ConGen 2019

Applications of New Sequencing Technologies to Understand Population Structure, Adaptation, and Environmental Influences on Genomic Variation

September 2-7, 2019

Goals & Overview

2019 Instructors

To provide training in conceptual and practical aspects of data analysis to understand the evolutionary and ecological genomics of natural and managed populations. Emphasis will be on next generation sequence (NGS) data analysis (RADs, DNA capture, and whole genome sequence analyses, gene expression, and epigenetics) and interpretation of output from recent novel statistical approaches and software programs. The course promotes daily discussions among early-career researchers (students/participants) and leaders in population genomics to help develop the “next generation” of conservation and evolutionary geneticists. We will identify and discuss developments needed to improve data analysis approaches. This course will cover analysis methods including the coalescent, Bayesian, and likelihood-based approaches. Special sessions will be held on population structure, detecting selection, genetic monitoring (of Ne, Nb, Fst, etc.), landscape genomics, and the use of GIS and remote sensing data to identify environmental variables influencing genetic diversity, Ne, and connectivity (Nm, Fst). It will also include lectures and hands-on activities on gene expression mechanisms underlying rapid adaptation to environmental change. This course is partially sponsored by the American Genetics Association, the Journal of Heredity, NASA (the National Aeronautics and Space Administration), and NSF (the Dimensions in Biodiversity program).  It includes >12 expert instructors and hands-on data analysis using your own data (with instructors) and dummy data sets provided by instructors. It could lead to a publication describing main goal outcomes of the course, as in past years, with the goal of facilitating data analysis for genomicists worldwide (see Benestan et al. 2016:;  and Andrews and Luikart 2014 -, and also Andrews et al. 2016 -