Biology subjects

von Seth, J.

Publications and source records attributed to von Seth, J..

4 recordsLinked to original sources

bioRxiv2026

Rethinking human AMY1 copy number evolution in light of demographic history

Dietary change is often invoked as a major selective force in recent human evolution, with increased copy number of the salivary amylase gene (AMY1) widely cited as an adaptation to starch-rich agricultural diets. However, most evidence for this model comes from limited geographical sampling and analyses that do not fully account for shared ancestry. Here we combine newly generated droplet digital PCR estimates from 390 individuals representing 30 Sub-Saharan African populations with published copy number data from up to 1,307 individuals worldwide and re-evaluate AMY1 evolution using ancestry-aware and phylogenetically informed models. Across Africa, AMY1 copy number shows no consistent association with agriculture once population structure is accounted for. At a global scale, differences between agriculturalists and non-agriculturalists are substantially smaller than previously reported and are largely explained by shared ancestry rather than diet. Phylogenetic analyses further reveal baseline differences in AMY1 copy number between Sub-Saharan and non-Sub-Saharan populations, pointing to deep demographic processes shaping present-day variation. These results challenge the long-standing "agriculture hypothesis" and identify demographic history, rather than subsistence strategy, as the primary driver of AMY1 CN evolution worldwide.

evolutionary biology↗

bioRxiv2023

Recurrent connectivity supports higher-level visual and semantic object representations in the brain

Visual object recognition is a dynamic process by which we rapidly extract meaningful information about the things we see. However, the functional relevance of inter-regional feedforward and feedback signals in the human ventral visual pathway remain largely unspecified, while its unclear whether computational models of vision alone can accurately capture object-specific representations. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500ms after stimulus onset. These results demonstrate the distinct contributions made by semantic object properties in explaining neural activity and connectivity, highlighting it as a core part of object recognition not fully accounted for by biologically inspired neural networks.

neuroscience↗

bioRxiv2022

GenErode: a bioinformatics pipeline to investigate genome erosion in endangered and extinct species

BackgroundMany wild species have suffered drastic population size declines over the past centuries, which have led to genomic erosion processes characterized by reduced genetic diversity, increased inbreeding, and accumulation of harmful mutations. Yet, genomic erosion estimates of modern-day populations often lack concordance with dwindling population sizes and conservation status of threatened species. One way to directly quantify the genomic consequences of population declines is to compare genome-wide data from pre-decline museum samples and modern samples. However, doing so requires computational data processing and analysis tools specifically adapted to comparative analyses of degraded, ancient or historical, DNA data with modern DNA data as well as personnel trained to perform such analyses. ResultsHere, we present a highly flexible, scalable, and modular pipeline to compare patterns of genomic erosion using samples from disparate time periods. The GenErode pipeline uses state-of-the-art bioinformatics tools to simultaneously process whole-genome re-sequencing data from ancient/historical and modern samples, and to produce comparable estimates of several genomic erosion indices. No programming knowledge is required to run the pipeline and all bioinformatic steps are well-documented, making the pipeline accessible to users with different backgrounds. GenErode is written in Snakemake and Python3 and uses Conda and Singularity containers to achieve reproducibility on high-performance compute clusters. The source code is freely available on GitHub (https://github.com/NBISweden/GenErode). ConclusionsGenErode is a user-friendly and reproducible pipeline that enables the standardization of genomic erosion indices from temporally sampled whole genome re-sequencing data.

bioinformatics↗

bioRxiv2020

Competitive mapping allows to identify and exclude human DNA contamination in ancient faunal genomic datasets

BackgroundAfter over a decade of developments in field collection, laboratory methods and advances in high-throughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data. ResultsHere we investigate whether human contaminating DNA can be found in ancient faunal sequencing datasets. We identify variable levels of human contamination, which persists even after the sequence reads have been mapped to the faunal reference genomes. This contamination has the potential to affect a range of downstream analyses. ConclusionsWe propose a fast and simple method, based on competitive mapping, which allows identifying and removing human contamination from ancient faunal DNA datasets with limited losses of true ancient data. This method could represent an important tool for the ancient DNA field.

genomics↗