Clinical mastitis is an inflammation of the mammary gland and causes significant costs to dairy production. It is unfavourably genetically correlated to milk production, and, thus, knowledge of the mechanisms that underlie these traits would be valuable to improve both of them simultaneously through breeding. A quantitative trait locus (QTL) that affects both clinical mastitis and milk production has recently been fine-mapped to around 89 Mb on bovine chromosome 6 (BTA6), but identification of the gene that underlies this QTL was not possible due to the strong linkage disequilibrium between single nucleotide polymorphisms (SNPs) within this region. Our aim was to identify the gene and, if possible, the causal polymorphism(s) responsible for this QTL through association analysis of high-density SNPs and imputed full sequence data in combination with analyses of transcript and protein levels of the identified candidate gene.group-specific component (GC) gene. This gene encodes the vitamin D-binding protein (DBP) and has multiple roles in immune defense and milk production. A 12-kb duplication that was identified downstream of this gene covered its last exon and segregated with the QTL allele that is associated with increased mastitis susceptibility and milk production. However, analyses of GC mRNA levels on the available samples revealed no differences in expression between animals having or lacking this duplication. Moreover, we detected no differences in the concentrations of DBP and its ligand vitamin D between the animals with different GC genotypes that were available for this study.Associations between SNPs and the studied traits were strongest for SNPs that were located within and immediately upstream of the GC as the gene that underlies the QTL for clinical mastitis and milk production. However, since only healthy animals were sampled for transcription and expression analyses, we could not draw any final conclusion on the absence of quantitative differences between animals with different genotypes. Future studies should investigate GC RNA expression and protein levels in cows with different genotypes during an infection.Our results suggest The online version of this article (doi:10.1186/s12711-016-0257-2) contains supplementary material, which is available to authorized users. Clinical mastitis (CM) is an inflammation of the mammary gland, which is generally caused by the introduction and multiplication of microorganisms. The consequences of CM include reduced animal welfare, economic losses due to reduced milk yield and altered milk composition, increased costs due to veterinary treatment of diseased animals, increased use of antibiotics, and early culling of animals. Thus, there is a high demand for solutions that help reduce the incidence of mastitis. Genetic improvement of resistance to CM by traditional breeding is possible , in spitIGJ gene at 87.8 Mb that encodes the immunoglobulin J polypeptide, have been suggested as candidates for susceptibility to mastitis , CM2 (31 to 120 dpp) and CM3 (121 to 305 dpp), two periods during the second lactation: CM4 (−15 to 30 dpp) and CM5 (31 to 120 dpp); and two periods during the third lactation: CM6 (−15 to 30 dpp) and CM7 (31 to 120 dpp).Five milk production traits, i.e., 305 d lactation yields for kg milk, kg protein, kg fat, protein percentage and fat percentage, were based on monthly test-day records of milk yield and bimonthly records of kg fat and kg protein. Animal repeatability models that were based on data from the first to third lactation were used for the genetic evaluation of milk production traits.The number of bulls with trait data varied for each trait, especially in the case of CM traits because many cows were culled before they reached the last period of the last lactation. In addition, selection of animals for genotyping was performed to fit the needs of several studies, and hence the number of bulls with both trait and genotype data varied among analyses as described below.Genotypes from the Affymetrix 25K SNP array , the Illumina BovineSNP50 BeadChip (54K) and the Illumina BovineHD Genotyping BeadChip (777K)  were combined to produce a dataset for BTA6 that consisted of 26,737 SNPs. First, 2552 NR AI bulls were genotyped on the 25K chip. Then, 1575 NR bulls were genotyped on the 54K chip with 536 of these 1575 animals already genotyped on the 25K chip. Finally, 384 of the 1575 bulls were genotyped on the 777K chip. The three datasets were filtered to remove SNPs with a minor allele frequency lower than 0.05. Map positions were based on the bovine reference genome assembly . SNPs ohttp://www.sequencing.uio.no) on a HiSeq 2500 platform according to the manufacturer’s protocols. Samples were prepared for paired-end sequencing (2 × 125 bp) by using the TruSeq DNA PCR-free library preparation kits and sequenced with the Illumina V4 kit  to generate an average coverage of 9×. Average sequencing coverage in a 1-Mb region that included the most significant markers and the duplication agreed with the genome-wide average coverage.Whole-genome sequencing data were obtained for 147 animals at three different time points in batches of 5, 16 and 126 animals. A first batch of five bulls were sequenced by FASTERIS (Switzerland) on an Illumina Genome Analyzer GAIIx instrument  with 2 × 108 bp paired end reads. The TruSeq SBS V2-GA kit was used to prepare libraries and the FASTX-toolkit v0.0.13  for adaptor- and quality-trimming of raw reads in FASTQ-format. An average sequence coverage of ~4× and 10× was obtained for three and two bulls, respectively. A second batch of 16 elite bulls were sequenced by AROS (Denmark) on a HiSeq 2000 platform according to the manufacturer’s protocols. Samples were prepared for paired-end sequencing (2 × 100 bp) using the TruSeq kit and sequenced with the Illumina V3 kit  to generate an average coverage of 10×. Finally, the third batch that included 96 NR elite bulls and 31 NR cows were sequenced by the Norwegian Sequencing Centre (http://www.1000bullgenomes.com/), and were chosen to minimize the number of false positive variants, while sacrificing some degree of variant calling sensitivity.The quality of the Illumina reads was checked with fastQC . AdaptorGenotypes of the called variants were refined and phased using Beagle version 4.1 . The iniSingle marker analyses were first performed for all 12 traits and the 26,737 SNPs on BTA6 from the high-density BTA6 dataset, and then for the six traits that were significant in the first analyses and 65,156 markers (SNPs and indels) between 84 and 94 Mb on BTA6.DYD is the vector of bull performances weighed by the number of daughters, 1 is a vector of ones, μ is the overall mean, X is a vector of SNP genotypes coded as 0, 1, or 2 depending on the number of copies of the first allele, b is the fixed effect of the marker, Z is an incidence matrix relating phenotypes to the corresponding random polygenic effects, a is a vector of random polygenic effects, and e is a vector of residual effects. Genetic and residual variances were estimated from the data. a was assumed to follow a normal distribution ~A is the relationship matrix derived from the pedigree, and e was assumed to follow a normal distribution ~The initial association analysis of the high-density dataset was performed with the ASREML package version 2.0 . The modFor the association analysis of the imputed full sequence data, we used the GCTA package  with the mixed linear model based association analysis (MLMA) option  for compDYD is now an unweighted vector of bull performances, and g is the random polygenic effect, i.e. the accumulated effect of all SNPs as captured by the genetic relationship matrix which was estimated by GCTA as described by Yang et al.  concentrations were determined by applying a liquid chromatography–tandem mass spectrometry (LC–MS/MS) method developed at the Hormone laboratory . More details on these procedures are in Additional file Single marker association analyses were performed for all seven CM traits and five milk production traits and ~27,000 SNPs on BTA6. Highly significant results were detected for CM1, CM4, CM6, kg milk, kg protein, kg fat and protein percentage in a region between ~88.7 and 88.9 Mb on BTA6 (Table GC gene. GC is transcribed on the reverse strand between 88.74 and 88.69 Mb (according to the UMD 3.1 assembly of the bovine genome) and is the only known gene in close vicinity of this QTL position. The closest genes on either side are SLC4A4  at ~88.2–88.5 Mb and NPFFR2 (neuropeptide FF receptor 2) at ~89 Mb, but the significance levels of all traits were markedly reduced for SNPs within and closer to these genes, and hence our analyses indicate GC as the strongest positional candidate gene for the QTL.The most significant SNP in this analysis, rs110300280, was located approximately 5400 bp 5′ from the transcription start site of the GC gene as well as within a flanking region of ~5 Mb on each side of the gene were analyzed in order to verify that GC was the causal gene and, if possible, identify the causal variations within this gene. For computational reasons, the analyses were performed with a different software than in the previous step. The p values were lower in this step than in the previous one, most likely due to differences in the algorithms used.The traits CM1, CM4, CM6, kg milk, kg protein and kg fat were reanalyzed for a total of 65,156 SNPs between 84 and 94 Mb on BTA6 that were detected by whole-genome resequencing. All detected sequence level variations within the GC  = 11.52 and 12.75, respectively], which is located ~2.5 kb 5′ of the transcription start site. The G allele of rs110483492 was associated to higher mastitis incidence and higher milk yield than the A allele, and showed a frequency of ~34 % among the genotyped bulls. Nearly similar significant p values were found for several other SNPs in intron 1 of GC and immediately upstream of the gene. kg fat was most significantly associated to rs381872595 [−log10 = 7.76], an indel at ~88,743,767 bp that was also among the most significant markers for CM1 and kg milk. kg protein, CM4 and CM6 showed the strongest associations with markers at ~88.9 Mb and with markers within and immediately close to GC. Results for all traits and markers are in Additional file GC towards ADAMTS3, and the possibility that the QTL effect is due to polymorphisms closer to NPFFR2 or ADAMTS3 could not be completely ruled out. On the other hand, none of the markers in the coding or promoter sequences of these genes were among the most significant.For all traits tested, a series of SNPs with very similar p values were detected within and close to GC Fig. ; Table 2The more telomeric positions of the most significant markers for kg protein, CM4 and CM6 could suggest that additional polymorphisms affected these traits. However, when the effect of rs110483492 at 88,741,762 bp was corrected for, no markers remained significant for any of the six traits. Hence, all significant markers are most likely in strong LD with the same causal polymorphism.GC could be excluded as causal. Moreover, the analysis suggests that the underlying causal variation has a regulatory role since no variants within the coding sequences were among the most significant ones.The use of full sequence data did not refine the QTL region much more compared to the first