Spatial modelling and analysis of adaptive genomic variation (SPGN01)
17 June 2019 - 21 June 2019
Local adaptation to climate and other environmental drivers increasingly is being studied at the molecular level using high-throughput sequencing methods, with applications spanning both model and non-model organisms. At the same time, statistical tools for modeling and mapping patterns of biodiversity have seen increasing application, including to the challenge of understanding the drivers of spatial variation in adaptive genomic variation and mapping these patterns under current and future climate. This 5-day course will provide the skill set necessary to analyze sequence data for signatures of natural selection and to apply spatial modeling techniques to these patterns to quantify and map population-level genetic variation using two spatial modelling algorithms – Generalized Dissimilarity Modelling (GDM) and Gradient Forest (GF).
The course will include introductory lectures, instruction on using the Linux command line for manipulation of genomic data, guided computer coding in R, and exercises for the participants, with an emphasis on visualization and reproducible workflows. Portions of each day will be allotted for students to work through their own datasets with the instructors.
This course is intended for research scientists, postdoctoral researchers, and graduate students interested in learning how to analyze genomic data for signals of adaptation using population genetic tools and the application of spatial modeling understanding and mapping landscape genomic patterns in R.
After successfully completing this course students will:
- Understand the theory and techniques for detecting signals of natural selection using genomic data, focusing on multi-population and landscape approaches
- Understand the statistical underpinnings of spatial modeling methods (GDM and GF) for analyzing and mapping adaptive genomic variation
- Be able to develop, evaluate and apply GDM and GF for quantifying and mapping spatial genetic patterns
- Estimate population-level vulnerability to climate change
- Students are highly encouraged to bring their own data to the course.
Full details can be found using the link below