Biology subjects

Hussin, J.

Publications and source records attributed to Hussin, J..

18 recordsLinked to original sources

bioRxiv2026

Epistatic evolution drives HLA-dependent CD8+ T Cell escape risk in diverse populations

Understanding how viral evolution shapes HLA-dependent T cell escape is crucial to identify individuals at risk of reduced cellular immunity to emerging variants. Nevertheless, we lack frameworks to model HLA diversity and the evolutionary feasibility of T cell-evading mutations. Here, we construct an HLA map capturing variation in epitope specificity across HLA-typed cohorts. Enhancing this framework with SARS-CoV-2 CD8 T cell escape reveals heterogeneous escape across HLA-defined groups, with clusters enriched for HLA-B*07:02, HLA-A*03:01 and HLA-A*02:01 showing higher epitope loss. To assess the evolutionary plausibility of escape, we model viral sequence fitness using an epistasis-aware protein language approach trained on coronaviruses to systematically score mutations across viral lineages. We find that the fitness effect of mutations dynamically changes with evolving sequence context, and that T cell-evading mutations become fitter with additional escape mutations. This study links host HLA diversity to viral fitness landscapes for surveillance and vaccine design.

genomics↗

bioRxiv2026

CETP alternative splicing variation impacts human traits

The cholesteryl ester transfer protein (CETP) is an important protein in reverse cholesterol transport and has been identified as a significant factor associated with cardiovascular disease (CVD), making it a widely studied pharmaceutical target. Three protein-coding isoforms of CETP exist, distinguished by the alternative splicing of one exon each. The isoform primarily responsible for cholesterol-related functions in the plasma is well studied, but specific functions of each isoform remain poorly understood. In this study, we demonstrate the significance of considering CETPs isoforms in analyses of human traits. Using bulk RNA-seq data from multiple tissues, we characterized the expression patterns and genetic regulation determinants of CETP transcripts. Leveraging publicly available GWAS summary statistics, we conducted multivariable Mendelian Randomisation (MVMR) to estimate the impact of variation in isoform proportions on phenotypes, highlighting the importance of CETPs isoforms in pituitary and thyroid glands. Furthermore, we uncovered tissue-specific associations between CETPs isoforms and CVD-associated phenotypes. Additionally, we observed that the epistatic interaction previously reported between CETP and ADCY9, a gene implicated in modulating a CETP modulators response, may be mediated through the regulation of alternative splicing of exon 9. Our results underscore the importance of a comprehensive understanding of CETPs isoforms, which can significantly impact both fundamental and clinical research efforts.

genetics↗

bioRxiv2025

Chromatin landscape and enhancer-gene interactiondifferences between three cardiac cell types

Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNP) associated with a specific traits and diseases, however, uncovering the true disease-relevant SNPs remains challenging. One limitation for prioritizing true disease-relevant SNPs from GWAS is that most of the identified SNPs are non-coding, making it difficult to unravel their mechanism of action. Nevertheless, mapping non-coding SNPs to enhancers is a validated approach to link SNPs to their target genes through the analysis of enhancer-gene interactions (EGI) and thus provide insight into their mechanism of action. While previous studies linking cardiac disease-relevant SNPs to enhancers and their target genes have focused on the principal cardiac cell type, cardiomyocytes (CMs), the analysis of other non-CM cell types has been largely ignored and has only gained attention recently. We hypothesize that characterizing cell-type-specific enhancer-gene interactions (EGIs) for these non-CMs, namely cardiac fibroblasts (CFs), endothelial cells (ECs), and smooth muscle cells (SMCs), followed by mapping cardiac-disease-associated non-coding SNPs to those enhancers will identify novel disease-relevant genes and provide insights for future mechanistic research. To identify the landscape of cell-type-specific EGIs in these cardiac cells, we have employed the activity-by-Contact (ABC) model. It integrates assay for transposase-accessible chromatin sequencing (ATAC-seq), H3K27ac chromatin immunoprecipitation with sequencing (ChIP-seq), and high-throughput chromosome conformation capture with H3K27ac immunoprecipitation (H3K27ac HiChIP) data to identify EGIs. We have identified the landscape of cell-type-specific EGIs in these cardiac cells. Furthermore, a higher similarity of the chromatin accessibility profile (ATAC-seq) between CF and SMC, compared to CF and EC, and SMC and EC was observed. Finally, overlapping identified EGIs with cardiac-disease-associated non-coding variants has allowed the identification of a QT-interval-associated SNP that is mapped to the enhancer region of an EC-specific EGI.

