Single nucleotide polymorphisms (SNPs) located within the human genome have been shown to have utility as markers of identity in the differentiation of DNA from individual contributors. Massively parallel DNA sequencing (MPS) technologies and human genome SNP databases allow for the design of suites of identity-linked target regions, amenable to sequencing in a multiplexed and massively parallel manner. Therefore, tools are needed for leveraging the genotypic information found within SNP databases for the discovery of genomic targets that can be evaluated on MPS platforms.1000 Genomes Project SNP database, human genome regions were identified that contain globally ubiquitous identity-linked SNPs and that were responsive to targeted resequencing on MPS platforms. Algorithmic filters were used to exclude target regions that did not conform to user-tunable SNP island target characteristics. To validate the accuracy of TIA for discovering these identity-linked SNP islands within the human genome, SNP island target regions were amplified from 70 contributor genomic DNA samples using the polymerase chain reaction. Multiplexed amplicons were sequenced using the Illumina MiSeq platform, and the resulting sequences were analyzed for SNP variations. 166 putative identity-linked SNPs were targeted in the identified genomic regions. Of the 309 SNPs that provided discerning power across individual SNP profiles, 74 previously undefined SNPs were identified during evaluation of targets from individual genomes. Overall, DNA samples of 70 individuals were uniquely identified using a subset of the suite of identity-linked SNP islands.The SNP island target identification algorithm (TIA) was developed as a user-tunable system to leverage SNP information within databases. Using data within the TIA offers a tunable genome search tool for the discovery of targeted genomic regions that are scalable in the population frequency and numbers of SNPs contained within the SNP island regions. It also allows the definition of sequence length and sequence variability of the target region as well as the less variable flanking regions for tailoring to MPS platforms. As shown in this study, TIA can be used to discover identity-linked SNP islands within the human genome, useful for differentiating individuals by targeted resequencing on MPS technologies.The online version of this article (10.1186/s12859-018-2133-2) contains supplementary material, which is available to authorized users. As comparative genomics studies increase their fidelity with which the genomes of the human population are examined, the intricacies of the SNP diversity found within the population are described , making × 10− 19 , a panel× 10− 19 , and lat× 10− 19  for appl× 10− 19 –11 and t× 10− 19  for use 1000 Genomes Project [International HapMap Project [Encyclopedia of DNA Elements (ENCODE) [1000 Genomes Project has made available a database containing records of verified SNP locations found in the current version of the human reference genome across 2504 unrelated complete human genomes, representing 26 populations around the world. The SNPs within this database are an informative repository of potential target regions for the development of a suite of genetic markers with utility for determining identity.Leveraging advances in MPS and post-sequencing bioinformatics processing technologies, SNPs are characterized within the genome with increasing speed and fidelity. Projects such as the  Project , Interna Project , and the(ENCODE)  offer ne1000 Genomes Project. Further, each island was required to be flanked by low-variance regions of at least 150 bp, containing only SNPs with frequencies of ≤0.5% or ≥ 99.5%. The identified SNP islands were down-selected for unique genome locations with conserved primer targeting sites within the flanking regions as compared to the whole human genome. These conserved primer sites allow target-specific amplification of the SNP islands while minimizing mis-priming amplicon noise, resulting in increased target sequence resolution. Within the resulting 54 SNP islands, 166 SNP markers for identity passed computational filters, experimental amplification, and massively parallel DNA sequencing. An additional 143 SNPs were identified within the SNP islands. Each of the SNPs provided identity-relevant information, defined as displaying discerning information across individual DNA profiles. Application of the resulting SNP array to contributor genomic DNA produced profiles of the contributing individuals that were targeted, individually specific, and reproducible. Overall, the SNP panel differentiated all 70 individuals that is was tested against. In this report, a subset of this SNP array (27 islands) was used to differentiate 15 individual identity profiles obtained using buccal swab DNA samples (~ 10 ng/μL).In this study, we identified 54 SNP islands that we define as haplotypes of multiple identity-linked SNPs located within the same discrete genomic region. The SNP islands were identified using a state-based computational algorithm filtering through the genome and demonstrated utility for differentiating human identity. SNPs within the islands were selected to provide representation within the human population with a frequency that allows general variability but not niche specificity, avoiding selection of a highly-specific population. The islands were computationally targeted in compact regions of the genome (≤ 400 base pairs in length) containing three to five SNPs with a SNP frequency between 30 and 70% for the global population as defined by data from the 1000 Genomes Project Database. The user-defined parameters of the algorithm optimized the number of targeted SNP islands from the exhaustive options available throughout the human genome. Under SNP island search parameters, suitable target regions were located, consisting of a high-variance segment of sequence with a defined maximal length, flanked by low-variance (conserved) sequence of defined minimal length.The SNP island target identification algorithm  establishing low-variance segment I, 2) establishing high-variance segment, and 3) establishing low-variance segment II.1000 Genomes Project Database in a SNP-by-SNP manner along the human reference genome to find relevant targets. The algorithm first located a span of at least the specified sequence length where no relevant SNPs existed, establishing the Low-Variance Segment I state. Once this segment was established, TIA began to incrementally count the number of impactful SNPs as it moved along the genome sequence until either the maximal user-defined target window length was reached or a user-defined number of impactful SNPs was reached, establishing the High-Variance Segment state. For this study, impactful SNP targets were defined as those with a reported global frequency within the threshold of 30% to 70% variance. If the maximal target window length was reached prior to counting the defined number of impactful SNPs, TIA regressed to the establishing Low-Variance Segment I state. If the impactful SNP count was reached within the maximal window length, TIA advanced to the establishing Low-Variance Segment II state. In this last state, TIA again looked for a span of sufficient base length where no relevant SNPs existed; however, this second span was required to be located before the overall target window exceeded the maximum allowed length. If a low-variance segment was not found within the specified overall region length threshold, TIA regressed to the establishing Low-Variance Segment I state. If a segment of sufficient base length was found, the location of the target window was recorded, and TIA reset to the beginning state, establishing Low-Variance Segment I.TIA began by scanning the SNP contents of the TIA accepts user-defined variables for tailored genome searches, including the minimum length for a low-variance segment, the maximum length of a high-variance segment (identity-linked target window), the frequency range of relevant SNPs based on global SNP frequency, and the minimum number of impactful SNPs to establish a high-variance segment. The TIA process was performed for SNP islands containing a minimum of five, four, and three identity-linked SNPs, occurring within a high-variance sequence window of less than 400 bp. Low-variance flanking regions were limited to a minimum length of 150 bp.SNP island targets containing repeat stretches or patterns of the same nucleotide composition, that potentially interrupt Illumina DNA sequencing chemistries , 18, werNational Center for Biotechnology Information. SNP islands were discarded from the viable collection if they did not contain flanking region subsequences larger than 25 unique base pairs with no similarity to other regions within the reference genome. Any SNP island with at least one unique sequence for each flanking region was retained.Primers for targeted amplification of surviving SNP islands were designed to fall within the 150-bp flanking regions of the targets. A separate Python module was developed and used to evaluate the flanking regions of the SNP islands, determining conserved segments within the flanking regions and retaining only those SNP islands meeting the conservation stringency criteria for both flanking regions. Though this algorithmic filter is conceptually a single operation, it was performed in a four-step process. The algorithm located all unique subsequences within the flanking regions of all previously identified target regions, as compared to the entire genome. These subsequences had a minimum sequence length of 15 bp. All SNP islands, lacking unique subsequences in one of the flanking regions, were discarded. The identified unique subsequences from the remaining SNP islands were compared to the human genome reference sequence  for similarity of sequence using the BLASTn tool of the HindIII (New England Biolabs), and 6 μL of UltraPure DNase/RNase-free water (Thermo Fisher Scientific). 1 μL of each target-specific probe was used within the dual-target reaction. VIC fluorophore-labeled TaqMan copy number reference probe for telomerase reverse transcriptase gene (TERT) (Thermo Fisher Scientific) was used as a single-target autosomal marker, and FAM fluorophore-labeled TaqMan copy number probe for sex-determining region Y gene (SRY) (Thermo Fisher Scientific) was used as a single-target Y-chromosome sex-linked marker. Buccal swab DNA samples (4 μL) were evaluated within their respective reactions in an empirically determined dilution range of 10− 2, 10− 3, and 10− 4.Human genomic DNA (gDNA) was collected as buccal swabs from 15 contributors using SecurSwab DUO-V collectors (Bode Technologies). DNA was extracted from the buccal swabs, using the QIAamp DNA Mini Kit (Qiagen) according to the application notes for DNA purification from buccal swabs (version 05/2016). The absolute concentration of recovered human gDNA was quantitated by droplet digital polymerase chain reaction (ddPCR). Reactions were constructed using 10 μL of ddPCR Supermix for Probes containing no dUTP (Bio-Rad), 2 U of Droplets were generated using Automated Droplet Generation Oil for Probes (Bio-Rad) in an Automated Droplet Generator (Bio-Rad). DNA within droplet reactions was amplified using a C1000 Touch Thermal Cycler (Bio-Rad). Droplets were evaluated for the presence of target-amplified fluorescence using a QX200 Droplet Reader with excitation wavelengths at 494 nm (FAM) and 538 nm (VIC). Data acquisition and analyses were performed on two fluorescence channels (518 nm and 554 nm) using the QuantaSoft software (Bio-Rad), where concentrations (copies/μL) of the TERT and SRY targets were calculated for the undiluted DNA samples. Reactions were performed in triplicate, and measured DNA concentrations were converted from copies/μL to pg/μL using the conversion estimate of 3.3 pg/haploid copy of the human genome .Primer sets were computationally designed for the unique sequences of the low-variance SNP island flanking regions using the SeqBuilder module of the DNASTAR software suite (version 11.2.1.25). During the searches, primer length range was set to between 25 and 45 bp with a target annealing temperature of 70 °C. Identified primers were compared to the NCBI GenBank database human reference genome (version GRCh37.p13) using BLASTn.Multiplexed targeted amplification was used to amplify SNP island profiles from the genomic DNA (gDNA) of 15 contributing individuals. Contributor gDNA for each individual, ranging from 25 ng to 300 pg of starting DNA mass, was amplified in multiplexed reactions. 