Biology subjects

Jia, P.

Publications and source records attributed to Jia, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

bioRxiv2026

MaskTalk: cell-identity-gated spatial lag for target-aware cell-cell communication inference in high-resolution spatial transcriptomics

MotivationHigh-resolution spatial transcriptomics enables single-cell ligand-receptor analysis, but unmasked receptor spatial lags include receptor expression from non-target neighbors, complicating the attribution of local communication signals to specified source-target cell-type pairs. ResultsWe present MaskTalk, a Python package implementing the cell-identity-gated spatial lag model (CIG-SLM). CIG-SLM restricts receptor-side neighborhoods to target cells through W(C) = WD(C). In cell-level breast cancer Visium HD data, CIG-SLM produced target-cell-dependent communication profiles relative to the matched LIANA+ bivariate unmasked baseline, and masked-specific records showed larger between-condition effect sizes and shorter physical source-target distances. Public breast cancer Xenium data demonstrated that MaskTalk runs on external cell-level spatial data and exhibits target-aware masking behavior. Availability and ImplementationImplemented in Python with AnnData; code is available at https://github.com/JiaPP1994/MaskTalk. Software and data archive DOIs are 10.5281/zenodo.21735883 and 10.5281/zenodo.21735951, respectively. Contactshaobin79@aliyun.com; cuihw2001423@163.com Supplementary InformationSupplementary Methods S1-S4, Figures S1-S3, and Tables S1-S5.

bioinformatics↗

bioRxiv2026

MEIsensor: a deep-learning method for mobile element insertion discovery

Mobile element insertions (MEIs) are a critical source of structural variation in the human genome, yet their accurate detection remains challenging, particularly within repetitive regions and for structurally complex insertions. While long-read sequencing enables the direct recovery of inserted sequences, current analytical pipelines rely heavily on repeat-library alignment, which can limit both computational efficiency and accuracy. Here, we present MEIsensor, a deep learning framework designed to detect and classify MEIs directly from long-read sequencing data. Unlike conventional methodologies, MEIsensor performs direct, sequence-based annotation of Alu, LINE1, and SVA insertions. Evaluated against HGSVC benchmarks using HiFi long-read datasets, MEIsensor consistently outperformed existing tools across major MEI classes while substantially improving computational efficiency, demonstrating particularly pronounced gains for structurally complex SVA insertions. Furthermore, manual curation confirmed that MEIsensor successfully identifies MEIs hidden in highly repetitive genomic regions, including centromeric higher-order repeats, that were previously absent from benchmark datasets. Together, these advancements establish MEIsensor as an efficient framework for MEI analysis that promises to significantly advance our understanding of human genomic architecture and evolution.

bioinformatics↗

bioRxiv2025

DOMINO: diffusion-optimised graph learning identifies domain structures with enhanced accuracy and scalability

Spatial transcriptomics enables in situ molecular profiling, allowing to measure the cellular transcriptional output within the tissue. As the tissue architecture is conserved, spatial domains with specific transcriptional patterns can be identified, facilitating the discovery and understanding of functional tissue compartments. Thus, several methods to uncover and identify these spatial domains have been developed. However, most of these existing methods do not scale to rapidly increasing data sizes and focus only on local structure while missing the global view of the tissue. Here, we present DOMINO, a diffusion-optimised contrastive learning framework for spatial domain detection. DOMINO utilises graph diffusion convolution to propagate information beyond immediate neighbours and jointly optimises local and, importantly, global graph structure via contrastive learning. This novel framework yields biologically interpretable domains with clearer boundaries and scales to large datasets, outperforming state-of-the-art methods across healthy and malignant benchmark datasets. We apply DOMINO to a newly generated spatial transcriptomic dataset of endometriosis-associated ovarian cancers, which could not be processed by existing domain detection methods owing to its size. We uncovered conserved proliferative and non-proliferative tumour states that recurred across these tumours and were independently validated in an external clear cell ovarian cancer spatial transcriptomic dataset. Proliferative domains were characterised by elevated expression of EIF4A1 and HSPA8, increased cell cycle activity, reduced mast cell abundance, and coordinated stromal remodelling, including altered fibroblast states and spatial organisation. In parallel, integrative analysis across tumours revealed subtype-specific multicellular ecosystems associated with either endometrioid or clear cell ovarian carcinomas, together with a tumour-excluded stromal domain that could only be resolved through the integration of spatial and transcriptional information. These findings demonstrate how well DOMINO scales up and that it uncovers biologically meaningful spatial programs spanning tumour intrinsic states, tumour microenvironment interactions, and subtype-specific tissue architecture that are not recovered by conventional expression-based clustering approaches.

