The average MAF for both breedswas 0.27 ± 0.14 with a higher SNP proportion having high MAF values (≥ 0.3). TheLD levels and distribution of allele frequencies found in this study suggestthat it is possible to have adequate coverage throughout the genome of thesebreeds using the BovineSNP50K_v2, capturing the effect of most QTL related withproductive traits, and ensuring an adequate prediction capacity in genomicanalysis.The linkage disequilibrium (LD) between molecular markers affects the accuracy ofgenome-wide association studies and genomic selection application. High-densitygenotyping platforms allow identifying the genotype of thousands of singlenucleotide polymorphisms (SNPs) distributed throughout the animal genomes, whichincreases the resolution of LD evaluations. This study evaluated thedistribution of minor allele frequencies (MAF) and the level of LD in theColombian Creole cattle breeds Blanco Orejinegro (BON) and Romosinuano (ROMO)using a medium density SNP panel (BovineSNP50K_v2). The LD decay in these breedswas lower than those reported for other taurine breeds, achieving optimal LDvalues (r Gene alleles which are physically close in a chromosome are inherited in aninterconnected manner by inheritance properties, and these are not passed to theoffspring independently but as blocks of alleles or haplotypes provided by eachparent  (The LD is the foundation that supports gene mapping and genome-wide associationstudies (GWAS), which are important tools for the exploration of the genetic basisthat regulates the expression of economically important traits in cattle  (n=500) and Romosinuano (ROMO) (n=366) were used in this study. These weregenotyped for 54,609 SNPs using the BeadChip BovineSNP50K_v2 , and DNAQuality control (QC) procedures were applied to SNP genotype data independentlyfor each racial group according to the rules described by After performing QC, PLINK was used to estimate the minor allele frequency (MAF)for all autosomal markers included in the two data sets. According to thedistribution of allele frequencies, five categories were establishedrepresenting the proportion of SNPs with MAF values that fall within thefollowing ranges: ≥ 0.03 to < 0.1, ≥ 0.1 to < 0.2, ≥ 0.2 to < 0.3, ≥0.3 to < 0.4 and ≥ 0.4 to ≤ 0.5. Note that values that fell less than 0.03were eliminated. The results of the comparison between the two breeds wereplotted using R.2) wasused as the LD measure, indicating the ability of the alleles present in amarker to predict the presence of alleles on a second marker located on acertain genetic distance measured in base pairs .The square of the correlation coefficient between two loci , and 40,421 autosomal SNP on ROMO cattle (74.02%). Thedistribution of MAF shows a similar trend in both breeds. A higher proportion ofSNPs had high MAF values with approximately 45% of the SNPs falling into the lasttwo categories (MAF ≥ 0.3), and a lower percentage were classified in the firstcategories, with 32 to 33% of the SNPs showing a MAF value lower than 0.2 .Overall, all chromosomes followed the same trend in both breeds with a higherproportion of SNPs located in the last two categories (MAF ≥ 0.3) . In the 2). In order to consider all possible SNP pairs with a distance ofless than or equal to 100 kb between markers, 28,830 SNP combinations pairs wereobtained to estimate LD across the 29 autosomes in the BON breed, and 28,924 SNPcombinations pairs in the ROMO breed. Up to a distance of ≤1 Mb, the number ofcombinations of SNP pairs (r2 ≥ 0.1) increased to 177,395 in the BONbreed and to 222,480 in the ROMO breed.After attaining autosomal SNPs through a QC process, the level of LD betweenautosomal markers was assessed using a correlation coefficient between two loci(r2 levels > 0.9 in the first windows (1 kb), followed by arapid decline, with a quite variable trend of up to 20 kb, reaching at this point anaverage r2 levels of 0.38 in the ROMO breed and an average of 0.41 in theBON breed. From this distance the LD decay pattern becomes more stable and follows amore defined trend. At a distance of 200 kb the mean r2 found was 0.27for the ROMO breed and 0.25 for the BON breed . On the other hand, the BON breed showed anaverage r2 level of 0.26 ± 0.17, and ca. 34% of the comparisons betweenSNPs pairs show an r2 ≥ 0.3. Finally, at 1 Mb distances the ROMO breedshows LD levels of 0.23, while the BON breed shows consistently lower LD levels,with an average r2 level of 0.19 at a distance of 1 Mb between pairs ofSNPs (TableS1).In the LD decay analysis for distances greater than 100 kb and up to 1 Mb and using100 kb windows, a decreasing trend in the mean ras found , but witBosindicus), i.e. between 0.19 and 0.20 (Bostaurus), with a higher proportion of low allele frequencies (< 0.2) showing high MAF values (> 0.4) . These rTableS1). These results suggest that using the set ofSNPs contained in the BovineSNP50K_v2 chip, there is no consistency in the LD levelsthat can be expected for genomic distances of less than 20 kb. These results arehowever, consistent with those reported by The LD decay analysis of up to 200 kb using 1 kb windows is shown in 2 values larger than 0.3, and this wasconsidered as the minimum required level to develop reliable association studies andto obtain accurate genomic predictions. In this study the ROMO breed presented anaverage value of r2 = 0.3 up to a distance of 100 kb, while the BON breedshowed a faster decay, reaching the same value up to a distance of 70 kb. Theseresults differ from the average LD levels reported in other taurine breeds by 2=0.3 up to a distance of 40 to 50 kb, whileindicine breeds as Gyr and Nelore show a faster LD breakdown, reaching the samer2 value up to a distance of about 20 kb. Similar results were foundin other taurine dairy and beef breeds that reached similar r2 values fordistances less than or equal to 30 kb (2≥ 0.3) at distances close to 100 kb, which is similar to our results. On the otherhand, at distances of 1 Mb the ROMO breed showed LD levels of 0.23, while the BONbreed showed an average r2 levels of 0.19 (TableS1), and these values differ from those reportedfor other cattle breeds that generally show r2 levels below 0.1 atdistances of 1 Mb  in other cattle breeds, can be related to one or more of thefollowing factors: a higher ancestral relatedness  at distances between40 to 50 kb  in a distance of up to 70 kb forthe BON breed, and 100 kb for the ROMO breed, which is possibly associated withseveral issues: their status as a closed population, having a reduced effectivepopulation size, and being subject to a limited occurrence of recent geneticintrogression events. The LD levels and allele frequencies distribution found inthis study suggests that using the BovineSNP50K_v2 chip as a genotyping platform canresult in an adequate coverage throughout the genome for this type of Creole cattle.This in turn may allow capturing the effect of most of the QTLs associated withproductive traits and in this way an adequate prediction capacity in the genomicanalysis  can be ensured.In BON and ROMO Creole breeds a high proportion of SNPs (45%) with high values of MAF(≥ 0.3) were found, and this may be related with high LD levels found in thesepopulations. These values were superior to those reported in other taurine breedsand reached optimal LD levels (r


Root system morphology is important for sustainable agriculture, but the genetic basis of root traits and their relationship to shoot traits remain to be elucidated. The aim of the present study was to dissect the genetic basis of root traits at late growth stages and its implications on shoot traits in wheat.Among 323 wheat accessions, we investigated phenotypic differences in root traits at booting and mid-grain fill stages in PVC tubes, shoot traits including plant height (PH), canopy temperature (CT) and grain yield per plant (YPP) in a field experiment, and performed a genome-wide association study with a Wheat 660K SNP Array.Co-6A, Co-6B and Co-6D) associated with root depth at both booting and mid-grain fill stages, as well as CT and YPP. Minirhizotron system scanning results suggested that the causal genes in the three SALs may regulate root elongation in the field. The heritable independence between root depth and PH was demonstrated by linkage disequilibrium analysis. The YPP was significantly higher in genotypes which combined favourable marker alleles (FMAs) for root depth and PH, suggesting that a deep root and shorter plant height are suitable traits for pyramiding target alleles by molecular marker-assisted breeding.Deep-rooted accessions had lower CT and higher YPP than those with shallow roots, but no significant relationship was identified between root dry weight and shoot traits. Ninety-three significantly associated loci (SALs) were detected by the mixed linear model, among which three were hub SALs (These results uncovered promising genomic regions for functional gene discovery of root traits in the late growth period, enhanced understanding of correlation between root and shoot traits, and will facilitate intensive study on root morphology and breeding through molecular design. Triticum aestivum L.) is a worldwide staple food grain. As a result of global climate change and scarcity of water and nutrients, abiotic stresses increasingly curtail wheat yield  associated with the number of adventitious roots in soybean was validated in near-isogenic backgrounds as a reliable marker for waterlogging-tolerant breeding in multiple environments  investigate the phenotypic association between root and shoot traits at late growth stages; (2) dissect the genetic basis of root traits at late growth stages and its implication on shoot traits; and (3) identify hub SALs (significantly associated loci) controlling root and shoot traits.The germplasm used in this study consisted of 323 winter wheat accessions , most of–3, placed in the PVC pipes and watered to 80 ± 5 % of field capacity  eight seeds were sown in each pipe; (4) seedlings were thinned to three plants; (5) each PE bag was weighted before winter, at the jointing, booting and flowering stages, to evaluate moisture loss, and re-watered to 80 ± 5 % field capacity; (6) PE bags were withdrawn from the PVC pipes at the booting and mid-grain fill stage (All accessions were grown in polyvinyl chloride (PVC) pipes at the Institute of Crop Science Experimental Station in Beijing  from the beginning of October 2014 to May 2015, in a randomized complete block design with three replicates. The root traits were measured at the booting and mid-grain fill stages. The phenotyping workflow was as follows: (1) PVC pipes  were buried in a soil pit ; (2) polcapacity . The soill stage ; (7) rooll stage ; and (9)ll stage , shearedTo measure root traits of accessions in the field, 60 minirhizotron pipes  