%0 Journal Article
%A Huang, Yilei
%A Carmi, Shai
%A Ringbauer, Harald
%+ Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Haplo Group, Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Haplo Group, Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
%T Estimating effective population size trajectories from time-series identity-by-descent segments : 
%G eng
%U https://hdl.handle.net/21.11116/0000-0010-F98F-5
%U https://doi.org/10.1093/genetics/iyae212
%7 2025-01-24
%D 2025
%* Review method: peer-reviewed
%X Long, identical haplotypes shared between pairs of individuals, known as identity-by-descent (IBD) segments, result from recently shared co-ancestry. Various methods have been developed to utilize IBD sharing for demographic inference in contemporary DNA data. Recent methodological advances have extended the screening for IBD segments to ancient DNA (aDNA) data, making demographic inference based on IBD also possible for aDNA. However, aDNA data typically have varying sampling times, but most demographic inference methods for modern data assume that sampling is contemporaneous. Here, we present Ttne (Time-Transect Ne), which models time-transect sampling to infer recent effective population size trajectories. Using simulations, we show that utilizing IBD sharing in time series increased resolution to infer recent fluctuations in effective population sizes compared with methods that only use contemporaneous samples. To account for IBD detection errors common in empirical analyses, we implemented an approach to estimate and model IBD detection errors. Finally, we applied Ttne to two aDNA time transects: individuals associated with the Copper Age Corded Ware Culture and Medieval England. In both cases, we found evidence of a growing population, a signal consistent with archaeological records.
%K population genetics, evolutionary biology, ancient DNA, bioinformatics, statistical genetics
%J Genetics
%V 229
%N 3
%] iyae212
%I Oxford; Genetics Society of America
%@ 0016-6731