### General Information

Course Code | AM_1214 |
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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 |
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### 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, Genesin 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

non-additive)

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

3.

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

test.

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

3.

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

heritability.

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

Tuesday.

Lecture 5: Beyond GWAS: how to interpret GWAS findings using biological

knowledge

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).

Literature:

Lecture notes + (tutorial) articles.

### Method of Assessment

Examination: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

course.

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.

Resit:

Offered for the two written exams. Part 1 and 2 are offered in a single

resit.

Students can miss a maximum of 1 practical (with valid reason). A missed

practical has to be compensated.

### Literature

Lecture notes + (tutorial) articles.### Target Audience

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