ADP‐glucose pyrophosphorylase, comprising two small subunits and two large subunits, is considered a key enzyme in the endosperm starch synthesis pathway in wheat (Triticum aestivum L.). Two genes, AGP‐S1‐7ATa and AGP‐L‐1BTa, were investigated in this study. Haplotypes of these genes were associated with thousand kernel weight (TKW) in different populations. Mean TKWs of favoured haplotypes were significantly higher than those of nonfavoured ones. Two molecular markers developed to distinguish these haplotypes could be used in molecular breeding. Frequencies of favoured haplotypes were dramatically increased in cultivars released in China after the 1940s. These favoured haplotypes were also positively selected in six major wheat production regions globally. Selection of AGP‐S1 and AGP‐L‐1B in wheat mainly occurred during and after hexaploidization. Strong additive effects of the favoured haplotypes of with other genes for starch synthesis were also detected in different populations. Triticum aestivum) will become a greater challenge in the future. Although average yields in China increased by 2.12% in the last 10 years (http://faostat3.fao.org), the annual rate of increase is gradually decreasing. Yield potential can be increased in three ways: higher spike number per unit area (SN), higher grain number per spike (GN) and higher thousand kernel weight (TKW). Crop yields at the physiological level are determined mainly by source and sink strengths , sink capacity has proved more important than source accumulation   is a rate‐limiting enzyme in the synthesis of starch in plant endosperm  increased seed weight by 7%–24%  by 35%  and Brittle2 (Bt2) loci in maize endosperm separately encode the large and small subunits  lines , a mini core collection (MCC) and modern cultivars (MC). Based on their effects, these haplotypes were artificially classified into subgroups Hap‐I and Hap‐II and cleaved amplified polymorphic site (CAPS) markers were developed to identify them. There has been strong selection for the two favoured haplotypes over the past seventy years of wheat breeding in China. These favoured haplotypes also occur at high frequencies in cultivars released in other geographic regions during the last century.Most crops undergo significant genetic changes during domestication and breeding , TaAGP‐S2  and TaAGP‐L  were cloned from Chinese Spring (CS). TaAGP‐S1‐A and TaAGP‐L‐D were located on chromosomes 7AL  and 1DL , respectively, by sequence alignments to the Triticum urartu and Aegilops tauschii genome sequences. TaAGP‐S1, TaAGP‐S2 and TaAGP‐L were located on homoeologous chromosome groups 7, 5 and 1, respectively, and verified by PCR amplification of DNA from CS and its nulli‐tetrasomic lines with genome‐specific primers  and DNA (40%) levels . Dendrogram analysis showed that this site was Ala in most species and the amino acid sequences around it were highly homologous  at position 5090 in the third exon caused an amino acid difference between ‐Hap‐1/2 and ‐Hap‐3/4; other polymorphic sites were all in introns. Significant differences in TKW were detected between ‐Hap‐1 and ‐Hap‐3 in both landraces and modern cultivars of the MCC (Table ‐Hap‐1 and ‐Hap‐2 in 2014 plot tests (Table ‐Hap‐1 was not significantly different from that of ‐Hap‐2 at 15 days postanthesis (DPA) (Table ‐Hap‐1 and ‐Hap‐2 had similar effects on TKW.Another eight SNPs and two insertions/deletions (InDels) were identified among the 245 sequenced accessions, forming four haplotypes, named CC Table a. There ts Table a. The reA) Table b, suggesTaAGP‐L‐1B formed four haplotypes, ‐Hap‐1, ‐Hap‐2, ‐Hap‐3 and ‐Hap‐4 , 150 (C–T) and 283 (G–C) in TaAGP‐L‐1B. The SNP at position 150 involved a synonymous mutation (AAC‐AAT) in the first exon, position 283 was in the first intron, and position −122 was located in an E2F‐DP‐binding motif (WTTSSCSS). Rose et al.  was not mentioned because this SNP was present only at H5 in their study, and only in one cultivar (Chinese Spring).There were three SNPs between e et al.  reported‐Hap‐I and ‐Hap‐II promoter‐GUS were employed to measure promoter‐driven activity. Gus expression was mainly located in the aleurone layer at 21 DPA  fell within the restriction site CTNAG that was cleaved by restriction endonuclease DdeI  fell within the restriction site GCAATGNN that was cleaved by BSrDI  markers based on restriction enzyme digestion were developed to distinguish each haplotype. Position 5090 of I Figure a althougTaAGP‐S1‐7A‐Hap‐I was significantly higher than that of ‐Hap‐II by 5.7 g in 2002, 4.9 g in 2006 and 4.7 g in 2010 . Favoured haplotypes at TaAGP‐S1‐7A and TaAGP‐L‐1B reached very high frequencies (from 82% to 100%) in all five groups, indicating they had undergone overall positive selection in wheat breeding.Frequencies of favoured haplotypes at s Figure a,b. Theset al., TaAGP‐S1‐7A, TaSus1‐7B, TaAGP‐L‐1B, TaSus2‐2A, TaSus1‐7A and TaSus2‐2B) were compared in the MC . Polyploid wheats were closer to T. urartu, supporting the currently held opinion that T. urartu is the A genome donor to polyploid wheats  showed that the FST between cultivated and wild tetraploid wheats was only −0.036, but this value reached 0.436 between hexaploid landraces in the MCC and cultivated tetraploid wheat. This value was 0.154 when modern cultivars and landraces in the MCC were compared  dramatically declined with polyploidization Figure a. The πDd Figure c. These AGP‐L‐1B was higher than that at AGP‐S1‐7A .Diversity at 7A Table . It alsoTaAGP‐S1‐7A and TaAGP‐L‐1B expressed in developing wheat endosperm. Four haplotypes of each gene were detected in modern cultivars. Some haplotypes at each locus showed similar expression patterns and no variation in TKW in the MCC and NILs  and therefore may change TaAGP‐L‐1B into an E2F target gene. This motif is not present in the 1‐kb sequence upstream of ‐Hap‐I. E2F transcription factors can be inhibitors or activators of E2F target genes in Arabidopsis thaliana  Table  and 348 5 population from cross Youzimai/Zhou 18*3//Handan 6172 were used for estimating SN, GN, and TKW and transcript level differences between TaAGP‐S1‐7A‐Hap‐1 and ‐Hap‐2. Another set of NILs from an F4 population from cross Yangmai 158/Zhou 18*3//Handan 6172 were used for measuring the same yield components and transcript level differences between TaAGP‐L‐1B‐Hap‐1 and ‐Hap‐3 in 2014. Two NILs derived from a BC3F5 population from Isengrain/5*Yanzhan 4110 and a BC3F6 population of Jianmai/6*Zhou 18 were used for detecting TKW differences between TaAGP‐L‐1B‐Hap‐1 and ‐Hap‐2, and TaAGP‐L‐1B‐Hap‐1 and ‐Hap‐4, respectively. The data for all NILs are listed in Table Wheat NILs derived from an FTaAGP‐S1‐7A and TaAGP‐L‐1B genes and frequency changes of the favoured haplotypes during the last 100 years. Release dates and origins of all materials are provided in Hou et al. , 82 Russian and 51 Australian cultivars were surveyed to investigate the global distribution of favoured haplotypes of the u et al. .TaAGP‐S1‐7A and TaAGP‐L‐1B (Table Thirty‐seven accessions of diploid wheat and 36 accessions of tetraploid wheat were sequenced for evolutionary studies of 1B Table .http://www.premierbiosoft.com/) and synthesized by BGI Tech . PCR was conducted in reaction volumes of 15 mL containing 50–100 ng DNA, 7.5 mL buffer, 2.4 mL of 2.5 mm deoxynucleotide triphosphates, 0.5 mL of 10 mm forward and reverse primers, and 0.15 mL of LA Taq polymerase (TaKaRa Biotechnology). PCR was carried out in a Veriti 96‐Well Thermal Cycler (Applied Biosystems) with the following steps: denaturing at 95 °C for 5 min, followed by 32 cycles of 95 °C for 30 s, annealing at 55–63°C for 1 min, 72 °C for extension (1 kb/min) and a final extension of 72 °C for 10 min. PCR products were purified by purification kit DP‐1502 (TIANGEN), then cloned by the pGEM‐T Easy Cloning Vector (TIANGEN), and transformed into Escherichia coli cells by the heat shock method. Plasmids were extracted with a DP‐1002 Kit (TIANGEN). DNA was sequenced by an ABI 3730XI DNA Analyser (Applied Biosystems). Sequence alignments and SNP identification were made using DNASTAR (http://www.dnastar.com/). The digestion sites of restriction endonucleases were detected by Primer Premier 5.0. Two restriction endonucleases used in the study were produced by New England Biolabs (Beijing).Primers were designed by Premier 5.0  cv. Kitaake as described by Hiei et al.  was added, and centrifuged at 12 000 g for 10 min at 4 °C. Protein concentration in the supernatant was measured following the Bradford (GUS activity with 4‐MUG (4‐methylumbelliferyl‐β‐D‐glucuronide) substrate as described by Hänsch et al. .Data processing was carried out using Excel 2010 and SPSS Statistics 17.0 (D tests of the two genes were carried out by DnaSP 5.10 (http://www.ub.es/dnasp). FST tests at AGP‐S1‐7A were carried out by Arlequin 3.5.1.2 (http://cmpg.unibe.ch/software/arlequin3).Diversity analysis and Tajima The authors declare that they have no conflict of interests.Figure S1 Alignments of TaAGP‐S1‐7A, ‐7B, ‐7D, TaAGP‐S2‐5A, ‐5B, ‐5D, TaAGP‐L‐1A, ‐1B and ‐1D based on cDNA (a) and genomic DNA (b) sequences.Figure S2 Mean TKWs of two genes in the MCC.Figure S3 Differences between TaAGP‐S1‐7A‐Hap‐1 and ‐Hap‐2 in F5 NILs derived from Youzimai/Zhou 18*3//Handan 6172.Figure S4 Differences between TaAGP‐L‐1B haplotypes.Figure S5 Haplotype combinations in two populations.Figure S6 Global distribution of haplotypes.Figure S7 Dendrogram based on AGP‐S1‐7A sequences among diploid, tetraploid and hexaploid wheat accessions. Major polymorphic sites in tetraploid accessions are shown in the table below.Click here for additional data file.Table S1 Primers used in this study.Table S2 Near‐isogenic lines derived from four combinations.Table S3 Haplotype transcript measurements at five stages of developing seeds.Table S4GUS activity measurements in transgenic and wild type rice.Table S5 Accessions of hexaploid wheats in the Mini Core Collection.Table S6 Accessions of modern cultivars.Table S7 Progenitor accessions used in this study.Click here for additional data file.


Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds. Our aim was to identify genomic segments and tag single nucleotide polymorphisms (SNPs) associated with tick-resistance in Hereford and Braford cattle. The predictive performance of a very low-density tag SNP panel was estimated and compared with results obtained with a 50 K SNP dataset.BayesB (π = 0.99) was initially applied in a genome-wide association study (GWAS) for this complex trait by using deregressed estimated breeding values for tick counts and 41,045 SNP genotypes from 3455 animals raised in southern Brazil. To estimate the combined effect of a genomic region that is potentially associated with quantitative trait loci (QTL), 2519 non-overlapping 1-Mb windows that varied in SNP number were defined, with the top 48 windows including 914 SNPs and explaining more than 20% of the estimated genetic variance for tick resistance. Subsequently, the most informative SNPs were selected based on Bayesian parameters (model frequency and t-like statistics), linkage disequilibrium and minor allele frequency to propose a very low-density 58-SNP panel. Some of these tag SNPs mapped close to or within genes and pseudogenes that are functionally related to tick resistance. Prediction ability of this SNP panel was investigated by cross-validation using K-means and random clustering and a BayesA model to predict direct genomic values. Accuracies from these cross-validations were 0.27 ± 0.09 and 0.30 ± 0.09 for the K-means and random clustering groups, respectively, compared to respective values of 0.37 ± 0.08 and 0.43 ± 0.08 when using all 41,045 SNPs and BayesB with π = 0.99, or of 0.28 ± 0.07 and 0.40 ± 0.08 with π = 0.999.Bayesian GWAS model parameters can be used to select tag SNPs for a very low-density panel, which will include SNPs that are potentially linked to functional genes. It can be useful for cost-effective genomic selection tools, when one or a few key complex traits are of interest.The online version of this article (doi:10.1186/s12711-017-0325-2) contains supplementary material, which is available to authorized users. Rhipicephalus (Boophilus) microplus tick is one of the main causes of economic losses in cattle production and affects negatively the performance of their hosts both directly by blood sucking and indirectly as a vector of viral, bacterial and protozoal diseases [Bovine ticks are endemic throughout some of the most productive livestock farming regions in the world . In Brazdiseases . Resistadiseases . In addidiseases , 5. Therdiseases .The use of genome-wide single nucleotide polymorphism (SNP) panels of varying densities to detect statistical associations between phenotypes of interest and SNPs is a powerful method to identify the major genes that are involved in the control of complex traits. However, confounding factors, such as multicollinearity and estimability, which are embedded within multidimensional genotypic and/or phenotypic complex datasets must be considered, since it is necessary to weight the rate of false associations for the interpretation of results .To date, several genomic regions associated with tick burden in dairy and/or beef cattle have been identified through association studies based on different regression methods , 8–14. HBayesian methods provide a flexible approach to solve high-dimensional problems and enable simultaneous estimation of the effects of high-density SNPs . The appIn this study, Bayesian methods were used on 50 K SNP panel data from Hereford and Braford cattle to identify genomic regions and tag SNPs associated with tick resistance. The predictive performance of the very low-density panel based on a selected subset of significant SNPs was estimated and compared with results obtained with the full SNP panel.All Hereford (HH) and Braford (BO) samples were derived from eight herds associated with the Delta G Connection breeding program . A subset of 3455 phenotyped animals was genotyped with the Illumina BovineSNP50 BeadChip. Total tick counts from one side of the body were recorded for each animal born between 2008 and 2011, two to three times consecutively, during the post-weaning period. In total, 10,673 tick counts were available for analyses. Variance components and breeding values were generated from log-transformed tick counts  with the−7) and if identical genotypes were found with other SNPs in neighboring positions. Moreover, only the SNP with the highest MAF was retained within groups of SNPs at the same position or that were highly correlated (>98%). A total of 41,045 SNPs (78%) and 3455 animals (98%) were retained for further analyses; these 3455 animals included 2803 BO and 652 HH and comprised yearling bulls, steers and heifers with respective phenotypes for tick count. Sporadically missing genotypes were imputed using FImpute software [SNP data quality control (QC) was performed using the R version 3.0.2/snpStats package  and the software .X−2  with v = 4 degrees of freedom and a scale S = 0.0091. In addition, a prior distribution for the residual variance was also considered as X−2 , but with v = 10 and scale S = 0.0572. Prior expected values of these Chi square distributions for the dispersion parameters that were equal to 0.0182 and 0.0715, respectively for the genetic and residual variances, were based on estimates previously obtained for tick counts in these BO and HH populations [Estimated breeding values (EBV) were obtained by adjusting a pedigree-based repeatability animal model to the tick count data. This model considered fixed effects for contemporary groups, regression coefficients with the linear additive effect for the zebu breed proportion, zebu–HH dominance effect, zebu–HH additive by additive epistatic effect , and liny is a vector of phenotypes (DEBV); k is the total number of SNPs; δi indicates whether SNP i is included in (δi = 1) or excluded (δi = 0) from the model for a given iteration of the MCMC; zi is a vector of genotypes of the fitted SNP i, coded −10/0/10; ai is the random substitution effect of the fitted SNP i with its own variance e is the vector of normally distributed random residuals. In BayesCπ, the probability that a SNP has a zero effect was treated as unknown and a common effect variance was assumed for all the SNPs having a non-zero effect, while for BayesA δi was always equal to 1. Initial SNP effects were estimated for all individuals with BayesCπ  as proposed by Sun et al. [Prior specification for SNP effects in BayesB allows a proportion of the SNPs to have a zero effect, with a fixed probability π, while the remaining SNPs have normally distributed effects with a locus-specific variance and a probability 1-π. Conversely, in BayesA all SNP covariates are fitted, i.e., π = 0, for each Markov chain Monte Carlo (MCMC) cycle. The statistical model used for Bayesian analyses was: n et al.  and de On et al. . Subsequn et al.  using thn et al. .SNPs were allocated to 2519 non-overlapping 1-Mb genome windows that contained different numbers of SNPs based on the physical map order derived from the bovine genome assembly UMD3.1 . GeneticGenome regions that potentially contained quantitative trait loci (QTL) associated with tick resistance, referred to as top windows, were identified based on a threshold that is defined in terms of genetic variance contribution as described by Schurink et al. . Top wint-like statistic (TL), linkage disequilibrium (LD) and minor allele frequency (MAF) parameters. In GenSel, MF reflects the proportion of post-burn-in iterations that included that particular covariate (SNP) in the model, while TL is the absolute value of posterior mean effects (for only those chains that included the SNP in the model) divided by the respective standard deviations of those effects. The R/snpStats package [To identify potential SNPs to construct a low-density panel, a tag SNP selection strategy was tested within the top windows by considering model frequency (MF),  package  was used package  was appl2 values higher than 0.4 [We selected SNPs with the maximum MF within each top window as top SNPs. Then, we also selected all SNPs within top windows that had MF values above the minimum observed MF value for top SNPs. This step aimed at selecting SNPs that were not at the top of their own windows, but that had sufficiently large MF to exceed the MF value of the top SNPs located in other selected windows. A similar approach was used to evaluate consistency of SNP effects by considering TL. Within those pre-selected SNPs based on MF, the minimum TL value was determined and set as the threshold to select the remaining SNPs within top windows that exceeded this minimum TL value. The final step to construct the tag SNP panel aimed at removing redundant SNPs due to observed high LD among subsets of SNPs pre-selected by MF and TL. Thus, when two SNPs were observed with rthan 0.4 , only thTo check the effectiveness of choosing only the most informative SNPs for genetic prediction of tick resistance, genotypic and tick count data from 3455 animals were divided into five sub-groups based on two strategies: K-means clustering according to SNP relationship distance, or randomly, using the R 3.0.2/base package. Cross-validation was carried out within the grouping strategy by selecting subsets of SNPs as described above using data from four of the five groups and then testing the derived tag SNP panel for genomic prediction in the group that was not included in the selection process.jth individual:zji represents the genotype for the ith SNP from the total K SNPs included in the very low-density panel.For individuals within each testing group, direct genomic values (DGV) were calculated based on their tag SNP genotypes and corresponding allele substitution effects estimated from training data, which consisted in data on tick counts and genotypes from the four other groups. In this step, we used the BayesA method, such that all selected SNPs had non-zero effects. For the Acc is the numerator relationship matrix within cluster c.Pooled prediction accuracies of DGV were derived from their genetic correlations with tick count data in a bivariate analysis using a within-group pedigree-based numerator relationship matrix  and werment}A∗; . For ourc, as proposed by Legarra et al. [Prediction accuracies were also estimated within each cluster a et al. . Additioa et al.  for the ntagSNP/41,045).To further check the effectiveness of our selection process, prediction accuracies of DGV were also obtained with the same fivefold cross-validation with BayesB considering all 41,045 SNPs and π = 0.999. With this model, the built-in selection process fits, within each cycle, a number of SNPs that is comparable to that included in our proposed panel , which corresponded to a small fraction of the pedigree-based heritability (h2 = 0.19) obtained with the same dataset [A. hebraeum tick counts on the tail of South African Nguni cattle (0.02). Some cycles contained no fitted SNPs when an extremely high value of π (0.9999) was used in BayesB, which resulted in the absence of any predictive SNPs, and thus this model contributed mostly to the estimated residual variance. These results suggest that BayesCπ could not estimate π appropriately based on the present data.The BayesCπ analysis that included all animals and SNPs simultaneously resulted in a posterior (π) of 0.9999 and therefore, only approximately four SNPs (0.01%) were fitted in each iteration of the MCMC chain. Using π = 0.9999 in a BayesB analysis resulted in a very low estimated heritability  top windows number 163 located at 4 Mb (identified according to the first SNP position in the window) and number 214 at 55 Mb, top windows number 364 at 68 Mb on BTA3, number 553 at 14 Mb on BTA5, number 794 at 13 Mb on BTA7, number 1190 at 77 Mb on BTA10, number 1283 at 65 Mb on BTA11, and number 1553 at 54 Mb on BTA14 , i.e. oHomo sapiens chromosome) close to the RPS15P8 pseudogene . Other positional candidate genes close to SNP rs110144789 (BTA11) are LAMC3 , ABL1 , FIBCD1 (fibrinogen C domain containing 1), QRFP (pyroglutamylated RFamide peptide); and to rs29019899 (BTA10) are ADAM10 , LIPC  and the gene 5S_rRNA ; all these genes are annotated on the bovine genome sequence.The first 