Hands-on: genome association analysis
Course infoSchedule
Course moduleMED-BMS17
Credits (ECTS)3
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Medical Sciences; Biomedische wetenschappen;
Contactperson for the course
dr. ir. H.H.M. Vermeulen
Other course modules lecturer
dr. ir. H.H.M. Vermeulen
Other course modules lecturer
Academic year2017
4  (27/11/2017 to 26/08/2018)
Starting block
Course mode
RemarksPeriod 4a, Monday and Tuesday
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registration openfrom 01/04/2017 up to and including 30/10/2017
Waiting listYes
Placement procedureDone manually by Back Office
ExplanationDone manually by Back Office
The main objectives of this module are:
This course is set up to help you reach the following goals efficiently:
  1. You can define the terminology and explain the research tools that are given in the list below.
  2. You can interpret and critically appraise the results of application of these tools as presented in research articles.
  3. You can perform basic statistical association analyses for a (provided) genome variation data set using selected software packages, interpret the results, and report results and conclusions.
  • Haplotype, linkage disequilibrium
  • Next-generation sequencing (exome/genome), SNV chips/arrays
  • Hardy-Weinberg equilibrium
  • Multifactorial disease
  • Heritability
  • Common and rare allele hypotheses
  • LD mapping, association via LD
  • Population stratification bias
  • Genome-wide association study
  • Candidate gene
  • Fine-mapping
Research tools:
  • Browsing genome structure and variation (ENSEMBL, 1000 Genomes, GoNL, ExAC, LD plots, GWAS catalog)
  • Study designs for evaluation of genome association (cohort study, case-control study, LD mapping, candidate gene, genetic (genome-wide) association study, next-generation sequencing study, replication study, meta-analysis)
  • Statistical analysis of genetic data (genetic association (Cochran Armitage, allelic test, genotypic test, regression analysis), meta-analysis, imputation, power, multiple testing, set- based analysis, polygenic risk scores)
  • Presentation of results of genome association studies (Manhattan plot, QQ-plot, locus-zoom plot)
  • Dealing with population stratification bias (PCA, MDS, genomic control)
  • Quality control procedures (HWE, relatedness, sample and variant call rates)
  • Statistical genetics software packages (Plink, SNPtest, IMPUTE, Metal, Linux environment, genetic power calculator)
  • Data management for big genome data analysis
The module 
It is safe to say that DNA variation in the human genome contributes to every disease, except perhaps trauma-related injuries. The far majority of human diseases are so-called multifactorial traits: they are caused by a combination of germline (inherited) DNA variants and environmental factors. Insight into disease-related DNA variants is highly relevant as this knowledge can, among others, be used for identification of treatment targets or estimation of disease risk or prognosis. In the past 15 years, tremendous progress in DNA variant measurement techniques and knowledge about human genome variation have paved the way for big genome association studies. These have boosted insight into human disease by the discovery of thousands of DNA variants.
This course is focused on these big, population-based genome association studies. Do you want to understand the design, analysis, and results of these studies? And do you want to be able to perform a genome association analysis and identify disease-related DNA variants yourself? Then this is the right course for you.
Note that attending PhD students are encouraged to bring their own data to the course!

Instructional modes
Working group

Period 4a, Monday and Tuesday

Course examination
Test weight1
OpportunitiesBlock 4, Block 4