We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance  in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set . Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP “radius” as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures.We expanded the set of reference SNPs to 39,083 (the OSU18 set) and extracted CERENKOV SNP feature data. We computed radius empirical likelihoods and likelihood densities for rSNPs and control SNPs, and found significant likelihood differences between rSNPs and control SNPs. We fit parametric models of likelihood distributions for five different distance measures to obtain ten log-likelihood features that we combined with the 248-dimensional CERENKOV feature matrix. On the OSU18 SNP set, we measured the classification accuracy of CERENKOV with and without the new distance-based features, and found that the addition of distance-based features significantly improves rSNP recognition performance as measured by AUPVR, AUROC, and AVGRANK. Along with feature data for the OSU18 set, the software code for extracting the base feature matrix, estimating ten distance-based likelihood ratio features, and scoring candidate causal SNPs, are released as open-source software CERENKOV2.Accounting for the locus-specific geometry of SNPs in data-space significantly improved the accuracy with which noncoding rSNPs can be computationally identified.The online version of this article (10.1186/s12859-019-2637-4) contains supplementary material, which is available to authorized users. Functio changes ; however regions . Within  regions . Various regions , for exa regions  and expr regions . But the regions  (HGMD),  regions  or ClinV regions . While v regions –21, conv regions  and othe regions –26 suggeMany types of genomic data have been used to derive SNP annotation features that have proved useful in supervised models for rSNP recognition . The picA variety of supervised classification algorithms have been proposed for identifying functional noncoding SNPs, including the support vector machine (SVM) , 23, 32,In our previous work , we descIn this work we report on CERENKOV2, a next-generation machine-learning approach for rSNP recognition that improves upon our previous approach, CERENKOV  in termsk-Nearest-Neighbors [k-NN), and in the case of k-NN, the choice of distance measure can be a key determinant of the accuracy of the classifier [grouped machine-learning problem), and (2) in the reference SNP set, each associated locus has at least one rSNP in it and usually many cSNPs , assifier . Given ttructure , 44), wetructure , the pots, we denote by L(s) the set of SNPs within the same locus as s , we define an intralocus average SNP-to-neighboring-SNP distance or “intralocus radius” λs|d by Based on our initial observation that SNPs within the same locus tend to be clustered in data space, we investigated whether there are class label-specific biases in the locus-based average SNP-to-neighboring-SNPs distances that could be exploited to improve accuracy for discriminating rSNPs from cSNPs. In mathematical terms, for a SNP d=1−r, where r is the Pearson correlation coefficient [s and s′. With Pearson distance being applied, we found that the distribution of intralocus radii for rSNPs were markedly different from cSNPs’, with rSNPs often having higher intralocus radii than cSNPs, i.e., λr|Pearson>λc|Pearson. Given the sparsity of rSNPs in the genome (cSNPs outnumber rSNPs 14.5 to one in the OSU18 SNP set) and the typically large linkage disequilibrium-defined locus sizes in the human genome [s in general mostly contains cSNPs. Together, these observations suggest that in feature-space, the SNPs of a given locus have an “atom”-like structure with respect to Pearson distance–a core rSNP and a “cloud” of cSNPs with higher average distance from the it , Manhattan distance, cosine distance (defined as 1.0 minus the cosine similarity) and Pearson distance, each on unscaled data and min-max scaled data ” in each case). We first analyzed the intralocus SNP-SNP radius distributions for the two SNP classes (rSNPs and cSNPs) within 248-dimensional feature-space using kernel density estimation for radius values conditioned on the class label (rSNP or cSNP) of the reference SNP. As seen in Fig. We computed intralocus radii for each of the OSU18 SNPs see “” sectionFor cosine and Pearson distances, the intralocus radius distributions for rSNPs are slightly more skewed to the left and more platykurtic than the distributions for cSNPs; in terms of Euclidean and Manhattan distances, the intralocus radius distributions for rSNPs are left-skewed and more leptokurtic, while the cSNPs’ are right-skewed and less leptokurtic; for the rest distances, the intralocus radius distributions for cSNPs are slightly more skewed to the right and more leptokurtic than the distributions for rSNPs  would be useful for discriminating rSNPs from cSNPs. To visualize the potential class-label discriminating power of each of the ten methods for computing intralocus radii, we empirically estimated the rSNP/cSNP log-likelihood ratios (LLRs) for the ten different methods for computing intralocus radii using binned counts of SNPs for posterior probability estimation Fig. . ConsistNext, we extracted features from intralocus radii for use in the CERENKOV classifier, using sets of SNPs that were reserved for training within a cross-validation framework see “” sectionOn an identical starting set of reference SNPs , AUROC from 0.830 to 0.839 (p<10−18), and AVGRANK from 11.172 to 10.994 (lower is better for AVGRANK [p<0.004)  interpretation.To illustrate the biological utility of CERENKOV2, we used CERENKOV2 to compute rSNP prediction scores for noncoding SNPs in the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) database. We identified two noncoding SNPs that are trait-associated in GRASP and that have CERENKOV2 rSNP prediction scores greater than 0.7: rs2239633 (associated with acute lymphoblastic leukemia), and rs11071720 , with CCERENKOV2 significantly improves upon our previous framework and classifier, CERENKOV, in its ablity to score noncoding SNPs based on their regulatory potential. CERENKOV2—by virtue of its training-set construction criteria  and its novel feature set including geometric ones—is specifically designed for the problem of identifying candidate causal noncoding SNPs in GWAS summary regions. We have demonstrated, using side-by-side comparisons on identical assignments of SNPs to cross-validation folds, that CERENKOV2’s performance exceeds that of our previous CERENKOV, by both classical global rank-based measures (AUPVR and AUROC) and by the GWAS-oriented performance measure (AVGRANK) that we previously proposed. In particular, CERENKOV2’s validation-set AUPVR performance, 0.404, is a significant improvement over CERENKOV’s AUPVR of 0.359 on the same reference SNP set (OSU18). The results reported in this work are based on a significantly expanded reference SNP set , which should increase the generalizability and robustness of the performance results reported herein.The source code, feature data files, and instructions for installing and running CERENKOV2 are freely available online . By making the software, the data files, and in particular the OSU18 SNP set (with benchmark results) available, we hope to accelerate development of methods for functional analysis of noncoding SNPs and ultimately increase the yield of molecular insights from GWAS.r2≥0.8 in 1,000 Genomes (1KG) Phase 1 [We obtained minor allele frequencies (MAFs) for all SNPs from the dbSNP-based  snp146 S Phase 1 , with da Phase 1  used insThe CERENKOV feature extraction software is based on Python and SQL. We extracted 248 SNP features for each of the OSU18 SNPs, using information and measurements from SNP annotation databases, epigenomic and chromatin datasets and phylogenetic conservation scores . We extracted additional SNP annotation information by (i) coordinate-based joins to other genome annotation tracks in the UCSC database; and (ii) by joining with non-UCSC data sources using the SNP coordinate. For triallelic and quadrallelic SNPs, we used the two most frequent alleles, for the purpose of obtaining features that depend on allele-dependent scores. We derived DNase I hypersensitive site (DHS) features from data tracks from published genome-wide assays with high-throughput sequencing-based detection (DNase-seq) from the ENCODE project  (the masWe used the BioMart tool to download (i) TFBS motif occurrences (based on the 2014 release of the Jaspar database ) and Chrp-value across genes and tissues.We obtained SNP-to-gene associations for 13 tissues  from the Genotype Tissue Expression (GTEx) Project  Analysisd, we first computed the intralocus radius λs|d for each SNP s in our OSU18 dataset, in the data-space of all the 248 features ; then we separated those intralocus radii according to SNP classes, making two sets Λr|d={λr|d|r is an rSNP} and Λc|d={λc|d|c is a cSNP}. For empirical estimation of likelihoods, we used the R hist function with 11 bins on Λr|d and Λc|d and then gathered the bin counts, i can be computed with formula d, 2 probability density functions, pr|d(·) and pc|d(·), can be estimated from Λr|d and Λc|d. And for any given SNP s, its likelihood ratio is defined as the ratio of its probability densities, i.e. s inclines to be an rSNP, observing its intralocus radius.For each distance metric n 1.0.9)  in R andn 1.0.9)  for distFor loci where only one SNP  was located, we set its likelihood ratio to 1. For each of the OSU18 SNPs, and using the parametric distributions fitted as described above, we computed log-likelihood-ratio scores for each of the ten combinations of distance metric and scaled/unscaled data listed in “The feature extraction and distance computation were done in Python 3 under Ubuntu 16.04 and would take about two hours with a single core of an Intel Core i7-4790 CPU. Peak RAM usage was approximately 12 GB.For the machine learning framework, we used the R statistical computing environment (version 3.4.4) , also unIn order to compare CERENKOV2 with GWAVA , we annolocus-based sampling, in which we assigned rSNPs to folds (stratifying on the number of cSNPs per rSNP), and then assigned cSNPs to the same fold to which it’s LD-linked rSNP was assigned. Thus, in the case of locus-based sampling, an rSNP and its linked cSNPs are always assigned to the same cross-validation fold. Especially for those 10 geometric features, distribution parameters were estimated only on training data to prevent data leakage. For every prediction performance metric we report, the fold composition was exactly the same across all of the rSNP recognition models studied. We set base_score = 0.06918531 . We estimated 95% confidence intervals on the sample mean using 1,000 iterations of bootstrap resampling [For the gradient boosted decision trees (GBDT) classifier, we used the R API for xgboost  version sampling .η=0.1, γ=10, nrounds = 30, max_depth = 7, subsample = 1.0 and scale_pos_weight = 1; we used these hyperparameter values for all subsequent analyses using xgboost-GBDT. .We tuned the xgboost-GBDT classifier with a hyperparameter septuple grid size of 3,888, with locus-based sampling. The tuning hyperparameter tuple that maximized the validation AUPVR was: P-values were less than the accepted human genome-wide significance level (5 ×10−8) and whose CERENKOV rSNP prediction score was at least 0.7.We downloaded the full GRASP 2.0.0.0 catalog in tab-delimited value (TSV) format and joined the GRASP data with the CERENKOV2 prediction matrix using the dbSNP refSNP ID as the join key. We then filtered the resulting data matrix to include only SNPs whose GRASP trait-association Additional file 1Supplementary Tables. This PDF file contains 2 supplementary tables. The first one provides a view of comparison of validation-set performance measures between GWAVA, CERENKOV and CERENKOV2 on the OSU18 reference SNP set. The second one lists the skewnesses and kurtoses of intralocus radii computed using Canberra, Euclidean, Manhattan, cosine, and Pearson distances, applied to scaled and unscaled feature data, and conditioned on the type of reference SNP (rSNP or cSNP). (PDF 90 kb)


