Analysis of Twin Data in Health Sciences

This course introduces the modern analysis in studies of twins. The students will be able to understand founding theory and apply basic methods and biometric models in a research context. The students learn how to investigate genetic and environmental influences on traits under various twin designs and assumptions. They also learn how twins can be used effectively in studying association controlling for various sources of confounding.

Contents:

The course introduces statistical concepts that are typically targeted in analysis of twin and family data and touches some more advanced methods. Findings and insights from studies with twins will be highlighted. Time is devoted for presentations by the participants including discussion and feedback.

Main topics:

• Classic biometric modelling of a single trait (univariate outcome).
• Time to event analysis of twin data
• Biometric modelling of multivariate outcomes in twins.
• The matched case co-twin design for inferring association
• Workshop on contributed projects
• The future of twin studies

Course arrangements:

4 full days followed by an exam, either oral presentation of

Course credit:

3 ECTS

Type of evaluation:

Presentation of project report project or multiple choice web-exam.

Teaching arrangements:

Lectures and exercises including use of statistical software R. The typical arrangement is lectures for the first half of the day and individual course work for the second half.

Course leader:

Jacob Hjelmborg, Department of Epidemiology and Biostatistics, SDU.

Course faculty:

Professor Jaakko Kaprio (Epidemiology, University of Helsinki); Professor Jennifer Harris (Epidemiology, University of Oslo); Professor Thomas Scheike (University of Copenhagen); Professor Kaare Christensen, Professor Qihua Tan and Professor Jacob Hjelmborg (University of Southern Denmark).

Prerequisites:

Knowledge about statistics corresponding to what is acquired from a graduate level health science course or alike. Students should bring their own laptop pre-installed with R – details will be provided.

Course fee:

The course is free of charge.