Genetic and Genomic Epidemiology (SL_124)
About Study Course
Objective
The objective of this course is to equip the students with the knowledge on the main methods in genetic epidemiology, as well as with practical skills to implement them in practice. This course teaches how to use both genotyping and whole-genome (and whole-exome) sequencing data for association analysis. The software package R as well as several UNIX-based software packages (PLINK, SNPTEST) will be used for computer practical classes.
Prerequisites
• Familiarity with probability theory and mathematical statistics.
• Basic knowledge in R software.
• Basic knowledge of linear models and statistical estimation techniques.
Learning outcomes
1.The students will acquire knowledge on the following topics:
• get to know the concept of mendelian inheritance, main principles of population genetics; monogenic and polygenic traits and diseases;
• will be able to demonstrate in-depth knowledge and understanding of main research questions in genetic epidemiology;
• learn and will be able to identify different types of genetic markers;
• understand and independently implement linkage and association analysis;
• learn and be able to independently analyse the most common data types and data formats in genetic epidemiology: genotyping data, sequence data;
• the concept of genotype imputation and the formats of imputed data;
• able to identify and commonly used software for genome-wide association analysis (GWAS);
• able to demonstrate expanded knowledge through familiarity and analysis formats of GWAS summary statistics and the methodology of polygenic risk scores, which ensure framework for innovative research.
1.The students who have completed the course, will be able to:
• plan a research study in genetic epidemiology, implementing a commonly used study design (such as a GWAS in a single cohort);
• handle common formats of genotyping and sequencing data on a UNIX server;
• select an appropriate software and use it in quality control of genomic data;
• conduct a GWAS, using either linear or logistic regression;
• critically assess the GWAS results and recognize some common issues, such as confounding by population structure, and take some measures to control for these issues;
• use a common software package to compute a polygenic risk score (PRS) and assess the effect of the PRS in an independent cohort.
1.The students will acquire competence to:
• understand, evaluate and critical assess of the published research on genome-wide association studies and their meta-analysis.
• understand and critical assess of the published research on polygenic risk scores.
• a student who has successfully passed the course, is able to independently decide on appropriate study design in genetic epidemiology, understand the data structures for genotype and sequence data as well of tools to link genetic and phenotype datasets, and conduct the genome-wide association study or a study on a polygenic score, is able to find and apply information to solve non-standard problems.
