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.
Each of these methods comes with its 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).
Prep Work
- Skim the papers posted on your team’s resources list below
- Some of these papers (especially GWAS) will include MANY methods within the same source. This week, pay particular attention (i.e. search, ctrl-F) to any section(s) about the results of the application of your team’s method.
- 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 sources applying the method to one of the phenotypes we’ll be covering in Week 7.
Participation Activities
Read & Discuss via Perusall: Harden, K.P. (forthcoming, 2021). Alternative Possible Worlds. from The Genetic Lottery: Why DNA Matters for Social Inequality.
Find and summarize an empirical article that uses your team’s method. An Empirical Article is a scholarly source that does a new analysis of data (that is, it is not just a review of previous research; it will almost certainly include sections labeled Methods and Results).
- Make the subject of your post: [summary] Article title (N = number of participants).
- For the body of your post, fill out an Empirical Article Summary template
- Post your completed Empirical Article Summary to your team’s duscussion forum.
Find a popular source 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.
- In the body of your post, include:
- A link to the popular souce
- A brief (1 sentence) summary description of the source.
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.
- Make the subject of your post: [tweets] First few words of the first tweet.
- 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 the text/images in that style. Make the subject of your post: [tweets] First few words of the first tweet.
Journal Response: Editing
- Do some editing of your team’s Method Summary draft (in the team google doc, produced during the Thursday live Zoom session). For example:
- Add examples from posts in your team’s discussion forum.
- Clarify details about the method.
- Add any particularly helpful figures you found to describe your method - or make your own!
- Add links to recommend particularly useful popular sources describing the method.
- For participation credit, write a couple of sentences reflecting on what you changed or added and how those changes impact the summary overall.
- Do some editing of your team’s Method Summary draft (in the team google doc, produced during the Thursday live Zoom session). For example:
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
- Work together to draft a Method Summary for your team’s method.
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
- Tafforin & Segal 2019 Twins in Space- Review and Perspectives. https://doi.org/10.22330/he/34/123-129
- Iuliano-Burns et al 2009 The Age of Puberty Determines Sexual Dimorphism in Bone Structure- A Male Female Co-Twin Control Study. https://doi.org/10.1210/jc.2008-1522
- Andel et al 2008 Physical Exercise at Midlife and Risk of Dementia Three Decades Later- A Population-Based Study of Swedish Twins. https://doi.org/10.1093/gerona/63.1.62
- Verweij et al 2013 Is the relationship between early-onset cannabis use and educational attainment causal or due to common liability. https://doi.org/10.1016/j.drugalcdep.2013.07.034
- Joyner et al 2020 Using a co-twin control design to evaluate alternative trait measures as indices of liability for substance use disorders. https://doi.org/10.1016/j.ijpsycho.2019.11.012
- Sadler et al 2011 Subjective Wellbeing and Longevity- A Co-Twin Control Study. https://doi.org/10.1375/twin.14.3.249
- Lyons et al 2002 Nicotine and familial vulnerability to schizophrenia- A discordant twin study. https://doi.org/10.1037/0021-843X.111.4.687
Gene-Environment Correlation
- Saltz 2018 Gene–environment correlation in humans- lessons from psychology for quantitative genetics. https://doi.org/10.1093/jhered/esz027
- Tubbs et al 2020 The Genes We Inherit and Those We Dont- Maternal Genetic Nurture and Child BMI Trajectories. https://doi.org/10.1007/s10519-020-10008-w
- Guerreiro et al 2016 Genome-wide analysis of genetic correlation in dementia with Lewy bodies, Parkinson’s and Alzheimer’s diseases. https://doi.org/10.1016/j.neurobiolaging.2015.10.028
- Rimfeld et al 2021 The winding roads to adulthood- a twin study. https://doi.org/10.1101/2021.02.16.431456
- Linner et al 2020 Multivariate genomic analysis of 1.5 million people identifies genes related to addiction antisocial behavior and health. https://doi.org/10.1101/2020.10.16.342501
- Nivard et al 2017 Genetic Overlap Between Schizophrenia and Developmental Psychopathology- Longitudinal and Multivariate Polygenic Risk Prediction of Common Psychiatric Traits During Development. https://doi.org/10.1093/schbul/sbx031
Gene-Environment Interaction
- McAllister et al 2017 Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. https://doi.org/10.1093/aje/kwx227
