietal volumes thought to be part of the central executive network ‐ tended to be somewhat more susceptible towards age ( r = −0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability ( r g = 0.17–0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain‐wide morphometry and cognitive ageing.

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Cognition and emotion: Family member similarity and intergenerational transmission.

Deater-Deckard,

Hong,

Bertrand

et al. 2024

Child Development at the Intersection of Emotion and Cognition (2nd Ed.).

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C hildren arrive in the world ready for cognitive and affective action and experience. Over time, they will develop into full-fledged cognitive regulators of their emotions. Guiding each child through this developmental journey is a set of experiences integrated with biological mechanisms. As individuals, children differ from each other in that biology (some aspects of which they are born with, e.g., structural differences in their genotypes) and in what they experience-even within the same household. These distinct influences work together within a family context and set of relationships. Our emphasis in the current chapter is that each person in the family is unique but that there also are clear patterns of family member resemblance (i.e., observed similarity) arising from familial transmission (i.e., causal mechanisms involving biological-environmental transactions) of cognitive and affective components of emotion regulation. We present examples of research on familial resemblance and transmission involving two cognitive emotion regulation processes: effortful control (EC) and executive functioning (EF), and their connections with negative emotions-anger/frustration and fearfulness in particular. Examining family member resemblance and transmission processes allows scientists to operationalize mechanisms more precisely and infer potential causal effects more accurately, which in turn informs improvements to prevention and intervention efforts.

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Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Cited by 9 publications

References 63 publications

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

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

General dimensions of human brain morphometry inferred from genome‐wide association data

Cognition and emotion: Family member similarity and intergenerational transmission.

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