bioinformatics↗

bioRxiv2025

Melatonin enhances peanut productivity by enriching root-associated nitrogen fixing bacteria

Melatonin, a pleiotropic phytohormone, is widely recognized as a promising bio-stimulant, yet its integrative effects on root development, yield gain, and microbiome assembly in legumes remain underexplored. In this study, we investigated the effects of melatonin seed treatment across three peanut genotypes, focusing on plant productivity, and the composition and structure of bacterial communities in root, rhizosphere, and bulk soil compartments. Melatonin treatment substantially improved root biomass, nodulation, nitrogen balance index, and yield-related traits, with the highest response observed in the genotype Kainong 308. Amplicon sequencing revealed that melatonin induced distinct genotype and compartment specific shifts in bacterial community composition, with the root bacteria showing the increased remodeling, including a 45.9% increase in unique amplicon sequence variants (ASVs). Melatonin selectively enriched key Proteobacteria taxa such as Rhizobium, Sphingomonas, and Enterobacter hormaechei, known for their plant-growth promoting and biocontrol capabilities. Co-occurrence network analysis indicated that melatonin-treated roots harbored more complex bacterial networks, and module #4 dominated by melatonin-induced Proteobacteria was strongly correlated with most of the plant traits. Collectively these findings highlight melatonin dual role as a bio-stimulant and microbiome modulator, promoting a functionally enriched and responsive bacteria that supports enhanced plant performance. This study provides novel insights into the melatonin-mediated coordination of plant performance and bacterial assembly, offering a foundation for microbiome-informed crop improvement strategies.

plant biology↗

bioRxiv2025

SpaFoundation: a visual foundation model for spatial transcriptomics

Spatial transcriptomics (ST) has revolutionized biological research by enabling the joint profiling of gene expression and spatial context, along with histological images. However, current existing ST technologies remain high-cost and time-consuming, hindering their broader clinical applications. Although computational methods have been developed to infer gene expression directly from histology images, these methods still suffer from limited accuracy and spatial resolution due to insufficient training data and model capacity. Here, we introduce SpaFoundation, a large-scale histology foundation model designed to accurately predict spatial gene expression from tissue images. SpaFoundation employs a teacher-student Vision Transformer (ViT) architecture to learn generalizable histological representations by modeling potential dependencies among image patches. Notably, we incorporated self-distillation and masked image modeling (MIM) jointly to capture high-level semantic representations and fine-grained structural features, enriching spots representations. The model is pretrained on 1.79 million patches spanning 26 tissue types, with 80 million parameters. We validated SpaFoundation using 117 samples, demonstrating its flexibility across different spatial resolutions and superior performance in spatial gene expression prediction, as well as strong transferability to downstream tasks such as tumor detection and spatial domain clustering. Our results highlight the potential of large-scale foundation model to learn informative histological representations and underscore the benefits of domain-specific pretraining in extracting task-relevant representations, paving the way for foundation model-driven spatial gene expression inference. The implementation and pre-trained weights of SpaFoundation are publicly available at https://github.com/NingZhangCSUBio/SpaFoundation.

bioinformatics↗

bioRxiv2025

An evolutionary leap in plant reproduction: From archegonia and motile sperm to embryo sacs and pollen tubes

The transition of plants from water to land marks a pivotal step in their evolutionary history, with the change in reproductive mode being the most fundamental. This change freed plant reproduction from aquatic dependence, fostering terrestrial lifes prosperity. Key to the shift from archegonial to embryo sac reproduction are pollen tube emergence and female gametophyte changes, though their evolutionary trajectories and mechanisms remain unclear. Ginkgo, a basal gymnosperm, occupies a pivotal position in this transition, retaining motile sperm, simplified archegonia, and primitive pollen tubes, making it an ideal model. To study how the transition between these two reproductive modes occurred, we collected transcriptome data from the reproductive organs of Ginkgo and evolutionarily representative species. Our findings reveal the MYB98-CRP-ECS module- critical for angiosperm pollen tube guidance--in Ginkgos mature archegonia, with pollen tubes containing guidance components. While egg cells are transcriptionally and functionally conserved across land plants, changes in the cell fate of other female gametophyte component cells drove the shift from archegonia to embryo sacs. Pollen tubes may originate from fern male gametophyte internalization and modification. These findings clarify the evolutionary path and molecular basis of this major reproductive transition.