genomics↗

bioRxiv2025

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

1Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic studies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep learning architecture proven effective in handling high-dimensional data, to learn population ancestry from single nucleotide polymorphisms (SNPs) data using the populational Thousand Genomes Project dataset. Our results highlight the models ability to generalize to diverse populations in the CARTaGENE and Montreal Heart Institute biobanks and that predictions remain robust to high levels of missing SNPs. We show that, despite the lack of North African populations in the training dataset, the model learns latent representations that reflect meaningful population structure for North African individuals in the biobanks. To improve model transparency, we apply Saliency Maps, DeepLift, GradientShap and Integrated Gradients attribution techniques and evaluate their performance in identifying SNPs leveraged by the model. Using DeepLift, we show that models predictions are driven by population-specific signals consistent with those identified by traditional population genetics metrics. This work presents a generalizable and interpretable deep learning framework for genetic ancestry inference in large-scale biobanks with genetic data. By enabling more widespread genomic ancestry characterization in these cohorts, this study contributes practical tools for integrating genetic data into downstream biomedical applications, supporting more inclusive and equitable healthcare solutions.

bioinformatics↗

bioRxiv2025

LIPTER, a cardiomyocyte-enriched long noncoding RNA, controls cardiac cytoskeletal maturation and is regulated by a cardiomyocyte-specific enhancer.

Cardiac development is characterized by a complex series of molecular, cytoskeletal and electrophysiological changes that guarantee the proper functioning of adult cardiomyocytes (CMs). These changes are defined by cell-type-specific transcriptional rewiring of progenitor cells to form CMs, and are regulated by various epigenetic elements, such as long noncoding RNAs (lncRNAs). LncRNAs are versatile epigenetic regulators as they may act in cis or in trans to orchestrate important gene programs during cardiac development and may concurrently encode micropeptides. LIPTER is one such lncRNA, previously shown to regulate lipid droplet transport in cardiomyocytes and thus an important regulator of cardiomyocyte metabolism. Here we show that LIPTER also plays a role in the cytoskeletal maturation of CMs, as loss of LIPTER leads to persistent expression of fetal genes, changes in chromatin accessibility, disorganized sarcomeres and impaired calcium homeostasis in CMs. Furthermore, we have identified a cardiomyocyte-specific regulatory enhancer that regulates the expression of LIPTER in CMs. CRISPR-mediated inhibition of this enhancer led to reduced LIPTER expression in CMs and increased expression of fetal genes. This CM-specific enhancer could therefore be manipulated to control the expression of LIPTER for therapeutic benefit. In summary, we have unravelled a novel role of LIPTER in CMs cytoskeletal maturation and have identified a CM-specific enhancer for LIPTER.

genomics↗

bioRxiv2024

Insights into Heart Failure Metabolite Markersthrough Explainable Machine Learning

