A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.

The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.

Comparative transcriptome analysis of T lymphocyte subpopulations and identification of critical regulators defining porcine thymocyte identity.

Introduction: The development and migration of T cells in the thymus and peripheral tissues are crucial for maintaining adaptive immunity in mammals. However, the regulatory mechanisms underlying T cell development and thymocyte identity formation in pigs remain largely underexplored. Method: Here, by integrating bulk and single-cell RNA-sequencing data, we investigated regulatory signatures of porcine thymus and lymph node T cells. Results: The comparison of T cell subpopulations derived from porcine thymus and lymph nodes revealed that their transcriptomic differences were influenced more by tissue origin than by T cell phenotypes, and that lymph node cells exhibited greater transcriptional diversity than thymocytes. Through weighted gene co-expression network analysis (WGCNA), we identified the key modules and candidate hub genes regulating the heterogeneity of T cell subpopulations. Further, we integrated the porcine thymocyte dataset with peripheral blood mononuclear cell (PBMC) dataset to systematically compare transcriptomic differences between T cell types from different tissues. Based on single-cell datasets, we further identified the key transcription factors (TFs) responsible for maintaining porcine thymocyte identity and unveiled that these TFs coordinately regulated the entire T cell development process. Finally, we performed GWAS of cell type-specific differentially expressed genes (DEGs) and 30 complex traits, and found that the DEGs in thymus-related and peripheral blood-related cell types, especially CD4_SP cluster and CD8-related cluster, were significantly associated with pig productive and reproductive traits. Discussion: Our findings provide an insight into T cell development and lay a foundation for further exploring the porcine immune system and genetic mechanisms underlying complex traits in pigs.

Hi-Tag: a simple and efficient method for identifying protein-mediated long-range chromatin interactions with low cell numbers.

Protein-mediated chromatin interactions can be revealed by coupling proximity-based ligation with chromatin immunoprecipitation. However, these techniques require complex experimental procedures and millions of cells per experiment, which limits their widespread application in life science research. Here, we develop a novel method, Hi-Tag, that identifies high-resolution, long-range chromatin interactions through transposase tagmentation and chromatin proximity ligation (with a phosphorothioate-modified linker). Hi-Tag can be implemented using as few as 100,000 cells, involving simple experimental procedures that can be completed within 1.5 days. Meanwhile, Hi-Tag is capable of using its own data to identify the binding sites of specific proteins, based on which, it can acquire accurate interaction information. Our results suggest that Hi-Tag has great potential for advancing chromatin interaction studies, particularly in the context of limited cell availability.

H3K27ac modification and transcription characteristics of adipose and muscle tissues in Chuxiang Black pig.

The establishment of high-quality pork breeds for improving meat quality in the pig industry is needed. The Chuxiang Black (CX) pig is a new breed developed from Chinese local pigs and Western lean pigs that has a high proportion of lean meat and excellent meat quality. However, the characteristics of cis-regulatory elements in CX pigs are still unknown. In this study, cis-regulatory elements of muscle and adipose tissues in CX pigs were investigated using ChIP-seq and RNA sequencing. Compared with the reported cis-regulatory elements of muscle and adipose tissues, 1768 and 1012 highly activated enhancers and 433 and 275 highly activated promoters in CX muscle and adipose tissues were identified, respectively. Motif analysis showed that transcription factors, such as MEF2A and MEF2C, were core regulators of highly activated enhancers and promoters in muscle. Similarly, the transcription factors JUNB and CUX1 were identified as essential for highly activated enhancers and promoters in CX adipose tissue. These results enrich the resources for the analysis of cis-regulatory elements in the pig genome and provide new basic data for further meat quality improvement through breeding in pigs.

AGIDB: a versatile database for genotype imputation and variant decoding across species.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

Characterizing structural variants based on graph-genotyping provides insights into pig domestication and local adaption.

Structural variants (SVs), such as deletions (DELs) and insertions (INSs), contribute substantially to pig genetic diversity and phenotypic variation. Using a library of SVs discovered from long-read primary assemblies and short-read sequenced genomes, we map pig genomic SVs with a graph-based method for re-genotyping SVs in 402 genomes. Our results demonstrate that those SVs harboring specific trait-associated genes may greatly shape pig domestication and local adaptation. Further characterization of SVs reveals that some population-stratified SVs may alter the transcription of genes by affecting regulatory elements. We identify that the genotypes of two DELs (296-bp DEL, chr7: 52,172,101-52,172,397; 278-bp DEL, chr18: 23,840,143-23,840,421) located in muscle-specific enhancers are associated with the expression of target genes related to meat quality (FSD2) and muscle fiber hypertrophy (LMOD2 and WASL) in pigs. Our results highlight the role of SVs in domestic porcine evolution, and the identified candidate functional genes and SVs are valuable resources for future genomic research and breeding programs in pigs.