12 top windows jointly explained more than 10% of the genetic variance and three genomic regions (top three windows) individually explained more than 1% of the genetic variance for tick resistance  gene that is associated with increased tick resistance in cattle. The ADAM10  gene (BTA10) encodes a characterized member of the ADAM-family of metalloproteases, which has a prominent role in inflammation [ITGA11  gene are associated with tick burden in dairy cattle breeds  and Brahman beef cattle. Although this gene is functionally described as related with cellular adhesion control, these authors suggested that it had a role in modulating cellular immune responses. Both of these genes (ADAM10 and ITGA11) are on BTA10 and may be involved in the control of cellular adhesion and migration during the process of skin infection caused by tick burden. Other studies based on microsatellite whole-genome scans [ADAM10 gene (~50 Mb), three other top windows (Table The SNP with the largest effect on tick count rs110197574) was mapped to the  subunit . Analysi197574 waammation . Furtherammation . Accordiammation  at approme scans , 54 and ws Table  were detAnaplasma marginale) that is mainly transmitted by ticks [A. phagocytophilum, an obligatory intracellular parasite of human granulocytes, causes a similar disease and was shown to activate the ABL1 signaling pathway during cell invasion. This protein is critical for intracellular invasion and infection establishment. Thus, a novel strategy for the treatment of human granulocytic anaplasmosis was proposed through inhibition of the host cell Abl-1 signaling pathway [ABL1 gene and other genes that are associated with the most informative SNPs found to affect tick count , which indicates that a gene network may be involved in cattle resistance to ticks. Two other top windows on BTA11 with putative QTL were also reported by Machado et al. [Anaplasmosis is an infectious rickettsial disease (by ticks  and negaby ticks . In huma pathway . In addiR. evertsi evertsi tick count in African cattle [double cortin-like kinase 2 (DCLK2) gene, near the LRBA gene, which is suggested by these authors to be associated with protein kinase A that supports the secretion and/or membrane deposition of immune effector molecules.Other genomic regions on BTA17 at 7 Mb flanked by SNPs ARS-BFGL-NGS-5880 and BTA-122662-no-rs  provided by GenSel, in the strategy used for tag SNP selection, a minimum MF value of 0.0898 was determined among all top SNPs representing each of the 48 top windows. Nine additional SNPs with an MF above this threshold were selected from the list of 914 SNPs within the top windows. Within those 57 (48 + 9) pre-selected SNPs, the minimum observed TL of 0.902 was set as another threshold to select SNPs within the 914-SNP list that exceeded this lower bound TL value. The subset of SNPs that were pre-selected based on MF and TL contained 63 SNPs, which were subsequently analyzed in terms of LD and MAF, resulting in a final list of 58 SNPs. These selected SNPs were distributed on most of the bovine chromosomes, except BTA9, 12, 18, 19, 21, 23, 24, 26 and 27. It is interesting to mention that nine of the 58 SNPs were located on BTA2. Previous studies identified significant allele effects associated with tick burden in a GWAS analysis, as well as positional candidate genes on chromosome BTA2 [Based on the model frequency (MF) and ome BTA2 , 11, 58.This strategy to select more informative SNPs that are uniquely linked to QTL related to cattle tick resistance within top windows favored those that were more often included in the Bayesian mixture model (greater MF) and with a more consistent effect (greater TL), but avoided redundancy due to LD. Based on that, our goal was to retain SNPs that had a suitable prediction ability to build very low-density panels for cost-effective genomic selection of tick resistance in cattle.r = 0.99) to the SNP adjusted effect, r = 0.46). Since TL is an alternative measure of SNP effect (i.e. i was included in the model) and due to its incomplete correlation with MF, we were able to combine both parameters, MF and TL, to select informative SNPs for which the estimated effects were consistent [The proportion of fitted models that included a SNP and used it to infer associations with the phenotype under study, represented by MF , 59, wasnsistent .According to some authors , 36, SNP2 > 0.4) with another SNP with a higher MF (rs110043221). SNPs within this window were mapped to a bovine genomic region that contains three genes, EPS8L3 (EPS8-like 3), GSTM1/3 (glutathione S-transferase mu 1 and 3), CSF1 (colony stimulating factor 1—macrophage) and a microRNA bta-mir-2413. De Rose et al. [Rhipicephalus (Boophilus) microplus in sheep. The second top window with an effect on tick resistance is located at ~54 Mb on BTA14 , activation of immune response (e.g. ABL-1), positive regulation of macrophage derived from cell differentiation (e.g. CSF1), regulation of inflammatory response and leukocyte chemotaxis (e.g. ADAM10), cell–cell junction organization (e.g. CDC42) and leukocyte activation involved in immune response .The genes that map to the regions containing the 58 SNPs that were selected to compose the proposed very low-density panel are listed in Additional file The proposed BayesB (π = 0.99) GWAS and tag SNP selection strategy was applied to each of five K-means and five random cross-validation subsets and generated 10 alternative SNP panels, which included 47 to 86 SNPs Table . Three hh2), that were estimated with BayesA using the very low-density panel derived for each of the 10 cross-validation groups ranged from 0.09 to 0.12, and were very similar to the h2 estimated with BayesB (π = 0.99) using the full set of 41,045 SNPs  were lower and ranged from 0.04 to 0.06 , while allowing for SNPs to have specific variances and consequently different effect sizes .The effectiveness of the applied strategy for selecting more informative SNPs for genomic prediction of cattle tick resistance was assessed by pooled breeding value prediction accuracies measured as the genetic correlation between cross-validation DGV and tick count data, which were equal to 0.27 ± 0.09 for the K-means clustering groups and 0.30 ± 0.09 for the random groups.Accuracies within each cluster were also obtained using the method of Legarra et al.  and subsUsing the full set of 41,045 SNPs, pooled cross-validation accuracies for K-means and random clustering, respectively, were equal to 0.37 ± 0.08 and 0.43 ± 0.08 for BayesA, 0.37 ± 0.08 and 0.43 ± 0.08 for BayesB with π = 0.99, and 0.28 ± 0.07 and 0.40 ± 0.08 for BayesB with π = 0.999. When compared to the above results, accuracies that are derived using the proposed very low-density tag SNP panel with 58 SNPs would represent at least 68% of the accuracies of predictions obtained using all 41,045 SNPs with BayesB or BayesA methods. These results demonstrate that tag SNP panels may be used in commercial applications for genomic predictions in beef cattle as an alternative to more costly high-density panels. Nevertheless, the decision about the most suitable SNP density should be trait- and population-specific, depending on the relative accuracy and cost of the alternative SNP panels.Cardoso et al.  reportedBayesian approaches have been proposed for predicting genomic breeding values with high-density SNP panels, but in practice they may be more useful for low-density panels . DecreasGenome-wide association studies allow for a much finer description of the genome and genomic selection results in increased genetic gains because early and accurate selection decisions are made possible, for traits that were previously ignored because of high associated phenotyping costs. Observed trends of decreasing genotyping costs in contrast to increasing expenses for phenotyping are expected to lead various livestock sectors to widely adopt genomic technology. However, the development of beef cattle training populations has been generally conducted by private companies and at a significantly slower pace compared to the dairy industry . For a wFine-mapping investigation using next-generation sequencing could also be used to target flanking regions around the currently identified tag SNPs, which may be involved in the biological mechanisms of tick resistance in Hereford and Braford cattle. The identification of causal mutations, along with the availability of a larger training population or suitable blending with historical data, would be decisive to propose cost-effective genomic evaluations based on very low-density marker panels to improve tick resistance in commercials herds.t-like statistics. The resulting very low-density panel included SNPs that are potentially linked to functional gene networks and accounted for most of the genetic variance in tick resistance. The accuracy of genomic predictions derived from the proposed very low-density SNP panel using BayesA was moderate and may be useful for delivering cheaper genomic tests to the industry and for further studies related to fine-mapping for causal variants discovery.BayesB appears to be a suitable method for selecting tag SNPs based on Bayesian model frequency and


Fagus grandifolia Ehrh.), native to eastern North America, is ecologically important and provides high quality wood products. This species is susceptible to beech bark disease (BBD) and is facing high rates of mortality in North America. The disease occurs from an interaction between the woolly beech scale insect (Cryptococcus fagisuga), one of two species of the fungus Neonectria (N. faginata or N. ditissima), and American Beech trees.The American Beech tree (In this case-control genome-wide association study (GWAS), we tested 16 K high quality SNPs using the Affymetrix Axiom 1.5 K – 50 K assay to genotype an association population of 514 individuals. We also conducted linkage analysis in a full-sib family of 115 individuals. Fisher’s exact test and logistic regression tests were performed to test associations between SNPs and phenotypes.Mt), which encodes a mRNA for metallothionein-like protein  in Fagus sylvatica. Metallothioneins represent Cys-rich metal chelators able to coordinate metal atoms and may play an important role in the resistance mechanisms against beech scale insect.Association tests revealed four highly significant SNPs on chromosome (Chr) 5 for a single gene  contains supplementary material, which is available to authorized users. Fagus grandifolia Ehrl.) is native to the eastern North American deciduous forests and is the only species of this genus in North America  is currently under investigation  and the estimated odds ratio [OD] for the association between the minor allele [A1] and the disease phenotype were calculated. Resulting p-values underwent genomic inflation control.A logistic regression model was used to perform association tests between SNPs and disease scores. Population stratification was controlled using the first 20 ancestry principal components (PC’s) as covariates in the logistic model. GWAS was performed using the PLINK 1.9 package  and 3222) was used to determine the pairwise correlation between genetic markers.Haplotypes were identified using Haploview (v.4.2) , and witThe sequencing of 10 cDNA libraries yielded 1,406,316 reads covering 508,764,432 bases. The libraries from BBD-resistant trees yielded between 87,964 and 210,340 reads, while 70,218 to 205,945 reads were obtained for libraries from BBD-susceptible trees (Table Assemblies of 454 sequence reads into contigs were conducted using both the SeqMan NGEN (DNASTAR) program and the 454 Newbler (Roche) assembler. Contigs were built using reads from, either five BB-resistant libraries, five BBD-susceptible libraries or combination of both libraries.F. grandifolia transcriptome assemblies using the NGEN program.The NGEN assembly resulted in a total of 28,592, 27,544 and 44,065 contigs for pools of BBD-resistant, BBD-susceptible and the combination of both libraries, respectively. NGEN assemblies incorporated 76%, 73% and 73% of the sequence reads from the BBD-resistant, BBD-susceptible and the combined samples. NGEN contig lengths averaged approximately 360 bases, with a median varying from 475 to 542 bases. The