Universiteit Leiden

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Statistical Modelling of Gene Environment Interactions (CANCELLED)

THIS COURSE IS CANCELLED. This one-day course focuses on understanding and awareness of (many) issues in genetic interaction analyses. It does NOT focus on all technicalities in singular genetic main effect associations such as those in GWA studies.

Target group
Lecturer
Researcher
Postdoctoral researcher
PhD candidate
Guest
Teacher
Ralph Rippe  (Associate professor)
Method
e-Learning

Course description

We aim to provide an overview of common theories and their accompanying models in Gene-Environment research, including for example Diathesis Stress and Differential Susceptibility. A strong focus will be on estimation intricacies, as well as how to statistically distinguish such models.

We aim to provide awareness of pittfalls in analysis and interpretation of such models. Approaches are discussed to deal with genotype uncertainty in SNP data and to deal with cumulative effects of small individual effects, of both SNPs and genes.

Participants will perform basic analyses in the programme R on example data sets. Skeleton scripts are provided in the exercises. Exercises are discussed in plenary discussions. Coding solutions via scripts and fully worked solutions are provided at the end of the day.

Targets

After this course, you are able to

  • Understand the differences between types and structures of genetic data.
  • Understand different GxE theoretical models.
  • Understand the difference between GxE and ExG framing.
  • Perform in R: analyses of different interaction models, including uncertainty corrections.
  • Perform in R: run competing framework analyses.

Literature

Several articles to be announced.

Prerequisites

  • basic knowledge of R (e.g., first lessons of online course coursera: “Statistics with R”).
  • laptop with R (and, if you want, R-studio). (The instructions will be based on basic R, but they also work in RStudio)
  • a basic understanding of genes (and SNPs) (e.g., at the level of “genetics for dummies”)
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