WUR) single nucleotide polymorphism (SNP) can be used as a genetic marker for host response to porcine reproductive and respiratory syndrome (PRRS), PRRS vaccination, and co-infection with porcine circovirus type 2b (PCV2b). Objectives of this study were to identify genomic regions other than WUR associated with host response to PRRS vaccination and PRRSV/PCV2b co-infection and regions with a different effect on host response to co-infection, depending on previous vaccination for PRRS.The WUR1000125 (n = 171 AA and 198 AB pigs) where B is the dominant and favorable allele. Half of the pigs were vaccinated for PRRS and 4 weeks later, all pigs were co-infected with PRRS virus and PCV2b. Average daily gain (ADG) and viral load (VL) were quantified post vaccination (Post Vx) and post co-infection (Post Co-X). Single-SNP genome-wide association analyses were then conducted to identify genomic regions associated with response to vaccination and co-infection.Commercial crossbred nursery pigs were pre-selected for WUR genotype (−log10P ≥ 5.5), regardless of prior vaccination for PRRS. Several SNPs were also significantly associated with ADG Post Vx and Post Co-X. SNPs with a different effect on ADG, depending on prior vaccination for PRRS, were identified Post Vx (−log10P = 5.6) and Post Co-X (−log10P = 5.5). No SNPs were significantly associated with vaccination VL (−log10P ≤ 4.7) or PRRS VL (−log10P ≤ 4.3). Genes near SNPs associated with vaccination VL, PRRS VL, and PCV2b VL were enriched (P ≤ 0.01) for immune-related pathways and genes near SNPs associated with ADG were enriched for metabolism pathways (P ≤ 0.04). SNPs associated with vaccination VL, PRRS VL, and PCV2b VL showed overrepresentation of health QTL identified in previous studies and SNPs associated with ADG Post Vx of Non-Vx pigs showed overrepresentation of growth QTL.Multiple SNPs near the major histocompatibility complex were significantly associated with PCV2b VL  contains supplementary material, which is available to authorized users. GBP5), located on Sus scrofa chromosome (SSC) 4, was identified as a major gene for host response to porcine reproductive and respiratory syndrome (PRRS) ) and co-infection (0 to 42 dpi) periods on both vaccinated (Vx) and non-vaccinated (Non-Vx) pigs. Serum samples were collected on Vx pigs following vaccination (Post Vx) at −28, −24, −21, −17, −14, and −7 dpi and on both Vx and Non-Vx pigs following co-infection (Post Co-X) at 0, 4, 7, 11, 14, 21, 28, 35, and 42 dpi. Serum samples were used to quantify PRRSV and PCV2b viremia using real-time polymerase chain reaction according to Niederwerder et al. ijklm = is the observed phenotype; Trialj = fixed effect of the jth trial (1 or 2); WURk = fixed effect of WUR SNP genotype (AA or AB); SNP = fixed effect of the lth genotype ; βp = partial regression coefficient for the covariate weight at −28 dpi (WtVx) (P = 1), age at −28 dpi (VxAge) (P = 2), and PCV2b viremia at 0 dpi (P = 3); Animali = random animal genetic effect of the ith individual, with a variance-covariance structure proportional to the GRM with the assumption ~ P > 0.10).Including WUR genotype in the model as a fixed effect allowed for the identification of regions other than WUR associated with host response to PRRS vaccination, while simultaneously accounting for the effect of this marker. Level of PCV2b viremia at 0 dpi was fitted as a covariate because 37 (24 Non-Vx and 13 Vx) pigs had non-zero PCV2b viremia titers at 0 dpi, suggesting that they were exposed to PCV2b prior to entering the facility, likely from their mothers; all phenotypes were adjusted to PCV2_0 = 0 to model the situation that all pigs were negative for PCV2b at 0 dpi.main effect) and the interaction of SNP genotype with vaccination status (interaction effect):To identify the effect of vaccination on the effect of genomic regions for host response to co-infection, bivariate animal models were used to analyze ADG Post Vx, PRRS VL, PCV2b VL, and ADG Post Co-X of Vx versus Non-Vx groups as two separate traits, as described in Dunkelberger et al. . For eacModel ); bN(bV) = vector of solutions for fixed effects; WiN(WiV) = matrix of fixed genotype effects for the ith SNP genotype and giN(giV) = corresponding vector of solutions; αN(αV) = corresponding vector of solutions;lN(lV) = corresponding vector of solutions; pN(pV) = corresponding vector of solutions. The covariance matrix of random animal genetic, litter, pen, and residual effects was specified as follows:G represents the GRM for the Non-Vx(N) and Vx(V) individuals and I represents the identity matrix. For all analyses, covariances were allowed between traits for the animal genetic effect and litter effect. However, covariances were constrained to 0 for pen effects and residuals since Vx and Non-Vx pigs were allocated to different rooms and no pig had records for both traits.Model \documentprincomp function of R software [n < 369). The number of independent tests per chromosome segment was then summed to calculate the total number of independent tests across the genome. Unmapped SNPs were not included in this calculation. Bonferroni correction was then applied using the following equation:10p-value threshold was 5.4.Multiple test correction was performed to determine the appropriate significance threshold to interpret GWAS results. First, principal component analysis was used to determine the number of independent tests (i.e. SNPs) per chromosome as the number of principal components required to capture 99.5% of the variation , 12 usinsoftware . Results10 p-value thresholds  for the combined additive and dominance effect (2-df test) of each SNP were generated for each trait. Next, 1-megabase (Mb) windows were constructed for each SNP, which spanned 0.5 Mb on either side of each SNP. Then, for each SNP, Ensembl IDs (http://www.ensembl.org) [GO) Slim feature of the PANTHER software [http://www.pantherdb.org) [P-values for statistical overrepresentation of protein pathways were corrected by PANTHER software using the Bonferroni multiple testing correction method. The number of SNPs and genes corresponding to each SNP list for each trait are shown in Additional file Pathway analyses were conducted to identify protein pathways that