scholarly journals Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults

2020 ◽  
Author(s):  
Dorret I. Boomsma ◽  
Toos C. E. M. van Beijsterveldt ◽  
Veronika V. Odintsova ◽  
Michael C. Neale ◽  
Conor V. Dolan
Keyword(s):  
Age Groups ◽  
Covariance Structure ◽  
Genetic Component ◽  
Important Predictor ◽  
Genetic Covariance ◽  
Genetic Components ◽  
Analysis Application ◽  
Covariance Structure Model ◽  
Inattention And Hyperactivity ◽  
Adult Twins

AbstractWe present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but age-appropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

2019 ◽  
Vol 50 (3) ◽  
pp. 384-395 ◽  
Author(s):  
Yamin Zhang ◽  
Mingli Li ◽  
Qiang Wang ◽  
Jacob Shujui Hsu ◽  
Wei Deng ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Rare Variants ◽  
Clinical Symptoms ◽  
Gray Matter Volume ◽  
Structural Mri ◽  
Genetic Component ◽  
Major Depressive ◽  
Genetic Components ◽  
Burden Test

AbstractBackgroundMajor depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.MethodsWe sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.ResultsTwo genes (CSMD1, p = 5.32×10−6; CNTNAP5, p = 1.32×10−6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10−5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10−6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10−4) and Alzheimer Disease Up (FDR q = 6.12×10−4).ConclusionsBoth rare variants analysis and imaging–genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

scholarly journals The Bayesian Covariance Structure Model for Testlets

2020 ◽  
pp. 107699862094120
Author(s):  
Jean-Paul Fox ◽  
Jeremias Wenzel ◽  
Konrad Klotzke
Keyword(s):  
Random Effects ◽  
Random Effect ◽  
Covariance Structure ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Random Effects Model ◽  
Structure Model ◽  
Modeling Framework ◽  
Irt Models ◽  
Covariance Structure Model

Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.

scholarly journals The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register

2020 ◽  
Author(s):  
Ruifang Li-Gao ◽  
Dorret I. Boomsma ◽  
Eco J. C. de Geus ◽  
Johan Denollet ◽  
Nina Kupper
Keyword(s):  
The Netherlands ◽  
Variance Components ◽  
Genetic Model ◽  
Additive Genetic Variance ◽  
Type D Personality ◽  
Continuous Measure ◽  
Genetic Covariance ◽  
Type D ◽  
Genetic Components ◽  
Increased Risk

Abstract Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the ‘Mendel’ software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4–9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.

scholarly journals Variance modelling of longitudinal height data from a Pinus radiata progeny test

2000 ◽  
Vol 30 (4) ◽  
pp. 645-654 ◽  
Author(s):  
Luis A Apiolaza ◽  
Arthur R Gilmour ◽  
Dorian J Garrick
Keyword(s):  
Repeated Measures ◽  
Model Fitting ◽  
Covariance Structure ◽  
Progeny Test ◽  
Genetic Covariance ◽  
Alternative Structures ◽  
Natural Logarithm ◽  
Unequally Spaced ◽  
Residual Matrix ◽  
Autoregressive Correlation

Variance components were estimated using alternative structures for the additive genetic covariance matrix (G0), for height (m) of trees measured at 10 unequally spaced ages in an open-pollinated progeny test. These structures reflected unstructured, autoregressive, banded correlation and random regressions models. The residual matrix (R0) was unstructured, and the block and plot strata matrices were autoregressive. The best model for G0 considering the likelihood value and number of parameters was the autoregressive correlation form with age-specific variances and time on a natural logarithm basis. The genetic correlation between successive measures ranged from 0.93 at age 1 to 0.99 at age 14 years. Heritability increased with age from 0.09 (age 1) to 0.24 (age 7) and then declined to 0.13 at age 15. Heritabilities from the unstructured model were similar, while heritabilities assuming banded correlations were lower after age 7. The covariance structure implicit in the random regressions model was considered unsatisfactory. Using structures in G0 facilitated model fitting and convergence of the likelihood maximisation algorithm. Fitting a structured matrix that reflects the relationships present in repeated measures may overcome problems of nonpositive definiteness of unstructured matrices from longitudinal data, especially when genetic variation is small.

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