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Liu, Z.

Publications and source records attributed to Liu, Z..

At least 217 records · Page 12Linked to original sources
bioRxiv2025

FAK inhibitor treatment systemically regulates the glioblastoma immune environment via blocking monocyte trafficking

Glioblastoma (GBM) is the most common primary malignant brain tumour in adults with dismal survival rates, and current therapies, including most immunotherapies, are not efficacious due to the highly immunosuppressive microenvironment. Studies in other solid cancers report that impairment of the integrin effector pathway involving focal adhesion kinase (FAK) can promote anti-tumour immune responses. Therefore, we set out to address whether, and if so how, suppressing FAK function may influence GBM by using both tumour cell-specific FAK gene deletion and systemic delivery of a clinically relevant FAK kinase inhibitor (FAKi) VS-4718 in an orthotopic murine stem cell model of GBM. We found that treatment with the FAKi, but not tumour cell-specific FAK gene deletion, resulted in GBM clearance and improved survival. This was dependent on adaptive immunity, and tumour-infiltrating T cells in FAKi-treated tumours displayed increased cytotoxic potential and reduced exhaustion. We also found a significant reduction in immuno-suppressive peripherally-derived macrophages and FAKi treatment caused sequestration of inflammatory monocytes within the bone marrow, resulting in impaired monocyte trafficking to tumours as judged by adoptive transfer. This is due to suppression of key adhesion and migration signalling through 4{beta}1 integrin and CX3CR1 in peripheral monocytes. Our work here describes a previously unidentified role for FAK in trafficking of peripheral suppressive macrophages to GBM tumours, reducing T cell exhaustion and promoting anti-tumour immunity. This highlights a new way in which systemic FAK inhibitors can be used to provide a beneficial immune modulatory strategy for the treatment of GBM.

immunology↗
bioRxiv2025

Full-length structure of CPR-containing self-sufficient cytochrome P450

Cytochrome P450 (P450s) are heme-thiolate monooxygenases that exploit electrons sourced from pyridine nucleotide to reduce the oxygen and heme iron to catalyze hydroxylation of inactivated C-H bond1-4. Self-sufficient P450s that contain the substrate-binding heme-domain and the electron-donating NADPH:cytochrome P450 reductase (CPR) domain in the same polypeptide chain are highly effective and have been engineered to catalyze various challenging reactions. The high efficacy attributes to the effective electron transfer rate, but how the electrons travel among the redox centers remains elusive owing to the lack of structural information of the full-length protein. Here, we report the structure of a homologue of the most extensively studied P450BM3 from Shimazuella soli (SoP450) resolved by single particle cryo-electron microscopy (cryo-EM) and X-ray crystallography. SoP450 primarily exists as a homodimer formed via the intertwined CPR-domains. The spatial alignment of the heme-domain that is linked via an extensive loop was also determined. Notably, a class of structure that lacks one heme-domain was identified from the cryo-EM analyses, indicating that the heme-domain is mobile. We suspected that the heme-domain could move to reach the CPR-domain for electron acquisition and built a model of putative catalytic state to reveal how the electrons are relayed from NADPH to heme. These results are of fundamental importance to understand the catalytic reaction of CPR-containing self-sufficient P450s, which shall provide critical information to the engineering and applications of these enzymes.

synthetic biology↗
bioRxiv2025

IgG-Bridging-Seeded Synergistic Aggregation of SARS-CoV-2 Spikes Underlies Potent Neutralization by A Low-Affinity Antibody