longest transcripts obtained in the resistant, susceptible and combined assemblies were 11,704, 8168 and 10,800 bases, respectively. Table The Newbler filtered assembly resulted in 10,690 contigs for the BBD-resistant data, 7630 contigs for the BBD-susceptible data, and 16,285 contigs for all libraries combined. The Newbler assemblies incorporated 84%, 81% and 86% of the sequence reads from the BBD-resistant, BBD-susceptible and the combined data, respectively. The longest transcripts obtained by Newbler in the resistant, susceptible and combined assemblies were 4651, 4336 and 10,681 bases respectively. The average length of the Newbler contigs across the three assemblies was 679 bases. The detailed Newbler assembly statistics are shown in Table gsMapper SNP calling program generated two output files. One output file contained all possible SNPs. The other output file contained only the high confidence SNP calls, which was used as the starting point for selecting SNPs for the mapping study. A summary of the SNP discovery results is displayed in Table SNP site discovery was performed using both the DNASTAR NGEN and Newbler combined assemblies as reference transcriptomes. The Newbler R = 249 and S = 257). Of the initial 16,709 SNPs, 5838 Poly High Resolution SNPs passed Affymetrix filtering metrics and only these were included in downstream analysis. A set of 5838 SNPs was visualized for the cluster pattern in SNPolisher R Package v.1.5.1 (Affymetrix Inc.).In total, 514 DNA samples were submitted to genotyping using the Affymetrix Axiom™ Genome-Wide 1.5 K - 50 K array and after quality filtering, resulted in genotypes for 506 samples  - based linkage genetic map for American beech. Single locus Mendelian segregation was first tested using p-values, which displayed systematic deviation from the expectation  was applied to 506 individuals and 5838 SNPs. DAPC revealed three genetic clusters , revealed high correlation among individuals in the sample population. The IBS test allowed the removal of individuals with the highest number of correlated “partners”, indicating high likelihood of relatedness. Our method of choice was to test for population outliers by performing IBS-based nearest neighbor analysis. In total, 179 individuals from the five different stands were identified as possible very close relatives and were removed from the further downstream analysis Table .Table 5Dp-values expected by random chances was detected, except for the top associated SNPs   based significant threshold to adjust for multiple testing, where α represents Bonferroni’s coefficient 0.05 and n represents the number of SNPs after filtering for quality parameters (0.05 / 3155 = 1.58 × 10−5).The initial logistic regression test was performed with 3220 SNPs, however after filtering SNPs to compute PCA and IBS score, an independent set of 2116 SNPs remained. No Genomic inflation from GWAS −5) Fig. . For the−5, which is strong evidence of association. All four SNPs are located on chromosome (Chr) 5  = 0.2573), AX-156988334  = 0.2758) and AX-157000652  = 0.2773)  from a single contig (contig 03321), within which fell all four identified SNPs  encodes an mRNA for metallothionein-like protein   analyses were performed against the NCBI database  for non-redundant protein database. The best hit resulted in the identification of the single gene (g) Table .Mt. gene. With the exception of the individuals (1504R and DN00726S), the constitutive expression of the candidate Mt. gene was higher in the BBD resistant individuals than in the BBD susceptible individuals   encoding for a metallothionein-like protein was found to be physically linked to these genetic markers and may play an important role in the resistance mechanisms against Nectria sp. - beech scale insect. This is consistent with genetic studies of several different small full-sibling families that suggest involvement of a few as two genes [Genome Wide Association Analysis has identified a single locus contributing to resistance to beech bark disease (BBD). There were four SNPs in chromosome (Chr) 5 significantly associated with the scale resistance trait analyzed. A candidate gene  resistance to the fusiform rust disease [Disease resistance in plants can involve any number of genes, from a single major gene to many loci determining resistance. Single-gene resistance mechanisms with large effects are more common in agricultural crops but only a few have been described in forest species, which reflects the greater genetic diversity of the host and pathogen populations in forest pathosystems . In fore disease , 42, res disease , 44 and  disease .Pinus taeda (Loblolly pine) at only 5–10%. Overall, the number of informative SNPs was sufficiently high to provide us with association power on the genome scale for the disease resistance.In this study, we used Affymetrix Axiom™ Genome-Wide 1.5 K – 50 K array  to genotype 327 individuals used for association mapping. Although SNP discovery was performed specifically for BBD, a very small proportion of the SNPs deemed informative in downstream analysis. The conversion rate, provided by Affymetrix genotypng facility, corresponded to 34.04% and was quite high compared to The knowledge of genetic architecture is important for breeding resistant varieties to develop resistant planting stock for restoration of impacted habitats. Molecular markers have also contributed to improved breeding strategies for monogenic resistance genes when combining them in a “gene pyramiding” strategy for a more durable resistance  and can Plant metallothioneins are proteins thought to sequester excess amounts of certain metal ions . These lFagus sylvatica type) was described in Norway spruce (Picea abies), whose expression pattern was analyzed via ESTs from cDNA libraries [Cryptomeria japonica) [Metallothionein-like protein class II (ibraries . Type 4 aponica) . ESTs enaponica) .Changes in expression of metallothioneins and metallothionein-like proteins have been previously reported in response to biotic stresses in plants, including insect herbivory and fungal infections (reviewed in ). There Fagus, mapping populations have been developed to discover QTLs for traits correlated to BBD. The tree improvement program included crosses to study inheritance of resistance to Cryptococcus fagisuga . Ho. HoFagusuga see , 59)..Fagus, mDeployment of resistant planting stock can help to reduce disease incidence throughout natural stands of American beech. Markers found in this study that exhibit a significant association with the resistant phenotype, can be further refined to develop efficient and cost effective indirect selection techniques such as MAS (marker assisted selection) and genomic selection (GS) or combination of both (see ).Mt), that encodes for Fagus sylvatica mRNA for metallothionein-like protein . Once a reference genome sequence is available, it will be possible to gain more insight into functional annotation of the four SNPs and determine the exact number of genes associated to BBD.To our knowledge, this is the first study designed to determine the genetic factors of disease resistance to beech bark disease (BBD) with genome scan analysis in American beech tree. The results presented identified four highly significant markers associated with a single locus located on chromosome (Chr) 5. All four loci were localized to the same contig within a single gene Additional file 2:RNA‐seq data quality assessment. (PDF 853 kb)Additional file 3:Examples of RNA vs. mRNA Agilent Bioanalyzer quality profiles. (PDF 288 kb)Additional file 4:The sequences and statistics for the high quality and most informative SNPs used in the GWAS. (XLSX 1080 kb)Additional file 5:The list of mapped SNPs in 12 linkage groups (LGs). (XLSX 91 kb)Additional file 6:p-values where on x-axis, are expected –log10 P values and on y-axis observed –log10 P values. The plot is showing large deviation from the null distribution where the inflation factor was higher than the threshold of 1, indicating a high genomic inflation in Beech association data and an existence of the population stratification. (PDF 90 kb)(A) DAPC analysis revealed three main genetic clusters where the individuals shown as dots and the groups as inertia ellipses. Eigenvalues of the analyses are displayed inset. (B) Quantile-quantile (QQ) plot of GWA Additional file 7:The statistics for Fisher exact test and Logistic regression test. (XLSX 4534 kb)Additional file 8:P value >1.585 × 10–5) located on the chromosome (Chr) 5. (B) Alignment of four nucleotide sequences to reference sequence Fagus sylvatica mRNA (Sequence ID: AJ130886.1). The FASTA sequence order corresponds as AX-156994126 (SEQ_1), AX-156989406 (SEQ_2), AX-156988334 (SEQ_3) and AX-157000652 (SEQ_4). Highlighted red nucleotides refer to polymorphism to reference sequence and green nucleotides present diagnostics SNPs, respectively. (DOCX 4155 kb)(A) Four highlighted markers with significance level higher than genome-wide threshold RNA sequence reads from each cDNA library mapped to the full-length copy of the candidate gene transcript sequence from contig 03321, representing the expression of the candidate Additional file 10:Linkage disequilibrium (LD) structure across four SNPs associated to BBD. (Red) strong LD between markers; (white) no LD. The block-like pattern represents the regions of high LD. Pairwise LD among four SNPs listed as squared allelic correlation (r2)  and Lewo


Selection for sound conformation has been widely used as a primary approach to reduce lameness and leg weakness in pigs. Identification of genomic regions that affect conformation traits would help to improve selection accuracy for these lowly to moderately heritable traits. Our objective was to identify genetic factors that underlie leg and back conformation traits in three Danish pig breeds by performing a genome-wide association study followed by meta-analyses.Sus scrofa Build 10.2 on Ensembl to search for candidate genes.Data on four conformation traits  for three Danish pig breeds  were used for association analyses. Estimated effects of single nucleotide polymorphisms (SNPs) from single-trait association analyses were combined in two meta-analyses: (1) a within-breed meta-analysis for multiple traits to examine if there are pleiotropic genetic variants within a breed; and (2) an across-breed meta-analysis for a single trait to examine if the same quantitative trait loci (QTL) segregate across breeds. SNP annotation was implemented through LRPPRC, WRAP73, VRTN and PPARD likely control conformation traits through the regulation of bone and muscle development, and IGF2BP2, GH1, CCND2 and MSH2 can have an influence through growth-related processes. Meta-analyses not only confirmed many significant SNPs from single-trait analyses with higher significance levels, but also detected several additional associated SNPs and suggested QTL with possible pleiotropic effects.Among the 14, 12 and 13 QTL that were detected in the single-trait association analyses for the three breeds, the most significant SNPs explained 2, 2.3 and 11.4% of genetic variance for back quality in Landrace, overall conformation in Yorkshire and back quality in Duroc, respectively. Several candidate genes for these QTL were also identified, i.e. Our results imply that conformation traits are complex and may be partly controlled by genes that are involved in bone and skeleton development, muscle and fat metabolism, and growth processes. A reliable list of QTL and candidate genes was provided that can be used in fine-mapping and marker assisted selection to improve conformation traits in pigs.The online version of this article (doi:10.1186/s12711-017-0289-2) contains supplementary material, which is available to authorized users. Lameness and leg weakness are issues of concern in pig production due to economic and welfare aspects. Leg weakness has been reported as the second most common reason for involuntary culling in pigs for many years, and accounted for 8.6 to 15% of the sows being removed from commercials herds in Nordic countries . SeveralReliability of genomic prediction can be increased by including single nucleotide polymorphisms (SNPs), that are significant in genome-wide association