ConGen Population Genomic Data Analysis Course/Workshop, South Africa 2025
Goals: To train Ñý¼§Ö±²¥, postdocs, faculty, and agency researchers to understand and use population genetics principles and DNA-marker data to improve biodiversity conservation and management. ConGen will teach applications of genetics concepts and data in research, monitoring, and management of populations and species. The course teaches participants to understand, analyze, and interpret genetics datasets including microsatellites, SNPs, and genome-scale sequencing datasets (RADseq, amplicon-seq, targeted capture, WGseq). We will teach genome assembly, and how to take raw reads through to genotyping (with & w/o a reference genome). The course will help bridge the gap between researchers and management to improve conservation. This course is urgently needed given the biodiversity crisis and the recent Kunming-Montreal Global Biodiversity Framework in which 196 parties “committed to reporting the status of genetic diversity for all species"– wild and domestic (Mastretta-Yates et al. 2024; Hoben et al. 2024).
Target audiences:
- Advanced undergrads and technicians
- Master's and Ph.D. Ñý¼§Ö±²¥
- Postdocs, faculty, government researchers or PIs
- Conservation managers (especially day 1)
When: Monday-to-Friday, December 7-14.
Where:
Register:
*Recommended knowledge and background:
- Basic understanding of DNA markers (Microsatellites, SNPs, RADseq WGseq), population genetic diversity metrics (H, Allelic richness) and the mechanisms of evolutionary change: genetic drift, gene flow, selection, & mutation. Hardy-Weinberg and LD testing (especially for Days 2-6).
- Experience in R and Linux (for days 3-5). You can learn the basic R skills via a Zoom lecture the week before the course.
- Participants should understand English (written, spoken), and posess some understanding of basic pop gen terms and concepts: (locus, allele, heterozygosity, effective population size, and inbreeding, inbreeding depression, outbreeding depression).
Main instructors:
Eric Anderson (NMFS, Colorado State U), Ellie Armstrong (UC Riverside), Gordon Luikart (U Montana), Will Hemstrom (Colorado State University), Marli de Bruyn and Monica Mwale (SANBI Pretoria), Jessica Da Silva (SANBI Cape Town), Karen Ehlers and Paul Grobler (University of Free State), Marty Kardos (NOAA, NMFS), Laura Bertola (National Centre for Biological Sciences (NCBS) in Bangalore, India, Robin Waples (NMFS/U Washington). Additional instructors to be announced.
Workshop content:
ConGen teaches fundamental statistical and computational approaches that will help prepare Ñý¼§Ö±²¥ and professionals to use population genetic and genomic data in their work. Microsatellites and SNP datasets will be discussed and analyzed – with forensics and other applications. Emphasis will be on next-generation sequence (NGS) data analysis (RADs and genome sequencing/resequencing) and interpretation of output from fundamental and novel statistical approaches and software programs (including R and Linux command line). The course promotes interactions among early-career researchers e.g., grad Ñý¼§Ö±²¥ & postdocs), mid-career faculty and agency researchers, and leaders in population genomics to help develop our "next generation" of conservation and evolutionary geneticists. We will identify and discuss developments needed to improve data analysis approaches to advance the field. This course often feels like a workshop because multiple instructors ask questions and provide helpful comments during another instructor’s lecture to help advance learning of basic and advanced concepts and approaches.
This course will cover concepts and methods including the coalescent, Bayesian, and likelihood-based approaches. Special lecture sessions and hands-on exercises will be conducted on assessing population structure, testing for HW proportions, detecting selection, genetic monitoring (of Ne, FST, Nm, etc.), inbreeding detection (RoH), population assignment (with microsatellites then lcWGseq data), whole-genome sequencing & assembly, and more.
We will use popular programs like Structure, NeEstimator, and packages in Rstudio. We’ll analyze datasets (hands-on) using key software packages including GenePop, Structure, NeEstimator, bottleneck programs, Rstudio, GeneClass, Rubias, WGSassign, etc.). We’ll discuss approaches for detecting illegal trafficking/killing and dispersal of individuals using population assignment tests. Finally, participants will learn to assess Ne without genetic data and other genetic monitoring approaches to help countries address the recent Kunming-Montreal Biodiversity Framework adopted by the UN Convention on Biodiversity (CBD) (see Mastretta-Yanes et al. 2024; Hoban et al. 2024).
