Lecture: Statistical genetics, breeding informatics and experimental design - Details

Lecture: Statistical genetics, breeding informatics and experimental design - Details

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General information

Course name Lecture: Statistical genetics, breeding informatics and experimental design
Subtitle
Course number 740823
Semester WiSe 2024/25
Current number of participants 55
expected number of participants 20
Home institute Züchtungsinformatik
Courses type Lecture in category Teaching
First date Tuesday, 22.10.2024 16:15 - 17:45, Room: (L05 (Tierzucht-Institutsgebäude))
Type/Form
Performance record →Ab hier automatisch erfasste Informationen / Beyond this point, the information is filled in automatically←

Prüfungsleistung(en) je Modul / Exam details per module:

* [(M.iPAB.0003.Mp) Statistical genetics, breeding informatics and experimental design][1]
* Klausur: Do, 06.02.2025, von 15:30:00 bis 17:00:00
* Klausur: Mi, 16.04.2025, von 10:00:00 bis 12:00:00 ([E-Prüfungsraum MZG 1.116 (MZG/Blauer Turm)][2])

[1]: https://ecampus.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&unitId=23490&periodId=277
[2]: https://www.geodata.uni-goettingen.de/lageplan/?ident=5237_1_1.OG_1.116

Rooms and times

(L05 (Tierzucht-Institutsgebäude))
Tuesday: 16:15 - 17:45, weekly (14x)
(L06 (Tierzucht-Institutsgebäude))
Thursday: 14:15 - 15:45, weekly (13x)

Fields of study

Module assignments

Comment/Description

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.