Genomic profiling of pediatric hematologic malignancies and diagnosis of cancer predisposition syndromes: tumor-only versus paired tumor-normal sequencing.

Not available.

The Inactivated ISKNV-I Vaccine Confers Highly Effective Cross-Protection against Epidemic RSIV-I and RSIV-II from Cultured Spotted Sea Bass Lateolabrax maculatus.

The genus Megalocytivirus of the family Iridoviridae is composed of two distinct species, namely, infectious spleen and kidney necrosis virus (ISKNV) and scale drop disease virus (SDDV), and both are important causative agents in a variety of bony fish worldwide. Of them, the ISKNV species is subdivided into three genotypes, namely, red seabream iridovirus (RSIV), ISKNV, and turbot reddish body iridovirus (TRBIV), and a further six subgenotypes, RSIV-I, RSIV-II, ISKNV-I, ISKNV-II, TRBIV-I, and TRBIV-II. Commercial vaccines derived from RSIV-I , RSIV-II and ISKNV-I have been available to several fish species. However, studies regarding the cross-protection effect among different genotype or subgenotype isolates have not been fully elucidated. In this study, RSIV-I and RSIV-II were demonstrated as the causative agents in cultured spotted seabass, Lateolabrax maculatus, through serial robust evidence, including cell culture-based viral isolation, whole-genome determination and phylogeny analysis, artificial challenge, histopathology, immunohistochemistry, and immunofluorescence as well as transmission electron microscope observation. Thereafter, a formalin-killed cell (FKC) vaccine generated from an ISKNV-I isolate was prepared to evaluate the protective effects against two spotted seabass original RSIV-I and RSIV-II. The result showed that the ISKNV-I-based FKC vaccine conferred almost complete cross-protection against RSIV-I and RSIV-II as well as ISKNV-I itself. No serotype difference was observed among RSIV-I, RSIV-II, and ISKNV-I. Additionally, the mandarin fish Siniperca chuatsi is proposed as an ideal infection and vaccination fish species for the study of various megalocytiviral isolates. IMPORTANCE Red seabream iridovirus (RSIV) infects a wide mariculture bony fish and has resulted in significant annual economic loss worldwide. Previous studies showed that the phenotypic diversity of infectious RSIV isolates would lead to different virulence characteristics, viral antigenicity, and vaccine efficacy as well as host range. Importantly, it is still doubted whether a universal vaccine could confer the same highly protective effect against various genotypic isolates. Our study here presented enough experimental evidence that a water in oil (w/o) formation of inactivated ISKNV-I vaccine could confer almost complete protection against RSIV-I and RSIV-II as well as ISKNV-I itself. Our study provides valuable data for better understanding the differential infection and immunity among different genotypes of ISKNV and RSIV isolates in the genus Megalocytivirus.

Production and characterization of monoclonal antibodies against mandarinfish ranavirus and first identification of pyloric caecum as the major target tissue.

Mandarinfish ranavirus (MRV), also known as a variant of largemouth bass virus (LMBV), is an emerging pathogen in mandarinfish aquaculture. In this study, monoclonal antibodies (mAbs) against MRV were produced and characterized, and 7 mAbs were obtained through Western blotting screening and all 7 mAbs specifically recognized MRV/LMBV but not several piscine iridoviruses as ISKNV, GIV and TFV. By LC MS/MS analysis, the recognized viral proteins by seven mAbs were identified as MRV-pORF47L, MRV-pORF55R, MRV-pORF57L, MRV-pORF77L and MRV-pORF78L, respectively, and all five viral proteins are late expression structural proteins by Western blotting. Based on mAb 1C4, immuno-histochemistry and immuno-histo-fluorescence were performed to re-assess the tissue tropism of MRV. The result showed that abundant reactive signals were observed in infected spleen, kidney as well as intestine and pyloric caecum. Real-time quantitative PCR also demonstrated that spleen as well as pyloric caecum and intestines are the major target tissue upon MRV infection. In infected intestines and pyloric caecum, numerous enlarged, multinucleated cells with intracytoplasmic inclusions were identified as the target cells of MRV, suggesting that MRV serves as a digestive tract pathogen to mandarinfish, which may explain why acute infection of MRV can cause the typical clinicopathology featured by severe ascites.