studies (GWAS), in the SNP chips used for routine genomic prediction . Thus, oFew studies have focused on the mapping of genes for leg conformation traits in pigs. Thus, the objectives of this study were (1) to identify the QTL that are associated with conformation traits in three Danish pig breeds  by performing a GWAS; and (2) to examine if the identified genetic variants are associated with multiple conformation traits within one breed and if the same QTL are segregating across breeds by performing meta-analyses. The biological functions of the genes that were closest to the most significant SNP within the detected QTL were also examined to unravel the genetic background of conformation traits.http://www.danavl.dk/). In Denmark, purebred pigs in nucleus herds are performance-tested. Conformation traits are evaluated by trained technicians when the pigs are around five months of age and weigh approximately 100 kg. The data used here were recorded from 2002 to 2015 and included four conformation traits: front leg quality (FRONT), back quality (BACK), hind leg quality (HIND) and overall conformation trait (CONF). The first three traits  were scored using a three-point scale from 1 to 3, with 3 corresponding to the best conformation. For CONF, a five-point scale from 1 to 5 was used to score the animals, with scores of 1 corresponding to animals that have serious legs or back problems, 3 to average animals, and 5 to animals with excellent conformation. The number of observations in each breed and the means and standard deviation of each trait are in Table Data on conformation traits from the three Danish breeds, Landrace, Yorkshire and Duroc, which were analyzed in this study, were provided by the Danish pig breeding company DanAvl, Axeltorv, Copenhagen, Denmark  and SNPs that were not mapped in the porcine reference genome build Sscrofa10.2 (http://www.ensembl.org/Sus scrofa/Info/Index) were also excluded. Missing genotypes for the remaining SNPs on the 60 K chip were imputed using Beagle version 3.3.2 [Genotyping was carried out using three types of SNP chip: Illumina PorcineSNP60 BeadChip , and two GeneSeek® custom SNP arrays  namely Genomic Profiler (GGP) Porcine LD array featuring over 8500 SNPs (8.5 K) or GGP Porcine HD array, featuring over 70,000 SNPs (70 K). SNPs were quality-controlled within each breed using the following criteria: SNPs with a call-rate lower than 80% across all samples genotyped with each chip or SNPs with a minor allele frequency (MAF) lower than 0.01 were excluded; SNPs that deviated strongly from the Hardy–Weinberg equilibrium  .The GRM between individuals within each breed was used to perform principal component analysis and single-trait association analyses. The method that was used to estimate GRM between individuals using SNP data is described in Yang et al. . In thisPrincipal component analysis was performed to assess the population structure within each breed. The top ten eigenvectors with the largest eigenvalues were subsequently included as covariates in the association analysis model to account for the confounding effect of population structure . Includi1 is a vector of 1s; Association analyses were carried out using a mixed-linear model with the leaving-one-chromosome-out approach described by Yang et al. . Mixed-ln et al. .−6. Thus, the 5% genome-wide significance level used to avoid type I errors was 1.38 × 10−6 for all three breeds.The Bonferroni correction was applied to correct for multiple testing. Since the number of SNPs was relatively similar for the three breeds, the Bonferroni correction threshold was calculated for all three breeds based on the number of SNPs for the Landrace breed, i.e. 0.05/36,080 = 1.38 × 10i across traits , P value <1.38 × 10−6) was applied for these within-breed multi-trait analyses.A multiple trait meta-analysis was performed within each breed using the approximate multi-trait test statistic described by Bolormaa et al. . EffectsP values from each study i were converted into z-scores. These z-scores for each SNP were weighted by the sample size of each study and combined in a weighted sum across breeds. The test statistic follows the standard normal distribution and was calculated as follows:P value for study i; i; and i. A Bonferroni correction was applied with a significance threshold of 0.05/44,390 = 1.13 × 10−6 for all four traits.Meta-analyses across the three breeds for a trait was carried out using the METAL software developed by Willer et al. . The dirSince the software used does not report model convergence, SNPs with an allele substitution effect estimate that fell outside the range of ±2SD of the corrected phenotypes were filtered out to avoid their probably unrealistic large effect due to inappropriate convergence in the parameter estimation. Also, only SNPs that passed the Bonferroni correction threshold were selected to mark the QTL region. A QTL region was defined by extending the position of the most significant SNP (top SNP) on either side until all SNPs within that region had a The size of the effect of a QTL, which was defined as the contribution of the most significant SNP within that QTL to the phenotypic variance of the trait, was calculated as http://www.ensembl.org/Susscrofa/Info/Index).Genes that harbored the most significant SNPs within each QTL region from single-trait analyses and across-breeds meta-analyses were searched based on the pig genome assembly, Sscrofa10.2  and genomic correlations were estimated from signed t-values of all SNPs between traits in each breed; they are presented on the lower and upper diagonals, respectively in Table Sus scrofa chromosome (SSC) 1, 2, 3, 4, 5, 6, 7, 10, 12, 13 and 18. In general, the number of associated regions was larger for CONF than for the other traits.Genomic regions that were found to be associated with conformation traits for Landrace, Yorkshire and Duroc are in Table PPARD gene on SCC7. This SNP explained 0.2% of phenotypic variance (2% of genetic variance) of BACK. The second most significant SNP, rs81389032 (SSC6: 74.4 Mb), was not within any reported coding gene, but was located in a region between the genes ENSSCT00000032147 and ENSSCT00000003913.In total, 14 regions were significantly associated with conformation traits in Landrace, of which five were associated with BACK, one with HIND, and eight with CONF, but none with FRONT Table . The mosENSSSCT00000005518; explained 0.2% of the phenotypic variance (2.3% of the genetic variance) of CONF. The next two most significant SNPs were located between the genes RIPPLY2 and SNAP91 (SNP on SSC1: 92.7 Mb), and between the genes CD79B and GH1 (SNP rs81440562 on SSC12: 15.0 Mb).For the Yorkshire breed, 12 regions were associated with the traits analyzed: one region with FRONT, one with BACK, three with HIND, and seven with CONF Table . The mosEPAS1 and PRKCE. The next two most significant SNPs were rs81373756 (SSC3: 100.4 Mb) within PRKCE and rs81333163 (SSC6: 57.6 Mb) between A1BG and RPS5. They were both associated with CONF and each explained 0.5% of the phenotypic variance of this trait.Among the 13 QTL regions identified for the Duroc breed, the region on SSC3 between 100.2 and 100.4 Mb showed the highest peak (Table PPARD (SSC7: 36.2 Mb) was highly associated with both BACK and CONF in Landrace; (2) the QTL region located between RIPPLY2 (92.6 Mb) and SNAP91 (92.8 Mb) on SSC1 exhibited high association with both CONF and HIND in Yorkshire; and (3) the region that comprises PRKCE (SSC3: 100.5 Mb) showed high association with both CONF and BACK in Duroc. In the single-trait within-breed association analyses, the only region that overlapped between breeds was on SSC7 and it was associated with CONF in Yorkshire and Duroc.Some regions and genes showed significant association with more than one trait within a breed, suggesting the presence of a variant that may affect multiple conformation traits. For example, strong associations were found: (1) The numbers of significant SNPs identified in within-breed multi-trait meta-analyses for Landrace, Yorkshire and Duroc  were larger than in single-trait analyses (not shown). Their distribution on the genome was showed in the Manhattan plots in Additional file ENSSCG00000003243 and ZNF614, was not identified in the single-trait analyses. Similarly, the two second most significant SNPs are also new and are intergenic on SSC7: rs342640079 (34.9 Mb) between GRM4 and HMGA1 and rs80894106 (103.5 Mb) between VRTN and SYNDIG1L. The latter SNP is associated with CONF in both Yorkshire and Duroc in the single-trait analyses, but reached the higher level of significance in the across-breed meta-analysis.In total 36 regions were associated with the traits analyzed in the across-breed meta-analyses: three regions were associated with FRONT, eight with BACK, seven with HIND and 18 with CONF [Fig. LRPPRC, WRAP73, VRTN and PPARD are involved in bone and muscle development at different levels. Liu and McKeehan [LRPPRC has a role in the regulation of cytoskeleton network activity by analyzing its sequence and the proteins it interacts with. WRAP73 belongs to the WD repeat protein gene family, which includes the WDR8 gene that plays an essential role in the ossification process. Expression of WDR8 was observed in bone-forming cells and in bone and cartilage tissue during the early stage of ectopic ossification in mouse [VRTN gene was found to be related to the number of vertebrae in domestic pigs with one allele resulting in an additional segment in the thoracic vertebrae compared with the wild type allele [PPARD, a candidate gene for BACK and CONF in Landrace, was reported to be linked with muscle development and metabolism in pigs [PPARD inhibits the formation of myotube and increases adipocyte differentiation in mouse myoblasts [PPARD with limb bone length [HMGA1 which is located in a QTL region that we identified on SSC7 in Duroc, was reported to be associated with length of limb bone in pigs [HMGA1 enhanced the proliferation of chondrocyte cells by regulating the expression of a chondrocyte-specific marker [VRTN, PPARD and HMGA1 are candidate genes for conformation traits in pigs by regulating bone and muscle development.The present GWAS detected several chromosomal regions that are associated with conformation traits in the three pig breeds, and in many cases, we were able to identify candidate genes within these regions. Results from GWAS in humans and other species suggest that conformation traits are complex and affected by various factors such as bone and cartilage development, muscle growth, fat accumulation and body weight gain , 24, 25.McKeehan  suggestein mouse . A polyme allele . PPARD,  in pigs . Expressyoblasts . In addie length  and growe length  in pigs. in pigs , 32 and c marker . These fIGF2BP2, GH1, CCND2 and MSH2. The protein IGF2BP2 plays an important role in controlling the action of IGF [CCND2 for BACK in Landrace are essential for growth of pancreatic islets [MSH2, regulates the activity of the melanocortin system which is involved in fat accumulation, feed intake and daily weight gain in pigs [Growth-promoting factors, including insulin and IGF, are known to participate in bone and fat metabolism and in the growth process , 35. In n of IGF  which arn of IGF . Similarn of IGF . Membersc islets , which p in pigs . These rRIPPLY2 (SSC1: 92.6 Mb) and SYNDIG1L (SSC7: 103.5) are associated with vertebrae and rib development. RIPPLY2 is involved in somitogenesis during the embryo stage in mice, and knockout mice for this gene die during the perinatal period due to severe vertebrae and rib malformation [RIPPLY2 are associated with segment defects of the vertebrae [SYNDIG1L was associated with number of teats while Duijvesteijn et al. [SYNDIG1L may have a role in the development of vertebrae in pigs. Many causal genetic factors have been previously reported to be located in the regulatory regions of genes which may explain why several candidate SNPs were identified at intergenic locations in this study. However, it may also indicate that the density of the SNP chips used was not sufficiently high. In that case, an association analysis with imputed whole-genome sequence might be able to further pinpoint the causal mutations [In