plant biology↗

bioRxiv2025

SpatialMap: A Scalable Deep Learning Method for Cell Typing in Subcellular Spatial Transcriptomics

Subcellular imaging transcriptomics has offered unprecedented resolution of spatial mapping of gene expressions at subcellular level. However, accurate and robust cell type annotation at cellular level remains a challenge. Here we present SpatialMap, a deep weakly supervised cell typing framework that combines graph neural networks (GNN) with transfer learning to annotate cellular identities in subcellular spatial transcriptomics (ST). SpatialMap consistently outperforms state-of-the-art annotation methods on both simulated and real-world ST datasets. It demonstrates strong scalability across diverse tissue types and platforms, including NanoString CosMx, MERFISH and 10x Genomics Xenium. When applied to a mouse primary motor cortex dataset, SpatialMap successfully identified Micro, L4/5 IT, and L6 IT Car3 absent from the reference data, showcasing its capability to discover novel cell types. Furthermore, SpatialMap accurately annotated malignant cells in human liver cancer samples. Its design also enables seamless extension to cross-omics cell typing and cross-platform annotation within proteomics.

bioinformatics↗

bioRxiv2025

Deciphering complex interactions between LTR retrotransposons and three Papaver species using LTR_Stream

Long terminal repeat retrotransposons (LTR-RTs), a major type of class I transposable elements (TEs), are the most abundant repeat element in plants. The study of the interactions between LTR-RTs and the host genome relies on high-resolution characterization of LTR-RTs. However, for non-model species, this remains a challenge. To address this, we developed LTR_Stream for sub-lineage clustering of LTR-RTs in specific or closely related species, providing higher precision than current database-based lineage-level clustering. Using LTR_Stream, we analyzed Retand LTR-RTs in three Papaver species. Our findings show that high-resolution clustering reveals complex interactions between LTR-RTs and the host genome. For instance, we found autonomous Retand elements could spread among the ancestors of different subgenomes, like retroviruses pandemics, enriching genetic diversity. Additionally, we identified that specific truncated fragments containing transcription factors motifs such as TCP and bZIP may contribute to generation of novel TAD-like boundaries. Notably, our pre- and post-allopolyploidization comparisons show that these effects diminished after allopolyploidization, suggesting that allopolyploidization may be one of the mechanisms by which Papaver species cope with LTR-RTs. We demonstrated the potential application of LTR_Stream and provided a reference case for studying the interactions between LTR-RTs and the host genome in non-model plant species.

bioinformatics↗

bioRxiv2025

A Telomere-to-Telomere Diploid Reference Genome and Centromere Structure of the Chinese Quartet

Recent advances in sequencing technologies have enabled the complete assembly of human genomes from telomere to telomere (T2T), resolving previously inaccessible regions such as centromeres and segmental duplications. Here, we present an updated, higher-quality, haplotype-phased T2T assembly of the Chinese Quartet (T2T-CQ), a family cohort comprising monozygotic twins and their parents, generated using high-coverage ONT ultralong and PacBio HiFi sequencing. The T2T-CQ assembly serves as a crucial reference genome for integrating publicly available multi-omics data and advances the utility of the Quartet reference materials. The T2T-CQ assembly scores highly on multiple metrics of continuity and completeness, with Genome Continuity Inspector (GCI) scores of 77.76 (maternal) and 76.41 (paternal), quality values (QV) > 70, and Clipping Reveals Assembly Quality (CRAQ) scores > 99.6 for both haplotypes, enabling complete annotation of centromeric regions. Within these regions, we identified novel 13-mer higher-order repeat patterns on chromosome 17 which exhibited a monophyletic origin and emerged approximately 230 thousand years ago. Overall, this work establishes an essential genomic resource for the Han Chinese population and advances the development of a T2T pan-Chinese reference genome, which will significantly enable future investigations both into population-specific structural variants and the evolutionary dynamics of centromeres.

genomics↗

bioRxiv2025

PROBind: A Web Server for Prediction, Analysis and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues

Protein-protein and protein-nucleic acids interactions are fundamental to numerous cellular functions, yet only a small fraction have been experimentally characterized. Although modern computational methods have been developed for predicting interacting residues in proteins, they are challenging to use due to individual installation and execution requirements, lack of a standardized input or output format, and absence of support for result analysis. Moreover, methods trained using structures of complexes or intrinsically disordered regions, may not perform well on other types. To overcome these challenges, we develop PROBind, a web server for predicting, analyzing, and interactively visualizing protein, DNA and RNA binding residues from both protein sequences and structures. PROBind integrates 12 predictors trained on structural or disordered proteins, and supports the upload of results from external predictors. By normalizing and averaging predictions from multiple predictors targeting the same ligand type, PROBind generates meta-predictions that balance discrepancies among different methods. Furthermore, it provides interactive graphical tools for result analysis and contextualization. Overall, PROBind accommodates diverse ligand types and supports predictions and analysis based on both structure and sequence data, overcoming the limitations of existing tools. PROBind is freely accessible at https://www.csuligroup.com/PROBind.

bioinformatics↗

bioRxiv2025

Infectome Analysis of Small Mammals in Southern China Reveals Ecological Associations and Emerging Threats from Diverse Pathogens

Small mammals harbor a diverse array of zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viral diversity, with limited attention paid to bacteria and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome of 2,408 individual samples, representing lung, liver, and gut tissues from 858 animals collected throughout Guangdong province, China, with a study design that accounted for host species, tissue, season, and geographic location. We identified 76 pathogen species, including 29 RNA viruses, 12 DNA viruses, 5 bacteria, and 30 eukaryotic pathogens, 33 of which were newly discovered. Tissue distribution analysis revealed distinct organotropisms, suggesting varied transmission routes, while host distribution analysis showed that each animal carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Our characterization of the geographic and seasonal patterns revealed that pathogen richness was primarily influenced by region, host species, and season, while pathogen composition was largely shaped by host genetic distance. Collectively, these data provide the first comprehensive insight into the dynamics of the pathogen infectome in these key mammalian disease reservoirs, highlighting the major factors driving pathogen diversity and transmission.

microbiology↗

bioRxiv2023

MSIsensor-RNA: microsatellite instability detection for bulk and single-cell gene expression data

Microsatellite instability (MSI) is an indispensable biomarker in cancer immunotherapy. Currently, MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI detection. However, the MSI detection method on expression data, especially single-cell expression data, is still lacking, limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell level. Herein, we developed MSIsensor-RNA, an accurate, robust, adaptable, and standalone software to detect MSI status based on expression values of MSI-associated genes. We demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA-seq, microarray, and single-cell RNA-seq. MSIsensor-RNA is a versatile, efficient, and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and applications. MSIsensor-RNA is available at https://github.com/xjtu-omics/msisensor-rna.

bioinformatics↗

bioRxiv2023

Pindel-TD: a tandem duplication detector based on a pattern growth approach

Tandem duplication (TD) is a major type of structural variation (SV), and plays an important role in novel gene formation and human diseases. However, TDs are often missed or incorrectly classified as insertions by most of modern SV detection methods due to the lacking of specialized operation on TD related mutational signals. Herein, we developed a TD detection module of Pindel referred as Pindel-TD based on a TD specific pattern growth approach. Pindel-TD detects TDs with a wide size range at single nucleotide resolution. Using simulation and real read data of HG002, we demonstrate that Pindel-TD outperformed other leading methods in terms of precision, recall, F1-score and robustness. Further applying Pindel-TD on data generated from K562 cancer cell line, we identified a TD located at the seventh exon of SAGE1, explaining its high expression. Pindel-TD is available at https://github.com/xjtu-omics/pindel and free for non-commercial use.

bioinformatics↗

bioRxiv2023

New machine learning method identifies subtle fine-scale genetic stratification in diverse populations

Fine-scale genetic structure impacts genetic risk predictions and furthers the understanding of the demography of populations. Current approaches (e.g., PCA, DAPC, t-SNE, and UMAP) either produce coarse and ambiguous cluster divisions or fail to preserve the correct genetic distance between populations. We proposed a new machine learning algorithm named ALFDA. ALFDA considers both local and global genetic affinity between individuals and also preserves the multimodal structure within populations. ALFDA outperformed the existing approaches in identifying fine-scale genetic structure and in retaining population geogenetic distance, providing a valuable tool for geographic ancestry inference as well as correction for spatial stratification in population health studies.

