Salmonella enterica serovar Pullorum is a host-restricted serotype causing infection in poultry. The pathogen can not only cause acute infection in young chicks with high mortality and morbidity, but also persist in adult chickens without evident clinical symptoms and lead to vertical transmission. To eradicate S. Pullorum in poultry farms, it is necessary to establish an efficient method to monitor the prevalence of the pathogen in adult chickens. The protein IpaJ is a specific immunogen in S. Pullorum and is not detected in closely related serotypes, such as S. Gallinarum and S. Enteritidis.E. coli. The purified MBP-IpaJ was used as a coating antigen to develop an indirect ELISA assay, which was applied to the detection of S. Pullorum infection in chickens. The indirect ELISA assay demonstrated that antibodies produced against IpaJ were detectable in antisera of chickens infected with S. Pullorum in the second week, stably increased until the tenth week, and persisted at a high level in the following two weeks. Furthermore, the ELISA method detected four positive samples out of 200 clinical antiserum samples collected from a poultry farm, and the positive samples were confirmed to be reacted with S. Pullorum using the standard plate agglutination test.In the present study, IpaJ was expressed as a recombinant fusion protein MBP-IpaJ in S. Pullorum infection, and contribute to eradication of pullorum disease in the poultry industry.The established indirect ELISA using the IpaJ protein is a novel method for specific detection of The online version of this article (10.1186/s12917-018-1753-0) contains supplementary material, which is available to authorized users. Salmonella enterica serovar Pullorum (S. Pullorum), a host-restricted serotype mainly infecting poultry [S. Pullorum persistently exists in infected adult chickens, the pathogen can be transmitted vertically to offspring. Therefore, to reduce the prevalence of the disease in poultry farms, efficient surveillance has become increasingly important and essential. Although bacterial culture is the standard method for monitoring Salmonella infection due to the accuracy of the testing method, culture-based procedure for Salmonella isolation and serotype identification are labor intensive, expensive to perform, and time-consuming, thereby limiting its application in clinical diagnosis [S. Pullorum in poultry farms.Pullorum disease remains a problem in the poultry industry in developing countries such as China and Brazil. The causative agent is  poultry . Becauseiagnosis –5. Thus,Salmonella detection method in chickens is the agglutination test, which played a significant role in eradicating S. Pullorum in Europe [Salmonella, and cannot differentiate S. Pullorum from S. Gallinarum and S. Enteritidis because of the shared O9 antigen in the three serotypes [S. Pullorum infection is useful for surveillance in the poultry industry.Serology detection has frequently been used to identify infected and carrier animals in both surveillance and epidemiological studies . The goln Europe . Howevererotypes . ELISA merotypes –11, but S. Pullorum, and not detected in S. Gallinarum and S. Enteritidis. Our previous studies revealed that > 99% S. Pullorum isolates carry the ipaJ gene, which is expressed during S. Pullorum infection [ipaJ gene was cloned into the bacterial expression vector pMBL-c5x and expressed in E. coli. Using the purified MBP-IpaJ protein, an indirect ELISA method was developed and applied to sera from S. Pullorum-infected chickens. The method has the potential to contribute to rapid and specific diagnosis of S. Pullorum infection in chickens.The IpaJ protein is a new antigen reported to be specific to nfection –14. In tipaJ gene, 840 bp in length, was successfully amplified from the strain C79–13 and cloned into the prokaryotic vector pMAL-c5X to create pMAL-c5X-ipaJ. The sequence of ipaJ was identified via sequencing analysis and restriction digestion -infected chickens with that from chickens infected with ipaJ-positive S. Pullorum(∆pSPI12::pipaJ)  Fig. . SensitiS. Pullorum-infected chickens during 12 successive weeks. As shown in Fig. The indirect ELISA assay was used to detect the changes in levels of antibodies against IpaJ in antiserum from ten S. Pullorum has been reported so far; the plate agglutination test (PAT) was used to confirm the reaction of these positive antisera with S. Pullorum strain C79–13 , this test cannot identify serotypes of Salmonella, with interpretation of the results being somewhat subjective and prone to error [S. Pullorum infection, while the PAT results confirmed that these antisera could react with S. Pullorum, S. Gallinarum and S. Enteritidis. These findings demonstrated that the specificity of the indirect ELISA method was better than that of PAT. However, MBP fusion protein, which was used as the coating antigen could also react with antibodies against MBP, and such antibodies have been found to have minor seroreactivity [S. Pullorum or other Salmonella serotypes, and assess the specificity of the method.In the present study, the indirect ELISA method revealed that chickens infected by to error . In the activity . MBP-tagactivity . The indS. Pullorum infection in chickens based on detection of antibodies against IpaJ protein. The method can not only differentiate pullorum disease from other avian Salmonellosis, but can also serve as a novel method for monitoring the S. Pullorum infection and contribute to eradication of pullorum disease in the poultry industry.In summary, the present study developed an indirect ELISA method for identifying S. Pullorum reference strain C79–13 obtained from the China Institute of Veterinary Drug Control was used in this study. The ipaJ deleted strain ∆pSPI12 and complementary strain ∆pSPI12::pipaJ were preserved in our lab (14). The prokaryotic expression vector pMAL-c5X  was used to construct recombinant expression plasmids in the host strain E. coli ER2523 . Bacteria with recombinant plasmids were grown in Luria Bertani (LB) broth containing ampicillin (100 μg/ml).The ipaJ gene sequence (Accession Number: GU949535) in GenBank, the primes pMAL-c5X-ipaJ-F/R  were designed to amplify the gene from the strain C79–13 using PCR. PCR analysis was performed in a 50 μl volume containing 2.5 U of Taq DNA polymerase, 10 μl 5 x SF buffer, 200 μM dNTP mix, 0.4 μM upstream and downstream primers, and 1 μl DNA template. PCR amplification was done with a pre-denaturation step at 95 °C for 5 min followed by 30 cycles of 95 °C for 50 s, 61 °C for 50 s, and 72 °C for 1 min, and the reaction terminated with a final extension at 72 °C for 10 min. The PCR products were then resolved in a 1% agarose gel after electrophoresis and purified using the MiniBEST Agarose Gel DNA Extraction kit . The purified ipaJ PCR products were then ligated to pMAL-c5X vector. After transformation of the ligated products into the ER2523 competent cells, the colonies containing the recombinant plasmid were subject to PCR identification and sequencing analysis. The recombinant bacterial strain carrying the pMAL-c5X-ipaJ plasmid was named ER2523-pMAL-c5X-ipaJ.Using the published ipaJ was inoculated into fresh LB medium with ampicillin at a 1:100 dilution. When the OD600 reached 0.4–0.6, IPTG was added to the medium with at a final concentration of 0.3 mM to induce protein expression, and the bacteria cultured at 37 °C for 4 h at a shaking speed of 180 rpm. The bacterial pellets were collected for ultrasonic lysis, the supernatants of lysed bacterial cultures containing the MBP-IpaJ protein were subjected to SDS-PAGE and stained with 0.025% coommassie brilliant blue R-250 . Purification of the protein was performed following the manufacture’s instruction by using the pMAL™ Protein Fusion and Purification System .An overnight culture of the recombinant strain ER2523-pMAL-c5X-The cell lysates or purified recombinant proteins were subjected to SDS-PAGE and transferred to NC membrane using a Pyxis™ Gel Processor . The NC membrane was blocked in 5% BSA and then incubated in anti-MBP antibodies at a 1:8000 dilution, or anti-serum against IpaJ at a 1:1000 dilution for 2 h at 37 °C. After incubation with goat anti-mouse IgG-HRP at a 1:10000 dilution for 1 h, the NC membrane was stained with an ECL chromogenic kit  and scanned by using an Amersham Imager 600 imagers .S. Pullorum-infected chickens were diluted from 1:10 to 1:5120, and 100 μl per well of each dilution was added to the plate and incubated at 37 °C for 2 h. Following incubation with 100 μl per well of 1:10000 rabbit anti-chicken IgG-HRP at 37 °C for 1 h, the plate was stained with TMB for 10 min at 37 °C and the reaction terminated with 2 M H2SO4. The OD450 value was measured to determine the best concentration of MBP-IpaJ and the appropriate dilution of the antiserum.The recombinant MBP-IpaJ was used as the coating protein in the ELISA assay. To determine the best concentration of the coating protein, MBP-IpaJ was diluted to the following concentrations: 8 μg/ml, 6 μg/ml, 4 μg/ml, 2 μg/ml, 1 μg/ml, 0.5 μg/ml, 0.25 μg/ml, and 0.125 μg/ml. Hundred microliters per well of each dilution was then added to 12 wells of the ELISA plate, and incubated at 4 °C for 14–16 h. After washing with PBST three times, the plate was blocked with 200 μl per well of 1% BSA in PBS at 37 °C for 2 h. Antisera from Salmonella serotypes including S. Gallinarum, S. Enteritidis, S. Typhimurium, and other poultry bacteria, including E. coli, and Campylobacter jejuni. The antisera from chicken infected with these bacteria were collected and subjected to the indirect ELISA assay to evaluate the specificity of the method. The sensitivity test of the indirect ELISA method was performed using positive antisera against S. Pullorum from infected chickens. The antisera were serially diluted from 1:100 to 1:12800 to be subjected to the indirect ELISA analysis, while the corresponding diluted antisera from non-infected SPF chickens were used as the negative controls.The specificity of the indirect ELISA assay was evaluated using other avian S. Pullorum infection, the production of antibodies against IpaJ during S. Pullorum infection in SPF chickens  was monitored. All of one-week old female SPF chickens were given antibiotic-free feed and water. The animal experiments were undertaken with the permission of the Animal Care and Ethics Committee of Yangzhou University. The S. Pullorum reference strain C79–13 was inoculated intramuscularly (i.m.) to ten one-week old SPF chickens with 1 × 106 CFU per chicken. The antiserum was collected every week during the following 12 weeks. The collected antisera were preserved at − 70 °C, and then subjected to the indirect ELISA assay.To evaluate the indirect ELISA assay for the detection of S. Pullorum and S. Gallinarum [Two hundred antiserum samples were collected from a poultry farm. All of the samples were monitored using the indirect ELISA assay and the results compared with those of the PAT method used to detect antisera from chickens infected with llinarum .t-test with pairwise analysis. A p < 0.05 was considered as statistically significant. All analyses were performed in the software GraphPad Prism version 7.The data was analyzed using the Student’s Additional file 1:Identification of recombinant MBP-IpaJ protein via Western blot analysis. The immunoblotting assay was used to detect the expression of MBP-IpaJ protein using anti-MBP antibody recognizing the tag. Two bands were detected and found to represent MBP-IpaJ (73.5 kDa) and MBP (42 kDa). (DOCX 148 kb)


Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server. Over the past decade, genotyping technologies for genome-wide association studies (GWAS) have allowed for extensive and rapid genotyping of common variants . CommercMeta-analysis is a powerful and commonly used technique, but if the study data were generated using different platforms, there may be a reduction in statistical power due to minimal overlap between the genotyped markers. To overcome this reduction in power, imputation may be used to increase the marker overlap between datasets, thereby improving the power of a meta-analysis .Imputation is dependent on the adequate matching of haplotypes based on LD and thus it is essential that the reference population is genetically similar to the population being imputed. Numerous reference datasets are freely available online and can be used for imputation via suitable imputation software. These include amongst others, the 1000 Genomes phase 3 data (1000G) , the Hum1  are three resources which have recently become a viable option for accurate imputation of African populations.African and admixed populations are more heterogeneous in their haplotype block structure and, as such, would benefit from a larger reference dataset incorporating more genetic diversity . Referen1 contains the largest collection of haplotypes of African origin, with all the 1000G samples and an additional 2000 samples from Uganda, 100 samples from each of a set of five populations from Ethiopia, Egypt, Namibia (Nama/Khoesan), and South Africa (Zulu). The AGR contains 97 004 203 biallelelic SNPs spanning the autosomes and the X chromosome for 4 956 samples1. The 1000G reference panel contains 84 237 642 biallelic SNPs for 2 504 samples selected from 26 populations across Europe, Asia, the Americas, South-, and East-Asia  but at a slightly reduced quality and accuracy.Two sources of data for the SAC cohort were available, namely genotypes obtained using the Affymetrix 500k array containing 500 000 SNP markers  and the Illumina  multi-ethnic genotyping array (MEGA) with 1.7 million markers. This study was carried out in accordance with the recommendations of the Health Research Ethics Committee of Stellenbosch University  before participant recruitment and written informed consent was obtained from all study participants prior to blood collection. All subjects gave informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Health Research Ethics Committee of Stellenbosch University.Genotype data obtained using the Affymetrix and MEGA arrays were subjected to iterative quality control (QC) using PLINK v1.9  as previThese QC steps were iterated until no additional variants or individuals were removed, and concluded with a sex-concordance check to remove individuals with incorrect sex information. Genotype Harmoniser version 1.4.15  was usedThree different reference panels were used to conduct five protocols of phasing and imputation in order to assess which performed best for our admixed population . The fir1 (SIS). Genotypes from the Affymetrix 500k array in PLINK file format were converted to Variant Call Format (VCF) using PLINK v1.9 and then uploaded to the server where phasing was performed using SHAPEITv2.r790  in different ways. Impute2 uses the Markov-chain to implement the HMM, while minimac3 uses a Monte-Carlo procedure to implement HMM . PBWT alAlthough haplotype pre-phasing has been shown to decrease imputation accuracy slightly it was used in this study for consistency between the protocols (the Michigan server did not have an option to not phase data) and to increase the speed of imputation .For all imputation runs, the reference panels included all available populations since using an all-inclusive reference panel is known to improve imputation accuracy . Of the Imputed data were returned from the imputation software in one of two formats: either in the form of a VCF file, or in Impute2 (gen/sample) format and based on the format, one of two QC procedures was employed to convert the imputed data from genotype probabilities to actual genotypes. Data output from the two procedures were compared and showed complete overlap and can thus be used interchangeably.2 version 0.7.5. R version 3.2.4 was used to identify INDELS, which were removed using GTOOL  and coding all no-call alleles as N (PLINK command: – output-missing-genotype N). INDELS and SNPs with no-call alleles were removed and the files were converted to PLINK bed format (bed/bim/fam).To assess imputation quality we considered the internal quality metrics obtained from each imputation protocol: the INFO score (in the case of IMPUTE2) and the r-squared value (for PBWT and Minimac3). Although, the info score and r-squared quality metrics are not directly comparable, they have shown to be highly correlated in two notable studies: one by Marchini and Howie , and anoImputation accuracy was assessed by extracting the overlapping individuals from the MEGA and imputed Affymetrix data and using PLINK, any variants that overlapped between the two platforms prior to imputation were removed. Between the two arrays there were only 41 815 variants genotyped on both platforms and they were evenly distributed across the genome and should not affect the analysis if removed post-imputation. The analysis was performed per chromosome and for each SNP the alleles were compared between the imputed Affymetrix data and the MEGA data. If both alleles of a SNP matched it would be considered a complete match . If only one allele matched it was considered a half match and if no alleles matched it was considered a no-match. For each chromosome the total number of imputed variants was recorded and their distribution by MAF was plotted to determine how the number of variants correlated with MAF between the different imputation protocols.To determine the imputation accuracy, the SNP overlap between the MEGA and imputed Affymetrix data was assessed. Within this overlap the number of SNPs that were complete-, flip-, half- or non-matched were recorded along with their average INFO score or r-squared value. Since SNPs that are flipped can be flipped to align a reference, or a different dataset if a meta-analysis is planned, the flipped SNPs were considered matches for the purposes of calculating imputation accuracy. Accuracy was calculated by comparing the proportion of SNPs in the overlap that were complete (or flipped) matches to the number of overlapping SNPs. This provided an indication of accuracy and error rate within the overlapping region and should be a good indication of overall imputation accuracy. These calculations were performed for the autosomes and the X chromosome separately in order to determine how accurately and with what quality the X-linked variants were imputed compared to the autosomal variants.After QC and strand alignment, 919 individuals and 239 612 variants with a genotyping rate of 99.39% remained in the Affymetrix 500k dataset, and 771 Individuals with 1 491 347 variants remained in the MEGA dataset with a genotyping rate of 99.43%. A total of 325 individuals were genotyped on both the Affymetrix and MEGA array and 43 140 SNP markers overlapped between the two platforms. Following imputation the 325 individuals with genotype data from both MEGA and Affymetrix were extracted from both the MEGA data and imputed Affymetrix data so that their imputed genotypes (Affymetrix) could be directly compared to their actual genotypes (MEGA) in order to determine genotyping accuracy. The 43 140 SNPs that were genotyped on both platforms were removed from both datasets after imputation in order to not skew the accuracy analysis.For the SAC cohort, the best genotype imputation results obtained were from the in-house IMPUTE2 (with 1000G reference panel) and the Sanger imputation server (with the AGR reference panel) methods. The in-house method resulted in the most imputed variants across both the autosomes 60 438 387) and X chromosome (2 574 793), followed by SIS (AGR) , while the SIS with 1000G reference panel had slightly fewer imputed variants than with the AGR panel . The Michigan imputation server had only about half as many imputed variants as the other methods, for either reference panel . The num38 387 anFor the autosomes and X chromosome, the SIS (AGR) produced the best imputation quality across all MAF ranges, closely followed by the in-house method where quality was second to SIS (1000G) only for low MAF (0-1%) variants on the X chromosome . The MicImputation accuracy was previously evaluated in African and three-way admixed populations, but we have performed the first evaluation in a five-way admixed population. The imputation accuracy in African-American individuals (considered to be three-way admixed) ranges from 78%  to 89% . Bantu-sSince four of the five protocols were capable of imputing X-linked variants, and since the quality and accuracy of X chromosome imputation has not been previously tested, we included it for this analysis. The X chromosome had only slightly lower or higher imputation quality for all imputation runs when compared to the autosomes, indicating that X chromosome imputation can be performed with confidence , 3. AlthThe biggest limitation for imputation in the five-way admixed population is the lack of a suitable reference panel. Imputation in the San population has been shown to have the lowest imputation accuracy (89%) compared to other African populations , which cIn conclusion, we have shown that imputation of the SAC population is feasible and produces quality data on both the autosomes and X chromosome. While the SIS (AGR) imputation had the best quality and accuracy, the in-house protocol using Impute2 and 1000G Phase 3 also produced imputed data of a high standard and had the highest number of imputed variants. This protocol may prove especially useful in the case of a meta-analysis where one wishes to maximize SNP overlap between datasets. As the number of applicable reference populations and individuals grows, imputation accuracy will improve for African and admixed populations, but it remains the gold-standard to Sanger sequence a variant of interest to confirm that the imputed variant is present in the population prior to conducting further research.marlom@sun.ac.za.Summary statistics for the quality and accuracy assessment of the SAC data will be made available to researchers who meet the criteria for access to confidential data after application to the Health Research Ethics Committee of Stellenbosch University. Requests can be sent to: MM, E-mail: HS, SM, GT, CK, and MM conceived the idea for this study. HS and SM performed the data QC. SM conducted phasing, imputation, and quality assessment. HS performed the accuracy assessment and wrote the first draft. All authors contributed to writing and proofreading for approval of the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


SNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. PCA is not a single method that is always done the same way, but rather requires three choices which we explore as a three-way factorial: two kinds of PCA graphs by three SNP codings by six PCA variants. Our main three recommendations are simple and easily implemented: Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA . We also document contemporary practices by a literature survey of 125 representative articles that apply PCA to SNP data, find that virtually none implement our recommendations. The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications. Single nucleotide polymorphism (SNP) data is common in the genetics and genomics literature, and principal components analysis (PCA) is one of the statistical analyses applied most frequently to SNP data. These PCA analyses serve a multitude of research purposes, including increasing biological understanding, accelerating crop breeding, and improving human medicine. This article focuses on the one research purpose identified in its title, elucidating population structure—although its discussion and citations make evident the broader relevance of the results and principles presented here.PCA is not a single method that is always done exactly the same way. Rather, three methodological choices are implicated necessarily in each and every PCA analysis and graph of SNP data. They are indicated in this article’s title: the kind of graph produced, the way that SNP reads  are coded numerically, and the transformation applied to the data prior to PCA analysis. These three choices impact which kinds of structure and patterns in SNP data can be displayed and discovered in PCA graphs.Current practices—as documented by a literature survey of 125 representative articles that apply PCA to SNP data—suffice to justify the well-deserved popularity and abundant success of PCA for elucidating population structure . But detFirst, we consider two kinds of PCA graphs. PCA is applicable to a two-way factorial design, that is, a data matrix, and it provides a dual analysis of both the rows and the columns of a matrix. The standard term for a figure showing both is a “biplot.” The contrasting term used here for showing only rows or only columns is a “monoplot.” And our generic term for either a monoplot or a biplot is a “graph.” Biplots were first introduced by Gabriel  and haveSecond, we compare three SNP codings. Consider a data matrix comprised of a number of SNPs observed for a number of Individuals. The original reads of nucleotides  constitute categorical data, whereas PCA requires numerical data. But there is no natural and unique method for translating from this categorical data to the required numerical data, so the SNP literature exhibits multiple methods for coding SNP data numerically. Three options for SNP coding are discussed: code the rare allele as 1 and the common allele as 0 for each SNP, the reverse, and a mixture of rare coded 1 or 0 (and hence common 0 or 1). We name them SNP coding rare = 1, common = 1, and mixed. We recommend SNP coding rare = 1 and document its several advantages for elucidating population structure. However, to the best of our knowledge, the consequences of different SNP codings for the appearance and interpretation of PCA graphs have not yet been addressed. Most articles in our survey fail to report which SNP coding was used, and none explicitly specify the recommended SNP coding, which thereby compromises the interpretation and repeatability of published PCA graphs.Third, we explore six PCA variants. A SNPs-by-Individuals data matrix comprises a two-way factorial design. Although analysis of variance (ANOVA) has not been used in the present context of PCA analysis of SNP data, it provides important insight by distinguishing three sources of variation that have quite different biological meanings: the SNP main effects, Individual main effects, and SNP-by-Individual (S×I) interaction effects . The sixeven one of our three recommendations—biplots, SNP coding rare = 1, and PCA variant DC-PCA. Consequently, the likelihood that any published PCA analysis of SNP data has yet implemented all three recommendations is quite small. Awareness of the consequences of these three choices—which are made in every PCA analysis of SNP data necessarily—creates new opportunities to elucidate population structure more effectively.Our literature survey encountered no clear implementation of that we chose SNP coding rare = 1 and PCA variant DC-PCA, but the following two subsections will explain the exact meanings of these choices and the reasons why we prefer them. To distinguish DC-PCA from other variants of PCA, its principal components are called interaction principal components (IPCs). By definition, after row and column averages have been subtracted from a data matrix, what remains is the matrix of interactions, as will be explained in greater detail in the third subsection and the appendix.The increase in biological information that is achieved upon upgrading from a monoplot to the recommended biplot can be illustrated by discussing left half, Avena sativa L.), and Kathy Esvelt Klos kindly shared with us this dataset with 635 oat lines by 1341 SNPs . All SNPs are biallelic, and there are no missing data. Experienced oat breeders had classified the 635 oats into three groups [In its e groups . The 411entire figure, In the in both panels. Furthermore, when some SNPs have been associated with traits of agricultural or medical importance, such as the 25 SNP associations with agriculturally important traits found in this oat dataset [Beginning with the approach using expert knowledge, we devised a method that transfers expert knowledge of the oats to the SNPs in order to classify the 1341 SNPs into three corresponding groups, again using the same color scheme that had been applied to the 635 oats. It is trivially simple: The color assignment for each SNP is based on which of the three oat groups has the highest percentage of the rare allele, and the outcomes were: 372 SNPs colored green had the highest percentage for spring oats, 345 colored blue were highest for world diversity oats, and 624 colored red were highest for winter oats. Thereby, this biplot shows what a monoplot cannot possibly show, the joint structure of SNPs-and-oats with green mostly on the left and red mostly on the right Progressing to the approach using automated statistics, we must begin by discussing the PCA arch. When interpreting a PCA graph, such as the biplot for oats in A diagonal data structure implies a PCA arch. Importantly, the reverse implication also holds frequently, as the ecological literature makes clear, and it merits greater attention in the genomics literature: A PCA arch often implies a diagonal data structure. However, it is possible for a PCA arch to emerge from other data structures, such as samples taken from a set of nine populations in a three-by-three lattice with two of those populations sampled more than the others , and thThe oat lines in The foremost opportunity is a novel method to give both panels of a biplot a coherent color scheme by means of automated statistics, in marked contrast to the above method based on expert knowledge. Because Furthermore, the PCA arch in The gradient in Occasional discrepancies may occur amidst an overall similarity when comparing the population structure shown by expert knowledge and by automated statistics, as in Figs In review, it is axiomatic that a biplot shows more information than a monoplot. A biplot can, but a monoplot cannot, display and discover joint structure in the SNPs-and-Individuals. Either expert biological knowledge or automated statistical analysis can be used to reveal patterns in PCA biplots—provided that the data are structured by a single dominant causal factor. Expert knowledge and automated statistics are deeply complementary, so this combination makes results more reliable and objective. The simple change of switching from monoplots of Individuals only to biplots of both Individuals and SNPs offers new opportunities to display joint structure, which can increase biological insight. The following section on Materials and Methods provides additional information on the construction and interpretation of PCA graphs.The biological information displayed in a biplot using the recommended SNP coding rare = 1 is clear because this coding uniquely orients the information in both panels of a biplot in the same way, thereby facilitating straightforward and intuitive interpretation—especially of the joint structure of SNPs-and-Individuals. Given the recommendation of biplots in the previous subsection, this subsection evaluates SNP codings primarily in terms of their consequences for the appearance, interpretation, and utility of biplots. However, to accommodate researchers who may prefer monoplots, at least on some occasions, we also discuss the consequences of SNP codings for monoplots. Also, given the recommendation of PCA variant DC-PCA in the next subsection, this subsection explores the consequences of SNP codings for only DC-PCA, leaving further exploration to the next subsection.One option, here called SNP coding rare = 1, is to code the rare allele as 1 and the common allele as 0. This coding is of special interest for reasons that emerge momentarily. Another option, here called SNP coding common = 1, is the opposite: to code the common allele as 1 and the rare allele as 0. This coding is the default in TASSEL, which is widely used for crop plants . The other two codings can also be generalized.The SNP coding rare = 1 can be generalized for SNP datasets having more than the 2 codes of biallelic data. One possibility is to create a variable for each allele, where each variable signifies the presence/absence of that allele. For a diploid species with three codes—one for each of the two homozygotes and another for the heterozygote—express the data as 0 and 2 for the homozygotes and 1 for the heterozygote. Then recode each SNP to give the rarest homozygote the value 2 and the most common homozygote the value 0. These ideas can be elaborated for polyploids such as hexaploid (AABBDD) bread wheat, We begin our exploration of the consequences of SNP coding with the simple dataset that Morrison used as the initial example in his first blogpost on the PCA arch distortion in genomics data . His toyboth Individuals and SNPs. However, DC-PCA has distorted that single gradient into an arch with its ends involuted or bent toward the middle for Individuals 1 to 20, and likewise for SNPs 1 to 24. This arch is a problem that complicates the interpretation of PCA graphs, but provided that one knows about this distortion, the gradient is still apparent: Both arches move clockwise from Individual 1 to 20 and from SNP 1 to 24. Also, those Individuals and SNPs that are located at similar positions along the gradient  are placed in similar directions from the origin—related Individuals and SNPs track each other in a clear and intuitive manner.DC-PCA biplots for these three datasets are also shown in this figure. On the left, the PCA arch for the 20 individuals shown with black triangles was included in Morrison’s first blogpost. But this figure presents a biplot, so it also includes the PCA arch for the 24 SNPs shown with red dots. The concentration of ones along the matrix diagonal constitutes a single gradient with evident joint structure that involves In the middle, the biplot shows the same gradient as at the left, but with the opposite option, SNP coding common = 1. The arch for Individuals 1 to 20 is shown by black triangles, and the arch for SNPs 1 to 24 by green dots. Compared to Morrison’s original dataset, the arch for