were buried in soil with a 45° angle of inclination. There were two pipes per plot, set 20 cm apart. The space between plots was 60 cm . Ten acc3 ha−1: before winter, at booting and at flowering when the amount of rainfall was insufficient during each corresponding period  in two growing seasons from October 2014 to June 2016, and at the Changping Experiment Station, Beijing  in one growing season from October 2015 to June 2016. These two sites and the Institute of Crop Science Experimental Station had similar environmental conditions and soil types. The experimental fields were irrigated three times with 750 mg period . Each ex® was applied to genotype all 323 wheat accessions. This array was developed from the transcriptome and genome sequence and consisted of 630 517 SNPs. The physical locations of SNPs were identified based on the IWGSC wheat genome sequence (IWGSC RefSeq v1.0). After removing nucleotide variations with missing rates ≥0.2 and minor allele frequency (<0.05), 395 675 SNPs were used for GWAS (Genomic DNA was extracted from seedling leaves according to the protocol of for GWAS .H2) and a combined mixed linear model-based analysis of variance were calculated using the analysis of variance (ANOVA) procedure in IciMapping 4.1 software  was set from 2 to 9. For each k, ten runs were performed separately; each run was carried out with 100 000 iterations and 100 000 burn-in periods. A Neighbor–Joining tree was constructed using the Megacc software on the basis of a distance matrix, using the whole-genome SNPs shared by all the accessions. Principal component analysis (PCA) was performed via GCTA software. Marker–trait relationships were analysed using both general linear models (GLMs) and mixed linear models (MLMs) in TASSEL 5.0 software,. Population structure was represented by the first five principal components, which were fitted as fixed effects; kinship (family relatedness) was used to define the random variables for the total genetic effects of the 323 accessions. Because the Bonferroni correction at the 0.05 level (0.05/395 675 = 1.26 × 10–7) was too conservative and could lead to false negatives (10(P) >4 was used to declare the significance of SNP–trait associations. Linkage disequilibrium (LD) was estimated as the squared allele frequency correlation (r2), for each SNP pair on the same chromosome, and r2 values were evaluated with the −r2 command in the software PLINK 1.9. An LD decay graph of each chromosome was plotted according to the average value of r2 of SNP pairs within 100 kb; step size was set to 100 kb. The pairwise r2 values were averaged across each sub-genome and the whole genome to get the LD delay graph of the corresponding genome. The extended region where LD between the significantly associated SNP and nearby SNPs decayed to r2 = 0.5 was regarded as the candidate region. For the same trait, each candidate region or overlapping candidate regions was categorized as an SAL; for different traits, the degree of linkage between each two SALs in the same chromosome was represented by their average LD using a previously reported method (C. The population structure of the 323 wheat accessions was assessed using 1800 SNP markers (at least 5 Mb apart and uniformly distributed across the genome) with the software STRUCTURE 2.3.4 based on admixture and correlated allele frequency models. The number of sub-populations (P < 0.001), and among environments ; further, PH, YPP and CT of each genotype responded differently to environment  (H2) for all traits were moderate to high, ranging from 56.0 % for YPP to 94.6 % for PH, suggesting that these traits were suitable candidates for genetic analysis.The mean value of root depth increased 97.2 % from booting (57.4 cm) to mid-grain fill (113.2 cm), and the root dry weight increased 72.0 % from 0.25 to 0.43 g per plant during the same period. These results indicated that wheat roots rapidly elongated from the booting to mid-grain fill stages . The coe < 0.01) . Broad-sCorrelation analysis showed that the same root traits measured at different growth stages were positively correlated, with Pearson correlation coefficients of 0.65 for root depth and 0.51 for root dry weight. The only correlation between root depth and root dry weight was a significant but weak relationship of 0.15 at mid-grain fill . AlthougTo clarify further the effect of root depth on shoot traits, RDB and RDG of the 323 accessions were set into three types separately: deep (root depth > mean + s.d.), intermediate (mean + s.d. > root depth > mean – s.d.) and shallow (root depth < mean – s.d) . The avek = 2) according to the methodology of Evanno (r2) dropped to 0.5 at 0.5 Mb for the whole genome  . Consideectively .P-value in GWAS). Each peak SNP has two types of marker alleles, one is responsible for higher yield and is called the favourable marker allele (FMA). We observed that YPP increased and PH decreased along with the combination of FMAs associated with YPP and PH simultaneously  for common FMAs associated with YPP and PH was 0.046, three times that of FMAs controlling YPP and RDG (0.015) , group II (1970s), group III (1980s), group IV (1990s), group V (2000s) and group VI (post-2010). We observed that the average frequency  of common FMAs associated with YPP and PH ranged from 50.4 to 68.9 %, higher than that of common FMAs associated with YPP and RDG (ranging from 26.0 to 35.8 %) . In addi (0.015) , suggestCo-6A, Co-6B and Co-6D, because these not only control YPP and CT, but also exhibited stable associations with root depth at different growth stages. The peak SNPs detected by MLM for different traits in each of the three loci are the same or highly linked. To be specific, for Co-6A, AX-110520701 is the shared peak SNP for RDB and RDG, AX-111048945 is the peak SNP for YPP and CT, and the LD between these two SNPs is 0.88; for Co-6B, AX-94940485 is the shared peak SNP for YPP, CT and RDB; for Co-6D, AX-108848017 is the peak SNP for CT, AX-94788899 is the peak SNP for RDB and RDG, and the LD between these two SNPs is 1.00. These results implied that the causal genes for different traits in Co-6A, Co-6B and Co-6D are the same or highly linked. Using the TriAnnot Pipeline, genes in the three loci were predicted. Interestingly, the peak SNPs in Co-6A, Co-6B and Co-6D were all near to predicted transporter genes, in particular for AX-94940485 on Co-6B, which were inside the predicted transporter genes, and it is noteworthy that 13 transporter genes were within each of the three loci , with sequence identities ranging from 80.2 to 100 %  in Co-6A, Co-6B and Co-6D. Group II consisted of five accessions with alternative shallow root marker alleles in each peak SNP. The average root depths in PVC pipe culture of Group I accessions at booting and mid-grain fill were 68.7 and 147.3 cm, respectively, compared with Group II with corresponding values of 48.1 and 73.9 cm. Scanning by the minirhizotron system showed that the root depth in the field was significantly correlated with root depth in the PVC pipe culture (Pearson correlation coefficient was 0.797 at the booting stage), and average root depths in the field of Group I were 109.3 cm at booting and 127.3 cm at mid-grain fill (maximum depth of the minirhizotron was 127.3 cm), significantly deeper than those of Group II   . Furtherctively) . These rin situ, permitting observations on root traits at different growth stages in the field.Measurement of root traits at the flowering and grain development growth stages is very difficult. There are two major phenotyping methods, namely destructive methods   . It may Co-6A, Co-6B and Co-6D) controlling RDB, RDG, CT and YPP. Using a minirhizotron system, we found that total root length of accessions with favourable marker alleles of peak SNPs at Co-6A, Co-6B and Co-6D were much longer than those of accessions with alternative marker alleles in these peak SNPs, suggesting that the causal genes at these SALs were associated with root elongation and account for differences in root depth; hence the association with canopy temperature and grain yield. Interestingly, 13 NRT2 genes were located in each of the SALs and were close to the peak SNPs. The NRT2 gene family encodes proteins that were identified as components of the homological AtNRT2.1 high-affinity transport system, which is expressed mainly in roots. In rice, upregulated expression of OsNRT2.1 improves nitrogen utilization efficiency and yield (OsNRT2.4 resulted in a 20–40 % decrease of total later root length (NRT2 genes. Our results pinpoint the significance of NRT2 genes in wheat improvement. Work is underway to verify the relationship and to elucidate the mechanism of the effectg NRT2 genes on root growth in wheat, and thus the relationship among root traits and shoot performance.The hub SALs identified are expected to possess functional genes with application value (https://academic.oup.com/aob and consist of the following. Table S1: information on 323 wheat accessions. Table S2: summary of the significantly associated loci detected by GLM. Table S3: summary of the significantly associated loci detected by MLM. Table S4: protein sequence similarity between reported wheat NRT2 genes and predicted NRT2 genes in Co-6A, Co-6B and Co-6D. Figure S1: frequency histogram of days to flowering for 323 winter wheat accessions. Figure S2: the density of ﬁltered SNPs used for GWAS. Figure S3: root type classification at the booting stage and comparison of shoot traits. Figure S4: the population structure of the 323 wheat accessions. Figure S5: decay of linkage disequilibrium for each chromosome and sub-genome. Figure S6: GWAS results for root and shoot traits based on the general linear model. Figure S7: associations between polymorphisms within hub SALs and the phenotypic variations, canopy temperature, root depth at booting stage and root depth at mid-grain fill stage, based on the mixed linear model. Figure S8: physical location of peak SNPs and predicted NRT2 genes within Co-6A, Co-6B and Co-6D.Supplementary data are available online at mcz041_Suppl_Supplementary_Figure-S1Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S2Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S3Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S4Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S5Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S6Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S7Click here for additional data file.mcz041_Suppl_Supplementary_Figure-S8Click here for additional data file.mcz041_Suppl_Supplementary_Table-S1Click here for additional data file.mcz041_Suppl_Supplementary_Table-S2Click here for additional data file.mcz041_Suppl_Supplementary_Table-S3Click here for additional data file.mcz041_Suppl_Supplementary_Table-S4Click here for additional data file.