step. However, since the sequence data include all the variants that are present in a larger region, other genes than GC gene and several kb from its 5′ end, and were dispersed in-between other SNPs that had much lower significance levels. All the SNPs with a p value lower than 1*e−11 for CM1 were in high LD with each other, most often with an r2 higher than 0.95. One unexpected exception was the most significant SNP for CM1 at 88,741,762 bp, which showed an r2 with the other top SNPs of ~0.84. The top-ranking SNPs were distributed across a large number of haplotype blocks, and most blocks contained SNPs with highly variable significance levels. For CM1, most of the SNPs with a p value lower than 1*e−12 were in three blocks that covered the region between 88,739,045 and 88,744,985 bp, i.e., a region that spanned from the beginning of intron 1 to ~5 kb from the transcription start site. For each of these SNPs, the allele that was associated with the highest mastitis incidence and milk yield was unique to a haplotype with a frequency of 37 %, which is in agreement with the frequency of the individual alleles.LD analyses revealed a relative complex LD structure, which reflects the history of the NR breed. Highly significant SNPs were located in a larger region that spanned the entire Such an LD pattern that consists of a high LD between rather distant SNPs, which are interspersed in-between many other SNPs with a much lower LD is typical for most commercial cattle breeds that have undergone a rapid recent decrease in effective population size from a large ancestral population due to selection and breed formation. The long-ranging LD is most likely due to the presence of some long haplotypes that are the result of the recent extensive use of AI bulls and that have not been broken apart by recombination, in a background of much shorter haplotypes that reflect the large ancestral population. The size and the combinational effects of such haplotypes limit our ability to fine map and precisely identify the causal polymorphism(s) that underlie the QTL based only on association mapping results.GC, see below) for animals that are homozygous AA at rs110675140 , thus one possibility is that the presence of two copies of this exon may affect the level, stability or function of this transcript. A study that mapped regions enriched for acetylated lysine 27 on histone H3 (H3K27ac) via chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) showed that binding sites for enhancers were situated within the duplicated region (http://nagrp.ansci.iastate.edu/eric/epidb/rsem_expression.php#type1chipseq). Having two copies of this site could, in theory, affect transcription of GC.We aligned the entire sequence of the duplication against the human genome in order to search for conserved functional elements. The duplicated region overlapped the position of the alternative last exon 13 found in the predicted The DNA sequence of the duplicated region showed remarkably little variation, since only 19 SNPs were detected in the entire ~12 kb region. None of the markers within or near the duplication were among the most significant for any of the traits.GC gene is mainly transcribed in the liver [GC expression in humans and rat was shown to be regulated through binding sites for the hepatocyte nuclear factor 1 (HNF1) that are situated 5′ of the transcription start site [https://genome.ucsc.edu), which reports a conserved HNF1 site 5′ of the human GC gene in a region that aligns to around 88,739,350 bp in the bovine sequence. This site is located near three of the most significant SNPs for CM1, i.e. rs109360654 at 88,739,045 bp (intron 1), rs108964685 at 88,739,413 bp (5′ region) and rs109541079 at 88,739,458 bp (5′ region). The most likely binding sites for C/EBP were found in the region between 88,727,907 and 88,727,918 bp, which is between rs109056048 at 88,727,480 bp and rs110675140 at 88,728,121 bp in intron 1. The human GC gene may also be regulated via two binding sites for the CCCTC-binding factor (zinc finger protein) . The sequences of these two sites align to regions that overlap the SNPs rs109056048 at 88,727,480 bp and rs109360654 at 88,739,045 bp, respectively. When searching the sequence that surrounds the most significant SNP for CM1 at 88,741,762 bp for putative transcription factor binding sites using the JASPAR database, putative binding sites for different members of the C/EBP family were detected in regions between 88,741,755 and 88,741,765 bp and between 88,741,764 and 88,741,773 bp, respectively.Since none of the markers in the coding regions were among the most significant, the causal polymorphism was expected to have a regulatory role. The he liver . Liver-sart site , 35, andart site . First, GC in cattle show that it is predominantly expressed in liver, with no expression in the mammary gland . To investigate if the QTL effect was due to differences in alternative splicing or expression levels of the GC mRNA, we tested liver samples from ten healthy individuals with different genotypes at rs110675140 . Results from RNAseq showed that all animals carried three transcripts with no significant difference between genotypes (not shown), i.e. the RefSeq transcript (NM_001035380.2) that consists of exons 1–13, the predicted transcript variant X1 (XM_005208092.1) in which exon 13 is replaced by an alternative last exon, and transcript variant X2 (XR_234992.1), which is similar to the RefSeq transcript except that it contains an additional small exon between exons 12 and 13 , with no significant difference between genotypes D3 and 25(OH)D2, respectively [The vitamin D-binding protein (DBP) encoded by the ectively , 37. SerStage of lactation had a significant effect (p < 0.01) on the concentrations of DBP, 25(OH)D2, 25(OH)D3, free 25(OH)D2 and bioavailable 25(OH)D2. Concentrations of DBP and 25(OH)D3 increased while that of 25(OH)D2 decreased as lactation progressed. Concentrations of free 25(OH)D2 and bioavailable 25(OH)D2 were highest during the first month after calving and decreased thereafter. This is consistent with previous studies in humans , 39. In GC as the best positional candidate gene and agrees with results from other studies e.g. [GC and NPFFR2. They reported slightly different positions for this QTL in the two analyzed breeds, with the most significant SNPs located at 88.72 Mb (intron 1 of GC) in Nordic Red cattle, and at 88.97 Mb in Nordic Holstein. High LD in this region limited their ability to identify the causal polymorphism. The authors also tested for co-segregation with milk production traits in their Danish Holstein population and detected significant associations for kg milk, kg protein and kg fat between ~88.5 and 89.5 Mb [Although high LD and long-range haplotypes in the QTL region complicate fine mapping and make it impossible to identify a causal polymorphism for the QTL, the results strongly support ies e.g. –10. Sahaies e.g.  used whodeoxycytidine kinase (DCK) gene, the second overlapped with the solute carrier family 4, sodium bicarbonate co-transporter, and member 4 (SLC4A4) gene, and the last QTL overlapped with the neuropeptide FF receptor 2 (NPFFR2) gene. No significant QTL within or near the GC gene were detected in Danish Holstein, which may indicate that different polymorphisms affect susceptibility to CM in Holstein as compared to Nordic and Norwegian Red breeds. However, the LD between the SNPs in the third QTL (i.e. at NPFFR2) and the most significant SNPs in our study was high (not shown), and indicates that this third QTL in Danish Holstein is the same as the QTL detected in Nordic Red and Norwegian Red.In a follow-up study on Danish Holstein cattle, associations with CM were observed at three different locations in the region between 88.06 and 89.07 Mb . One QTLGC as the gene that underlies a QTL for milk production in an Australian Holstein population. Again, in this study, the high LD in the QTL region made it difficult to pinpoint a causal polymorphism. However, they identified the most significant SNPs at 88,741,762 and 88,743,767 bp depending on the methods. These SNPs are the most significant and third most significant SNPs for CM1 in our study, and among the most significant for the remaining traits.In addition, Goddard et al.  reportedGroup-specific component encodes DBP, which has multiple important biological roles in the immune system and host defense. DBP is the major plasma protein carrier of vitamin D3 and its metabolites. Vitamin D3 has a function as a cytokine that is generated to protect the host against microbial invaders [invaders . During invaders –45, whicinvaders . Recent invaders , 45, 47,invaders .Vitamin D-binding protein was also suggested to be a precursor of the macrophage activating factor Gc-MAF . ActivatDBP may have a direct effect on milk production. Goddard et al.  suggesteHence, the QTL could either affect host immune response to pathogens and indirectly decrease milk production in diseased animals, or the main effect could be on increased milk production with a secondary effect on mastitis, for instance due to lack of energy for immune defense or strain on the teat canal and sphincter muscle from increased volume at each milking. Alternatively, the mechanisms behind milk production and immune response could be affected more or less independently by the same underlying polymorphism, or by multiple polymorphisms in high LD with each other.GC gene is most highly expressed in this tissue [In this study, we did not succeed in identifying the functional variant that is responsible for the QTL. A limitation of our study was the choice of the animals used for RNA expression and protein analyses. Ideally, such analyses should use samples from cows with different genotypes during a mastitis infection. However, sampling such material with uniform quality and in sufficiently large amounts for proper statistical analysis poses a rather complicated logistics challenge, at least in Norway where the farms are small and situated far apart, and would need a long period of time. This was beyond the duration and resources of this project, and analyses were therefore restricted to material that was readily available. RNA expression was analyzed in liver samples, since the s tissue . HoweverGC gene. We also detected a duplication downstream of the GC gene that segregated with the alleles of this QTL. The duplication covered the alternative last exon of the GC gene and might contain binding sites for enhancers. No differences neither in GC transcript levels nor in protein levels and binding were detected under the examined conditions, but our data did not permit any final conclusions to be drawn on differences in transcription or protein binding during an infection. Further functional studies including both healthy and infected animals are needed to reveal the causative variants that underlie this QTL.Association analysis of full sequence data mapped a highly significant QTL that affects both clinical mastitis in early lactation and milk production to a region within or immediately upstream of the