SNPs shown in green has been rotated by 180° relative to the Individuals shown in black. This rotation happens because reversal of the polarity of a SNP sends its point to the opposite location relative to the origin in a PCA graph—or more precisely, approximately to the opposite location because a low-dimensional graph approximates its high-dimensional data. Therefore, Individual 1 and SNP 1 are far apart with common = 1, although they are near each other with rare = 1 as they should be; and the same applies to Individual 20 and SNP 24. On the other hand, Individual 15 and SNP 8 nearly coincide with common = 1 , although they are far apart with rare = 1 as they should be; and the same applies to Individual 6 and SNP 17 (at the left). Consequently, the biplot in the middle, unlike the original at the left, is counterintuitive and confusing because related Individuals and SNPs can be widely separated, and distant Individuals and SNPs can be quite close.On the right, the biplot illustrates SNP coding mixed. In this dataset, the coding rare = 1 and common = 1 alternates, with columns using rare = 1 shown in red, and those using common = 1 shown in green. Incidentally, other mixed coding schemes give qualitatively the same results, such as selecting the coding at random for each SNP, or reversing the coding for every fourth SNP instead of every other SNP. The DC-PCA biplot on the right combines features of the left and middle biplots. Individuals 1 to 20 are shown with black triangles, odd-numbered SNPs 1 to 23 with red dots, and even-numbered SNPs 2 to 24 with green dots. The orientation of the green arch is rotated by 180° relative to the black and red arches. Taken together without distinguishing red from green, the dots for the SNPs roughly approximate a circle around the origin, rather than the typical arch, so awareness of the arch distortion would not be enough to guide proper interpretation. The biplot on the right inherits the problems from the middle biplot that related Individuals and SNPs can be widely separated, and distant Individuals and SNPs can be quite close. Furthermore, it has the additional problems that SNPs near each other along the gradient can be far apart in this biplot (such as SNPs 1 and 2), whereas SNPs far apart from each other along the gradient can be near each other (such as SNPs 4 and 15). Consequently, SNP coding mixed produces biplots that are quite confusing.both row and column PCA scores, not just one or the other, so the orientation of the row arch and column arch relative to each other is invariant, not arbitrary.From However, the verdict on SNP codings for monoplots is different. Although the DC-PCA biplots in Similarly, Nevertheless, although Figs that SNP coding rare = 1 was used to construct the CA1 arranged matrix in why SNP coding matters for CA. As ecologists have long known, although both PCA and CA have the arch distortion, PCA does but CA does not involute the arch, and therefore only CA can produce an arranged matrix that concentrates the larger values along the matrix diagonal . Likewise, for a given Individual, its mean across SNPs is simply the frequency of the allele coded 1. Ordinarily, it makes sense to avoid burdening or distracting a PCA graph with such simple information, so it is best to remove main effects. Also, these means often lack any straightforward or interesting biological meaning. Indeed, it may be especially difficult to attach any biological meaning to the Individual means, not least because of alternative choices for SNP coding. When SNP or Individual means are biologically meaningless, they merely add noise to a PCA graph. That said, occasionally there can be special circumstances, so it is the prerogative of researchers to decide which of the three sources—SNP main effects, Individual main effects, and S×I interaction effects—they find of interest for a given SNP dataset and research purpose.R rows and C columns. These data can be conceptualized as a high-dimensional cloud of R points in C-dimensional space, or conversely as C points in R-dimensional space . DC-PCAg 4.1 in ). The orFor SNP coding rare = 1, the total SS is composed of 88.0% for S×I interaction effects, 10.7% for SNP main effects, and 1.3% for Individual main effects. SNP coding common = 1 is not shown in and S×I interaction effects combined, denoted by I&S×I, which has a SS of 2077.808 + 138492.217 = 140570.025. Researchers who are familiar with PCA and are accustomed to the automatic monotonic decrease in the SSs for successive PCs should note that the SSs for the Individuals and S×I portions are not necessarily monotonic.The application of PCA to a combination of two sources of variation, unlike the single source of S×I interaction effects for DC-PCA in more S×I interactions than PC1 and PC2 from SNP-Centered PCA because DC-PCA is not distracted by any main effects. This comparison can be understood in geometrical terms by saying that obviously without centering PCA is wasting effort to capture the non-central centroid of the data, and that distraction compromizes PCA’s visualization of the data.All seven PCs and the residual of SNP-Centered PCA contain a mixture of Individual and S×I effects. Such mixtures always occur for any dataset whenever PCA is applied to a combination of main and interaction effects . For thiSNP-Centered PCA has four possible outcomes. The oat example in Individual-Centered PCA also has four possible outcomes. Like their centered counterparts, PCs from SNP-Standardized and Individual-Standardized PCA contain a mixture of main and interaction effects, so these PCA variants also have four possible outcomes. Furthermore, SNP-Standardized PCA, unlike SNP-Centered PCA, cannot be used with VCF coding because the reference genome has a standard deviation of zero.Grand-Mean-Centered PCA produces a mixture of SNP main effects, Individual main effects, and S×I interaction effects in each PC. Therefore, the situation for Grand-Mean-Centered PCA is quite complex and undesirable: It has seven possibilities, not counting additional possibilities involving a PC with a substantial mixture of main and interaction effects. The supporting information includes the augmented ANOVA tables for these additional variants, using the oat data with SNP coding rare = 1  model, which combines ANOVA for the main effects with PCA for the multiplicative effects . AMMI anThe interpretive principles for an AMMI1 biplot are that displacements along the abscissa reflect differences in main effects, whereas displacements along the ordinate reflect differences in S×I interaction effects. An Individual and SNP with IPC1 scores of the same sign have a positive S×I interaction, whereas those with opposite signs have a negative S×I interaction, and an Individual (or SNP) with a score near zero has small interactions with all SNPs —at least for those interactions that are captured in the AMMI1 biplot. Like DC-PCA, because AMMI1 has a single kind of information in its abscissa and in its ordinate, an AMMI1 biplot has a single set of interpretive principles that applies to all datasets.further choice between confounded main and interaction effects from PCA variants other than DC-PCA, including SNP-Centered PCA, or else non-confounded main and interaction effects from AMMI. Given this further choice between confounded or non-confounded effects, we are not aware of any plausible arguments in favor of confounded effects. Accordingly, we recommend DC-PCA or AMMI1 to accommodate researchers’ diverse interests, but disfavor all of the other five variants of PCA because of confounding and the consequent multiple outcomes for how their biplots should be interpreted.A researcher may make a choice between not having, or else having, interest in main effects—which equates to the difference between DC-PCA and the other five variants of PCA considered here. However, the existence of AMMI provides a only approximate because every component has a mixture of main and interaction effects. Between Five additional PCA biplots for the oat dataset are shown in the supporting information, using SNP coding rare = 1 and the same color scheme as the main text . SNP-CenOryza sativa L.) [Glycine max (L.) Merr.) [Zea mays L.) [Solanum tuberosum L.) [The findings from ANOVA tables in this subsection reinforce the value of biplots that was stressed in the first subsection. The joint structure of SNPs-and-Individuals just is the S×I interactions, which only biplots can display, and S×I is commonly large. From tiva L.)  has 91.5) Merr.)  has 88.7mays L.)  has 65.2osum L.)  has 30.1In review, both SNP coding and PCA variant can affect which kind of information—Individual or SNP or S×I effects—dominates in each PC for all variants of PCA other than DC-PCA. An augmented ANOVA table quantifies the outcome for any dataset, and thereby facilitates proper interpretation of PCA results and graphs. Our default recommendation is DC-PCA because it uniquely applies PCA to a single source of variation, namely the S×I interactions that are often of primary biological interest, so its IPCs always contain this one kind of information and hence there is no need for its ANOVA table to be augmented. When main effects are of interest, we recommend an AMMI1 biplot because it uniquely displays main and interaction effects without confounding them.Because PCA monoplots of only Individuals provide some insight into population structure, they are deservedly popular in the literature. However, a monoplot cannot show interaction structure, which is often the dominant source of variation in a dataset and is usually the variation of principal interest. Production of a useful biplot is an unlikely prospect apart from understanding the consequences of SNP codings and PCA variants.Elucidating population structure helps to accomplish many other research purposes. For example, population structure is important for genome-wide association studies (GWAS) because the first few PCs can be used as covariates in a regression to address the problem of spurious associations produced by population structure . In turnThis article addresses just one statistical analysis, PCA, applied to just one kind of genomics data, SNPs. But this is just the tip of the iceberg: The genetics and genomics community uses many statistical analyses for many kinds of data. Indeed, PCA graphs of SNP data commonly occur in multi-panel figures that also show neighbor-joining trees and clustering by Bayesian or other methods, as well as geographical or other relevant information –34, and every PCA graph of SNP data requires and implements some choices of PCA graph, SNP coding, and PCA variant—whether or not these fateful choices are reported and justified. And having already collected SNP data and produced a PCA graph with whatever choices, very little additional effort would be required to repeat the PCA analysis with the three choices recommended here, and then to discern what additional population structure is displayed and discovered.Finally, perhaps the most significant and promising result from this investigation is the opportunity for researchers to ask new questions that have not been asked before: What population structure has been present in SNP datasets all along, which cannot be displayed by contemporary practices of PCA analysis, but can be elucidated by the recommended choices of PCA graph, SNP coding, and