Multiple studies have investigated selection signatures in domestic cattle and other species. However, there is a dearth of information about the response to selection in genomes of highly admixed crossbred cattle in relation to production and adaptation to tropical environments. In this study, we evaluated 839 admixed crossbred cows sampled from two major dairy regions in Tanzania namely Rungwe and Lushoto districts, in order to understand their genetic architecture and detect genomic regions showing preferential selection. Animals were genotyped at 150,000 SNP loci using the Geneseek Genomic Profiler (GGP) High Density (HD) SNP array. Population structure analysis showed a large within-population genetic diversity in the study animals with a high degree of variation in admixture ranging between 7 and 100% taurine genes (dairyness) of mostly Holstein and Friesian ancestry. We explored evidence of selection signatures using three statistical methods . Selection signature analysis identified 108 candidate selection regions in the study population. Annotation of these regions yielded interesting genes potentially under strong positive selection including ABCG2, ABCC2, XKR4, LYN, TGS1, TOX, HERC6, KIT, PLAG1, CHCHD7, NCAPG, and LCORL that are involved in multiple biological pathways underlying production and adaptation processes. Several candidate selection regions showed an excess of African taurine ancestral allele dosage. Our results provide further useful insight into potential selective sweeps in the genome of admixed cattle with possible adaptive and productive importance. Further investigations will be necessary to better characterize these candidate regions with respect to their functional significance to tropical adaptations for dairy cattle. Livestock genomes have undoubtedly undergone significant changes following domestication about 10,000 years ago  and subsIn developing countries, crossbreeding is considered an effective management strategy that allows exploitation of a combination of the high adaptability to environmental conditions, resistance to diseases and overall hardiness possessed by local indigenous breeds alongside the relatively high productive potential of exotic breeds . ConsequTajima’s D; Fu and Li test) (b) within population measures based on extended haplotype homozygosity  and (c) measures based on differentiation between and within species  as reviewed by Various statistical methods have been developed to detect footprints of selection based on neutral evolutionary theory . These mIn this study, we applied three complementary approaches: iHS, XP-EHH, and pcadapt to explore selection signatures in the autosomal genome of Tanzanian crossbred dairy cows, with a view of understanding the possible determinants of adaptation and production and to relate these signals to adaptation and productivity traits. In addition, we examined the local ancestral allele dosage of the putative selection regions using ELI algorithm  to underThis study was undertaken according to the International Livestock Research Institute (ILRI) Institutional Animal Care and Use Committee (IACUC) guidelines, with approval reference number 2014.35. Animals were handled by experienced animal health professionals during blood and hair sampling to minimize discomfort and injury. Prior to sample collection, meetings were held with farmers to explain the purpose of the study and obtain informed consent.Samples were collected from two districts of Tanzania, namely Rungwe and Lushoto located in the Southern and Northern Highlands, respectively. These study sites were chosen based on the availability of a wide range of breeds, the population density of improved dairy cattle, the presence of complimentary dairy projects led by ILRI under the ‘Maziwa Zaidi’ program, and the sites having been identified as emerging high dairy potential regions. Both districts have similar agro-ecological climates owing to their locations in high altitude zones with mixed crop-livestock farming being one of the major economic activity. However, unlike Lushoto district, there is greater emphasis on dairy farming in Rungwe with the majority of dairy animals being fed under zero grazing conditions . The hisBlood samples were collected by venipuncture using approved procedures that avoid unnecessary pain and suffering. The procedure was undertaken by qualified veterinarians. Hair samples were collected from the tail switch of the animals, taking care to avoid fecal contamination following the protocol described by the Animal Genetics Laboratory (2013). A total of 839 cows were sampled from smallholder dairy farms consisting of 490 samples from the Rungwe district and 349 samples from the Lushoto district.A panel of genotypes from commercial international taurine dairy breeds was used as a reference for breed composition assignment. These included Friesian (28 samples), Holstein (63), Norwegian Red (17), Jersey (36), and Guernsey (21) breeds. To capture genetic signatures representative of African cattle, an African taurine breed (N’Dama (24)) and two indicine breeds, the East African Shorthorn Zebu (EASZ) (50) and Gir (30) were also included in the analysis.Samples were genotyped at Geneseek  using the Geneseek Genomic Profiler (GGP) High Density (HD) SNP array consisting of 150,000 SNPs, while SNPs for the reference breeds had been genotyped with the Illumina HD Bovine (777K SNPs) array. The SNPs in GGP array are optimized for use in dairy cattle having the most informative SNPs from Illumina Bovine 50 and 770 k chips and additional variants known to have a large effect on disease susceptibility and performance. Before analysis, the study genotypes were merged with the reference genotypes using PLINK , resultiMinor allele frequencies (MAF) were estimated using PLINK . The disThe observed heterozygosity estimates for each population were calculated from observed genotype frequencies obtained from PLINK  as folloThe inbreeding coefficient (F) was calculated using PLINK based on the observed versus expected number of homozygous genotypes as follows:if is the probability of individual i being homozygous by descent, (1 - if) is the probability that individual i is homozygous by chance for a specific SNP with known allele frequencies p and q  using GENESIS package  in R v. The unsupervised model-based clustering method implemented by the program ADMIXTURE v. 1.3.0  was usedfastPHASE   and the astPHASE  by applyastPHASE . Additio package  in R v. p-values were calculated as described in Using EASZ as a reference population, we calculated XP-EHH scores for Tanzanian crossbred cattle. XP-EHH compares the extended haplotype homozygosity between two populations at each focal SNP and allows detection of recent selection events, in which haplotypes have almost or fully risen to fixation . As in iTo identify outlier loci, we performed analysis using pcadapt package which implements PCA . pcadaptz-scores and  is the vector of the K z-score means  based on the chi-square distribution. To identify outlier SNPs, we applied the approach of where, Σ is the (K × K) covariance matrix of the re means . The p-vWe inferred local ancestry using Efficient Local Ancestry Inference (ELAI) algorithm  in orderWe run ELI assuming three source populations: (a) European taurine based on a dataset that combined all the Commercial European dairy breeds used as reference (b) African taurine (represented by N’Dama) and (c) indicine breed (represented by EASZ). Analysis was performed only for the chromosomes harboring strong candidate selection regions detected by iHS or pcadapt approaches . Data quality checks included removal of SNPs with MAF < 0.05 as well as SNP with missingness > 0.05. The upper and lower layer clusters were set as 3 and 15, respectively. ELI requires specification of the number of admixing generations. Thus, we specified 10 admixture generations, assuming a 5-year generational interval. These generations correspond to the recent history of crossbreeding in Tanzania supported by the reports indicating that crossbreeding in Tanzania became prominent 1960s when smallholder dairy farming began in earnest post-independence . As reco1 based on UMD v3.1 bovine genome assembly. To limit possible false positives, we designated a region as candidate selective sweep if it passed the 1% FDR threshold and contained at least two SNPs separated by not more that than 500 kb. This was done by first identifying any two SNPs which are not separated by 500 kb and then cumulating consecutive SNPs on both sides until the last SNP is separated by >500 kb. The window chosen was informed by previous evidence that the LD in cattle do not exceed 500 kb  are shown in Figure The observed heterozygosity estimates for the study populations are provided in Table p < 0.001).The study populations showed low detectable levels of inbreeding. The inbreeding coefficient estimates were slightly higher for cattle in Lushoto (3.3 ± 3.6%) compared to Rungwe (2.0 ± 3.7%), as shown in Table The first principal component (PC1), accounted for 46% of the total variation and separated European taurine breeds from non-European breeds as shown in Figure K = 2 to K = 8 are presented in Figure K increased in value in the combined dataset of reference and Tanzania data, hence no clear indication of the appropriate K for our population was obtained using this statistic. Based on visual inspection of the admixture plot, scrutiny of the separate CV error plots and the PCA plots, K = 7 represented the most appropriate population number for the dataset. Importantly, increasing K above 7 did not reveal any detectable population substructure and the breed clusters remained the same.ADMIXTURE results for K = 7, most animals were crosses of Holstein, Friesian and Red breeds (which formed a single cluster in the ADMIXTURE plot), which contributed on average 50% of the total genes in the crossbred animals. The predicted absolute exotic breed gene content in the crossbred cattle ranged from 7 to 100%. Rungwe cattle