Microvessel density (MVD) corresponds to the intensity of neo-angiogenesis. MVD assessments are based on the expression levels of the vascular endothelium markers such as, e.g., CD34 or CD105. The goal of this study was to assess MVD among patients with head and neck squamous cell carcinoma (HNSCC), and to evaluate the predictive value of MVD in head and neck cancers.The study included 49 patients treated for HNSCC and 11 patients with dysplasia of the upper respiratory tract epithelium. Control tissues consisted of 12 normal mucous membranes of the throat. Expression levels of MVD markers were assessed by immunohistochemistry (IHC) using tissue microarrays (TMA). Clinicopathological factors and patients’ survival over the 5-year follow-up period were analyzed.p = 0.001) and to dysplastic tissues. (p = 0.02). Significantly higher MVD/CD105 values were also seen in the tumor compared to the control tissues (p = 0.001) or the dysplastic tissues (p = 0.001). Unexpectedly, significantly lower MVD/CD34 values were seen in the tumor tissues of patients with the T3–T4 tumors compared to those with T1–T2 tumors (p = 0.01).The MVD/CD34 values were found to be significantly elevated in the HNSCCs compared to the non-malignant control tissues  of HNSCCs. This counterintuitive observation suggests that assessments of MVD as performed on TMA by IHC using anti-CD34 or anti-CD105 antibodies considered to be specific for endothelial cell markers might underestimate the extent of the tumor vascularity in HNSCC. Squamous cell carcinomas of the head and neck (HNSCC) remains a significant therapeutic challenge, and the effectiveness of current treatments remains the same as it was 40 years ago . Accordi0 stage, and anti-CD34 Abs react with the largest number of endothelial cells compared to other antibodies used in studies of tumor angiogenesis. However, anti-CD34 Abs may also interact with various elements of the matrix, limiting their specificity in MVD assessments [CD34 is a membrane glycoprotein found on the external surface of endothelial cells. During angiogenesis, the CD34 protein is responsible for adhesion of leukocytes to the internal surface of a vascular wall and for migration of vascular endothelial cells [essments .The CD105 antigen (endoglin) is a homodimeric membrane glycoprotein consisting of two subunits connected by a disulfide bridge. CD105 is a component of the TGF-ß receptor complex . CD105 iThe reports of MVD in head and neck cancers show high levels of variability, which might reflect a diversity in detection methodologies used in the previous studies. The goal of this work was to evaluate MVD in HNSCCs, using tissue microarrays (TMA) to ensure the uniform tissue quality for a reliable MVD measurements and to assess the relationship between the MVD values and selected clinical and pathomorphological features of the disease. These data were expected to indicate whether MVD plays a role in HNSCC as a potential predictive marker of survival.n = 49) and patients with dysplasia of the endothelium in the upper respiratory tract (n = 11) was conducted. Control group consisted of mucus membranes acquired from patients undergoing soft palate surgery due to obstructive sleep apnea (OSA) (n = 12). All patients were treated at the Department of Otolaryngology of the Faculty of Medicine and Dentistry at the Medical University of Warsaw between 2007 and 2011. The study was approved by the Ethics Committee at the Medical University of Warsaw, and all patients signed informed consent forms (AKBE/50/12# to T.S.). Clinicopathological assessment was conducted based on the TNM and UICC classifications. Only patients fulfilling the requirement for 5-year follow-up were included in this study  according to the producer’s recommendations. To identify the cells of vascular origin, we used the murine monoclonal antibody antiCD34 Class II (clone:QBEnd 10) at 1:50 dilution (Dako); murine monoclonal antibody antiCD105 (clone:SN6h) at 1:25 dilution (Dako). Slices were deparaffinized in xylene, and dehydrated in ethanol solutions and distilled water. They were incubated in the TBS buffer  for 10 min at a room temperature (RT). To uncover relevant antigen determinants, slides were incubated with TRS High pH buffer  (Novocastra). Endogenous peroxidase activity was blocked with a 3% hydrogen peroxide solution . Incubation with reagents was conducted overnight in a moist chamber of the refrigerator at a 4 °C. Each TMA was covered with 300 µl of antibodies. Negative controls consisted of specimen where IHC Diluent (Novocastra) was used instead of a primary antibody. Positive controls consisted of reactions conducted on tissues recommended by the producer of the antibody.2.Specimens were assessed blinded using a light microscope by two independent examiners (T.S. and M.J.S.) and the results were averaged. MVD was calculated as an arithmetic mean of the number of microvessels from three fields of view with the greatest vascularization located on two different casts from the same patient. Under 100× magnification, we chose the sites of the greatest vascularity (hotspots), and individual microvessels were counted under 400× magnification. Each immunopositive structure  separated from other profiles or tissue elements was counted as a single vessel. Each single colored cell or a group of cells were considered a vessel regardless of the presence of vessel lumen. Vessels with visible muscular layer and morphotic elements apparent in the lumen were not counted as a microvessel. The final result was expressed as a number of vessels in the field of view under 400× magnification. The size of microscopic field was 0.236 mmt test or Mann–Whitney test depending on their distribution demonstrated differences between groups. Kaplan–Meier analysis was used to draw total survival curves in groups depending on MVD value. Differences in survival were assessed using the log-rank test. Logistic regression model with odds ratios (OR) was used in the analysis of likelihood of survival. The level of significance was set at p < 0.05. All values represent means ± SD.The acquired results were analyzed using SAS 9.4 software. The Chi-square test was used for binary variables. Comparison of continuous variables using Student’s t test demonstrated significantly higher mean MVD/CD34 values in the tumor region (21.04 ± 6.47) compared to the normal control group (9.91 ± 1.67), (t = 5.87 for p = 0.001) and the dysplasia group (16.27 ± 3.25), (t = 2.36 for p = 0.02). Similar results were obtained for the assessment of endoglin (CD105) expression. Student’s t test confirmed a significantly higher mean MVD/CD105 ratio within the tumor (14.04 ± 4.29) compared to the normal control group (2.5 ± 10), (t = 17.03 for < 0.001) and the dysplasia group (9.00 ± 1.54), (t = 10.65 for p < 0.001) . This was an unexpected finding. However, no such relationship was found for the MVD/CD105 values and the demographic, clinical, and histopathological characteristics, such as age, sex, and histopathological grading (G feature) of the HNSCC patients, as shown in Figs. The Mann–Whitney test demonstrated significantly lower MVD/CD34 ratios among patients with T3–T4 tumors compared to those with T1–T2 cancers , clinical staging (TNM), MVD/CD34, and MVD/CD105, we built a multivariate logistic regression model containing characteristics considered significant for patient survival. No characteristics influencing probability of patient survival were identified. Kaplan–Meier analysis was used to obtain total survival curves for the patient groups divided based on the MVD values, and no differences in 5-year survival were found in relation to the MVD values.Remodeling of the existing vessels and formation of new ones is the key component of the neoplastic transformation, enabling the tumor to grow. Density of microvessels (MVD) reflects the intensity of angiogenesis within the tumor. In 1991, Weidner et al. proposed the MVD assessment as one of the measures of tumor vascularity –12. HoweMany subsequent studies have used endothelial cell markers such as CD34 or CD105 for the analysis of MVD. In 2002, Schimming and Marme were the first to analyze MVD based on CD105 identification in HNSCC. They demonstrated a correlation between tumor size and the higher MVD/CD105 ratio in oral cancers . Based oIn this study, no statistically significant correlations were observed between the MVD values in HNSCCs and the tumor stage or other clinicopathological characteristics. Nor was there a correlation between the MVD values and patients’ survival. This lack of correlations could be attributed to the use of TMAs for the MVD assessment. Since microvascularization differs within the tumor, MVD is usually averaged from several or several dozen places throughout tumor cross-section, whereas the TMA technique allows for microvessel assessment only in its selected fragments. Thus, TMAs might not fully reflect the true degree of microvascularization in a tumor, as also suggested by others , 22.2 for HNSCC and 2.61/mm2 for normal mucus membranes. Bodnar et al. reported higher MVD values in a group of patients with laryngeal cancers [2) in HNSCC and 10.1 (42.61 mm2) in normal mucus membranes. Others have reported variable results [To the best of our knowledge, our study is the first to simultaneously assess MVD/CD105 and MVD/CD34 in HNSCC using TMAs. According to various reports, the results of MVD assessments using CD105 expression in HNSCCs vary over a wide range. In our data set, mean MVD in the study group was 61.34/mm cancers . Zvrko e cancers , 25. How cancers , 17, 26. results –30.Our results suggest that MVD, as currently measured, does not accurately reflect the degree of tumor vascularity. The higher MVD/CD34 values in the T1–T2 compared to T3–T4 cancers are counterintuitive and suggest that the current practice of using CD34 as a marker of MVD may be questioned. CD34 is expressed on hematopoietic cells, including early progenitor cells, and its abundance on microvessels in T1–T2 HNSCCs could be taken as an indication of an early differentiation stage of endothelial cells in the newly forming vessels. On the other hand, anti-CD34 Ab is considered as the “para-endothelial Ab” that reacts with antigens located on the surface of all endothelial cells regardless of their functional status. It was also demonstrated that anti-CD34 Abs react with the largest number of endothelial cells compared to other commonly used Abs . Indeed,The variability of the MVD data in the literature suggests a need for a closer scrutiny of the reasons behind these discrepancies. The most critical concerns relate to: (a) the disease heterogeneity, emphasized by the fact that HNSCC is a disease encompassing several distinct sites and tumor histologies; (b) the antibodies used for MVD assessments vary in their quality and specificity; (c) methods for the tissue selection and handling differ; and (d) the assessment algorithms used to quantify MVDs are selected using disparate criteria that are based on the experience of individual investigators. There is an unmet need for applying a uniform IHC methodology to MVD assessments, including the use of antibodies that are specific for all endothelial cells and are appropriately used according to the standard experimental protocol. Currently, neither endoglin nor CD34 expression adequately reflects true MVD values in tumor tissues, and the use of Abs to both antigens may be necessary to achieve realistic results. Additional studies in larger patient cohorts with antibodies specific for all endothelial cells are to define the role of MVD as a predictive parameter in HNSCC and other malignancies.