27 primer sets from those identified by TIA were selected for the evaluations. Each reaction  was composed of 25 ng of contributor DNA, 1 U of Phusion High-Fidelity DNA polymerase, 400 μM of each dNTP, and 0.5 μM of both forward and reverse primers for 27 SNP island targets. Thermal cycle conditions were 98 °C for 3 min, followed by 40 cycles of 98 °C for 10 s, 70 °C for 3 min, and 72 °C for 1 min, with a final extension at 72 °C for 5 min. After amplification, the multiplex reactions were cleaned using AMPure XP reagent (Beckman Coulter), using a 1.8× bead to total volume ratio and eluting in a volume of 50 μL of UltraPure DNase/RNase-free water. The primer specificities and amplicon sizes were evaluated using a DNA 1000 assay chip (Agilent Technologies) on a BioAnalyzer 2100 (Agilent Technologies).Illumina paired-end sequencing libraries were prepared using the Accel-NGS 2S DNA Library kit for Illumina Platforms (Swift Biosciences), following manufacturer protocol version 2.0. Each individual DNA pool was given a unique multiplex identifying adapter (MID). Quality and concentration evaluations for amplicon pools, pre- and post-adapter addition, were visualized using a DNA 1000 Assay chip on a BioAnalyzer 2100. Library quality was assessed using the ddPCR Library Quantification Kit for Illumina TruSeq (Bio-Rad), according to manufacturer specifications, on the ddPCR platform. Prepared libraries were sequenced on an Illumina MiSeq platform at the Institute for Genome Sciences Genomics Resource Center at the University of Maryland School of Medicine. The libraries were sequenced in a paired-end manner using the MiSeq Reagent Kit v3 , generating paired-end 300-bp read lengths.Paired sequence read files generated from Illumina sequencing were organized and binned into paired, sample-specific files according to MID sequences. The paired files were evaluated and trimmed based on sequence quality using Trimmomatic  in paireQuality-trimmed reads for each sample were aligned in a paired-end sequence alignment to the human reference genome (version GRCh37.p13) using the Burrows-Wheeler Aligner Maximal Exact Matches (BWA-MEM) program . The resSNP calls were performed using the sorted and indexed BAM files in the Genome Analysis Tool Kit Haplotype Caller  and outpnπ(1-π) for the population distribution. The sample proportion (p) of calls that were returned as reference was calculated asThe DOC at each sequenced base position was used to determine statistical confidence intervals for each haplotype call for each sample. The independence of the haplotype call and the DOC allowed a binomial distribution with a mean of π, where π is the proportion of reference calls, and a variance of x is the number of times a call is classified as reference and n is the number of times that a given SNP is sequenced or DOC. To determine if the sample size was large enough to use the normal distribution to calculate a statistical confidence interval for the sample proportion, the criteria, where p isZ = 1.96. Z is a standardized normal random variable with a mean of 0 and a standard deviation of 1. Z measures the number of standard deviations that an observation is from the mean. For a 95% confidence level (CL), α = 1-CL = 0.5. The corresponding Z value is 1.96, which is found using a standard normal probability distribution table [, where the standard deviation of on table .p and the confidence limits (margin of error) for each SNP location using the ggplot2 package [R statistical software [p ≤ 0.1 were classified as homozygous variant, that included or were greater than p ≥ 0.9 were classified as homozygous reference, and that included 0.4 ≤ p ≤ 0.6 were classified as heterozygous.The distribution of haplotype calls for SNP classifications within a given profile was visualized by scatterplot of the sample  package  and by m package  of the Rsoftware . GenomicThe relation of the SNP profiles between the individuals was visualized based on the zygosity determination for each SNP location. SNP profile calls were assigned values of 1 for the homozygous reference zygosity, 2 for the heterozygous zygosity, or 3 for the homozygous variant zygosity. A total of 116 SNP locations within 28 SNP islands for each profile were evaluated. The relationship between the zygosity profiles of identity-relevant SNP locations for fifteen individual genomic samples were evaluated by the percent similarity, calculated as the number of matched SNP calls divided by the total number of calls between the individual pair.R statistical software. The zygosity of each evaluated SNP location for each profile was represented as a green band (homozygous reference), a yellow band (heterozygous), or a red band (homozygous variant). The Euclidean distance of the relatedness of the SNP zygosity profiles and the complete agglomeration method for clustering were used to construct an agglomerative hierarchical clustering dendrogram.A haplotype heatmap and relatedness dendrogram were generated using the heatmap.2 function of the gplots package  in the R1000 Genomes Project Database and those identified in this study, were located within the SNP islands , sequence repetition rejection filter , and the primer design feasibility filter (54 targets remaining) narrowed the scope of regions for targeted SNP island amplification within the genome  at an average trimmed read length of 240 bp. Figure p, green), heterozygous , and homozygous variant . Figure Using the DOC for the same 27 SNP locations, the variation in zygosity pattern within each SNP island was calculated across all individuals Fig. , indicatFifteen single-contributor SNP profiles (116 identity-relevant SNP locations) generated from the identity-linked SNP island targets were evaluated for similarity Table  and SNP Driven by natural selection, the genetic diversity of the modern human population spans the globe and results in unique inheritable markers of distinction within the population . These g1000 Genomes Project SNP Database [Database of Single Nucleotide Polymorphisms (dbSNP) [International HapMap Project [Allele Frequency Database (ALFRED) [SNPs are widely reported as having utility for determining identity, ancestry, phenotype, and disease states , 30, 31.Database , the Dat (dbSNP) , the Int Project , and the(ALFRED)  continue(ALFRED) . The all1000 Genomes Project Database. According to tunable parameters, the Python algorithms focused on SNP island target selections that not only contain identity-relevant SNPs but also are amenable to analysis using the Illumina MPS platform. The filtering algorithms located identity-linked SNP islands in the human genome, of which 54 islands continued forward within the study after trial validation screening. In application of the 27 islands, the average DOC for evaluated identity-linked SNP locations indicated variability in the amplification and sequencing efficiencies across the SNP islands , heterozygous (0.4–0.6 p), and homozygous variant (0–0.1 p). The variance from the three expected zygosity determinant regions observed for the multiple-contributor sample indicated a mixed sample with alternate DOCs and the resulting distribution proportions overlaying each other.Increasing the number of identity-relevant SNPs contained within each sequenced target provided more identity-discerning information for each SNP island. In the evaluation of 27 identity-linked SNP islands across the genomes of fifteen individuals, the SNP variation, contained within a subset of the total number of SNP islands located by TIA, effectively differentiated the identities of all individuals assessed Table . EvaluatIn comparison of 15 single-contributor samples, the similarity of the resulting evaluated SNP profiles produces a typical similarity score between 40% and 60% Table . This obThe algorithmic narrowing of the scope of genomic regions provided SNP island options conducive to analysis by targeted amplification. In addition, the algorithms allowed the efficient evaluation of the greater human genome for regions that were amenable to uniquely identifying individual contributors within the Illumina MPS workflow. These algorithms were developed in a manner that makes them tunable for determining the desired genomic features. The operator can change the maximum length of the SNP island, the minimum length of the low-variance primer regions, the number, frequency, and type of repeat sequences allowed within the SNP island, and the minimum number of known SNPs having a defined global or population-specific frequency range. By tuning the algorithms to desired target goals, the resulting SNP panel can be applied to discovery of identity-, ancestry-, or phenotype-linked information. In addition, the target islands can be modified to accommodate the requirements and advantages of other sequencing platform chemistries.Systems and methods like ForenSeq  for the TIA provides a genome analysis tool for the selection and development of target regions within the genome, leveraging SNP locations and population frequency information in SNP databases. The target regions discovered by TIA are tunable for the target sequence length and quality, conserved flanking sequence length, allowable variability within the conserved flanking sequence, the global population frequency of the target region SNPs, and the minimum and maximum number of qualifying SNPs contained within the target regions. The target regions identified by TIA are amenable to evaluation by targeted resequencing using MPS platforms. The advantages of TIA are that it allows the user to develop tailored SNP islands, according to SNP allele frequency and population information. As a result, the algorithms provide flexibility to rapidly identify and validate new informative SNP panels as global population SNP databases mature and gain in fidelity and as new MPS technologies come online.Additional file 1:Table S1. Identity-Linked SNP and SNP Island Genomic Locations. Reported global allele frequencies are those represented within the 1000 Genomes Project Database. For SNP loci labeled NSV, no SNP variant was previously reported for that genomic location. Deletion events are represented as a dash (−). (DOCX 56 kb)


Dactylis glomerata L.) is a well-known perennial forage species, rust diseases cause serious reductions in the yield and quality of orchardgrass; however, genetic mechanisms of rust resistance are not well understood in orchardgrass.While orchardgrass  associated with the rust trait. The bulk analysis discovered a number of 5,211 SNPs related to rust trait. Two candidate genes, including cytochrome P450, and prolamin were implicated in disease resistance through prediction of functional genes surrounding each high-quality SNP (P < 0.01) associated with rust traits based on GWAS analysis and bulk analysisIn this study, a genome-wide association study (GWAS) was performed using specific-locus amplified fragment sequencing (SLAF-seq) technology in orchardgrass. A total of 2,334,889 SLAF tags were generated to produce 2,309,777 SNPs. ADMIXTURE analysis revealed unstructured subpopulations for 33 accessions, indicating that this orchardgrass population could be used for association analysis. Linkage disequilibrium (LD) analysis revealed an average rThe large number of SNPs associated with rust traits and these two candidate genes may provide the basis for further research on rust resistance mechanisms and marker-assisted selection (MAS) for rust-resistant lineages.The online version of this article (doi:10.1186/s41065-017-0027-3) contains supplementary material, which is available to authorized users. Dactylis glomerata L.) is a cool-season forage grass that is wildly cultivated in all mild, subtropical regions of the world [Orchardgrass  gene [Arabidopsis thaliana, leaf architecture [Zea mays), and blight resistance [Triticum aestivum L.).In recent studies on rice (Oryza sativa), several genes that have large effects on traits involved in determining yield, morphology, and salinity tolerance have been identified [Hordeum vulgare) [Glycine max) [Avena sativa) accessions were evaluated for disease resistance and further genotyped with 15,000 Diversity Arrays Technology (DArT) and 31 simple sequence repeat and markers to disclose association with disease resistance trait and found five markers were associated with rust resistance [Genome-wide association studies (GWASs) have become one of the most commonly strategies for identifying genes underlying complex traits in plants. In model species, such as D6) gene . This apD6) gene  of Arabiitecture  of maizesistance  of wheatentified –13. In tvulgare) , the Psyvulgare) , and theine max)  have beeine max)  through ine max) . A numbesistance .Consequently, several reduced-representation sequencing technologies, including restriction site-associated sequencing (RADseq), double digest RADseq, and two-enzyme genotyping-by-sequencing (GBS), were developed as cost-effective methods for SNP discovery and high-throughput genotyping . RecentlCucumis sativus L.) through SLAF-seq to detect fruit-related QTLs. Additional studies have created high-density genetic maps in order to provide a platform for gene/QTL fine-mapping [SLAF-seq is highly automated due to the development of bioinformatics tools and high-throughput sequencing technology applications. SLAF sequencing methods ascertain the uniformity, efficiency, and density of marker development, and they have been applied in several studies. For example, Li et al.  construc-mapping , 26, 27.