were enriched for genes near SNPs associated with each trait according to the procedure described by Waide et al. . First, mbl.org)  were obtsoftware . Comparerdb.org) . A custoQTL Test, was also used to provide evidence for regions associated with each trait in this study. A master list of previously reported QTL for all traits in the pig genome was downloaded from the QTL database for pigs at animalgenome.org [. In the database, each QTL entry is assigned to one of the following trait categories: exterior, health, meat and carcass, production, and reproduction. In total, 16,032 QTL entries were downloaded. Filtering of the master list was then performed according to the following steps: duplicate QTL entries  were removed, QTL for the same trait from the same study with overlapping positions were concatenated, QTL spanning more than 5 Mb were removed, and all ADG, body weight, and growth-related QTL, which belong to the “production” category, were re-assigned to a new category entitled “growth”. After filtering, 9892 unique QTL entries remained.A second type of analysis, hereafter referred to as the nome.org . In the The filtered master QTL list was then used to construct a list of all QTL that mapped to each SNP. A QTL was mapped to a SNP if the QTL was either entirely contained within or entirely overlapped the 1-Mb SNP window. This list was used to determine the number of health and growth QTL that mapped to SNPs associated with VL and ADG traits, respectively. These lists were then used to assess overrepresentation of health or growth QTL mapping to SNPs for each list.I as the trait QTL  mapped to one of the three SNP lists for a trait , QTLT as the list of all QTL  mapped to one of the three SNP lists, and QTLG as the list of all QTL  throughout the genome, the null hypothesis (H0) for this test was that the ratio P1 = QTLI:QTLT was equal to the ratio P2 = QTLI:QTLG. The alternative hypothesis (Ha) was that P1 > P2.Using QTLP-values for the QTL Test were calculated as the probability of identifying QTLI or more trait QTL for a given SNP list under H0 using the binomial distribution, as follows:I; K = QTLT; and P = P2. Analyses were performed separately for health and growth QTL for each SNP list.SNP Test, was also performed to assess overrepresentation of health or growth QTL, but using slightly different information than the QTL Test. Using SNPI as the number of unique SNPs within a health or growth QTL (i.e. QTLI), SNPT as the number of SNPs in each SNP list , and SNPG the number of SNPs across the genome , the H0 for this test was that P3 = SNPI:SNPT was equal to P4 = SNPI:SNPG (H0: P3 = P4). The alternative hypothesis (Ha) was that P3 > P4. P-values for the SNP Test were also calculated using the above binomial distribution but using k = SNPI, K = SNPT, and P = P4.A third test, hereafter referred to as the -log10P = 4.65)  Fig. .Fig. 1Si−log10P ≥ 5.6) associated with ADG Post Vx for Vx pigs (Table −log10P ≥ 5.4) associated with ADG Post Vx of Non-Vx pigs , 11_53, and from 12_5 to 12_6 Fig.  were siggs Table  were sig−log10P ≥ 5.6) main effect on ADG Post Vx (Table −log10P = 5.6)  or Non-Vx (−log10P = 1.0) pigs separately  Fig. , and was.6) Fig. . Howeverely Fig. .Table 2S−log10P = 6.1) associated with PCV2b VL of Non-Vx pigs (Table −log10P ≤ 5.1) Fig. , PRRS VL4.3)Fig. , or PRRS.3) Fig. .Fig. 3GW−log10P ≥ 5.5) main effects on PCV2b VL (Table −log10P = 6.1), but not Vx pigs (−log10P = 4.8)   main or interaction effect on PRRS VL Post Co-X  Fig. . The int.4) Fig. . No SNPso-X Fig. .Fig. 4Ma−log10P ≥ 5.5) associated with ADG Post Co-X of Vx pigs (Table −log10P ≥ 5.4) associated with ADG Post Co-X of Non-Vx pigs  main effect on ADG Post Co-X (Table −log10P = 6.9), but not Non-Vx pigs (−log10P = 0.5)  associated with ADG Post Co-X for Non-Vx pigs, but not Vx pigs (−log10P = 1.0)  Fig. . Convers.0) Fig. .−log10P = 5.5) interaction effect of SNP genotype by VxStatus identified in this study was associated with ADG Post Co-X (Table −log10P = 5.2) or Non-Vx (−log10P = 0.5) pigs  with a significant  associated with genes near SNPs for each SNP list  are presented. These significant associations include several protein pathways that were significantly underrepresented, which are presented but will not be discussed. The number of mapped Ensembl IDs and the total number of Ensembl IDs (i.e. mapped and unmapped) are presented in Additional file For each trait, only GO terms that were significantly (GO terms enriched for genes near SNPs for host response to PRRS vaccination are presented in Table (Table ) or for genes near SNPs associated with ADG Post Vx for the main or interaction effects of SNP -γ, and primary metabolic process pathways were significantly enriched for genes near SNPs associated with the main effect of SNP for PCV2b VL  overrepresented for health QTL for T2 (Table P = 4.6E-7) overrepresented for health QTL for T3 (Table P = 7.9E-7) overrepresentation of health QTL for SNPs associated with PRRS VL of Vx pigs for T3  overrepresented for health QTL for T2 (Table P = 6.7E-12) and Non-Vx (P = 3.3E-18) pigs for T2  overrepresentation of health QTL (Table P = 6.7E-5 to 6.0E-4) and Non-Vx (P = 1.7E-9 to 4.0E-4) pigs  or Non-Vx (P ≥ 0.04) pigs  and T3 (P = 0.03) (Table Results indicated little evidence of overrepresentation of growth QTL for SNPs associated with ADG Post Vx of Vx P ≥ 0.5 or Non-3) Table .P ≥ 0.36) or Non-Vx (P ≥ 0.74) pigs  or interaction (P ≥ 0.12) effects of SNP by VxStatus  overrepresented for health QTL for T2 (Table P = 0.06) for significant overrepresentation of health QTL was also detected for T2.5, but not T3 (P = 0.26) (Table P = 0.88) for the SNP Test  overrepresented for health QTL  overrepresentation of health QTL  Table . For ana6) Table . An excest Table . SNPs asP = 0.01) and T2.5 (P = 7.1E-6)(Table P = 0.05) and T3 (P = 2.8E-6) (Table P = 2.0E-4) Table . SNPs as6) Table . For T2,P ≥ 0.11) and interaction effect (P ≥ 0.44) of SNP on ADG Post Co-X were not significantly overrepresented for any of the SNP lists (Table P ≥ 0.29) or Non-Vx (P ≥ 0.53) pigs Post Co-X  associated with host response to PRRS MLV vaccination and co-infection with PRRSV and PCV2b. Significant regions were detected for PCV2b VL, ADG Post Vx, and