The project aims to apply methods for inference of biological fitness developed by the applicants and collaborators over several years to ancient human DNA data. This is more challenging (larger genomes), hence we will not be able to use local resources. Furthermore, the data is already available in NAISS infrastructure from our collaborators / associates, which would make it much more convenient to access the data if we are also on NAISS. Collaborators / associates: Hongli Zeng (Nanjing University of Post and Telecommunications, presently visiting KTH as CSC Scholar), John Barton, University of Pittsburgh, USA, Mattias Jakobsson ((Uppsala University, owner of the data). Two relevant recent references from the methods side: "Fitness inference tested by in silico population genetics", Hong-Li Zeng, Yu-Han Huang, Erik Aurell, John Barton, Phys. Rev. E 113, 044415 (2026) DOI: https://doi.org/10.1103/4ljm-gy5p; Two fitness inference schemes compared using allele frequencies from 1068 391 sequences sampled in the UK during the COVID-19 pandemic", Hong-Li Zeng, Cheng-Long Yang, Bo Jing, John Barton and Erik Aurell*, Phys. Biol. 22 016003 (2025) DOI 10.1088/1478-3975/ad9213. One relevant reference on the data side: "Homo sapiens-specific evolution unveiled by ancient southern African genomes" Mattias Jakobsson, Carolina Bernhardsson, James McKenna, Nina Hollfelder, Mario Vicente, Hanna Edlund, Alexandra Coutinho, Per Sjödin, James Brink, Bernhard Zipfel, Helena Malmström, Marlize Lombard & Carina M. Schlebusch Nature volume 650, 156–163 (2026), https://doi.org/10.1038/s41586-025-09811-4