Advanced Soft Porous Organic Crystal with Multiple Gas-Induced Single-Crystal-to-Single-Crystal Transformations for Highly Selective Separation of Propylene and Propane.

Soft porous organic crystals with stimuli-responsive single-crystal-to-single-crystal (SCSC) transformations are important tools for unraveling their structural transformations at the molecular level, which is of crucial importance for the rapid development of stimuli-responsive systems. Carefully balancing the crystallinity and flexibility of materials is the prerequisite to construct advanced organic crystals with SCSC, which remains challenging. Herein, a squaraine-based soft porous organic crystal (SPOC-SQ) with multiple gas-induced SCSC transformations and temperature-regulated gate-opening adsorption of various C1-C3 hydrocarbons is reported. SPOC-SQ is featured with both crystallinity and flexibility, which enable pertaining the single crystallinity of the purely organic framework during accommodating gas molecules and directly unveiling gas-framework interplays by SCXRD technique. Thanks to the excellent softness of SPOC-SQ crystals, multiple metastable single crystals are obtained after gas removals, which demonstrates a molecular-scale shape-memory effect. Benefiting from the single crystallinity, the molecule-level structural evolutions of the SPOC-SQ crystal framework during gas departure are uncovered. With the unique temperature-dependent gate-opening structural transformations, SPOC-SQ exhibits distinctly different absorption behaviors towards C3 H6 and C3 H8 , and highly efficient and selective separation of C3 H6 /C3 H8 (v/v, 50/50) is achieved at 273 K. Such advanced soft porous organic crystals are of both theoretical values and practical implications.

Cognitive status and its risk factors in patients with hypertension and diabetes in a low-income rural area of China: A cross-sectional study.

OBJECTIVES: The proportion of older people with dementia in China is gradually increasing with the increase in the aging population over recent years. Hypertension and diabetes are common non-communicable diseases among rural populations in China. However, it remains unclear whether these conditions affect the occurrence and development of cognitive impairment as there is limited research on cognitive status and its risk factors among residents of rural areas. METHODS: A multi-stage stratified cluster random sampling method was used to select 5400 participants from rural permanent residents. A self-designed structured questionnaire was used to investigate demographic data of the participants. Cognitive function was assessed using the Montreal Cognitive Function Assessment Scale (MoCA). The results were analyzed using chi-square test, ANOVA and multiple linear regression analysis. RESULTS: A total of 5028 participants returned the survey, giving a response rate of 93.1%. Higher education (odds ratio (OR) = 3.2, 95% confidence interval (CI) 2.87-3.54, p < 0.001), higher income (OR = 1.61, 95% CI 1.16-2.07, p < 0.001), and dietary control (OR = 0.66, 95%CI 0.34-0.98, p < 0.001) were protective factors. A visual representation of the relationship between annual income and MoCA score showed an inverted U-curve, the group with an annual income of 6000-7999 RMB had a maximum OR of 1.93 (95%CI 0.12-2.74, p < 0.001). While difficulty in maintaining sleep were risk factors for cognitive impairment (OR = -2.28, 95% CI-4.18-0.39, p = 0.018). CONCLUSIONS: Participants with middle incomes had better cognitive status than those with the highest incomes. Higher education, proper diet control and good sleep are beneficial to the cognitive status of residents in rural areas.

Sensitive and Specific Exonuclease III-Assisted Recombinase-Aided Amplification Colorimetric Assay for Rapid Detection of Nucleic Acids.

The development of a contamination-free and on-site nucleic acid detection platform with high sensitivity and specificity but low-cost for the detection of pathogenic nucleic acids is critical for infectious disease diagnosis and surveillance. In this study, we combined the recombinase-aided amplification (RAA) with the exonuclease III (Exo III)-assisted signal amplification into a platform for sensitive and specific detection of nucleic acids of African swine fever virus (ASFV). We found that this platform enabled a naked eye visual detection of ASFV at a detection limit as low as 2 copies/µL in 30 min. As expected, no cross-reactivity was observed with other porcine viruses. In addition, to avoid aerosol contamination, a one-tube RAA-Exo III colorimetric assay was also established for the accurate detection of ASFV in clinical samples. Taken together, we developed a rapid, instrument-free, and low-cost Exo III-assisted RAA colorimetric-assay-based nucleic acid detection platform.

