Biology subjects

Tuke, J.

Publications and source records attributed to Tuke, J..

7 recordsLinked to original sources

bioRxiv2025

Detecting and Quantifying Networks of Biological Kinship via Exponential Family Random Graph Models

Genetic relatedness between ancient humans can help to identify close and distant connections between groups and populations, uncovering signatures of demographic histories such as identifying mating networks or long-range migration. Critical to researchers are the characteristics that connected individuals, or groups of individuals, share, and how these characteristics interact and are correlated. Here we use Exponential Random Graph models as a method to explore demographic and contextual parameters that may help to explain the significant drivers of the topology of mating networks, as well as to quantify their effects. We show through simulations that model selection and coefficient estimators facilitate the exploration of such networks, and apply the method to individuals from a collection of Avar-associated cemeteries from the Carpathian Basin dating to the 6th to the 9th centuries CE.

bioinformatics↗

bioRxiv2023

BREADR: An R Package for the Bayesian Estimation of Genetic Relatedness from Low-coverage Genotype Data

Robust and reliable estimates of how individuals are biologically related to each other are a key source of information when reconstructing pedigrees. In combination with contextual data, reconstructed pedigrees can be used to infer possible kinship practices in prehistoric populations. However, standard methods to estimate biological relatedness from genome sequence data cannot be applied to low coverage sequence data, such as are common in ancient DNA (aDNA) studies. Critically, a statistically robust method for assessing and quantifying the confidence of a classification of a specific degree of relatedness for a pair of individuals, using low coverage genome data, is lacking. In this paper we present the R-package BREADR (Biological RElatedness from Ancient DNA in R), which leverages the so-called pairwise mismatch rate, calculated on optimally-thinned genome-wide pseudo-haploid sequence data, to estimate genetic relatedness up to the second degree, assuming an underlying binomial distribution. BREADR also returns a posterior probability for each degree of relatedness, from identical twins/same individual, first-degree, second-degree or "unrelated" pairs, allowing researchers to quantify and report the uncertainty, even for particularly low-coverage data. We show that this method accurately recovers degrees of relatedness for sequence data with coverage as low as 0.04X using simulated data, and then compare the performance of BREADR on empirical data from Bronze Age Iberian human sequence data. The BREADR package is designed for pseudo-haploid genotype data, common in aDNA studies.

bioinformatics↗

bioRxiv2020

Introgression, hominin dispersal and megafaunal survival in Late Pleistocene Island Southeast Asia

The hominin fossil record of Island Southeast Asia (ISEA) indicates that at least two endemic super-archaic species - Homo luzonensis and H. floresiensis - were present around the time anatomically modern humans (AMH) arrived in the region >50,000 years ago. Contemporary human populations carry signals consistent with interbreeding events with Denisovans in ISEA - a species that is thought to be more closely related to AMH than the super-archaic endemic ISEA hominins. To query this disparity between fossil and genetic evidence, we performed a comprehensive search for super-archaic introgression in >400 modern human genomes. Our results corroborate widespread Denisovan ancestry in ISEA populations but fail to detect any super-archaic admixture signals. By highlighting local megafaunal survival east of the Wallace Line as a potential signature of deep, pre-H. sapiens hominin-faunal interaction, we propose that this understudied region may hold the key to unlocking significant chapters in Denisovan prehistory.

evolutionary biology↗

bioRxiv2020

Is YouTube(R) promoting the exotic pet trade?Analysis of the global public perception of popular YouTube(R) videos featuring threatened exotic animals.

The exploitation of threatened exotic species via social media challenges efforts to regulate the exotic pet trade and consequently threatens species conservation. To investigate how such content is perceived by the global community, sentiment analysis techniques were employed to explore variations in attitudes expressed through text and emoji usage in public comments associated with 346 popular YouTube(R) videos starring exotic wild cats or primates in captive or domestic situations. Although a negative trend in mean text sentiment was observed in 2015 for primates, an otherwise consistent positive mean sentiment text and emoji score through time was revealed in response to both exotic wild cat and primate videos, implying the societal normalisation and acceptance of exotic pets. These findings highlight the urgency for effective YouTube(R) policy changes and content management to promote public education and conservation awareness, whilst extinguishing false legitimisation and potential demand for the exotic pet trade.

scientific communication and education↗

bioRxiv2020

Ancient human genomes reveal a hidden history of strong selection in Eurasia

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied upon genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has likely been underappreciated in the evolutionary history of humans and suggests that our current understanding of the tempo and mode of selection in natural populations may be quite inaccurate.

genomics↗

bioRxiv2018

Dimension Reduction and Visualization for Single-copy Alignments via Generalized PCA

Single-copy sequence alignments have been a valuable source of information for genetic studies; their lack of recombination makes phylogenetic analyses tractable [1]. Specifically, mitochondrial DNA will continue to play an important role in genetic studies due to its high mutation rate and high copy per cell count of the molecule [2]. In this paper we develop a new method for the analysis of single-copy sequence data that simultaneously considers the relationships between sequenced individuals and positions of interest in the genome. We then show that tests for relationships between genetic information and qualitative and quantitative characteristics can be calculated. We motivate the use of our method with examples from empirical data.

bioinformatics↗

bioRxiv2017

GHOST: Recovering Historical Signal from Heterotachously-evolved Sequence Alignments

Molecular sequence data that have evolved under the influence of heterotachous evolutionary processes are known to mislead phylogenetic inference. We introduce the General Heterogeneous evolution On a Single Topology (GHOST) model of sequence evolution, implemented under a maximum-likelihood framework in the phylogenetic program IQ-TREE (http://www.iqtree.org). Simulations show that using the GHOST model, IQ-TREE can accurately recover the tree topology, branch lengths and substitution model parameters from heterotachously-evolved sequences. We develop a model selection algorithm based on simulation results, and investigate the performance of the GHOST model on empirical data by sampling phylogenomic alignments of varying lengths from a plastome alignment. We then carry out inference under the GHOST model on a phylogenomic dataset composed of 248 genes from 16 taxa, where we find the GHOST model concurs with the currently accepted view, placing turtles as a sister lineage of archosaurs, in contrast to results obtained using traditional variable rates-across-sites models. Finally, we apply the model to a dataset composed of a sodium channel gene of 11 fish taxa, finding that the GHOST model is able to infer a subtle component of the historical signal, linked to the previously established convergent evolution of the electric organ in two geographically distinct lineages of electric fish. We compare inference under the GHOST model to partitioning by codon position and show that, owing to the minimization of model constraints, the GHOST model is able to offer unique biological insights when applied to empirical data.

evolutionary biology↗