Mechanistic studies of viral neutralization typically prioritize high-affinity antibodies, relegating low-affinity binders to the sidelines. We report P5-1C8, a Class 1 SARS-CoV-2 antibody that exemplifies this underexplored "low-affinity yet high-potency" phenotype, retaining strong neutralization of Omicron JN.1 despite markedly weakened trimer binding (KD = 225 nM; IC50 = 0.06 nM). Structural and biophysical analyses reveal that P5-1C8 engages WT and BA.1 spikes through canonical intra-spike bivalency, but with JN.1 it induces aggregation. Using virion-like nanoparticles displaying multiple spikes, we show that IgG remains bound with no detectable dissociation and triggers pronounced aggregation. Coarse-grained molecular dynamics delineate the stepwise pathway in which weak IgG-spike contacts seed aggregation via transient inter-spike bridging. Together, these findings establish the first mechanistic framework demonstrating how weak-binding antibodies can nonetheless achieve potent neutralization through higher-order aggregation, thereby expanding the conceptual landscape of antibody function and opening new directions for antibody evaluation and design. Graphical AbstractLow-affinity antibodies are frequently disregarded in discovery pipelines. This work reports P5-1C8, a Class 1 SARS-CoV-2 antibody with weak trimer binding (KD-to-IC50 > 3,700-fold) yet potent neutralization of Omicron JN.1. Structural, biophysical, functional and coarse-grained simulations collectively demonstrate that transient inter-spike IgG bridging seeds higher-order aggregation, which in turn drives neutralization and provides a mechanistic framework. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/691454v1_ufig1.gif" ALT="Figure 1000"> View larger version (79K): org.highwire.dtl.DTLVardef@1d4f132org.highwire.dtl.DTLVardef@1274400org.highwire.dtl.DTLVardef@e186a6org.highwire.dtl.DTLVardef@4f1870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗
bioRxiv2025

High-resolution MRI Guided Whole Mouse Brain Cell Type Atlas using Deep Learning

Cell types represent groupings of cells defined by shared anatomical and functional properties. Traditional mouse brain cell atlases rely heavily on single-cell sequencing, which provides valuable molecular detail but lacks whole-brain, isotropic resolution. Diffusion MRI (dMRI) offers a complementary approach for probing cytoarchitecture and myeloarchitecture, with quantitative metrics increasingly used as biomarkers of brain development and neurodegenerative disorders. Although prior work has linked dMRI metrics with gene expression, the capacity of dMRI to directly predict cell types remains unclear. Here, we present a deep learning framework that integrates high-resolution dMRI with three-dimensional light-sheet microscopy (LSM) registered to the Allen Mouse Brain Common Coordinate Framework (CCFv3). We investigated correlations between dMRI and spatial transcriptomics-derived cell types and generated a whole-brain cell type atlas at 10 {micro}m isotropic resolution. Together, these results establish an efficient, high-resolution strategy for brain cell atlas generation and underscore the potential of advanced imaging to illuminate cellular mechanisms of the brain.

neuroscience↗
bioRxiv2025

MARRVEL-MCP enables natural language variant interpretation through autonomous workflow construction

AO_SCPLOWBSTRACTC_SCPLOWRare disease variant interpretation requires navigating multiple genomic databases with strict input formats and synthesizing heterogeneous evidence, creating barriers for non-experts and cognitive burdens even for specialists. MARRVEL exemplifies this challenge by requiring precise queries (e.g., HGVS notation) and returning complex, difficult-to-synthesize outputs. To address this input-output asymmetry, we developed MARRVEL-MCP, a natural language interface enabling large language models (LLMs) to perform end-to-end variant interpretation via structured tool access. This work demonstrates the impact of context engineering--the deliberate design of domain-aware tool environments and information scaffolding--in reshaping the role of model scale in genomics. MARRVEL-MCP equips LLMs with 39 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded pipelines, LLMs autonomously infer workflows, performing named-entity recognition, identifier normalization, and multi-database synthesis from narrative queries. Using 45 expert-curated questions, lightweight models (3B-20B parameters) with MARRVEL-MCP matched or outperformed larger models lacking tool access. A 20B-parameter model (gpt-oss-20b) achieved a 95% pass rate versus 33% without MARRVEL-MCP, approaching state-of-the-art proprietary performance. Although accuracy remains below the 100% required for autonomous diagnosis and token usage is considerable, these results show that well-designed context can compensate for limited model capacity. These findings establish context engineering as a core principle for biomedical AI, enabling efficient, scalable integration of LLMs with curated genomic resources. MARRVEL-MCP is available at https://github.com/hyunhwan-bcm/MARRVEL_MCP/.