ADG Post Co-X and results from functional annotation analyses provided biological evidence that supported these statistically associated regions. Results of the functional annotation analyses also supported the many regions with small effects that were detected. Multiple SNPs on SSC7 were significantly associated with PCV2b VL, regardless of prior vaccination for PRRS. However, regions with a significantly different effect on ADG, depending on prior vaccination for PRRS, were detected for ADG Post Vx and Post Co-X. These findings indicate that multiple genomic regions have the potential to be used to select pigs for decreased PCV2b VL following PRRSV/PCV2b co-infection, regardless of prior vaccination for PRRS, but the same is not true for ADG.This study also introduced a novel approach of using previously-reported QTL to provide evidence for statistically-associated regions from GWAS. Other studies have assessed clustering of health QTL throughout the rice genome  and the WG) under PRRSV-only infection, respectively [GBP5 [NVSL) and KS2006-72109 (KS06) PRRSV isolates, but not WG following infection with KS06 [The WUR SNP, a genetic marker for a major QTL for PRRS on SSC4, was identified in a previous study. Results showed that WUR was associated with 15.7% and 11.2% of genetic variation in PRRS VL and weight gain  complex (7_23 to 7_31), one of the most gene-dense regions of the genome which is known to harbor many genes associated with the immune response [When Vx and Non-Vx pigs were analyzed separately, H3GA0020199, located at SSC7_20, was the only SNP significantly associated with PCV2b VL, and only in Non-Vx pigs. This SNP is located within the gene n humans . This SNresponse , 28.A SNP within the SLA was also associated with host response to PCV2b infection in a previous study. This SNP,  located at 7_28 (SLA Class III) was associated with host response to experimental infection with PCV2b in commercial crossbred pigs [There is also evidence of associations of SNPs within the SLA region with host response to PRRSV-infection from other studies, including experimental PRRSV-infection  and foll(−log10P = 4.25) associated with PRRS VL in Vx pigs for the current study.Despite these sizeable associations of the SLA region with PRRS antibody response, no SNPs within this region, or any other region of the genome, were significantly associated with vaccination VL or PRRS VL for the current study. In general, the lack of significant associations for vaccination VL and PRRS VL is consistent with the conclusion reported by Waide et al.  that genContrary to analysis of vaccination VL and PRRS VL, we identified multiple SNPs that were significantly associated with ADG Post Vx and Post Co-X. This finding conflicts with results reported by Waide et al.  who repoGRB2)-reltated adaptor protein 2 gene in pigs, also known as GRB2-related adaptor downstream of Shc (GADS). In humans, GADS is involved in the formation of the T cell receptor complex [Most genomic regions associated with ADG Post Vx in the current study were also associated with ADG in previous studies. However, for all but one of the identified regions, associations with ADG in other studies were in a disease-free environment. For example, the SSC15_129 region associated with ADG of Non-Vx pigs Post Vx, was also associated with ADG in non-challenged pigs by Rückert and Bennewitz . Regions complex  and has  complex . However(RNF125) gene, which is a negative regulator of the RIG-1 like receptor signaling pathway [(−log10P = 3.16) association of this SNP with the main effect of SNP genotype on PRRS VL was also detected.None of the SNPs that were significantly associated with ADG of Non-Vx pigs were also significantly associated with ADG of Vx pigs. However, several other SNPs had a significant effect on ADG Post Vx, regardless of vaccination status, which agrees with the high, positive (0.92 ± 0.92) genetic correlation identified between ADG of Vx and Non-Vx pigs Post Vx reported in our previous study using these same data . These S pathway . The RIG pathway . SuggestThe effect of ASGA0077518 (17_57) on ADG Post Vx depended on prior vaccination for PRRS, where the effect of this SNP on ADG was near genome-wise significance for Vx pigs, but not Non-Vx pigs. The nearest QTL for ADG reported in a previous study spans 17_64 to 17_66 . During Similar to analysis of ADG Post Vx, several SNPs were also significantly associated with ADG Post Co-X, including the only SNP that was previously associated with growth under disease challenged conditions in a separate study . This SNPCNXL2) gene. In humans, PCNXL2 has been associated with susceptibility to colorectal cancer [Other significant SNPs associated with ADG Post Co-X included a SNP (WU_10.2_15_140171163) located at 15_140. This same region was associated with ADG of non-challenged pigs in a previous study . MARC002l cancer .Similar to results for ADG Post Vx, none of the SNPs that were significantly associated with ADG of Non-Vx pigs were also significantly associated with ADG of Vx pigs Post Co-X. However, several other SNPs had a significant effect on ADG regardless of prior vaccination for PRRS, which agrees with our previous finding of a moderate to high 0.75 ± 0.37) genetic correlation between ADG of Vx and Non-Vx pigs 5 ± 0.37 .In addition to identifying genomic regions associated with host response to PRRS vaccination and PRRSV and PCV2b co-infection, another objective of this study was to provide biological evidence for regions identified from GWAS. PANTHER software was used to test for enrichment of genes near SNPs associated with each trait.Pathways enriched in genes near SNPs associated with vaccination VL included cell proliferation, cell movement, cell signaling, and cytokine signaling. Enrichment of these pathways is consistent with the literature regarding PRRS MLV vaccination response. Compared to infection with a field isolate of PRRSV, PRRS MLV vaccination is known to result in a delayed humoral and cell-mediated immune response . Cell-meConsistent with results reported by Waide et al. , the G-pImmune-related pathways were also enriched for genes near SNPs associated with PCV2b VL, including but not limited to: cellular defense