The identification of genes involved in variation of peach fruit quality would assist breeders to create new cultivars with improved fruit quality. Peach is a genetic and genomic model within the Rosaceae. A large quantity of useful data suitable for fine mapping using Single Nucleotide Polymorphisms (SNPs) from the peach genome sequence was used in this study. A set of 94 individuals from a peach germplasm collection was phenotyped and genotyped, including local Spanish and modern cultivars maintained at the Experimental Station of Aula Dei, Spain. Phenotypic evaluation based on agronomical, pomological and fruit quality traits was performed at least 3 years. A set of 4,558 out of a total of 8,144 SNPs markers developed by the Illumina Infinium BeadArray (v1.0) technology platform, covering the peach genome, were analyzed for population structure analysis and genome-wide association studies (GWAS). Population structure analysis identified two subpopulations, with admixture within them. While one subpopulation contains only modern cultivars, the other one is formed by local Spanish and several modern cultivars from international breeding programs. To test the marker trait associations between markers and phenotypic traits, four models comprising both general linear model (GLM) and mixed linear model (MLM) were selected. The MLM approach using co-ancestry values from population structure and kinship estimates (K model) identified a maximum of 347 significant associations between markers and traits. The associations found appeared to map within the interval where many candidate genes involved in different pathways are predicted in the peach genome. These results represent a promising situation for GWAS in the identification of SNP variants associated to fruit quality traits, potentially applicable in peach breeding programs. Prunus persica (L.) Batsch] is a model plant inside the family Rosaceae mainly due to its relatively small genome , stone adhesion-flesh texture (F-M), fruit shape (S) or sub-acidity  have previously been used for SNP genotyping in soybean  is the non-random association of alleles at distinct loci in a sample population. AM is now being largely applied, using SSRs, to many crops, such as maize , potato  soybean , wheat  Batsch] cultivars  were used in this study, as described by A germplasm collection of 94 peach and nectarine [2), yield efficiency (kg/cm2), fruit weight (g), flesh firmness (N), soluble solids content , titratable acidity , and ripening index . Also, biochemical traits as vitamin C , anthocyanin content , total phenolics , flavonoid content , relative antioxidant capacity , and sucrose, glucose, fructose, sorbitol, and total sugars (g/kg FW) were evaluated. All the procedures used in this study were previously described . The platform was developed by the International Peach SNP Consortium (IPSC)  following the manufacturer’s instructions. DNA concentration and quality was checked using PicoGreenon assay  and Illum (IPSC) , which i2.They also include SNPs identified from Illumina 80 bp paired-end genome sequencing of 22 important founder peach accessions  and the almond cultivar ‘Nonpareil’  Naïve-model (GLM without any correction for population structure); (b) Q-model (GLM with Q-matrix as correction for population structure); (c) QK-model (MLM with Q-matrix and K-matrix as correction for population structure and kinship relationships); (d) K-model (MLM with K-matrix as correction for kinship relationships structure). A structured association approach could avoid spurious associations and the results were compared to determine the best model. The significance of marker-traits associations was declared at P < 0.05 and a standard correction was performed by applying Bonferroni procedure at P < 0.00001097  was calculated using a simple regression equation implemented in GLM procedure in TASSEL.Association analysis between DNA marker and agronomical and fruit quality traits was conducted using the software TASSEL v3.0 . Differe00001097 . Alleles00001097 . Among tribution . To deteAll the bioinformatics analysis were performed on local servers at the UC Davis Genome Center . Reads were mapped to the reference sequences from the peach genome (Peach v1.0).Computer simulations were used to determine the power of detection of QTLs with the current population and marker set. For this analysis, a modified version of the phenosym R program  was usedThe Tassel pipeline initially identified 8,144 SNP loci distributed across all major scaffolds of the peach genome. Data were filtered to remove SNPs with low read support. Data for individual SNPs were retained only if they possessed polymorphic markers , and with the gene train score lower than 0.4 . In addition, data were filtered after removing the markers with similar pattern and with minor allele frequency (lower than 5%). After discovery and amplification on the Infinium HD BeadChips Illumina, 4,558 high quality SNPs remained for the final analysis of genetic structure and association studies, as summarized in Table K value of 2  and Q-model, detected a large number of associations between the markers and phenotypes which after Bonferroni correction for multiple testing of accessions, reduced down the number of associations. The Q-model showed 66 associations for the evaluated traits. It appears that these models may not have accounted for the heterogeneity of the genetic background, which may have resulted in false positive associations.P-values (P < 0.00001298), while the other models were characterized by the excess of small P-values  and those used in the present analysis (SNPs). Although SNPs are less polymorphic than SSR markers because of their biallelic nature, they easily compensate this drawback by being abundant, ubiquitous, and amenable to high- and ultra-high-throughput automation , and blooming and harvest dates  in the promoter of the candidate gene ppa025636m that was associated with total sugars content and sorbitol, respectively, in LG4 and in the interval comprised between 18,085 to 20,165.259 bp. Also, the SNP_IGA_450711 position, is in agreement with the position of CPPCT028 SSR marker found in the study of 5. Second one, the lower region of the LG6 and close to marker CPPCT030 and one gene, ppa006828m, were identified very close to the position of SNP_IGA_700469, and associated with harvest date and sorbitol content. It may be responsible for the protein involved in peroxisome biogenesis5. This gene is expressed during the flowering stage, the mature plant embryo stage, and the petal differentiation and expansion stage. This is a fatty acid oxidation process that results in the complete oxidation of a long-chain fatty acid and a fatty acid beta-oxidation begins with the addition of coenzyme A to a fatty acid, and occurs by successive cycles of reactions during each of which the fatty acid is shortened by a two-carbon fragment removed as acetyl coenzyme A5. This candidate gene is also in agreement with that proposed on almonds trees  play a role in the biosynthesis of lutein, which is a member of the carotenoids pathway  to know if there is any impact on the results of GWA mapping in peach. Peach association mapping is an alternative to QTL mapping based on crosses between different cultivars because of the multiple advantage compared to bi-parental populations. Finding specific regions of the genome will provide further information searching for the genes involved in peach fruit quality, and to identify new candidate genes. Additionally, this work provide promising results concerning association mapping with pomological traits that could be applied in other CFiF and MM designed the study and the experiments. MM provided the material from the peach germplasm collection. CFiF, VG, and SC-W performed the statistical and biochemical analysis. CFiF, VG, and MM discussed the data, wrote and reviewed the manuscript. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL (quantitative trait loci) for recombination rate in two purebred layer chicken lines.A total of 1200 white-egg layers (WL) were genotyped with 580 K SNPs and 5108 brown-egg layers (BL) were genotyped with 42 K SNPs (single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each 0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL, respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.Differences in the recombination rates, hotspot locations, and QTL regions associated with genome-wide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.The online version of this article (10.1186/s40104-019-0332-y) contains supplementary material, which is available to authorized users. Charactng genes . Recombing genes .Recombination rates have been reported to differ by sex –6, chromRNF212 (ring finger protein 212), which controls genome-wide recombination rate in males and females [PRDM9 (PR domain containing 9), which has been identified as a regulator of recombination hotspots [PRDM9, and their recombination is concentrated at gene promoters [PRDM9 [.Genome-wide association studies (GWAS) have identified several QTL that regulate genome-wide recombination rates in humans , 18, 19, females , 24 and hotspots . It is rromoters , 27. Thes [PRDM9 .Recombination rates assessed from high density SNP panels have been less thoroughly investigated in chickens. Crossover events were concentrated in promoters and CpG islands based on the whole-genome re-sequencing in 11 collared flycatcher . GroenenTwo chicken breeds were examined: white egg layers (WL) and brown egg layers (BL). The genotyping data was available for 448 half-sib families (282 sires and 166 dams) averaging 3.5 ± 2.7 birds per family for a total of 1200 birds in the WL line. This consisted of 969 sire-offspring pairs and 332 dam-offspring pairs. In BL line genotypes were available for 1717 half-sib families (621 sires and 1096 dams) with an average of 6.0 ± 4.9 birds per family for a total of 5108 birds. This consisted of 4719 sire-offspring pairs and 4573 dam-offspring pairs, respectively.Gallus gallus  autosomes (GGA) and the Z chromosome remained. This 173 K (173 K-WL) data was subsequently randomly trimmed down to 23 K (23 K-WL) to simulate a lower density panel. Sex chromosome information was not used for recombination detection but was included in GWAS analysis.The WL birds were genotyped using the Affymetrix 580 K SNP chip. After removing SNPs with call rate < 0.95, minor allele frequency (MAF) < 0.025, or Mendelian inconsistency rate between parent-offspring > 0.05, a total of 172623 (173 K) segregating SNPs across 28 The BL birds were genotyped using an Illumina 42 K chip. Using the same quality criteria as for 173 K-WL, 22956 segregating SNPs were retained resulting in a 23 K-BL panel, including 5510 SNPs overlapping between the 173 K-WL and 23 K-BL panels.Missing genotypes (~ 0.006% in WL and ~ 0.01% in BL) were imputed using FImpute .cw) within each window (window recombination rate) was computed within line as:n is the total number of recombination events observed on the corresponding whole chromosome, xi is the length of overlapped region (in Mb) between the 0.5-Mb window and recombination interval i, ri is the length (in Mb) of the recombination interval, and T is the total number of parent-offspring pairs. Window recombination rates were estimated across all parents and for sires and dams separately.Recombination events were determined on autosomes within half-sib families within each line using LINKPHASE version 3.0 . Only haFor each chromosome, the 10% of 0.5-Mb