bioinformatics↗
bioRxiv2025

Restoration of MAIT cell function in healthcare-associated bacterial infections supports recovery of carbapenem efficacy against resistant bacteria

Antimicrobial resistance (AMR) presents a major clinical challenge to patients with healthcare-associated infections (HAIs), particularly among immunocompromised individuals and patients with comoribidites, who often exhibit an impaired mucosa-associated invariant T (MAIT) cell pool. MAIT cells are innate-like T cells enriched in mucosal tissues, possess potent antibacterial activity, and restoration of their function may offer a host-directed strategy against drug-resistant pathogens. We evaluated how stimulation with cognate antigen in combination with various cytokines, modulates MAIT cell cytotoxicity and enhances carbapenem activity. Under optimal conditions, MAIT cells exhibited increased expression of antimicrobial cytolytic proteins and efficiently killed cells pulsed with MAIT cell antigen. IL-15 or IL-2 plus IL-7 were particularly effective in promoting polyfunctional cytotoxic responses. Secretomes from cytokine-stimulated MAIT cells restored the activity of imipenem against engineered E. coli expressing the clinically relevant carbapenemases blaNDM-1, blaKPC-2, and blaOXA-48, strongly reducing bacterial growth, viability, and metabolic activity. Notably, IL-2 plus IL-7 stimulation enabled expansion and functional restoration of MAIT cells from HAI patients, whose baseline MAIT cell numbers and responses were diminished. These findings demonstrate that tailored stimulation can reinvigorate MAIT cell effector function and augment antibiotic efficacy, supporting a role for MAIT cells in adjunct immunotherapeutic strategy to combat AMR in vulnerable patient populations. Category of manuscript: Research Article.

immunology↗
bioRxiv2025

From Circles to Signals: Representation Learning on Ultra-Long Extrachromosomal Circular DNA

Extrachromosomal circular DNA (eccDNA) is a covalently closed circular DNA molecule that plays an important role in cancer biology. Genomic foundation models have recently emerged as a powerful direction for DNA sequence modeling, enabling the direct prediction of biologically relevant properties from DNA sequences. Although recent genomic foundation models have shown strong performance on general DNA sequence modeling, their application to eccDNA remains limited: existing approaches either rely on computationally expensive attention mechanisms or truncate ultra-long sequences into kilobase fragments, thereby disrupting long-range continuity and ignoring the molecules circular topology. To overcome these problems, we introduce eccDNAMamba, a bidirectional state space model (SSM) built upon the Mamba-2 framework, which scales linearly with input sequence length and enables scalable modeling of ultra-long eccDNA sequences. eccDNAMamba further incorporates a circular augmentation strategy to preserve the intrinsic circular topology of eccDNA. Comprehensive evaluations against state-of-the-art genomic foundation models demonstrate that eccDNAMamba achieves superior performance on ultra-long sequences across multiple task settings, such as cancer versus healthy eccDNA discrimination and eccDNA copy-number level prediction. Moreover, the Integrated Gradient (IG) based model explanation indicates that eccDNAMamba focuses on biologically meaningful regulatory elements and can uncover key sequence patterns in cancer-derived eccDNAs. Overall, these results demonstrate that eccDNAMamba effectively models ultra-long eccDNA sequences by leveraging their unique circular topology and regulatory architecture, bridging a critical gap in sequence analysis. Our codes and datasets are available at https://github.com/zzq1zh/eccDNAMamba.

