Please view the complete Course Syllabus of 2016 in pdf here.

Short subject description

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.

Target group

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.

We are currently finalizing the course outline of 2017. In the meantime, you may browse this information from 2016 to get an idea of what may be offered next summer.

Course outline

The summer school will consist of 5 lectures in the morning (3 hours each) and 5 computer tutorials in the afternoon (2 hours each).

Number of credits: Participants who joined at least 80% of all sessions can obtain a certificate of participation upon request.

Furthermore, participants who are interested in obtaining formal study credits (3 ECTS) can complete an essay assignment at the end of the course that will be graded. Note that it is the student’s own responsibility to get these credits registered at their own university

1) Introduction (July 11)

a) Lecture – Part 1
– Terminology
– Overview of possible applications
– Genetic data

b) Lecture – Part 2
– Conceptual framework for studying genetic effects on human traits

c) Lecture – Part 3
– Statistical power
– Credibility of findings (Bayes rule)

d) Computer tutorial
Getting familiar with PLINK

2) Heritability and genetic discovery (July 12)

a) Lecture – Part 1
– Broad- vs. narrow-sense heritability
– Interpreting heritability estimates
– Estimating heritability using molecular genetic data with GREML
– Bivariate GREML analyses

b) Lecture – Part 2
– Candidate gene studies
– Genome-wide association studies (GWAS)
– Imputation
– Meta-analysis
c) Lecture – Part 3
– Quality control of GWAS results
d) Computer tutorial (Rietveld)
Getting familiar with GCTA

3) Molecular genetic basics and population stratification (July 13)

a) Lecture – Part 1
– Mendel’s laws of heredity
– Exceptions to Mendel’s laws
– Genetically complex traits
– Hardy-Weinberg equilibrium

b) Lecture – Part 2
– Linkage disequilibrium
– Genotyping vs. sequencing
– Interpreting the results of genetic association studies

c) Lecture – Part 3
– Population stratification
– Cryptic relatedness
– Genomic control
– Principal components

d) Computer tutorial
– Population stratification correction with PLINK, GCTA, and R
– Visualizing GWAS results and quality control in R


4) Genetic discovery – continued (July 14)

a) Lecture – Part 1
– LD-score regression

b) Lecture – Part 2
– The endo- and proxy-phenotype approach
– The effect sizes of hits
– Winner’s curse correction

c) Lecture – Part 3
– Examples: Educational attainment, subjective well-being, neuroticism, and depression

d) Computer tutorial
Meta-analysis with METAL
LD Score Regression


5) Polygenic scores and applications (July 15)

a) Lecture – Part 1
– Constructing polygenic scores
– Accuracy of polygenic scores

b) Lecture – Part 2
– Imperfect genetic correlation across samples in GWAS meta-analysis
– Polygenic scores as control variables
– Polygenic scores as proxies for unobservable characteristics

c) Lecture – Part 3
– Genes as instrumental variables (a.k.a. Mendelian Randomization)

d) Computer tutorial
Constructing and working with polygenic scores in PLINK and R
– Mendelian randomization in R