E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives.

BACKGROUND: Genome-wide association studies (GWAS) are an effective way to explore genotype-phenotype associations in humans, animals, and plants. Various GWAS methods have been developed based on different genetic or statistical assumptions. However, no single method is optimal for all traits and, for many traits, the putative single nucleotide polymorphisms (SNPs) that are detected by the different methods do not entirely overlap due to the diversity of the genetic architecture of complex traits. Therefore, multi-tool-based GWAS strategies that combine different methods have been increasingly employed. To take this one step further, we propose an ensemble-like GWAS strategy (E-GWAS) that statistically integrates GWAS results from different single GWAS methods. RESULTS: E-GWAS was compared with various single GWAS methods using simulated phenotype traits with different genetic architectures. E-GWAS performed stably across traits with different genetic architectures and effectively controlled the number of false positive genetic variants detected without decreasing the number of true positive variants. In addition, its performance could be further improved by using a bin-merged strategy and the addition of more distinct single GWAS methods. Our results show that the numbers of true and false positive SNPs detected by the E-GWAS strategy slightly increased and decreased, respectively, with increasing bin size and when the number and the diversity of individual GWAS methods that were integrated in E-GWAS increased, the latter being more effective than the bin-merged strategy. The E-GWAS strategy was also applied to a real dataset to study backfat thickness in a pig population, and 10 candidate genes related to this trait and expressed in adipose-associated tissues were identified. CONCLUSIONS: Using both simulated and real datasets, we show that E-GWAS is a reliable and robust strategy that effectively integrates the GWAS results of different methods and reduces the number of false positive SNPs without decreasing that of true positive SNPs.

Genome-scale CRISPR screen identifies TRIM2 and SLC35A1 associated with porcine epidemic diarrhoea virus infection.

Porcine epidemic diarrhoea (PED) caused by the porcine epidemic diarrhoea virus (PEDV) is the most devastating disease in the global pig industry due to its high mortality rate in piglets. The host factors critical for PEDV replication are poorly understood. Here, we designed a pooled African green monkey genome-scale CRISPR/Cas9 knockout (VeroCKO) library containing 75,608 single guide RNAs targeting 18,993 protein-coding genes. Subsequently, we use the VeroCKO library to identify key host factors facilitating PEDV infection in Vero E6 cells. Several previously unreported genes associated with PEDV infection are highly enriched post-PEDV selection. We discovered that knocking out the tripartite motif 2 (TRIM2) and the solute carrier family 35 member A1 (SLC35A1) inhibited PEDV replication. Virtual screening and molecular docking approaches showed that chem-80,048,685 (M2) s ignificantly inhibited PEDV attachment and late replication by impeding SLC35A1. Furthermore, we found that knocking out SLC35A1 in Vero E6 cells upregulated a disintegrin and metalloprotease protein-17 (ADAM17) by splicing porcine aminopeptidase N (pAPN) and angiotensin-converting enzyme 2 (ACE2) ectodomains to reduce PEDV-infection in a CMP-Sialic Acid (CMP-SA) cell entry-independent manner. These findings provide a new perspective for a better understanding of host-pathogen interactions and new therapeutic targets for PEDV infection.

Development of a Naked Eye CRISPR-Cas12a and -Cas13a Multiplex Point-of-Care Detection of Genetically Modified Swine.

The Rapid Visual CRISPR (RAVI-CRISPR) assay employs Cas12a and Cas13a enzymes for precise gene detection in a sample. However, RAVI-CRISPR is limited in single-tube multiplex detection applications due to the lack of specific single-strand (ss) DNA-fluorescently quenched (ssDNA-FQ) and RNA-fluorescently quenched (ssRNA-FQ) reporter cleavage mechanisms. We report the development of a sensitive and specific dual-gene Cas12a and Cas13a diagnostic system. To optimize the application for field testing, we designed a portable multiplex fluorescence imaging assay that could distinguish test results with the naked eye. Herein, dual gene amplified products from multiplex recombinase polymerase amplification (RPA) were simultaneously detected in a single tube using Cas12a and Cas13a enzymes. The resulting orthogonal DNA and RNA collateral cleavage specifically distinguishes individual and mixed ssDNA-FQ and ssRNA-FQ reporters using the green-red-yellow, fluorescent signal conversion reaction system, detectable with portable blue and ultraviolet (UV) light transilluminators. As a proof-of-concept, reliable multiplex RAVI-CRISPR detection of genome-edited pigs was demonstrated, exhibiting 100% sensitivity and specificity for the analysis of CD163 knockout, lactoferrin (LF) knock-in, and wild-type pig samples. This portable naked-eye multiplex RAVI-CRISPR detection platform can provide accurate point-of-care screening of genetically modified animals and infectious diseases in resource-limited settings.