Novel ATXN1/ATXN1L::NUTM2A fusions identified in aggressive infant sarcomas with gene expression and methylation patterns similar to CIC-rearranged sarcoma.

CIC-rearranged sarcomas are newly defined undifferentiated soft tissue tumors with CIC-associated fusions, and dismal prognosis. CIC fusions activate PEA3 family genes, ETV1/4/5, leading to tumorigenesis and progression. We report two high-grade CNS sarcomas of unclear histological diagnosis and one disseminated tumor of unknown origin with novel fusions and similar gene-expression/methylation patterns without CIC rearrangement. All three patients were infants with aggressive diseases, and two experienced rapid disease deterioration and death. Whole-transcriptome sequencing identified an ATXN1-NUTM2A fusion in the two CNS tumors and an ATXN1L-NUTM2A fusion in case 3. ETV1/4/5 and WT1 overexpression were observed in all three cases. Methylation analyses predicted CIC-rearranged sarcoma for all cases. Retrospective IHC staining on case 2 demonstrated ETV4 and WT1 overexpression. ATXN1 and ATXN1L interact with CIC forming a transcription repressor complex. We propose that ATXN1/ATXN1L-associated fusions disrupt their interaction with CIC and decrease the transcription repressor complex, leading to downstream PEA3 family gene overexpression. These three cases with novel ATXN1/ATXN1L-associated fusions and features of CIC-rearranged sarcomas may further expand the scope of "CIC-rearranged" sarcomas to include non-CIC rearrangements. Additional cases are needed to demonstrate if ATXN1/ATXN1L-NUTM2A fusions are associated with younger age and more aggressive diseases.

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

MOTIVATION: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. RESULTS: This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. AVAILABILITY AND IMPLEMENTATION: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection.

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.

Scale Drop Disease Virus Associated Yellowfin Seabream (Acanthopagrus latus) Ascites Diseases, Zhuhai, Guangdong, Southern China: The First Description.

Scale drop disease virus (SDDV), an emerging piscine iridovirus prevalent in farmed Asian seabass Lates calcarifer in Southeast Asia, was firstly scientifically descripted in Singapore in 2015. Here, an SDDV isolate ZH-06/20 was isolated by inoculating filtered ascites from diseased juvenile yellowfin seabream into MFF-1 cell. Advanced cytopathic effects were observed 6 days post-inoculation. A transmission electron microscopy examination confirmed that numerous virion particles, about 140 nm in diameter, were observed in infected MFF-1 cell. ZH-06/20 was further purified and both whole genome and virion proteome were determined. The results showed that ZH-06/20 was composed of 131,122 bp with 135 putative viral proteins and 113 of them were further detected by virion proteome. Western blot analysis showed that no (or weak) cross-reaction was observed among several major viral proteins between ZH-06/20 and ISKNV-like megalocytivirus. An artificial challenge showed that ZH-06/20 could cause 100% death to juvenile yellowfin seabream. A typical sign was characterized by severe ascites, but not scale drop, which was considerably different from SDD syndrome in Asian seabass. Collectively, SDDV was confirmed, for the first time, as the causative agent of ascites diseases in farmed yellowfin seabream. Our study offers useful information to better understanding SDDV-associated diseases in farmed fish.

Evaluating recommender systems for AI-driven biomedical informatics.