genomics↗

bioRxiv2023

PSTPIP2 ameliorates aristolochic acid nephropathy by suppressing interleukin-19-mediated neutrophil extracellular trap formation

Aristolochic acid nephropathy (AAN) is a progressive kidney disease caused by herbal medicines. Previously, we found that proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2) and neutrophil extracellular traps (NETs) play important roles in kidney injury and immune defense, respectively; however, the mechanism of AAN regulation by PSTPIP2 and NETs remains unclear. We found that renal tubular epithelial cell (RTEC) apoptosis, neutrophil infiltration, and inflammatory factor and NET production were increased in a mouse model of AAN, while PSTPIP2 expression was low. Conditional knock-in of PSTPIP2 in mouse kidneys inhibited cell apoptosis, reduced neutrophil infiltration, suppressed the production of inflammatory factors and NETs, and ameliorated renal dysfunction. In contrast, restoring normal PSTPIP2 expression promoted kidney injury. In vivo, the use of Ly6G-neutralizing antibody to remove neutrophils and peptidyl arginine deiminase 4 (PAD4) inhibitors to prevent NET formation reduced apoptosis, thereby alleviating kidney injury. In vitro, damaged RTECs released interleukin-19 (IL-19) via the PSTPIP2/nuclear factor (NF)-{kappa}B pathway and induced NET formation via the IL-20R{beta} receptor. Concurrently, NETs promoted the apoptosis of damaged RTECs. PSTPIP2 affected NET formation by regulating IL-19 expression via inhibition of NF-{kappa}B pathway activation in RTECs, inhibiting their apoptosis and reducing kidney damage.

pharmacology and toxicology↗

bioRxiv2022

Ring-finger protein 34 facilitates nervous necrosis virus evading antiviral innate immunity by targeting TBK1 and IRF3 for ubiquitination and degradation

Ubiquitination, as one of the most prevalent posttranslational modifications of proteins, enables a tight control on host immune responses. Many viruses hijack the host ubiquitin system to regulate host antiviral responses for their survival. Here, we found that fish pathogen nervous necrosis virus (NNV) recruited an E3 ubiquitin ligase ring finger protein 34 (RNF34) to inhibit RLRs-mediated interferons (IFN) response via ubiquitinating TBK1 and IRF3. Ectopic expression of RNF34 greatly enhances NNV replication and prevents IFN production, while deficiency of RNF34 led to the opposite effect. Furthermore, RNF34 targets TBK1 and IRF3 via its RING domain. Of note, the interactions between RNF34 and TBK1 or IRF3 were conserved in different fish species. Mechanically, RNF34 promote K27-linked ubiquitination and degradation of TBK1 and IRF3, which in turn diminishing TBK1-induced translocation of IRF3 from cytoplasm to nucleus. Ultimately, NNV capsid protein (CP) was found directly bind with RNF34 and this interaction was conserved in different fishes, and CP induced TBK1 and IRF3 degradation and IFN suppression was depended on RNF34. Our finding demonstrated a novel mechanism by which NNV CP evaded host innate immunity via RNF34, and provided a potential drug target for the control of NNV infection. Author SummaryUbiquitination plays an essential role in the regulation of innate immune responses to pathogens. NNV, a kind of RNA virus, is the causal agent of a highly destructive disease in a variety of marine and freshwater fish. Previous study reported NNV could hijack the ubiquitin system to manipulate the hosts immune responses, however, how NNV utilizes ubiquitination to facilitate its own replication is not well understood. Here, we identified a novel distinct role of E3 ubiquitin ligase RNF34 as an IFN antagonist to promote NNV infection. Nervous necrosis virus capsid protein utilized RNF34 to target TBK1 and IRF3 for K27 and K48-linked ubiquitination degradation. Importantly, the interactions between RNF34 and CP, TBK1 or IRF3 are conserved in different fishes, suggesting it is a general immune evasion strategy exploited by NNV to target the IFN response via RNF34.

immunology↗

bioRxiv2022

Searching across-cohort relatives via encrypted genotype regression

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author SummaryEstimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated.

genetics↗