Integrated analysis of genome-wide association studies and 3D epigenomic characteristics reveal the BMP2 gene regulating loin muscle depth in Yorkshire pigs.

BACKGROUND: The lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of techniques as ChIP-seq and Hi-C, the annotations of cis-regulatory elements in the pig genome have been established, which offers a new opportunity to elucidate the genetic mechanisms and identify major genetic variants and candidate genes that are significantly associated with important economic traits. Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD. RESULTS: Five single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD. CONCLUSIONS: Based on the results of GWAS, Hi-C, and cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics.

Cathepsin C promotes colorectal cancer metastasis by regulating immune escape through upregulating CSF1.

Since metastasis remains the primary reason for colorectal cancer (CRC) associated death, a better understanding of the molecular mechanism underlying CRC metastasis is urgently needed. Here, we elucidated the role of Cathepsin C (CTSC) in promoting CRC metastasis. The expression of CTSC was detected by real-time PCR and immunohistochemistry in the human CRC cohort. The metastatic capacities of CTSC-mediated metastasis were analyzed by in vivo metastasis model. Elevated CSTC expression was positively associated with tumor differentiation, tumor invasion, lymph node metastasis, and AJCC stage and indicated poor prognosis in human CRC. CTSC overexpression in CRC cells promoted myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) recruitment by the CSF1/CSF1R axis. In contrast, the knockdown of CSF1 reduced CTSC-mediated MDSCs and TAMs infiltration and CRC metastasis. Depletion of either MDSCs or TAMs decreased CTSC-mediated CRC metastasis. In human CRC tissues, CTSC expression was positively associated with intratumoral MDSCs and TAMs infiltration. Furthermore, the combination of CTSC inhibitor AZD7986 and anti-PD-L1 antibody blocked CTSC-induced CRC metastasis. CTSC overexpression promoted MDSCs and TAMs infiltration by CSF1/CSF1R axis. Interruption of this oncogenic loop may provide a promising treatment strategy for inhibiting CTSC-driven CRC metastasis.

Genomic evaluation for two-way crossbred performance in cattle.

BACKGROUND: Dairy cattle production systems are mostly based on purebreds, but recently the use of crossbreeding has received increased interest. For genetic evaluations including crossbreds, several methods based on single-step genomic best linear unbiased prediction (ssGBLUP) have been proposed, including metafounder ssGBLUP (MF-ssGBLUP) and breed-specific ssGBLUP (BS-ssGBLUP). Ideally, models that account for breed effects should perform better than simple models, but knowledge on the performance of these methods is lacking for two-way crossbred cattle. In addition, the differences in the estimates of genetic parameters (such as the genetic variance component and heritability) between these methods have rarely been investigated. Therefore, the aims of this study were to (1) compare the estimates of genetic parameters for average daily gain (ADG) and feed conversion ratio (FCR) between these methods; and (2) evaluate the impact of these methods on the predictive ability for crossbred performance. METHODS: Bivariate models using standard ssGBLUP, MF-ssGBLUP and BS-ssGBLUP for the genetic evaluation of ADG and FCR were investigated. To measure the predictive ability of these three methods, we estimated four estimators, bias, dispersion, population accuracy and ratio of population accuracies, using the linear regression (LR) method. RESULTS: The results show that, for both ADG and FCR, the heritabilities were low with the three methods. For FCR, the differences in the estimated genetic parameters were small between the three methods, while for ADG, those estimated with BS-ssGBLUP deviated largely from those estimated with the other two methods. Bias and dispersion were similar across the three methods. Population accuracies for both ADG and FCR were always higher with MF-ssGBLUP than with ssGBLUP, while with BS-ssGBLUP the population accuracy was highest for FCR and lowest for ADG. CONCLUSIONS: Our results indicate that in the genetic evaluation for crossbred performance in a two-way crossbred cattle production system, the predictive ability of MF-ssGBLUP and BS-ssGBLUP is greater than that of ssGBLUP, when the estimated variance components are consistent across the three methods. Compared with BS-ssGBLUP, MF-ssGBLUP is more robust in its superiority over ssGBLUP.

Detecting Melanocortin 1 Receptor Gene's SNPs by CRISPR/enAsCas12a.