A comprehensive examination is made of physiological and transcriptional variations and genetic diversity of rapeseed genotypes differing in their response to boron deficiency, and transcriptomics-assisted QTL-seq analyses are found to expedite the identification of quantitative trait genes in plant species with complex genomes. Brassica napus L. AnAnCnCn, 2n=4x=38) is highly susceptible to boron (B) deficiency, a widespread limiting factor that causes severe losses in seed yield. The genetic variation in the sensitivity to B deficiency found in rapeseed genotypes emphasizes the complex response architecture. In this research, a B-inefficient genotype, ‘Westar 10’ (‘W10’), responded to B deficiencies during vegetative and reproductive development with an over-accumulation of reactive oxygen species, severe lipid peroxidation, evident plasmolysis, abnormal floral organogenesis, and widespread sterility compared to a B-efficient genotype, ‘Qingyou 10’ (‘QY10’). Whole-genome re-sequencing (WGS) of ‘QY10’ and ‘W10’ revealed a total of 1 605 747 single nucleotide polymorphisms and 218 755 insertions/deletions unevenly distributed across the allotetraploid rapeseed genome (~1130Mb). Digital gene expression (DGE) profiling identified more genes related to B transporters, antioxidant enzymes, and the maintenance of cell walls and membranes with higher transcript levels in the roots of ‘QY10’ than in ‘W10’ under B deficiency. Furthermore, based on WGS and bulked segregant analysis of the doubled haploid (DH) line pools derived from ‘QY10’ and ‘W10’, two significant quantitative trait loci (QTLs) for B efficiency were characterized on chromosome C2, and DGE-assisted QTL-seq analyses then identified a nodulin 26-like intrinsic protein gene and an ATP-binding cassette (ABC) transporter gene as the corresponding candidates regulating B efficiency. This research facilitates a more comprehensive understanding of the differential physiological and transcriptional responses to B deficiency and abundant genetic diversity in rapeseed genotypes, and the DGE-assisted QTL-seq analyses provide novel insights regarding the rapid dissection of quantitative trait genes in plant species with complex genomes.Allotetraploid rapeseed ( Boron (B) is an essential micronutrient for the normal growth and development of higher plants . In vascBrassica napus L., AnAnCnCn, ~1130Mb, 2n=4x=38) originated from spontaneous interspecific hybridization between the diploid progenitors B. rapa  and B. oleracea  about 7500 years ago, followed by chromosome doubling, a process known as allopolyploidy (B. napus genome (Allotetraploid rapeseed (Brassica napus is widely cultivated and is the world’s second leading crop source of vegetable oil (after soybean) for human consumption  technology, whole-genome re-sequencing (WGS) and RNA-seq/digital gene expression (DGE) profiling have been widely employed to identify genomic variations  to reveal genomic variations and transcriptional differences between B-efficient and B-inefficient genotypes under B deficiency; and (iii) to identify the candidate genes underlying B efficiency in allotetraploid rapeseed through combining analyses of QTL-seq and the DEGs. This research facilitates our understanding of the differential tolerance to B deficiency in B. napus genotypes, and provides novel insights into the rapid cloning of QTGs in diverse plant species with complex genomes.In this current research, we aimed to achieve the following three objectives: (i) to phenotypically discriminate between B-efficient and B-inefficient The B-efficient (B-deficiency-resistant) rapeseed genotype ‘Qingyou 10’ (‘QY10’) and the B-inefficient (B-deficiency-sensitive) genotype ‘Westar 10’ (‘W10’), were used to perform analyses of the phenotypic and physiological differences in response to B deficiency during vegetative and reproductive development. The leaves and roots of 10-d-old ‘QY10’ and ‘W10’ seedlings exposed to B deficiency were subjected to DGE profiling in order to identify genome-wide DEGs. The ‘QY10’, ‘W10’, B-efficient and B-inefficient pools of the doubled haploid (DH) lines derived from ‘QY10’ and ‘W10’ were subjected to WGS to identify genomic variations and delineate the QTLs or genes underlying B efficiency. Using a hydroponic culture system, the plants were cultivated in an illuminated chamber for 10 d, and 25 μM and 0.25 μM B were used as the high and low B conditions, respectively. Using a pot culture system (see http://rsb.info.nih.gov/ij/). Root hairs of fresh seedlings were examined using an Olympus SZX16 stereoscopic microscope . The pattern of accumulation of reactive oxygen species (ROS) in the root tips was detected using dihydroethidium (DHE) . The length of the non-root-hair zones (NRHZs) in root tips with 10 replicates was quantified using ImageJ (2) from the fresh seedlings were subjected to transmission electron microscopy (TEM)  to characterize differences in cell morphologies, plasma membranes (PMs) and cell walls (CWs). At the full-blossom stage, anthers and stigmas were isolated from the stamens and pistils, respectively, and subjected to scanning electron microscopy (SEM)  to characterize the PGs and mastoids.Pieces of juvenile leaves .Lipid peroxidation was evaluated by determining the malondialdehyde (MDA) concentration in the leaves and roots of fresh seedlings as described by An Illumina HiSeq 2000 system (read length = 100bp)  was used to perform WGS to distinguish the genomic variations  between ‘QY10’ and ‘W10’, which generated a total of 40 Gb of data. To construct B-efficient and B-inefficient bulk DNA, the doubled haploid (DH) population comprising 190 lines derived from ‘QY10’ and ‘W10’, was subjected to B-efficiency assessment through an integrated analysis of B-deficiency symptoms and total dry biomass under the hydroponic culture system. ‘B-efficient’ plants were assumed to be higher in total dry weight or seed yield and without obvious B-deficiency symptoms when grown under B-deficient conditions compared to ‘B-inefficient’ plants. Based on the B efficiency assessment, individuals representing the two outermost ends of the normal frequency distribution curve of B efficiency were selected from the DH population of ‘QY10’ × ‘W10’ for QTL-seq analyses. After isolation and quantification of genomic DNA and the pooling of equal concentrations of DNA to constitute the B-efficient (BE) and B-inefficient (BinE) bulk samples, we used an Illumina HiSeq 3000 platform (read length = 150bp)  to perform WGS. The high-quality homozygous SNPs between the BE and BinE bulk samples were further structurally identified and functionally annotated with the reference genome.Leaves and roots of ‘QY10’ and ‘W10’ seedlings that had been exposed to B deficiency for 10 d were subjected to DGE profiling. The total RNA of each sample was subsequently sequenced on an Illumina Hiseq 2500 platform  to generate 50-bp single-end (SE) reads.B. napus ‘Darmor-bzh’ transcriptome reference, and then the mRNA abundances of the unigenes, which were identified by TopHat (http://ccb.jhu.edu/software/tophat/index.shtml) and Cufflinks (http://cole-trapnell-lab.github.io/cufflinks/)  less than 0.05 (http://www.tm4.org/mev.html) (http://www.pantherdb.org/data/)   was estimated as described by B. napus ‘Darmor-bzh’ genome in a given genomic interval.A sliding-window approach with a 1-Mb window size and 10-kb increment was utilized to measure the average distribution of Δ(SNP-index) of the SNPs physically mapped across the Supplementary Table S1 at JXB online) were used to verify the DGE results according to a previously described protocol  assays  was performed for the analysis of all statistical tests using the software Statistical Product and Service Solutions 17.0 . All the sequencing data of the WGS and DGE profiling were submitted to the National Centre for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/) with the Bioproject PRJNA340053.Fisher’s least-significant difference (LSD) test . Furthermore, the over-accumulated MDA in ‘W10’ showed that it suffered from more severe lipid peroxidation in both the shoots and roots than ‘QY10’ , cells of both ‘QY10’ and ‘W10’ appeared structurally intact, and the chloroplasts were arrayed in an orderly manner along the plasma membranes (PMs) . HoweverSupplementary Fig. S2A), fewer siliques at the main inflorescence , and split stems  and 1000-seed weight . Of special interest, we also found that ‘QY10’ possessed a higher BEC and oil content than ‘W10’ . Thus, ‘QY10’ represents an elite germplasm resource that can be used for B-efficient rapeseed breeding in the agricultural industry.To further determine the differential responses of ‘QY10’ and ‘W10’ to B deficiency in reproductive development, they were grown in a pot culture system. Under B limitation, the floral buds of ‘W10’ displayed protruding stigmas and shorter anther filaments with abnormal morphologies compared with ‘QY10’ , leadingit stems . After lit stems , which wGiven the great differences in the fertilities of ‘QY10’ and ‘W10’ , we paidB. napus genome  . Mono-nucleotide InDels  were the most frequent type, followed by di-  and tri-nucleotides  .To identify the genomic variations between the B-efficient and B-inefficient genotypes, we performed WGS of ‘QY10’and ‘W10’, generating a total of 20 Gb (18×) data for each of them, which revealed 218 755 InDels unevenly distributed over the , C1–C9) , ranging, C1–C9) . The lenB. napus (π (average number of SNPs per nucleotide) ranged from 1.04×10–3 (chr. C5) to 3.97×10−3 (chr. A5), with an average value of π=2.15×10−3. The SNPs detected were categorized into two groups, transitions  and trans-versions , based on the nucleotide variations between ‘QY10’ and ‘W10’. Among the 1 605 747 SNPs, 920 229 (57.3%) belonged to the transition type, which was more common than the trans-versions  . With regards to the transitions, the frequency of A/G transitions  was similar to the frequency of C/T ; however, among the trans-versions, the frequency of the A/C sub-type  showed it to be the most common and C/G to be the least  . We then used the annotations of the reference ‘Darmor-bzh’ genome to examine the distribution of SNPs and InDels within various genomic features. Overall, a similar distribution of SNPs and InDels was observed . Only 11.6% of total SNPs and 11.1% of total InDels were detected in genic regions, whereas a significant proportion of SNPs (87.3%) and InDels (87.6%) were detected in 2-kb upstream (promoter), 1-kb downstream, and other inter-genic regions. Further, we analyzed the effect of SNPs on amino acid substitution. Of the SNPs present in coding sequence regions, a smaller fraction (~40%) was detected to be of the non-synonymous than the synonymous type (~60%) .In total, 1 605 747 SNPs were identified across the 19 chromosomes of B. napus , rangingB. napus . The freSupplementary Table S2). In total, 21 743 and 14 343 DEGs between the two lines were identified in the shoots and roots, respectively, which were unevenly distributed on the 19 chromosomes of B. napus . In contrast, in the roots, ‘QY10’ had more genes  with higher mRNA abundances than ‘W10’  , including the transporters principally responsible for nutrient uptake and transport , which highlights the pivotal role of roots in regulating B efficiency between