This lesson introduces genetic mapping using qtl2, a R package for analyzing quantitative phenotypes and genetic data from complex crosses like the Diversity Outbred (DO). Genetic mapping with qtl2 allows researchers in fields as diverse as medicine, evolution, and agriculture to identify specific chromosomal regions that contribute to variation in phenotypes (quantitative trait loci or QTL). The goal is to identify the action, interaction, number, and precise location of these regions.

Participants will learn to

- calculate genotype and allele probabilities
- perform a genome scan and plot the results
- evaluate statistical significance of results
- find estimated effects of a QTL on a phenotype
- account for relationships among individuals by using a kinship matrix
- perform SNP association analysis

The lesson concludes with a complete analytical workflow from a study of DO mice.The lesson is adapted from Karl Broman’s software, tutorials, and book co-authored with Saunak Sen, A Guide to QTL Mapping with R/qtl.

## Prerequisites

Understand fundamental genetic principles

Know how to access files not in the working directory by specifying the path

Know how to install a R package

Know how to assign a value to a variable

Know how to apply a built-in function

Setup | Download files required for the lesson | |

00:00 | 1. Introduction | What is quantitative trait mapping? |

00:15 | 2. Input File Format |
How are the data files formatted for qtl2?
Which data files are required for qtl2? Where can I find sample data for mapping with the qtl2 package? |

01:00 | 3. Calculating Genotype Probabilities |
How do I calculate QTL at positions between genotyped markers?
How do I calculate QTL genotype probabilities? How do I calculate allele probabilities? How can I speed up calculations if I have a large data set? |

02:00 | 4. Special covariates for the X chromosome | How do I find the chromosome X covariates for a cross? |

02:30 | 5. Performing a genome scan |
How do I perform a genome scan?
How do I plot a genome scan? |

03:30 | 6. Performing a permutation test | How can I evaluate the statistical significance of genome scan results? |

04:00 | 7. Finding LOD peaks | How do I locate LOD peaks above a certain threshold value? |

05:00 | 8. Calculating A Kinship Matrix |
Why would I calculate kinship between individuals?
How do I calculate kinship between individuals? What does a kinship matrix look like? |

06:00 | 9. Performing a genome scan with a linear mixed model |
How do I use a linear mixed model in a genome scan?
How do different mapping and kinship calculation methods differ? |

06:30 | 10. Performing a genome scan with binary traits | How do I create a genome scan for binary traits? |

07:00 | 11. Estimated QTL effects | How do I find the estimated effects of a QTL on a phenotype? |

07:30 | 12. SNP association mapping | How do I identify SNPs in a QTL? |

08:30 | 13. QTL analysis in Diversity Outbred Mice |
How do I bring together each step in the workflow?
How is the workflow implemented in an actual study? |

09:30 | Finish |

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

This lesson was funded by NIH grant R25GM123516 (Churchill) and The Jackson Laboratory Director's Innovation Fund (McClatchy & Churchill).