Biology subjects

Rymbekova, A.

Publications and source records attributed to Rymbekova, A..

4 recordsLinked to original sources

bioRxiv2026

A meta-analysis of ancient and present-day Central Eurasian genome data to revise archaic hominin ancestry

Archaic introgression has shaped the evolutionary history of Eurasian populations, yet Central Eurasian region remains understudied despite being at the crossroads of ancient human migration. Here, we analyzed the whole-genome data of five Central Eurasian (CE) individuals from Early Bronze Age (EBA) and five present-day CE individuals to characterize the archaic introgression landscape. We estimated that archaic introgression from Neanderthal and Denisovan archaic hominins comprises approximately 2.2% of the Central Eurasian genomes. Both amount and chromosomal distribution of archaic introgression remained largely unchanged between the EBA and present-day CE individuals. Putative introgressed fragments matching the Altai Neanderthal and the Altai Denisovan were retrieved. Our results suggest that while the archaic introgression levels seemingly remained stable over the past several thousand years, larger modern CE genomes panels will be required to fully characterize the genomic landscape of archaic ancestry in the region.

genomics↗

bioRxiv2025

Genetic insights into Iron Age Saka culture: Ancient DNA analysis of the Boz-Barmak burial ground, Kyrgyzstan

The nomadic cultures of the Iron Age played an important role in shaping the genetic and cultural landscape of Eurasian populations. Yet despite its key geographical location, the Central Eurasian region remains underrepresented in ancient DNA studies of humans. We address this gap through genomic analysis of 12 individuals from the Boz-Barmak burial site in Kyrgyzstan associated with Saka pastoralists (4th-2nd centuries BCE), 9 of which yielded low-coverage genomes. Genetic clustering analysis placed these individuals within the genetic variation of ancient and modern Central Eurasian and Siberian populations. We found no evidence of first-degree relatives in a kinship analysis, however a network of second- and third-degree relationships seems to be present. Notably, all male individuals share the same Y-chromosomal haplotype, common in present-day Kyrgyz and Tajik groups, while mitochondrial DNA showed comparably high diversity, with distinct haplogroups observed across the analysed individuals. These findings are in line with archaeological and ethnographic evidence of patrilocality in Early Iron Age Saka, where male lineages remained stable across generations, while female mobility contributed to genetic diversity. Our study complements our understanding of the interplay between kinship, social organization and population history in nomadic cultures.

genomics↗

bioRxiv2024

Screening great ape museum specimens for DNA viruses

Natural history museum collections harbour a record of wild species from the past centuries, providing a unique opportunity to study animals as well as their infectious agents. Thousands of great ape specimens are kept in these collections, and could become an important resource for studying the evolution of DNA viruses. Their genetic material is likely to be preserved in dry museum specimens, as reported previously for monkeypox virus genomes from historical orangutan specimens. Here, we screened 209 great ape museum specimens for 99 different DNA viruses, using hybridization capture coupled with short-read high-throughput sequencing. We determined the presence of multiple viruses within this dataset from historical specimens and obtained several near-complete viral genomes. In particular, we report high-coverage (>18-fold) hepatitis B virus genomes from one gorilla and two chimpanzee individuals, which are phylogenetically placed within clades infecting the respective host species.

genomics↗

bioRxiv2022

Predicting Dog Phenotypes from Genotypes

We analyze dog genotypes (i.e., positions of dog DNA sequences that often vary between different dogs) in order to predict the corresponding phenotypes (i.e., unique observed characteristics). More specifically, given chromosome data from a dog, we aim to predict the breed, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare the performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, we show that the reverse is true for height and weight regression. Finally, we evaluate the results of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences, resulting in input sequences ranging from 20 SNPs to [~]200k SNPs. In doing so, we explore the impact of using a very limited number of SNPs for prediction. Our experiments demonstrate that these phenotypes in dogs can be predicted with as few as 0.5% of randomly selected SNPs (i.e., 992 SNPs) and that dog breeds can be classified with 50% balanced accuracy with as few as 0.02% SNPs (i.e., 40 SNPs).

genomics↗