PCA variant? And how vital is that additional biological insight for achieving research objectives? Answers to these questions can emerge only from the accumulated experiences of many researchers working with many SNP datasets and diverse research objectives. What can be concluded already, however, is that necessarily and unavoidably, Three principal recommendations emerge from this investigation into PCA analysis of SNP data. (1) Use biplots, not monoplots, since only they can display S×I interaction information, that is, the joint structure of SNPs-and-Individuals. (2) Use the SNP coding 1 for the rare allele and 0 for the common allele. (3) Use the PCA variant DC-PCA if only S×I interactions are of interest, as is often the case; otherwise, use AMMI1 if main effects are also of interest. Additionally, report which SNP coding and PCA variant were selected, and ideally also provide reasons for those particular choices, so that readers can interpret PCA results properly and reproduce PCA analyses reliably. Finally, if the recommended DC-PCA (or AMMI1) is not used, then provide an augmented ANOVA table in order to quantify the amount of main and interaction effects in each PC. These conclusions should be understood as useful default recommendations because some dataset properties or research purposes may constitute exceptions, such as a focused research interest in only the Individuals that calls for publication of a monoplot—although it may still be worthwhile to produce the biplot and inspect it carefully before making a final decision about which graph to publish. An understanding of the consequences of choices between two PCA graphs, three SNP codings, and six PCA variants is an asset for elucidating population structure.Drc is the datum for row r and column c, μr is the mean for row r, μc is the mean for column c, μ is the grand mean, is λn the singular value for component n and γrn is the eigenvector value for row r and component n, δcn is the eigenvector value for column c and component n, with both eigenvectors scaled as unit vectors, and ρrc is the residual for row r and column c when the number of components used is fewer than the full model . The singular values and eigenvectors are obtained by SVD, as explained in the appendix.A convenient form of the equation for DC-PCA is:λ, and to call these values “PCA scores.” The PCA literature does not have consistent terminology since sometimes the results for rows  are called “PCA scores” whereas results for columns (or SNPs) are called “PCA loadings.” Because PCA is symmetric regarding matrix rows and columns, as the above equation makes evident, we prefer to use the single and consistent terminology of PCA scores. However, other contexts may call for a different approach, such as a PCA monoplot of Individuals, for which multiplication by the singular value is preferable because this optimizes the two-dimensional approximation of distances between Individuals. For a comprehensive explanation of ways to handle the singular value, see Malik and Piepho [The convention adopted here is to multiply eigenvector values by the square root of the singular value to obtain d Piepho .not need to use the same scale.The axes in PCA graphs are often scaled to obtain a convenient shape, but actually the axes should have the same scale for many reasons . Howeverλnγrnδcn are products of the two scores both scores leaves their products unchanged. Because of arbitrary polarity, different software may produce PCA analyses for a given dataset with opposite polarity for some or all components. This has two implications for producing and interpreting PCA graphs, including biplots. First and most important, polarity reversal leaves all distances between points unchanged, so mathematically and geometrically, it is absolutely inconsequential. Indeed, reversing the polarity of an ordinate amounts to flipping a graph over from left to right, or reversing the polarity of an abscissa amounts to flipping a graph over from top to bottom, and reversing both axes amounts to rotating a graph by 180°—nothing changes. Second, especially when several PCA graphs of the same or related data are being compared with each other, adopting a consistent orientation constitutes good pedagogy, making it easier for readers to perceive the salient differences between graphs without distraction from arbitrary orientations.The solution to SVD is unique, up to simultaneous sign change of both eigenvectors for a given component. This is evident from the above PCA equation because the terms in the equation The PCA arch or horseshoe distortion is important because it generates both problems and opportunities as explained in the Results section, and also because it occurs in the PC1-PC2 plane, which is what researchers show most often in PCA graphs. But this distortion, which amounts to a quadratic representation of PC1 in PC2, is only the first of a polynomial sequence. Using simulated genomics data, graphs of PC1 through PC4 on the abscissa and a one-dimensional habitat or environmental gradient on the ordinate show a sequence of linear, quadratic, cubic, and quartic polynomials . The samThe following principles for interpreting biplots pertain specifically to DC-PCA. For two points of the same kind , nearby points have similar interactions, whereas distant points have dissimilar interactions. For two points of different kinds , Individuals in a given direction from the origin have positive interactions for SNPs in that same direction, Individuals in a given direction have negative interactions for SNPs in the opposite direction, and Individuals in a given direction have small interactions for SNPs at right angles. Accordingly, interactions are large when an Individual and SNP are both far from the origin (but not at right angles), whereas an Individual near the origin has small interactions with all SNPs, and likewise a SNP near the origin has small interactions with all Individuals. More exactly, these interpretive principles address the interaction structure captured in the IPC1-IPC2 plane of a biplot.The percentage of variation captured by each PC is often included in the axis labels of PCA graphs. In general this information is worth including, but there are two qualifications. First, these percentages need to be interpreted relative to the size of the data matrix because large datasets can capture a small percentage and yet still be effective. For example, for a large dataset with over 107,000 SNPs and over 6,000 persons, the first two components capture only 0.3693% and 0.117% of the variation, and yet the PCA graph shows clear structure . ContraThe objection may be raised that PCA biplots would be impractical for datasets with many thousands of SNPs, making graphs unworkably cluttered. In fact, high-density PCA graphs appear in the literature routinely, such as Fig 4 in  showing Enormous SNP datasets are becoming increasingly common, and fast PCA algorithms can readily handle large-scale genome-wide data. The remarkably efficient software FastPCA computes the top several PCs with time and memory costs that are linear in the number of matrix entries . The sofThe 125 articles applying PCA analysis to SNP data were taken from the literature more or less at random, with some emphasis on agricultural crop species and on researchers at Cornell University. They span many species and many journals. This survey is included in the supporting information .The oat dataset supplied by Kathy Esvelt Klos is included in two formats: SNP coding mixed is the data as received, except that the original coding of 1 and 2 was shifted to 0 and 1; and SNP coding rare = 1, which required polarity reversal for 772 of the 1341 SNPs  Text.Our R code for comparing six PCA variants and correspondence analysis (CA) is included in the supporting information . From thThis appendix concerns which variants of PCA are, or else are not, immune to changes in SNP coding as regards PCA monoplots of Individuals, where “Individuals” is a generic term for samples such as persons or cultivars. The main text already showed in Y be the p × n SNP data matrix with SNPs in p rows and Individuals in n columns. Without loss of generality, assume that p≥n. The matrix Y may be SNP-Centered as follows: In is the n-dimensional identity matrix and 1n is an n-vector of ones. Let YC = USVT be a singular value decomposition of YC, where U is a p × n orthonormal matrix of left singular vectors holding the row scores, V is an n × n orthogonal matrix right singular vector holding the column scores, and S is a diagonal matrix of order n holding the ordered singular values. From the orthonormality of U we have UTU = In and from the orthogonality of V we have VTV = VVT = In.First, consider SNP-Centered PCA. Let r-th SNP is changed by swapping 0s and 1s in this r-th row of Y, this operation can be written as P is a diagonal matrix of order p with P{r,r} = 1 if the polarity of the r-th SNP is unchanged and P{r,r} = −1 if the polarity is changed. It is important to note that PPT = PTP = IP. Now V is the matrix of right singular vectors of both YC and If the polarity of the YS = D−1/2YC where D = diag (W) with D. Therefore, the above results for SNP-Centered data carry over fully to SNP-Standardized data.Second, consider SNP-Standardized PCA. For standardized data, PYC needs to be computed before the centering for Individuals. Thus, we need to compute P and YDC in the same way as the SVD of YC. This explains why Individual scores before and after changing the polarity of some SNPs are not perfectly correlated.Third and finally, consider Double-Centered PCA. DC-PCA is not immune to changes in SNP polarity as regards PCA monoplots for Individuals. Double-Centering pertains to the matrix However, when the SS for Individual main effects is small relative to that for SNP-by-Individual interaction effects, centering by Individual has little effect on the Individual scores based on SVD. The verdicts on immunity to SNP coding will be nearly the same for DC-PCA and SNP-Centered PCA when Individual main effects are small, and SNP-Centered PCA was already proven earlier in this appendix to be immune. Therefore, correlations for Individual scores between different SNP codings are expected to be very close to 1 or -1 for DC-PCA, but not exactly 1. A small SS for Individual main effects compared to that for SNP-by-Individual interaction effects is a necessary and sufficient condition for DC-PCA monoplots of Individuals to be virtually immune to changes in SNP coding.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 FigAs in the main text, to reduce clutter, all of these biplots use two panels, with oat lines on the left and SNPs on the right. The color scheme is the same as in (DOCX)Click here for additional data file.S1 TextIt has 635 oat lines and 1341 SNPs. The format of this dataset is that used by our R code.(TXT)Click here for additional data file.S2 TextThe format of this dataset is that used by our R code.(TXT)Click here for additional data file.S3 TextThis R code was produced for our own in-house research purposes, not as polished and public software, but it is made available here for the sake of transparency in research. It makes basic PCA biplots and ANOVA tables, but not the final figures and tables and the CA1 arranged matrix that appear in this publication.(TXT)Click here for additional data file.