MOTIVATION: Many researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly. Here, we study a method of automating biomedical data science using a web-based AI platform to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user's experiments as well as prior knowledge. To validate this framework, we conduct an experiment on 165 classification problems, comparing to state-of-the-art, automated approaches. Finally, we use this tool to develop predictive models of septic shock in critical care patients. RESULTS: We find that matrix factorization-based recommendation systems outperform metalearning methods for automating ML. This result mirrors the results of earlier recommender systems research in other domains. The proposed AI is competitive with state-of-the-art automated ML methods in terms of choosing optimal algorithm configurations for datasets. In our application to prediction of septic shock, the AI-driven analysis produces a competent ML model (AUROC 0.85±0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort. AVAILABILITY AND IMPLEMENTATION: PennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses.

BACKGROUND: A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. RESULTS: We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj . CONCLUSIONS: In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. RESULTS: We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual. AVAILABILITY AND IMPLEMENTATION: Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction to: Investigating the parameter space of evolutionary algorithms.

[This corrects the article DOI: 10.1186/s13040-018-0164-x.].

Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens.

The impact of intratumoral heterogeneity (ITH) and the resultant neoantigen landscape on T cell immunity are poorly understood. ITH is a widely recognized feature of solid tumors and poses distinct challenges related to the development of effective therapeutic strategies, including cancer neoantigen vaccines. Here, we performed deep targeted DNA sequencing of multiple metastases from melanoma patients and observed ubiquitous sharing of clonal and subclonal single nucleotide variants (SNVs) encoding putative HLA class I-restricted neoantigen epitopes. However, spontaneous antitumor CD8+ T cell immunity in peripheral blood and tumors was restricted to a few clonal neoantigens featuring an oligo-/monoclonal T cell-receptor (TCR) repertoire. Moreover, in various tumors of the 4 patients examined, no neoantigen-specific TCR clonotypes were identified despite clonal neoantigen expression. Mature dendritic cell (mDC) vaccination with tumor-encoded amino acid-substituted (AAS) peptides revealed diverse neoantigen-specific CD8+ T responses, each composed of multiple TCR clonotypes. Isolation of T cell clones by limiting dilution from tumor-infiltrating lymphocytes (TILs) permitted functional validation regarding neoantigen specificity. Gene transfer of TCRαß heterodimers specific for clonal neoantigens confirmed correct TCR clonotype assignments based on high-throughput TCRBV CDR3 sequencing. Our findings implicate immunological ignorance of clonal neoantigens as the basis for ineffective T cell immunity to melanoma and support the concept that therapeutic vaccination, as an adjunct to checkpoint inhibitor treatment, is required to increase the breadth and diversity of neoantigen-specific CD8+ T cells.

Comparison of protective efficacy between two DNA vaccines encoding DnaK and GroEL against fish nocardiosis.

Fish nocardiosis is a chronic granulomatous bacterial disease mainly caused by three pathogenic bacteria, including Nocardia seriolae, N. asteroids and N. salmonicida. Molecular chaperone DnaK and GroEL were identified to be the common antigens of the three pathogenic Nocardia species in our previous studies. To evaluate the immune protective effect of two DNA vaccines encoding DnaK or GroEL against fish nocardiosis, hybrid snakehead were vaccinated and the immune responses induced by these two vaccines were comparatively analyzed. The results suggested it needed at least 7 d to transport DnaK or GroEL gene from injected muscle to head kidney, spleen and liver and stimulate host's immune system for later protection after immunization by DNA vaccines. Additionally, non-specific immunity parameters (serum lysozyme (LYZ), peroxidase (POD), acid phosphatase (ACP), alkaline phosphatase (AKP) and superoxide dismutase (SOD) activities), specific antibody (IgM) production and immune-related genes (MHCIα, MHCIIα, CD4, CD8α, IL-1ß and TNFα) were used to evaluate the immune responses induced in vaccinated hybrid snakehead. It proved that all the above-mentioned immune activities were significantly enhanced after immunization with these two DNA vaccines. The protective efficacy of pcDNA-DnaK and pcDNA-GroEL DNA vaccines, in terms of relative percentage survival (RPS), were 53.01% and 80.71% respectively. It demonstrated that these two DNA vaccines could increase the survival rate of hybrid snakehead against fish nocardiosis, albeit with variations in immunoprotective effects. Taken together, these results indicated that both pcDNA-DnaK and pcDNA-GroEL DNA vaccines could boost the innate, humoral and cellular immune response in hybrid snakehead and show highly protective efficacy against fish nocardiosis, suggesting that DnaK and GroEL were promising vaccine candidates. These findings will promote the development of DNA vaccines against fish nocardiosis in aquaculture.