-mapping . Moreove-mapping , 29–31.In this study, we use the SLAF-seq technology to perform a GWAS of rust traits in orchardgrass to identify the associated SNPs and predict functional genes. These results will provide a basis for orchardgrass breeding and may be helpful in enhancing rust resistance in orchardgrass.A total of 76.74 M reads were obtained from this experiment. The average Q3 value was 86.91%, and the average GC content was 46.97%. Details are listed in Additional file In total, 76.74 M reads were generated for the 33 genotypes, encompassing 4.30 Gb of the orchardgrass genomic DNA sequence. A total of 2,334,889 SLAF tags were identified throughout the genome Table . These SThe 33 orchardgrass samples could not be clearly divided into subgroups by the neighbor-joining analysis using MEGA software. However, the results from this phylogenetic analysis showed that the HS (high susceptibility of disease) materials were relatively concentrated, comparing with HR (high level of resistance of disease) materials Fig. .Fig. 1ThThe best dataset was produced by using a K-value of 2, indicating that our samples probably derived from 2 ancestors. With the maximum membership probability, 5 accessions were assigned to group 1 (G1), while 28 accessions were assigned to group 2 (G2). All 5 accessions from G1 were HR samples, and all 16 HS accessions (16) were included in G2  showed that our samples probably derive from two ancestors. It is important to use population-based methods to separate accessions from mixed populations into unstructured subpopulations, allowing for association analyses between phenotypes and molecular bands to be conducted in homogeneous subpopulations , 34. In A principal component analysis (PCA) was performed using the 2,309,777 SNPs from all 33 accessions to estimate the clusters of population. Principal Component 1 (PC1) explained 7.94% of the variation in the genotypic data, while PC2 and PC3 explained 5.20% and 2.57% of the variation, respectively. Although there are intermediate accessions that make the groupings less clear, the PCA results indicate that the HS samples can be clustered into one group  were considered to be in LD with strong linkage at r2 > 0.8  associated with the rust trait  analysis of 2,309,777 SNPs, 4,604 SNPs were significantly (2+-binding protein (TaCab1), were implicated with disease resistance in the Nonredundant NCBI nucleotide sequences (NT) database, and their predicted molecular functions and biological processes are specified in Table We predicted 555 genes from these 4,604 SNPs in our study . Cytochrome P450 plays an important role in the synthesis of secondary metabolites of fungi and metabolism of foreign compounds . Studies defense . Pc68Lrk domains . Several domains , 39. In ipe rust . In concWe also associated the results between GWAS analysis and bulk analysis to confirm these four predicted genes; however, two types of analysis only shared three markers representing two predicted genes including Cytochrome P450 and Prolamin gene Table . TherefoA total of 33 orchardgrass accessions, with high level of resistance of disease (HR) and high susceptibility of disease (HS), as evaluated by Yan et al. , were chTotal orchardgrass genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen USA); the quality and quantity of DNA was then inspected using 0.8% gel electophoresis. The quantified DNA was diluted to 20 μg/μL and was stored at −20 °C before use.The orchardgrass genomic DNA was analyzed with SLAF-seq . SequencBased on the SNP genotype data from the 33 orchardgrass samples, a dendrogram was constructed in MEGA5  using th2) between all combinations of SNPs  were used to evaluate the significance of pairwise linkage disequilibrium (LD) using Haploview [http://www.softpedia.com/get/Science-CAD/Haploview.shtml). Each pair of SNPs was considered to have strong linkage if r2 > 0.8. Based on SNP genotype data, the GWAS analysis was conducted using a GLM in the TASSEL software [The squared correlations  were used to conduct a bulk analysis, and the procedures were followed as previously described . To idenhttp://ensemblgenomes.org/) and NCBI (http://www.ncbi.nlm.nih.gov/). The NT databases were used to conduct pathway analysis and functional annotation for predicted genes [The genetic data relating to rust traits of a 500-Kb window surrounding each SNP were downloaded from Ensembl (ed genes .P < 0.01) associated with the rust trait, while a total number of 5,211 SNPs were filtered by bulk analysis. Based on bioinformatics analysis for GWAS results, four candidate genes, including cytochrome P450, Pc68LrkC4, prolamin, and TaCab1 were predicted involving in disease resistance, and the bulk analysis further confirmed cytochrome P450 and prolamin genes were reliable. Besides, further research should be done to determine how these two genes work for resisting diseases. The results can also provide basic information for MAS of rust-resistant lineages.In this study, SLAF-seq technology was used to conduct the GWAS in orchardgrass, and a number of 2,334,889 SLAF tags were produced to generate 2,309,777 SNPs. The GWAS showed that a total number of 4,604 SNPs were significantly (


Gossypium hirsutum, but there are difficulties with precise selection of traits. Therefore, it is important to improve the understanding of the genetic basis of phenotypic variation. In this study, we conducted phenotyping and genetic variation analyses of 719 diverse accessions of upland cotton based on multiple environment tests and a recently developed Cotton 63K Illumina Infinium SNP array and performed a genome‐wide association study (GWAS) of fibre quality traits. A total of 10 511 polymorphic SNPs distributed in 26 chromosomes were screened across the cotton germplasms, and forty‐six significant SNPs associated with five fibre quality traits were detected. These significant SNPs were scattered over 15 chromosomes and were involved in 612 unique candidate genes, many related to polysaccharide biosynthesis, signal transduction and protein translocation. Two major haplotypes for fibre length and strength were identified on chromosomes Dt11 and At07. Furthermore, by combining GWAS and transcriptome analysis, we identified 163 and 120 fibre developmental genes related to length and strength, respectively, of which a number of novel genes and 19 promising genes were screened. These results provide new insight into the genetic basis of fibre quality in G. hirsutum and provide candidate SNPs and genes to accelerate the improvement of upland cotton.Genetic improvement of fibre quality is one of the main breeding goals for the upland cotton,  CorrelaThe genotypes of 719 accessions were examined using the Cotton 63K Illumina Infinium SNP array. All the SNP data were analysed using Illumina GenomeStudio software. First, SNPs without allele polymorphism were eliminated. Low‐quality SNP loci  were also deleted. A final set of 10 511 high‐quality SNPs was screened and used for genetic variation and GWA analysis Figure . These Set al., To understand the genetic diversity of our association panel, molecular phylogenetic analysis among these accessions was conducted based on the genetic distances of these SNPs. The neighbour‐joining (NJ) tree results showed two divergent groups Figure b, designet al., et al., r2 drops to half the maximum value  values continuously increased with K from 1 to 10 Figure a; howeveet al., et al., To explore the genetic factors associated with the fibre quality, we conducted a GWAS, which took into account the population structure and familial relatedness , and Gh_A07G1759 and Gh_D06G1953 code for ADP glucose pyrophosphorylase and UDP‐arabinose 4‐epimerase, respectively (Tables Gene ontology (GO) analysis indicated that the functions of most genes were binding and catalytic reactions, regardless of the intervals Figure b. We alsy Tables , S4. We We analysed major SNP loci relevant to the two critical quality traits, fibre length and strength, that breeders most often consider in selection. For SNPs associated with FL, a significant peak appeared in chromosome Dt11 Figure a, b, andSeven significant SNPs on chromosome At07 were simultaneously analysed for fibre length and strength across multiple environments Table a, b. We For the genetic effect of each SNP in Hap3, taking locus i39753Gh for example, the average fibre length and strength in the accessions with the GG genotype were 29.90 mm and 31.37 cN/tex, respectively Figure . The thr2(1+RPKM) > 1) of all 212 and 161 genes related to FL and FS, respectively , ten (excluding two genes found in both FL and FS) of these 17 predominately expressed genes were not previously reported in cotton (Table All 17 selected genes (nine in FL and eight in FS) were related to diverse functions Table  and weres Figure c. We desay Table  and cataon Table . Additioon Table .Gh_D07G1799, coding for galacturonosyltransferase (GAUT), is involved in pectin biosynthesis, which is part of glycan metabolism  in Dt11 for FL contained a significant SNP locus  and Gh_D11G1929 on Dt11 codes for KIP‐related protein 6 (KRP6) in Arabidopsis. The Rab family encodes key factors of vesicle‐targeting specificity proteins in A. thaliana , made plant cells undergo cell expansion, acting as a cell wall‐loosening enzyme , was implicated in the regulation of sugar metabolism/embryo development  was used for the association analysis in this study. These accessions were from different countries, 588 were collected from China and 131 were from other countries , fibre strength , fibre micronaire (FM), fibre uniformity  and fibre elongation , were measured using a high volume instrument (HVI). Statistical analyses, Pearson correlation between traits and significance analyses were conducted using SPSS 22.0 software. Differences were tested for significance at the 1% probability level.et al., et al., G. hirsutum TM‐1 reference genome  between pairwise SNPs (MAF > 0.05) was estimated using TASSEL 3.0 software. The significance of the associations between SNPs and traits was based on the threshold of the Bonferroni correction for multiple tests (1/n), where n was the total number of SNPs used in the association analysis.For the genome‐wide association analysis, TASSEL 3.0 software  and two normal‐quality upland cotton cultivars (CCRI8 and ND601) were grown in the field of Baoding, China, in 2014. For the cotton fibre samples, bolls were collected at 0, 5, 10, 15, 20, 25 and 30 DPA. Samples from different plants were pooled. Total RNA was extracted from these samples using the EASY spin Plant RNA kit . The qualified RNA was used for sequencing analysis using TopHat v2.0 on a HiSeq 2500 platform at the Novogene Bioinformatics Institute, Beijing, China.The authors declare no conflict of interest.Figure S1. Correlation analysis between five traits related to fibre quality.Figure S2. Manhattan plots showing the GWAS for FL in eight environments.Figure S3. Manhattan plots showing the GWAS for FS in eight environments.Figure S4. Manhattan plots showing the GWAS for FM in eight environments.Figure S5. Manhattan plots showing the GWAS for FU in eight environments.Figure S6. Manhattan plots showing the GWAS for FE in eight environments.Figure S7. Boxplots depicting the genetic effects of SNPs with significant associations with fibre length in Dt11.Figure S8. Boxplots depicting the genetic effects of SNPs with significant associations with fibre length and strength in At07.Figure S9. Expression of all candidate genes related to fibre length and strength.Click here for additional data file.Table S1. List of 719 upland cotton accessions used for association mapping.Table S2. Analysis of variance (ANOVA) results of the fibre quality traits.Table S3. List of 612 candidate genes with fibre quality traits.Table S4. KEGG analysis of all candidate genes.Table S5. Phenotypes of the fibre length and strength of 47 upland cotton accessions belonging to Hap3.Click here for additional data file.


