Biology subjects

Elhaik, E.

Publications and source records attributed to Elhaik, E..

13 recordsLinked to original sources

bioRxiv2026

Sedimentary ancient DNA reveals rhizosphere-like plant-microbe association signals in a 2-million-year-old Arctic ecosystem

Plant-microbe interactions in the rhizosphere are central to nutrient cycling and ecosystem functioning. Sedimentary ancient DNA (sedaDNA) is a promising yet underexplored tool for reconstructing past microbial communities and investigating ecological interactions among plants, animals, and microorganisms. Here, we reanalyse the previously published Kap Kobenhavn Formation (Northern Greenland) sedaDNA dataset to move beyond taxonomic ecosystem reconstruction and test whether ancient sediments preserve structured, rhizosphere-compatible plant-microbe association signals. Our results show that this ancient boreal ecosystem hosted several rhizosphere-associated taxa, comparable to those in modern boreal soils. Several bacterial genera co-occurred repeatedly with specific plant families, forming a rhizosphere-like taxonomic core with predicted plant-growth-promoting traits related to nutrient acquisition, colonisation, and stress tolerance. Although sedaDNA co-occurrence cannot demonstrate direct symbiosis, the consistency of taxonomic, network, and functional signals suggests that ancient sediments preserve interconnected ecological structure. Our findings extend sedaDNA-based ecosystem reconstruction beyond taxonomy and provide a possibility for investigating plant-microbe association signals in deep time.

plant biology↗

bioRxiv2025

Unexpected D-tour Ahead: Why the D-Statistic, applied to Humans, Measures Mutation Rate Variation not Neanderthal Introgression

The D statistic is widely used to infer introgression and played a pivotal role in establishing as fact that humans interbred with Neanderthals, leaving a lasting genetic legacy. However, D relies on two untested assumptions: mutation rate constancy between groups and a negligible contribution from recurrent mutations. Together, these preclude a previously ignored alternative explanation of the data based on mutation-rate variation across human populations. Here we show that neither assumption is valid and infer that essentially all sites contributing to non-zero D carry recurrent mutations. When D is calculated separately for different sequence motifs, it varies far more than can be explained using a model based on introgressed haplotypes, where non-independence due to being located on the same haplotype largely prevents different motifs from generating different values of D. Large variation in D between triplets appears only possible if D is driven by processes acting at the level of individual bases. We further show that D is dominated by sites where the Neanderthal allele matches the human major allele and, more specifically, where the Neanderthal allele is fixed in humans outside Africa. This is the exact opposite of expectations under the introgression hypothesis, where Neanderthal alleles should mostly be rare and found only outside Africa. Finally, we tested a new model in which heterozygosity modulates mutation rate. Remarkably, heterozygosity differences, relative mutation rate, and D are all strongly inter-correlated, not only in African - non-African comparisons but also in comparisons among African and among non-African population pairs, and even across the genome. We conclude that the mutation rate variation hypothesis provides a more compelling explanation for the observed patterns in human-Neanderthal genetic relationships than the introgression hypothesis and that the concept of archaic introgression into humans and its implications for hominin-derived traits must be rethought.

evolutionary biology↗

bioRxiv2024

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable app and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

neuroscience↗

bioRxiv2023

MORPHIX: Resolving sample identification bias in morphometrics analysis with a supervised machine learning package

Evolutionary biologists, primarily palaeoanthropologists, anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps: Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCAs central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCAs accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. Throughout this manuscript, we address the recent and contentious use of PCA in physical anthropology and phylogenetic inference, such as the case of Homo Nesher Ramla, an archaic hominin with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume. We also found that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings applied in 18,400 to 35,200 Physical anthropology studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.

evolutionary biology↗

bioRxiv2021

Systems Biology Analysis of Human Genomes Points to Key Pathways Conferring Spina Bifida Risk

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance and environmental influences that hamper GWAS approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases vs. controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation and essential transcriptional regulation, indicating their impact on the pathogenesis of human SB. Additionally, interrogation of conserved non-coding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to interrogation of coding and non-coding sequence variant contributions to rare complex genetic disorders.

genomics↗

bioRxiv2021

Why most Principal Component Analyses (PCA) in population genetic studies are wrong