Author Correction: ATF4 couples MYC-dependent translational activity to bioenergetic demands during tumour progression.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

ATF4 couples MYC-dependent translational activity to bioenergetic demands during tumour progression.

The c-Myc oncogene drives malignant progression and induces robust anabolic and proliferative programmes leading to intrinsic stress. The mechanisms enabling adaptation to MYC-induced stress are not fully understood. Here we reveal an essential role for activating transcription factor 4 (ATF4) in survival following MYC activation. MYC upregulates ATF4 by activating general control nonderepressible 2 (GCN2) kinase through uncharged transfer RNAs. Subsequently, ATF4 co-occupies promoter regions of over 30 MYC-target genes, primarily those regulating amino acid and protein synthesis, including eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), a negative regulator of translation. 4E-BP1 relieves MYC-induced proteotoxic stress and is essential to balance protein synthesis. 4E-BP1 activity is negatively regulated by mammalian target of rapamycin complex 1 (mTORC1)-dependent phosphorylation and inhibition of mTORC1 signalling rescues ATF4-deficient cells from MYC-induced endoplasmic reticulum stress. Acute deletion of ATF4 significantly delays MYC-driven tumour progression and increases survival in mouse models. Our results establish ATF4 as a cellular rheostat of MYC activity, which ensures that enhanced translation rates are compatible with survival and tumour progression.

Investigating the parameter space of evolutionary algorithms.

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients.

Clusters of differentiation (CD) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (mABs) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (SLE) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB) to profile de novo gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (in silico) of BCL7A (padj=1.69e-9) and STRBP(padj=4.63e-8) with CD22; NCOA2(padj=7.00e-4), ATN1 (padj=1.71e-2), and HOXC4(padj=3.34e-2) with CD30; and PHOSPHO1, a phosphatase linked to bone mineralization, with both CD22(padj=4.37e-2) and CD30(padj=7.40e-3). Utilizing carefully aggregated secondary data and leveraging a priori hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.

Detection of genomic signatures of recent selection in commercial broiler chickens.

BACKGROUND: Identification of the genomic signatures of recent selection may help uncover causal polymorphisms controlling traits relevant to recent decades of selective breeding in livestock. In this study, we aimed at detecting signatures of recent selection in commercial broiler chickens using genotype information from single nucleotide polymorphisms (SNPs). A total of 565 chickens from five commercial purebred lines, including three broiler sire (male) lines and two broiler dam (female) lines, were genotyped using the 60K SNP Illumina iSelect chicken array. To detect genomic signatures of recent selection, we applied two methods based on population comparison, cross-population extended haplotype homozygosity (XP-EHH) and cross-population composite likelihood ratio (XP-CLR), and further analyzed the results to find genomic regions under recent selection in multiple purebred lines. RESULTS: A total of 321 candidate selection regions spanning approximately 1.45 % of the chicken genome in each line were detected by consensus of results of both XP-EHH and XP-CLR methods. To minimize false discovery due to genetic drift, only 42 of the candidate selection regions that were shared by 2 or more purebred lines were considered as high-confidence selection regions in the study. Of these 42 regions, 20 were 50 kb or less while 4 regions were larger than 0.5 Mb. In total, 91 genes could be found in the 42 regions, among which 19 regions contained only 1 or 2 genes, and 9 regions were located at gene deserts. CONCLUSIONS: Our results provide a genome-wide scan of recent selection signatures in five purebred lines of commercial broiler chickens. We found several candidate genes for recent selection in multiple lines, such as SOX6 (Sex Determining Region Y-Box 6) and cTR (Thyroid hormone receptor beta). These genes may have been under recent selection due to their essential roles in growth, development and reproduction in chickens. Furthermore, our results suggest that in some candidate regions, the same or opposite alleles have been under recent selection in multiple lines. Most of the candidate genes in the selection regions are novel, and as such they should be of great interest for future research into the genetic architecture of traits relevant to modern broiler breeding.