Multifunctional polymer-based composites have been widely used in various research and industrial applications, such as flexible and stretchable electronics and sensors and sensor-integrated smart structures. This study investigates the influence of particle coalescence on the mechanical and electrical properties of spherical nickel powder (SNP)/polydimethylsiloxane (PDMS) composites in which SNP was aligned using an external magnetic field. With the increase of the volume fraction of the SNP, the aligned SNP/PDMS composites exhibited a higher tensile strength and a lower ultimate strain. In addition, the composites with aligned SNP showed a lower percolation threshold and a higher electrical conductivity compared with those with randomly dispersed SNP. However, when the concentration of the SNP reached a certain level (40 vol. %), the anisotropy of the effective material property became less noticeable than that of the lower concentration (20 vol. %) composites due to the change of the microstructure of the particles caused by the coalescence of the particles at a high concentration. This work may provide rational methods for the fabrication of aligned composites. They weet al. have aligned a small amount of ferromagnetic particles in epoxy using a magnetic field for improved mechanical properties of the composite, such as Young’s modulus, tensile, and tear strength [et al. have effectively controlled the alignment and the orientation of nickel nanowires in polydimethylsiloxane (PDMS) for anisotropic material behaviors of the composite in both mechanical and magnetic properties [In addition to the type of embedded conductive filler particles, the alignment of particles using mechanical, electrical, or magnetic forces is also a useful technique to enhance the overall material properties of a composite ,25,26,27strength . Sun et operties . In partoperties ,31,32. Toperties . Howeveroperties ,34 in thIn this study, we fabricated conductive polymer composites composed of spherical nickel powder (SNP) and polydimethylsiloxane (PDMS) with different particle concentrations. Different concentrations of SNP were aligned in pre-polymer using an external magnetic field during the curing process. An optical microscope was used to characterize the alignment and the coalescence of the SNP particles. The composites were experimentally characterized to evaluate the effect of the coalescence on the anisotropic material properties with different particle concentrations.−14 S/m. Spherical nickel powder  was obtained from NOVAMET . The average diameter of the SNP is 11.4 μm, and the electrical conductivity is about 1 × 106 S/m. Densities of the PDMS and the SNP are 0.96 g/cm3 and 8.65 g/cm3, respectively. PDMS (Sylgard 184) was obtained from Dow Corning . PDMS is a non-conductive material with a conductivity of approximately 2.0 × 10After PDMS and as-received SNP were weighed for the target weight fractions , they were mixed using a polymer mixer  at 2000 rpm for three minutes at room temperature in order to obtain a uniform mixture. After dispersion, a curing agent was added to the mixture at a weight ratio of 10:1 for the elastomer and the curing agent, respectively, and mixed again for another three minutes. The mixture was then degassed in vacuum for 10 min to eliminate air voids in the matrix and poured into a mold. An external magnetic field (approximately 1.0 Tesla) was applied to the sample for the alignment of SNP at room temperature, as shown in σ) was calculated as following:R is the measured resistance, A is the cross-sectional area of the composite; and L is the length between the electrodes. Tensile test of the samples was conducted using a motorized materials test stand  at room temperature according to ASTM D412 [The alignment of SNP in the pre-polymer was characterized using an optical microscope. The direct current (DC) conductivity was measured by a standard two-probe method using two multimeters. For the electrical conductivity, the samples were cut into 10 mm × 30 mm. The electrodes were coated with high-purity silver on both sides of the sample to minimize contact resistances b. Some sSTM D412 . The samSince optical investigation did not make it easy to understand the microstructural change in particles in the pre-polymer in a three-dimensional (3D) space, we used a 3D dynamic simulation for the particle alignment in the pre-polymer state. For the low volume fraction of particles, as shown in On the other hand, the nickel particles formed lateral coalescence in some places with a higher concentration, as shown in Now, we investigated the influence of SNP coalescence on the mechanical properties of the SNP/PDMS composites. −14 S/m, showing no change in electrical conductivity up to 20 vol. % of SNPs regardless of the direction. This means that the SNPs do not make a conductive network in the matrix. Based on the percolation theory, the electrical conductivity of composites is mainly dependent on the volume fraction, the size, the shape of the fillers, and the impurity [In addition to the mechanical property, the electrical property of the SNP/PDMS composites was also characterized. impurity ,42,43. Ni.e., parallel and perpendicular) regardless of the magnetic field. This effect may be attributed to the morphology change in SNP in the matrix as observed in the experiment. The loss of anisotropy in electrical conductivity can be explained by the loss of anisotropy in the distribution of the SNP in the matrix due to high particle concentration.Rapid increases in electrical conductivity were observed with SNP/PDMS composites between 15 vol. % and 20 vol. % and between 20 vol. % and 25 vol. %, in parallel with and perpendicular to the magnetic field, respectively. This phenomenon can be explained because the aligned SNP in the matrix enhanced the conductivity of the SNP network due to the alignment process under the magnetic field. However, the SNP/PDMS composites at above 35 vol. % showed similar electrical conductivities in both directions (The main contribution of this work is to examine the influence of coalescence of nickel particles on the mechanical and electrical properties of SNP/PDMS composites. Although conductive polymer composites have been proposed and tested for various smart composite structures, relatively small concentrations of filler particles have been taken into account in most cases, which explains only the effect of the alignment of the filler particles. In our study, the alignment of SNPs in the matrix was achieved by an external magnetic field as a function of particle concentrations up to 40 vol. %. Visual inspections and dynamic simulation confirmed the alignment of SNP and the coalescence process with time as well as volume fractions of SNP. SNP-aligned composites showed significant anisotropy in both mechanical and electrical properties depending on the volume fractions. While the anisotropy material properties were clearly observed at low SNP concentrations in terms of the mechanical and electrical properties, the material properties were changed from anisotropic to isotropic with an increase of the SNP concentration, which is due to the magnetization of the filler particles, forming the coalescence of nickel particles and making structural rearrangements. In addition, the effects of the alignment of SNP were maximized at 10 vol. % and 20 vol. % SNP/PDMS composites for mechanical and for electrical anisotropies, respectively. This work provides a practical understanding of mechanical and electrical properties of conductive polymer composites with magnetically aligned filler particles influenced by the coalescence of particles. The SNP/PDMS composite can be used as smart composites to monitor electrical and/or thermal properties, and thus it is important to show a stable signal response under external loading ,45,46,47


AIFM1 gene are causally linked to severe forms of mitochondrial disorders. Here we investigate the consequence of WAH-1/AIF deficiency in the survival of the nematode Caenorhabditis elegans. Moreover, we assess the survival of C. elegans strains expressing a disease-associated WAH-1/AIF variant. We demonstrate that wah-1 downregulation compromises the function of the oxidative phosphorylation system and reduces C. elegans lifespan. Notably, the loss of respiratory subunits induces a nuclear-encoded mitochondrial stress response independently of an evident increase of oxidative stress. Overall, our data pinpoint an evolutionarily conserved role of WAH-1/AIF in the maintenance of proper mitochondrial activity.Impaired mitochondrial energy metabolism contributes to a wide range of pathologic conditions, including neurodegenerative diseases. Mitochondrial apoptosis-inducing factor (AIF) is required for the correct maintenance of mitochondrial electron transport chain. An emerging body of clinical evidence indicates that several mutations in the  Furthermore, impaired maintenance of mitochondrial biogenesis, structure, and bioenergetics may lead to the development of a variety of human diseases, including cardiomyopathies, encephalomyopathies, rare metabolic syndromes, peripheral neuropathies, and liver failure. In this regard, a growing body of evidence supports the notion that altered oxidative phosphorylation (OXPHOS) is causally associated with several sporadic and inherited pathologies known as mitochondrial disorders10. Their clinical manifestations range from limited damage of a single tissue to devastating systemic failure of various organs. In most of the inherited forms, mitochondrial disorders exhibit a chronic and progressive course with an onset that often occurs perinatally or within the first years of life13. The pathophysiology of mitochondrial disorders is challenging, as they may be due to mutations either in nuclear- or mitochondrial DNA-encoded genes and can directly or indirectly affect the OXPHOS system. To make diagnostic and therapeutic interventions even more difficult, different mutations in a single gene can give rise to distinct syndromes, whereas a large spectrum of pathogenic mutations in various genetic loci can lead to an identical clinical outcome, as in the case of Leigh syndrome14. Despite the relatively low incidence within the population15, an increasing number of new disease-causing mutations have been isolated in patients with mitochondrial disorders.Mitochondria contribute to energy production and biosynthetic processes in eukaryotic cells. Abnormal mitochondrial function contributes to the pathogenesis of age-dependent