Understanding molecular traits through metabolomics offers an avenue to tailor cardiovascular prevention, diagnosis and treatment strategies more effectively. This study focuses on the application of machine learning (ML) and explainable artificial intelligence (XAI) algorithms to detect discriminant molecular signatures in heart failure (HF). In this study, we aim to uncover metabolites with significant predictive value by analyzing targeted metabolomics data through ML models and XAI methodologies. After robust quality control procedures, we analyzed 55 metabolites from 124 plasma samples, including 53 HF patients and 71 controls, comparing Logistic Regression (Logit) models with Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGB), all achieving high accuracy in predicting group labels: 84.20% ({sigma} =5.46), 85.73% ({sigma} =6.25), and 84.80% ({sigma} =7.84), respectively. Permutation-based variable importance and Local Interpretable Model-agnostic Explanations (LIME) were used for group-level and individual-level explainability, respectively, complemented by H-Friedman statistics for variable interactions, yielding reliable, explainable insights of the ML models. Metabolites well-known for their association with heart failure, such as glucose and cholesterol, but also more recently described association such C18:1 carnitine, were reaffirmed in our analysis. The novel discovery of lignoceric acid (C24:0 fatty acid) as a critical discriminator, was confirmed in a replication cohort, underscoring its potential as a metabolite marker. Furthermore, our study highlights the utility of 2-way variable interaction analysis in unveiling a network of metabolite interactions essential for accurate disease prediction. The results demonstrate our approachs efficacy in identifying key metabolites and their interactions, illustrating the power of ML and XAI in advancing personalized healthcare solutions. See Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/616718v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@d50da1org.highwire.dtl.DTLVardef@171d4d7org.highwire.dtl.DTLVardef@4c8aaaorg.highwire.dtl.DTLVardef@123c1ed_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-Scale Sequencing Data

Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral lineages, which are crucial for predicting and mitigating future viral threats. Despite the potential of next-generation sequencing (NGS) to capture these iSNVs, the process is fraught with challenges, particularly the risk of capturing sequencing artifacts that may result in false iSNVs. To tackle this issue, we developed a workflow designed to enhance the reliability of iSNV detection in large heterogeneous collections of NGS libraries. We use over 130,000 publicly available SARS-CoV-2 NGS libraries to show how our comprehensive workflow effectively distinguishes emerging viral mutations from sequencing errors. This approach incorporates rigorous bioinformatics protocols, stringent quality control metrics, and innovative usage of dimensionality reduction methods to generate representations of this high-dimensional dataset. We identified and mitigated batch effects linked to specific sequencing centers around the world and introduced quality control metrics that consider strand coverage imbalance, enhancing iSNV reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how related groups of data points are within a two-dimensional space, enhancing our ability to explain clustering patterns based on their shared genetic characteristics. Our workflow sheds light on the complexities of viral genomic analysis with state-of-the-art sequencing technologies and advances the detection of accurate intra-host mutations, opening the door for an enhanced understanding of viral adaptation mechanisms.

bioinformatics↗

bioRxiv2024

Temperature-dependent Spike-ACE2 interaction of Omicron subvariants is associated with viral transmission

The continued evolution of SARS-CoV-2 requires persistent monitoring of its subvariants. Omicron subvariants are responsible for the vast majority of SARS-CoV-2 infections worldwide, with XBB and BA.2.86 sublineages representing more than 90% of circulating strains as of January 2024. In this study, we characterized the functional properties of Spike glycoproteins from BA.2.75, CH.1.1, DV.7.1, BA.4/5, BQ.1.1, XBB, XBB.1, XBB.1.16, XBB.1.5, FD.1.1, EG.5.1, HK.3 BA.2.86 and JN.1. We tested their capacity to evade plasma-mediated recognition and neutralization, ACE2 binding, their susceptibility to cold inactivation, Spike processing, as well as the impact of temperature on Spike-ACE2 interaction. We found that compared to the early wild-type (D614G) strain, most Omicron subvariants Spike glycoproteins evolved to escape recognition and neutralization by plasma from individuals who received a fifth dose of bivalent (BA.1 or BA.4/5) mRNA vaccine and improve ACE2 binding, particularly at low temperatures. Moreover, BA.2.86 had the best affinity for ACE2 at all temperatures tested. We found that Omicron subvariants Spike processing is associated with their susceptibility to cold inactivation. Intriguingly, we found that Spike-ACE2 binding at low temperature was significantly associated with growth rates of Omicron subvariants in humans. Overall, we report that Spikes from newly emerged Omicron subvariants are relatively more stable and resistant to plasma-mediated neutralization, present improved affinity for ACE2 which is associated, particularly at low temperatures, with their growth rates.