Characterization of a novel chicken muscle disorder through differential gene expression and pathway analysis using RNA-sequencing.

BACKGROUND: Improvements in poultry production within the past 50 years have led to increased muscle yield and growth rate, which may be contributing to an increased rate and development of new muscle disorders in chickens. Previously reported muscle disorders and conditions are generally associated with poor meat quality traits and have a significant negative economic impact on the poultry industry. Recently, a novel myopathy phenotype has emerged which is characterized by palpably "hard" or tough breast muscle. The objective of this study is to identify the underlying biological mechanisms that contribute to this emerging muscle disorder colloquially referred to as "Wooden Breast", through the use of RNA-sequencing technology. METHODS: We constructed cDNA libraries from five affected and six unaffected breast muscle samples from a line of commercial broiler chickens. After paired-end sequencing of samples using the Illumina Hiseq platform, we used Tophat to align the resulting sequence reads to the chicken reference genome and then used Cufflinks to find significant changes in gene transcript expression between each group. By comparing our gene list to previously published histology findings on this disorder and using Ingenuity Pathways Analysis (IPA®), we aim to develop a characteristic gene expression profile for this novel disorder through analyzing genes, gene families, and predicted biological pathways. RESULTS: Over 1500 genes were differentially expressed between affected and unaffected birds. There was an average of approximately 98 million reads per sample, across all samples. Results from the IPA analysis suggested "Diseases and Disorders" such as connective tissue disorders, "Molecular and Cellular Functions" such as cellular assembly and organization, cellular function and maintenance, and cellular movement, "Physiological System Development and Function" such as tissue development, and embryonic development, and "Top Canonical Pathways" such as, coagulation system, axonal guidance signaling, and acute phase response signaling, are associated with the Wooden Breast disease. CONCLUSIONS: There is convincing evidence by RNA-seq analysis to support localized hypoxia, oxidative stress, increased intracellular calcium, as well as the possible presence of muscle fiber-type switching, as key features of Wooden Breast Disease, which are supported by reported microscopic lesions of the disease.

Linkage disequilibrium in crossbred and pure line chickens.

BACKGROUND: Both genome-wide association (GWA) studies and genomic selection depend on the level of non-random association of alleles at different loci, i.e. linkage disequilibrium (LD), across the genome. Therefore, characterizing LD is of fundamental importance to implement both approaches. In this study, using a 60K single nucleotide polymorphism (SNP) panel, we estimated LD and haplotype structure in crossbred broiler chickens and their component pure lines (one male and two female lines) and calculated the consistency of LD between these populations. RESULTS: The average level of LD (measured by r(2)) between adjacent SNPs across the chicken autosomes studied here ranged from 0.34 to 0.40 in the pure lines but was only 0.24 in the crossbred populations, with 28.4% of adjacent SNP pairs having an r(2) higher than 0.3. Compared with the pure lines, the crossbred populations consistently showed a lower level of LD, smaller haploblock sizes and lower haplotype homozygosity on macro-, intermediate and micro-chromosomes. Furthermore, correlations of LD between markers at short distances (0 to 10 kb) were high between crossbred and pure lines (0.83 to 0.94). CONCLUSIONS: Our results suggest that using crossbred populations instead of pure lines can be advantageous for high-resolution QTL (quantitative trait loci) mapping in GWA studies and to achieve good persistence of accuracy of genomic breeding values over generations in genomic selection. These results also provide useful information for the design and implementation of GWA studies and genomic selection using crossbred populations.