response, cell-to-cell adhesion, and nucleobase metabolic processes for Vx pigs only, and the chromatin organization pathway for both Vx and Non-Vx pigs. This result agrees with the finding that several SNPs near the SLA region were significant for the main effects of SNP on PCV2b VL. It is also consistent with the high, positive genetic correlation between PCV2b VL of Vx and Non-Vx pigs (0.99 ± 0.94) reported for our previous analyses of these same data . Based oFor PCV2b VL, genes near SNPs associated with the main effect of SNP were enriched for primary metabolic process and response to IFN-γ. The “primary metabolic process” pathway was previously found to be associated with PRRS VL following infection with the KS06 and NVSL PRRSV isolates . EnrichmResults of protein pathways analyses showed enrichment of metabolism-related pathways for genes near SNPs associated with ADG of Vx and Non-Vx pigs Post Co-X. Lipid transport was also enriched for genes near SNPs associated with the interaction effect of SNP genotype by VxStatus on ADG Post Co-X. Metabolic processes are clearly associated with growth rate and such pathways were also enriched for WG following PRRSV-only infection in a previous study .For the protein pathway analyses, it is important to note that all analyses were performed using the 10.2 build of the swine genome. Therefore, repeating these analyses using the newer (11.1) build could change results for any regions previously misassembled and/or regions for which additional annotation information is now available.The QTL and SNP Tests proposed in this study were then used to provide another piece of evidence for regions identified from GWAS. A significant result for the QTL Test indicated that SNPs identified from GWAS mapped to significantly more unique trait QTL  reported in previous studies than expected by chance. The SNP Test was designed to answer a similar question, where the objective was to assess whether the proportion of SNPs within a SNP list that mapped to health or growth QTL was greater than expected by chance. In general, similar results were obtained for the QTL and SNP Tests for analyses of each trait.Results of the QTL Test for the main and interaction effects of SNP for PRRS and PCV2b VL were consistent with results of the SNP Test, except that for T3, the main effect of SNP was not significant for SNPs associated with PRRS VL for the SNP Test. When PRRS and PCV2b VL were analyzed separately by vaccination status, every SNP list showed significant overrepresentation of health QTL, except for T3 for PRRS VL of Non-Vx pigs. These results were consistent with those obtained for the SNP Test, except that none of the SNP lists were significant for analysis of PRRS VL of Non-Vx pigs. This was driven by the fact that, compared to PRRS VL of Vx pigs, similar numbers of SNPs were identified for each SNP list, but fewer SNPs mapped to health QTL for Non-Vx versus Vx pigs.10p-value thresholds were used to construct the SNP lists, thereby including SNPs with smaller effects on the trait of interest. Non-significant results for the main/interaction effects of SNP on PRRS VL and for the interaction effect for PCV2b VL indeed appear to be consistent with GWAS results for these traits, for which no SNPs reached genome-wise significance for these effects.Collectively, results of these tests indicate that regions associated with PRRSV and PCV2b VL showed significant overrepresentation for health QTL identified from previous studies, especially when Vx and Non-Vx pigs were analyzed separately. Results of the SNP Test show that mapping of SNPs to health QTL was non-random. Non-significant results obtained for the main/interaction effects of SNP likely reflect noise since relaxed –logFew significant results were obtained for the QTL Test for ADG. Only two SNP lists, both for the main effect of SNP on ADG Post Vx, showed a significant result. Overrepresentation of growth QTL was not detected for any of the SNP lists for SNPs associated with ADG Post Co-X, except for T3 for ADG of Non-Vx pigs. An additional SNP list (T2) showed significant overrepresentation of growth QTL for ADG of Non-Vx pigs Post Vx for the SNP Test. Possibly, significant results for the QTL and SNP Tests were obtained for Non-Vx pigs Post Vx only because this trait represents growth in a healthy environment, which is the same condition under which growth traits were measured for the majority of previously-identified growth QTL. Therefore, non-significant results for analysis of ADG of Vx pigs Post Vx or ADG Post Co-X might indicate the identification of novel QTL for ADG, and/or QTL specific to growth under challenge.Collectively, results of the QTL and SNP Tests for SNPs associated with ADG indicate significant overrepresentation of growth QTL for growth rate under non-challenged conditions, but not under challenged conditions. For growth under challenged conditions, significant results for the SNP Test also indicate non-random associations of SNPs mapped to growth QTL.The QTL Test and SNP Test used in this study are novel approaches used to provide evidence for statistically associated regions from GWAS. Although these tests have shown to provide valuable evidence for regions identified in this study, it is also important to note that results obtained from these tests are contingent on several factors. For example, several necessary steps for preparing the filtered QTL list are subject to modification, including the criteria used to determine a “unique” QTL entry and the length of the QTL interval used to retain unique entries. In addition, a limiting factor of the QTL and SNP Tests is that it is only possible to test for significant overrepresentation of QTL using QTL that have already been identified. Therefore, a non-significant result merely implies that QTL of a particular category are not significantly overrepresented for a subset of SNPs based on the current catalog of QTL for the pig genome. Consequently, non-significant results are obtained for SNPs mapping to undiscovered QTL.Other possible limitations are that results are also subject to the window size used to identify genes mapping to significant SNPs, as well as the significance thresholds used to construct the SNP