Principal Component Analysis (PCA) is a multivariate analysis that allows reduction of the complexity of datasets while preserving data covariance and visualizing the information on colorful scatterplots, ideally with only a minimal loss of information. PCA applications are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics), implemented in well-cited packages like EIGENSOFT and PLINK. PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We employed an intuitive color-based model alongside human population data for eleven common test cases. We demonstrate that PCA results are artifacts of the data and that they can be easily manipulated to generate desired outcomes. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the literature of population genetics and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations. An alternative mixed-admixture population genetic model is discussed.

genetics↗

bioRxiv2020

Diverse genetic origins of medieval steppe nomad conquerors - a response to Mikheyev et al. (2019)

Recently, Mikheyev et al. (2019) have produced a preprint study describing the genomes of nine Khazars archeologically dated from the 7th to the 9th centuries found in the Rostov county in modern-day Russia. Skull morphology indicated a mix of "Caucasoid" and "Mongoloid" shapes. The authors compared the samples to ancient and contemporary samples to study the genetic makeup of the Khazars and their genetic legacy and addressed the question of the relationships between the Khazar and Ashkenazic Jews. A careful examination reveals grave concerns regarding all the aspects of the study from the identification of the "Khazar" samples, the choice of environment for ancient DNA sequencing, and the analyses. The authors did not disclose the data used in their study, and their methodology is incoherent. We demonstrate that their analyses yield nonsensical results and argue that none of the claims made in this study are supported by the data unequivocally. Provided the destruction of the bone samples and the irreproducibility of the analyses, even by the forgivable standards of the field, this study is irreplicable, wasteful, and misleading.

genomics↗

bioRxiv2019

A genomic dating tool for ancient genomes resolves the origins of hundreds of Eurasian genomes

Radiocarbon dating is the gold-standard in archaeology to estimate the age of skeletons, a key to studying their origins. Nearly half of all published ancient human genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we developed the Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Upper Palaeolithic to modern-day samples and applied it to 1559 ancient Europeans, Asians, and Caucasus individuals and to 2117 modern worldwide individuals. We show that TPS predictions for dated skeletons align with their known dates and correctly account for kin relationships. The TPS-dating of poorly dated Eurasian samples resolves conflicts and sheds new light on disputed findings, as illustrated by four test cases. We discuss the phenotypic traits of the Time Informative Markers (TIMs) that underlie TPS. SummaryTPS is a novel method to date humans from the Upper Palaeolithic to modern time from their DNA sequences. Accurate dating is essential to the interepretation of paleogemonic data.. The gold-standard method in archaeology is radiocarbon dating1. However, a major limitation of radiocarbon dating is the high amount of collagen extraction (500 mg) involved in the process2. Consequently, half of all published ancient human genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we present the Temporal Population Structure (TPS), the first genomic dating method for ancient genomes ranging from the Upper Palaeolithic to modern-day samples. We show that TPS predictions for 961 radiocarbon-dated Eurasian skeletons align with their known dates. We replicate these findings on 598 other Europeans, Asians and Caucasus individuals. Using kin-pairs, we demonstrate that TPS has produced more accurate results than radiocarbon and other dating. We show how our findings resolve conflicts and sheds new light on disputed findings as illustrated by four test cases. Finally, we discuss the phenotypic traits of the Time Informative Markers (TIMs) that underlie TPS. TPS is a novel dating technique, which can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses. TPS cannot be used for older (<14,000 years ago) samples, and its accuracy depends on the temporal and geographical breadth of radiocarbon-dated samples in the training dataset, though this limitation can be improved over time. Overall, TPS can improve the accuracy of archeological and paleogenomic studies.

genomics↗

bioRxiv2019

Global Genetic Cartography of Urban Metagenomes andAnti-Microbial Resistance

We have created a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities across 3 years. This is the first systematic, worldwide study cataloging the urban microbial ecosystem. We identify taxonomically-defined microorganisms collected across three years. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics AMR markers, and novel genetic elements, including 10,928 viral, 1302 bacteria, and 2 archaea novel species. We identify 4,424 species of urban microorganisms and a consistent "core" of 31 species found in nearly all samples that is largely distinct from any human commensal microbiome. Profiles of AMR genes show geographic variation in type and density. Together, these results constitute a high-resolution, global metagenomic atlas, which enables the discovery of new genetic components, highlights potential forensic applications, and provides an essential first draft of the global AMR burden of the worlds cities.