windows with the highest recombination rates were considered to be recombination hotspots regions and windows with no recombination detected across all individuals were considered to be cold spots. The genome-wide recombination number (GRN) was calculated for each parent as the average number of recombination events over 28 autosomes per meiosis. A t-test was used for significance testing of GRN between white and brown layers. The genome-wide hotspot usage (GHU) was calculated for each parent as the proportion of recombination events for that parent that fell within hotspots regions across the genome.The effect of marker density was evaluated only in the WL, by comparing whole-genome recombination number and 0.5-Mb window recombination rates from the 173 K-WL and the simulated lower density 23 K-WL panels.r2 > 0.5 within each non-overlapping 50-SNP window. The relationship between genomic inbreeding coefficient and GRN was assessed in both lines.Genomic inbreeding coefficients were calculated for each individual based on the observed against the expected (under Hardy-Weinberg Equilibrium) number of homozygous genotypes using PLINK v1.07 . To miniHaplotype blocks with a fixed length of 0.5 Mb were constructed using the galGal4.0 map assembly. The number of common haplotype alleles was computed, which is the number of haplotype alleles with at least 1% frequency in a 0.5-Mb window. The number of common haplotype alleles was determined for every 0.5-Mb window in each of the 2 lines.The relationships of recombination rate with chromosome size (Mb), GC content, and CpG island density, in each 0.5-Mb window, was assessed in both lines. The GC content and CpG island density on each chromosome was calculated for each 0.5-Mb window using the hgTables tool from the UCSC genome browser .y is the vector of repeated GHU or GRN observations for each parent, b represents the means for each gender of parents treated as fixed effects, u is the vector of random animal genetic effects with A is the pedigree relationship matrix among parents and p is the vector of permanent environmental effects with X and Z are design matrices, and e is the vector of residual effects, with Since recombination was detected for each parent-offspring pair, parents with multiple offspring had repeated records, which allowed estimation of heritability and repeatability of GHU and GRN. This was undertaken separately for each line using a repeatability model in ASReml3.0 , represeπ equal to 0 [y, X, and b are as for model [M is a n × m matrix of SNP genotype covariates , n is the number of parents, m is the number of SNPs, α is a vector of random allele substitution effects for each SNP, and e is a residual effect with heterogeneous variance according to the number of offspring. The weighting factor En [h2 is the narrow sense heritability estimated from pedigree, t is the repeatability, and n is the number of observations contributing to the average GHU or GRN for that parent.Marker-based heritabilities of GHU and GRN were estimated using the average GHU or GRN for each parent in a weighted BayesC model with ual to 0 , 36 implual to 0 , 38. Theentclass1pt{minimaactor En , 40 was π equal to 0 is that every SNP effect in [αj is the marker effect for SNP j, and The prior assumption of BayesC with ffect in  follows π of SNP to have zero effects, and the non-zero distribution of SNP effects followed an identical and independent univariate-t distribution with null means, an inverse chi-squared prior for the SNP variances with degrees of freedom υa, and scale parameter π were 0.999 in WL and 0.99 in BL so that the number of fitted markers per MCMC iteration was about the same for the two lines, accounting for the difference in SNP density between the two lines.A genome-wide association study (GWAS) of average GRN for each parent was performed for each line using a weighted BayesB method , 42 implment}Sa2 , 38. PriThe expected proportion of genetic variance (GV%) explained by each non-overlapping 1-Mb region is approximately 0.04% under a polygenic model. Those 1-Mb regions that explained at least 0.8% (> 20 times the expected %) of the genetic variance and their flanking regions on both sides of these regions (±1 Mb) were considered as QTL regions , 37, 43.The window posterior probabilities of association (PPA) of each QTL region, which is the percentage of samples for that region that contained at least one non-zero effect SNP for the trait, was also calculated. The SNP with the highest SNP posterior probability of inclusion (SPPI) was selected as the lead SNP for each QTL region. The SPPI was defined as the proportion of MCMC samples in which that SNP had been sampled with non-zero effects. Each lead SNP was fitted separately from the other SNP in the QTL region in a separate BayesB GWAS analysis described above, to estimate the GV% explained by that SNP . Single P < 0.0001). Figure The average recombination rate across each 0.5-Mb window in each of the 28 chromosomes estimated from the 23 K-WL and 23 K-BL SNP data sets was calculated for the WL and the BL lines and is summarized in Table Among the 190 and 199 recombination hotspots regions detected within 173 K-WL and 23 K-BL SNP sets respectively, 28 were common to both lines. There were 551 and 45 recombination cold spots detected in WL and BL, respectively, of which 22 were common to both lines. Overall, 14746 and 215808 recombination events were detected across the genomes in 1301 and 9292 meioses in WL and BL, respectively. The average GRN per meiosis were 11.33 in WL and 23.22 in BL. Several strategies were used to further investigate the cause of the higher estimate of recombination rate in the BL compared to the WL.P = 0.0046) than the average recombination rate obtained when using 173 K SNPs (0.013 ± 0.0070). The correlation of recombination rates on GGA1 based on these two different sized sets of segregating SNPs in white layers was 0.68. The average number of SNPs within a 0.5-Mb window was 103.3, 12.5, and 12.2 on GGA1 in 173 K-WL, 23 K-WL, and 23 K-BL, respectively. The average GRN detected in 23 K-WL was 8.58 ± 0.045, which was significantly lower (P < 0.0001) than the average GRN using 173 K-WL (11.89 ± 4.13), and both of which were lower than that detected in 23 K-BL (23.78 ± 4.37). A similar result was seen for other chromosomes, even if their size was different, and for the sex chromosome.GGA1 was used as an example  than brown layers (0.031). The correlation between individual GRN and the genomic inbreeding coefficient was − 0.19 in WL and − 0.47 in BL. The regression coefficient between individual GRN and the genomic inbreeding coefficient was − 13.44 in WL and − 26.19 in BL. Crossover events that occur within a long chromosome segment of homozygous SNPs cannot be identified. Individuals with higher genomic inbreeding coefficients had more and longer lengths of homozygous regions, which might hinder the identification of recombination events. The 42 K panel had 23 K (55%) segregating SNPs in the BL, whereas the 580 K panel had only 173 K (30%) segregating SNPs in the WL. Although, panel creation is subject to ascertainment bias, neither panel was specifically created for either of these lines, and the segregation percentages imply that the WL was more inbred than the BL, in agreement with unpublished knowledge of the history of the line.P < 0.0001) than that in BL (10.90 ± 3.33), which is consistent with the WL being more inbred than the BL. The correlation between number of common haplotype alleles and window recombination rate was 0.49 in WL and 0.71 in BL. Recombination hotspots regions had significantly higher number of detected common haplotype alleles, compared to cold spots, in both lines. In WL, the numbers of common haplotype alleles were 6.60 ± 3.29 and 2.95 ± 1.21, in hotspots and cold spots regions, respectively. In BL, the numbers of common haplotype alleles were 15.78 ± 3.19 and 4.98 ± 0.46, in hotspots and cold spots regions, respectively.Figure Recombination breaks down haplotype alleles inherited from parental generations and creates new haplotype alleles. Fewer haplotype alleles were observed and more homozygous haplotype alleles were preserved in windows with lower recombination rate. The BL had a much higher detected recombination rate and a corresponding larger number of common haplotype alleles within 0.5-Mb window than WL. The prevalence of homozygous haplotype alleles is expected to reduce the number of identifiable recombination events.Based on the above results, the observed window recombination rates and GRN differed significantly between the 2 lines, due to the breed-specific characteristics of recombination events. Groenen et al.  reportedP < 0.0001) and BL . In Fig. P < 0.0001) and BL (P = 0.040).Table P = 0.0001) and BL (P = 0.0052). Based on the random samples, dams (16.36 ± 1.10) had higher GRN than sires (11.11 ± 0.52) in WL. In BL, GRN was 23.27 ± 0.20 in dams, and 22.38 ± 0.16 in sires.In WL, the average size of half-sib families was 4.0 ± 3.0 for sires, and 2.5 ± 1.5 for dams. In BL, the average size of sire families was 8.0 ± 5.7 and 4.7 ± 4.0 for dam families. Random samples of 10 sire and 10 dam half-sib families (10 offspring per family) were used to recalculate 0.5-Mb window recombination rates and average GRN, separately in sires and dams, in order to avoid the impact of numbers of observations and family sizes on identification of recombination events between the sexes. This did not change our findings, as the average window recombination rate was still higher in dams than sires in both WL  and brown layers (P < 0.0001). The average 0.5-Mb window recombination rates in WL were: macrochromosomes = 0.0067 ± 0.0092, intermediate chromosomes = 0.0069 ± 0.0091, and microchromosomes = 0.0085 ± 0.013. Whereas, in BL the respective window recombination rates were: 0.011 ± 0.0086, 0.016 ± 0.013, and 0.022 ± 0.018 on macrochromosomes, intermediate chromosomes and microchromosomes. A negative correlation between the average window recombination rate and chromosome length was observed in both the white (− 0.16) and brown (− 0.50) layer lines. Rodionov [The average window recombination rates changed depending on chromosome size, with recombination rates per 0.5-Mb on the microchromosomes being significantly higher than on the macrochromosomes for both white  and CpG island density (P = 0.042) in both lines. The association of recombination with GC content and CpG island density was stronger in BL than in WL. Although it is known that recombination rate is related to genome structure, the strength of this relationship varies among organisms. For example, GC content is positively correlated with recombination rate in mammals [In WL, the correlation of recombination rate was 0.13 with both GC content and CpG island density , was identified on GGA20 near 10 Mb in WL. SPO11 produces a meiosis-specific protein, which could initiate recombination [SPO11 induces double-strand break during meiosis in mice. Weng et al. [SPO11 as a positional candidate gene for genome-wide recombination number in Limousin beef cattle.One of the strongest candidate genes, bination  reportedg et al.  identifiPRIM2 , which is located at 86.05–86.13 Mb. It was reported that PRIM2 is active in both the initiation of DNA replication and synthesis [IGF2 (insulin-like growth factor 2), MRPL23 , RAG1 (recombination activating gene 1), and RAG2 (recombination activating gene 2). IGF2 is a growth factor that controls hormone activity, regulation of mitosis, and cell