Breadcrumb
Twin & family designs
All
A1. Introduction to OpenMx Part 1: The basics and conducting regression
Elizabeth Prom-Wormley, June 2022 Virtual Workshop
This video will introduce students to twin modeling using R and OpenMx. By the end of the video, students will be able to (1) recognize the major steps involved in an OpenMx model and (2) translate implementation of a linear regression between a statistical equation, structural equation model, and an OpenMx model
A2. Introduction to Analysis of Twin Data Using R and OpenMx - Part 2
Elizabeth Prom-Wormley, June 2022 Virtual Workshop
This video builds on the basics introduced in Part 1. By the end of the video, students will be able to: (1) summarize the goals of analyzing twin data for a single phenotype, (2) summarize the general process by which to analyze twin data for a single phenotype, (3) apply basic R functions for twin-focused data analysis and visualization, and (4) translate the implementation of a basic ACE model between a structural equation model and an OpenMx model
A3. Introduction to Path Analysis and twin SEM models
Elizabeth Prom-Wormley, June 2022 Virtual Workshop
This video presents the basic rules used for path tracing in structural equation modeling, which is the basis for developing basic and more complex twin models. By the end of the video, students will be able to: (1) identify the advantages of applying path tracing rules and their use in structural equation models, (2) summarize the basic path tracing rules, and (3) apply basic path tracing rules to derive the expected parameters generated from simple regression models of unrelated individuals as well as basic twin models
A4. Getting acquainted with twin modeling using OpenMx
Elizabeth Prom-Wormley, June 2022 Virtual Workshop
This practical will help students learn the basics of running a simple twin analysis, including challenges and useful strategies. Students will explore the development and estimation of parameters from Saturated and ACE models.
B1. Twin Models using OpenMx Part 1: Introduction
Hermine Maes, June 2021 Virtual Workshop
This video provides a broad overview of twin modeling using Structural Equation Models (SEMs). 听It provides the background and rationale for听fitting genetic models, such as the ACE and ADE model, to data collected in the classical twin design (CTD: MZ & DZ twins reared together).
B2. Twin Models using OpenMx Part 2: Saturated Models
Hermine Maes, June 2021 Virtual Workshop
Discusses how to fit the baseline (saturated) twin model in OpenMx, which estimates the twin means, variances, and covariances without estimating latent variances (VA, VD, and VC). Then briefly discusses estimating VA, VC, and VE or VA, VD, and VE.
B3. Twin Models using OpenMx Part 3: Genetic Models
Hermine Maes, June 2021 Virtual Workshop
Discusses fitting latent variance components (VA, VC, and VE, or VA, VD, and VE) in twin SEM models using OpenMx.
B4. Twin Models using OpenMx Part 4: Estimating variances vs. paths
Hermine Maes, June 2021 Virtual Workshop
Discusses two alternative approaches to estimating variance components in twin models: (a) estimating the path coefficients from the latent variables to the phenotypes and fixing the variances of the latent variables; or (b) estimating the variances of the latent variables but fixing the path coefficients to the phenotypes at 1.
B5. 7 Ways to do a Univariate Twin Model
Hermine Maes, June 2021 Virtual Workshop
Discuss the basic approach to estimating variance components using twins, and then discuss multiple alternative approaches to doing this, including their advantages and disadvantages.
C1. Multivariate Twin Models using OpenMx Part 1: ACE models for two phenotypes and genetic correlation
Michael Hunter, June 2021 Virtual Workshop
This video takes us from the univariate ACE model to a multivariate ACE model: from ACE to MACE.听 We begin with a review of the univariate ACE model, and then extend this to two phenotypes as a Cholesky model, introducing the concept of genetic correlation along the way.
C2. Multivariate Twin Models using OpenMx Part 2: atheoretical and theoretical multivariate models
Michael Hunter, June 2021 Virtual Workshop
This video introduces a theory-driven multivariate behavior genetics model: the common pathway model.听 The common pathway model, also called the psychometric factor model, first creates a phenotypic factor model and then biometrically decomposes the factor and residual variances.
C3. Multivariate Twin Models using OpenMx Part 3: Common pathway model
Michael Hunter, June 2021 Virtual Workshop
This video introduces a theory-driven multivariate behavior genetics model: the independent pathway model.听 The independent pathway model, also called the biometric factor model, first creates biometric factors for the A, C, and E variance components and then further biometrically decomposes the residual variances.
C4. Multivariate Twin Models using OpenMx Part 4: Independent vs. Common Pathway models
Robert Kirkpatrick, June 2021 Virtual Workshop
D1. Multivariate twin models: from univariate to bivariate
Conor Dolan, June 2022 Virtual Workshop
In this lecture, the univariate twin model is extended to the bivariate twin model. It is explained how the variance in two traits is decomposed into additive genetic, non-additive genetic, and environmental effects.
D2. Multivariate twin models: from bivariate to multivariate
Conor Dolan, June 2022 Virtual Workshop
In this lecture, the bivariate twin model is extended to the multivariate twin model. It is explained how the variance in four traits is decomposed into additive genetic, non-additive genetic, and environmental effects.
