Summer School: Introduction to Genome-Wide Data Analysis
 
The goal of the summer school is to introduce researchers from various fields to key concepts, state-of-the-art methods, and computer tools in statistical genetics that can be applied in the social and medical sciences. The course will be highly quantitative and interactive, covering topics such as the estimation and interpretation of heritability using molecular genetic data, genetic association studies, polygenic prediction, and identification strategies to isolate causal effects using genetic insights. It will emphasize methodological issues such as appropriate study design, data integrity, multiple testing, detecting and controlling for potential confounds, as well as factors influencing the accuracy of polygenic scores.
 
The summer school welcomes (research) master students, PhD students, post-docs, and professionals from various disciplines (e.g. behavioral genetics, economics, medicine, sociology, political sciences, psychology) who are interested in learning state-of-the art methods for genome-wide data analysis. The summer school will attempt to “bridge” specific knowledge gaps that participants from different backgrounds may have. Specifically, social scientists will get a short, formal introduction to genetics, while students from medicine or genetics will benefit from the formal treatment of statistical methods and the discussion of how investigating the genetics of social scientific outcomes may lead to medically relevant insights.
 
A formal background in statistics or econometrics is required from students (at the level of a first year course in a graduate school PhD program in economics, psychology, or epidemiology), but no formal background in genetics will be assumed. The course is taught in English and will be accessible to students from both in- and outside the Netherlands.
 
The course will be taught by professors Ronald de Vlaming and Aysu Okbay. Computer tutorials will be given by Hermon Kweon.
 
For more information and applications, see https://businessdatascience.nl/introduction-to-genome-wide-data-analysis