microbiology↗

bioRxiv2023

Towards Computing Attributions for DimensionalityReduction Techniques

We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification - using the gradients of target outputs with respect to the inputs - on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets.

bioinformatics↗

bioRxiv2023

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD [≤]1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

systems biology↗

bioRxiv2022

Intra-host viral populations of SARS-CoV-2 in immunosuppressed patients with hematologic cancers

Throughout the SARS-CoV-2 pandemic, several variants of concern (VOC) have been identified, many of which share recurrent mutations in the spike proteins receptor binding domain (RBD). This region coincides with known epitopes and can therefore have an impact on immune escape. Protracted infections in immunosuppressed patients have been hypothesized to lead to an enrichment of such mutations and therefore drive evolution towards VOCs. Here, we show that immunosuppressed patients with hematologic cancers develop distinct populations with immune escape mutations throughout the course of their infection. Notably, by investigating the co-occurrence of substitutions on individual sequencing reads in the RBD, we found quasispecies harboring mutations that confer resistance to known monoclonal antibodies (mAbs) such as S:E484K and S:E484A. Furthermore, we provide the first evidence for a viral reservoir based on intra-host phylogenetics. Our results on viral reservoirs can shed light on protracted infections interspersed with periods where the virus is undetectable as well as an alternative explanation for some long-COVID cases. Our findings also highlight that protracted infections should be treated with combination therapies rather than by a single mAbs to clear pre-existing resistant mutations.

genomics↗

bioRxiv2022

Selection for immune evasion in SARS-CoV-2 revealed by high-resolution epitope mapping combined with genome sequence analysis

A deeper understanding of the molecular determinants that drive humoral responses to coronaviruses, and in particular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is critical for improving and developing diagnostics, therapies and vaccines. Moreover, viral mutations can change key antigens in a manner that alters the ability of the immune system to detect and clear infections. In this study, we exploit a deep serological profiling strategy coupled with an integrated, computational framework for the analysis of SARS-CoV-2 humoral immune responses of asymptomatic or recovered COVID-19-positive patients relative to COVID-19-negative patients. We made use of a novel high-density peptide array (HDPA) spanning the entire proteomes of SARS-CoV-2 and endemic human coronaviruses to rapidly identify B cell epitopes recognized by distinct antibody isotypes in patients blood sera. Using our integrated computational pipeline, we then evaluated the fine immunological properties of detected SARS-CoV-2 epitopes and relate them to their evolutionary and structural properties. While some epitopes are common across all CoVs, others are private to specific hCoVs. We also highlight the existence of hotspots of pre-existing immunity and identify a subset of cross-reactive epitopes that contributes to increasing the overall humoral immune response to SARS-CoV-2. Using a public dataset of over 38,000 viral genomes from the early phase of the pandemic, capturing both inter- and within-host genetic viral diversity, we determined the evolutionary profile of epitopes and the differences across proteins, waves and SARS-CoV-2 variants, which have important implications for genomic surveillance and vaccine design. Lastly, we show that mutations in Spike and Nucleocapsid epitopes are under stronger selection between than within patients, suggesting that most of the selective pressure for immune evasion occurs upon transmission between hosts.

genomics↗

bioRxiv2021

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajimas D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

bioinformatics↗

bioRxiv2021

ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clades information, which means it could be used for predictive tasks using virus sequence analysis.

