Contents:
• Gene Expression Analysis
• Genome-wide association analysis
• QTL mapping
• Statistical hypothesis testing
• Regression methods
• Analysis of variance
• Multiple testing
• Experimental designs (block designs, randomized designs, Latin squares)
• Sample size estimation
• Introduction to programming
• Fundamentals of databases
Novel biotechnological methods allow the production of very large data sets (gene
sequences, genotypes, transcriptomes) at decreasing costs. Students learn about
statistical and computational methods to use these records for breeding issues.
Furthermore, the main experimental designs to plan, implement, and evaluate targeted
and efficient experiments for data generation will be treated.
Examination requirements:
Profound knowledge of statistics and informatics methods to use them for breeding
issues. |
