Week 6 Do Genes Cause Behavior? I: Methods Teams

This week you’ll learn about one specialized analysis, including what kinds of questions it can address, how its results are is usually reported, and its limitations. Each team will produce a reference resource to explain the method. Team assignments will be posted on Moodle.

Objectives

  • Learn about a new method from the context(s) in which it has been used.
  • Look for common applications across phenotypes.

Lecture Notes

This week our goal is to learn about some more specialized methods that you may encounter when reading the behavior genetic literature, and understanding how these methods do (or do not) allow us to potentially understand causation when we are limited to correlational/non-experimental data in our studies of human behavior.

As soon as possible:

  • Check your team assignment on Moodle.
  • Make sure you can access your team discussion forum for Week 6 (posted in Moodle below your team’s pre-selected readings).
  • Make sure you can access (or request access to) your team google doc for Week 6 (posted below your team’s discussion board; access to the doc does require a login so I can see who contributed what).
  • Skim your team’s pre-selected papers (note: SKIM, you are not expected to do a deep-read of all of these).

This week we will have Zoom meetings on both Tuesday and Thursday (11:00 am - 12:20 pm Central). During these sessions, you will work together to:

  • [Tuesday] Complete article summaries for some of your pre-selected papers, focusing on the section(s) dealing with your team’s method in particular.
  • [Thursday] Assemble a Method Summary for others to use as a reference.

As always, there are four asynchronous options to contribute to your team effort this week. However, I encourage you to attend this week’s Zoom sessions if at all possible, because working through these activities as a group is the easiest way to develop these skills.

The readings for Week 6 and Week 7 are essentially the same. This week, you’ll be developing expertise in your particular method. Next week, you’ll be rotating into phenotype-based teams, where you’re goal will be to synthesize research from a variety of methods applied to a single phenotype, with the goal of answering the question: Do genes cause (human) behavior? The challenge is that most of our expectations for causal reasoning rely on the availability of experimental evidence. When working with human genetics, or in many fields of observational relationships between (for example) environments and phenotypes, we can’t randomly assign folks to different conditions (whether those conditions are genetic, or environmental - for example, we can’t randomly assign people to different families, or to smoke or not smoke, or to different socioeconomic statuses). Behavior genetics provides a variety of methods to potentially address some of the expectations and assumptions that would arise from a causal relationship, even in the absence of direct experimental manipulation.

As the Briley et al. (2018) reading posted for this week discusses, each of these methods comes with their own benefits, limitations, things we can learn, and assumptions they rely on. Drawing causal conclusions often relies on the principle of “triangulation” among methods: no single non-experimental method will be conclusive, but if we draw similar conclusions from applying a broad diversity of available methods, we can become reasonably confident that a causal relationship exists even in the absence of experimental research in humans. A classic example of this is the relationship between smoking and cancer. There has never been a randomized control trial of the long term health consequences of smoking in humans because you couldn’t (either practically or ethically) randomly assign people to smoke or not smoke over the course of decades. Rather, we look at converging evidence from many methods to conclude that either the relationship is causal OR all of the methods (again, based on different assumptions) just happen to be biased in the same wrong direction (a situation that I personally take to be rather unlikely, but your mileage may vary).

In particular in the Briley et al. (2018) paper, pay attention to Figures 1, 2, & 3, which illustrate alternative causal pathways that might result in similar observed correlational patterns. Depending on the true causal pathway, intervening in the genes (if you wanted to) wouldn’t necessarily change the outcome even though a genetic association or heritability would have been robustly observed. Similarly, genetic methods can help us disentangle the causal ordering of phenotypic and environmental characteristics. For example, if we want to improve educational outcomes, should we try to change children (grit, mindset, test training) or change schools (resources, structures) or change families (reading together, resources stabilities, parenting approaches)? Whenever our goal is to change outcomes, we need to identify the causal relationships among a whole bunch of correlated variables to predict which features may be most effective to target for interventions (and we must always make choices about where and when to intervene, prevent, or treat; these decisions are costly and often necessarily mean NOT intervening/preventing/treating other areas). Genetically informative research designs don’t only tell us about genes; they offer an opportunity to anchor our understanding of the relationships among non-genetic factors, as well, even when human experimentation is impractical, unethical, or impossible.

Prep Work

  • Skim the papers posted on your team’s resources list below
    • Your team will be working together to create formal summaries of these papers during Tuesday’s Zoom session, but you should become generally familiar with them before/even if you’re not attending Tuesday’s Zoom session. Each team starts with one method overview and 4-6 empirical papers applying the method to one of the phenotypes we’ll be covering in Week 7.

Participation Activities

  • Find an empirical article (other than those that have been pre-selected) that uses your team’s method, fill out an Empirical Article Summary template, and post your summary to your team’s discussion forum. Make the subject of your post: [summary] Article title (N = number of participants).
  • Find a popular media piece about your team’s method (for example, a blog post or youtube tutorial describing how it works) and post it to your team’s discussion forum. Make the subject of your post: [scicomm] Popular media piece title.
  • Write a tweet thread (4 or more tweets, <280 characters each, link/image/gifs optional) about how your team’s method works (citing the pre-selected readings and/or papers that have been posted to your team’s discussion forum) and post it to your team’s discussion forum. For some inspiration, check out this twitter list of authors whose work you have/will read in this class, plus some of my favorite scicommers. Important: You are not required to actually tweet anything; it’s enough to prepare & post here the text/images in that style. Make the subject of your post: [tweets] First few words of the first tweet.
  • Read & Discuss via Perusall: Briley et al 2018 Behaviour Genetic Frameworks of Causal Reasoning for Personality Psychology. European Journal of Personality, 32(3), 202-220. https://doi.org/10.1002/per.2153
  • Team Learning Project on Tuesday, 11:00 am - 12:20 pm
    • Work together to fill out the Empirical Article Summary and Review Article Summary templates describing your team’s pre-selected papers. Each team starts with one method overview and 4-6 empirical papers applying the method to one of the phenotypes we’ll be covering in Week 7.
  • Team Learning Project on Thursday, 11:00 am - 12:20 pm

Team Resources

Pre-selected articles are listed below. Each team starts with an article introducing the methods concept plus articles applying the method to one or more phenotypes that will be summarized in Week 7. Team discussion forums and shared google docs are available in Moodle.

Co-Twin Control Studies

Gene-Environment Correlation

Gene-Environment Interaction

Mendelian Randomization

Non-Human Animal Models

Genomic Heritability

Polygenic Scoring