bioinformatics↗

bioRxiv2021

The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner

The rapid, global dispersion of SARS-CoV-2 since its initial identification in December 2019 has led to the emergence of a diverse range of variants. The initial concerns regarding the virus were quickly compounded with concerns relating to the impact of its mutated forms on viral infectivity, pathogenicity and immunogenicity. To address the latter, we seek to understand how the mutational landscape of SARS-CoV-2 has shaped HLA-restricted T cell immunity at the population level during the first year of the pandemic, before mass vaccination. We analyzed a total of 330,246 high quality SARS-CoV-2 genome assemblies sampled across 143 countries and all major continents. Strikingly, we found that specific mutational patterns in SARS-CoV-2 diversify T cell epitopes in an HLA supertype-dependent manner. In fact, we observed that proline residues are preferentially removed from the proteome of prevalent mutants, leading to a predicted global loss of SARS-CoV-2 T cell epitopes in individuals expressing HLA-B alleles of the B7 supertype family. In addition, we show that this predicted global loss of epitopes is largely driven by a dominant C-to-U mutation type at the RNA level. These results indicate that B7 supertype-associated epitopes, including the most immunodominant ones, were more likely to escape CD8+ T cell immunosurveillance during the first year of the pandemic. Together, our study lays the foundation to help understand how SARS-CoV-2 mutants shape the repertoire of T cell targets and T cell immunity across human populations. The proposed theoretical framework has implications in viral evolution, disease severity, vaccine resistance and herd immunity.

immunology↗

bioRxiv2020

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

1The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

bioinformatics↗

bioRxiv2020

Fine-scale population structure confounds genetic risk scores in the ascertainment population

Polygenic risk scores, or PRS, are a tool to estimate individuals liabilities to a disease or trait measurement based solely on genetic information. One commonly discussed potential use is in the clinic to identify people who are at greater risk of developing a disease. In this paper, we compare three PRS models that incorporate a large number of genetic markers for coronary artery disease (CAD). In the UK Biobank, the cohort which was used at some point in the creation or validation of each score, we calculated the association between CAD, the scores, and population structure for the white British subset. After adjusting for geographic and socioeconomic factors, CAD was not associated with the first principal components of genetic diversity, which reflect fine-scale population structure. In contrast, all three scores were confounded by these genetic components, highlighting that PRS may be influenced by genetic factors not directly causal for CAD, thereby raising concerns about their biases in clinical application.Furthermore, we investigated the differences in risk stratification using four different UK Biobank assessment centers as separate cohorts, and tested how missing genetic data affected risk stratification through simulation. We show that missing data impact classification for extreme individuals for high- and low-risk, and quantiles of risk are sensitive to individual-level genotype missingness. Distributions of scores varied between assessment centers, revealing that thresholding based on quantiles can be problematic for consistency across centers and populations. Based on these results, we discuss potential avenues of improvements of PRS methodologies for usage in clinical practice.

genetics↗

bioRxiv2017

Insights into platypus population structure and history from whole-genome sequencing

The platypus is an egg-laying mammal which, alongside the echidna, occupies a unique place in the mammalian phylogenetic tree. Despite widespread interest in its unusual biology, little is known about its population structure or recent evolutionary history. To provide new insights into the dispersal and demographic history of this iconic species, we sequenced the genomes of 57 platypuses from across the whole species range in eastern mainland Australia and Tasmania. Using a highly-improved reference genome, we called over 6.7M SNPs, providing an informative genetic data set for population analyses. Our results show very strong population structure in the platypus, with our sampling locations corresponding to discrete groupings between which there is no evidence for recent gene flow. Genome-wide data allowed us to establish that 28 of the 57 sampled individuals had at least a third-degree relative amongst other samples from the same river, often taken at different times. Taking advantage of a sampled family quartet, we estimated the de novo mutation rate in the platypus at 7.0x10-9/bp/generation (95% CI 4.1x10-9 - 1.2x10-8/bp/generation). We estimated effective population sizes of ancestral populations and haplotype sharing between current groupings, and found evidence for bottlenecks and long-term population decline in multiple regions, and early divergence between populations in different regions. This study demonstrates the power of whole-genome sequencing for studying natural populations of an evolutionarily important species.

genomics↗