microbiology↗

bioRxiv2019

Regulation of neuron-specific gene transcription by stress hormone signalling requires synaptic activity in zebrafish

The Glucocorticoid Receptor (GR) co-ordinates metabolic and behavioural responses to stressors. We hypothesised that GR influences behaviour by modulating specific epigenetic and transcriptional processes in the brain. Using the zebrafish as a model organism, the brain methylomes of wild-type and grs357 mutant adults were analysed and GR-sensitive, differentially methylated regions (GR-DMRs) were identified. Two genes with GR-DMRs exhibited distinct methylation and transcriptional sensitivities to GR: the widely expressed direct GR target fkbp5 and neuron-specific aplp1. In larvae, neural activity is required for GR-mediated transcription of aplp1, but not for that of fkbp5. GR regulates metabotropic glutamate receptor gene expression, the activities of which also modulated aplp1 expression, implicating synaptic neurotransmission as an effector of GR function upstream of aplp1. Our results identify two distinct routes of GR-regulated transcription in the brain, including a pathway through which GR couples endocrine signalling to synaptic activity-regulated transcription by modulating metabotropic glutamate receptor expression.

developmental biology↗

bioRxiv2018

Adversarial childhood events are associated with Sudden Infant Death Syndrome (SIDS): an ecological study

Sudden Infant Death Syndrome (SIDS) is the most common cause of postneonatal infant death. The allostatic load hypothesis posits that SIDS is the result of perinatal cumulative painful, stressful, or traumatic exposures that tax neonatal regulatory systems. To test it, we explored the relationships between SIDS and two common stressors, male neonatal circumcision (MNC) and prematurity, using latitudinal data from 15 countries and over 40 US states during the years 1999-2016. We used linear regression analyses and likelihood ratio tests to calculate the association between SIDS and the stressors. SIDS prevalence was significantly and positively correlated with MNC and prematurity rates. MNC explained 14.2% of the variability of SIDSs male bias in the US, reminiscent of the Jewish myth of Lilith, the killer of infant males. Combined, the stressors increased the likelihood of SIDS. Ecological analyses are useful to generate hypotheses but cannot provide strong evidence of causality. Biological plausibility is provided by a growing body of experimental and clinical evidence linking adversary preterm and early-life events with SIDS. Together with historical evidence, our findings emphasize the necessity of cohort studies that consider these environmental stressors with the aim of improving the identification of at-risk infants and reducing infant mortality.

pathology↗

bioRxiv2018

Ancient ancestry informative markers for identifying fine-scale ancient population structure in Eurasians

The rapid accumulation of ancient human genomes from various areas and time periods potentially allows the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations known to misrepresent the population structure. Past studies addressed these problems using ancestry informative markers (AIMs). However, it is unclear whether AIMs derived from contemporary human genomes can capture ancient population structure and whether AIM finding methods are applicable to ancient DNA (aDNA) provided that the high missingness rates in ancient, oftentimes haploid, DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperforms all competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of population microarrays and the interpretation of aDNA results.

genetics↗

bioRxiv2017

Pair Matcher (PaM): Fast Model-Based Optimisation Of Treatment/Case-Control Matches Using Demographic And Genetic Data

In clinical trials, individuals are matched for demographic criteria, paired, and then randomly assigned to treatment and control groups to determine a drugs efficacy. The successful completion of pilot trials is a prerequisite to larger and more expensive Phase III trials. One of the chief causes for the irreproducibility of results across pilot to Phase III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups. Pair Matcher (PaM) addresses stratification bias by optimising pairing assignments a priori- and\\or posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on racial criteria or Principal Component Analysis (PCA) alone. Homogenising the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the study. PaMs ability to infer the ancestry of the participants further allows identifying subgroup of responders and developing a precision medicine approach to treatment. PaM is simple to execute, fast, and can be used for clinical trials and association studies. PaM is freely available via R scripts and a web interface.

bioinformatics↗