Week 7 Do Genes Cause Behavior? II: Phenotype Teams

This week, you’ll take your methodological expertise developed in Week 6 and rotate into phenotype-based teams, to bring together a variety of different methods to understand a particular phenotype.

Team assignments will be posted on Moodle.

Objectives

• Understand how a variety of different methods can address the same topic.
• Synthesize research across methods to identify areas of common agreement, limitations of one method addressed by another, and areas where the different approaches disagree.

Lecture Notes

This week our goal is to apply a variety of genetically informative methods to understanding what causes (or is caused by) a particular phenotype.

As soon as possible:

• Check your team assignment.
• Make sure you can access your team discussion forum for Week 7 (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 7 (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, starting from what was accomplished last week when the methods teams were working with the same readings.
• [Thursday] Assemble a Topic Summary about your phenotype, focusing on the question(s): What causes (or is caused by) your team’s phenotype?

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. Last week, you developed expertise by examining how a specific method has been applied across many phenotypes. This week, your goal is to synthesize research from across a variety of methods to develop your understanding of how your team’s phenotype fits into a complex network of potential causal influences.

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 4-7 empirical papers applying one or more methods we covered in Week 6.

Participation Activities

• Find an empirical article (other than those that have been pre-selected) about the genetics of your team’s phenotype, 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 the genetics of your team’s phenotype, find the published journal article that it is reporting, and post both 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 the genetics of your team’s phenotype (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. Make the subject of your post: [tweets] First few words of the first tweet.
• Journal: Editing
  • Do some editing of your team’s Topic Summary draft (in the team google doc, produced during the Thursday live Zoom session). For example:
    • Add information from posts in your team’s discussion forum.
    • Add details about individual studies to existing text: sample sizes, participant characteristics, phenotype operationalizations, effect sizes, replication attempts.
    • Paraphrase direct quotes; only quote something directly if the original phrasing was so perfect and beautiful that to rephrase it would be a crime against language.
    • Work through one or more of the steps of Randy McCarthy’s suggestions for self-editing your writing to edit your team’s Topic Summary draft.
  • For participation activity credit: Write a couple of sentences here reflecting on what you changed or added and how those changes impact the summary overall.
• Team Learning Project on Tuesday, 11:00 am - 12:20 pm
  • Using the article summaries generated in Week 6 as a starting point, work together to fill out Empirical Article Summary templates describing your team’s pre-selected papers. Each team starts with 4-7 empirical papers applying one or more methods we covered in Week 6.
• Team Learning Project on Thursday, 11:00 am - 12:20 pm
  • Work together to draft a Topic Summary about your team’s phenotype.

Team Resources

Pre-selected articles are listed below. Each team starts with an article applying one or more methods from Week 6 to the phenotype. Team discussion forums and shared google docs are available in Moodle.

Body Size

• 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
• 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
• 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
• Mann et al 2019 Using genetic path analysis to control for pleiotropy in a Mendelian randomization study. https://doi.org/10.1101/650192
• 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
• 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

Cognitive Aging

• 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
• 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
• 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
• 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
• 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
• 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
• 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

Educational Attainment

• 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
• Demange et al 2020 Parental influences on offspring education- indirect genetic effects of non-cognitive skills. https://doi.org/10.1101/2020.09.15.296236
• Tucker-Drob & Bates 2016 Large cross-national differences in gene × socioeconomic status interaction on intelligence. https://doi.org/10.1177/0956797615612727
• Mann et al 2019 Using genetic path analysis to control for pleiotropy in a Mendelian randomization study. https://doi.org/10.1101/650192
• 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
• 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

Externalizing

• 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
• DiLalla & DiLalla 2018 Gene–Environment Correlations Affecting Children’s Early Rule-Breaking and Aggressive Play Behaviors. https://doi.org/10.1017/thg.2018.30
• Hicks et al 2009 Environmental adversity and increasing genetic risk for externalizing disorders. https://doi.org/10.1001/archgenpsychiatry.2008.554
• Mann et al 2019 Using genetic path analysis to control for pleiotropy in a Mendelian randomization study. https://doi.org/10.1101/650192
• Pearish et al 2019 Social environment determines the effect of boldness and activity on survival. https://doi.org/10.1111/eth.12939
• Linner et al 2019 Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. https://doi.org/10.1038/s41588-018-0309-3

Neuroticism

• Sadler et al 2011 Subjective Wellbeing and Longevity- A Co-Twin Control Study. https://doi.org/10.1375/twin.14.3.249
• 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
• 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
• 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

Schizophrenia

• Lyons et al 2002 Nicotine and familial vulnerability to schizophrenia- A discordant twin study. https://doi.org/10.1037/0021-843X.111.4.687
• 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
• 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
• Ripke et al 2020 Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. https://doi.org/10.1101/2020.09.12.20192922
• Sekar et al 2016 Schizophrenia risk from complex variation of complement component 4. https://doi.org/10.1038/nature16549