lists. For the current study, 1-Mb SNP windows were used to allow for the possibility of trans-acting QTL and SNP list thresholds were selected to be consistent with the procedure described by Waide et al. , as prevIn conclusion, other than WUR, several other genomic regions were associated with host response to PRRS MLV vaccination and co-infection with PRRSV and PCV2b, but in general, host response was highly polygenic. Multiple SNPs near the SLA region were associated with PCV2b VL, regardless of previous vaccination for PRRS. Several regions associated with ADG Post Vx and Post Co-X were also identified, but SNPs with a significantly different effect on ADG, depending on vaccination status, were identified for ADG during both periods. Taken together, results indicate that multiple SNPs near the SLA region have the potential to be used to select pigs for decreased PCV2b VL following PRRSV/PCV2b co-infection, but that different SNPs were associated with ADG following PRRS vaccination and PRRSV/PCV2b co-infection, depending on previous vaccination for PRRS.Results from the protein pathway enrichment analyses supported GWAS results, showing that immune-related pathways were enriched for genes near SNPs associated with vaccination VL, PRRS VL, and PCV2b VL and that metabolic pathways were enriched for genes near SNPs associated with ADG. Results of the QTL and SNP Tests provided additional evidence for the identified regions and similar results were obtained for both tests. Results showed that SNPs associated with vaccination VL, PRRS VL, and PCV2b VL were significantly overrepresented for health QTL when Vx and Non-Vx pigs were analyzed separately and results of the SNP Test showed that mapping of SNPs to health QTL was non-random. Results for ADG showed that, for Non-Vx pigs prior to co-infection, SNPs associated with ADG were significantly overrepresented for growth QTL and results of the SNP Test showed that mapping of SNPs to growth QTL was non-random. These findings likely reflect the fact that most QTL used for these tests were associated with growth under non-challenged conditions.Collectively, results of functional annotation analyses provide valuable insight regarding the biological pathways underlying host response to PRRS MLV vaccination and PRRSV/PCV2b co-infection, biological evidence for regions statistically associated with the traits of interest, and a means of summarizing GWAS results for complex traits, including host response to disease challenge.


P < 0.05) smaller than those in the A genome. Intense selection (domestication and breeding) had a stronger effect on the A than on the B genome chromosomes. Based on the genetic pedigrees, many blocks can be traced back to a well-known Strampelli cross, which was made one century ago. Furthermore, polyploidization of wheat (both tetraploidization and hexaploidization) induced revolutionary changes in both the A and the B genomes, with a greater increase of gene diversity compared to their diploid ancestors. Modern breeding has dramatically increased diversity in the gene coding regions, though obvious blocks were formed on most of the chromosomes in both tetraploid and hexaploid wheats. Tag-SNP markers identified in this study can be used for marker assisted selection using haplotype blocks as a wheat breeding strategy. This strategy can also be employed to facilitate genome selection in other self-pollinating crop species.A Chinese wheat mini core collection was genotyped using the wheat 9 K iSelect SNP array. Total 2420 and 2396 polymorphic SNPs were detected on the A and the B genome chromosomes, which formed 878 haplotype blocks. There were more blocks in the B genome, but the average block size was significantly ( Triticum aestivum L.) is one of the most important global staple food. It was originated in the middle east about 8000 years ago and expanded to most parts of the world23Triticum monococcum, and continued in tetraploid and hexaploid wheats. Therefore, besides common evolution effects found in other crops such as maize, rice, soybean, etc., polyploidization played additional important role in shaping the wheat evolution. Tracking the footprints of wheat evolution by molecular markers is an interesting but challenging research, because until now, all of the published results concerning wheat domestication was mainly based on single genes5Common wheat , PCA, and neighbour-joint tree analyses to show the genetic differentiation between species and populations in the same species. The Fst values between T. dicoccum and T. dicoccoides were 0.063 and 0.067 for the A and the B genomes, respectively. However, these values were three times higher when comparing tetraploid wheats T. dicoccoides either with T. uratu or with Aegilops speltoides at 0.161 and 0.199, respectively. Furthermore, three times higher differentiation was found between T. aestivum landrace and T. dicoccum than between hexaploid modern cultivar and landrace on both the A and the B genomes. The Fst values were 0.181 vs. 0.064, and 0.171 vs. 0.058 for the A and the B genomes, respectively  on the short arm of chromosome 1A spanning a region between 48.5 and 64.4 cM, that was present in the founder parents and was maintained in most of the newly released Chinese cultivars . The welT. dicoccum and T. durum  based on single SNP markers and haplotypes separately. From the 5,156 SNPs that mapped on single chromosome location, 4,434 polymorphic SNPs with minor allele frequency (MAF) ≥ 0.05 were chosen for GWAS. The mean value for each trait was calculated using BLUP. SNP-based association analysis identified 234 loci  associated with traits at D and 7D . Among tP < 1.2 × 10−3, distributing on all the A and the B genome chromosomes  were found associated with yield-related traits at omosomes . FavorabThe use of haplotype was able to increase the power of associations and the amount of phenotypic variation explained , as indi15Glu-1PPd1Polyploidy has been important in the evolution of angiosperms and may significantly affect population genetic diversity and structure. The common wheat was formed through two steps of polyploidization, allotetraploidization (approximately 0.5 million years ago) and hexaploidization 18r2) on the