Multi-locus genome wide association study was undertaken using a set of 320 diverse spring wheat accessions, which were each genotyped for 9,626 SNPs. The association panel was grown in replicated trials in four environments [two each in irrigated (IR) and rainfed (RF) environments], and phenotypic data were recorded for five traits including days to heading, days to maturity, plant height, thousand grain weight and grain yield. Forty-six significant marker-trait associations (MTAs) were identified for five traits. These included 20 MTAs in IR and 19 MTAs in RF environments; seven additional MTAs were common to both the environments. Five of these MTAs were co-localized with previously known QTL/MTAs and the remaining MTAs were novel and add to the existing knowledge. Three desirable haplotypes for agronomic traits, one for improvement in RF environment and two for improvement in IR environment were identified. Eighteen (18) promising candidate genes (CGs) involved in seven different biological activities were also identified. The expression profiles of four  of the 18 genes showed that they were induced by drought stress in the wheat seedlings. The MTAs, haplotypes and CG-based markers may be used in marker-assisted breeding for drought tolerance in wheat. Triticum aestivum L.) is an important staple crop that is widely grown in a range of environments and provides about 20% of the daily required protein and calories for 4.5 billion people world-over1. In recent years, the annual growth in wheat production has declined from 3% in the past to <0.7% in recent years, which is a cause of concern2. It has also been recognized that currently there is limited water supply for irrigation in 70% of the land area under wheat cultivation; this area with limited water supply may increase in future and would thus become the major cause of limiting global wheat production5. Therefore, development of drought-resilient and water-use efficient cultivars is a thrust area of research for wheat breeders world-over to meet the future demands of wheat production.Wheat  genomics/phenomics approaches are currently being utilized to study the genetics of yield and its contributing traits under water stress. Hopefully, the information generated from these newer approaches will prove useful for further improvement in wheat productivity not only under drought, but also under limited water availability7.The development of drought-resilient and high yielding wheat cultivars continues to be a challenge, also partly due to low heritability and large “genotype × environment” interactions associated with yield as a trait, particularly under droughtArabidopsis also, SWI2/SNF2 remodeling was found to maintain equilibrium between plant growth and drought stress tolerance8. Transcription factors like NAC, DREB, WRKY, MYB, bZIP have also been reported to play a major role in drought stress tolerance9. Similarly, CDPK7, CIPK03 and CIPK12 genes in rice were reported to be involved in signal transduction and protein kinase activity to provide tolerance against drought stress11. Further, in brassica, the gene BnPIP1 involved in ion and osmotic homeostasis12 and the gene BnPtdIns-PLC2 was found to provide tolerance against drought though phospholipid metabolism10. A number of QTLs have also been reported for drought stress tolerance in several crops including rice13, barley14 and sorghum15.Genetic basis of drought tolerance has been studied in several crops. In 16. Interval mapping also allowed identification of stable QTLs, which showed expression across multiple environments21. Similarly, GWAS identified marker-trait associations (MTAs) for yield and its component traits30. However, limited efforts have been made to study the genetics of grain yield and its components under drought conditions experienced by wheat crop in Indian sub-continent. Also, only few studies involving multi-locus and multi-trait GWA analysis have been conducted33; more such studies need to be undertaken to overcome the limitations of routine single locus single trait GWAS.A number of studies involving QTL analysis for different agronomic traits including grain yield and its component traits under favourable and stress  environments were conducted in the recent past. These involved interval mapping and genome wide association studies (GWAS) in hexaploid and durum wheatThe present study was conducted to identify MTAs involving yield and related traits using LD-based multi-locus GWA mapping using GBS (genotyping by sequencing)-derived SNP markers in a large association panel consisting of 320 spring wheat accessions. The Wheat association mapping panel grown under different water regimes [i.e. irrigated (IR) and rainfed (RF] during growing seasons (2011–12 and 2012–13) and phenotypic data were collected for five different traits. Post-GWAS haplotype analysis was also undertaken to study the joint effect of associated SNPs and to identify desirable genotypes. Candidate genes (CGs) underlying some of the MTAs were identified and annotated through expression analysis. The results from the present study should prove useful for genetic improvement of yield and yield contributing traits via marker-assisted breeding to enhance productivity under limited water availability.A wide range of variability for each of the five traits was available in the WAM panel in all the four environments including both locations (Meerut and Powerkheda) each with two environmental conditions (IR and RF); the data is summarised in Supplementary Table Pearson’s correlation coefficient analysis revealed that all the five traits were significantly correlated among themselves in IR as well as RF environments . Maximum genetic diversity was observed for sub-genome B (0.33) followed by sub-genome A (0.30), and sub-genome D (0.22) , 46 significant MTAs were identified for five traits  in four different environments; 39 (85%) MTAs were detected in individual environment only; the remaining 7 15%) occurred in two or more environments. MTAs were distributed on 18 wheat chromosomes excluding chromosomes 1D, 3D, and 7D , each with varying number of haplotypes for different traits involving four chromosomes  were identified. Each haplotype was associated with two to four traits out of five traits considered during present study. The number of SNPs in individual haplotypes ranged from two to five. The number of observed haplotypes in individual GRs ranged from 4  to 14 (GR5). Univariate general linear model (GLM) suggested that there were significant differences for associated phenotypes among haplotypes  was solely identified in RF environment and was associated with TGW and DTH. The GR2 (four haplotypes associated with PH and DTM) and GR5  were uniquely identified in the IR environment. GR1 and GR4 were associated with DTH and DTM; and these were identified in both RF and IR environments. In each of the five GRs, most desirable haplotypes were also identified . In order to determine the effect of number of desirable alleles of associated SNPs on phenotype, joint effect was estimated using linear regression. In case of DTH, DTM and PH, SNP alleles that lower the trait value were considered as desirable; however, in case of TGW and GY, SNP alleles that increases the trait value were considered as desirable. Interestingly, in all the cases, significant joint effects were observed except in case of PH_E4. For example, three SNPs  were associated with DTH under E1; significant difference were observed for DTH among genotypes with one, two, three and without any desirable alleles , contrasting genotypes were selected for each of the five traits under different environmental conditions  wheat CGs . This also provided further evidence of their potential involvement in the trait phenotype in different wheat developmental stages and tissues, and under drought stress condition .During GWAS rare variants  are mostly ignored due to statistical reasons. However, in order to examine the importance of rare variants in determining phenotypic variation in the present study, we selected 99 SNPs with MAF ranging from 0.03 to 0.04, and tested each individual SNP for association with GYPP in all the four environments using “t test”. Total seven SNPs were found associated with GYPP in E1 and E2 environments. These included two SNPs each uniquely identified in each of the E1 and E2 and three SNPs that were found in both the E1 and E2 environments  for each of the five traits under IR/RF environments suggested that the panel was suitable for a study of the genetics of quantitative traits like yield. Moreover, genetic diversity and PIC (based on marker data) also suggested that WAM panel is highly diverse. Significant and strong correlation of four traits  with GY under IR and RF environments  gene bank] under different water regimes in different locations of India in order to conduct GWAS. This diverse panel was never utilized earlier for a study of the genetics of drought tolerance using GWAS, although it was utilized for conducting GWAS for other traits like yield related trait41. Being a multi-locus model, confounding arising due to population structure may also be corrected by including kinship (K-model) and principal components (Q-model) in association test model. During present study, appropriateness of multi-locus association model was confirmed by solid lines in quantile-quantile (Q-Q) plots  for GWAS to overcome the limitations arising due to single locus GWAS44. Out of above co-localized QTL/MTAs, a QTL Qyld.csdh.7AL was associated with GY under RF environments42; the other three co-localized MTAs were earlier identified using GWAS. For instance, a genomic region between 148.43–161.31 cM (wsnp_CAP7_c1321_664478-IACX7848) was associated with GY under DS28. The other two co-localized genomic region between 150.54–178.42 cM (wsnp_Ex_c11047_17915103-wsnp_Ku_c8437_14341371) and at 156.23 cM  were associated with TGW44. Another MTA associated with TGW and located on chromosome 6A at 88.9 cM was co-localized with a QTL for TGW (QTkw.aww.6A) associated with wmc0256A mapped at 90 cM45. Similarly, the other important MTA associated with TGW identified in RF environment during the present study and located on chromosome 2A at 150.26 cM was co-localized with an MTA for GY under DS located at 149 cM28. The above three genomic regions  associated with GY under drought stress identified during the present study could be subjected to fine mapping and CG identification, so that diagnostic molecular markers can be developed for deployment in breeding programs, particularly those targeting drought prone regions.The 46 MTAs identified in this study were also compared to MTAs/QTL