had significantly (p < 0.001) higher levels of taurine admixture (mean 78.3 ± 13%) compared to those in Lushoto (mean 56.4 ± 16%).Based on results obtained with Focusing on Tanzanian crossbred cattle, several significant regions were detected after FDR adjustment at 1% on BTA, 1, 5, 6, 13, 14, 20, 22, and 26  intronic to PLAG1 gene at position 25015640 . This poThe distribution of XP-EHH scores for Tanzanian crossbred cattle is shown in Figure K = 3) should be retained.We used pcadapt to detect outlier loci based on the PCA . As recoSeveral significant regions were detected after FDR adjustment at 1% across the genome Figure . Of thesBased on the criteria of t = 4.53, df = 506.72, p < 0.00001).We used Efficient Local Ancestry Inference (ELAI) algorithm  to invesG = 2.93, U = 0.998, p-value = 1). Similarly, we observed elevated AT ancestry (>2 SD above the mean) on BTA 6 sweep region at 74.68 – 78.32 Mb (data not shown). When considering only the unrelated individuals from Rungwe population (N = 178) and the putative source populations , we observed a substantial increase in AT ancestry (>3 SD) on BTA 6 at ∼77 Mb , we detected the highest African taurine allele dosage (39%) as shown in Supplementary Figure Focusing on the strong candidate selection region on BTA14 at 23.28 – 26.99 Mb, a quite striking observation is the excess AT ancestry dosage (>2 SD above the mean) and a corresponding decline in European taurine ancestry Figure . The higGiven that the EASZ which is the major base population used for crossbreeding in East Africa is a stable admixed breed consisting of African taurine and Asian indicine ancestral background , we needDairy farmers in the tropics face many challenges including disease pressure, poor feed availability, high temperatures and generally inappropriate management strategies. Understanding the genetic basis for adaptation and production in this environment is critical if productivity is to be maximized. Characterizing the genetic structure of the population under study is important to the evaluation of the location of specific differentiation and effect of management practices and the production system on the gene pool.Bos taurus breeds and has a very low representation of indicine breeds  as did the bovine 50K SNP chip  and African taurine (N’Dama) breeds compared to European taurine (ET) breeds. This is possibly due to the fact that the GGP HD array is optimized for use in SNP chip . This asSNP chip . The relWe observed differences in inbreeding coefficient estimates between European taurine and indicine breeds. Additionally, there was a trend for Lushoto cattle to have higher inbreeding estimates compared to Rungwe cattle. During field-work, it was noted that farmers in Lushoto had limited access to breeding options compared to those in Rungwe, such that they mostly had access to bulls for breeding as opposed to artificial insemination. Since the available dairy bulls were limited in number and distribution, the inbreeding estimates in these animals would be higher.To accurately assess population structure, we utilized PCA-AiR for PCA analysis. Unlike other standard PCA approaches, PCA-AiR uses estimates of kinship coefficients to accurately capture population structure, even in the presence of admixture . The cluOur main goal for selection signature analysis was to detect regions that show preferential selection in the genome of Tanzanian crossbred dairy cows. To accomplish this, we used three different but complementary statistical methods: iHS and XP-EHH and pcadapt. Use of a combination of methods for selection sweep detection enables many different emerging patterns of selection to be identified, while also improving the robustness of the reliability and accuracy of the analyses . The iHSFailure to detect significant selection signals by XP-EHH approach is related to the FDR threshold used. For example, we found that minimum FDR threshold of 10% was required to capture comparable number of selection signals to that for iHS and pcadapt. Another reason could due to the heterogeneity of our admixed study sample. It is important to point out that we used a combined dataset of Rungwe and Lushoto cattle populations in all the analyses. This limits the power of XP-EHH since it is designed to detect differentiation of alleles among populations .The putative selective sweeps regions that were detected by iHS and pcadapt have been widely reported in the literature. Nonetheless, distinguishing between true signatures of selection and those arising from natural phenomena such as admixture and genetic drift remains a challenging task . AdditioIt is not surprising that most of the selective sweep regions that were detected as significant at 1% FDR threshold, but excluded by the clustering criterion were obtained by pcadapt, which is based on single SNP analysis , suggesting a strong sweep for the derived allele , which was reported to be significantly associated with subcutaneous rump fat thickness in indicine and taurine–indicine composite cattle . Another strong selection region on BTA 6 at 71 Mb mapped to a well-known KIT gene responsible for white spotting in Holstein cattle  for outlier SNPs. This can be expected given few (∼10 – 15) admixture generations in the Tanzanian crossbred cattle which may have not allowed enough time for selection to generate significantly detectable levels of AT ancestry in the sweep region and across the genome. As noted by While admixed populations offer a unique opportunity in localizing selection signatures, the likely false positives arising due to post-admixture genetic drift remains a concern especially for recently admixed breeds such the Tanzanian crossbred cattle . HoweverThe African taurine breeds generally have small body size  and possIn this study, we have characterized population structure and demonstrated evidence of selection signatures in the Tanzanian crossbred cattle. Population structure analysis shows that the crossbred cows are mostly crossbreds of Holstein and Friesian cattle but with a wide variation of admixture. Selection signature analysis revealed several selection signals involved in multiple biological pathways related to production and adaptation. Local ancestry analysis revealed elevated African taurine ancestry dosage in the major candidate selective sweep regions. These results will complement previously reported findings and allow a better understanding of the genetic architecture of admixed cattle in tropical environments. This understanding is critical in order to maximize production through the use of animals better equipped to cope with stresses in tropical dairy production systems.10.6084/m9.figshare.7332845. Access to reference genotypes is available by making direct requests to the respective owners as indicated in the acknowledgments section of the article.The genotype data for the study population (Tanzanian crossbred cattle) is available at FM conceived, designed and obtained funding for the study. EC and FM analyzed the data. EC drafted the manuscript. RB, YZ, JA, RM, and FM contributed to revisions and edits of the manuscript. All authors read and approved the final manuscript.EC was an employee of USOMI LTD at the time of manuscript preparation. FM is currently an employee of USOMI LTD. The remaining 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 BS and handling Editor declared their shared affiliation.


Ta (Variety Identification System of Triticum aestivum) (http://webtom.cabgrid.res.in/vista) can be used in DUS testing having dispute resolution of sovereignty and access benefit sharing (ABS) issues. This model approach can be used in other crops with pan-global level management of crop germplasm in endeavour of crop productivity.Crop varieties or genotypes of a given species are pivotal for agricultural production and ownership, management and improvement of their germplasm is a great challenge. Its morphological identification requires time, cost and descriptors are often compromised statistically due to phenotypic plasticity. Development of DNA based signature of varieties can overcome these limitations. There is a global need to implement world trade organization (WTO) and intellectual property rights (IPR) guidelines of Plant Breeders Rights (PBR) where DUS  testing can be supplemented by DNA profile. Universalization and minimization of SNP number without compromising identification accuracy is the major challenge in development of varietal profile by rapid genotype assay. Besides this, there is no server-based approach reducing computational skill with global accessibility of referral phenotypic and genotypic data. We report world’s first model web server for crop variety identification using >350 Indian wheat varieties and Axiom 35 K SNP chip data. Standard filtering and linkage disequilibrium approach were used to develop varietal signature in Linux using HTML, Java, PHP and MySQL with provision of QR code generator to facilitate bar-coding. Phylogenetic tree constructed by selected SNPs confirms six major trait based clusters of varieties and their pedigree. Our user friendly server based tool, VIS Crop variety resources are pivotal for agricultural production, their selection and management is an integral part of variety development2. The identification of large number of plant varieties solely on the basis of morphological characters is difficult due to growth stage specificity and phenotypic plasticity3. Development of DNA based signature profiles of released varieties to compare with candidate variety or future variety is required.Out of 250–300 thousands edible plant species, only 5% are relevant to agriculture. Among these, three species, namely maize, rice and wheat fulfills 60% of the caloric needs of humans3. To protect intellectual property (IP) of variety by granting statutory Plant Breeders Rights (PBR), it requires testing of distinctness, uniformity and stability (DUS) called DUS testing. It has limitations like growth stage specificity, environmental influence, phenotypic plasticity, ineffectiveness over large collections, lack of rapidity besides statistical compromise in the values of descriptors. If VIS tool is available to breeders, growers and the general industry, it would be instrumental in rapid identification, germplasm registration and traceability of the produce of concerned crop variety. Such tool can also prevent economic loss of variety developer’s investment, which may happen due to deceptive indication of variety status by unauthorized user of variety. The majority of the crop diversity is present in developing countries and global use of germplasm is often encountered with sovereignty and access benefit sharing (ABS) issues4.In sweep of globalization having WTO and IPR guidelines, it is imperative to have rapid and highly reliable method of plant variety identification system (VIS). Traditional approaches of VIS were exclusively dependent on morphological characters (descriptors) having compromised precision and time consuming. For example, in potato varieties having 50 characters, 12 of them are time dependent with growth