932013 Management and analysis of high density genomic data

Type
Exercise and seminar
Semester hours
3
Lecturer (assistant)
Organisation
Offered in
Sommersemester 2023
Languages of instruction
Englisch
Content

The analysis of single nucleotide polymorphisms (SNP), genotyped at high density, is becoming practice in animal and plant breeding and genetics. A SNP data set can be used to answer various research questions. Due to the large amount of data, special treatment is required, using specific tools and basic programming skills.

This lecture will guide the students through each of the steps from SNP data acquisition, preparation and utilization for selected research questions.

Tentative lecture schedule:
Week 1: An introduction to genomic data analysis
Week 2: An introduction to R and Linux
Week 3: Introduction to PLINK
Week 4: Extracting data with PLINK
Week 5: Quality control
Week 6: Imputation and phasing
Week 7: Linkage disequilibrium and effective population size
Week 8: Genomic relationships
Week 9: Inbreeding and runs of homozygosity
Week 10: Admixture analysis
Week 11: Genome wide association studies
Week 12: Selection signatures
Week 13: Genomic selection
Previous knowledge expected

Basic knowledge of principles of genetics with applications to animal or plant genetics
Recommended pre-requisite courses: Molecular animal/plant genetics or genomics; Quantitative genetics course
Objective (expected results of study and acquired competences)

After completion of this course the students will be able to recognize the structure of the SNP data sets and identify standard file formats. They will be able to extract parts of the data and modify it according to specific requirements in both Windows and Linux operating systems. The students will be familiar with the most common utilizations of the genomic data and the standard software tools required for their analysis. They will be able to analyze genomic data in order to answer specific research questions and interpret the results.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.