Advanced Biometric Analyses
in Behavior Genetics

Learn the most common models for analyzing twin, sibling, and/or cousin data in this hands-on course. Useful for researchers in the social, behavioral, and medical sciences. Unlock a whole new world of inferential power in your observational data by developing behavioral genetic expertise!

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Instructors:
S. Alexandra Burt, PhD (Michigan State University)
D. Angus Clark (Department of Defense)

Workshop Format:
Three-Day Independent Workshop

Video Availability:
Archival Videos from 2024 Available Now!

Twin and family kinship data can dramatically enhance the inferential power of non-experimental research projects. They allow researchers to determine if an association between a predictor and an outcome is really environmental in origin and/or to evaluate whether the etiologic underpinnings of your outcome variable vary across social contexts (e.g., it is genetically influenced in X context but not in Y context). Perhaps not surprisingly then, kinship data have been intentionally embedded in most large-scale publicly available databases within the social, behavioral, and medical sciences (e.g., ABCD, AdHealth, and others).

However, relatively few applied researchers outside of particular sub-specialty areas are able to conduct these sorts of biometric kinship analyses. Neither the concepts underlying biometric kinship designs nor the specific structural equation models typically employed are taught in most graduate programs. Moreover, although it is now possible to run biometric kinship models in Mplus (the approach we will take in this workshop), the statistical programs used to analyze these kinds of data have historically required either a unique coding language (Mx) or in-depth knowledge of R and a deep knowledge of the concepts underlying family-based models (Open Mx), collectively making them inaccessible to many. This three-day workshop is designed to provide hands on didactic and practical training for the structural equation models most commonly used in modern-day twin-family studies, including univariate, multivariate, and genotype-environment interaction (GxE) structural equation models.

This workshop picks up where the Introduction to Behavior Genetics workshop leaves off, focusing on more advanced, state of the science biometric structural equation models. At the end of this workshop, attendees will have learned how to compute the proportion of genetic and environmental variance in an outcome variable, how to estimate sources of genetic and environmental covariance between variables, and how to evaluate whether the etiologic underpinnings of an outcome variable vary across social contexts. Although these constitute more advanced skills, the workshop teaches them in a highly accessible manner that emphasizes their practical applicability and utility for social, behavioral, and medical scientists.

What you’ll learn

  • Conceptual underpinnings of kinship data, with a focus on twin-families

    • Sources of twin-family similarities and differences

    • Definitions of additive genetic, shared environmental, and non-shared environmental variance

    • Falconer’s formulas

  • Overview of structural equation models

  • Biometric structural equation models

    • Univariate variance decomposition model, with and without sex differences

    • Correlated factors multivariate variance decomposition model, with and without sex differences

    • Genotype-environment interaction and correlation models

  • Necessary data preparation and management techniques

  • How to correctly interpret model results

Syllabus

  • The principles and concepts behavioral genetic decompositions of twin-sibling data

  • Sources of sources of similarities and differences in twin-sibling pairs

  • Practical demonstrations of a series of progressively more sophisticated models (i.e., univariate decomposition model, correlated factors multivariate decomposition model, genotype-environment interaction and correlation models)

    • How to choose the correct family-based analysis for your question

    • Conducting appropriate data preparation and management for a given analysis

    • How to troubleshoot while running analyses

    • How to interpret the results

  • To solidify their learning, students will engage in significant hands-on learning, applying each method to existing data. These learning activities will include preparing the data for analysis, running the analyses, and interpreting the results

  • Office hours with the instructors will be offered at the end of the course for students to troubleshoot analyses in their own data

Registration Options

Advanced Biometric Analysis in Behavior Genetics

  • Professional
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • Click Register Below
  • Trainee
  • 33% Discount for
    Students and Postdocs
  • Use code "TRAINEE" at Checkout

Combo 1: Advanced Biometric Analysis in Behavior Genetics + Introduction to Family-bases Analyses

  • Professional
  • Baseline Price
    20% Combo Discount
  • Click Register Below
  • Trainee
  • 33% Discount for
    Students and Postdocs
  • Use code "TRAINEE" at Checkout

 FAQs

  • This intermediate workshop was custom built for researchers in the social, behavioral, and medical sciences who want to learn classic family-based and behavioral genetic analyses. It is designed to be highly accessible to researchers in child development, medicine, clinical, social, and personality psychology, social work, among many others. This workshop is appropriate for faculty, staff, and students at both the graduate and advanced undergraduate levels.

  • Intermediate

  • This workshop assumes that attendees have no prior knowledge of or experience with behavioral genetic models. It does, however, assume that learners have basic computing skills such as the ability to download and manage (e.g., copy and paste) files, install and run software, and use a web browser to access workshop materials. Participants are also expected to have a working knowledge of regression, as well as SPSS and MPlus. Knowledge of SEM is also useful though not required (there will be a brief primer on SEM). Knowledge of family-based methods would be useful (and can be obtained via the Introduction to Family-based Analyses workshop.

  • Data preparation, management, and preliminary analyses will be conducted in SPSS. Structural equation models will be run in Mplus.

  • Registered learners will gain access to the Zoom link and passcode to attend the synchronous workshop, as well as video recordings of the lectures, downloadable slideshows, and example data files.