Expression quantitative trait loci (eQTL) analysis identifies loci that explain variation in transcript abundance.
Prerequisites
This lesson teaches expression QTL analysis using the qtl2 package in R. Learners should:
- have a basic knowledge of genetics
- have basic R programming skills
- be familiar with QTL mapping using the qtl2 package in R. This lesson in quantitative trait mapping will help.
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What are expression quantitative trait loci (eQTL)?
How are eQTL used in genetic studies? |
| 00:20 | 2. Genetic Drivers of Pancreatic Islet Function |
How I can learn more from an example eQTL study?
What is the hypothesis of an example eQTL study? |
| 00:50 | 3. Load and explore the data | What data are required for eqtl mapping? |
| 01:35 | 4. Review Mapping Steps | What are the steps involved in running QTL mapping in Diversity Outbred mice? |
| 02:35 | 5. Mapping A Single Gene Expression Trait | How do I map one gene expression trait? |
| 03:35 | 6. Mapping Many Gene Expression Traits | How do I map many genes? |
| 04:35 | 7. Creating A Transcriptome Map | How do I create and interpret a transcriptome map? |
| 05:35 | 8. Transcriptome Map of cis and trans eQTL | How do I create a full transcriptome map? |
| 06:05 | 9. Maximum eQTL Peaks and Nearby Genes | How do I display maximum eQTL peaks and nearby genes? |
| 06:35 | 10. Interpreting qtl2 results | How do I interpret qtl2 results? |
| 06:47 | 11. Mediation Analysis |
What is mediation analysis?
How is mediation analysis used in genetics and genomics? How do I explore causal relations with mediation analysis? |
| 06:59 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.