Spatial modelling and analysis of adaptive genomic variation (SPGN01)
17th June 2019 - 21st June 2019£275.00 - £495.00
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.
Any researchers (from postgraduate students to senior investigators) interested in the use of spatial modelling for quantifying and visualizing patterns of biodiversity, including those in applied fields and basic science.
Venue – PR statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 25 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments and accommodation. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 16th June and departure Friday 21st May PM (accommodation must be vacated by 9am).
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRstatistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRstatistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Familiarity with geospatial data. A basic understanding of statistical modeling concepts and inference, including regression methods and model validation. Basic familiarity in working with species, community, or genetic data.
Assumed computer background
Experience with Linux command line helpful, but not required. Basic familiarity with R, including ability to import/export data, manipulate data frames, fit and plot standard / basic statistical models. Familiarity with genetics also helpful.
Equipment and software requirements
A laptop computer with RStudio installed and access to Linux (e.g., emulator, virtual machine, access to server, etc.).
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available.
Students are highly encouraged to bring their own data to the course.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Meet at the accommodation at approx. 17:00 onwards
Monday 8th – Classes from 09:00 to 17:00
Day 1 – Background and introduction
1) Introduction to approach
2) Data types (spatial, environmental, and genomic)
3) Considerations (quality control, SNP calling, filtering)
Tuesday 9th – Classes from 09:00 to 17:00
Day 2 – Natural Selection I
4) Introduction to the genomics of natural selection
5) Review of population genomic approaches to inferring selection
6) Implementing FST- and differentiation-outlier tests and interpreting results
Wednesday 10th – Classes from 09:00 to 17:00
Day 3 – Natural selection II
7) Principles of inferring selection from environmental association analyses
8) Review of landscape genomics approaches to inferring selection
9) Preparing SNP data from VCF and environmental data from rasters
10) Implementing multiple association methods (e.g., LFMM, RDA, etc.) and interpreting results
Thursday 11th – Classes from 09:00 to 17:00
Day 4 – Spatial Modeling I
11) Introduction to GDM and GF
12) Review of genetic and environmental data preparation
13) Interpreting model results
14) Model fitting / testing / validation / variable selection
Friday 12th – Classes from 09:00 to 16:30
Day 5 – Spatial Modeling II
15) Predictions & Applications of GDM / GF
16) Transforming environmental grids
17) Visualizing spatial variation in genetic composition
18) Calculating genetic differences between locations / times
19) Projecting patterns under climate change
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)