In population genomics, there are a dizzying array of potential data analysis approaches to infer population history, aspects of natural selection, or other evolutionary properties from data. Although methods developers try to evaluate their approaches, those evaluations can be unconsciously biased or may not reflect the experiences of real-world users. GHIST is an annual forum in which the community can test inference approaches in an unbiased fashion. Each year, the GHIST organizers release simulated population genomic data sets and host a competition to infer various aspects of the processes that generated those data. From the competitors’ solutions, the community will learn what approaches perform well or poorly in particular circumstances. Participating the competition is also great training for new students!
GHIST 2024 is now live, with four demographic history inference challenges of escalating expected difficulty. For each challenge, we provide a VCF file with genomic data, and you submit a simple text file with your inferences. We look forward to your submissions!
To join the competition, visit our page on Synapse. (You will need to create a Synapse account.)
Top competitors will be invited as co-authors on the publication describing the competition.
Entries will close on November 15, 2024.
To help you get started, we've created an hour-long introductory workshop. In the workshop, we'll introduce GHIST, use dadi-cli on a cloud instance to analyze the data from the first challenge, and submit our inference to the tournament. Join in!
Questions? Contact Ryan Gutenkunst at rgutenk@arizona.edu.
Thank you to the Design Committee:
- Katie Lotterhos - Northeastern University
- Andrés Moreno-Estrada - Centro de Investigación y de Estudios Avanzados del IPN
- Peter Ralph - University of Oregon
- Adam Siepel - Cold Spring Harbor Laboratory
Also thank you to Travis Struck, who has implemented the 2024 competition.
This competition is supported by NIH NIGMS grant GM149235 to Ryan Gutenkunst.