and familial forms of neurodegenerative disorders17. It was initially described as a death effector that is released from mitochondria in response to toxic insults18. Its pro-apoptotic role is highly conserved from yeast to invertebrates to mammals19. The mature form of AIF is mainly tethered to the mitochondrial inner membrane21. Structural and biochemical characterizations of in vitro purified AIF show that NADH incorporation determines the folding, the redox status, and the dimerization of the FAD-containing AIF protein25. In addition to its death-related role, AIF also contributes to cellular bioenergetics, as it assists in the assembly and/or stabilization of the electron transport chain (ETC)26. In this regard, our group and others have demonstrated that AIF physically interacts and stabilizes the oxidoreductase CHCHD4/MIA40, hence assisting the correct biogenesis of the respiratory chain complexes28. Over the last years, linkage analysis and exome sequencing of patients have identified an array of deleterious mutations in the AIFM1 gene that are associated with rare inherited X-linked mitochondrial disorders. The first deleterious mutation in the AIFM1 gene was found in two infant males showing progressive mitochondrial encephalomyopathy and born from twin sisters29. The disease-segregating mutation is an inherited trinucleotide deletion in exon 5 that causes the ablation of the Arg201 (R201 del). As a result, recombinant mutant AIF protein is structurally unstable, shows aberrant FAD incorporation, and, consequently, impaired redox properties29. To date, a significant array of mutations in the AIFM1 gene have been identified in patients showing a wide range of clinical presentations38.Apoptosis-inducing factor (AIF) is a FAD-containing protein with an NADH-dependent oxidoreductase activity39. Despite the presence of putative FAD- and NADH-binding domains, it seems that WAH-1 does not incorporate cofactors and undergoes conformational changes according to the redox status of the surrounding milieu. At least in vitro, oxidized WAH-1 forms inter-molecular disulphide bonds and exists as dimers or high-order oligomeric states, whereas it dissociates to monomers in response to reduced conditions40. Apart from development, the importance of WAH-1 in other physiological contexts, including mitochondrial activity, has not been fully addressed yet. Here we investigate the downstream consequences of WAH-1/AIF genetic modulation in C. elegans. We demonstrate that wah-1 downregulation by RNA interference (RNAi) affects the expression of ETC complexes and mitochondrial respiration, resulting in reduced lifespan. Moreover, we present new CRISPR/Cas9-modified knockin strains in which a disease-associated WAH-1 variant is expressed. Our data indicate that, along with its well-established role in cell death, WAH-1/AIF is required for the correct maintenance of the OXPHOS system in the nematode C. elegans.The Worm AIF Homolog WAH-1 is a mitochondrial protein that is released into the cytosol during apoptosis. In association with CPS-6/Endonuclease G, WAH-1 translocates to the nucleus and contributes to DNA degradation during caspase-dependent cell deathC. elegans physiology, synchronized wild-type N2 nematodes were exposed to control and wah-1 RNAi. Quantitative real-time PCR confirmed wah-1 downregulation  super-resolution microscopy analysis showed mitochondria with an irregular appearance already in young WAH-1 deficient animals . To test whether WAH-1 deficiency regulates the lifespan through the ETC, we performed epistasis analysis using gas-1(fc21) and mev-1(kn1) mutants. The gas-1 gene encodes for a 49 kDa subunit of complex I, whereas the mev-1 encodes for a subunit of complex II. We found that wah-1 RNAi did not alter the median lifespans of neither gas-1 nor mev-1 mutant animals , gst-4 (glutathione-S-transferase), and sod-3 (superoxide dismutase) promoters  levels in wah-1-silenced animals. Surprisingly, nematodes fed with wah-1 double-stranded RNA (dsRNA)-expressing bacteria showed decreased levels of ROS as measured with the two genetically encoded fluorescent biosensors Hyper and roGFP44  mutants showed an unexpected increase, rather than a decrease, of oxygen consumption compared with wild-type nematodes  animals do not show any difference in survival compared to wild type nematodes. To a certain extent, WAH-1 modulation of mitochondrial function and survival is consistent with the effect of AIF in higher organisms. Indeed, defects of the OXPHOS system varies considerably according to the level of AIF protein loss, with the complete AIF knockout that compromises multiple OXPHOS subunits, whereas AIF downregulation determines mainly aberrant complex I activity46. As in mammals, it may be that WAH-1 controls the OXPHOS system through the interaction with the four CHCHD4/MIA40 orthologs encoded in C. elegans. Alternatively, WAH-1 may modify the electron flow through the respiratory chain complexes, promoting proton pumping across the mitochondrial membrane in a similar manner to the yeast NADH-ubiquinone oxidoreductase Ndi1 as recently proposed48. Ultimately, WAH-1 may bind factors that directly regulate mitochondrial biogenesis. In this regard, it is known that the redox state of WAH-1 affects the activity of CPS-6/EndoG40. As CPS-6 and EndoG mediates the depletion of mitochondrial DNA in nematodes and mammals, respectively50, it may be that the archaic WAH-1-dependent regulation of the OXPHOS system is functionally linked to the mitochondrial DNA homeostasis. Further work is necessary to elucidate this biological aspect.AIF is a mitochondrial protein essential for the maintenance of the OXPHOS system. A significant number of 52. Multiple lines of evidence suggest that the respiration rate induces metabolic changes, favoring alternative survival pathways as well as protective homeostatic mechanisms55. Moreover, mild mitochondrial stress induces the expression of nuclear-encoded genes through epigenetic modifications of chromatin, including posttranslational modification of histones and nucleosome turnover59. Thus, inhibition of mitochondrial respiration can mediate stress response and adaptive processes with a wide array of homeostatic functions60, although the engagement of this complex surveillance pathway is not a predictor of C. elegans lifespan61. In our study, we demonstrate that nematodes subjected to wah-1 RNAi exhibit a nuclear stress response that includes the upregulation of hsp-6, sod-3, gst-4, and HIF-1 target genes. However, such a transcriptional response is insufficient to counteract OXPHOS defects upon wah-1 knockdown, contrary to other genetic manipulations62. As a result, wah-1 downregulation shortens, rather than extends, C. elegans lifespan. One reasonable possibility that explains the decreased survival is an enhanced level of ROS due to ETC impairment. Perhaps surprisingly, multiple approaches indicate that animals lacking WAH-1 exhibit much lower ROS levels compared with control ones. At least in vivo, it is unlikely that WAH-1 has an antioxidant function or a free radical-scavenging activity, although we do not rule out that it contributes to redox reactions through direct interaction with other proteins as previously shown40. Our data further support the concept that increased oxidative stress in certain AIF-deficient models may be a consequence, rather than the cause, of mitochondrial dysfunction. Moreover, our findings demonstrate the conserved role of WAH-1/AIF in the correct maintenance of a functional OXPHOS system in the nematode C. elegans.It is worth noting that, although severe mitochondrial dysfunction contributes to several human pathologies, mild mitochondrial uncoupling or modest reduction of OXPHOS during development promotes longevity in some model organismsThe following antibodies were obtained from commercial sources as indicated: mouse anti-Myc ; rabbit and mouse anti-GFP (Invitrogen and Roche); mouse anti-ATP synthase subunit alpha (CV), mouse anti-complex IV subunit 1, mouse anti-complex I subunit NDUFS3 (CI), mouse anti-complex I subunit NDUFA9 (CI) ; mouse anti-actin, mouse anti-tubulin (Sigma). Secondary horseradish peroxidase-conjugated anti-mouse and anti-rabbit antibodies were purchased from Pierce.Escherichia coli strain OP50 or HT115 (DH3) as a food source and kept at 20 °C as described previously65. The following strains were used: N2 , BAN271 wah-1(bon14[R309 del]), BAN272 wah-1(bon15[R309 del]), BAN278 wah-1(bon14[R309 del]) 2 × backcrossed, BAN279 wah-1(bon14[R309 del]) 4 × backcrossed, BAN280 wah-1(bon14[R309 del]) 6 × backcrossed, CF1553 muls84[pAD76(sod-3::GFP)], CL2166 dvIs19[pAF15(gst-4::GFP::NLS)], CW152 gas-1(fc21)X, jrIs1[Prpl-17::Hyper], jrIs2 [Prpl-17::Grx-1-roGFP2], SJ4100 zcIs13[hsp-6::GFP], SJ4103 zcIs14[myo-3p::GFP(mit)], TK22 mev-1(kn-1)III, and ZG580 iaIs28[hif-1p::hif-1a::tag]. The following strains were obtained from Knudra Transgenics: BAN271 wah-1(bon14[R309 del]) and BAN272 wah-1(bon15[R309 del]).Nematode strains were maintained on Nematode Growth Medium (NGM) plates seeded with E. coli HT115 bacteria expressing dsRNAs against target genes . The expression of dsRNA was induced by isopropyl-β-d-1-thiogalactopyranoside (1 mM). A bacterial strain carrying the empty pL4440 vector served as control. Progeny was grown on NGM plates until adult stage and then transferred to fresh plates at groups of 30–45 nematodes per plate. Animals were transferred to fresh plates every 1–4 days and examined every second day for touch-provoked movement and pharyngeal pumping, until death. Worms that died abnormally due to internally hatched eggs, protruded vulva or dried on the edge of the plates were considered censored. Survival curves were generated with GraphPad Prism Software  using the method of Kaplan and Meier. p-values were calculated using the log-rank (Mantel–Cox) test.Unless otherwise stated, all experiments were conducted at 20 °C. RNAi was performed using a bacterial feeding protocol. Briefly, adult gravid nematodes were treated with hypochlorite solution to obtain synchronous populations. Bleached eggs were rinsed with M9 buffer and immediately transferred to NGM plates carrying p-values were determined using t-test or analysis of variance. Stimulated emission depletion microscopy: STED microscopy was performed with an inverted microscope (Leica DMI6000 CS) equipped with a × 100 oil objective and a white light laser. Images were taken at a resolution of ~50 nm following the use of a depletion laser at 592 nm. ROS quantification by