Webinars


The 15th KISS Webinar

 

We are pleased to announce that Dr. Wooseok Ha from UC Berkeley will give a talk at 5 pm (ET) on November 29. Please use the zoom link below to register for the KISS webinar. The webinar title and abstract are as follows. We look forward to seeing you all at the KISS webinar.

 

Date/Time: 5-6 pm ET (4-5pm CT) on November 29 (Tuesday)

 

Recorded video:

https://drive.google.com/file/d/1SdlO5SoVeZg07bLZZQXrqcwFrbCMYwS4/view?usp=sharing

 

After registering, you will receive a confirmation email containing information about joining the meeting.

 

Speaker: Dr. Wooseok Ha (UC Berkeley)

 

Title: Fast and flexible estimation of effective migration surfaces.

 

Abstract: An important feature in spatial population genetic data is often “isolation-by-distance,” where genetic differentiation tends to increase as individuals become more geographically distant. Recently, Petkova et al. (2016) developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. In this talk, I will present a new method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node as in EEMS. When tested with coalescent simulations, FEEMS accurately recovers effective migration surfaces with complex gene-flow histories, including those with anisotropy. Applications of FEEMS to population genetic data from North American gray wolves show it to perform comparably to EEMS, but with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.