this study, we identified several SNPs and chromosomal regions that were significantly associated with the traits analyzed. However, some of the top SNPs were located in intergenic regions between coding genes. Some of these genes, such as ormation . In humaertebrae . In pigsertebrae  reportedn et al.  showed tutations .PRKCE, is involved in several biological processes. PRKCE is a well-known key factor in cell proliferation and differentiation, muscle contraction, gene expression, cell growth and apoptosis, metabolism and diabetes, as reviewed by Akita [PRKCE results in malignant tumors and diabetes [PRKCE with conformation traits.The highly significantly associated gene with CONF in Duroc, by Akita  and Geraby Akita . PRKCE pby Akita  or IGF1 by Akita . Howeverdiabetes , 46. ThiSLC14A2, PPM1G and NGFR [SLC14A2 which is a member of the SLC super family, is mainly expressed in brain [Not all candidate genes that were identified here have an obvious biological function on conformation traits. For example, some of the candidate genes are expressed in neuronal tissues and are related to the activity of the neuronal system, i.e. and NGFR –49. The and NGFR , 50, 51.and NGFR . Anotherin brain  and assoin brain . Since tIn this study, the detection of QTL was enhanced in the multi-trait meta-analyses compared with the single-trait analyses. In other words, larger numbers of significant SNPs and higher significance levels of the top SNPs were observed in the meta-analyses. This phenomenon was also reported by Bolormaa et al.  for statPhenotype and genotype data from all three breeds were available, and thus a GWAS analysis with pooled data could have been done instead of the across-breed meta-analysis. However, these breeds have been separated for many generations and have undergone strong artificial selection and genetic drift, which means that different sets of QTL may be segregating in each breed. Moreover, GWAS relies on linkage disequilibrium (LD) between markers and causal variants. The marker-QTL linkage phase can differ by breed and, thus, a joint analysis will have less power to detect such QTL. The genome-wide and local pattern of LD and the persistence of LD phase have been investigated in these three breeds , which sPRKCE, SLC14A2, HMGA1, VRTN and SYNDIG1L. The greater significance level of the top SNPs was expected due to the improved power of detection for common SNPs in the meta-analyses compared with single-breed analyses. This advantage of meta-analyses has been reported in pigs [SOS2, TRIM24 and ELMO1. Mutations in SOS2 are associated with the Noonan syndrome in humans, i.e. patients with this syndrome have short stature, weak muscles and malformed skeleton [SOS2 is associated with conformation traits via growth-regulated processes. TRIM24 belongs to the superfamily of tripartite motif-containing proteins which have a role in the immune system. A member of this protein family, TRIM76, was reported to be highly expressed in porcine skeletal muscle and significantly associated with carcass traits such as ham percentage and intramuscular fat [ELMO1, which is associated with the development and progress of diabetes nephropathy in humans, is a candidate gene for CONF [HOX gene clusters  are known to be associated with the formation and development of vertebrae [HOXB5 and HOXB13 were the closest genes to the top SNP in the associated region. An association between HOXB genes and number of lumbar and thoracolumbar vertebrae was also reported in pigs [The results of the meta-analysis confirmed the associations of a number of candidate genes that had been detected in the single-breed analyses, such as  in pigs , cattle  in pigs , 58, and in pigs , 50. In skeleton . They arskeleton , which sular fat . The resfor CONF . Anotherertebrae . The comertebrae . In this in pigs .The diversity in the candidate genes and their biological functions found in this study confirmed the complex pattern of the genetic mechanisms that underlie conformation traits in pigs. Bone and skeleton development, muscle and fat metabolism and growth processes probably interact together to determine the general conformation and movement of a pig. However, these interactions are still unclear and should be investigated further.LRPPRC, WRAP73, VRTN, PPARD, IGF2BP2, GH1, CCND2 and MSH2 genes with conformation traits in pigs. We show that meta-analysis is a powerful QTL detection approach since we were able to detect possible QTL with pleiotropic effects in the multi-trait meta-analyses, and novel relevant candidate genes such as SOS2, TRIM24 and ELMO1 in the across-breed meta-analyses. Our findings are reliable and can be used in fine-mapping to confirm the effects of the genes identified, as well as in marker-assisted selection to improve the conformation in pigs.Conformation traits are complex and appear to be controlled by genes that are involved in different biological processes, including bone and skeleton development, muscle and fat metabolism and body growth. Our results suggested the association of the


Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits.Flax ( Linum usitatissimum L.) is one of the oldest plants cultivated for fiber and edible oil and remains an important cash crop worldwide. Breeding selection for fiber flax or linseed flax has resulted in two plant types, which differ considerably in agronomic performance  has not been reported.In recent years, genome-wide association studies (GWAS) based on next-generation sequencing (NGS) technology have become the new approach for improving crop traits. GWAS is suitable for phenotypic data under multiple environments, thereby reducing environment-induced errors and enhancing results accuracy -assisted breeding and improvement of the main agronomic traits in flax.The core germplasm resources of 224 flax accessions were collected from institutions in China and other countries Table . They weGenomic DNA was isolated from fresh leaves harvested from 20-day-old seedlings of flax. The Tiangen plant total genomic DNA extraction kit  was used for the genomic DNA extraction. A NanoDrop 2000  was used to determine the DNA concentration and quality to ensure that DNA samples met the requirements of the sequencing reaction .First, the restriction enzyme HaeIII was used to digest the genomic DNA in a 50 μL aqueous solution containing 500 ng of genomic DNA, 41 μL of NEB Buffer (10 ×), and 0.12 μL of HaeIII (1 U/μL). This solution was incubated at 37°C for 15 h. The resulting DNA was column-purified using a QIAGEN kit and solubilized in 50 μL of EB (0.01 mol/L). The sticky ends of the digested DNA fragments were filled in, and their 5′ ends were then phosphorylated using a 100 μL solution containing 30 μL of purified DNA (50 ng/μL), 10 μL of T4 DNA Ligase Buffer , 4 μL dNTP Mix (10 mmol/L), 5 μL of T4 DNA polymerase (5 U/μL), 1 μL of Klenow fragment (5 U/μL), and 5 μL of T4 polynucleotide kinase (10 U/μL). This solution was incubated at 20°C for 30 min in a thermocycler. The DNA was column-purified using a QIAGEN kit and solubilized in 33 μL of EB (0.01 mol/L). Next, a base was added to the 3′ ends of the 5′ phosphorylated DNA fragments, allowing them to connect to the Solexa adaptor, which has a T base at its 5′ end, using the following conditions: 32 μL of purified DNA (50 ng/μL), 5 μL of Klenow Buffer (10 ×), 10 μL of dATP (1 mmol/L), and 3 μL of Klenow Exo (5 U/μL). The solution was placed in a 37°C water bath for 30 min. The DNA was column-purified using a QIAGEN kit and solubilized in 10 μL of EB (0.01 mol/L). Finally, the Solexa adapter was attached to the DNA fragments to allow them to hybridize in the flow cells in the sequencing reactions. The reaction conditions were as follows: the solution containing 10 μL of purified DNA (50 ng/μL), 25 μL of DNA Ligase Buffer (2 ×), 10 μL of Adapter (5 pmol/μL), and 5 μL of DNA Ligase (5 U/μL) was incubated at 20°C for 15 min in the polymerase chain reaction (PCR) system. The DNA was column-purified using a QIAGEN kit and solubilized in 30 μL of EB (0.01 mol/L).http://www.paproc.de/)   under accession SRP116365 or SRS2474942 for leaf.Raw sequencing reads were separated using barcode sequences: Illumina SLAF libraries were barcoded with standard Illumina multiplex adaptors and pooled for sequencing in sets of three samples to generate an average of 6-fold sequence coverage per sample . The number of genetic clusters was predefined as r2) in Tassel version 3.0  between pairs of SNPs was estimated by using squared allele frequency correlations  = 5, red line) and P ≤ 1.268 × 10−6  = 6, blue line) were defined as the genome-wide control threshold and suggestive threshold, respectively. The genes within 10 Kb of a significant SNP's flanking region were reported as candidate genes.The efficient model was performed with both GLM and MLM using Tassel software. The population structure matrix generated from Admixture was used as the Q matrix for the GLM model. Under three different environmental conditions, plant height had a minimum value of 42.20 cm, a maximum value of 125.40 cm, and a maximum coefficient of variation of 18.09%; technical length had a minimum value of 27.60 cm, a maximum value of 103.20 cm, and a maximum coefficient of variation of 22.76%; number of branches had a minimum value of 2, a maximum value of 12, and a maximum coefficient of variation of 55.57%; number of fruits had a minimum value of 2, a maximum value of 39, and a maximum coefficient of variation of 53.47%; and 1,000-grain weight had a minimum value of 3.18 g, a maximum value of 9.21 g, and a maximum coefficient of variation of 16.27%. The results therefore indicated that the test germplasm resources of flax contained extraordinary genetic variation . Considering both the SLAF and SNP data, we defined the SLAF markers associated with SNPs as polymorphic SLAF markers to thereby examine the SLAF polymorphisms. Our analysis yielded a total of 146,959 polymorphic SLAF markers with a mean depth of 7.2. After quality control, there were 34,932 SNP loci used for subsequent GWAS analyses Table .K-value was 3 . In other words, our results implied that the collection most likely originated from three ancestors. Given that population stratification might affect the accuracy of association analysis, we generated QQ plots of individual traits . The relevant Manhattan plots and QQ plots of the two models and three environments are shown in Supplementary Figure UGT (UDP-glycosyltransferase) and PL (Pectate lyase). Moreover, the genes closest to the other seven SNP loci were CBP , PI-PLC X (PI-PLC X domain-containing protein), SPP (Squamosa promoter-binding-like protein), PPR (PPR repeat family), PSP (Pectate lyase superfamily protein), UF (Ubiquitin family), and CS (Cellulose synthase)  , VTP (Vesicle transport protein), and MIF (Macrophage migration inhibitory factor)  Table .P < 1.26E-06). The relevant Manhattan plots and QQ plots of the two models and three environments are shown in Supplementary Figure GRAS (GRAS domain family), GST (Glutathione S-transferase), PORR (Plant organelle RNA recognition domain), PIP5K (Phosphatidylinositol-4-phosphate 5-Kinase), XTH (Xyloglucan endotransglucosylase/hydrolase), and DDR (DNA-damage-repair) (Table Twenty-one SNP loci exhibited a significant association with the number of branches (P < 1.26E-06). The corresponding Manhattan plots and QQ plots of the two models and three environments are shown in Supplementary Figure TATP (Transmembrane amino acid transporter protein), Contig1437 (Linum usitatissimum clone Contig1437 microsatellite sequence), LU0019C12 (Linum usitatissimum clone LU0019C12 mRNA sequence), FH (Fumarate hydratase), and RP  (Table Nine SNP loci exhibited significant associations with the number of fruits (n) Table .P < 1.26E-06). The corresponding Manhattan plots and QQ plots of the two models and three environments are shown in Supplementary Figure HP , NAD-DEF (NAD dependent epimerase/dehydratase family), TS (Terpene synthase), TPI (Trypsin and protease inhibitor), STK (Serine/threonine protein kinase), CAP , PHO1 (SPX and EXS domain-containing protein), and ARP (Autophagy-related protein) (Table Twenty-three SNP loci exhibited significant associations with 1000-grain weight P < 