microscopy: for the quantification of H2O2 in jrIs1 [Prpl-17::Grx-1-Hyper] and GSSG/2GSH ratios in jrIs2 [Prpl-17::Grx-1-roGFP2] animals, an Andor Spinning Disk setup was used. The system consisted of a fully motorized inverted Nikon microscope in association with a Yokogawa Spinning Disk connected to a back-illuminated EMCCD camera . The setup was equipped with a × 40 oil-immersion lens (Nikon). Fluorescence was measured with a 525 nm emission filter, exciting the probes with 405 and 488 nm excitation lasers. Animals were analyzed at 4, 7, and 14 days of age.Nematodes were paralyzed with 25 mM levamisole in M9 buffer mounted on 2 % agarose pads or immobilized with 30 % PEG/25 % glycerol solution on glass slides. Alternatively, animals were fixed on 5 % agarose pads with a solution of polybead polystyrene (0.10 μm microspheres) (Polysciences). For quantification, we used Image J software unless noted otherwise. Statistical analysis was performed with GraphPad Prism Software and wah-1 (RNAi), washed with M9 buffer and transferred into XF24 Cell Culture Microplates at a final number of 50 animals per well. Nematodes were left to equilibrate at 20 °C for 90 min, then transferred to the Seahorse machine. OCR was recorded by the Seahorse XF24 Software under basal conditions as well as in response to 10 mM sodium azide (NaN3). Statistical analysis was performed using the GraphPad Prism Software package (GraphPad Software Inc.) and p-values were determined using a t-test.OCR was measured using the Seahorse XF24 Analyzer . Nematodes were grown for 4 days on control and beta-actin 5′-tgtgatgccagatcttctccat-3′ and 5′-gagcacggtatcgtcaccaa-3′; wah-1 5′- gctgatgctgtcgaggaga-3′ and 5′-tggtggtgttctcttctgtaga-3′; nhr-57 5′-tcggaatgaatccggaagt-3′ and 5′-atgcaggggaagatgaacag-3′.RNA extraction, purification and reverse transcription were performed by using the QIAshredder, RNeasy RNA extraction kit (Qiagen), and qScript cDNA Supermix (Quanta Biosciences). Quantitative RT-PCR was carried out in a Step One Plus Real Time PCR System (Applied Biosystems) and analyzed using the comparative ΔΔCt method. mRNA levels of β-actin were used for normalization. The average of at least three technical repeats was used for each biological data point. The following oligonucleotides were used in this study: g for 10 min. Proteins were separated on 10–15 % poly-acrylamide gels and subsequently transferred onto nitrocellulose membranes using a wet blot chamber or a semi-dry blot cassette (Bio-Rad). Nitrocellulose filters were saturated in Tris Buffer Saline with 5 % milk powder and 0.05 % Tween-20 for 2 h at room temperature. Primary and secondary antibodies were incubated for 1 h at room temperature or overnight at 4 °C in blocking solution. Immunoblots were developed in ECL and images taken with the chemiluminescent analyzer Chemidoc imaging system (Bio-Rad).Approximately 200 nematodes were collected in 1.5 ml sample tubes and resuspended in RIPA buffer. Mechanical disruption was achieved by sonication and total lysate was quantified with Bradford reagent (Sigma). Samples were denatured in Laemmli buffer at 95 °C for 5 min and spun down at 10,000


SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost‐effectiveness and sample throughput. Genotyping‐by‐sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS‐transcriptomics (GBS‐t) approach developed to be broadly applicable, cost‐effective and high‐throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS‐t was validated as a simple and largely automated, cost‐effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS‐t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity.The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker‐dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism ( This trend is expected to continue and has generated major opportunities for genotyping applications, specifically for highly multiplexed SNP‐based systems for implementation in GS and genomewide association studies (GWAS).Since the delivery of the Illumina genome analyser platform in 2006, the ability to generate sequence data has experienced more than a 1000‐fold increase with a corresponding average annual threefold reduction in the price of data generation  and genome complexity reduction are two distinct approaches to GBS applications. A third potential option is the generation of a skim genome sequence with genotype imputation, and as the cost of sequencing continues to decrease, an increasing number of species will be amenable to whole‐ or partial genome sequencing in a cost‐effective manner. However, to date this approach has only been applied to model species with small, well‐characterized genomes such as et al., et al., et al., Target capture is based on selective isolation of regions of interest prior to sequencing, with three main methods currently in common use: PCR amplification, molecular inversion probes (MIPs) and hybrid capture. PCR amplification uses primers to amplify regions of interest from the genome. The MIPs system  method of Elshire et al. , which u  and Brachypodium distachyon L. identified a conserved set of sequences present in mRNA of 60‐90 Mbp in size  has been described for genotyping applications in plants in a handful of studies in canola (Bancroft n = 2x = 14) outbreeding forage grass; phalaris, a putative segmental allotetraploid (2n = 4x = 28) outbreeding forage grass; lentil, a diploid (2n = 2x = 14) inbreeding grain legume; and canola, an allotetraploid (2n = 4x = 38) partially outbreeding oilseed. The diversity of species under investigation has demonstrated the versatility and effectiveness of the method.This study describes a GBS method based on transcript sampling that is cost‐effective and applicable to a broad range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding, including perennial ryegrass, a diploid  could be assigned to the seven chromosomes of lentil in a uniform manner.1 mapping population. The SNP loci were evaluated for strict adherence to either 1:1 or 1:2:1 segregation ratios based on chi squared statistics, obtaining 50 216 loci. SNP loci that failed to segregate in the mapping population, but identified in all samples reliably with a homozygous genotypic class were also included, adding a further 89 556 loci to the prior set. In total, 139 772 SNP loci from a total of 11 787 reference transcriptome contigs and scaffolds were identified as a high‐confidence set.For perennial ryegrass, variant site identification was performed as a two‐step process. A total of 83 samples were initially processed, representing a broad range of diversity. Indels, sites with >2 alleles called and sites with >50% missing data were removed, resulting in 1 079 770 variant sites. A requirement for presence of all three genotypic classes at a given locus reduced this number to 449 713. The second step involved SNP validation by genotyping of 95 full‐sibs from a two‐way pseudo‐testcross FA collection of 285 phalaris samples that represented key parental genotypes from a breeding programme were assembled and processed. Using SAMtools mpileup, a collection of 6 008 286 variant bases were identified. However, SAMtools can only call genotypes for disomic SNPs. A range of other computational variant calling programmes were evaluated (Freebayes and GATK), but proved unsuitable for analysis of the putative segmental allotetraploid. Manual inspection of individual variant base positions and associated haplotypic configurations were therefore used to identify loci with either five genotypic classes  or three genotypic classes .All of the currently available variant calling software packages are designed for genomes of consistent ploidy and currently do not provide genotype identification from data sets with mixed inheritance patterns as described above. A simple computational process based on a custom R script was developed for variants identified using SAMtools mpileup, to return the bases aligned at the specific designated base location and call allelic proportions based on the aligned observed bases. Genotypes were called based on a ±5% variation around the expected frequencies. For the alternative allele, <5%, 20–30%, 45–55%, 70–80% and >95% were the designated bins relating to the presence of 0, 1, 2, 3, 4 copies of the given allele. From the 6 008 286 initially identified variant bases, a total of 89 738 sites from the 285 samples could be reliably called using these parameters. Further analysis of the segregation ratios for these SNPs revealed a 4:1 ratio of tetrasomic to disomic inheritance patterns.A total of 575 lines of canola, representing both spring‐type lines primarily from Australia and winter‐type lines from various geographic locations, were subjected to GBS‐t. Initial filtering of the entire data set resulted in 104 809 high‐confidence SNPs. Examination of population structure revealed significant differences between spring‐ and winter‐type lines, and so filtering was performed separately, resulting in 123 469 SNPs in the spring‐type samples and 179 656 SNPs in the winter‐type samples. Of these, 76 270 were common between both groups, resulting in a total of 226 855 unique SNPs.Reprocessing all bam files with mpileup using this SNP list to provide complete genotype profiles and filtering across the whole data set resulted in 185 787 high‐confidence SNPs from 521 individuals using stringent filtering parameters, including an increased minor allele frequency (MAF) of 0.05 to account for reduced sample size due to separate processing of spring and winter types, removal of individuals with large amounts missing data, and omission of loci with a high degree of heterozygosity. SNP loci with a >0.4 proportion of heterozygous calls were deemed to be the result of misalignment between homoeologous sequences on the A and C subgenomes, and so were subsequently removed. This was performed on a locus‐by‐locus rather than gene‐by‐gene basis, as evidence was found of well‐aligning SNPs within the same gene. As an example, in Figure An evaluation of missing data compared to number of sequence reads generated was performed for representative samples from the diploid  and polyploid (canola) species. The diploid species generated a profile where c. 3 million sequencing reads (c. 1% of a HiSeq run‐lane) resulted in missing data across the 139 772 SNP markers of c. 15%, while for the polyploid species a much higher level of missing data (c. 50%) was observed . Nei's genetic distance was calculated for the complete data set, and neighbour joining (NJ) dendrograms were