Beyond its powerful genome-editing capabilities, the CRISPR/Cas system has opened up a new era of molecular diagnostics due to its highly specific base recognition and trans-cleavage activity. However, most CRISPR/Cas detection systems are mainly used to detect nucleic acids of bacteria or viruses, while the application of single nucleotide polymorphism (SNP) detection is limited. The MC1R SNPs were investigated by CRISPR/enAsCas12a and are not limited to the protospacer adjacent motif (PAM) sequence in vitro. Specifically, we optimized the reaction conditions, which proved that the enAsCas12a has a preference for divalent magnesium ion (Mg2+) and can effectively distinguish the genes with a single base difference in the presence of Mg2+, and the Melanocortin l receptor (MC1R) gene with three kinds of SNP sites (T305C, T363C, and G727A) was quantitatively detected. Since the enAsCas12a is not limited by PAM sequence in vitro, the method shown here can extend this extraordinary CRISPR/enAsCas12a detection system to other SNP targets, thus providing a general SNP detection toolbox.

Systemic regulation of nodule structure and assimilated carbon distribution by nitrate in soybean.

Background: The nitrate regulates soybean nodulation and nitrogen fixation systemically, mainly in inhibiting nodule growth and reducing nodule nitrogenase activity, but the reason for its inhibition is still inconclusive. Methods: The systemic effect of nitrate on nodule structure, function, and carbon distribution in soybean (Glycine max (L.) Merr.) was studied in a dual-root growth system, with both sides inoculated with rhizobia and only one side subjected to nitrate treatment for four days. The non-nodulating side was genetically devoid of the ability to form nodules. Nutrient solutions with nitrogen concentrations of 0, 100, and 200 mg L-1 were applied as KNO3 to the non-nodulating side, while the nodulating side received a nitrogen-free nutrient solution. Carbon partitioning in roots and nodules was monitored using 13C-labelled CO2. Other nodule responses were measured via the estimation of the nitrogenase activity and the microscopic observation of nodule ultrastructure. Results: Elevated concentrations of nitrate applied on the non-nodulating side caused a decrease in the number of bacteroids, fusion of symbiosomes, enlargement of the peribacteroid spaces, and onset of degradation of poly-ß-hydroxybutyrate granules, which is a form of carbon storage in bacteroids. These microscopic observations were associated with a strong decrease in the nitrogenase activity of nodules. Furthermore, our data demonstrate that the assimilated carbon is more likely to be allocated to the non-nodulating roots, as follows from the competition for carbon between the symbiotic and non-symbiotic sides of the dual-root system. Conclusion: We propose that there is no carbon competition between roots and nodules when they are indirectly supplied with nitrate, and that the reduction of carbon fluxes to nodules and roots on the nodulating side is the mechanism by which the plant systemically suppresses nodulation under nitrogen-replete conditions.

HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data.

Human diseases and agricultural traits can be predicted by modeling a genetic random polygenic effect in linear mixed models. To estimate variance components and predict random effects of the model efficiently with limited computational resources has always been of primary concern, especially when it involves increasing the genotype data scale in the current genomic era. Here, we thoroughly reviewed the development history of statistical algorithms used in genetic evaluation and theoretically compared their computational complexity and applicability for different data scenarios. Most importantly, we presented a computationally efficient, functionally enriched, multi-platform and user-friendly software package named 'HIBLUP' to address the challenges that are faced currently using big genomic data. Powered by advanced algorithms, elaborate design and efficient programming, HIBLUP computed fastest while using the lowest memory in analyses, and the greater the number of individuals that are genotyped, the greater the computational benefits from HIBLUP. We also demonstrated that HIBLUP is the only tool which can accomplish the analyses for a UK Biobank-scale dataset within 1 h using the proposed efficient 'HE + PCG' strategy. It is foreseeable that HIBLUP will facilitate genetic research for human, plants and animals. The HIBLUP software and user manual can be accessed freely at https://www.hiblup.com.

Both human diseases and agricultural traits can be predicted by incorporating phenotypic observations and a relationship matrix among individuals in a linear mixed model. Due to the great demand for processing massive data of genotyped individuals, the existing algorithms that require several repetitions of inverse computing on increasingly big dense matrices (e.g. the relationship matrix and the coefficient matrix of mixed model equations) have encountered a bottleneck. Here, we presented a software tool named 'HIBLUP' to address the challenges. Powered by our advanced algorithms (e.g. HE + PCG), elaborate design and efficient programming, HIBLUP can successfully avoid the inverse computing for any big matrix and compute fastest under the lowest memory, which makes it very promising for genetic evaluation using big genomic data.