entire genomes  at the same locus, otherwise, strong selection in breeding will lead to declining of gene diversity at most loci. Though intensive selection increased the LD  in various genetic backgrounds evaluated in multiple environmentsSNP is becoming one of the most popular marker systems employed in association mapping for being highly abundant in the genomes and for its high throughput analysis capabilities. It is not surprising that millions of SNPs can be discovered within a single crop after resequencing hundreds of collections. However, much fewer genomic regions were found associated with agronomic traits than expected. Most of the studies published in the past years mainly showed the SNPs significantly associated with genes previously identified to validate their association results. This might be due to the biallelic characteristic of SNP markers. The use of haplotypes containing a group of SNP markers can improve the levels of polymorphisms and should help overcome the drawback of using single SNP markers by creating much more combinations (haplotypes) . This ca33Modern plant breeding has promoted gene exchanges and recombination between parents used for crossing. Hence the activities can enhance the gene diversity in modern cultivars. This was strongly supported by comparing genetic diversity using haplotype blocks between homoeologous chromosome pairs on the A and the B genomes . HoweverMany introgression, NAM, and MAGIC populations have been developed in major crops, such as rice, maize, wheat and oilseed, where elite cultivars were usually used as the major genetic background in the crossing design363837A Chinese wheat mini core collection (MCC)1012Triticum urartu, 4 T. boeoticum, 4 T. monococcum, 6 Aegilops speltoides, 3 Ae. longissima, 3 Ae. sharonensis and 1 Ae. searsii, and 16 tetraploid wheats, including 5 T. dicoccoides, 5 T. dicoccum, and 6 T. durum, were also used in the current study -Luoyang Experiment Station in Henan province , and at the CAAS-Shunyi Experiment Station in Shunyi, Beijing  for the 2009–2010 growing season. Each accession was planted in a 2 m four-row plot with 30 cm between rows, with 40 seeds planted in each row. The filed management followed local practices.Data from yield traits of all accessions in the MCC were collected from ten plants selected from the middle of each plot. These traits included heading date , maturity date , spike length , spikelet number per spike , plant height , kernel numbers per spike , effective tiller number , 1000-kernel weight , kernel length , kernel width , and kernel thickness . The data were collected in all four environments, i.e. in 2002, 2005, and 2006 at Luoyang, Henan province, and in 2010 at Shunyi, Beijing; they were named as 02LY, 05LY, 06LY, and 10SY, respectively. The detailed phenotyping data are listed in www.tiangen.com) according to the manufacturer’s instructions (Tiangen). The DNA concentration was measured and diluted to approximately 50 ng/uL for SNP genotyping.Genomic DNA was isolated from 100 mg fresh leaves of 10-day-old seedlings of each accession using DNA quick Plant System by Tiangen Biotech (Beijing) Co., Ltd  between populations, and Pearson correlation analysis between traits within a single environment and between environments for a trait were analyzed using SPSS 16.0 software (http://www.brothersoft.com/downloads/spss-16.html). The mixed mean value of each trait from multiple environments in the MCC was estimated by the best linear unbiased predictor (BLUP) method according to Bernardo4041http://www.maizegenetics.net/)43h2) of each test trait in different environments, defined as the proportion of genetic variance over the total variance, was also calculated according to the formula h2 = σa2/(σa2 + σe2) with the MLM options of no compression and re-estimation for each marker. Here, σa2 means genetic variance, and σe2 indicates the residual variance.Basic statistics, analysis of variance (One-Way ANOVA) according to Tukey test at the significance level of 5% (Fst (coefficient of gene differentiation) was employed to measure genetic differentiation between populations using POPGENE softwareStandard statistics for genetic diversity at each SNP locus, including polymorphism information content  and gene diversity, were carried out using PowerMarker v3.25 softwareet al.In order to reduce the false positive or spurious associations, genetic structure in the population level  was evaluated uisng STRUCTURE v2.2 softwarehttp://www.broadinstitute.org/haploview/haploview)Haplotype structure in wheat A and B genome chromosomes was evaluated using Haploview 4.2 software package (Fst) within and between landraces and modern cultivars were further evaluated by treating one haplotype as a single marker on individual chromosome.Gene diversity and coefficient of gene differentiation 43P < 1/n) was declared at a P value threshold P < 2.3 × 10−4 (−LogP > 3.65) and P < 1.2 × 10−3 (−LogP > 2.93) for SNP markers and haplotypes, respectively.The markers with the minor allele frequencies less than 0.05 were treated as rare alleles and filtered out. The compressed mixed linear model (MLM)44R2) by the markers was calculated using ANOVA function in SPSS 16.0 software, based on a formula R2 = SSA/SST where SSA being the sum of squares between groups, and SST being the total sum of squares44Additionally, phenotypic variation explaned .