Struck Travis J, Vaughn Andrew H, Daigle Austin, Ray Dylan D, Noskova Ekaterina, Sequeira Jaison J, Antonets Svetlana, Alekseevskaya Elizaveta, Grigoreva Elizaveta, Raines Evgenii, McMaster Eilish S, Kovacs Toby G L, Ragsdale Aaron P, Moreno-Estrada Andrés, Lotterhos Katie E, Siepel Adam, Gutenkunst Ryan N
Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
Center for Computational Biology, University of California, Berkeley, CA, USA.
bioRxiv. 2025 Aug 11:2025.08.05.668560. doi: 10.1101/2025.08.05.668560.
Evaluating population genetic inference methods is challenging due to the complexity of evolutionary histories, potential model misspecification, and unconscious biases in self-assessment. The Genomic History Inference Strategies Tournament (GHIST) is a community-driven competition designed to evaluate methods for inferring evolutionary history from population genomic data. The inaugural GHIST competition ran from July to November 2024 and featured four demographic history inference challenges of varying complexity: a bottleneck model, a split with isolation model, a secondary contact model with demographic complexity, and an archaic admixture model. Data were provided as error-free VCF files, and participants submitted numerical parameter estimates that were scored by relative root mean squared error. Approximately 60 participants competed, using diverse approaches. Results revealed the current dominance of methods based on site frequency spectra, while highlighting the advantages of flexible model-building approaches for complex demographic histories. We discuss insights regarding the competition and outline the next iteration, which is ongoing with expanded challenge diversity. By providing standardized benchmarks and highlighting areas for improvement, GHIST represents a substantial step toward more reliable inference of evolutionary history from genomic data.
由于进化历史的复杂性、潜在的模型错误设定以及自我评估中无意识的偏差，评估群体遗传推断方法具有挑战性。基因组历史推断策略竞赛（GHIST）是一项由社区推动的竞赛，旨在评估从群体基因组数据推断进化历史的方法。首届GHIST竞赛于2024年7月至11月举行，设有四个不同复杂程度的人口历史推断挑战：一个瓶颈模型、一个带有隔离的分裂模型、一个具有人口复杂性的二次接触模型以及一个古老混合模型。数据以无错误的VCF文件形式提供，参与者提交数值参数估计值，并根据相对均方根误差进行评分。大约60名参与者使用不同的方法参赛。结果揭示了基于位点频率谱的方法目前的主导地位，同时突出了灵活的模型构建方法在复杂人口历史中的优势。我们讨论了有关竞赛的见解，并概述了正在进行的下一轮竞赛，其挑战多样性有所增加。通过提供标准化的基准并突出改进领域，GHIST朝着从基因组数据更可靠地推断进化历史迈出了重要一步。