bioinformatics↗
bioRxiv2025

TXNDC5 Governs Extracellular Matrix Homeostasis in Pulmonary Hypertension

BACKGROUNDPulmonary hypertension (PH) is characterized by vascular remodeling without effective treatments. Thioredoxin domain containing 5 (TXNDC5), a member of the protein disulfide isomerases (PDI) family, regulates protein folding and vascular homeostasis, yet its role in PH remains unknown. METHODSLabel-free proteomics profiled protein expression in lungs from PH patients. TXNDC5 was analyzed by single-cell RNA sequencing, immunofluorescence, and Western blot. Endothelial gain- and loss-of-function approaches were applied in Sugen5416/hypoxia (SuHx)-induced rodent PH models. RNA sequencing and protein-protein interaction analysis were used to investigate underlying mechanisms. RESULTSTXNDC5 was significantly upregulated in the lungs of patients with PH and in experimental PH models, with predominant localization in endothelial cells (ECs) of remodeled distal pulmonary arteries. Endothelial TXNDC5 overexpression exacerbated pulmonary vascular remodeling, elevated right ventricular systolic pressure, and promoted right ventricular hypertrophy, whereas global or endothelial-specific TXNDC5 deficiency conferred protection against SuHx-induced PH. Hypoxia-induced factor (HIF)-2 transcriptionally activated TXNDC5 to drive PH development. Single-cell RNA sequencing identified a distinct subpopulation characterized by TXNDC5high extracellular matrix (ECM)-producing ECs. Bulk RNA sequencing combined with protein-protein interaction analysis revealed that TXNDC5 regulated ECM homeostasis through biglycan (BGN). Pharmacological inhibition of TXNDC5 with E64FC26 and endothelial-targeted TXNDC5 gene therapy significantly attenuated PH severity in rats. CONCLUSIONSOur study reveals that TXNDC5 is a main modulator to regulate ECM homeostasis and may serve as a promising target for the treatment of PH.

molecular biology↗
bioRxiv2025

A stress-dependent TRIM28-ALKBH2 feedback loop modulates chemotherapy response

Chemoresistance to DNA-damaging agents, including platinum and alkylating compounds, limits treatment efficacy in non-small cell lung cancer (NSCLC) and is frequently associated with elevated DNA repair activity. Here, we identify a stress-responsive feedback loop between the E3 ligase TRIM28 and the demethylase ALKBH2. TRIM28 binds ALKBH2 and promotes its K48-linked polyubiquitination and proteasomal degradation, whereas ALKBH2 enhances TRIM28 transcription and protein levels, forming a feedback loop. Notably, alkylation stress induces a biphasic response: acute MMS exposure enhances TRIM28-ALKBH2 association and accelerates ALKBH2 degradation, whereas prolonged exposure promotes TRIM28 degradation, leading to ALKBH2 stabilization and transcriptional upregulation. Clinically, ALKBH2 is frequently elevated in lung adenocarcinoma and is associated with worse survival, whereas TRIM28 exhibits prognostic value specifically in chemotherapy-treated patients. Functionally, MMS-cisplatin co-treatment increased DNA damage and reduced clonogenic survival by counteracting ALKBH2-dependent alkylation tolerance, linking the TRIM28-ALKBH2 loop to chemoresistance in NSCLC.

biochemistry↗
bioRxiv2025

A unified language model bridging de novo and fragment-based 3D molecule design delivers potent CBL-B inhibitors for cancer treatment

The rational design of small molecules is central to drug discovery, yet current artificial intelligence (AI) methodologies for generating three-dimensional (3D) molecules are often siloed, focusing on either de novo design or fragment-based design. The lack of a holistic framework limits AIs application across the complex and multi-step pipeline spanning from novel scaffold identification to lead compound optimization, and prevents AI from effectively learning from the entire process. Here, we introduce UniLingo3DMol, a language model for 3D molecular generation, empowered by fragment permutation-capable molecular representation alongside multi-stage and multi-task training strategy. This integrated design enables UniLingo3DMol to seamlessly span both de novo and fragment-retained molecular design, demonstrating superior performance over existing generation models in in silico evaluations across more than 100 diverse biological targets. We further leveraged UniLingo3DMol in the design of inhibitors targeting CBL-B, a crucial immune E3 ubiquitin ligase and attractive immunotherapy target. This strategy led to a lead compound demonstrating excellent in vitro activity and robust in vivo anti-tumor efficacy. Our findings establish UniLingo3DMol as a generalized and powerful platform, showing the strong potential to advance AI-driven drug discovery.

bioinformatics↗
bioRxiv2025

The Cortical Temporal Axis: MEG-Based Cross-Frequency Gradients with Biological Anchors

