Dr. L heads to France to teach popgen at SSMPG 2015

Post date: Sep 4, 2015 7:52:43 PM

Software and Statistical Methods for Population Genetics (SSMPG 2015)

Aussois, Vanoise national park, September 7th-11st

I'm getting ready to head to France to teach at SSMPG 2015, where we're taking a new approach toward teaching people about genome scans through a contest.  Genome scans are methods that we use to analyze genomic data and look for genes that underlie adaptations to the environment.  We're showing the students how to use our genome scan methods and then letting them apply our programs to three datasets that I simulated.   For the contest, students will apply the methods that we teach them to analyze the simulated datasets and submit their list of loci that underlie the adaptation. The winner of the contest is the one whose list contains the most true positives and the least false positives.  It is our hope that through the contest, students will better learn the challenges of analyzing genomic data.

Except for me, no one---not even the other instructors---will know which genes are neutral and which genes underlie the adaptation.  The three datasets that I simulated are increasingly complex scenarios, both in demographic history and genetic architecture.  It will be interesting to see how the students and instructors compare! I'm really looking forward to meeting and working with the other instructors:

Instructors

Michael Blum, UGA Grenoble (France)

PCAdapt: detecting genes involved in adaptation with principal component-analysis.

Olivier Francois, UGA Grenoble (France)

LEA: An R package for landscape and ecological genome-wide association studies.

Mathieu Gautier, INRA Montpellier (France)

BAYPASS: detecting adaptive loci with and without environmental variables using the covariance matrix.

Katie Lotterhos, Northeastern University (USA)

OutFLANK: a procedure to find Fst outliers based on an inferred distribution of neutral Fst.

Bertrand Servin, INRA Toulouse (France)

hapFLK and FLK tests for the detection of selection signatures based on multiple population genotyping data.

Renaud Vitalis, INRA Montpellier (France)

SELESTIM: Detecting and measuring selection from gene frequency data.