identified in earlier important studies using chromosome position, where the mapping population/germplasm/association panel were phenotyped under different water regimes Table . Five MT46. Gene involved in drought tolerance also found to have homologous copies, and majority of them showed expression partitioning under stress condition to provide better adaptability and wide distribution of wheat46.It is well known that wheat genome is allopolyploid with three sub-genomes ; and thus, most of wheat genes (including CGs identified in present study) have three homoeologous copies one on each sub genomes . Polyploidization may provide sub-functionalization or neo-functionalization against stress to plant47. Haplotype analysis using multiple significant SNPs was recently reported in cotton49 and foxtail millet50 and desirable haplotypes impacting multiple traits were identified. Such haplotypes may prove useful in improvement of multiple associated traits simultaneously. During the present study, the haplotypes H3 (GR3) was found under rainfed environment only and this haplotype was associated with higher TGW and early maturity. Grain weight is a highly heritable trait and has high phenotypic stability51 and early maturity under rainfed environment allows the crop to avoid the ever-depleting soil moisture during the crop growth. Therefore, haplotype H3 may be exploited in MAS for breeding for water stress tolerant wheat genotypes. Under IR environments, the haplotypes H3 (GR4) and H8 (GR5) showed association with early maturity, reduced plant height, higher TGW and GYPP. These two haplotypes may be exploited for breeding high yielding early maturing wheat varieties for IR environments.Further, in comparison to the single SNPs, haplotypes involving multiple SNPs may be more useful in plant breeding53. A wheat TPP (T6P) was also found to be associated with TGW in bread wheat54. The second gene TraesCS5B02G193200 encodes APETALA2/Ethylene-responsive factor (AP2/ERF), which is a transcription factor. Genes belonging to this family of TFs are mainly plant-specific TFs and are known to be involved in regulation of tolerance to several abiotic stresses including DS56. Another important CG TraesCS5A02G401800 encodes DNA-binding One Zinc Finger (Dof) TF; this family of TFs contains a zinc finger domain, and plays an important role in imparting tolerance against DS in higher plants57. DoF TF was initially reported in maize (ZmDOF1), where it plays a major role in light-regulated gene expression57. In wheat, Dof1 has been shown to be involved in carbon metabolism by increasing the regulation of the C4 pathway58. Another wheat Dof gene WPBF has been reported to be involved in plant growth and development59. Recently in potato (Solanum tuberosum L.), a Dof gene (PGSC0003DMG400019528) was reported to be upregulated in response to 2 h DS60. The fourth gene, TraesCS2A02G547600 encodes Gibberellin-dioxygenases (GAox) that are involved in the biosynthesis of bioactive gibberellins (GAs). Abundance of GA regulates responses to environmental stress including DS61. GAox genes are also indirectly involved in DS regulation via GA biosynthesis61. Also, the role of these genes in drought tolerance in wheat is supported by their up-regulation in wheat seedling under DS , TraesCS5B02G193200 (AP2/ERF TF), TraesCS5A02G401800 (Dof TF), TraesCS2A02G547600 (GAox)]: Following are the reasons for the high level of confidence: (i) each of these genes exhibited a significantly higher expression under DS; (ii) MTAs linked with these CGs were identified exclusively in RF environments  that were associated with GY; these were eliminated during GWAS due to MAF. The minor allele of one (SNP_6794) of the seven SNPs was associated with significantly higher GYPP under IR environment. Similar to the present study, earlier also, some rare alleles were also found to be desirable for important agronomic traits in wheat63. Further, desirable alleles of associated SNPs, desirable haplotypes and CG-based markers together may prove useful in the breeding for improved wheat varieties; the CGs may also be validated using functional genomics approaches and CG-based association mapping. Significant joint effect of associated SNPs suggested that corresponding traits can be improved substantially through pyramiding of significant SNP alleles. Contrasting genotypes identified during the present study may serve as a promising material to develop mapping populations for further genetic dissection of the trait; and favourable genotypes may serve as donor in a breeding program.In summary, MTAs identified during present study may be further validated using joint linkage and association mapping (JLAM) or post-GWASThe wheat association mapping (WAM) panel for GWAS consisting of 320 diverse spring wheat genotypes was assembled at the International Maize and Wheat Improvement Center (CIMMYT) (details of 320 genotypes are available in Supplementary Table Data on each of 320 genotypes were recorded for the following five traits: (i) days to heading (DTH), recorded as number of days from the date of planting until the emergence of spikes in ~75% of plants in a plot; (ii) days to maturity (DTM), recorded as the number of days from the date of planting until physiological maturity achieved in ~75% of the spikes in a plot, (iii) plant height (PH), recorded at maturity as the mean height (from ground to tip of the main spike) of randomly selected five plants, (iv) thousand grain weight (TGW), recorded as average weight (g) of five random samples of 1000 grains each; (v) grain yield per plot (GYPP), recorded as grain weight of plot.64. Genotyping was undertaken using GBS developed by DArT Pty. Ltd., Yarralumla, Australia. The detailed methodology is described elsewhere26.Genomic DNA was extracted from fresh leaves collected from five plants per line using a modified CTAB method65 and PIC66 were calculated at chromosome, sub-genome and whole genome levels. Model-based cluster analysis was performed using 42 unlinked SNPs, one from each of the 42 chromosome arms, to infer population structure in the dataset. The software STRUCTURE version 2.267 was used for this purpose. The number of presumed sub-populations (K) was set from 2 to 20, and the process was repeated three times. For each run, burn-in and MCMC iterations were set to 50,000 and 100,000, respectively, and a model “without admixture and correlated allele frequencies” was used. The number of sub-populations was determined following delta K (ΔK) method68. Whole genome as well as chromosome wise LD was conducted as a part of another study using TASSEL 5, and decay distance was calculated using R software27.Using mapped SNPs , diversity analysis was conducted, and two diversity parameters, namely gene diversity40. This method is believed to be highly efficient in computation and also eliminates confounding issues arising due to population structure, kinship, multiple testing correction, etc. This method utilizes both Fixed Effect Model (FEM) and a Random Effect Model (REM), iteratively. REM tests estimate pseudo-quantitative trait nucleotides (QTNs), FEM tests marker using pseudo-QTNs as covariates. For GWAS, SNPs having <30% missing data and >5% minor allele frequency were utilized. Principle component analysis (PCA) was conducted using TASSEL 5.0, and first three components were incorporated as covariate in association test model. Bonferroni-corrected P-value threshold was set as 0.01  and SNP with −log (p) > 6.0 declared as significant MTA. Q-Q plots generated through FarmCPU were used to examine model fitting (account for population structure). Haplotypes were determined on the basis of interval distance between associated SNPs. Significant SNPs present within LD range (10 cM) were considered as one haplotype. Univariate GLM was used to determine significance difference for trait among haplotypes.During the present study we used a multi locus GWAS model named Fixed and random model Circulating Probability Unification (FarmCPU)vice-versa. All positive and negative allelic values were summed to estimate the breeding value of each genotype. For each trait, contrasting phenotypes were selected with minimum and maximum breeding value.Joint effects were estimated, when more than two SNPs were associated with the same trait. To determine joint effect, linear regression was performed using number of desirable SNP alleles for traits (independent variable) and corresponding trait values of the genotypes that contained more than one desirable SNP alleles (dependent variable). The breeding value of each accession was calculated from the allelic effects of all significant SNPs. The allelic effect was calculated by taking the difference in trait value between genotypes with contrasting alleles. The breeding value for each genotype was estimated, by using absolute value of the allelic effect of each significant SNP taken as a negative value if a genotype, had SNP allele that decreases the trait value and 69 available on the Ensemble database (http://www.ensembl.org/info/docs/tools/vep/index.html). High-confidence annotated genes were retrieved from a 1000 kb window for each identified MTA. The GO annotation information of these CGs were extracted from the IWGSC website (http://www.wheatgenome.org/). For annotated CGs, gene expression analysis was conducted utilizing wheat expression database70 hosted at http://wheatexpression.com. Transcripts per kilobase millions (TPM) values for every CG were downloaded. Log transformed (Log2X) value was used to generate a heatmap using online tool ClustVis71.CGs for individual MTAs were identified by aligning the associated GBS sequences to wheat genome assembly IWGSC1.072. All the samples were analyzed with two biological and three technical replicates. Student’s t-test was applied to see the significant differences between the control and treatments.Seedlings of wheat cv. ‘C306’ representing the well-known drought tolerant genotype were analysed for tolerance against DS. For applying water stress, 7 days old seedlings were transferred to modified Hoagland’s solution (+20% PEG 8000). The leaf samples of control and treated seedlings were harvested at 0, 1 and 6 h after water stress treatment and immediately frozen in liquid nitrogen and stored at −80 °C. Total RNA was isolated using the TRIzol Reagent (Ambion) as per the manufacturer’s specifications. First-strand cDNA was synthesized from DNaseI-treated total RNA using RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) according to the manufacturer’s instructions. qRT-PCR was performed in step-plus Real-Time PCR System (Applied Biosystems) using the SYBR Green (Applied Biosystem). Wheat β-actin gene was used as an endogenous control. The primers of CGs used for qRT-PCR are listed in Supplementary Table Supplementary Information