B. napus genotypes.A DGE profiling strategy was employed to identify genome-wide transcriptional differences between the B-efficient and B-inefficient lines under B deficiency. A total of 155 million clean reads were obtained with an average of 13 million reads (0.65 Gb data) for each sample . Regardless of the shoots or the roots, the most highly enriched GO term for CC was macromolecular complex, while catalytic activity was the most enriched in the MF category. In the BP annotations, response to stimuli was the most enriched. In PC, transporter, transferase, oxidoreductase, and kinase were the four strongest GO enrichments.A gene ontology (GO) enrichment analysis of functional significance allowed us to distinguish major biological functions of the DEGs between ‘QY10’ and ‘W10’. The differentially expressed transcripts could be grouped into the following four categories: molecular function (MF), cellular component (CC), biological process (BP), and protein class (PC) , nitrate reductases (NIAs), glutamine synthetases (GSs), and asparagine synthetases (ASs). Generally, there existed considerable similarities between the expression profiles of these genes in the shoots of ‘QY10’ and ‘W10’ .In order to refine the genomic regions harbouring QTLs for B efficiency, QTL-seq was conducted to compare the SNP profiles among the B-efficient ‘QY10’, B-inefficient ‘W10’, and the B-efficient and B-inefficient DH line pools on a genome-wide scale. Based on B-efficiency assessment, each of the DH line pools consisted of 22 plants selected from the 190-line DH population with 61% coefficient of variation, the dry biomasses of which ranged from 0.0085 to 0.24g per plant  values of which were negative and positive, respectively. Our previous study had determined that negative and positive Δ(SNP-index) values represented QTLs contributed by the B-inefficient and B-efficient lines, respectively  data for each bulk DNA was generated from WGS. According to the definition of the SNP-index and Δ(SNP-index) in the QTL-seq analysis, we identified two genomic regions on chromosome C2 showing great differences in the indices of individual SNPs presenting differentiation between the B-efficient and B-inefficient pools, which may harbour QTLs for B efficiency . We desiqBEC-C2a interval (23–31Mb)  and a QTL for root elongation length (REL), both of which were contributed by the B-inefficient parent ‘W10’, were identified under B deficiency conditions   in the shoots and roots, respectively, between ‘QY10’ and ‘W10’  play major roles in regulating B efficiency in Arabidopsis (B. napus (Supplementary Table S3), and, among them, more attention was paid to the BnaC02g29210D gene (Supplementary Table S3) because it is highly homologous (92.8%) to AtNIP5;1 (AT4G10380), which is an influx boric acid channel essential for efficient B uptake and normal plant development under B-limitation conditions   was co-lnditions   between ‘QY10’ and ‘W10’ in the shoots and roots, respectively . Among them, particular attention was paid to BnaC02g29210D (hereafter referred to as BnaC2.ABCG21) (ABCG21. ABCG21 is a member of the ABC transporter gene superfamily (including A–G and I types), some of which have been established to drive the exchange of a wide range of compounds across many different biological membranes in terrestrial plants  , 10D becnditions , and itsnditions , indicatB. napus (‘Darmor-bzh’) genome reference sequence  and ‘W10’ , has led to the discovery of millions of SNPs and InDels across the B. napus genome. A total of 1 605 747 SNPs and 218 755 InDels were identified, with an average density of 14.2 SNPs per kb , positional cloning, comparative mapping, and association studies in B. napus. An analysis of the number of SNPs and InDels in ‘QY10’ and ‘W10’ showed that the observed number of these polymorphisms in each chromosome was significantly different from the expected number based on their physical sizes , showing an upward bias towards transitions from the expected ratio of 0.5. This phenomenon, known as ‘transition bias’, has been reported in B. napus  and the InDels (88 873) in the Cn sub-genome (n sub-genome (525.8Mb) was obviously larger than the An sub-genome (314.2Mb)  was 1.3 . Among them, particular attention was paid to the transporter and oxidoreductase categorizes. A total of 953 and 770 DEGs related to transporters were identified in the shoots and roots, respectively, of ‘QY10’ and ‘W10’ . Given the crucial roles of roots in responses to B deficiency  , which was assumed to be a key factor for the stronger resistance to B deficiency in the B-efficient genotype. In addition, ROS production in the roots was observed in response to nutrient deficiency, which may function as an important component in signalling nutrient deprivation  and the powerful ability of ‘QY10’ to scavenge ROS also highlighted its stronger tolerance to B deficiency.B deficiency caused considerable differences in the transcript levels of a wide range of genes involved in several biological processes between ‘QY10’ and ‘W10’, which might arise from their differential sensitivities to B limitation conditions. Based on GO analyses of the DEGs, transporter, transferase, oxidoreductase, and kinase were the most enriched protein classes  , with hirivation . B deficrivation , showingA combination of QTL analyses and transcriptome profiling has been used as a powerful approach for the rapid identification of functional polymorphisms and candidate genes for traits of interest (qBEC-C2a and qBEC-C2b) with opposite additive effects for B efficiency on chromosome C2  of allotetraploid rapeseed  and an ABC transporter gene (BnaC2.ABCG21) were identified as the prior candidates underlying qBEC-C2a and qBEC-C2b, respectively  to AtNIP5;1 (AT4G10380), which is an influx boric acid channel essential for efficient B uptake and normal plant development under B-limitation conditions in Arabidopsis thaliana  underlying trait-associated high-resolution robust QTL(s), thereby expediting the genomics-assisted breeding and genetic improvement of diverse plant species with complex genomes.B. napus was controlled primarily by the roots, which allowed greater B uptake and accumulation in B-efficient cultivars than B-inefficient cultivars under low-B conditions  and single nucleotide polymorphisms (SNPs) identified between the B-efficient genotype ‘QY10’ and the B-inefficient genotype ‘W10’.Figure S4. Numbers of differentially expressed genes in the shoots and roots of the B-efficient genotype ‘QY10’ and the B-inefficient genotype ‘W10’.Figure S5. Gene ontology (GO) enrichment analysis of differentially expressed genes in the shoots and roots between the B-efficient genotype ‘QY10’ and the B-inefficient genotype ‘W10’.Figure S6. Frequency distribution of total dry weight in a 190-line DH population derived from the B-efficient genotype ‘QY10’ and the B-inefficient genotype ‘W10’.Table S1. Primer sequences used for RT-qPCR assays in this research.Table S2. Overview of reads generated from Illumina Hiseq 2500 for digital gene expression profiling.Table S3. Annotated genes in the QTL qBEC-C2a region on chromosome C2.Table S4. Annotated genes in the QTL qBEC-C2b region on chromosome C2.


NOS1, PHKG1, NEU3 and PIP5K1B were differentially expressed in Beijing-You, while CDC42, CSK, PIK3R3, CAMK4 and PLCB4 were differentially expressed in Cobb, suggesting that these might be key genes that contribute to RFI. The results of the present study identified numerous novel SNPs for RFI, which provide candidate biomarkers for use in the genetic selection for RFI. The study has improved knowledge of the genomic variants and potential biological pathways underlying RFI in chickens.Improving feed efficiency is a major goal in poultry production to reduce production costs and increase profitability. The genomic variants and possible molecular mechanisms responsible for residual feed intake (RFI) in chickens, however, remain poorly understood. In this study, using both local and commercial breeds, genome re-sequencing of low RFI and high RFI chickens was performed to elucidate the genomic variants underlying RFI. Results showed that 8,505,214 and 8,479,041 single nucleotide polymorphisms (SNPs) were detected in low and high RFI Beijing-You chickens, respectively; 8,352,008 and 8,372,769 SNPs were detected in low- and high-RFI Cobb chickens, respectively. Through a series of filtering processes, 3746 candidate SNPs assigned to 1137 genes in Beijing-You chickens and 575 candidate SNPs (448 genes) in Cobb chickens were found. The validation of the selected 191 SNPs showed that 46 SNPs were significantly associated with the RFI in an independent population of 779 Cobb chickens, suggesting that the method of screening associated SNPs with whole genome sequencing (WGS) strategy was reasonable. Functions annotation of RFI-related genes indicated that genes in Beijing-You were enriched in lipid and carbohydrate metabolism, as well as the phosphatase and tensin homolog (PTEN) signaling pathway. In Cobb, however, RFI-related genes were enriched in the feed behavior process and cAMP responsive element binding protein (CREB) signaling pathway. For both breeds, organismal development physiological processes were enriched. Correspondingly,  As with most animal production systems, the cost of feed in chicken meat production is a high proportion of the total farming expense, being nearly 70% of the total cost of poultry production . The selResidual feed intake (RFI), defined as the difference between the actual feed intake (FI) and predicted requirements based on growth and maintenance, was first proposed as an alternate measure of feed efficiency (FE) by Koch et al. . RFI is MAP3K5, PEX7 and DSCAM might be interesting markers for RFI. Functional annotation of genes indicated that regulation of protein and lipid metabolism, gap junction formation, inositol phosphate metabolism and the insulin signaling pathway were significant biological processes and pathways associated with RFI [Current whole genomic sequencing methodologies are important tools to unravel the mechanisms of complex traits, facilitate new understanding of the genetic regulation of phenotype and allow the identification of potential biomarkers for early or more accurate genetic prediction . In recewith RFI . Genome-with RFI . There are more than one hundred domestic chicken breeds available across China, and collectively, they have roughly the same market share in China as do introduced commercial lines. Beijing-You chickens, one of the representative domestic chickens, are known for their peculiar characteristics including higher intramuscular fat content and chewy texture , which aAll of the animal experiments were conducted in accordance with the Guidelines for Experimental Animals established by the Ministry of Science and Technology . Animal experiments were approved by the Science Research Department  of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (IAS-CAAS) . Ethical approval on animal survival was given by the animal ethics committee of IAS-CAAS (No. IASCAAS-AE-03). 