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits.G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content.The SNP markers distinctly separated Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton.The online version of this article (doi:10.1186/s12870-017-0981-y) contains supplementary material, which is available to authorized users. Gossypium spp.), primarily the tetraploid species, Gossypium hirsutum L., is produced in many diverse tropical, subtropical and warm temperate regions of the world. Because cotton cultivation is so extensive, ca. 31 million hectares worldwide in 2016 (USDA-NASS), the societal benefits from plant breeding-based gains in cotton performance are greatly magnified. These include improved natural resource conservation and preservation, reduced reliance on undesirable chemical protectants, more time- and energy-efficient production, and increased profits for growers and cotton-related businesses, as well as local and even national economies. Improvements in baseline crop production and product quality have been possible through breeding of cotton germplasm resources, especially genetically elite cultivars. The use of non-elite germplasm resources for genetic improvement has been increasing in part because molecular markers and sequence polymorphisms facilitate germplasm analysis, classification, categorization, genotyping, genomic comparisons, and various types of marker-assisted selection for breeding and breeding-related research.Cotton  is a subset of approximately 20% of the entire NCGC, created in 2013 via proportional representation across taxonomic levels and geographic origins [G. hirsutum and G. barbadense L. [G. hirsutum and G. barbadense are being developed for future genetic studies. The GDRS has been evaluated for seed oil, protein, and seed index traits [Cotton germplasm resources available worldwide  contain esources . In someesources –9. Conti origins . Simple  origins  and to idense L. . Based ox traits , and subDiverse resources within other worldwide cotton germplasm collections have also been characterized with molecular markers, primarily SSRs , 12–14. With decreasing resources available for germplasm preservation, optimization of these resources becomes essential. Unrecognized redundancy of germplasm within collections has played a large role in impeding optimal management and use of collections . It has Most recently in cotton, SSRs have been the marker of choice for researchers to identify genotypic differences , 17, 25.G. hirsutum cultivar set and to identify loci associated with seed nutritional traits.Compared to many field crops, cotton has only recently begun to benefit from the wide availability of SNP markers. Allotetraploid crops like cultivated cotton are especially challenging for the identification of SNPs , 29. MosG. hirsutum genotypes, 3) to investigate possible redundancies in NCGC accessions, 4) to relate this new technology with results from SSR markers, and 5) to explore the potential of the array for use in genome-wide association studies.To obtain a more thorough characterization of the genetic diversity of cotton, we used the CottonSNP63K array to analyze a total of 395 cotton samples provided by the NCGC and collaborators worldwide. The data collected on the samples shows how this technology can be applied 1) to evaluate genetic diversity and population structure between groups of germplasm, 2) to gauge the effectiveness of the array to identify differences among individual Gossypium genotypes following the protocol described in Hulse-Kemp et al. [G. hirsutum samples; of which 292 were improved/cultivated or previously cultivated types and 71 were non-cultivated relatives  and four tetraploid species . All plant materials used in this study were obtained from other researchers or national germplasm collections as indicated in Additional file Genomic DNA was extracted from young leaves of single plants representing a panel of 395 diverse p et al. . DNA froG. hirsutum were selected to span global regions of cotton breeding efforts. Within improved types, 185 cultivars were developed in the United States, and these were further classified into four breeding regions: eastern, mid-south, plains, and western. For a detailed description of criteria used for classification into global and US breeding regions see Hinze et al. [G. hirsutum, 15 examples of genotypes with the same designations were included to examine whether genotypes with the same names were also genetically similar. In 12 examples, the genotypes came from different breeding programs , and in the remaining examples, the genotypes came from the same seed source  , primarily of Brazilian origin [G hirsutum, i.e., from the seven geographical landraces, will be further and collectively referred to as “wild” even though it is recognized that the only truly wild landrace is yucatanense, while the remaining six landraces display some traces of domestication and are considered ferals, or relics from ancient pre-Columbian domesticated forms.Analyses of non-cultivated atanense . These an origin . For conn origin , 10, 11,Gossypium germplasm. The cluster file was developed using 1,156 cotton samples including the subset of plant materials used in this diversity analysis. Genotypes were determined for all samples in the present analysis using the 38,822 SNPs classified as polymorphic by Hulse-Kemp et al. [G. hirsutum and five other species, namely G. barbadense, G. tomentosum Nuttal x Seemann, G. mustelinum Miers x Watt, G. armourianum, and G. longicalyx, but not among G. hirsutum genotypes.SNP genotypes were generated for each panel member according to Hulse-Kemp et al. , using thttps://www.cog-genomics.org/plink2) [Standard summary statistics for all SNPs were generated using PLINK v. 1.90b3m (/plink2) , 41. SNP/plink2) .Gossypium species, (2) a dataset of only G. hirsutum samples, (3) a dataset of only improved  G. hirsutum, and (4) a dataset of only wild  G. hirsutum. The subset of improved G. hirsutum cultivars originating in US breeding programs was also evaluated.Summary statistics and diversity analyses were independently evaluated on (1) the full dataset containing multiple Diversity was analyzed using SNPs with a MAF greater than 0.01 and a genotyping rate greater than 0.90. A genetic similarity matrix for all pair-wise combinations of individuals was calculated using PLINK (‘--cluster --matrix’ option) on the basis of the genome-wide average proportion of alleles shared which were identical by state (IBS) between any two individuals . Multidihttps://www.cottongen.org/data/trait/NCGC_rating_scale). These descriptors are primarily inherited as qualitative traits whose expression shows negligible environmental interaction; therefore, characters were scored in a single replicate at a single location.Putatively identical accessions from the NCGC, as determined by the matrix derived from SNP-based IBS values (IBS > 0.98) were compared for phenotypic similarity at the Southern Plains Agricultural Research Center (SPARC) in 2015. Seeds representing each