Gentiana tongolensis (Gentianaceae). The plastid genome of G. tongolensis is 145,757 bp in size and is missing parts of petD gene when compared with other Gentiana species. A total of 112 single nucleotide polymorphisms (SNPs) and 31 indels with frequencies of more than 2% were detected in the plastid genome, and most were located in protein coding regions. Most sites with SNP frequencies of more than 10% were located in six genes in the LSC region. After verification via cloning and Sanger sequencing at three loci, heteroplasmy was identified in different individuals. The cause of heteroplasmy at the nucleotide level in plastome of G. tongolensis is unclear from the present data, although biparental plastid inheritance and transfer of plastid DNA seem to be most likely. This study implies that botanists should reconsider the heredity and evolution of chloroplasts and be cautious with using chloroplasts as genetic markers, especially in Gentiana.Chloroplasts are typically inherited from the female parent and are haploid in most angiosperms, but rare intra-individual heteroplasmy in plastid genomes has been reported in plants. Here, we report an example of plastome heteroplasmy and its characteristics in  Chloroplasts transform solar energy into chemical energy stored in the form of carbohydrates, supporting life on Earth. In angiosperms, plastomes are generally highly conserved with a quadripartite structure that contains a pair of inverted repeats (IRs) that separate the remaining regions into one small single-copy region (SSC) and one large single-copy region (LSC) . PlastomChloroplast data are ubiquitous in plant systematics research due to its uniparental inheritance, haploid nature, highly conserved structure, and slower evolutionary rate of change compared to nuclear genomes . TherefoGentiana contains 15 sections and 362 species  Satake & Toyokuni ex Toyokuni. In addition to these plastomes, a number of plastid fragments have been sequenced and used to reconstruct phylogenetic relationships in Gentiana and Gentianaceae . The species was identified by Dr. Peng-Cheng Fu, and its voucher specimens (No. Fu2016162) were deposited in the herbarium of the College of Life Science, Luoyang Normal University. Each sample was collected from a single plant and fast dried by silica gel. One sample (Fu2016162.05) was used for genome sequencing in this study. We performed total genomic DNA isolation, DNA fragmentation, and sequencing library construction following the process described by Gentiana, we obtained approximately seven Gb of raw data that was deposited in the Genome Sequence Archive of the BIG Data Center (Accession No. CRA001588). The raw reads were processed in a relatively standard manner in Trimmomatic 0.38 (G. tongolensis was deposited in GenBank (Accession No. MK251985).Five individuals of tic 0.38 , beginnitic 0.38 . The plaGentiana species that represent the different groups reported , and genome comparisons were performed to identify the differences among taxa using Geneious Basic 5.6.4  with no SNPs were also chosen as controls . The PCR cycling profile included an initial step of 5 min at 95 °C, followed by 35 cycles of denaturation at 95 °C for 50 s, 50 s of annealing at 54 °C, and 30 s at 72 °C, and a final extension at 72 °C for 7 min. PCR products were purified by an eZNA DNA Gel Extraction Kit . The concentration of purified PCR products was measured by a NanoDrop 2000c Spectrophotometer , and then the PCR products were ligated into a pMD19-T vector  and transformed into Trans5α Chemically Competent Cells . Positive clones were tested in a 20 μL PCR reaction volume containing 10–100 ng template DNA, 1× PCR Buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.2 mM of M13F/R, and 1 unit of Taq DNA polymerase . PCR was performed under the following program: an initial step of 5 min at 95 °C followed by 20 cycles of 30 s at 95 °C, 1 min at 53 °C, and 30 s at 72 °C, followed by a final extension step at 72 °C for 7 min. The positive clones were sequenced using the Sanger method with M13 universal primer.For verifying the SNPs in the plastome, three regions  located in the LSC with high SNP frequencies were chosen . GTcpSNPcontrols . GTcpSNPcontrols , and theMK251953–MK251984, MN602314–MN602324). Phylogenetic relationships among haplotypes were reconstructed in PhyML 3.0  had a frequency more than 10%, and all were located in the LSC region. A total of 22 SNPs or indels were detected in the SSC region, and none in the IR regions. The top 20 SNPs or indels with highest frequencies are presented in atpA, psbB, rpoB, rpoC1, trnH, psbC, rpoA and psaA. There were 22 C→T plus 15 G→A transitions, and 16 T→C plus 23 A→G transitions in the plastome of G. tongolensis, and thus no excess of mutations was attributable to 5mC deamination which is a clue as to whether the sequence is nucleus-derived or plastid-derived  or to biological reasons. DNA damage during sample storage with silica gel maybe one potential artificial factor leading to the appearance of SNPs in Illumina read data. However, the agarose electrophoresis before library construction did not show any signs of degradation. Besides, DNA damage that occurred due to biological processes would lead to random mutations whose frequencies should be very low. However, our data showed that SNPs were mainly located in genes in the LSC region, and the frequencies of a number of SNPs were high. On the other hand, compromise in plastid DNA repair is a potential reason that cannot be excluded by our present data. Because the rate with which we have detected variants among clones within a verified individual was well above expected somatic mutation levels, it seems unlikely that this is a cause for the heteroplasmy in G. tongolensis.Polymorphisms are common in plastid genomes at the level of population or species, but intra-individual heteroplasmy has rarely been reported (rpl22, rpl32, mC deamination, detected by C→T plus G→A transitions vs. T→C plus A→G transitions, is a clue as to whether the sequence is nucleus-derived or plastid-derived (mC deamination were observed in this study, it seems that we may not attribute the individual mutations to the transfer of plastid DNA to the nucleus in G. tongolensis. However, more evidence is necessary to uncover the reason for the heteroplasmy.The transfer of plastid DNA, ranging from individual genes (e.g., -derived . BecauseGentiana. Among the seed plants, chloroplasts can be inherited strictly from the female parent, strictly from the male parent, or biparentally (Medicago (Arabidopsis (Coreopsis grandiflora (Chamaecyparis obtusa (Medicago truncatula (Passiflora (G. tongolensis plastome may be observed in functionally paralogous copies within an individual caused by biparental plastid inheritance. Consistent with this idea is the fact that all high-frequency SNPs detected in the plastome of G. tongolensis were located in coding regions rather than intergenic regions.These findings raise fundamental questions about chloroplast inheritance in rentally . Most flMedicago  and Arabbidopsis . Howeverndiflora , Chamaecs obtusa , Medicaguncatula , and Capuncatula . Paternassiflora . Biparenssiflora , and it ssiflora . TherefoGentiana show that the plastome structure is conserved, except for the ndh gene being lost in section Kudoa (G. tongolensis, we detected partial loss of petD, which encodes subunit IV of the cytochrome b6/f complex (psbB, trnH and rpoB genes have distinctly high frequencies of SNPs within an individual in this study. Intra-genomic synonymous rate variations resulting from localized variations in mutation rate can lead to the distribution of SNPs among the coding region (psbB, trnH and rpoB were not mutational hotspots in Gentiana (G. tongolensis may support this hypothesis, but more evidence is required.Although the plastome is conserved in flowering plants, structural variations such as gene loss, IRs, and rearrangements are common . Recent on Kudoa . In the  complex . We alsog region . HoweverGentiana . AlthougGentiana , the posGentiana . One hypGentiana . The casPassiflora (Picea (G. tongolensis, the chloroplast phylogeny may be problematic for phylogenetic inference and not accurately represent ancestor–descendant relationships, especially in Gentiana. Besides phylogenetic studies, intra-individual chloroplast heterozygosity can also affect population studies that are sometimes based on the inference of genetic diversity within and between populations. Therefore, before chloroplast heteroplasmy is further studied in more taxa, additional attention should be paid when chloroplast sequences are used.Plastid DNA is a ubiquitous molecular marker in plant systematics and population genetics research, such as DNA barcoding  and planssiflora  and Picea (Picea . Since wG. tongolensis plastome, but the underlying cause of it is unclear from the present data. Biparental plastid inheritance and transfer of plastid DNA seemed to be most likely, and to better address this question, it will be necessary to collect more data from this species. Hopefully, this study will encourage botanists to reconsider the heredity and evolution of chloroplasts and exercise caution when using them as genetic markers, especially in Gentiana.In summary, we reported a case of heteroplasmy and its characteristics in the 10.7717/peerj.8025/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.8025/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.8025/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.8025/supp-4Supplemental Information 4Dots represent the first nucleotide in each column. Dashes represent deletion. Results of the three individuals are represented in purple, black and red color, respectively. The individual used for Illumina sequencing is the red one.Click here for additional data file.10.7717/peerj.8025/supp-5Supplemental Information 5Dots represent the first nucleotide in each column. Dashes represent deletion. Results of the five individuals are represented in blue, purple, black, green and red color, respectively. The individual used for Illumina sequencing is the red one. The nucleotide variation that appears in both Illumina and Sanger sequencing is bold.Click here for additional data file.10.7717/peerj.8025/supp-6Supplemental Information 6Dots represent the first nucleotide in each column. Dashes represent deletion. The individual is the one used for Illumina sequencing. The nucleotide variation that appears in both Illumina and Sanger sequencing is bold.Click here for additional data file.