70) and end of the FE test  and were fed ad libitum with daily measurement of FI for the intervening 28 days (Days 70–98 comprised the rapid growth phase for Beijing-You). The diet was based on the Feeding Standard for Chickens established by the Ministry of Agriculture . For the 28-day period, FI (FI70–98), weight gain (WG70–98) and RFI (RFI70–98), as well as the metabolic BW (BW0.75) at 70 days were calculated. The corresponding data for 220 male Cobb chickens from 64 male families . A population of 400 Beijing-You chickens  with a similar body weight, aged 70 days, was selected from the base population of the Institute of Animal Sciences (CAAS), and these birds came from 75 male and 153 female families . Upon refamilies  were mean = 48, HRFI and LRFI, in Beijing-You chickens) were measured by standard dissection procedures. Breast muscle and liver samples from subsamples (n = 8) of HRFI and LRFI male Beijing-You chickens were cut into small pieces, rapidly snap-frozen and stored at −80 °C for quantitative real-time PCR (qRT-PCR) analyses. Likewise, samples of breast muscle and hypothalamus from HRFI and LRFI Cobb chickens (each n = 8) were collected and saved the same way. Genomic DNA was isolated from whole blood samples collected from 48 HRFI and 48 LRFI chickens (the details of the group information are shown in All libraries were sequenced on the Hiseq 2500 platform (Illumina) instrument with 125-bp paired-end reads. Reads of extremely low quality (>10 consecutive nucleotides with Phred scores < 10), with adaptor contamination or without a quality control-passed paired read were discarded using NGSQC toolkit (v2.3.3), resulting in a qualified clean dataset. More than 20× coverages were determined for each pool . All clehttp://gsa.big.ac.cn/preview/preview.action?code=WkQSr8M0 (accession no. PRJCA000311).The sequence data reported in this paper have been deposited in the Genome Sequence Archive of BIG Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences and is publicly accessible at p-values of <0.05 were analyzed further. Pairwise fixation index (FST) values were calculated using the FST-test tool in the PoPoolation2 package. SNPs with FST values in the top 5% and allelic frequency changes greater than 35%  were identified as trait-related SNPs. Genes harboring these SNPs (50 kb of upstream and downstream of the SNPs) were selected for pathway and function enrichment analyses using the Ingenuity Pathway Analysis (IPA) system (http://www.ingenuity.com) according to the default parameters. Functional and canonical pathway analyses were used to identify significant biological functions and pathways; associated networks were also constructed.To identify SNPs with frequencies that consistently differed across replicates (pools) of these divergent populations, the divergence of allelic frequency between two phenotypically-extreme groups in the two breeds of chickens was statistically tested using analysis of variance and F-tests. SNPs with Benjamini-adjusted http://www.lynnon.com/), and ratios of bases occurring at SNP locations were recorded.Nine randomly-chosen SNPs were subjected to validation by PCR analysis and Sanger sequencing using 16 phenotypically-divergent chickens. Targeted sequences were first PCR-amplified using 50 ng of genomic DNA with a Taq PCR MasterMix . Verification of PCR was performed by 1% agarose gel electrophoresis. Amplicons were then purified according to the EasyPure PCR Purification Kit’s standard protocol  and sent to Tianyi Huiyuan  for Sanger sequencing using primers described in ® SNP arrays using the Affymetrix GeneTitan® system according to the procedure described by Axiom 2.0 Target Prep 384 Samples Protocol  (https://www.thermofisher.com/). Genetic analyses were performed using ASREML [y is the vector of observations; b, the vector of fixed effects of sex; a, the vector of random direct genetic effects; e, the vector of random residual effects; X and Z are incidence matrices relating the observations to the respective fixed and direct genetic effects.In the validation population, there were 779 Cobbs , which were the offspring of the generation for WGS bird screening. The RFIs of these chickens were measured by the methods described above. One hundred ninety SNPs were randomly selected from the candidate SNPs obtained from the WGS of Cobb. Genotyping was done on Axiomg ASREML  packagesg ASREML . BreedinThe association analyses of estimated breeding values (EBVs) and SNPs were conducted using the Wald test under the R v3.1.1 environment . The ass® FAST qPCR Master Mix (2×) Universal Cocktail . A corrected Ct (ΔCt) was calculated by subtracting the β-actin Ct value from that of the target genes for each sample. Relative differences from the control sample (the HRFI groups) were then calculated according to the 2−ΔΔCT method. Differences between the two groups were analyzed using Student’s t-test for independent samples in SAS 8.0 for Windows.To investigate whether the expression profiles of the variant genes differed between HRFI and LRFI groups, key genes  that wer70 of Beijing-You and BW28 of Cobb) and the final BW (BW98 of Beijing-You and BW42 of Cobb), nor the average daily gain (ADG). Notice, as expected, how different the means are for the Beijing-You and Cobb chickens. There were, however, significant differences (p < 0.01) in daily feed intake (DFI) between the phenotypically-divergent groups in both breeds. Consequently, the differences in mean RFI values and the feed conversion ratio (FCR) between LRFI and HRFI chickens in each breed were highly significant (p < 0.01). A summary of the phenotypic measurements of Beijing-You and Cobb chickens is presented in ST values in the top 5% shown in Gallus gallus) chromosomes (GGA) 1, GGA2, GGA4 and GGA Z. After filtering with allelic frequency divergence >35% between the HRFI and LRFI groups, there were 6288 SNPs identified as candidates for RFI traits  in the HRFI compared to the LRFI birds  signaling, as well as lipid and carbohydrate metabolism were enriched . CorrespFI birds A. ST values in the top 5% shown in In Cobb chickens, 8,352,008 and 8,372,769 SNPs were detected in the LRFI and HRFI sub-groups . There wCAMK4, PLCB4 and FGFR4 in the hypothalamus were higher in HRFI chickens than in LRFI chickens  in the breast muscle of LRFI chickens compared to HRFI chickens  in Rac and actin cytoskeleton signaling were consistently higher (p < 0.01) in breast muscle of LRFI than HRFI chickens . The details of these significant SNPs, including their positions in the genome, the nearest known genes and the p-values, are given in The association analysis to validate the 191 SNPs was done using the validation population of 779 offspring from the Cobb chickens making up the WGS resource . There wGenetic improvement of economically-important traits, such as growth rate and feed efficiency, has occurred for about 100 years in the Cobb chickens. There has been no systematic genetic selection in the conservation population of the local Beijing-You chickens. Consistent with the current findings, more genomic variants exist in the Beijing-You than Cobb chickens, because the high intensity of genetic selection in the latter would be expected to result in genomic regions with increased homozygosity. There were numerous SNPs associated with RFI in the Beijing-You and a larger proportional difference in FCR (HRFI/LRFI = 1.2), suggesting a high potential for genetically-increasing feed efficiency in Beijing-You chickens. Obviously, FCR is already much lower in Cobb chickens, and there was less difference (HRFI/LRFI = 1.08) between the phenotypic extremes.PHKG1, PIP5K1B and NEU3) that participate in carbohydrate and lipid metabolism [The two breeds examined here showed some unique characteristics, and only 127 genes associated with RFI were identified in both breeds. These represented a small proportion of the total (127/1137) for Beijing-You, but much higher (127/448) for Cobb chickens. PTEN signaling, as well as lipid and carbohydrate metabolism were only enriched in Beijing-You chickens. The PTEN/PI3K/AKT signaling pathway has been shown to regulate cell metabolism, growth and survival ,17. PTENtabolism ,27,28 wetabolism  have demtabolism . TherefoPLCB4 and CAMK4 mRNA levels were higher in the hypothalamus of HRFI than LRFI chickens. The upregulation of CAMK4 mRNA levels may increase FI through induction of CREB phosphorylation and activation of downstream signaling pathways, and upregulation of PLCB4 expression may affect activation of CAMKs through changes in Ca2+ concentrations. Therefore, the CREB signaling pathway may contribute to RFI in Cobb chickens by affecting feed behavior. Compared to the Beijing-You, the Cobb is a specialized broiler breed as a result of long-term selection for increased feed efficiency and growth rate, which may have resulted in part from changes in feed behavior and its related genetic basis. For Cobb chickens, the results showed that feed behavior and the related pathway  were enriched. Feed behavior plays a key role in feed intake, and variation in feed intake is associated with variation in RFI. The CREB transcription factor has been widely investigated as a key factor in the control of appetite and FI by the hypothalamus . A previDrosophila [CDC42, CSK and PIK3R was significantly upregulated in LRFI chickens, as compared to HRFI chickens. Therefore, all of these results showed that although there were small breed differences in the participating pathways, organismal development as the common physiological process contributed to the RFI in the two breeds.Organismal development as the common physiological process was enriched in both breeds. The concept of RFI was initially developed to determine differences among animals in feed conversion into body tissue. A previous study in cattle showed a variation in RFI of 37%, which was explained by tissue metabolism, protein turnover and stress response . Global osophila . Ras-relosophila . CSK-1 wosophila . In the In order to further validate the reliability of the WGS results, 191 SNPs were selected for genotyping and association analysis. The results showed that there were 46 SNPs (48 genes) significantly associated with the RFI, which demonstrated that the WGS result was reliable, and these SNPs could prove to be interesting markers for RFI. NOS1, PHKG1, NEU3 and PIP5K1B were differentially expressed in Beijing-You, while CDC42, CSK, PIK3R3, CAMK4 and PLCB4 were differentially expressed in Cobb, suggesting that these might be key genes that contribute to RFI. This present study, with chickens of very distinct backgrounds and characteristics, offers important knowledge of biomarkers, candidate genes and potential biological pathways underlying RFI.The present study provides novel insights into genomic variants related to RFI in two very distinct breeds of chicken. A total of 3746 reliable SNPs included in 1137 genes in Beijing-You chickens and 448 genes with 575 SNPs in Cobb chickens were identified as significant variant markers associated with RFI. Forty-six SNPs were significantly associated with the RFI in an independent population of 779 Cobb chickens, suggesting that the method of screening the associated SNP with WGS strategy was reasonable. Lipid and carbohydrate metabolism and the PTEN signaling pathway were enriched in the Beijing-You, while feed behavior and the CREB signaling pathway were enriched in Cobb chickens. Organismal development as the common physiological process was enriched in the two breeds.