D3. Multivariate twin models: independent pathway models part 1
Dirk Pelt, June 2022 Virtual Workshop
In this lecture, the independent pathway model is introduced. A general specification of the common factor model is provided first, which is applied to genetic and environmental correlation matrices to arrive at the independent pathway model.
D4. Multivariate twin models: independent pathway models part 2
Dirk Pelt, June 2022 Virtual Workshop
In this lecture, several competing independent pathway models are tested. It explains how results from independent pathway models can be interpreted and presented.
D5. Multivariate twin models: the common pathway model
Conor Dolan, June 2022 Virtual Workshop
The common pathway model is discussed, also in relation to independent pathway models.
D6. Practical walk through: Multivariate twin models, part 1 and 2
Conor Dolan, June 2022 Virtual Workshop
In this practical we use the knowledge from the lectures in this section to estimate multivariate twin models, and independent and common pathway models in R using the OpenMx package. A skinfold dataset with 4 phenotypes is used, and a dataset with Neuroticism items.
E1. The Direct Symmetric Matrix Approach to Fitting Twin Models
Brad Verhulst, June 2022 Virtual Workshop
A lecture describing the direct symmetric matrix approach to fitting twin models.
E2. Practical: ACE Models
Brad Verhulst & Katrina Grasby, June 2022 Virtual Workshop
This practical details several ways that an ACE model can be parameterised in OpenMx. It covers introducing siblings into a twin model and how measured genetic data can be incorporated instead of assuming that DZ twin pairs have a genetic correlation of 0.5.
E3. Practical on SEM
Katrina Grasby, June 2022 Virtual Workshop
These are practical exercises related to structural equation modeling.
F1. Heterogeneity of effects and interactions in twin models: Overview
Sarah Medland, June 2022 Virtual Workshop
This video talks about different models of heterogeneity and the terminology that is used in this literature.
F2. Heterogeneity of effects and interactions in twin models: GxE Part 1
Hermine Maes, June 2022 Virtual Workshop
An overview of the basic types of models that can be used to estimate interactions and to model heterogeneity in twin models.
F3. Heterogeneity of effects and interactions in twin models: GxE Part 2
Hermine Maes, June 2022 Virtual Workshop
A deeper dive into fitting interactions in twin SEM models using the "Purcell" approach.
F4. Practical walk through: GxE models and Sex Limitation models
Hermine Maes & Sarah Medland, June 2022 Virtual Workshop
G1. Assumptions of the Classical Twin Design and Biases when Violated
Matthew C Keller, June 2022 Virtual Workshop
Assumptions of the CTD and discussion of biases that occur听when these assumptions are violated. Participants will learn how to calculate biases by hand.
G2. Extended Twin Family Designs: The Motivation for Using Them
Matthew C Keller, June 2022 Virtual Workshop
The motivation for using Extended Twin Family Designs (ETFDs), including how ETFDs can reduce biases that occur in other designs arising from assortative mating and passive G-E covariance arising from vertical transmission.
G3. Extended Twin Family Designs: Path Tracing
Matthew C Keller, June 2022 Virtual Workshop
This lecture shows how to derive expectations of variances and covariances in ETFDs using path tracing rules. We use a 鈥淣uclear Twin Family Design鈥 as an example. throughout.
G4. The Augmented Classical Twin Design
David Evans, June 2022 Virtual Workshop
This video introduces a new structural equation model called the 鈥淎ugmented Classical Twin Design鈥 which relaxes the Equal Environments assumption in twin studies.
G5. Practical: Classical Twin Design and Extended Twin Family Design
Matthew C Keller, June 2022 Virtual Workshop
This practical uses the 鈥淚nteractive worksheet鈥 link along with the 鈥淐TD.NTFD.R鈥 file located under the 鈥淧ractical Files鈥 link to go through parameter indeterminacy in the CTD and how adding information (e.g., ETFDs) can help deal with this indeterminacy. This is a great way to check your understanding of the lectures in this subsection.
H1. Biometrical Age-Based Latent Growth Curve Modeling
Michael C. Neale, June 2022 Virtual Workshop
This 26 minute talk describes genetically-informative latent growth curve modeling, including how age-at-participation, rather than wave-of-assessment is used to model development instead of the effects of being measured several times.听 It also shows how genetic variance in the cortical structure of the brain decreases with age, while its heritability increases.
H2. Markov Modeling with Genetically Informative Data
Michael C. Neale, June 2022 Virtual Workshop
A 30-minute talk about Markov modeling generally, with specific reference to the seminal 1986 contribution of Professor Eaves, which described Markov processes for genetic and environmental variance components.听 The consequences of including random intercepts for these processes are described.
H3. Multivariate Longitudinal Modeling with Genetically Informative Data
Michael C. Neale, June 2022 Virtual Workshop
Extending the Eaves et al Markov model for genetically informative data to the multivariate case is described.听 At the time of writing, this model has not yet been implemented in a software script, so some suggestions as to how to do so are included in the 30-minute talk.
I1. Practical on testing genetic mean effects in twin models
Conor Dolan, June 2021 Virtual Workshop