1.2E-06. TheP < 1.26E-06). The Manhattan plots of the SNP loci  . In this study, we employed NGS sequencing technology coupled with SLAF-seq to perform GWAS to identify SNP loci associated with important traits and determined their candidate genes.Genome-wide association study (GWAS) is based on molecular markers, SNPs, that are present throughout a genome and facilitate direct association analysis for complex traits. It is considered an effective method to determine molecular markers that influence crucial traits . Afterwards, a more stringent screening was performed, in which the 42 SNP loci were subjected to analyses of co-identification by both the GLM and MLM as well as co-occurrence in 2015HRB, 2016HRB, and 2016LX. Ultimately, we identified two SNP loci associated with plant height, six SNP loci associated with the number of branches, five SNP loci associated with the number of fruits, and eight SNP loci associated with the 1,000-grain weight. Given that the aforementioned SNP loci displayed repeated occurrences (in both models and/or in at least two environments), they potentially have pivotal influences on the relevant agronomic flax traits. As such, they can be recruited as candidate genetic markers impacting these five important flax traits. The remaining SNP loci only had single occurrences of association (in only one model and in only one environment); therefore, their reliability must be investigated further.The two association models  and the phenotypic data derived from three environments  were used to perform GWAS for five important flax traits, generating a total of 107 loci  that displayed significant association with the five important flax traits causes phenotypes with shorter stature and more shoot branches. UGT84B1 overexpressors also have wrinkled leaves and reduced root gravitropism  in A thaliana to influence its development , including 15 SNP loci having co-identification either by both models or by co-occurrence in two or more environments. Next, candidate genes were screened in the 10 kb zone of each of the 15 SNP loci to identify 15 candidate genes possibly related to the five important agronomic traits. Our subsequent analyses determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for 1,000-grain weight. These SNP loci and candidate genes may serve as a biological basis for improving these important traits of flax.In this study, we employed SLAF-seq to perform GWAS for five important agronomic traits in 224 germplasm resources of flax. Using two models  for flax grown in three environments , we identified a total of 42 SNP loci displaying a significant association (ZD, ZY, LZ, and QT carried out most of the experimental work, and this study was conceived by JSu. Collections of flax germplasm resources were performed by DZ and XY. DX and JSun designed the research and wrote the manuscript. All authors 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.


Bos taurus the universality of the reference genome is biased towards genetic variation represented by only two related individuals representing the same Hereford breed. Therefore, results of genetic analyses based on this reference may not be reliable. The 1000 Bull Genomes resource allows for identification of breed-specific polymorphisms and for the construction of breed-specific reference genomes. Whole-genome sequences or 936 bulls allowed us to construct seven breed specific reference genomes of Bos taurus for Angus, Brown Swiss, Fleckvieh, Hereford, Jersey, Limousin and Simmental. In order to identify breed-specific variants all detected SNPs were filtered within-breed to satisfy criteria of the number of missing genotypes not higher than 7% and the alternative allele frequency equal to unity. The highest number of breed-specific SNPs was identified for Jersey  and the lowest—for the Simmental breed (197). Such breed-specific polymorphisms were annotated to coding regions overlapping with 78 genes in Angus, 140 in Brown Swiss, 132 in Fleckvieh, 100 in Hereford, 643 in Jersey, 10 in Limousin and no genes in Simmental. For most of the breeds, the majority of breed-specific variants from coding regions was synonymous. However, most of Fleckvieh-specific and Hereford-specific polymorphisms were missense mutations. Since the identified variants are characteristic for the analysed breeds, they form the basis of phenotypic differences observed between them, which result from different breeding programmes. Breed-specific reference genomes can enhance the accuracy of SNP driven inferences such as Genome-wide Association Studies or SNP genotype imputation.In  Bos taurus the universality of the UMD3.1 reference genome is highly biased towards genetic variation represented by only two related individuals representing the same Hereford breed–a bull “L1 Domino 99375” and his daughter “L1 Dominette 01449” [A reference genome is a DNA database, assembled as a representative of species' nucleotide sequence. Typically, it is based on genomic sequences from many individuals and, as a result, it is universal for the species. Nevertheless, we must be aware of the fact that it represents genetic diversity averaged over the polymorphisms represented in DNA of donor(s) genomes. It should also be borne in mind that most species are represented by distinctive breeds with specific phenotypic and thus also genotypic characteristics. By relating individual DNA sequence variation to the one universal template, we encounter a danger to miss population-specific variation, since all polymorphism detections are “biased” towards the sequence structure represented by the reference genome. That is why breed-specific reference genomes are essential for providing more accurate genomic inferences, which account for DNA sequence variation among particular breeds , 2. SuchDecreasing costs of obtaining individual whole genome sequences (WGS) enabled generation of large data sets consisting of DNA WGS of many individuals representing various breeds or populations. Such an initiative has been inaugurated as the 1000 Genomes Project for humans, which now harbours WGS for 3,900 individuals from 30 populations . In 2010This resource allows for identification of breed-specific polymorphisms and furthermore for construction of breed-specific reference genomes. This procedure was a major goal of our study and was conducted for Angus, Brown Swiss, Fleckvieh, Hereford, Jersey, Limousin and Simmental breeds represented by a large number of individual whole genome DNA sequences. However, since the analysed breeds differ in many aspects such as meatiness, milkiness, fertility and so on, genomic differences between breeds were translated into the functional genomic features by functional annotation of breed-specific SNPs.The material comprised whole genome DNA sequences of 936 bulls representing Angus 287 bulls), Brown Swiss (148), Fleckvieh (53), Hereford (75), Jersey (66), Limousin (82) and Simmental (225) breeds available within the frame of the 1000 Bull Genome project [7 bulls, In our analysis, aiming to identify breed-specific SNPs, the first step was to remove variants with two or more alternative alleles. The first filtering criterion was selection of a minimum number of alternative alleles observed on a forward and a reverse strand reads (threshold used was 1—removes variants never observed on forward and reverse reads). Then variants with overall quality below 20 and mapping quality below 30 were removed. The next criterion for filtering was selecting the minimum and maximum of read depth (respectively min = 10 and maximum = median of read depth + 3 standard deviation of read depth). Then two variants assigned the same base pair position were removed. Subsequently InDels, which revealed another InDel closer than 10 base pairs, SNPs, which revealed another SNP closer than 3 base pairs and SNPs closer than 5 base pairs to InDels were removed.In order to identify breed-specific variants all SNPs were filtered within-breed to satisfy criteria of the number of missing genotypes not higher than 7% and the alternative allele frequency equal to unity using he VCF Tools software . Those fThe total number of identified SNPs and the number of SNPs remaining after the filtration comprising the number of missing genotypes not higher than 7% and the alternative allele frequency equal to unity was summarized in Bos taurs as a species. Still a considerable number of variants were specific for only one breed. The highest number of breed-specific SNPs was identified for Jersey  and the lowest—for the Simmental breed (197). Furthermore, considering pairwise breed comparisons, the highest numbers of SNPs specific only for a combination of two breeds was identified for Jersey-Fleckvieh  and for Jersey-Brown Swiss  combinations, the latter may have been expected provided phenotypic similarities between both breeds as well as a similar milk production performance. The lowest numbers of breed-combination specific SNPs were always attributable to constellations involving the Simmental breed, i.e. Simmental-Hereford (16 SNPs), Simmental-Angus (24 SNPs), Simmental-Brown Swiss (171 SNPs) and Simmental-Jersey (231 SNPs). This remains in agreement with results presented by [www.interbull.org).The post-filtration variants were visualised using the Venn diagram  showing ented by  who showSNPs identified as breed-specific were genomically annotated to the UMD3.1 reference. Polymorphisms located in coding regions overlapped with 78 genes in Angus, 140 in Brown Swiss, 132 in Fleckvieh, 100 in Hereford, 643 in Jersey and 10 in Limousin. Note, that due to a low number of only 197 Simmental-specific SNPs, no polymorphisms located in coding regions were identified for this breed. Since such polymorphisms, potentially contribute to differences between breeds, we considered their functional consequences expressed by Sequence Ontology terms .As it might have been expected, in most of the breeds over the half of breed-specific variants located within coding regions was synonymous. However, the sets of Fleckvieh-specific and Hereford-specific polymorphisms were exceptions, since for those two breeds there were missense mutations, which composed the most of SNPs . Also for the remaining breeds, the number of missense point mutations was considerable ranging from 41% (485 SNPs) in Jersey to 36% (5 SNPs) in Limousin. Besides missense mutations, albeit less numerous, breed-specific SNPs with other high impact consequences were identified. These comprised SNPs located in splice regions, exonic SNPs resulting in a premature termination or prolongation of a translation process, as well as SNPs which alter the translation start codons. Further on, based on KEGG pathways, we checked whether whole metabolic processes representing interplay of several genes and their products, were specifically altered in the analysed breeds. Our results showed that genes harbouring breed-specific mutations in coding regions do not significantly represent any particular pathway.Phenotypic annotation of breed-specific SNPs to the OMIA data base (omia.org) has been done using the BioMart software . Genes hwww.animalgenome.org/cgi-bin/QTLdb/BT/index). Genes with mutations characteristic for the Angus breed overlapped with QTL related to iron content in mussels, which is supported by the earlier study [Next, it was checked which genes harbouring breed-specific SNPs overlapped with QTL found in the QTLdb (er study  demonstrBos taurus caused by breeding programmes [In cattle phenotypic differences between breeds are much more pronounced than in humans. They are enhanced by strong artificial selection for divergent production goals, like dairy or beef. Such phenotypic differences are driven by underlying changes in the genome structure, which emphasises an importance of breed-specific genomic inferences, for which breed-specific reference genomes are the prerequisite. Breed-specific reference genomes can enhance the accuracy of SNP based inferences such as Genome-wide Association Studies or SNP genotype imputation. In the future, when specific reference genomes will be available for many breeds, they can form a basis for describing interbreed volatility and dynamics of changes in the genome of ogrammes .


