GWAS results for lipid levels shown for chromosomes 7-19. Dashed line is genome-wide significance threshold.

Ilmoittaudu
10.12.2018 klo 08:00 - 28.2.2019 klo 23:59

Aikataulu

Tästä osiosta löydät kurssin opetusaikataulun. Tarkista mahdolliset muut aikataulut kuvauksesta.

PäivämääräAikaOpetuspaikka
Ti 15.1.2019
12:15 - 16:00
Ke 23.1.2019
10:15 - 12:00
Ke 23.1.2019
12:15 - 16:00
Ke 30.1.2019
10:15 - 12:00
Ke 30.1.2019
12:15 - 16:00
Ke 6.2.2019
10:15 - 12:00
Ke 6.2.2019
12:15 - 16:00
Ke 13.2.2019
10:15 - 12:00
Ke 13.2.2019
12:15 - 16:00
Ke 20.2.2019
10:15 - 12:00
Ke 20.2.2019
12:15 - 16:00
Ke 27.2.2019
10:15 - 12:00
Ke 27.2.2019
12:15 - 16:00

Kuvaus

Master's Programme in Life Science Informatics is responsible for the course.

Module where the course belongs to:

  • Biostatistics and Bioinformatics

The course is available to students from other degree programmes.

Basics of statistical inference, linear models and R-software. For example, courses Statistical inference (MAT12004), Linear models (MAT22004) and R-software (MAT12001 and MAT12002), or equivalent knowledge.

Recommended:

  • Statistical population genetics (LSI34004)

Other courses that support the further development of the competence provided by this
course:

  • High dimensional statistics (MAST32006).

Student understands and can apply common statistical models and statistical inference principles used in genome-wide association studies (GWAS). Student can apply and interpret linear and logistic regression in the GWAS context. Student understands what recent advances in statistical genetics have revealed about the genetic architecture of complex traits and diseases.

The recommended time for completion: First or second year.

The course is offered in spring term 3rd or 4th period. Course is not offered every year.

This course is about statistics needed to link modern genome data to quantitative measurements and disease endpoints. In particular, we will consider genome-wide association studies (GWAS).

  • Heritability of complex diseases and traits
  • Genome data: Genotyping and sequencing technologies and quality control
  • Genome analyses: Principal components analysis of genetic structure, linkage disequilibrium in genome, statistical imputation of genetic variation
  • Statistical concepts in GWAS: Significance and power, probability of association
  • Standard statistical models in GWAS: Linear regression, logistic regression, covariates, meta-analysis
  • Advanced statistical models in GWAS: Mixed models and missing heritability, rare-variant analyses, fine-mapping
  • Mendelian randomization

Timely research and review articles are read during the course.

Material available in the web (https://www.mv.helsinki.fi/home/mjxpirin/GWAS_course/)"

Students will use R-software to demonstrate the statistical methods in weekly exercises.

Course is assessed based on the weekly exercises that the students return, and an exam.

Course is graded from 1 to 5.

  • Can be taken as a distance learning course.
  • No attendance requirements.
  • Weekly exercises and an exam.