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Genome-wide association studies, Autumn 2015


Teacher:  Matti Pirinen, FIMM   (matti.pirinen at 

Scope: 5 cr (exercises and exam give 3 cr, optional project work additional 2 cr)

Type: Intermediate / advanced studies in statistics

Teaching: Lectures and computer class work


We are in the middle of a revolution in genomic science. Recent technologies make it possible to read genomes so quickly and cheaply that numerous genomics-related services will be offered to us in future, for example, in health care context for personalized risk prediction and choice of therapies / medication. This requires quantitative data analysis where statistics has a lead role. This course is about statistics used in modern genome analyses. In particular, we will consider genome-wide association studies (GWAS) that have been a discovery engine for the field for the last 10 years.

  • Heritability of complex diseases and traits
  • Data: Genotyping technologies - quality control
  • Statistical concepts: Significance and power, probability of association
  • Linear regression, logistic regression, covariates, PCA of genetic structure, meta-analysis
  • Linear mixed models, missing heritability, contributions from common and rare variants
  • Mendelian randomization
  • Linkage disequilibrium, fine-mapping, statistical imputation

Prerequisites: Studies in probability, statistical inference, linear models, and basic data-analysis with R-program.
The course is expected to be useful (also) for people who have been/will be doing GWAS in practice but do not necessarily have strong background in statistics.

Teaching schedule

II period, 14-17 in C128, Tuesdays 3.11, 10.11, 17.11, 24.11.

Course material

Slides: 1 (3.11), 2 (10.11), 3 (17.11), 4 (24.11)

Handouts: Significance thresholds (3.11), PCA (10.11), Height GWAS press release (17.11), LD (24.11)

Practicals: 1 (3.11), 2 (10.11), 3 (17.11), 4 (24.11)



Return the assignments of each week as a single PDF file that contains the R-scripts, the output of the scripts and the Figures. For example, you can use MS Word and save as / export as PDF. The assignments are returned through Moodle. ( If you do not have a UH student account for Moodle system, you can email your answers as a PDF to matti.pirinen'at' )

An example assignment ( assignment0.txt ) and an example answer ( answer0.pdf ).

Home exam

The answers must be returned by 14.15 (o'clock) on Tue 8.12. Return your answers as a single PDF through  Moodle. ( If you do not have a UH student account for Moodle system, you can email your answers as a PDF to matti.pirinen'at' .) Sufficient material for complete exam answers can be found above from 'Course material' and 'Assignments' sections.

How to answer the exam questions ?

  • Guiding principle: The idea of the exam is to verify that you yourself have the knowledge of GWAS as taught in this course. Therefore you should understand and have processed everything that is in included in your answers. 
  • Formulate the answers in your own words rather than copy-pasting from somewhere. 
  • You can use short definition-like pieces of text from the source materials on this webpage or from elsewhere you have discovered yourself.
  • If you copy anything else than definition-like text you should mark clearly where the material is taken from. The only reason that you would ever want to use such longer citations would be to give examples of the topic from the literature. In general, there is no need for long citations in this exam.
  • You are free to make figures that mimic figures you have seen during the course or elsewhere. If it is an almost direct copy of the original add a note "Adapted from --reference--" to the figure legend.  
  • Read each question carefully and answer to the question being asked. It does not help to include irrelevant pieces of information in the answers, no matter how great answers they were to some other question.
  • IMPORTANT: Every student does the home exam alone: do not share your answers with others, do not include any material that you haven't processed yourself.

Passing the course

The course is passed when a student has at least half of the exercise points and at least half of the exam points. The course will be graded from 1 to 5.

For students completing also the project work (see below), there will still be a single grade from the course. An excellent project work can increase the grade determined by the exam and assignments. 

Project work

After a successful completion of the lecture course (home assignments and home exams), students have an option to do a project work of 2 cr. Return your project report as a single PDF through  Moodle. ( If you do not have a UH student account for Moodle system, you can email your report as a PDF to matti.pirinen'at' . ) The deadline is Sun 31.1.2016 (23.59 o'clock).

Structure of the report:

  1. Start with a compact Abstract that tells what is included in the report and why it is important. In practice, Abstract may be the last thing you write/finish for the report, after you know exactly what is included.
  2. Have a short Introduction that puts the topic in its context with respect to GWA studies. With more statistical topics, you may also refer to a more general formulation of the problem in statistics or to some other fields of science that tackle similar problems.
  3. Use your own consideration how to best present the main content of the project from your own angle. If you use R or other software to demonstrate your topic, you should include the codes at the end of the report as an Appendix. Short and compact pieces of code (a few lines in easily readable form) can be included also in the main report. Choice is yours, think what is most clear.
  4. End the report with a compact Conclusion section that describes your own conclusion on the topic.
  5. Add References in the end, for example, by numbering them and referring with ('number') in the text. Or you can refer by ('Surname, year') in the text, in which case use alphabetical order in the reference section.
  6. You may include Appendixies

A  guideline for an amount of work expected to get the credits is to read carefully and with a good understanding at least two scientific publications, and reporting what you have learned in your own words. Note that in some projects you may spend most time on doing simulations and data analysis rather than reading papers and that is completely OK.

Possible topics include the following. Also your own topic is not only possible but also encouraged!

Extra material


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Course feedback

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