PCR allelic discrimination technologies have broad applications in the detection of single nucleotide polymorphisms (SNPs) in genetics and genomics. The use of fluorescence-tagged probes is the leading method for targeted SNP detection, but assay costs and error rates could be improved to increase genotyping efficiency. A new assay, rhAmp, based on RNase H2-dependent PCR (rhPCR) combined with a universal reporter system attempts to reduce error rates from primer/primer and primer/probe dimers while lowering costs compared to existing technologies. Before rhAmp can be widely adopted, more experimentation is required to validate its effectiveness versus established methods.Beta vulgaris L.). For each approach, assays were designed to genotype 33 SNPs in a set of 96 sugar beet individuals obtained from 12 parental lines. The assay sensitivity was tested using a series of dilutions from 100 to 0.1 ng per PCR reaction. PCR was carried out on the QuantStudio 12K Flex Real-Time PCR System . The call-rate, defined as the percentage of genotype calls relative to the possible number of calls, was 97.0, 97.6, and 98.1% for TaqMan, KASP, and rhAmp, respectively. For rhAmp SNP, 24 of the 33 SNPs demonstrated 100% concordance with other two technologies. The genotype concordance with either technologies for the other 9 targets was above 99% (99.34–99.89%).The aim of this study was to compare the accuracy, sensitivity and costs of TaqMan, KASP, and rhAmp SNP genotyping methods in sugar beet  contains supplementary material, which is available to authorized users. Plant breeding in the twenty first century uses the three pillars of phenotypic data, genetic variation, and molecular markers to develop improved crop varieties for a growing world . MoleculSingle-nucleotide polymorphisms (SNPs) are currently the most widely-used molecular markers due to their ubiquitous distribution throughout a given genome, as well as their low cost compared to other marker technologies , 4. ThesOne of the earliest methods for SNP genotyping was the TaqMan system   based onA new method called rhAmp based on RNase H2-dependent PCR (rhPCR) has been developed in an effort to address weaknesses of TaqMan and KASP technologies. This method uses RNase H2 to activate primers after successful binding to their target sites, reducing primer dimer formation and improving the specificity of the reaction . BecauseBeta vulgaris (sugar beet). The assays were conducted using the high-throughput genotyping pipeline based on the QuantStudio 12K Flex real-time PCR machine , allowing genotyping of 384 samples per run.In this report, we compare the accuracy, sensitivity, cost and data analysis time of rhAmp-based SNP genotyping system with two existing methods, TaqMan and KASP. These three chemistries were tested with 33 SNP markers on 12 inbred populations of Rz1). Before planting, seeds were surface-disinfected by soaking in ethanol 96% for 5 min, rinsed with sterile water, then incubated overnight in 50 ml of 0.3% hydrogen peroxide to obtain a greater homogeneity in plant germination. Seeds were planted in plastic pots and grown in a peat-based potting mix. Plants were grown in a climatic chamber for one month. Pots were placed in a growth chamber set at day/night temperatures of 25/18 °C, relative humidity of 70/90%, a 14-h photoperiod, and an irradiance of 60 W m−2.The study included 12 sugar beet genotypes from the DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment  collection , following the procedure described by Stevanato et al. . The quaA set of pre-validated SNPs  and custom TaqMan SNP assays (Thermo Fisher Scientific).KASP genotyping assay Genotyping by KASP was performed following manufacture’s instruction using 10 ng of DNA mixed with the KASP Genotyping Master Mix  and custom KASP SNP assays (LGC Genomics).rhAmp genotyping assay Genotyping by rhAmp was performed following manufacture’s instruction using 5 ng of DNA mixed with rhAmp Genotyping Mix, composed by rhAmp Genotyping Master Mix , rhAmp Reporter Mix  and custom rhAmp SNP assays (Integrated DNA Technology).The three reaction mixes are reported in Table g, 30 s) before thermal cycling. Thermal cycling parameters are described in Table PCR assays were prepared using a QIAgility liquid-handling robot (Qiagen). The robot was also set to pipette 96 individual DNAs per each run. The reactions were run in duplicates and with 4 non-template controls (NTC) in each run and for each custom assay. The total number of analyses in each assay was: 33 SNPs × 12 genotype × 8 plants in each line × 2 technical replication = 6392 analyses. Plates were sealed with adhesive film  and briefly centrifuged  location is the mean of FAM Rn and VIC Rn value of NTC reactions.Cluster to NTC distance is the mean distance of all data points in each cluster to NTC.Cluster angle separation is the angle difference between Homozygous allele clusters and Heterozygous cluster. Cluster angle is calculated as the mean angle of all data points in each cluster with respect to the x-axis and NTC cluster as the origin.Cluster spread is the standard deviation of distance between each data points to the center of cluster.Quantitative metrics such as “Call rate”, “No template control (NTC) location”, “Cluster to NTC distance”, “Cluster angle separation” and “Cluster spread” were calculated using the FAM and VIC Rn values from instrument software. Figure An analytical sensitivity test was performed at various DNA input ranging from 100 to 0.1 ng DNA per reaction. The limit of detection (LOD) of each method was evaluated using Rn, commonly referred as normalized reporter value.The data were subjected to analysis of variance using Statistica 10.0 package  and Fisher’s Protected Least Significant Difference was calculated for mean comparison.TaqMan, KASP and rhAmp assays successfully genotyped 29 out of the 33 SNPs on the 12 genotypes. SNP21, SNP204, SNP262 and SNP176 showed high failure rates across all three approaches and were withheld from subsequent analysis. Figure Table We analyzed the concordance rate of the three different methods, excluding any SNP with missing data in one or more samples. This resulted in exclusion of 1233 calls due to missing data. Thus, the concordance between the three chemistries was calculated on the 87% of theoretical maximum data, representing 2757 SNP/genotype combinations, each called with TaqMan, KASP and rhAmp. There were 32 cases of discordance between assays (1.16%), comprised of disagreement concerning heterozygous versus homozygous SNP state. We found that the concordance rates between KASP and TaqMan, and between KASP and rhAmp were 99.24 and 98.94%, respectively.The limit of detection (LOD) for SNP genotyping was determined for each method using a dilution series of input gDNA ranging from 100 to 0.1 ng DNA per reaction. Results showed that TaqMan and rhAmp were able to successfully genotype SNPs with as little as 0.2 ng DNA per reaction, while the KASP was unable to genotype SNPs at gDNA input lower than 0.9 ng per reaction Table . All thrA cost estimate for three SNP genotyping methods is shown in Table Time for 384-wells plate preparation and data analysis is reported in Table Determining the genetic composition of organisms and tissues is critical to diverse scientific pursuits. Improvements in the accuracy, time per run and cost of genotyping technologies have led to their widespread use in fields such as medicine, food safety, basic research and plant breeding. In the dynamic environment of molecular biology, it is challenging to keep up with the evolution of new technologies and methods. Here, we provide a comparison of a new genotyping assay, rhAmp, with two established methods, TaqMan and KASP. We focus the attention on quantitative metrics such as call rate, sensitivity, concordance, and the way in which these values differ across the three chemistries. For each method, we compare also the genotyping cost per sample and data turnaround time.The three PCR allelic discrimination technologies proved to be reliable allowing high-throughput genotyping. All the approaches present distinct and clear clusters in the allelic discrimination plots. The call rate was consistently above 97% with the highest values reported for rhAmp. In addition, rhAmp and TaqMan showed the highest concordance rate, above 99%. Discordant calls and undetermined results between the three chemistries may be due in part to variations in quantity and quality of DNA that can affect particularly TaqMan and KASP.As a result, rhAmp provides higher sensitivity, together with TaqMan, and more distinct clusters in the allelic discrimination plot. The sensitivity of each assay was tested to determine the lower limit of DNA required for successful allelic discrimination and defined as the lowest amount of target DNA detected by the assay . It has SNP genotyping calls can be determined by end-point PCR or combination of end-point and real-time PCR. TaqMan and rhAMP can perform both end-point and real-time PCR while KASP is end-point only , 15. TheIn all three evaluated techniques, allelic discrimination depends on hybridization in which a single base varies, and in certain cases, non-specific hybridization can occur . rhAMP tLooking at the cost per data point, based on our estimation, TaqMan is the most expensive method, while KASP and rhAmp provided much greater flexibility in terms of cost. Both rhAmp and KASP did not require labelling of the SNP-specific primers, which reduce the cost compared to TaqMan. Yuan et al.  report tIn all the three tests we observed low genotyping error rates, low labor cost, and tolerance of variations in quantity and quality of DNA. These properties are critical for plant selection in breeding programs because the optimization for a large number of DNA samples prior to conduct PCR reaction is not easily applicable.The three chemistries had largely similar concordance in SNP calling, with the rhAmp chemistry providing marginal increases in accuracy over KASP, as well as significant cost reductions compared with TaqMan. We conclude that rhAmp provides an improvement in the efficiency of SNP detection compared to TaqMan and KASP. These results should assist plant breeders in making informed decisions on how to use their resources to maximize gains through molecular breeding.Additional file 1: Table S1. Description of the sugar beet genotypes used in this study.Additional file 2: Table S2. Flanking sequences of evaluated SNPs indicated by brackets.