Neural oscillations have long been used to characterize the temporal dynamics of individual brain regions, yet a parsimonious, system-level representation that integrates multi-rhythmic activity has been lacking. Using source-localized resting-state magnetoencephalography (MEG) data, we computed regional power spectra and assessed spectral similarity, then applied diffusion map embedding to construct cross-frequency neurophysiological gradients that place whole-brain oscillations within a unified, low-dimensional coordinate system. We found the first three gradients accounted for over 40% of the variance, remained stable across individuals, and aligned with established functional, structural, and geometric cortical axes. Computational modeling showed that these gradients reflect local excitation-inhibition balance, while multimodal analyses revealed strong associations with neurotransmitter receptor distributions, cytoarchitecture, and cell-type-specific gene expression. Lifespan analyses further demonstrated systematic gradient reorganization, with distinct cognitive mappings onto functions such as language, memory, and multisensory integration. Clinically, Parkinsons disease patients displayed disrupted gradients, particularly in regions linked to language and social cognition. Finally, these gradient patterns exhibited high test-retest reliability. These findings establish MEG-derived neurophysiological gradients as robust, low-dimensional representations of cortical organization, offering a biologically grounded framework for studying brain aging and disease.

neuroscience↗
bioRxiv2025

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗
bioRxiv2025

Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex

Understanding how cellular architecture organizes cortical function requires mesoscopic approaches that resolve structure-function coupling in vivo. Here we introduce cerebral blood flow (CBF) and cell-body staining intensity (CSI) similarity index (CCSI), a localized similarity index between the CBF estimates from ultra-high-field 7T arterial spin labeling images and CSI from the BigBrain histology images to serve as a quantitative marker of laminar perfusion-cytoarchitecture coupling. CCSI revealed a reproducible, region- specific alignment between laminar vascular and cellular profiles across the cortex. Going beyond CBF, CCSI selectively tracked mitochondrial respiratory capacity and colocalized with capillary endothelial and mature non-myelination oligodendrocyte populations forming neurovascular interfaces. Transcriptomic enrichment highlighted pathways related to vascular remodeling, oxidative metabolism, and lipid-myelin homeostasis, indicating that CCSI reflects integrated metabolic-structural specialization. At the systems level, CCSI strengthened structure-function gradient correspondence in transmodal cortices, such as the default mode network. Together, these findings establish CCSI as a physiologically grounded, non-invasive marker of perfusion-cytoarchitecture alignment, providing a cross-scale framework linking cortical microstructure, metabolism, and functional organization.

neuroscience↗
bioRxiv2025

End-to-end high-throughput single-cell proteomics via SPRINT and dual-spray LC-MS

Single-cell proteomics (SCP) enables direct measurement of protein heterogeneity but remains constrained by throughput and limited applicability to primary tissues. Here, we present an integrated workflow developed to address both challenges. We engineered SPRINT, an AI-powered bioprinting platform that prepares more than 10,000 single cells per day, over tenfold faster than existing systems, while maintaining stability and enabling identification of over 6,000 proteins from individual HeLa cells. To expand analytical capacity, we further designed a dual-spray tandem direct injection (TDI) LC system that parallelizes non-analytical steps with peptide separation, doubling MS utilization efficiency and enabling 168 label-free SCP runs per day without sensitivity loss. Together with optimized tissue-dissociation protocols, this integrated workflow enabled creation of the first mouse tissue-derived SCP atlas, profiling >1,000 single cells across six organs. These advances establish SCP as a scalable platform ready for broad biological and biomedical applications.

systems biology↗
bioRxiv2025

Functional divergence of Capicua isoforms explains differential tissue vulnerability in neurological disease

Many neurological diseases impact specific brain regions despite widespread expression of the disease-related protein. Spinocerebellar ataxia type 1 (SCA1) primarily affects the cerebellum, though Ataxin-1 (ATXN1) is widely expressed. We previously showed that intensified interaction between mutant ATXN1 and Capicua (CIC) drives SCA1 pathogenesis in the cerebellum, whereas ATXN1 loss augments amyloid beta production in the hippocampus and cortex. CIC, however, forms a complex with ATXN1 and its paralog, Ataxin-1-like (ATXN1L)--yet knockout of either yields completely different phenotypes. To determine whether this could be due to CIC having two isoforms, we generated mice bearing either the long (CIC-L) or short (CIC-S) isoform. Loss of CIC-L led to cognitive deficits, whereas loss of CIC-S caused perinatal lethality, phenocopying, ATXN1 and ATXN1L knockout mice, respectively. Furthermore, CIC-L preferentially interacts with ATXN1, and CIC-S with ATXN1L. Our data underscore the importance of isoform-paralog interplay in studying regional vulnerability in neurodegenerative diseases.