Sponsors and course publications:
This course is sponsored by the American Genetic Association, the Journal of Heredity, NASA (the National Aeronautics and Space Administration), NSF, along with Nanopore and support from publishers of Environmental DNA, Molecular Ecology Resources, and Conservation Genetics. This course/workshop should lead to a publication (meeting review) describing the main topics, course outcomes, and recent advances in population genetics and phylogenetics worldwide. For example, see pubs below:
Registration fees: African Nationals (at an African institute) get a reduced registration fee thanks to scholarships (ask ConGen organizers). Non-African Nationals get the Early Bird discount if you pay before June 1st is $800 (so apply & pay early!). Cost for non-African nationals is $900 if you pay after June 1st.
Click for a Google sheets link for additional scholarship/fellowship opportunities (NOT associated with the course!)
Lodging and food: Participants will pay for their lodging ($30-$60 per night) and food ($5-30 per meal), both available within walking distance from the course. Course web page and organizers will advise on lodging and restaurants. Lodging costs less if you have/accept a roommate – in the UP dorms/housing.
Recommended lodging: (can be arranged through course organizers)
Field trips (stay tuned for more information):
Kruger National Park, December 14-17. We’ll safari around the southern half of the park, with local experts.
This course is held in collaboration with SANBI. See .
References cited:
Allendorf, F.W., W.C. Funk, S.N. Aitken, M. Byrne, G. Luikart. 2022. Conservation and the Genomics of Populations. [3rd Edition]. Oxford University Press. Pp. 784
Bailey, R. I. (2024). Bayesian hybrid index and genomic cline estimation with the R package gghybrid. Molecular Ecology Resources, 24(2), e13910.
Jombart T. and Ahmed I. (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. doi: 10.1093/bioinformatics/btr521
Hemstrom, W., & Jones, M. (2023). snpR: User friendly population genomics for SNP data sets with categorical metadata. Molecular Ecology Resources, 23(4), 962-973.
Hoban, S., Paz-Vinas, I., Shaw, R. E., Castillo-Reina, L., Silva, J. M., DeWoody, J. A., ... & Grueber, C. E. (2024). DNA-based studies and genetic diversity indicator assessments are complementary approaches to conserving evolutionary potential. Conservation Genetics, 1-7.
Jenkins, T. L. (2024). mapmixture: An R package and web app for spatial visualisation of admixture and population structure. Molecular Ecology Resources, 24(4), e13943.
Kamvar, Z. N., López-Uribe, M. M., Coughlan, S., Grünwald, N. J., Lapp, H., & Manel, S. (2016). Developing educational resources for population genetics in R: An open and collaborative approach. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.12558
Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6:208. doi: 10.3389/fgene.2015.00208
Kardos, M., and G. Luikart. 2021. The genomic architecture of fitness drives population viability in
changing environments. American Naturalist, 197:511–525. doi.org/10.1086/713469
Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller JM, Tallmon DA, Funk WC. The crucial role of genome-wide genetic variation in conservation. (2021) Proc Natl Acad Sci USA. 118(48):e2104642118. doi: 10.1073/pnas.2104642118.
Mastretta‐Yanes, A., Da Silva, J. M., Grueber, C. E., Castillo‐Reina, L., Köppä, V., Forester, B. R., ... & Hoban, S. (2024). Multinational evaluation of genetic diversity indicators for the Kunming‐Montreal Global Biodiversity Framework. Ecology Letters, 27(7), e14461.
Paradis, E. (2020). Population genomics with R. Chapman and Hall/CRC.
Zhang, R., Jia, G., & Diao, X. (2023). geneHapR: an R package for gene haplotypic statistics and visualization. BMC bioinformatics, 24(1), 199.
Yang, C., Mai, J., Cao, X., Burberry, A., Cominelli, F., & Zhang, L. (2023). ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. Bioinformatics, 39(8), btad470.