Pectoralis major ultimate pH (pHu) and which exhibit the muscular defect. An expression QTL (eQTL) detection was further performed for some candidate genes, either suggested by GWAS analysis or based on their biological function.White striping (WS) is an emerging muscular defect occurring on breast and thigh muscles of broiler chickens. It is characterized by the presence of white striations parallel to the muscle fibers and has significant consequences for meat quality. The etiology of WS remains poorly understood, even if previous studies demonstrated that the defect prevalence is related to broiler growth and muscle development. Moreover, recent studies showed moderate to high heritability values of WS, which emphasized the role of genetics in the expression of the muscle defect. The aim of this study was to identify the first quantitative trait loci (QTLs) for WS as well as breast muscle yield (BMY) and meat quality traits using a genome-wide association study (GWAS). We took advantage of two divergent lines of chickens selected for meat quality through Forty-two single nucleotide polymorphisms (SNPs) associated with WS and other meat quality traits were identified. They defined 18 QTL regions located on 13 chromosomes. These results supported a polygenic inheritance of the studied traits and highlighted a few pleiotropic regions. A set of 16 positional and/or functional candidate genes was designed for further eQTL detection. A total of 132 SNPs were associated with molecular phenotypes and defined 21 eQTL regions located on 16 chromosomes. Interestingly, several co-localizations between QTL and eQTL regions were observed which could suggest causative genes and gene networks involved in the variability of meat quality traits and BMY.The QTL mapping carried out in the current study for WS did not support the existence of a major gene, but rather suggested a polygenic inheritance of the defect and of other studied meat quality traits. We identified several candidate genes involved in muscle metabolism and structure and in muscular dystrophies. The eQTL analyses showed that they were part of molecular networks associated with WS and meat quality phenotypes and suggested a few putative causative genes.The online version of this article (10.1186/s12864-018-4598-9) contains supplementary material, which is available to authorized users. During the past few decades, a notable increase in the demand for poultry meat has been observed due to its convenience for cooking and processing, health benefits, and low price. To meet market demand and the world population increase, producers have had to increase their production while reducing the costs. Consequently, the production of broiler chickens has become more efficient mainly thanks to genetic selection. By comparing two genetic strains representative of the broilers being grown in 1957 or in 2001, Havenstein et al. . NeverthWS is characterized by white striations parallel to muscle fibers, mainly on breast, but sometimes on thigh and tender muscle of broilers. Histological observations of white-striped meat indicate an increase in degenerative fibers associated with regeneration phenomenon , variation in muscle fiber size, mononuclear cell infiltration, adiposis, and fibrosis . This my2 = 0.65) measured in two divergent lines of broilers selected for meat quality through Pectoralis major pHu [WS etiology remains poorly understood even if previous studies demonstrated that the prevalence of this defect is directly related to broiler growth performances , 16, 17.ajor pHu . A high ajor pHu . DifferePectoralis major pHu and which exhibit the muscular defect. A previous study showed that there is a higher incidence of WS in line with high pHu value [The aim of this GWAS was to identify QTL for WS as well as BMY and meat quality traits by taking advantage of the two divergent lines of broilers selected for Hu value . One hypAfter the first step of QTL mapping, an eQTL detection was performed for a few positional and/or functional candidate genes. This made it possible to better understand the associated molecular mechanisms, and provided candidate genes and markers that could be used in breeding programs to reduce the prevalence of WS and, more generally, to improve meat quality in broilers.2 homogeneity test was performed to characterize the distribution of normal fillets, fillets that were moderately affected, and fillets severely affected by WS defect between lines (pHu + and pHu-). The proportion of fillets with each degree of WS severity was not the same in both lines (p = 2.6 × 10− 15). There was a higher proportion of normal fillets in the pHu- line than in the pHu + line  and incidence of severe WS was higher in the pHu + line than in the pHu- line  (p = 7.5 × 10− 16).The descriptive statistics describing the distribution of carcass composition and meat quality traits of the 558 broilers used for the current GWAS are shown in Table These results are in agreement with previous studies reporting that meat with myopathy is often characterized by a higher pHu value than normal meat , 19–22.p-value was also the most significant SNP associated with WS in the entire population (Gga_rs13899127 on GGA1). Five SNPs significant at the GGA1 threshold co-localized with this SNP. Furthermore, we recovered the same SNP on GGA17 as the one associated with WS in the entire population (Gga_rs15804842).The GWAS performed on the whole population created from the two divergent lines identified three SNPs that were significantly associated with WS at the chromosome level and located on GGA1, GGA17, and GGA18 Fig. . This fi− 1 and β = − 6.5 × 10− 2, respectively). This illustrated that unless no co-localization was observed between WS and pHu QTLs, there might be an interaction between the genetic control of WS and the metabolic and physiological status of the two lines.For this SNP, the minor allele frequency (MAF) was similar in both lines , but the SNP effect was higher in the pHu + line than in the pHu- line . They defined 15 QTL regions distributed over 11 chromosomes. As for WS, these results strongly suggested a polygenic inheritance of the studied carcass and meat quality parameters.Three SNPs were significant at the genome threshold, while 39 SNPs were significant at the chromosome level for body composition and meat quality traits Table . These Sp = 3.5 × 10− 5, not shown in the table). The second one (QTL4) (90.82 to 91.20 Mb) contained three genome-wide significant SNPs for CL as well as several chromosome-wide significant SNPs for DL, lightness (L*) and yellowness (b*) of the meat. There were two common SNPs between DL, CL, L* and b* (Gga_rs1654745 at 91.09 Mb and Gga_rs14506759 at 91.11 Mb) and one more common SNP between CL and DL at 91.20 Mb (Gga_rs15648179). As illustrated in Fig. More than one third of the detected SNPs were located on GGA4 and defined two QTL regions . The first one (QTL3) was defined by a unique SNP  which controlled both PMY and BMY. Interestingly, this SNP was also close to significance for WS , one for DL and CL on GGA5 (20.55 to 20.97 Mb), and the other, for DL and b* on GGA24 (4.33 Mb).A significant positive genetic relationship between WS and intramuscular fat content (IMF) had previously been reported in this population , suggestp = 7.52 × 10− 5 and 6.60 × 10− 5 respectively).Identification of genomic regions involved in the control of WS is an opportunity to improve our understanding of the molecular mechanisms underlying the appearance of this defect. For that, we looked for candidate genes within several regions of interest. They comprised the three significant WS-QTL regions , the GGA4 region for which a pleiotropic effect with BMY was suggested, and two additional suggestive regions for which SNPs  close to significance were detected , adiposis and fibrosis (PDGFRα), extracellular matrix or sarcolemma composition , muscle metabolism (PNPLA7), and human neuromuscular disorders .Using an R software package (Gviz) , we visuMyosin Heavy chain 15 gene (MYH15) located in the region of interest on GGA1 , which is located upstream of the SNP of interest for WS and BMY on GGA4  is another gene of interest for the GGA4 region  is the closest gene to the WS QTL region on GGA7  is another interesting gene for this region . It encodes an ubiquitin-protein ligase with a role in sorting internalized cell-surface receptor proteins. Mutations in the LRSAM1 gene have been shown to cause an axonal form of Charcot-Marie-Tooth hereditary neuropathy (CMT2P), characterized by progressive muscle weakness and atrophy [, Patatin-like Phospholipase domain containing 7 gene (PNPLA7), is an insulin-regulated lysophospholipase expressed in muscle and fat. The regulation of its expression by nutritional status and insulin suggests a role in the catabolism of lipid precursors and/or mediators that affect energy metabolism in mammals [PNPLA2, is involved in neutral lipid storage disease associated with myopathy that is characterized by the accumulation of triglycerides in multiple tissues [Tubulin Beta-4B chain (TUBB4B), codes for a protein (tubulin) which is the major constituent of microtubules. Microtubules are major constituents of the cytoskeleton and have major roles in maintenance of the cell structure and intracellular transport.One candidate gene in the region of interest on GGA17  and myocardin (MYOCD) were selected. As shown in Additional file MYH1B) and neonatal (MYH1F) myosin heavy chain isoforms and a lower expression of adult myosin heavy chain (MYH1E) in the pHu + than in the pHu- muscle, which is consistent with a stronger muscle fiber regeneration process [MYH13 and MYOCD genes between normal and WS fillets [Finally, in addition to  process . In addi fillets .dysferlin (DYSF) gene (91.21–91.27 Mb) is close to the QTL region (90.82 to 91.20 Mb)  and inflammation [caveolin 3 and calpain 3) were added to the set of genes of interest both for their interaction with the dysferlin gene [Caveolin 3 (CAV3) may play a key role in the fusion of myoblasts into myotubes during the maturation process of muscle fibers. Calpain 3 (CAPN3) is an intracellular protease specific to the muscle, involved in the calcium-dependent proteolytic system. They are known to catalyze the limited proteolysis of proteins  involved in cytoskeletal modelling or signal transduction and to play a role in regeneration processes [Because of its high level of significance and pleiotropic effect on CL, DL, L* and b*, we looked for candidate genes within the QTL4 region Table . Visualiammation . Furtherammation , 39. Twolin gene , 41, andlin gene , 43. Cavrocesses , 45.COL6A3, CAV3, CAPN3, DYSF, SGCB) are associated with, or are members of the dystrophin complex. This complex contains dystrophin, which is at the origin of the genetic defect causing Duchenne muscular dystrophy (DMD) in humans, and establishes a direct link between the actin cytoskeleton and the extracellular matrix [Of the 16 candidate genes considered in the study, whether for WS or meat quality phenotypes, more than half are components of the extracellular matrix, sarcolemma, or muscles. Interestingly, several of them  and one was in the gene itself (Gga_rs15034052 at 2.04 Mb). Eleven out of these 29 SNPs were significant at the genome threshold  and located within a region containing the cluster of myosin heavy chain isoforms.The second cis eQTL relates to CAV3 gene. GWAS analysis identified 29 SNPs significantly associated with gene expression and which defined two eQTL regions, one cis eQTL on GGA12 (eQTL13)  . The second, on GGA6 (eQTL9), includes several SNPs for the MYH1B gene, and one single SNP for MYH1B, DYSF and PDGFRα (Gga_rs14581613 at 2.03 Mb). The third region is located on GGA22 (eQTL18) where two single SNPs are identified , for LRSAM1, PNPLA7 and TUBB4B gene expression. Finally, one last region appears to control several molecular phenotypes on GGA4 (eQTL6). This region is associated with the expression of FN1, MYH13, COL6A3, and