Back

Promises and limitations of local ancestry inference in imputed ancient genomes

Bougiouri, K.; Irving-Pease, E. K.; Frantz, L. A. F.; Racimo, F.; Petr, M.

2026-05-20 evolutionary biology
10.64898/2026.05.19.725905 bioRxiv
Show abstract

Recent advances in genome imputation have enabled the application of state-of-the-art statistical methods--originally developed for present-day genomes--to ancient genomes. One class of such methods, known as local ancestry inference (LAI), can model an individuals genome as a mosaic of tracts assigned to different putative ancestral sources, revealing patterns of genetic ancestry across the genome. However, most LAI methods have been designed to study recent admixture events in human history, and they generally assume large panels of present-day genomes. Despite the recent availability of high-quality imputed ancient genomes, it remains unknown to what degree LAI inference is reliable for such datasets. Ancient DNA is often characterized by heterogeneous geographic and temporal sampling, varying degrees of divergence between ancient source proxies and admixing populations, and complex demographic histories. Here, we performed an extensive set of population genetic simulations to evaluate the accuracy of four popular LAI methods-RFMix, FLARE, MOSAIC and simpLAI-under different demographic scenarios, various temporal sampling schemes, sample sizes, and admixture dates. We quantify the accuracy of these methods as a function of different parameters in practically relevant scenarios, and provide general guidelines for future studies utilizing LAI in ancient DNA research.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
Molecular Biology and Evolution
488 papers in training set
Top 0.2%
14.3%
2
Frontiers in Genetics
197 papers in training set
Top 0.9%
6.3%
3
eLife
5422 papers in training set
Top 17%
4.8%
4
PLOS Computational Biology
1633 papers in training set
Top 8%
4.3%
5
Genome Biology
555 papers in training set
Top 2%
4.3%
6
Molecular Ecology Resources
161 papers in training set
Top 0.3%
4.3%
7
Genome Biology and Evolution
280 papers in training set
Top 0.4%
3.9%
8
Bioinformatics
1061 papers in training set
Top 5%
3.9%
9
The American Journal of Human Genetics
206 papers in training set
Top 1%
3.1%
10
PLOS Genetics
756 papers in training set
Top 6%
2.9%
50% of probability mass above
11
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 27%
2.3%
12
Scientific Reports
3102 papers in training set
Top 50%
2.1%
13
Genome Research
409 papers in training set
Top 2%
2.1%
14
Nature Communications
4913 papers in training set
Top 48%
2.1%
15
Communications Biology
886 papers in training set
Top 6%
1.9%
16
GENETICS
189 papers in training set
Top 0.6%
1.8%
17
Genetics
225 papers in training set
Top 2%
1.7%
18
Briefings in Bioinformatics
326 papers in training set
Top 4%
1.7%
19
Nature Genetics
240 papers in training set
Top 4%
1.7%
20
Nature Computational Science
50 papers in training set
Top 0.6%
1.7%
21
Bioinformatics Advances
184 papers in training set
Top 3%
1.7%
22
Methods in Ecology and Evolution
160 papers in training set
Top 2%
1.5%
23
NAR Genomics and Bioinformatics
214 papers in training set
Top 2%
1.3%
24
Human Genetics and Genomics Advances
70 papers in training set
Top 0.5%
1.1%
25
BMC Bioinformatics
383 papers in training set
Top 6%
0.9%
26
PLOS ONE
4510 papers in training set
Top 62%
0.9%
27
European Journal of Human Genetics
49 papers in training set
Top 1.0%
0.9%
28
BMC Ecology and Evolution
49 papers in training set
Top 2%
0.9%
29
iScience
1063 papers in training set
Top 27%
0.9%
30
Computational and Structural Biotechnology Journal
216 papers in training set
Top 8%
0.9%