stage, which can never be rapid. Such limitations are encountered in almost every crop5 where Patent EP 445929 was revoked. Molecular markers especially protein profiling of glutenin and gliadins to supplement DUS features are reported more than a decade ago, where centralized publically accessible data on referral varieties can be used as “last resort” to resolve the dispute6.This is a great challenge for taxonomy, law, ethical consideration and technology to establish varietal status. For example, wheat varietal disputes over varieties Nap-Hal and Galahad having quality trait for biscuits, flour and dough making are globally best known example7. There is no dearth of molecular data for variety identification but VIS still has the challenge of universalization of SNPs across variety, SNP minimization, development of genotype assay, multiplexing, computational skill, online accessibility of molecular data along with variety descriptor. Pan-global approach of development of VIS is still lacking.Use of next generation sequence based approaches has emerged as a powerful tool for characterization of varieties based on genomic sequence differences. Single Nucleotide Polymorphisms (SNPs) are the most common type of genomic variations which represent differences in a single DNA building block. Therefore, SNP markers relate to their ease of data management along with their flexibility, speed and cost effectiveness. In fact >50 SNP arrays are available for >25 various crop genomesTill now there is no web-based methodology/ approach for varietal identification of any crop using throughput SNP data. There is a need to develop a user friendly server based tool where no computational skill is required and user can obtain variety identification results with its DUS features. Such tool can further supplement DUS varietal testing, which would be more relevant in era of globalization where transboundary movement of germplasm often leads to sovereignty disputes. We report world’s first model web server of any crop for variety identification using Indian wheat varieties and its Axiom 35 K SNP data as an example.http://plantauthority.gov.in/pdf/GBread%20Wheat.pdf). The quantitative characteristic, except days to heading and test weight, were recorded from ten plants in each of three replications. The days to heading was recorded on plot basis while thousand grains were randomly selected and weighed for test weight. The qualitative characters were recorded by the visual assessment on individual plant or on parts of the plants of individual genotypes. Out of 27 traits of latest 2017 UPOV guidelines 24 traits are covered in our recording barring three, namely, (i) straw: pith in cross section, (ii) Apical rachis segment: area of haireness on convex surface and (iii) lower glume: area of hairiness on internal surface.Extensive phenotyping was done to generate data of wheat DUS features to confirm the varietal status of a panel of 368 Indian spring wheat genotypes to be used in the study. The panel constituted were represented by 116 released varieties, 45 registered genetic stocks, 117 advanced breeding lines and 90 Indian landraces. Among 36 DUS features 28 were qualitative or categorical and 8 were quantitative. The DUS features represents 8 plant description traits, 9 ear characters, 6 flag leaf attributes, 6 glume features, 4 grain appearance and 3 grain quality traits. The characters were recorded according to DUS test guidelines framed by Protection of Plant Varieties and Farmers’ Rights Authority, India .DNA was isolated from the leaves of two week old seedlings using CTAB method10. In development of any crop variety identification methodology/system, the smallest number of markers discriminating all or maximum number of varieties in the panel are most desirable. Such approach of SNP minimization reduces the cost of genotyping without compromising the identification accuracy12. In fact 2–3 SNP/per chromosome are enough for variety identification13. In order to minimize number of SNPs, pairwise locus Linkage Disequilibrium (LD) was estimated by TASSEL 3.014. Further SNPs were selected based on following criteria: (1) SNP markers with PIC value more than 0.35, (2) the SNP marker were selected based on the consensus map derived from five mapping populations  where each SNP was mapped in at least in two mapping populations , (3) atleast two markers from each chromosome were selected for distinguishing genotypes and to differentiate closely related genotypes, more SNP markers were included (4) SNP markers were selected in such a way that they are not closely linked to each other except two markers each on chromosomes 2A, 6A and 7B. Graphical representation of the distribution of SNP markers on 21 chromosomes was done using GGT software 2.015. The position of SNP markers in terms of genetic distance (cM) were based on consensus genetic map generated from five mapping populations14. With the objective of not compromising the utility value of the SNP panel in resolving differences between close genotypes, markers differentiating those genotypes were also included. The genetic distances across the genotypes and neighbor-joining (NJ) tree based on Nei 1983 were calculated using PowerMarker v3.25 and NJ trees visualized using MEGA 416. BA codes were retrieved from CerealsDB  which corresponds to Affymetrix Axiom array SNP markers . Further these BA codes were used to search EnsemblPlants T. aestivum database (http://plants.ensembl.org/) to find the position of variants.Filtering of SNPs was done with parameters: call rate (<95%), monomorphic, >10% missing value, MAF (<0.05) and heterozygosity (>1%). Remaining SNPs were used for further analysis. Statistical values, namely, minor allele frequency (MAF), gene diversity, heterozygosity and Polymorphic Information Content (PIC) for each SNP were estimated using PowerMarker v3.25http://www.ncbi.nlm.nih.gov/) with the SNP sequences. The putative candidate genes identified from BLASTn were further searched in UniProtKB (https://www.uniprot.org/) to find the putative biological functions which were supported by existing literature. In case of hypothetical proteins, they were characterized by predicting its genes and translated proteins using protein BLAST .To find the putative candidate genes for the reported SNPs, we performed a BLASTn search of NCBI database (www.barcode-generator.org). Each accession and its genotype based SNP barcode was used as input to generate corresponding 2D barcode. Once the barcode was generated, it was scanned for the confirmation of information used for creating the 2D barcode.For accessions being used in this study, 2D barcode was generated using online tool (available at Triticum aestivum (VISTa) was developed in a LINUX operating system using HTML and JAVA as client-side scripting, PHP as server side scripting language and MYSQL as the RDBMS to store genotype data of 368 wheat verities with 54 SNPs markers. This is launched on Apache internet server. VISTa tool has provision to enter query data, search and measure the distance of query data against database along with the presentation of results in tabular and graphical mode. This web-based tool is easy to use and allows access to varietal identification and validation along with database through a user-friendly web- browser. The database is designed and developed on relational database concept. VISTa is intended to store and manage genotype data of 368 varieties of wheat and fast data retrieval required through web-interfaces. HTML and java scripting languages have been used for client side operations such as manual data entry, loading data from a text file and QR code image file. The sample data is kept in the server for users for ease of understanding and its implementation. PHP server-side language has been used for database connectivity, retrieval of data and calculation of distance measure of query data against database. HTML and Java scripting language has been used for illustration of results in tabular and graphical form. For most convenient remote location use, a mobile app has also been developed. We opted for QR code for information matrix due to its advantages, namely, being two-dimensional, it can hold more information , and thus minimizing space needed for printing in leveling of wheat seeds/produce. This will have more convenience and ease in labeling of germplasm especially while managing in wet-lab and crop-field both. Such approach has advantage of error-free, machine based retrieval of information and effective transmission required in germplasm management. It can be read in 360 degrees thus more convenient. It can also accommodate symbol/logo etc. of variety holder/ organization, if required.Variety Identification System for Of 35143 SNPs obtained with Axiom® Wheat Breeder’s Array, 6041 SNPs were removed having a call rate <95% leaving 29102 SNPs for downstream analysis. Out of these, 3.54% (1031) SNPs were monomorphic and 1.54% (448) showed >10% missing values. These SNPs were removed from the dataset. In addition, 7680 (26.39%) SNPs with a MAF of <0.05 and 3878 (13.33%) SNPs with >1% heterozygosity were also excluded from the dataset. Remaining 16065 (55.2%) SNPs were included for further analysis. The genotypes included in the study were released varieties, registered genetic stocks, advanced breeding lines and landraces. The MAF of SNPs ranged from 0.05 to 0.5 with average 0.2619. The gene diversity across 16065 loci ranged from 0.095 to 0.5 with average 0.3537. The mean PIC value of SNPs was 0.2837 with a range of 0.0905 to 0.375.R2 = 0.03) except a pair of markers on chromosome 5B and 6A , predominantly rainfed varieties (cluster 2), predominantly indigenous Indian collection and Mexican cultivars that paved the way for green revolution cluster 3), predominantly genetic stocks for disease  resistance (cluster 4), predominantly multi-parent derived recent genotypes having PASTOR and MILAN in their pedigree (cluster 5) and predominantly derivatives of PBW343 (‘Veery’ line) (cluster 6) , MOTHER OF FT AND TFL1 (MFT), ETERNAL TAPETUM 1 (EAT1), sn-glycerol-3-phosphate acyltransferase, phosphoserine aminotransferase, enolase, glucan endo-1,3-beta-glucosidase, receptor protein kinase TMK1, NAC transcription factor, aluminum-activated malate transporter 1 (ALMT1) and ABC transporters (Supplementary Table Wheat Breeders’ Array (35 K) designed from 820 K array representing highly informative SNPs have direct implications for wheat breeders especially interested in comparing hexaploid germplasmTriticum aestivum data in form of VISTa (http://webtom.cabgrid.res.in/vista/) tool which is based on minimum 54 SNPs and 36 DUS features differentiating >350 varieties. Data/ query can be uploaded and submitted in.txt or excel format or as QR code image in.jpeg format using PC, tablet or mobile device. This tool searches in all the 368 varieties across all the 54 SNPs to find the related varieties with similarity frequencies of each. User can also compare these varieties with 36 DUS features as well as 54 SNPs by checking the box. These tools can be of greater relevance