differentiation. MRPL23 is involved in the cellular process of translation and integration of membrane and ribosome. The proteins RAG1 and RAG2 carry out the formation of double-stranded breaks at recombination signal sequences [One candidate gene was identified on GGA3, namely ynthesis . The posequences .RNF212 on GGA4 was identified as influencing GRN in BL. The sequence variants in RNF212 are reported to affect genome-wide recombination rate in males and females in humans [The signal near n humans , 23. Itsn humans , 21, 46.RECQL (RecQ protein-like) located at 65.47–65.49 Mb, was found on GGA1. RECQL is involved in various types of DNA repair, including mismatch repair, nucleotide excision repair, and direct repair [RECQL was associated with GRN in Angus cattle [CDH10  located at 72.18–72.27 Mb, and MYC  located at 139.31–139.32, which plays a critical role in DNA replication, cell growth and cell cycle progression. The candidate gene of FANCD2  was identified on chromosome 12 at 2 Mb, which promotes gene conversion and DNA repair.One candidate gene, t repair . The winSPO11 in WL and RNF212 in BL. Other promising candidate genes include RAG1, RAG2, and RECQL, although further investigation is required. The different mapping results between WL and BL, and between chickens and other organisms  suggest that recombination is a species-specific polygenic trait.Only two genes previously identified as influencing recombination rate were present in regions detected in this study: P < 0.0001) from BL (20.3% ± 5.4%). Gender effects on GHU were significant in both lines . The average GHU was 9.1% ± 6.2% for sires and 10.4% ± 5.2% for dams in WL. The average GHU was 20.2% ± 4.6% for sires and 20.9% ± 5.8% for dams in BL. Heritability and repeatability estimates were 0.10 ± 0.041 and 0.14 ± 0.037 in WL and 0.10 ± 0.015 and 0.14 ± 0.014 in BL. These two estimated heritabilities were lower than the 0.21 value computed in cattle [Individual GHU ranged from 0 to 50% in WL and 0–83.8% in BL. The average GHU in WL was 9.6% ± 5.9%, which was significantly different  in WL and BL (results not reported). In WL, 6 QTL regions were identified on GGA3, 9, 10, 20, 21, and 28. In BL, 11 significant QTL regions were located on the Z chromosome. No QTL regions overlapped between the two lines, and no candidate genes were identified due to limited gene annotation information. Genetic variants controlling GHU might differ between breeds and species.Several studies have shown that the hotspots , 25. Parhotspots  observedGenome-wide recombination patterns and rates were characterized in two egg laying chicken breeds: white layers and brown layers. A large number of recombination events and recombination hotspots regions were identified. The BL were genotyped at lower density but had a higher number of detected recombination events than the WL, which were genotyped at higher density. Marker density can influence the identification of recombination events but different detectable recombination events were found between the 2 lines even with comparable SNP densities. Differences in the recombination rates (numbers) and hotspot locations were observed between lines, showing the breed-specific feature of detected recombination events. This breed-specific characteristic of detected recombination events may be due to inbreeding levels, haplotype structures, genetic variants, or other differences between the two breeds studied. The frequencies of recombination events were found to differ by sex. Dams had higher window recombination rates and more recombination events per meiosis than sires. Window recombination rate showed a negative correlation with chromosome size and positive correlations with GC content, and CpG island density in the two lines. Several QTL regions associated with genome-wide recombination were identified in the two breeds, suggesting that the control of recombination events is a complex polygenic trait.Additional file 1:Figure S1. Variation in recombination rate within 0.5-Mb windows across the 28 autosomes (except GGA6). The black line corresponds to recombination rates estimated from segregating 173K SNPs in WL. The grey line corresponds to recombination rate estimated from segregating 23K SNPs in BL. (PPTX 127 kb)Additional file 2:Figure S2. Recombination rate within 0.5-Mb estimated using segregating 173K SNPs (black line) and 23K SNPs (green line) in white layers on GGA1. (PDF 6 kb)Additional file 3:Figure S3. Variation in recombination rate in males (red solid line) and females (blue dashed line) within 0.5-Mb windows across the 28 autosomes in WL (except GGA16). (PPTX 152 kb)Additional file 4:Figure S4. Variation in recombination rate in males (red solid line) and females (blue dashed line) within 0.5-Mb windows across the 28 autosomes in BL (except GGA16). (PPTX 164 kb)Additional file 5:Figure S5. Relationship of GC content and CpG island density with recombination rate within 0.5-Mb window across chromosomes in WL and BL. (PDF 54 kb)Additional file 6:Figure S6. Proportion of genetic variance explained by 1-Mb regions across the genome for GRN in WL. (PDF 10 kb)Additional file 7:Figure S7. Proportion of genetic variance explained by 1-Mb regions across the genome for GRN in BL. (PDF 10 kb)


Oryza rufipogon Griff.) is invaluable genetic resource for rice resistance breeding. Whole-genome re-sequencing was conducted to systematically analyze the variations in two new inbred lines (Huaye 3 and Huaye 4) developed from a common wild rice. A total of 4,841,127 SNPs, 1,170,479 InDels, 24,080 structural variations (SVs), and 298 copy number variations (CNVs) were identified in three materials. Approximately 16.24 and 5.64% of the total SNPs and InDels of Huaye 3 and Huaye 4 were located in genic regions, respectively. Together, 12,486 and 15,925 large-effect SNPs, and 12,417 and 14,513 large-effect InDels, which affect the integrity of the encoded protein, were identified in Huaye 3 and Huaye 4, respectively. The distribution map of 194 and 245 NBS-LRR encoding homologs was constructed across 12 rice chromosomes. Further, GO enrichment analysis of the homologs with identical genotype variations in Huaye 3 and Huaye 4 revealed 67, 82, and 58 homologs involved in cell death, response to stress, and both terms, respectively. Comparative analysis displayed that 550 out of 652 SNPs and 129 out of 147 InDels were present in a widely used blast-susceptible rice variety (LTH). Protein-protein interaction analysis revealed a strong interaction between NBS-LRR candidates and several known R genes. One homolog of disease resistance protein (RPM1) was involved in the plant-pathogen interaction pathway. Artificial inoculation of disease/insect displayed resistance phenotypes against rice blast and brown planthopper in two lines. The results will provide allele-specific markers for rice molecular breeding.Common wild rice (The online version of this article (10.1007/s11105-018-1103-1) contains supplementary material, which is available to authorized users. Oryza rufipogon Griff.) has been proved an invaluable resistance genetics resource for rice genetic improvement due to its high level of resistance against various biotic and abiotic stresses , Pi-ta) , Bph18(t), Bph14)  provided a well-assembled reference genome for next-generation re-sequencing (NGR) technologies  , was used as control. When all the susceptible plants (control) died, six resistance levels  were used to score the BPH resistance according to the International Rice Research Institute (IRRI) guidelines , and the two inbred lines, Huaye 3 and Huaye 4, derived from S24, were planted at our research farm. Agronomic traits, including plant height, flag leaf length, flag leaf width, main panicle length, productive panicle number, seed setting, ratio of seed length to seed width, grain yield per plant, and 1000-grain weight, of Huaye 3 and Huaye 4 were measured at maturity during 2016 according to the protocols of the People’s Republic of China for the registration of a new plant variety DUS  test guidelines of rice (nes IRRI . Moreovehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then, three types of low-quality reads, viz., reads with sequencing adapter, reads with more than 10% N content, and reads with more than 50% low-quality bases (< 10), were filtered.Young leaves of the common wild rice progenitor S24 and two inbred lines Huaye 3 and Huaye 4 were collected and stored at − 80 °C for DNA extraction. Genomic DNA was extracted using a modified CTAB method  using BWA software (Li and Durbin https://sourceforge.net/projects/picard/) were used to eliminate the PCR duplication. Base recalibration and realignment near insertion or deletion regions were conducted using a Genome Analysis Toolkit (GATK). Reference genome coverage was estimated using SAMtools , and the sequencing reads were mapped onto these gene sequences using BWA software, and the coverage was calculated using SAMtools  InDels, and two or more InDels in a 10-bp or shorter window. We further retained the SNPs and InDels with a coverage depth ranged from 11× to 100×. SVs were identified by using the BreakDancer software, and SVs with a coverage depth ranged from 21× to 100× were retained . Polymerase chain reaction (PCR) was used to amplify the variation regions in a 20-μL volume containing 30 ng template, 0.15 μmol/L primer pairs, 1.0 μL dNTPs (2.0 mmol/L each), one 1 U Taq polymerase, and 1× PCR buffer . The PCR procedure was 94 °C for 5 min followed by 30 cycles of 94 °C for 45 s, 55 °C for 45 s, and 72 °C for 50 s, and a final extension at 72 °C for 5 min. PCR products were examined by agarose gel electrophoresis and further sequenced by Sanger sequencing method. The Sanger sequencing results were further assembled by using DNAMAN software. The assembled sequences were aligned to the reference genome sequences to validate the variations in all the selected regions using ClustalW software.The CDS sequences of randomly selected variation regions were used as template for primer design. Primer Premier 5 software was used for primer designing, and the primers were validated using NCBI Primer-BLAST (Arabidopsis (Holub http://www.repeatmasker.org/) was used to classify the transposon elements (TEs) in structure variations and copy number variations. The protein-protein interaction network was predicted using the STRING website tool , Swissprot protein database, Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG) of protein database, and NCBI non-redundant (NR) database using BLAST software  based on the Nipponbare reference genome and the VCF files. All the nucleotide sequences were translated to amino acid sequences by using a rice ORF find program, Beijing Gene Finder website tool . MEME_4.11.3 program was used to identify the conserved ten motifs . Multiple sequence alignment was completed using ClustalW software, and neighbor-joining phylogenetic tree was constructed using MEGA7 software with 1000 bootstrap replications  SRA database, and the accession number is “PRJNA396096.” The re-sequencing data of LTH were downloaded from the SRA database .Two new inbred lines were developed from a common wild rice (S24) by our research group in 2014, which were designated as “Huaye 3” and “Huaye 4” in 2016 Fig. . These tWhole-genome re-sequencing of S24, Huaye 3, and Huaye 4 generated 79,619,359, 95,853,301, and 77,152,550 raw reads, and about 97.85, 99.58, and 97.54% of these were high-quality reads. About 89.60, 87.98, and 86.85% of these bases were high-quality bases . The G/C contents of