generated Figure a,b. All 7/F8 generation level following single seed descent.Genetic distance between lentil genotypes was quantified Table , and fivAnalysis of the distribution of identified SNPs across the genome was performed for two species. Of the four species, only canola provided a full reference genome sequence, to which sampled SNP loci were mapped. For a second evaluation in perennial ryegrass, SNPs identified as segregating in the pair cross‐derived full‐sib population were assigned to genetic map locations, as a proxy exercise for genomic location.Single nucleotide polymorphism density and gene density were compared for canola Figure  and are B. distachyon and a comparative map alignment was generated  of the respective lines. In lentil, for example, PBA‐Bolt and PBA‐Herald were both derived from 96‐047L‐99R060, consistent with their close affinity. Interestingly, CIPAL0102, which was derived from both Indianhead and Northfield (located in a separate cluster), is located closer to the former, indicating a higher proportion of Indianhead‐derived genes.et al., et al., A. thaliana . Expression imbalances may be exclusive, such that only one allele is expressed, or preferential, such that both alleles are expressed, but at ratios deviating from 1:1. There have been variable estimates of ASE in plants, with 43–53%  have shown a more rapid decay of LD  following manufacturer's instructions, with a reduction in reaction volume. Libraries were evaluated using the TapeStation 2200 platform (Agilent Technologies), pooled and quantified using the KAPA library quantification kit , with the exception of canola, which was quantified using the Qubit (Life Technologies) Fluorometer according to manufacturer's instructions. Sequencing data were generated using Illumina  HiSeq 2000 or 3000 or NextSeq 500 systems.et al., et al., et al., et al., et al., in silico SNP genotyping process was implemented, involving initial fastq sequence quality trimming using a custom perl script as well as cutadapt v1.4.1 , a custom in‐house script was developed for data filtering. In‐house bash scripts were developed to extract the bi‐allelic genotypes (as a matrix) from the generated vcf files with a corresponding matrix of read depths . In‐house R scripts were also developed and used for MAF estimation and missing genotype data filtering, taking into account SNP loci performance within or between populations. For genotypes of canola, BAM files were sorted into spring and winter types due to the presence of significant population structure, and variants were called separately. The previous bioinformatic pipeline was altered such that the MAF threshold was adjusted to 5% within each population to allow more stringent variant calling. The two resulting SNP lists were then consolidated and all BAM files rerun through SAMtools mpileup to produce a complete SNP profile, removing SNPs found to be tri‐allelic between spring and winter types.Up to five seedlings were genotyped for each lentil line, and a consensus was determined based on the most common genotype observed within the line. SNP loci for which both homozygous classes were observed across the consensus calls were retained as a high‐confidence set. SNP loci for which only one homozygous class was observed in the consensus calls, but at least four heterozygous consensus calls were also observed, were retained. Additionally, SNP loci for which both homozygous calls were not observed across the consensus calls, but both homozygous classes were found within one or more lines were also retained for future SNP calling.For both polyploid canola and phalaris, additional SNP filtering was performed after the initial SNP list was developed. SNP loci with >0.4 proportion of heterozygous calls were deemed to be the results of misalignment between highly similar regions of the subgenomes and were therefore removed.Due to the putative segmental allopolyploid nature of the phalaris genome, allele frequencies were called rather than bi‐allelic genotypes and genotypes were scored based on binned allele frequencies. Allele frequencies not conforming to the defined bins were relabelled as missing data, and SNP loci were retained where three or more different pseudo genotype calls were present across samples. Allele frequencies at these SNP loci were used for all further analyses.http://www.mergemap.org) through the presence of bridging markers. Transcript sequences generated in the reference assembly were compared to reference assemblies from rice (Oryza sativa japonica group assembly build 4.0) and B. distachyon (version 1.0) using BLASTN  sampling probabilities.Click here for additional data file.Table S4 Description of lines used in each of the 4 species.Click here for additional data file.


TaPHS1 gene, were used to genotype the germplasm. The haplotype marker data and phenotypic PHS data were compared across all cultivars and different production regions. A relative change in observed phenotypic variation percentage was obtained per marker allele and across marker haplotype combinations when compared to the PHS susceptible cultivar, Tugela-DN. Clear favorable haplotypes, contributing 40–60% of the variation for PHS tolerance, were identified for QTL 3A and 4A. Initial analyses show haplotype data appear to be predictive of PHS tolerance status and germplasm can now be selected to improve PHS tolerance. These haplotype data are the first of its kind for PHS genotyping in South Africa. In future, this can be used as a tool to predict the possible PHS tolerance range of a new cultivar.Pre-harvest sprouting (PHS) has been a serious production constraint for over two decades, especially in the summer rainfall wheat production regions of South Africa. It is a complex genetic trait controlled by multiple genes, which are significantly influenced by environmental conditions. This complicates the accurate prediction of a cultivar's stability in terms of PHS tolerance. A number of reports have documented the presence of major QTL on chromosomes 3A and 4A of modern bread wheat cultivars, which confer PHS tolerance. In this study, the SSR marker haplotype combination of chromosomes 3A and 4A of former and current South African cultivars were compared with the aim to select for improved PHS tolerance levels in future cultivars. A total of 101 wheat cultivars, including a susceptible cultivar and five international tolerant sources, were used in this study. These cultivars and donors were evaluated for their PHS tolerance by making use of a rain simulator. In addition, five seeds of each entry were planted out into seedling trays and leaf material harvested for DNA isolation. A modified CTAB extraction method was used before progressing to downstream PCR applications. Eight SSR markers targeted from the well-characterized 3A and 4A QTL regions associated with PHS tolerance, were used to conduct targeted haplotype analysis. Additionally, recently published KASP SNP markers, which identify the casual SNP mutations within the  Triticum aestivum L.) producing areas of South Africa and has been well-documented over the past two decades  is a common phenomenon in the wheat , which confers PHS tolerance, was cloned and characterized further. Two important, functional SNP mutations within the third and fourth exons of the TaPHS1 gene-coding region, were identified. Both SNP mutations occurred together in all PHS susceptible cultivars covering a set of diverse genetic backgrounds and are considered critical for future PHS tolerant cultivar development  DNA extraction protocol by Saghai-Maroof et al.  and treawww.IDTDNA.com) and ordered through Whitehead Scientific PTY (Ltd) (www.whitesci.co.za). Initially, 31 different SSR marker primer sequences and relevant PCR conditions were obtained either from Röder et al. (https://wheat.pw.usda.gov/GG2/). These 31 SSR markers were screened on seven local cultivars and the five international sources to identify informative polymorphic markers. Table Barc57 and Barc12 (3A QTL) and DuPw004 and Wmc650 (4A QTL) were targeted from the well-characterized 3A and 4A QTL regions associated with PHS tolerance. The 96 cultivars, as well as the five international PHS tolerant donors were genotyped with the four SSR markers.All SSR marker primer pairs were synthesized by Integrated DNA Technologies  KAPATaq 2X Ready Mix, 0.5 μl (10 μM) per SSR primer and the remaining volume (5.0 μl) of DNAse Free water. The PCR reactions were performed in a MyCyclerTM Thermal Cycler (www.bio-rad.com) with the following cycling conditions: 3 min at 95°C, 40 cycles of 30 s at 95°C, 30 s at Tm°C, 30 s at 72°C and a final extension step of 5 min at 72°C. After amplification each specific SSR marker PCR amplicons were separated on a 3.0–3.5% (w/v) Certified Low Range Ultra Agarose high-resolution gel , made up in 1x TBE with 1x GRGreen Nucleic Acid gel stain solution  and run at 100–125 V for 1–4 h. SSR product sizes were determined according to 100 bp and/or 20 bp  DNA ladders. A digital gel picture under UV light exposure was taken with the Bio-Rad Molecular Imager Gel Doc™ XR Instrument. Observed SSR marker alleles were sized, recorded and analyzed per cultivar both visually and with image Lab™ gel analysis software.Extracted genomic DNA, totalling a 200 ng (4 μl) concentration was used as template DNA per sample in a 20 μl final volume PCR reaction. Reaction conditions recommended for the KAPA 2X Ready Mix PCR Kit  and TaPHS1-666 (TaPHS1-SNP2 marker), designed during the study of Liu et al. . The PCR reactions and fluorescence detection were performed in an Agilent Technologies Mx3500P Real-time Thermal Cycler as recommended by LGC (http://www.kbioscience.co.uk).Two KASP assays, namely u et al. , are conThe specific SNP allele for each KASP marker was recorded per cultivar. When one of the unfavorable alleles for either SNP marker was present, a cultivar was predicted as susceptible. When the allele that was present was favorable, but the other allele was missing, a cultivar was treated as unknown. When both alleles were missing, a cultivar was also treated as unknown.Barc57 and Barc12 (3A QTL) and DuPw004 and Wmc650 (4A QTL), were used in the final haplotype analyses of the 3A and 4A QTL. Additive allele identification was performed based on average PHS data for a particular marker haplotype combination on the comparison of mean PHS scores of the susceptible check, Tugela-DN. Mean PHS scores per SSR allele were used to calculate the percentage change in observed phenotypic variation in PHS tolerance