for wheat breeders for in silico and rapid identification of varieties based on DUS features and SNPs. The full information of the related varieties can be viewed by putting the cursor on the graphical view.We report here the first model of crop variety identification system using SNP array using 29. Barcode of the selected 368 genotypes have been developed. Figure chapati).SNP barcode provides a tool to discriminate very closely related accessions, traceability of minor crops in food supply chain, commercial frauds and dangerous substitutionsVISTa using varietal genomic data of wheat can be implemented in various other crops. While selecting SNP for varietal signature development, few other points must be considered. All SNP sets are not assayable due to genomic complexity and sequence dependent multiplexing parameters30. For example, in case of wheat, KASP assay development only 80% of SNP were assayable31. SNP present in intergenic or intronic region should be avoided as it might be casually associated, rather than causally. Similarly, silent mutation should be avoided as it is devoid of SAAP (single amino acid polymorphism). Preference should be given to SNPs present in coding region as they exhibit greater stability due to slow mutation rate and more likely to affect changes in the protein associated with phenotypic difference among varieties32. DUS feature associated SNP should be preferred33. Functional SNP selection offers the advantage due to their potential effect on plant phenotype differences34, for example, a non-sense mutation SNP in anthocyanidin synthase gene associated with phenotype uniqueness like yellow pigmentation in raspberry variety has been successfully used35. Similar functional SNP associated with DUS feature in barley is also reported36.The developed model server, Our approach can be a model for availability of allelic data in public domain obviating the need of genotyping data generation by multiple users/countries thus would be more logical and economic. In the era of digital communication using hand held communication devices, the present developed mobile app can further popularize this approach of server having huge data of SNP array and varietal signature for remote accessibility and rapid use. Such model user friendly tool can be popular for other crops also as it does not require computational expertise at user end. In era of globalization and best use of germplasm across country, variety identification system can play role in management of germplasm and issues of access benefit sharing (ABS).Ta is world’s first web server for variety identification of any crop using SNP data. In order to make cost effective and rapid genotyping, SNP varietal signature has been successfully made by reduction of SNP up to 54 without compromising identification of >350 varieties. These 54 SNPs based phylogenetic tree confirms six major trait based clusters of Indian wheat varieties along with their pedigree. This tool can also generate QR code to facilitate bar-coding of each variety required for germplasm management. This approach can overcome on limitation of phenotypic data based variety identification by supplementation of molecular data. This server can not only be a model for other crops but can also be used for DUS varietal testing, dispute resolution of sovereignty and ABS issues which is not very uncommon in germplasm movement and improvement in the endevour of wheat productivity and management.A model web server has been developed successfully for crop variety identification using throughput SNP data of >350 wheat varieties by 35 K SNP chip. VISSupplementary Table 1


Sorghum bicolor is the fifth most commonly grown cereal worldwide and is remarkable for its drought and abiotic stress tolerance. For these reasons and the large size of biomass varieties, it has been proposed as a bioenergy crop. However, little is known about the genes underlying sorghum’s abiotic stress tolerance and biomass yield.To uncover the genetic basis of drought tolerance in sorghum at a genome-wide level, we undertook a high-density phenomics genome wide association study (GWAS) in which 648 diverse sorghum lines were phenotyped at two locations in California once per week by drone over the course of a growing season. Biomass, height, and leaf area were measured by drone for individual field plots, subjected to two drought treatments and a well-watered control. The resulting dataset of ~ 171,000 phenotypic data-points was analyzed along with 183,989 genotype by sequence markers to reveal 213 high-quality, replicated, and conserved GWAS associations.The genomic intervals defined by the associations include many strong candidate genes, including those encoding heat shock proteins, antifreeze proteins, and other domains recognized as important to plant stress responses. The markers identified by our study can be used for marker assisted selection for drought tolerance and biomass. In addition, our results are a significant step toward identifying specific sorghum genes controlling drought tolerance and biomass yield.The online version of this article (10.1186/s12864-018-5055-5) contains supplementary material, which is available to authorized users. Sorghum bicolor (L.) Moench] could be bred to help address these challenges. Sorghum is the fifth most commonly grown cereal crop worldwide, and over half a billion people rely on it as a daily food staple. It is already essential to food security, as it can grow across a wide range of marginal climates, including regions too hot and dry to grow rice, corn, or wheat. Sorghum has also generated interest in recent years as a bioenergy crop because it can produce exceptionally large biomass yields on marginal lands with limited inputs [The plant and agricultural research community faces a grave challenge: in a mere three decades, we must reinvent agriculture to feed a growing global population, in an environmentally sustainable manner, while dealing with a projected increase in drought events –3. Sorghd inputs –6.In order to efficiently develop sorghum biomass varieties, several important research questions must be addressed. First, we must understand the genetic underpinnings of terminal biomass in sorghum and identify specific genes or genetic regions that can be targeted for breeding and engineering efforts. Second, if bioenergy crops are to be compatible with environmental stewardship and increased food production they cannot compete with food crops for productive croplands – they must be grown on underutilized, marginal lands. So, it is also necessary to gain a mechanistic understanding of stress tolerance. Unfortunately, our current understanding of the genetics underlying biomass and/or drought tolerance in sorghum is limited. While there have been many linkage mapping studies for drought tolerance quantitative trait loci (QTLs), there has been almost no validation of causal polymorphisms, which is a crucial step before results are of use to breeders, and the QTL regions identified in these linkage studies have been too large to identify meaningful candidate alleles –13. A geBy far, the best studied traits in sorghum are flowering time and plant height, in large part because these traits were the targets of the US Sorghum Conversion Program (SCP). Most sorghum varieties are photoperiod sensitive and do not flower at temperate latitudes, so in 1963, the SCP was begun with the goal of introgressing photoperiod insensitivity and dwarfing alleles into exotic sorghum backgrounds to produce what are now commonly referred to as converted lines or ‘SC’ lines –16. WhenDw1-Dw4, all of which have been cloned with the exception of Dw4 [Ma1-Ma6), of which Ma1 and Ma3 have been cloned [In sorghum plant height is thought to be primarily determined by four dwarfing loci, n of Dw4 . Flowerin cloned , 16, 18.n cloned –22. One n cloned –25.Approaches to field-based plant phenomics include gantry systems, ground-based vehicles, and aerial drones. With all phenomic strategies, however, the goals are high-throughput, non-invasiveness, accuracy, and automation. Gantry systems are able to lift large, imaging sensor suites, making them ideal for engineering research and development, but are fixed installations and cover a small area compared to the needs of commercial plant breeders. Ground-based vehicles, including high-clearance tractors and slim-profile autonomous robots which maneuver between crop rows, can measure traits that are challenging to observe from above, but can be physically impeded by downed plants, tall plants, and tillering, particularly in sorghum. Aerial drones, by contrast, can cover large areas quickly, allowing all genotypes in a study to be measured simultaneously, and are not impeded by plant height, which allows them to capture data throughout the entire growth season . RegardlWe present here the results of a GWAS in sorghum in which all of the phenotype data, including total fresh biomass (BWET), biomass at 65% moisture content (B65), leaf area index (LAI), and plant height (PH), were collected by aerial drone and ground-truthed, once per week, over the course of the growing season. The panel included 620 diverse public sorghum conversion (SC) lines and 26 proprietary sorghum hybrids provided by Chromatin Inc. To investigate the interaction between biomass and drought, as well as to better understand the genetic architecture of drought tolerance in sorghum, the panel was planted in three treatment plots including a control, pre-flowering drought stress, and post-flowering drought stress plots, at two locations in central California. We thus refer to the resulting phenotype dataset as ‘high density’, and the GWAS we performed as a ‘high-density phenotype’ GWAS, or an ‘HDP-GWAS’. Using a custom HDP-GWAS data analysis pipeline and novel data visualization tools, we identified 213 highly reliable GWAS peaks, a number of significant and reliable associations far larger than in any previously published GWAS performed in sorghum.Sorghum bicolor . Imputation accuracy  was calculated as 99.8% for major allele homozygotes, 92.9% for minor allele homozygotes, and 55.6% for heterozygotes. After filtering for bi-allelic SNPs with call rates ≥75%, 183,989 SNPs remained with an overall heterozygosity rate of 1.34%. When the hybrid lines were removed, only ~ 1% of SNP calls were heterozygous. A final set of 131,544 SNPs with minor allele frequency (MAF) ≥ 0.01 were used for all GWAS analyses. Two individuals with genotyping rates < 60% were dropped from all subsequent analyses, after which the mean call rate per genotype was 91.5%.Neighbor-joining (NJ) tree analysis, principle components analysis (PCA) analysis, and partitioning around k-medoids analysis (PAMK) each identified five subpopulation groups within the diversity panel Fig.  16, 22,, 28. Due2) was calculated for all SNPs on each chromosome. For each pair of SNPs, the relationship between pairwise distance and r2 was then calculated using a Gaussian kernel smoother (σ = 500). Across all chromosomes, this yielded an average baseline LD (r2) of 0.12  and Five Points, CA (WREC) once per week over 12 weeks during the 2016 growing season. At each location, each genotype was grown under three drought conditions: pre-flowering drought stress (PRE), post-flowering drought stress (POST) and control. The drone measured four plant traits: 1., vegetative biomass adjusted to 65% moisture content , a sorghum industry standard for measuring biomass yield and assessing the amount of bioenergy feedstock a sorghum line can provide, 2., fresh total plant biomass , 3., plant height  and 4., leaf area index (LAI).For each phenotype and drought stress treatment, drought tolerance was calculated as an additional phenotype by taking the deviation between PH, LAI, B65, and BWET for PRE and POST treatments and the control treatment, respectively, for an additional two derived phenotypes per individual, time-point, and location. Thus, the total number of drone phenotype data-points was 171,072 for a high-density phenomics (HDP) dataset. In addition to the phenotype data collected by drone, field-based phenotypes were collected for flowering time at KARE, and drone-collected photos were used to determine flowering time for WREC.Prior to running GWAS, a phenotype data filtering method was developed and applied to the drone-collected phenotype data to reduce noise and remove outliers. Briefly, for each individual, trait, treatment, and location combination, the drone measurements were plotted versus time. A kernel smoothing line was then fit for each plot, and outliers were identified based on the size of the residual of a given data point from the fitted spline  for each GWAS.Single-variate GWAS were run using GEMMA for every combination of trait, treatment, time-point, and location for a total of 460 GWAS. For each GWAS, the following covariates were tested for inclusion in the GEMMA model: no covariates, 1 principle component (PC), 2 PCs, 3 PCs, 4 PCs, 1PC + flowering time (FT), 2 PCs + FT, 3 PCs + FT, and 4 PCs + FT. Of these different models, the simplest model resulting in the best QQ-plot was selected for each GWAS. QQ-plots varied widely in terms of adherence to the null hypothesis, suggesting that for some trait by time by treatment by location combinations (TTLs), population structure, some of which was likely caused by linkage with SCP selection loci, was a confounding factor, however this was not the case for all TTLs, suggesting that false positives due to structure may be present in some GWAS, but not in others – the subsequent analysis methods take this into consideration  identified per SNP, after FDR correction, was 2.74, ranging from a maximum of 12.86 to the defined minimum of 1. The average distance spanned by the significant SNPs across all GWAS runs was 149.84 Kb, ranging from a minimum of 0 (only one significant SNP) to a maximum of 1.6 Mb , however on an M-blot, only the SNPs that were significant in at least one GWAS are plotted, and the size of the point is proportional to the number of times the SNP was significant. The SNPs are plotted on a per chromosome basis, and the exact y-axis position of each point is the median of the –log  across all GWAS where the SNP was significant. The physical boundaries of the GWAS peaks defined using our peak definition pipeline are delineated by alternating blue and green vertical lines . Like a Manhattan plot, the M-blot x-axis plots the SNP physical position and the y-axis plots the SNP –log , it was important we minimize the possibility that SNPs identified many times were the result of linkage to a subpopulation structure locus. We did so by removing peaks where all individual GWAS appeared to be the result of population structure artifact Fig. . The M-bp-value of all the SNPs in a given peak. The count of GWASs in which the SNP was identified as significant was then adjusted to equal the sum of the peak SNP counts as well as the sum of counts of all the other SNPs in the peak interval. The point size was then plotted as proportional to this grand sum for each peak. The advantage of the M-blot B is that by glancing only at the single circle, it is easier to gauge the total number of SNPs significant in a given interval across all 460 GWAS  was created to further simplify the data presentation  and phenotypic allele effect were calculated for each individual GWAS, for each SNP that was significant. Given that 460 individual GWAS were performed, these values are more interesting when summarized. PVE was plotted on Manhattan blots as described above, and the mean, median, standard deviation, minimum, and maximum PVE values for each SNP are given in Additional file decreased drought tolerance were only observed when deviation data were used. Far more minor allele effects were observed that increased drought tolerance, suggesting potential for large gains from selection for drought tolerance breeding programs.For all traits, allele effects of all sizes were identified. Allele effects for B65 ranged from − 1.87 to + 14.81 tons/acre, B65 deviation from − 4.2 to + 11.18 tons/acre, for BWET from − 2.35 to + 22.67 tons/acre, for BWET deviation from − 5.6 to + 16.04 tons/acre, for PH from − 0.08 to + 1.38 m, for PH deviation from − 0.45 to– + 0.61 m, for LAI from − 0.60 to + 1.73 and for LAI deviation, from − 0.72 to + 1.11 .In the dataset as a whole, a median of ~ 3.1% of the genotypes carried minor alleles at loci associated with the 213 GWAS peaks. When this percentage is broken down by subgroup, group K4 is enriched relative to this median, and greatly enriched relative to the other subgroups: ~ 4.4% of K4 genotypes carried minor alleles at effect loci compared to ~ 2.7% of K1 genotypes, ~ 1.4% of K5 genotypes, and ~ 0% of K2 and K3 genotypes, by median Fig. . These donly in a single subgroup were found for K4 than any other subgroup. One hundred twenty-two of the 1673 final unique significant SNPs linked to the end set of 213 GWAS peaks were private to K4, ~ 7.3% of the GWAS associated SNPs and ~ 0.5% of all SNP genotypes in K4. K1 had the second most private SNPs, 57, ~ 0.2% of K1 genotype calls. For the other three subgroups, less than 0.1% of genotype calls were for private GWAS associated SNPs  were exclusively NDV, ~ 9%  were excFor both the DV and NDV peaks, all four traits  were on par with each other for the number of peaks they identified, with the exception of LAI, which was linked to noticeably fewer peaks than the other three phenotypes, likely as a result the higher error for this phenotype Table  and 5. TAll annotated sorghum genes that fell into regions of each GWAS peak  were pulled from PhytoMine to generate lists of candidate genes , which suggests there is a great potential to better understand plant genes that confer drought tolerance and increased biomass traits under drought conditions. Of the significantly enriched gene ontology (GO) terms among all genes falling into the regions spanned by the 213 significant GWAS peaks, the top 10 included domains essential to oxidoreductase activity and the treatment reactive oxygen species (ROS), which are both stress regulatory signals as well as destructive cellular compounds that must be neutralized under stressful conditions , 34. OthThe top 10 significantly enriched protein domains suggested potentially important roles for hydrolases, F-box transcription factors, proteins involved in the circadian cycle, and unknown protein domains , for harvest index plasticity compared between well-watered and drought treatments, relative net root growth as compared between control and Al toxicity treatments, and panicle weight plasticity compared between well-watered and drought treatments. Twenty eight of our GWAS peaks overlapped with peaks identified for one or more of these three traits and suggest strong candidates for further functional validation  , and a vThe 213 regions and candidate genes we identified in this study provide strong targets for future experiments focused on gene validation, with the ultimate goal of identifying new alleles to deploy in the breeding/engineering of drought tolerant sorghum varieties. We conclude that HDP-GWAS will be a powerful tool for identifying all manner of genotype-trait associations in sorghum, and likely, in a wide variety of other crop species, as well.sorghum bicolor. The two field locations were the University of California Agriculture & Natural Resources (UC-ANR) Kearney Agricultural Research & Extension Center (KARE) in Parlier, CA and the UC-ANR Westside Research and Extension Center (WREC) in Five Points, CA. One hundred seeds of each converted line from the Sorghum Conversion program were requested from GRIN (https://www.ars-grin.gov) in the fall of 2014 for planting at KARE in the summer of 2015. Single row plantings, 6.1 m in length on .76 m beds, occurred on June 1, 2015 on a Hanford sandy loam soil and fully irrigated throughout the growing season. Fifty plants per plot were bagged and harvested for increase. In some cases, open pollinated plants were collected because of poor seed set under bags. Seed was then threshed, cleaned and stored in 10 °C cold storage units.648 lines including 620 SC lines, two other inbred lines and 26 sorghum hybrids provided by the USDA-ARS National Genetic Resources Program (GRIN) and Chromatin Inc., respectively, were selected to make up the diversity panel in order to assay the natural diversity of The 620 SC lines and 26 Chromatin breeding lines and hybrids were planted in two row plots (6.1 × .76 m), along with 6 (x two row) plots of two inbred lines with characterized drought tolerance: RTx430, a pre-flowering drought tolerant line, and BTx642, a post-flowering drought tolerant line, on June 1, 2016 at KARE and June 6, 2016 at WREC using a four-row Almaco research row planter , 45. Allo) was determined using on-site weather stations that are part of the CA Irrigation Management Information System (CIMIS), with the weather stations located approximately 150 m from the field plot sites at both KARE and WREC [The soil type at WREC is a Panoche clay loam and the soil at KARE is a Hanford sandy loam. At both sites, daily potential evapotranspiration  using the same kmer database, using FILLIN defaults , 52. ProPost-imputation, all SNPs with call rates < 75% were removed from the dataset for a total of 272,942 SNPs. Two individuals (358 and 638) with call rates ≤60% were also removed from the dataset. All other individuals had call rates ≥60%. Prior to GWAS analysis, SNPs were also filtered for minor allele frequency (MAF) ≥0.01, for a final GWAS dataset of 131,544 SNPs. In this final SNP dataset, an average of ~ 76.81% of genotype calls across each taxa were homozygous for the major allele, ~ 12.88% were homozygous for the minor allele, and 1.77% were heterozygous. Neighbor joining trees were created in R using the function nj from the ape library . PAMK wa2 -inter-chr -yes-really. Using the resulting LD matrices, the relationship between SNP pairwise distance (BP_B - BP_A) and LD, for each chromosome, was calculated as the Gaussian smoothed r2 by SNP pairwise distance, where smoothed r2 was found using the Gaussian kernel from Python3 scipy, function = scipy.ndimage.filters.gaussian_filter (σ = 500). Local LD was then defined as the smoothed r2 that corresponded to the location of a given SNP in the resulting tables of BP_A, BP_B, pairwise distance, and smoothed r2.Pairwise linkage disequilibrium (LD) was calculated for all SNPs on each chromosome using Plinkv1.9, function = −-rThe drone used to collect phenotyping data was a modified multirotor vehicle and Blue River’s proprietary imaging payload. The vehicle was a six propeller multirotor, flown at 25 m altitude and 6.5 m/s airspeed using an onboard autopilot with GPS waypoint navigation to ensure the target image resolution of 1 cm was achieved. The drone measured four plant traits: plant height , leaf area index (LAI), fresh total plant fresh biomass , and vegetative biomass adjusted to 65% moisture content . These trait measurements were derived from LiDAR (Light Detection and Ranging) and multispectral imagery collected by Blue River’s imaging payload. The LiDAR instrument used near-infrared lasers to create a 3D point cloud of the sorghum canopy < 3 cm spot spacing, which is in turn used to extract plant height. Images from the color and NIR cameras were stitched together into a single mosaic image of each field using the TraitMapper software. The color and near-infrared mosaics were then converted to reflectance, and a digital grid representing the field plots was overlaid onto the mosaics. The reflectance and LiDAR height data were then extracted for each field plot and converted to trait measurements using TraitMapper.Ground-truthing measurements were taken over a small subset of field plots to calibrate and validate the drone-based measurements, which covered all field plots. Manual field-based measurements of PH, LAI, BWET, and B65 were taken at 15 plots per week  within 1 day of each drone overflight. Due to the laborious nature of the field-based measurements, each of the 15 manual measurements were taken within a sub-plot (80 × 160 cm), centered within each field plot. The ground-truthing of PH was performed using a height pole to measure the height of all plants within each subplot and then the median height was reported for each subplot. Leaf area index (LAI) was ground-truthed by first measuring the surface area of every leaf within each subplot using a hand-held Licor LI-3000C portable leaf scanner, and then dividing the total leaf area by the area of the subplot. BWET was ground-truthed by harvesting and weighing the total plant mass of each subplot. B65 was ground-truthed by then drying harvested plant material for 5 days at 50 °C in forced air drying ovens and reweighing.2 was computed using the remaining 20% of the ground-truthing data. This process was then repeated 1000 times, each time randomly selecting a different 80% subset for calibration. Finally, the median R2 over the 1000 repetitions was computed and defined here as the “validation R2”. The validation R2 were: PH R2 = 0.98, n = 88; LAI R2 = 0.90, n = 63; BWET R2 = 0.90, n = 63; B65 R2 = 0.89, n = 100 .Performance of the drone-based models of height, leaf area, and biomass was assessed using the ground-truthing data and repeated k-fold cross validation (k = 5). Specifically, each of the models for PH, LAI, BWET, and B65 were first trained on a calibration dataset, randomly selected from 80% of the ground-truthing data, and the RFor the drone-collected phenotype data, two plots for each treatment and each location (2 × 3 × 2), were mistakenly identified ‘74’ and ‘475’, respectively, and could not be disambiguated. As a result, all plots labeled either ‘74’ or ‘475’ were removed from the dataset prior to analysis.Drone-collected phenotype data were also filtered for noise. For each single genotype x trait x treatment x location combination, the phenotype data were plotted over the 11 or 12 timepoints at which they were collected to ensure a biologically plausible growth pattern, . Time plots with sharp peaks or zig zags were interpreted as containing phenotype noise/outliers in need of filtering prior to performing GWAS. To standardize the removal of outliers and prevent the need to look at every time-plot by eye, a kernel smoothing spline line (σ = 2.5) was fit for each time plot. The standard deviation (sd) of the residuals was calculated and each plot was first classified as either high or low noise, where high noise plots had residual sd ≥0.4 for PH, ≥0.51 for LAI, ≥3.0 for B65, and ≥ 4.5 for BWET. Data points on high noise plots were considered outliers if the residual from the spline was ≥1.2 sd for PH, B65, and BWET, and ≥ 1.5 sd for LAI. Data points on low noise plots were considered outliers if the residual from the spline ≥2 sd for PH, B65, and BWET, and ≥ 3 sd for LAI. These parameters were determined empirically using 10–15 case (obvious outliers identified by eye) and 4–8 control plots (no outliers identified by eye) per trait, for a total set of 49 case and 25 control plots. Different sets of parameters were tested to select the set of final values that resulted in the removal of all conservatively defined outliers from the case plots and the removal no points from the control plots. Example of case and control sample plots are shown in Additional file Individual single variate GWAS were run for each combination of trait, treatment, time-point and location using both the raw phenotype data, and the deviations of the drought treatments from the control, equal to the control phenotype – pre-flowering drought treatment phenotype and control phenotype – post-flowering drought phenotype for pre-flowering and post-flowering drought deviations, respectively, for a total of 460 individual GWAS runs. All GWAS were run using GEMMA . IBD matFor every GWAS model, a QQ-plot was generated, and the simplest model (meaning the model with the fewest covariates) resulting in the QQ-plot with the least deviation from the null hypothesis was selected as the final GWAS model for a given data-file. Multiple test correction was performed using Benjamini-Hochberg method to a false discovery rate (FDR) of 0.1 .After all single-variate GWAS models were selected, all SNPs that passed the significance threshold after multiple test correction were concatenated into a single results file for processing using a custom HDP-GWAS pipeline Fig. .Conserved and reliable GWAS peaks were defined using the following pipeline, illustrated in Fig. 2 by physical position and bp pairwise distance. SNPs were considered linked to a given significant SNP the pairwise kernel smoothed r2 was ≥0.3 for chromosomes 6 and 9, and considered linked if r2 was ≥0.2 for all other chromosomes. The higher LD threshold was used for chromosomes 6 and 9 as a result for the higher baseline LD on these chromosomes. Max distance was defined as the largest distance spanned by linked SNPs around a given significant SNP, and calculated as maximum base pair (bp) distance between SNPs defined as linked based on the above local LD parameters , in order to confirm the diagnosis. In some cases, zooming back out around peaks reversed an initial decision to the split the peak. In this way, a final set of refined peaks was determined , and cases where there were not enough SNPs in the peak region to a draw a strong conclusion. By contrast, peaks were rated ‘3’ when they had very clear evidence to suggest they were not the result of data artifact, i.e., peaks were many linked SNPs lead to multiple SNPs that passed the significance threshold. All cases/peaks that fell between these two extremes and were, in other words, difficult to categorize as either definitely artifacts, or definitely not artifacts, were rated as ‘2’. Peaks with only plots rated as 1 were dropped from the final list of GWAS peak results  * 200.Percent variance explained (PVE) was calculated for each SNP, for each GWAS, as the squared Pearson correlation between the Best Linear Unbiased Predictors (BLUPs) of the given GWAS phenotype vector and the given SNP genotype vector, * 100. BLUPs were calculated in R using package rrBLUP, function ms.solve . PVE summaries including mean, median, minimum, and maximum values were found by calculating the respective descriptive statistics across all individual PVE values found for each SNP, for each GWAS. PVE should be considered as a rough guide to the impact of each SNP in this population rather than a truly quantitative measurement because it may overestimate PVE in some cases due to the large number of SNPs and presence of subpopulation structure. For Manhattan blots, a PVE medians were calculated for all SNPs shown on the Manhattan blot by calculating the statistic across the PVEs for the subset of GWAS at which a given SNP was significant. Then for all the SNPs including in a particular GWAS peak on the Manhattan blot, the SNP with the highest median PVE was chosen to have its PVE plotted on the blot as a red star.Allele effects were calculated as the differences between the mean phenotypes of the individuals homozygous for the effect/minor allele – the mean phenotypes of the individuals homozygous for the non-effect/major allele for each significant SNP identified for a given trait/phenotype for a particular trait x treatment x time x location combination. Allele effect heat maps were generated using Python3 seaborn.heatmap (cmp = “RedYlBu_r”), where the plotted allele effects were averaged across all time-points and locations.https://phytozome.jgi.doe.gov/phytomine/template.do?name=Region_Gene&scope=all), organism = Sorghum bicolor, chromosome = peak chromosome, location start > position of the first SNP associated with a particular peak, and location end < position of the last SNP associated with a particular peak Additional file 2:Figures S1-S10. (PDF 8610 kb)This file contains Additional file 3:S. bicolor in our GWAS diversity panel. (XLSX 17 kb)Summary of pairwise LD across the ten chromosomes of Additional file 4:Summary of GWAS peaks identified using original trait data. (XLSX 137 kb)Additional file 5:Summary of GWAS peaks identified using deviations between control and treatment data. (XLSX 65 kb)Additional file 6:Summary of percent variance explained across 460 GWAS runs. (XLSX 131 kb)Additional file 7:Average SNP allele effects for each GWAS peak by trait and treatment. (XLSX 50 kb)Additional file 8:Number private GWAS SNPs by genetic subgroup. (XLSX 11 kb)Additional file 9:Tables showing all genes in the regions spanned by significant SNPs for each peak. Gene information was obtained from the Phytomine tool in Phytozome v3.0. (ZIP 8760 kb)Additional file 10:Top candidate genes from PhytoMine and literature for 213 GWAS peaks. (XLSX 232 kb)Additional file 11:Significantly enriched gene ontologies. (XLSX 12 kb)Additional file 12:Significantly enriched protein domains. (XLSX 43 kb)Additional file 13:Sorghum bicolor GWAS results. (XLSX 11 kb)Generalized comparison of recent Additional file 14:Potential evapotranspiration (ETo) and Irrigation water applications by treatment and date ranges in grain sorghum drought response study at the UC Kearney and UC Westside REC sites in 2016. (XLSX 10 kb)