S24, Huaye 3, and Huaye 4 were 43.46, 44.97, and 41.43%, respectively. The high-quality reads were further mapped onto the Nipponbare reference genome (MSU7.0) uniformly using BWA software , 1,505,317 , and 1,867,413  DNA polymorphic sites were identified in S24, Huaye 3, and Huaye 4 compared to the Nipponbare reference genome, respectively Tables .Table 3NIn order to evaluate the accuracy of the variations detected by re-sequencing, 18, 13, and 13 genomic regions with abundant DNA variations in S24, Huaye 3, and Huaye 4 respectively were randomly selected for variation validation using Sanger sequencing. In total, 114, 88, and 85 DNA variation sites were validated in S24, Huaye 3, and Huaye 4, respectively. The results showed that the DNA variation sites detected by using Sanger sequencing were consistent with the re-sequencing data Table .The number and density distribution of DNA polymorphisms varied across different chromosomes. The highest SNP numbers were observed on Chr1, while Chr9, Chr12, and Chr8 had the lowest number of SNPs in S24, Huaye 3, and Huaye 4, respectively Fig. . SimilarThe average SNP densities were 483.57, 361.56, and 451.91 SNP/100 kb, and the average InDel densities were 119.01, 89.31, and 104.75 InDel/100 kb in S24, Huaye 3, and Huaye 4, respectively. However, the SNPs and InDels were not uniformly distributed along the chromosomes Fig. . DNA polSNPs are abundant markers distributed across the whole genome, so the SNP zygosity can represent genome zygosity. Analysis on the distribution of SNP zygosity across the 12 rice chromosomes indicated that the wild rice progenitor S24 genome contained much more heterozygous SNPs than the two inbred lines on each chromosome Table . In S24,Investigation on the nucleotide substitution type of SNPs indicated higher frequency of transitions  than transversions , and the ratios of Ti to Tv were 2.40, 2.50, and 2.53 in S24, Huaye 3, and Huaye 4, respectively Table . The numSNPs and InDels of S24, Huaye 3, and Huaye 4 showed a similar distribution in different genomic regions. Approximately 64% SNPs and 70% InDels were distributed in the upstream regions , intergenic regions (INTERGENIC), and downstream regions . About 36% SNPs and 30% InDels were observed in the genic regions, and 668,533, 487,884, and 599,574 SNPs and 136,396, 98,221, and 113,176 InDels were detected in genic regions of S24, Huaye 3, and Huaye 4, respectively. The SNPs and InDels, which were distributed in the initiation/termination codon  and splice site regions , and the InDels, which cause frameshift (FRAME_SHIFT), were defined as the large-effect variations that have severe impact on the integrity of encoded products and the gene function. A total of 17,277, 12,486, and 15,925 large-effect SNPs, and 16,816, 12,417, and 14,513 large-effect InDels were identified spanning 16,692, 12,248, and 14,434 variant genes in S24, Huaye 3, and Huaye 4, respectively Tables  and S5.Large segment variations, such as structural variations (SVs) and copy number variations (CNVs), can affect the genome stability. Numerous SVs and CNVs were identified in the three materials. In the intergenic, exon, and intron regions, 6489, 9728, and 7863 SVs in S24, Huaye 3, and Huaye 4 were detected, respectively. Among these SVs, 2055, 3403, and 2169 were in the exon regions Table . MoreoveVenn analysis of all Nipponbare transposon elements (TEs) and SV/CNV variant genes indicated that more than half of these genes were TEs Fig. c, d. TheIn order to identify the useful genomic regions that were specific to common wild rice lines, we compared our results to the newly released rice pan-genome data , LOC_Os09g16000; Pia, LOC_Os11g11790; Pi5, LOC_Os11g11810), two BPH resistance genes , two bacterial blight resistance genes , and one defense response gene   Table . These gis Holub , gene clIn order to detect the functional variant genes with identical genotypes in the two inbred lines, similar large-effect SNPs and InDels between Huaye 3 and Huaye 4 were selected for further analysis. A total of 6128 and 4853 identical large-effect SNPs and InDels, which present on 2022 and 3203 variant genes in Huaye 3 and Huaye 4, were identified, respectively.LOC_Os12g37280 (Bph26), which were involved in cell death and 82 variant genes were involved in response to stress, while 58 variant genes were involved in both terms  ranged from 1.00 to 3.67. The ratios of Ns to Sy SNPs in the two conserved domains of NBS-LRR encoding genes NB-ARC and LRR_1 were 2.32 and 2.24, indicating that these two domains have more amino acid substitutions in Huaye 3 and Huaye 4 Fig. . GO enrirms Fig. .Fig. 4GePi-d3, LOC_Os06g22460; Pi5, LOC_Os11g11810; Pi56(t), LOC_Os09g16000; Pi9, LOC_Os06g17900; Pia, LOC_Os11g11790; Pish, LOC_Os01g57340; Pit, LOC_Os01g05620; Pita, LOC_Os12g18360) and three BPH-resistant genes . A cytochrome C gene (LOC_Os05g34770) connected with the interaction network as a hub gene , the programmed cell death (PCD) following the recognition of pathogen effector (Avr protein) by the R protein is a sign of plant disease resistance. Through PCD in the cell of pathogen infection site, the host plant can get non-specific resistance to other pathogens, known as systemic acquired resistance (SAR). Thus, the host plant can get broad-spectrum resistance ESM 2(XLSX 353 kb)


Breeding programs benefit from information about marker-trait associations for many traits, whether the goal is to place those traits under active selection or to maintain them through background selection. Association studies are also important for identifying accessions bearing potentially useful alleles by characterizing marker-trait associations and allelic states across germplasm collections. This study reports the results of a genome-wide association study and evaluation of epistatic interactions for four agronomic and seed-related traits in soybean.Using 419 diverse soybean accessions, together with genotyping data from the SoySNP50K Illumina Infinium BeadChip, we identified marker-trait associations for internode number (IN), plant height (PH), seed weight (SW), and seed yield per plant (SYP). We conducted a genome-wide epistatic study (GWES), identifying candidate genes that show evidence of SNP-SNP interactions. Although these candidate genes will require further experimental validation, several appear to be involved in developmental processes related to the respective traits. For IN and PH, these include the Dt1 determinacy locus (a soybean meristematic transcription factor), as well as a pectinesterase gene and a squamosa promoter binding gene that in other plants are involved in cell elongation and the vegetative-to-reproductive transition, respectively. For SW, candidate genes include an ortholog of the AP2 gene, which in other species is involved in maintaining seed size, embryo size, seed weight and seed yield. Another SW candidate gene is a histidine phosphotransfer protein - orthologs of which are involved in cytokinin-mediated seed weight regulating pathways. The SYP association loci overlap with regions reported in previous QTL studies to be involved in seed yield.This study further confirms the utility of GWAS and GWES approaches for identifying marker-trait associations and interactions within a diverse germplasm collection.The online version of this article (10.1186/s12864-019-5907-7) contains supplementary material, which is available to authorized users. Glycine max [L] Merr.) is the world’s largest single source of vegetable protein and oil, accounting for more than 50% of world edible oil . Soybean is also a valuable component of many agricultural systems due to its N-fixing capacity. Improvement of seed yield is a major objective in soybean breeding. Seed yield  is a complex trait and is influenced by many developmental traits including seed weight (SW), internode number (IN) and plant height (PH). Like seed yield, these developmental traits are also quantitatively inherited. For example, SW is influenced by numerous physiological and morphological components , where σ2g = genotypic variance, σ 2g*l = genotype by location interaction variance, l = number of locations, and r = number of replicates, and σ2e is the error variance assuming all effects are random. We computed variance components estimation was computed using SAS version 9.3 .The model for phenotypic data collected for each trait was Additional file 1:Figure S1. Frequency distribution of observation of internode number, plant height, seed weight, and seed yield per plant in soybean. (DOCX 79 kb)Additional file 2:Figure S2. The mean level of linkage disequilibrium (LD) decay rate in euchromatic and heterochromatic chromosome regions. The mean decay of LD was estimated as squared correlation coefficient (r2) using all pairs of loci within 10 Mb of physical distance. The x-axis shows the distance between markers pairs in Mb and the y-axis shows LD in r2. The red line denotes euchromatic region and black line denotes the heterochromatic region. The dashed grey line shows the position where r2 dropped to half of its maximum value. (DOCX 53 kb)Additional file 3:P-values, distances to SNPs, and additional information about functional annotations. Counts of candidate genes are given for regions defined by two methods: by flanking non-significant markers around significant SNP(s), and within a window defined by the significant SNP(s) plus 100 kb before and after the significant SNP(s). (XLSX 22 kb)Summary of GWAS regions, genes of interest, and overlapping QTLs. Extends Table Additional file 4:Genes within GWAS regions along with gene annotations. Genes are given for regions defined by two methods: by flanking non-significant markers around significant SNP(s), and within a window defined by the significant SNP(s) plus 100 kb before and after the significant SNP(s). (XLSX 150 kb)Additional file 5:PI lines used in the study, arranged based on maturity group and country of origin. (XLSX 20 kb)Additional file 6:Significantly associated genome wide epistatic (SNP-SNP) interactions and associated candidate genes. (XLSX 14 kb)Additional file 7:Bayesian Information Criterion (BIC) mixed linear model with principal components (PCs) applied for association analysis of internode, plant height, seed weight and seed yield per plant. (DOCX 14 kb)