- Celis-Morales et al 2019 Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study. https://doi.org/10.1038/s41366-019-0381-5
- Reynolds et al 2016 Gene–Environment Interplay in Physical, Psychological, and Cognitive Domains in Mid to Late Adulthood- Is APOE a Variability Gene? https://doi.org/10.1007/s10519-015-9761-3
- Tucker-Drob & Bates 2016 Large cross-national differences in gene× socioeconomic status interaction on intelligence. https://doi.org/10.1177/0956797615612727
- Hicks et al 2009 Environmental adversity and increasing genetic risk for externalizing disorders. https://doi.org/10.1001/archgenpsychiatry.2008.554
- Hicks et al 2009 Gene–environment interplay in internalizing disorders- consistent findings across six environmental risk factors. https://doi.org/10.1111/j.1469-7610.2009.02100.x
- van Os et al 2020 Replicated evidence that endophenotypic expression of schizophrenia polygenic risk is greater in healthy siblings of patients compared to controls, suggesting gene–environment interaction- The EUGEI study. https://doi.org/10.1017/S003329171900196X
Mendelian Randomization
- Davies et al 2019 Within family Mendelian randomization studies. https://doi.org/10.1093/hmg/ddz204
- Mann et al 2019 Using genetic path analysis to control for pleiotropy in a Mendelian randomization study. https://doi.org/10.1101/650192
- Rasmussen et al 2018 Plasma apolipoprotein E levels and risk of dementia- A Mendelian randomization study of 106562 individuals. https://doi.org/10.1016/j.jalz.2017.05.006
- Nagel et al 2018 Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. https://doi.org/10.1038/s41588-018-0151-7
- Ripke et al 2020 Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. https://doi.org/10.1101/2020.09.12.20192922
Non-Human Animal Models
- Reynolds et al 2020 Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration. https://doi.org/10.1038/s41386-020-00795-5
- Church et al 2009 A mouse model for the metabolic effects of the human fat mass and obesity associated FTO gene. https://doi.org/10.1371/journal.pgen.1000599
- Raber et al 1998 Isoform-specific effects of human apolipoprotein E on brain function revealed in ApoE knockout mice- increased susceptibility of females. https://www.pnas.org/content/95/18/10914
- Merritt & Rhodes 2015 Mouse genetic differences in voluntary wheel running, adult hippocampal neurogenesis and learning on the multi-strain-adapted plus water maze. https://doi.org/10.1016/j.bbr.2014.11.030
- Pearish et al 2019 Social environment determines the effect of boldness and activity on survival. https://doi.org/10.1111/eth.12939
- Santangelo et al 2016 Novel primate model of serotonin transporter genetic polymorphisms associated with gene expression, anxiety and sensitivity to antidepressants. https://doi.org/10.1038/npp.2016.41
- Sekar et al 2016 Schizophrenia risk from complex variation of complement component 4. https://doi.org/10.1038/nature16549
Genomic Heritability
- Young 2019 Solving the missing heritability problem. https://doi.org/10.1371/journal.pgen.1008222
- Yengo et al 2018 Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. https://doi.org/10.1093/hmg/ddy271
- Reynolds & Finkel 2015 A meta-analysis of heritability of cognitive aging- minding the missing heritability gap. https://doi.org/10.1007/s11065-015-9280-2
- Lee et al 2018 Gene discovery and polygenic prediction from a 1-1-million-person GWAS of educational attainment. https://doi.org/10.1038/s41588-018-0147-3
- Linner et al 2020 Multivariate genomic analysis of 1.5 million people identifies genes related to addiction antisocial behavior and health. https://doi.org/10.1101/2020.10.16.342501
- Nagel et al 2018 Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. https://doi.org/10.1038/s41588-018-0151-7
- Ripke et al 2020 Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. https://doi.org/10.1101/2020.09.12.20192922
Polygenic Scoring
- Janssens 2019 Validity of polygenic risk scores- are we measuring what we think we are. https://doi.org/10.1093/hmg/ddz205
- Yengo et al 2018 Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. https://doi.org/10.1093/hmg/ddy271
- Zhang et al 2020 Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. https://doi.org/10.1038/s41467-020-18534-1
- Lee et al 2018 Gene discovery and polygenic prediction from a 1-1-million-person GWAS of educational attainment. https://doi.org/10.1038/s41588-018-0147-3
- Linner et al 2020 Multivariate genomic analysis of 1.5 million people identifies genes related to addiction antisocial behavior and health. https://doi.org/10.1101/2020.10.16.342501
- Nagel et al 2018 Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. https://doi.org/10.1038/s41588-018-0151-7
- Ripke et al 2020 Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. https://doi.org/10.1101/2020.09.12.20192922