from the susceptible check. This was done regardless of genetic background to attempt to reduce the effect that different genetic backgrounds might have on observed PHS tolerance levels. Tugela-DN was used as the susceptible check as a point of reference in the observed phenotypic variation analysis. The average PHS score per marker allele containing multiple genotypes was deducted from the average PHS score of the susceptible cultivar (Tugela-DN) and then divided by the Tugela-DN average to get an observed phenotypic variation percentage. The alleles were then classed as tolerant, moderate or susceptible based on these PHS averages.Four SSR markers, namely Example: Marker 1, Allele 1 = 6.4 (Tugela-DN)–2.9 (Marker 1/Allele 1) = 3.5/6.4 = 54.7% relative observed phenotypic variation (OPV).The 96 cultivars used in this study are listed alphabetically in Table The five international sources, namely AC Domain, RL4137, Rio Blanco, Transvaal and Renan, all had low PHS scores, namely 1.1, 1.2, 1.2, 1.6, and 1.1, respectively. This indicates excellent PHS tolerance.The PHS tolerance levels of the cultivars in the study varied from excellent (scores lower than 3.0) to moderate (scores between 3.0 and 4.5) to highly susceptible (scores higher than 4.5). The cultivars adapted to dryland conditions were more tolerant to PHS with 43% of the entries having excellent tolerance to PHS, compared to the 20% of excellent tolerance in irrigation cultivars. The number of cultivars with moderate tolerance was similar in both groupings .Figure Alleles were considered favorable for PHS tolerance when representative cultivars had PHS average scores of 3.0 or lower. An allele was classified as moderate if the average PHS scores ranged from 3.1 to 4.4. Finally, a marker allele was considered unfavorable for PHS tolerance if the average PHS scores of the representative cultivars were 4.5 or higher.Barc57 and Barc12, and their relative observed phenotypic variation percentage are shown.In Table Barc57 amplified five different alleles across the cultivars studied (Table Barc57220/240 contributed 53.1% to the observed phenotypic variation (OPV), with an average PHS score of 3.0 and appears to be more favorable for PHS tolerance than alleles Barc57210/240 and Barc57210. The Barc57220 allele, with a 40.6% contribution to the OPV (%), was moderate in its contribution. The Barc57240 allele, with a 20.3% OPV across cultivars, is not favorable for PHS tolerance. Barc57220/240 and Barc57240 should, therefore, be considered for positive and negative MAS, respectively.SSR marker ed Table . Allele Barc12 amplified six different alleles across the cultivars studied  are moderate contributing alleles with an OPV range of 32.8–40.6% and average PHS scores of 3.8, 3.9, 4.3, and 4.3, respectively. SSR Barc12 alleles 240 and 220 should be considered more favorable for MAS to improve PHS tolerance.SSR marker ed Table . Two favWmc650 and DuPw004 . The moderate class still remains difficult to define with a relevant score threshold as environmental effects might have a bigger influence on this group of cultivars than the other two classes. Cultivars from the moderate class can, depending on season and environment, move between classes.Barc57220, Barc12220, and Barc12160, are the contributors to favorable haplotypes 1 and 2. These marker alleles contribute additively to haplotypes 1 and 2 with overall improvements in the PHS averages.After the analyses of the allelic SSR marker data across the 3A QTL region, a total of 13 different haplotypes were observed Table . HaplotyBarc57220 and less favorable moderate marker allele Barc12210, suggesting additive allele interactions. Haplotype 4 is a combination of the moderate Barc57210 allele with the moderate Barc12200 allele, while haplotype 5 is a combination of the more favorable moderate Barc57220 allele and the less favorable Barc12180 moderate allele. Haplotype 6 is a combination of the less favorable moderate Barc57210/240 and the more favorable moderate Barc12200 allele combination. Haplotypes 4, 5, and 6 had average PHS scores of 3.4 with 46.9% OVP (%). Haplotype 5, with PHS score of 3.5 and OVP (%) of 45.3% is comprised of two moderate alleles, namely the less favorable Barc57220/240 and the more favorable Barc12220 allele. Haplotypes 8 to 11 are classed as less favorable moderate haplotypes with PHS averages of 3.8, 3.8, 4.3, and 4.3, respectively. These four haplotypes are all different combinations of less favorable moderate alleles from both flanking markers.Haplotypes 3 to 11 are considered moderate contributing haplotypes toward PHS tolerance in South African cultivars for the 3A QTL region Table . HaplotyBarc57220/240 and Barc12160 and Barc12180. These SSR allele combinations of haplotypes 12 and 13 appear to have negative interactions or contribute susceptibility factors as the PHS score averages are higher (indicating more susceptibility) than the single moderate contributing SSR marker alleles.Haplotypes 12 and 13 are susceptible haplotypes both with 4.8 PHS averages. These two haplotypes are made up of the less favorable moderate marker allele combinations For the 3A QTL region, haplotypes 1, 2, and 3 can be considered for potential MAS to improve PHS tolerance. Haplotypes 12 and 13 can be targeted negatively in MAS and should strictly be avoided during germplasm development.Wmc650170 and DuPw004190, working additively to confer a tolerant haplotype. The change of two moderately favorable marker alleles to a favorable haplotype elucidates to strong additive effects in this 4A QTL region or across both QTL regions. Haplotype 2 is a combination of tolerant marker allele Wmc650220 and moderate allele DuPw004190 with negating contributing effects to the haplotype PHS average. Allele Wmc650220 , 6 (PHS = 3.8), 7 (PHS = 4.1), and 8 (PHS = 4.3) are less favorable moderate haplotypes. These four haplotypes consist of combinations of less favorable moderate marker alleles and contribute less favorably to PHS tolerance than haplotypes 1, 2, 3, or 4.Haplotypes 3 and 4, with PHS mean values of 3.2 and 3.5, respectively Table , are lesWmc650170 and DuPw004190/280  in the range of 45.3–51.6% still contributed significantly to the observed phenotypic variation for PHS tolerance. Haplotypes 3, 4, and 5 Table  consist Haplotypes 6, 7, and 8  and T (for the TaPHS1-666 marker).PHS susceptibility is based on the presence of one or both of the unfavorable alleles A  cultivars were predicted into the correct PHS classes based on this analysis. This equates to a 9% improvement in prediction accuracy from the SSR haplotype predictions on the same dryland cultivars (Table From the joint SNP data of markers rs Table . Twelve TaPHS1-666 marker according to the methodology explained previously.The genotypic SNP data and PHS class prediction of the 34 irrigation cultivars is presented in Table Twenty-six of the remaining 33 cultivars (78.8%) were predicted correctly into tolerant or susceptible classes after comparison with the actual PHS scores. This is an 11% percent accuracy improvement from the 67.6% class prediction accuracy achieved with SSR haplotype data analysis. Nine of the 14 tolerant cultivars (64.3%) were predicted correctly and 17 of the 19 susceptible cultivars (89.5%) were accurately predicted as susceptible.TaPHS1 SNP data predicted 70% of the tolerant cultivars and 94% of susceptible cultivars correctly, based on the 3.5 PHS average threshold.Across all cultivars, the The cultivars that were assessed in this study were released by three different seed companies and represented diverse genetic backgrounds and growth types. All wheat cultivars grown in South Africa are red wheat types with exceptional bread making quality characteristics  might be different from the international PHS tolerant donors.According to the data from this study, the South African PHS tolerant check, Elands, compares favorably with the international sources. Based on pedigree comparisons there are no known PHS tolerance donor to confer tolerance. Data from this study are the first indication of the underlining genetic basis of the PHS tolerance in this cultivar to be predominantly as a result of the Qphs.pseru-3AS  were considered. In this case, cultivars that were phenotypically tolerant could be predicted through SNP haplotyping as susceptible or u et al. , possiblWith the SNP data it was harder to correctly predict tolerant cultivars than susceptible cultivars, possibly due to the masking effect of moderate cultivars, as well as the unknown effects of the 4A QTL and potential susceptibility factors. The accurate predictions of moderately tolerant cultivars remains a challenge. However, the data from this study has given more insight into the genetic variation within the moderate class, further emphasizing the complexity of the PHS traits influenced by the environment  specific to the 3A QTL can be used to select for better PHS tolerance cultivars. The newly published diagnostic markers for the Phs1-A1 locus need to be validated on this set of cultivars. Potentially the combination of using targeted MAS for TaPHS1 (3A) and Phs1-A1 (4A) with true diagnostic markers, may improve PHS class prediction accuracy in the future. It is envisaged that this methodology will be further fine-tuned and validated with in-season phenotyping screenings and leaf material sampling to assist with PHS tolerance classification and recommendations.In future, the SNP markers (The fact that the phenotypic PHS screenings of the 96 cultivars were conducted over a 25-year period and at several wheat producing localities throughout the wheat production areas of South Africa, could also have influenced the outcome of the data. It has been reported that environmental effects play a significant role in the PHS tolerance or susceptibility of certain cultivars Barnard, .The methodology explained in this study has the potential to be applied in a MAS approach to predict the PHS tolerance class during the development of germplasm, enabling breeders to select for and release cultivars with improved PHS tolerance.Both authors have contributed to the work and agreed to be in the author list. SS is the molecular scientist and contributed molecular data and the interpretation thereof. AB, as the plant physiologist, contributed phenological PHS data and the interpretation thereof. Both authors contributed to the writing of the manuscript and preparing it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FG and handling Editor declared their shared affiliation.