Plasmodium falciparum merozoite surface proteins 1 and 2 (msp-1 and msp-2), and the glutamate-rich protein is the gold standard for distinguishing reinfections from recrudescences in antimalarial drug trials. We compared performance of the recently developed 24-single-nucleotide polymorphism (SNP) Barcoding Assay against msp-1 and msp-2 genotyping in a cluster-randomized effectiveness trial of artemether–lumefantrine and dihydroartemisinin–piperaquine in Malawi. Rates of recrudescence and reinfection estimated by the two methods did not differ significantly . There was a strong agreement between the two methods in predicting treatment outcomes and resolving the genetic complexity of malaria infections in this setting. These results support the use of this SNP assay as an alternative method for correcting antimalarial efficacy/effectiveness data.Genotyping of allelic variants of  Without genotyping pretreatment and post-treatment parasites, it is difficult to resolve whether parasites persisting after therapy are due to treatment failure (recrudescence) or a new infection (reinfection) and to provide the true risk of treatment failure in the population.Plasmodium falciparum merozoite surface proteins 1 and 2 (msp-1 and msp-2), and glutamate-rich protein is the recommended genotyping method.2 However, it is labor intensive, has low discriminatory power, and produces results that are often ambiguous to interpret and reproduce between laboratories.3 Microsatellite genotyping is an alternative approach.6 However, the lack of capillary sequencers to amplify and score microsatellites has hampered its wide use. The 24-single-nucleotide polymorphism (SNP) Barcoding Assay has shown great potential7 but requires expensive reagents and real-time polymerase chain reaction (PCR) instruments. We compared the performance of the 24-SNP Barcoding Assay and msp-1 and msp-2 genotyping in an effectiveness trial.Genotyping of allelic variants of This study was part of a trial exploring neuro-ototoxic adverse effects in children repeatedly treated with artemisinin-based combination therapies (NCT01038063). Ethical approvals were obtained from Liverpool School of Tropical Medicine Research Ethics Committee (Protocol 09.07), University of Malawi College of Medicine Research and Ethics Committee (Protocol P.10/08/707), and Malawi’s Pharmacy, Medicines and Poisons Board (Protocol PMPB/CTRC/III/1211200904).Children with uncomplicated malaria were randomized to receive artemether–lumefantrine (AL) or dihydroartemisinin–piperaquine (DHA-PPQ) and followed up for 42 days. A filter paper blood sample was collected before treatment and 42 days posttreatment regardless of day 42 slide positivity.msp-1 and msp-2 genotyping as previously described.7 Investigators genotyping samples were blinded to d42 slide positivity. Infections with ≥ 2 and ≤ 1 heterozygous SNPs were classified as multiple- and single-haplotype infections, respectively.8 We performed a loci resampling analysis in GenClone v.2.09 to determine the minimum number of SNPs required to capture full haplotypic diversity amongst single-haplotype infections sampled.To determine if a child had recurrent parasitemia on day 42, parasite DNA was extracted from d0 and d42 samples using DNA Mini Kits  and genotyped using the 24-SNP Barcoding Assay, and Recurrent parasitemia was considered a reinfection if d0 and d42 parasites were genetically distinguishable; otherwise, it was deemed a recrudescence. All proportions and their binomial exact 95% confidence intervals (CIs) were computed using Stata version 11.0 .N = 71) showed no detectable parasite DNA on d42, whereas 38 had recurrent d42 parasitemia. Detailed effectiveness data for the trial will be presented elsewhere . Genotype data and treatment outcomes for 38 patients with recurrent parasitemia are shown in Supplemental Tables 1 and 2, whereas genotype data for 71 patients with no detectable parasite DNA on d42 are shown in Supplemental Tables 3 and 4. A sample size of 38 recurrent infections allows us to detect a 34% difference in rates of reinfection estimated by the two methods with 80% power and 95% CI. Repeat msp-1 and msp-2 genotyping was performed on ∼20% of samples because of contamination in the negative control or failure to amplify some loci during the initial genotyping attempt. However, genotyping failure rate for the 24-SNP Barcoding Assay was low with > 95% of SNP assays yielding data at the first genotyping attempt and < 5% allele drop out per sample.We evaluated 109 pairs of filter paper blood samples collected on days 0 and 42. Of these, 65%  . Relationships among 62 monoclonal samples identified using the 24-SNP Barcoding Assay are shown in the phylogenetic tree . We observed a modest concordance of 56.5%  between the two methods in estimating the multiplicity of infection for individual samples (msp-1 and msp-2 genotyping (P = 0.768). The small discrepancy between recrudescence rates estimated by the two methods resulted from classifying two recurrent infections either as treatment failures when using SNP genotyping or as as reinfections when using msp-1 and msp-2 genotyping . Treatment failures observed may be explained by nonadherence, pharmacokinetic variations, parasite resistance, and/or drug loss through vomiting. Study participants were given a full course of AL or DHA-PPQ with only the first dose given under supervision. This may promote noncompliance but accurately represents how drugs might be used in the community. In a previous study, 79% and 88% of AL- and DHA-PPQ–treated patients complied with recommended drug dosing schedules, respectively.10Rates of reinfection and treatment failure did not differ significantly between methods  . There wctively) . There wctively) . The pro samples . This pr12 attributable to shorter elimination half-life of the partner drug, lumefantrine. However, an ACT such as DHA-PPQ, with a long elimination half-life of the partner drug, may still fail to protect against reinfections if overwhelmed by intense transmission levels.11 To help reduce malaria transmission, new transmission reduction strategies such as mass drug administration, focal screening and treatment, or mass screening and treatment should be considered.13High rates of reinfection are of concern. Both genotyping methods showed that ∼30% of children treated for malaria are reinfected within 42 days post-treatment. This finding indicates that the intensity of transmission is very high. Compared with DHA-PPQ, AL is associated with higher risk of recurrent parasitemiamsp-1 and msp-2 genotyping. The main advantage of msp-1 and msp-2 genotyping is its low cost. We estimate that genotyping costs $11.45/sample versus $3.60/sample for the 24-SNP Barcoding Assay and msp-1 and msp-2 genotyping, respectively. Unlike the 24-SNP Barcoding Assay that relies on expensive real-time PCR instruments, msp-1 and msp-2 genotyping uses relatively inexpensive and common laboratory equipment such as gel electrophoresis equipment and ultra-violet (UV) transilluminators to genotype samples. Nonetheless, inherent limitations of msp-1 and msp-2 genotyping outweigh its low-cost attractiveness. This method is extremely labor intensive, prone to contamination, has limited resolution power, and generates data that are often ambiguous to interpret and reproduce between different laboratories because of dependency on visual interpretation of allele migration patterns on agarose gels. In contrast, the 24-SNP Barcoding Assay is less labor intensive, has better resolution power, and generates data that are easy to score and reproduce between laboratories. The 24-SNP Barcoding Assay has better discriminatory power because it interrogates 24 highly polymorphic SNPs rather than two msp-1 and msp-2 loci. Because of its excellent attributes, the 24-SNP Barcoding Assay should be adopted as an alternative genotyping method. However, high cost could derail its adoption. We investigated whether an abbreviated SNP set with fewer SNPs could equally identify all parasite haplotypes as 24 SNPs. Our results indicate that 17 SNPs, irrespective of their minor allele frequencies within the 62 single-haplotype infections identified, can reliably capture all parasite haplotypes identified by 24 SNPs . Our data also indicate that if SNPs with a high minor allele frequency (≥ 0.30) are selected, only 12 of these are required to identify all parasite haplotypes . It would cost $5.73 to genotype a single sample using the abbreviated SNP assay. Reduction in cost and availability of real-time instruments in most countries make the abbreviated SNP assay attractive and feasible to adopt.Our findings clearly demonstrate that the 24-SNP Barcoding Assay performs as well as msp-1 and msp-2 genotyping and should be adopted as an alternative method for PCR adjustment of antimalarial effectiveness/efficacy data. Resource-constrained laboratories should consider deploying an abbreviated SNP assay comprising 12 SNPs with high minor allele frequency to reduce genotyping costs while maintaining high assay resolution. Each continent must identify SNPs with high minor allele frequency to select informative SNPs.Our results demonstrate that the 24-SNP Barcoding Assay performs as well as Supplemental reference, figures, and tables


Mycobacterium bovis population responsible for bovine tuberculosis (bTB) outbreaks since 1970 has been described in detail. To further understand bTB evolution in France, we used single nucleotide polymorphisms (SNPs) based on whole genome sequence versus classical genotyping methods in order to identify accurate phylogenetic relationships between M. bovis strains. Whole genome sequencing was carried out on a selection of 87 strains which reflect the French M. bovis population’s genetic diversity. Sequences were compared to the M. bovis reference genome AF2122/97. Comparison among the 87 genomes revealed 9,170 sites where at least one strain shows a SNP with respect to the reference genome; 1,172 are intergenic and 7,998 in coding sequences, of which 2,880 are synonymous and 5,118 non-synonymous. SNP-based phylogenetic analysis using these 9,170 SNP is congruent with the cluster defined by spoligotyping and multilocus variable number of tandem repeat analysis typing. In addition, some SNPs were identified as specific to genotypic groups. These findings suggest new SNP targets that can be used for the development of high-resolving methods for genotyping as well as for studying M. bovis evolution and transmission patterns. The detection of non-synonymous SNPs on virulence genes enabled us to distinguish different clusters. Our results seem to indicate that genetically differentiated clusters could also display distinctive phenotypic traits.In recent years the diversity of the French  Mycobacterium bovis, the main etiological agent of bovine tuberculosis (bTB), is a member of the Mycobacterium tuberculosis complex (MTBC)  ,b. This canettii , include lineage , 2009. Tfrican 2 . In Franfrican 2 . Furtherproducts  particulGenetic differentiation of strains has become an indispensable tool to study the evolution, epidemiology and ecology of this pathogen.M. bovis population studying strains isolated mainly from livestock and wildlife that has been isolated since 1978 , two molecular tools that make it possible to discriminate strains by their genotypes  are currnce 1978 . During pulation .Whilst spoligotyping and MLVA are unquestioned tools for disease outbreak investigations, recent studies show the limitations of these methods for tracing the origin of a given infection . AlthougThese limitations can be overcome by the application of whole genome sequencing (WGS) for genome-based epidemiology especially for clonal populations belonging to highly concordant phylogenies with low homoplasy. WGS can provide comprehensive genetic information including all possible genomic targets as well as additional information on genome evolution, virulent determinants and drug resistance traits. Using the WGS, single nucleotide polymorphisms (SNPs) analyses become a robust tool for studying closely related strains of pathogenic mycobacteria .M. bovis strains. A set of 87 animal field strains representative of the French M. bovis population’s genetic diversity was selected based on a previous large-scale genotyping study ; pig (n = 1); sheep (n = 1); goat (n = 1); deer (n = 1) and mostly from cattle (n = 81). Details of the spoligotype and MLVA profiles of the isolates selected to be sequenced are indicated in M. bovis AF2122/97_BX24833 (EMBL sequence AF2122/97_BX248333); M. tuberculosis H37Rv (NC_000962.2); M. africanum MAL010084 (JKXR00000000.1); M. canettii CIPT 140010059 (NC_015848.1); M. bovis BCG Pasteur_1173P2 (NC_008769.1); and M. bovis BCG Tokyo_172 (NC_012207.1).A panel of 87 and 2011  was