Genetics in Neuroscience


Course Objective

To gain insight into
- the theoretical principles underlying human genetics
- the current state of the art in gene-finding for brain related traits
- the current state of the art in tools that can be applied to gain
biological insight into genetic findings
- how genetics and neuroscience can inform each other

Course Content

The course is offered to master students in two master programs, Genes
in behavior and health and Master of Neurosciences). The first four
lectures and the first three practicals are shared. After lecture 4,
the students are split into two groups (according to their choice of
masters). Lectures & practicals 5 to 7 are given separately by C.Dolan
(Course Behavior genetics in the master program Genes in behavior and
health) and D. Posthuma (Course Genetics in Neuroscience for the Master
of Neuroscience).
Lecture 1: introduction
CD: 1.1 Refresher statistics: variable, parameter, distribution,
population, sample, statistical inference, distribution, mean, variance,
standard deviation, (intraclass) correlation, concept of shared variance
(intraclass correlation), linear regression, statistical power (Note
students have previously done Data Analysis and Visualization), chi2
cross-tab test.
CD: 1.2 Genetic concepts: chromosomes, allele, genotype, Mendelian laws
of inheritance, major genes, minor genes (polygenes; QTL), SNP, complex
(polygenic) traits, Mendelian traits, polymorph, monomorph, gametic
phase (LD) disequilibrium
CD: 1.3 Biometrical genetics + basic population genetics: alleles,
allele frequency (common / rare variants), genotype, genetic variant,
genotype frequency, Hardy-Weinberg equilibrium, selection, mutation,
linkage disequilibrium (Mendelian laws).
CD: 1.4. Biometrical model: genotype - phenotype map (genotypic effect,
midparent), definition of additive genetic effects, dominance effects,
major gene distiribution, polygenic distribution, genetic resemblance in
kinships (Mendelian laws, IBD, IBS), variance due to locus (additive and
Practical 1: with quizzes (using R).
CD: 1.1. Calculate the alleles frequencies, and genotype frequencies,
calculate expected genotype frequencies (assuming HW equilibrium) of two
diallelic genes (one in HW eq; one in HW diseq).
CD: 1.2. Given parameters of biometric model, calculate additive genetic
and dominance variance
CD: 1.3. Measured genotype-phenotype relationship linear regression with
additive effects, including non-additive (dominance) effects. Show that
the results obtained in this manner correspond to the results obtained
in 2.
CD: 1.4. Linear regression with 10 snps (in LE) preliminary to practical
Lecture 2: Quantifying genetic effects with measured genotype.
CD: 2.1. The candidate (common) gene model. Revisit linear regression
model and biometric model. Case controle candidate gene model. Main
effect model + interaction model (power issue).
CD: 2.2. Genome wide association study (linear regression model) +
pitfalls (GC measures, interpretation, stratification).
CD: 2.3. Rare variants - brief discussion culminating in the burden
Practical 2: with quizzes (using R).
CD: 2.1. Calculate mean, variance of a given phenotype, and histogram.
CD: 2.2. Carry-out a candidate gene analysis report standardized beta
and explained variance, and R2 (explained variance / total variance)
CD: 2.3. Carry-out a candidate gene analysis with a covariate, main
effect and interaction, include power to detect the interaction.
CD: 2.4. Carry-out a candidate gene analyses where the effect is driven
by stratification (significant result), and include the stratifying
covariate (significant result disappears).
CD: 2.5. Linear regression with 10 snps (in LE) preliminary to practical
Lecture 3: Quantifying genetic effects with family members (inferring
the effects of unmeasured genotypes).
CD: 3.1. Genetic resemblance among family members - revisit IBD /IBS,
concept of shared variance.
CD: 3.2. Inferring genetic and share environmental effects from
(intraclass) correlations among twins, Falconer's equations, classical
twin design. Path diagram representation (tracing rules).
CD: 3.3. Assumptions of the twin design GxE interaction, G-E covariance,
assortative mating.
Practical 3: with quizzes (using R).
CD: 3.1. Phenotype 1 given calculate total variance and mean in mz and
dz twins
CD: 3.2. Phenotypic twin data. Calculate twin correlations, apply
Falconer's equations. Different phenotypes with different results.
CD: 3.3. Return to the dataset with 10 LE snps. But now twin data. The
proportion of explained variance in the regression model will equal the
heritability obtained in the twin analyses.
Lecture 4: Techniques using measured genotypes, focusing on omnibus test
of sets of genes (C Dolan), and a preview of snp annotation, gene based
tests, and gene network analyses. This is a joint lecture given by C
Dolan (CD) and D Posthuma (DP).
CD: 4.1. Follow up to GWAS: polygenic risk scores (relationship with
burden testing).
CD: 4.2. Introduction to GCTA: SNP-based (single nucleotide
polymorphism) heritability, as a method to determine the variance due to
a large number of measured genotypes. Relation to twin based
DP: 4.3. Applications of GWAS: findings from recent studies in the
context of psychiatric and neurodegenerative traits
Practical 4:
No practical as the students have an intermediate exam on the following
Lecture 5: Beyond GWAS: how to interpret GWAS findings using biological
DP: 5.1 Functional annotation of SNPs to understand risk loci: eQTL,
functional consequences, chromatin interactions
DP: 5.2 Genetic correlations between traits
DP: 5.3 Distinguising causality from pleiotropy: Mendelian Randomization
Practical 5: Using FUMA to understand GWAS
Lecture 6: Gene-set analyses
6.1 Gene-set models and tools
6.2 SNP heritability and functional partitioning
6.3 Biological pathway analysis, co-expression and protein interaction
6.4 Tissue specificity and cell-type specificity
Practical 6: Using LDSC and MAGMA
Lecture 7: Functional studies
DP: 7.1 How to use GWAS results to generate testable hypothesis
DP: 7.2 Functional follow-up: proteomics
DP: 7.3 Functional follow-up: iPSC
Practical 7: Design a functional experiment

Teaching Methods

Duration: Seven lectures (3 hours) and six practicals (3 hours)
Contact hours: 39 hours distributed over 7 days (7 lecture of 3 hours +
6 practicals of 3 hours).
Lecture material: ppts with slide notes.
Practical material:
R workbook, assignments with quizzes (to be answered during the
practicals, and returned on paper).
Lecture notes + (tutorial) articles.

Method of Assessment

Written exams in two parts: after lecture 4 and after lecture 7
(summative examination).
Grading: 1) practical attendance is a necessary condition to pass this
Practicals are mandatory, the quizzes have to be returned on paper
(formative examination).
Grades of the two written exams are averaged. No compensation, a minimum
of 5.5 for each part.
Offered for the two written exams. Part 1 and 2 are offered in a single
Students can miss a maximum of 1 practical (with valid reason). A missed
practical has to be compensated.


Lecture notes + (tutorial) articles.

Target Audience

Students in the master Genes in behavior and health and the Master of

General Information

Course Code AM_1214
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. D. Posthuma
Examiner prof. dr. C.V. Dolan
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Computer lab