Trifolium pratense L.) is a diploid, naturally cross-pollinated, cool-season species. As a perennial forage legume, red clover is mostly cultivated in temperate regions worldwide. Being a non-model crop species, genomic resources for red clover have been underdeveloped. Thus far, genomic analysis used in red clover has mainly relied on simple sequence repeat (SSR) markers. However, SSR markers are sparse in the genome and it is often difficult to unambiguously map them using short reads generated by next generation sequencing technology. Single nucleotide polymorphisms (SNPs) have been successfully applied in genomics assisted breeding in several agriculturally important species. Due to increasing importance of legumes in forage production, there is a clear need to develop SNP based markers for red clover that can be applied in breeding applications. In this study, we first developed an analytical pipeline that can confidently identify SNPs in a set of 72 different red clover genotypes using sequences generated by targeted amplicon sequencing. Then, with the same filtering stringency used in this pipeline, we used sequences from publicly available RNA-seq data to identify confident SNPs in different red clover varieties. Using this strategy, we have identified a total of 69,975 SNPs across red clover varieties. Among these, 28%  of them are missense mutations. Using Medicago truncatula as the reference, we annotated the regions affected by these missense mutations. We identified 2,909 protein coding regions with missense mutations. Pathway analysis of these coding regions indicated several biological processes impacted by these mutations. Specifically, three domains  were identified with five or more missense SNPs. These domain might also be a functional contributor in the molecular mechanisms of self-incompatibility in red clover. Future in-depth sequence diversity analysis of these three genes may yield valuable insights into the molecular mechanism involved in self-incompatibility in red clover.Red clover ( Trifolium pratense L.) is a diploid, naturally pollinated, cool-season species (Rhizobium species (Zea mays L.) to control soil erosion , we identified and confirmed a set of highly confident SNPs using targeted amplicon sequencing. Then, using the same analytical strategy, RNA-seq reads were used to identify a set of highly confidently SNPs in transcribed regions in red clover. SNPs generated from this study will contribute to further development of informative genomics tools for red clover breading studies and applications.Before performing SNP identification using RNA-seq reads, we conducted a pilot study by comparing genomic sequences of Cherokee cultivar (data not shown) with that of the draft genome of red clover  downloadTo confirm the final set of SNPs, primer sets were designed for each of these 36 SNPs for targeted amplicon size of 200 bp. Genomic DNAs from a total of 72 genotypes from an experimental breeding population, C328WS, were included in the confirmation study. C328WS is a red clover breeding population developed by Dr. Riday as part of his duties at USDA-ARS ; C328WS via MonsterPlex, a commercial service from FloodLight Genomics LLC (http://floodlightgenomics.com/). After sequencing, paired-end reads were mapped to the red clover reference genome using BWA  sing BWA . Mapped sing BWA  to keep sing BWA . VariantTo test the effectiveness of the identified SNPs in differentiating the 72 red clover genotypes, cluster analysis was performed using the R package pvclust . The SNPTwo RNA-seq data sets were obtained from NCBI SRA database: PRJNA287846 and PRJNA219226. The data from PRJNA287846 was done by our laboratory. For this project, leaf, root and flower tissues from red clover Kenland cultivar were used for RNA-seq . “Kenlanhttps://github.com/WLpython19/RedClover). To ensure that these SNPs were from unique regions in the genome, a genome-wide mapping search was performed using blat (Medicago truncatula (ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nt.gz). BLASTn  for each SNP was extracted from the reference genome (PRJEB9186). These flanking regions were used for PCR primer design. Primer pairs were designed using Primer3 , 0.20 μM of each primer, 0.50 mM additional MgCl2, 0.50 M betaine (Sigma-Aldrich), and approximately 20 ng template DNA. Thermal cycling was carried out on a DNA Engine Dyad (Bio-Rad) as follows: 95°C for 1 min; 45 cycles of 95°C for 20 s, 50°C for 2 min, and 72°C for 1 min; 72°C for 10 min; and a final step of 4°C for 1 min. Third, for sequencing of amplified targets, PCR products were submitted to the University of Wisconsin Biotechnology Center for Sanger sequencing. Sequencing traces were viewed and aligned using CodonCode Aligner 6.0.2 .Targeted amplicon sequencing was performed as follows.  Primer3  . All of the SNPs in the 36 loci were confirmed and additional nine were identified in eight of the loci (22% of the loci) Figure 1A total of 69,975 confident SNPs were identified using RNA-seq data  of them were missense mutations in protein coding regions. Annotation analysis identified 2,909 coding regions containing missense mutations. Out of these genes, 50% of them had two or more missense SNPs. Eighteen regions had the five or more missense SNPs in them Table 1.Arabidopsis, gene knock-out studies indicated that PPR proteins were critical during early embryogenesis  were suggested for vesicle secretion by stigmatic papillae, which is essential for hydration and pollen tube penetration of compatible pollen grains. Thus, this protein may play a very important role in the molecular mechanisms underlying self-incompatibility , potential role in cytoplasmic male sterility (PPR containing plant-like) and self-incompatibility (regulator of Vps4 activity in the MVB pathway). Homeobox domain is a DNA-binding domain of 60 amino acids, characterizing a large family of transcription factors . This moogenesis . In Brastibility .Red clover is known for its outcrossing nature with gametophytic self-incompatibility, which leads to high degree of genetic diversities ;a. Such As a species with high self-incompatibility, the genomic diversity of red clover deserves thorough investigation. Detailed genomic information can further facilitate breeding and molecular studies in red clover. However, genomics based diversity research is largely lacking in red clover. Our study clearly demonstrated the feasibility of targeted amplicon sequencing as an invaluable tool for genetic diversity identifications in red clover. Out of the 36 loci we pursued using targeted genomic amplicon amplification and sequencing, we discovered an additional 9 variants in 72 genotypes of red clover surveyed in the study. Taken together, 22% of the loci we investigated have additional SNPs aside from the original confirmed SNPs, with an average in-between SNPs spacing of 20 bp. Also, these 9 additional SNPs were identified based only on a 200 bp amplicon length. Our data strongly supports that red clover has a high degree of genomic variation among different cultivar varieties. Thus, it is reasonable to speculate that a large number of SNPs in red clover are still un-discovered. Currently, the most widely used genomic markers in red clover are SSR markers . These mde novo primer design as shown in our study. On the other hand, two restriction enzyme digestion based methods, Multiplex restriction amplicon sequencing (MRASeq)   and geno(MRASeq) .Newly identified SNPs should provide new opportunities for red clover genome research, including informed genomics assisted breeding with high density SNP markers, and even red clover seed-bank informatics development, with each germplasm’s barcoded SNP polymorphism profile recorded. Of note, using our SNP genotyping data, we were able to identify 11 sub-clades of red clover out of the initial 72 genotypes we had surveyed. This finding indicated that low-density SNP information was effective at identifying sub-population structure in red clover. Along with phenotypic data , SNP-assisted genotype clustering analysis becomes useful in the identification of cultivars with desired economic traits. Additionally, as a non-model organism, SNP surveys across populations becomes beneficial at identifying genes with high variance, like the three domains potentially linked to self-incompatibility we identified in our study. Future in-depth, population-wide deep sequencing of these domains and their predicted interacting partners will help yield insights into the dynamics of these genes in influencing self-incompatibility.For genome-wide SNP identification using a whole genome or whole transcriptome sequencing approach, long-read based technology might be especially beneficial for red clover. Currently, the most commonly used sequencing methods are short-read based. Short-read based sequencing technologies have transformed the fields of genetics and comparative biology. However, in the case of red clover genome sequencing, accurate identification of SNPs is likely compromised by its high levels of polymorphism, especially when divergent reads from the same genomic region are recognized as different loci, subsequently are assembled inaccurately, or when the reference genome is highly divergent from the target, causing inaccurate mapping . With thRed clover has a large number of SNPs identified in coding regions using RNA-seq data. Specifically, three genes/domains are identified with five or more missense SNPs. Future sequence diversity analysis of these three genes may yield valuable insights into the molecular mechanism involved in self-incompatibility in red clover. Given the highly diversified genome of red clover, targeted amplicon sequencing followed by sequencing can be an effective method for novel SNP identification in red clover.The data are available at NCBI under the accession number: PRJNA559390.The experimental research on plants carried out in this work complies with institutional, national, and international guidelines. Plant materials used in this study were from red clover C328WS and Kenland red clover. C328WS is currently stored and maintained at the US Dairy Forage Research Center. Kenland red clover is currently stored and maintained at USDA-ARS Forage-Animal Production Research Unit, in Lexington, KY. Both C328WS and Kenland are in the public domain and requires no licenses or permissions to use.WL, HR, and RD: conceived the research project and participated in the design of the study. CR and AE: contributed to the data acquisition and analysis of targeted amplicon sequencing. WL and HR: data analysis. WL, HR, and RD: manuscript preparation and data interpretation. All authors have read and approved the final version of the manuscript.WL, HR, and RD were further supported by USDA Agriculture Research Service 5090-31000-024-00D, 5090-31000-026-00-D, 5090-21000-056-00D, and 5042-21000-002-00D, respectively. CR and AE are partially supported by USDA CRIS project 5090-31000-024-00D. Any mention of trade names or products is for information purposes, as the USDA does not endorse any specific products or services.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.