careClones of the different isolates were enriched in 50 mL Middlebrook 7H9 broth and harvested at mid log phase growth and pelleted at room temperature at 5,500 g. Pellets were inactivated by heating at 80°C during 30 min. DNA was extracted by a chloroform protocol with a lysozyme pre-step followed by a proteinase K treatment. CTAB/NACL was added before the chloroform/isoamyl alcohol action. Precipitation of DNA was performed in isopropanol. DNA was conserved at -20°C in 1x TE buffer .1 (Spoligotyping of 43 spacers sequences contained in the direct repeat (DR) locus was performed on a Luminex . Spoligo1 . MLVA ty1  was perf1 , 2016.Whole genome paired end (2 × 100 bp) sequencing was performed using Illumina HiSeq technology by Genoscreen  . To avoiThe short reads were trimmed using Trimmomatic  when theM. bovis reference strain AF2122/97-BX24833 using SMALT (Sanger Institute). The mean coverage or depth obtained ranged from 27 to 105 and the similarity base to base to the genome reference ranged from 98.5 to 99.6% (The FASTQ were then mapped against the to 99.6% . Both paMycobacterium bovis BCG Pasteur_1173P2 (NC_008769.1) and M. bovis BCG Tokyo_172 (NC_012207.1) were included with the 87 genomes for SNP analysis.Data deposition: the 87 genomes have been deposited at GenBank under the Accession Nos. MINA00000000.1, MTZQ00000000, MTZS00000000, MTZR00000000, and under submission SUB4397761 to be released upon publication (Bioproject Accession No. PRJNA485121) see also The comparison of the 89 genomic sequences led to the identification of SNP mutations. SNP were screened using information in the whole genome including intergenic, synonymous, and non-synonymous mutations.SAMtools and BCFtools were used to transform the resulting BAM files format into VCF files. Then three tests were used to identify reliable SNPs; SNPs have a minimum of SAMtools quality score of 150. The number of forward (reverse) reads mapping with good quality onto the SNP site having the same base as in the reference had to be less than 20% of the total number of forward (reverse) reads mapping with good quality onto the SNP site having the variant base and the minimum forward (reverse) coverage on a SNP site has to be greater than 5. A total of 123 SNPs found in repetitive regions were filtered out. To perform this task, a window of 50 bp around the SNPs sites were blasted against the reference genome. The SNPs corresponding to sequences with more than one blast-hit (>99% match) were filtered out.Single nucleotide polymorphisms distant on less than 10 bp from one another were excluded as they may be due to sequencing mistakes, as described before .2), using the neighbor-joining method or maximum likelihood method with 1,000 bootstrap replicates, with distance calculated using the number of different SNPs.Multilocus variable number of tandem repeat analysis and spoligotype patterns were carried out using Bionumerics 7.6.2 software  by drawing minimum spanning trees (MSTs) and dendrograms . SNP data were concatenated, resulting in a single character string (nucleotide sequence) for each strain. Phylogenetic and molecular evolutionary analyses were conducted using MEGA , Catabolism of cholesterol (n = 8), Cell envelope proteins (n = 12), ‘mce’ families proteins (n = 21), Lipoproproteins (n = 6), Metal-transporter proteins (n = 9), PE and PE_PGRS families regulators (n = 12), Secretion system (n = 17), Proteases (n = 6), Genes and metabolisms regulation expression (n = 26), Defense mechanisms (n = 30), and Other unknown function (n = 9).We investigated whether it was possible to identify virulence traits related to the different phylogenetic lineages of French is study  were selis study : Lipid a∗   3.M. bovis isolates covering the genetic diversity of French strains previously distributed in six groups   . In a fiM. bovis AF2122/97 to determine a similarity coefficient based on categorical difference. The clustering method UPGMA was used to depict the evolutionary relationships between the French M. bovis field isolates’ genomes and the other genomes of the MTBC members presented in The sequence reads of each strain were mapped to the assembled genome of M. canettii, is the most distantly related to the M. bovis strains with a coefficient of 98 followed by M. tuberculosis and M. africanum, the two preferentially human strains. M. bovis field isolates are closely related, nonetheless well-differentiated groups were distinguished. The BCG strains and those of the SB0120 group that share the same spoligotype appeared to be closely related and more distant to the strain AF2122/97 used as reference.Consistent with the predicted evolutionary scenario, the phylogenetic analysis showed M. bovis AF2122/97 allowing us to identify possible phylogenetic lineage markers and thus to resolve the phylogeny within M. bovis field isolates. The new 87 genomes sequences of the M. bovis field isolates plus the sequences of reference strains  were mapped to the M. bovis reference strain (AF2122/97-BX24833) to identify SNPs.Whole-genome sequences were analyzed in order to detect SNP in comparison with the reference genome of A total of 9,170 SNPs were identified, 87% were located in coding regions, 2,880 of them were silent and 5,118 were missense. The distribution of SNPs covers the whole chromosome as depicted in M. bovis strains present in France  followed by MLVA (0.578) and significantly lower (0.004) by the use of SNPs .guaA  unique of this complex . The last and well-separated branch (119 SNPs) is subdivided into three different clusters. Cluster G (F9 group), defined by 98 SNPs, is closely related to a new cluster, cluster H, which is composed by only two strains from our selection. Cluster I (SB0120 group) is only separated from the two previous ones by five SNPs. This cluster is composed by 30 field strains and two BCG reference sequences. The reference sequences clustered with one field strain (A7) and are well-separated from the other field strains, which form a clear group defined by 66 SNPs. This analysis confirms the coexistence of several groups in France, some of them already defined as clonal groups. Three of them represent almost 90% of the French strains: group I (SB0120), the largest one with 44.7% of the strains, and group A (F4 family) and group C (SB0134 group), with 28.5% and 14.5% respectively. Three other groups had been described before but are less preponderant in the French population: the Eu2 complex, dominant in the Iberian Peninsula, represents 9% of the French strains; the Eu1 complex, dominant in the British Islands, represents 1% of the French diversity; finally the group G (F9 family), a small group representing only 0.3% of the French diversity but clearly defined by a particular spoligotype pattern (absence of spacer 1–17 and 23–34) and localized in the south west of France. The new groups B, E, and H are not defined by any other characteristics on their spoligotype pattern or MLVA markers but regarding the phylogenetic analysis they seem to be clearly separated from the other groups. Further analysis on a larger set of strains is necessary to better define these groups.The dendrogram presented in These results suggest that SNPs that are able to distinguish each cluster can be used to develop a novel SNP-based MLST to facilitate global epidemiology studies.n = 4)  variants were investigated on 181 virulence genes . On thesSome genes represent a higher degree of mutation (rate median = 2) . This isThe dendrogram shown in M. bovis strains responsible for tuberculosis outbreaks in France was accompanied by a significant reduction in their genetic diversity  and SNP based analyses are concordant, some differences may exist. For instance, strains belonging to the F9-family are closely related to the SB0120 group while conventional spologtyping-MLVA linked them to the Eu1 complex. Strains previously described as part of the F4-family by spoligotype and MLVA were confirmed by SNPs analyses. A previously defined complex, European 2, is characterized by a special mutation that appears mostly in the same SNPs group.M. bovis strains. With other different set of strains, SNP-based genotyping should be able to resolve misclassification of clonal clusters, to identify patterns of host or spatial association, and to specify their phylogeny and genealogy.However, to test the performance and the degree of resolution of these SNP-based typing, the panel of strains must be adapted. In this study, strains were chosen from our collection from 1983 to 2011 to represent as closely as possible the genetic diversity of French M. bovis strains and may be used for the development of high-resolution genotyping methods as well as other subjects in relationship to strain evolution and their transmission. This set of about 9,000 SNPs has to be reduced and detailed for a wider use.Our results strongly suggest that many new SNP targets can be identified by comparing the genomes of field M. bovis isolates. Our analysis made it possible to identify non-synonymous SNPs in virulence genes unique to certain groups.Non-synonymous SNPs targeting virulence genes were studied to reveal potential specific phenotypic traits within well-separated phylogenetically French Interestingly, we observed that six virulence genes have between 8 and 49 SNP variations. Four of these genes  belong to the pe/ppe family, characterized by conserved N-terminal proline-glutamate (PE) and proline-proline-glutamate (PPE) motifs, which clearly plays important roles in virulence . PE_PGRSpks7 and Mb2074c pks12) encode polyketide synthase. These modular enzymes are required for the production of unique lipids or glycolipid conjugates, which are critical for virulence, and also for other specific components of the complex mycobacterial cell envelope. It has been reported that pks7 plays an important role in virulence during the persistent phase of infection  synthesis (The two other genes presenting most mutations (Mb1689 nfection . Pks12 wynthesis .M. bovis groups still responsible for the persistence of outbreaks in cattle and wild life.The non-synonymous SNP detected in virulence genes are most likely to impact on protein function and contribute to phenotypic variation. Further investigations on secreted proteins, epitope recognition, or pattern of outer membrane lipids  are needM. bovis genome evolution if we consider that the horizontal gene transfer seems not playing a role for the evolution of contemporary modern strain (M. bovis (Sequential chromosomal nucleotide substitutions are the main driver of n strain . LikewisM. bovis .Differences in genome sequence, including gene families that are important for bacterial infection and transmission, highlight differences with putative functional implication between isolates otherwise classified within the same spoligotype group.de novo assemblies of combination of second and third generation sequencing of strains belonging to main clonal groups. This would enable us to perform studies which are not limited to the core genome and to improve our knowledge on specific phenotype traits of M. bovis strain, the dynamics of strain transmission and evolution in multi-host systems.A means to refine the clonal complex identity of the different groups identified in this study could include the analysis on the strains’ pangenomes to detect the presence or absence of large deletions or insertions. To do this, it is necessary to create new reference genome sequences obtained by All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.