molecular biology↗
bioRxiv2025

A subset of world barley collection was used in the identification of sources of resistance and marker-trait association for resistance for bacterial leaf streak

The bacterial leaf streak (BLS) disease of barley, caused by Xanthomonas translucens pv. translucens (Xtt), has become increasingly important worldwide in recent years. Inefficacy of chemical control methods leaves deployment of host resistance to be the only option to manage this disease. However, current commercial varieties are mainly susceptible to BLS. Therefore, our goal was to identify sources of resistance from diverse barley germplasms and map associated genetic factors. To do so, we evaluated a subset of the World Barley Core Collection (BCC), consisting of 198 accessions, on their reaction to BLS from 2013 to 2016 under natural or artificially inoculated disease pressures. Ten accessions exhibited consistently low disease severities over four years of evaluations. Using genotype data from the T3/Barley database, genome-wide association studies were conducted to identify marker-trait associations (MTAs) in this barley mini-core panel for BLS resistance. Utilizing four mixed-model analyses (MLM, MLMM, FarmCPU, BLINK), five significant MTAs were consistently identified from at least two mixed model analyses including two in chromosome 2H, and one each in chromosomes 5H, and 7H. Associations in chromosomes 2H and 5H appear to be in the same region with loci identified in a previous association study, reinforcing their potential relevance. The identified resistant barley accessions and associated markers will be valuable inbreeding BLS-resistant barley varieties. Core ideas- A subset of world barley collection was used in the identification of sources of resistance and marker-trait association for resistance for bacterial leaf streak. - Ten accessions consistently showed low disease severity across different years, highlighting their value in BLS resistance breeding. - Five MTAs were consistently identified using different models, with two aligning with previously identified resistance QTLs.

plant biology↗
bioRxiv2025

Marker effect p-values for single-step SNP-BLUP genomic models

BackgroundSingle-step Single Nucleotide Polymorphism best linear unbiased prediction (ssSNPBLUP) is a comprehensive method for obtaining genomically enhanced breeding values for animals and SNP effects in a single evaluation. The ssSNPBLUP model integrates phenotypic, pedigree, and genomic data for genomic evaluations. However, there has been no framework for estimating the reliability and p-values of the SNP effects obtained from a ssSNPBLUP genomic model. This study investigates the reliability and significance of the SNP effects estimated using a ssSNPBLUP framework in German Limousin (LIM) and Holstein (HOL) cattle populations. MethodsThis study introduces a novel approach for calculating p-values within the ssSNPBLUP framework and compares it to a conventional single-marker regression GWAS approach. SNP reliabilities were computed using prediction error variances of SNP effect estimates, enabling the identification of statistically significant SNP markers. LIM data included weaning weight (200-DW) evaluated with a maternal effect BLUP model, while HOL data comprised production traits (milk yield, protein yield, fat yield, and somatic cell score) analysed via a random regression test-day model. ResultsThe results reveal significant SNP effects in both LIM and HOL evaluations, with notable differences attributed to the size of the reference populations. Average SNP reliabilities were higher in HOL (Mean SNP reliability: 0.42) compared to LIM (Mean SNP reliability: 0.02), underscoring the critical role of the size of the reference population in determining the accuracy and reliability of SNP effects obtained from genomic evaluations. ConclusionsThe calculation of p-values from the ssSNPBLUP framework offers an efficient approach to identify quantitative trait loci (QTL) that significantly influences traits in populations. Our approach provides a framework that could be implemented in large and complex datasets such as those used in many national routine evaluations, where only a proportion of the animals are genotyped.

genomics↗