CAPN3. Of the six SNPs significantly associated with these molecular phenotypes, half are associated with at least two gene expressions: one single SNP between FN1 and MYH13 (Gga_rs16454745 at 91.10 Mb), another between FN1 and COL6A3 (Gga_rs14506759 at 91.11 Mb), and finally a last between the four genes (Gga_rs15648179 and 91.20 Mb)  or genetic markers (COL6A3 and CAPN3).Among all Interestingly, several co-localizations between QTL and eQTL regions were observed on GGA4, GGA5, and GGGA17, which could suggest causative genes and gene networks involved in the variability of meat quality traits or breast meat yield.LRSAM1 gene and WS defect (eQTL15 and QTL12)  were tested for this region, only LRSAM1 was significantly regulated and appeared to be a putative causative gene. In addition to being a positional and expressional candidate gene, LRSAM1 is also a functional candidate gene, since a mutation in this gene results in a form of hereditary motor and sensory neuropathy where slowly progressive distal muscle weakness and atrophy were observed [The first co-localization concerns the region of GGA17 associated with both the expression of observed .cis eQTL for the neonatal myosin heavy chain form MYH1F (0.30 to 0.83 Mb) was also observed near the QTL associated with WS on the region of interest on GGA18 (2.77 Mb). Embryonic (MYH1B) and neonatal (MYH1F) forms of myosin heavy chain are found in breast muscle during its early development or in muscle fibers regeneration. A characterization of the muscle fibers was previously performed in the pHu + and pHu- lines. The muscle fibers of the pHu + birds were characterized by a higher number of cells expressing the developmental embryonic and neonatal isoforms of the fast myosin heavy chain [MYH1F could be an interesting molecular marker of WS in relation to the regeneration process. However, we cannot conclude that there is a single mutation affecting this molecular phenotype and WS, since the positions of the QTL and eQTL were rather distant.A vy chain , as wellvy chain . MYH1F cSGCB-eQTL (eQTL7)  and FN1, COL6A3, MYH13, and CAPN3 expression (eQTL6). This region seems to regulate meat quality phenotypes possibly related to muscle cell integrity and expression of genes involved in human neuromuscular disorders, as well as in the structure and composition of muscle fibers.Another co-localization was found on GGA4, between the previously identified pleiotropic region (QTL4) containing the SNPs strongly associated with CL, DL, and color parameters (L* and b*), and the eQTL region controlling MYH15, SGCB, and DYSF did not evidence any co-localizations with WS nor with meat quality traits. However, we cannot exclude an effect of these genes. They remain interesting candidate genes whose action could be modulated by mutations in the coding region, leading to variations in protein stability or enzymatic activity, or by post-translation modification [RN gene (Rendement Napole) in pigs where a loss of function mutation in the PRKAG3 gene is associated with an excess of glycogen content in the skeletal muscles [MSTN gene (myostatin), for which a loss of function mutation results in muscular hypertrophy in cattle and sheep [The QTL mapping revealed several regions of importance for the control of meat quality phenotypes. The eQTL analysis suggested some candidate genes and molecular networks associated with WS and meat quality traits. On the other hand, the eQTL analysis for fication . This wa muscles ; or for nd sheep . FurtherThis study reported the first QTLs for WS in chicken. It did not support the existence of a major gene, but was in favor of a polygenic inheritance of the defect and of the other studied meat quality traits. We identified several candidate genes involved in muscle metabolism and structure, and also in neuromuscular disorders for some of them. The eQTL analyses confirmed that they were part of molecular networks associated with WS and meat quality phenotypes and suggested a few putative causative genes. This study provides a first set of molecular and genetic markers for WS that will have to be validated and enriched by the study of additional genetic populations and the integration of molecular and genetic information.Pectoralis major pHu according to a breeding scheme described in Alnahhas et al. [Meat pHu reflects the level of muscle glycogen content and is a determining criterion of meat quality. In this study we used two broiler lines divergently selected for s et al.  and whicP. major were collected fifteen minutes after slaughter, snap-frozen in liquid nitrogen and stored at − 80 °C until further analysis. Breasts were visually graded a day after slaughter by one trained person. The categories of WS were defined according to a modified version of the scale of Kuttappan et al. [, PmY and thigh yield (TY) expressed in relation to body weight (BW). Breast meat quality was evaluated on Pectoralis major muscle through the measurement of color parameters , CL, DL, Warner-Bratzler shear force (SF) of cooked meat, curing-cooking yield (CCY), IMF, and TBA-RS index. All these measurements were realized as described in Alnahhas et al. [WS, body composition, and meat quality traits were evaluated in 558 birds  from the 6th generation of divergent selection (G6). Of this total, 278 broilers  were from the pHu + line  and 280 broilers  from the pHu- line . Birds were reared as a single population , following standard rearing practices and had ad libitum access to feed and water. They were produced in two successive batches and slaughtered at 6 weeks of age at the PEAT experimental unit . After hanging the birds on the processing chain, they were automatically stunned in a water bath using a constant electric current and slaughtered manually by cutting the carotid artery and the jugular vein at the ventral surface of the neck from inside the oral cavity. Samples of s et al. .The 558 birds were genotyped at Labogena Laboratory  using the Illumina chicken SNP 57 K Beadchip containing 57,636 SNPs. Quality control of the genotype data was performed using PLINK 1.9 and included animal call rate (> 95%), SNP call rate (> 95%), and minor allele frequency (> 5%). Finally, after filters, all animals were kept and 40,195 out of 57,636 SNPs distributed along 28 autosomal chromosomes were validated for further analyses.Pectoralis major muscle sampled for each individual, using RNA NOW . Ten μg of RNA from each sample were reverse-transcribed using RNase H−MMLV reverse transcriptase  and random primers . Primers targeting the studied genes were designed with Primer3 version 4.0.0 [Gene expression was determined by RT-qPCR performed on one batch of animals from the 6th generation of divergent selection , as described by Beauclercq et al. [on 4.0.0 , 52. TheMYH15 and PDGFRα genes were added to the list of candidate genes later, their expression was quantified by RT-qPCR using a Roche LightCycler® 480 II  and the Takyon® , according to the manufacturer’s recommendations. Quantitative PCR conditions were set at 95 °C for 5 min, followed by forty-five cycles of 10 s at 95 °C, 20 s at 60 °C and 10 s at 72 °C. Two references were used: PDE3B (phosphodiesterase 3B), which is invariant whatever the sex and degree of severity of WS within and between the lines, was used as the housekeeping gene to normalize the Ct values; and a mix of chicken Pectoralis major muscle cDNA. The calculation of absolute mRNA levels was based on the PCR efficiency and the threshold cycle (CT) deviation of an unknown cDNA versus the control cDNA according to the equation proposed by Pfaffl [The level of mRNA expression of candidate genes was quantified using a Fluidigm Biomark microfluidic device  according to the manufacturer’s protocol (ADP37). Because y Pfaffl . The norGenome-wide association studies were performed on classical and molecular phenotypes using an univariate linear mixed model (LMM) implemented in GEMMA (Genome-wide Efficient Mixed Model Association) software . The mody is a n-vector of phenotypes for n individuals, W the n x c matrix of covariates which contains fixed effects of sex and batch (only sex effect for eQTL detection) and including a column of 1 s for the general mean, α the c-vector of the corresponding coefficients including the intercept, x the n-vector of marker genotypes, β a p-vector of marker effects, u the n vector of polygenic effects, and ϵ the n-vector of the residuals. u follows a multivariate normal distribution: u ~ MVNn where τ− 1 is the variance of the residual errors, λ is the ratio between the genetic variance (variance explained by the SNPs) and the residual variance, and K is a known n x n relationship matrix estimated from genotypes, using a n x p matrix of genotypes (X):K where xi is the ith column representing genotypes of ith SNP, n is a n × 1 vector of 1’s. ϵ follows a multivariate normal distribution: ϵ ~ MVNn where In is an n x n identity matrix.This expression was used to calculate P-value < 1.21 × 10− 6 was considered to be significantly associated at the genome-wide threshold. The chromosome-wide significance thresholds ranged from 7.56 × 10− 6 to 3.36 × 10− 4, depending on the chromosome size.To control the family-wise error rate (FWER) in the context of multiple hypothesis testing, a Bonferroni correction at 0.05 significant level was applied for both genome-wide and chromosome-wide thresholds (significant level = α/number of SNPs). Any SNP with a Additional file 1:Figure S1. Manhattan plot showing the association of SNPs with WS in the pHu + (a) and the pHu- (b) line. Black line represents the 5% genome-wide threshold, red line the 5% GGA1-wide threshold, blue line the 5% GGA17-wide threshold and purple line the 5% GGA20-wide threshold. (DOCX 1204 kb)Additional file 2:Figure S2. Gene environment of the region of interest for WS on GGA1. The first track corresponds to the chromosome 1 where the region of interest is indicated by a red line. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP for WS is highlighted in red. (PDF 8 kb)Additional file 3:Figure S3. Gene environment of the region of interest for WS and BMY on GGA4. The first track corresponds to the chromosome 4 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The two last tracks correspond to the Manhattan plot of BMY and WS respectively, on the region of interest, where the orange line represents the chromosome threshold and the most significant SNP for WS and BMY is highlighted in red. (PDF 10 kb)Additional file 4:Figure S4. Gene environment of the region of interest for WS on GGA7. The first track corresponds to the chromosome 7 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 8 kb)Additional file 5:Figure S5. Gene environment of the region of interest for WS on GGA17. The first track corresponds to the chromosome 17 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 9 kb)Additional file 6:Figure S6. Gene environment of the region of interest for WS on GGA18. The first track corresponds to the chromosome 18 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 10 kb)Additional file 7:Figure S7. Gene environment of the pleiotropic region on GGA4. The first track corresponds to the chromosome 4 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of CL, which has the most significant SNP in the pleiotropic region. Orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 9 kb)Additional file 8:Table S1. Significant SNPs for the level of expression of the 16 candidate genes. (DOCX 36 kb)Additional file 9:Figure S8. Manhattan plot showing the association of SNPs with MYH1F expression. Black line represents the 5% genome-wide threshold and blue line the 5% GGA18-wide threshold. (DOCX 560 kb)Additional file 10:Figure S9. Manhattan plot showing the association of SNPs with CAV3 expression. Black line represents the 5% genome-wide threshold and green line the 5% GGA12-wide threshold. (DOCX 585 kb)Additional file 11:Table S2. Primer sequences of the 16 candidate genes and the housekeeping gene (PDE3B). (DOCX 14 kb)