accession were planted in the greenhouse in early April and grown for 3 weeks before transplanting to field plots. Field plots were 10.06 m × 1.02 m with 20 plants per plot to represent each accession. During the growing season, 26 morphological descriptors were scored as a plot average. This group of descriptors included leaf hair, leaf color, leaf shape, stem color, stem glands, stem hair, leaf size, leaf glands, leaf nectaries, bract nectaries, boll nectaries, petal color, pollen color, petal spot, stigma, bract type, bract teeth size, bract teeth number, bract color, boll shape, boll point, boll size, boll color, boll glanding, boll pitting, and fruiting type  were selected from the GDRS that were previously genotyped with SSR markers [G. hirsutum accessions were found to be admixtures based on SSR analyses and were removed prior to comparison of SNP and SSR results  123 improved G. hirsutum accessions. Loci that were monomorphic within the respective subset were removed prior to calculation of genetic similarities. A genetic similarity matrix was calculated for each marker system based on Jaccard’s coefficient [Among the 395 genotypes of the current investigation, a subset of 195  markers , 10  method [G. raimondii (D5) reference genome and/or the NBI (Novogene Bioinformatics Institute) G. hirsutum  draft genome [Phenotypic data for seed traits including percent oil content, percent protein content and seed index (grams per 100 seeds) were obtained from Hinze et al.  for the ) method . The FDRt genome , 50.Gossypium species measured , which are useful for introgression research and breeding.We used a high-throughput genotyping platform to appraise the diversity of cultivated cotton and its wild relatives  ranged from 26,299 (68%) within 71 non-cultivated landraces to 23,145 (60%) in 292 cultivars; 25,829 (67%) SNPs were polymorphic within this set of G. hirsutum. The distribution of MAF across these groups indicated that improved cultivars, and more specifically, those from the western US breeding region, had the most monomorphic SNPs    were common to both groups, indicating that most genetic variation from wild types was also found in improved G. hirsutum. Whereas 3,135 or 10.8% of the total SNPs found in wild G. hirsutum were unique, only 1,927 or 6.6% of the SNPs observed in improved G. hirsutum were unique , followed by the 18 Australian samples (149) and the 13 samples from northern Africa (118)  and all other countries (N = 107). A large proportion of SNPs   as compared to all other global breeding regions combined .SNPs were initially characterized by their unique occurrence in several groups of que Fig. . Among g0%) Fig.  were shaWithin US improved germplasm, cultivars that were not assigned to a regional breeding group  possessed almost three times as many unique SNPs  as cultivars assigned to specific breeding regions Fig. . The “n/G. hirsutum (HE = 0.232) than in improved germplasm (HE = 0.195) of the species (Table E = 0.193) was found in the group of improved samples that could not be assigned to one of the four breeding regions. This supports the high number of unique SNPs within this group and suggests the presence of a distinctive combination of SNPs within a very diverse group of germplasm. The lowest diversity was observed in the mid-south (HE = 0.151) which corresponds to the observed low number of unique SNPs.Genetic diversity was greater in wild germplasm of m HE = 0.95 of theG. hirsutum, the highest similarity was observed in cultivars (IBS = 0.709) and the lowest similarity occurred among wild types (IBS = 0.652)  values between pairs of individuals was used to estimate average genetic similarity within specific breeding groups. For the two groups of 2) Table . Pairs o2) Table . Of part2) Table . Within 2) Table . This in2) Table . Within 2) Table . Approxi samples  where diGossypium species were analyzed together, and the SNPs efficiently separated G. hirsutum from all other species along the horizontal axis  were noted at the edge of the range of wild samples of G. hirsutum. These samples included a synthetic tetraploid of G. hirsutum x G. longicalyx J.B. Hutchinson x G. armourianum Kearney (HLA); Peale1B, an unnamed new species from Wake Atoll, formerly G. hirsutum [G. ekmanianum Wittmack [G. hirsutum. Within G. hirsutum, the wild and improved samples tended to separate along the horizontal axis with greater dispersion noted within the wild group. The improved cultivars generally separated into two clusters along the vertical axis. We were unable to determine a specific cause for this separation; however, it seems that it may be due to background in cultivars coming from the G. hirsutum genotype ‘TM-1’ [G. hirsutum and thus has been included in a majority of genetic studies, including those for SNP discovery. Independent analysis of cultivars from the United States and other countries revealed a general mixing and overlapping of these two groups . Multidimensional scaling (MDS) of pair-wise IBS values was used to visualize the relationships among several groups. First, all xis Fig. . Three ohirsutum ; and TX-Wittmack  rather te ‘TM-1’ . This geups Fig. . Analysiups Fig. .Fig. 3TwG. hirsutum diversity structure were run using fastSTRUCTURE software for three data sets: a) all G. hirsutum, b) only improved, and c) only wild or landrace samples. Only one of these analyses was able to determine a value of K that significantly clustered the data into more than one group. This occurred for the wild group where K = 7 was found to be significant, however there did not appear to be a known classification of samples to explain the observed clusters. The broad clustering of the improved samples into two clusters (indicated by MDS) was not detected by fastSTRUCTURE. The groupings suggested at higher K values did not correspond to any prior classifications and was generally consistent with the homogeneity of variation observed between countries in the MDS analyses. While it was not significant in explaining the genetic structure, the split between wild and improved groups was obvious at K = 2, in an analysis of all G. hirsutum , only 7 of 26 morphological descriptors were found to differ within pairs. The descriptor ratings for leaf and boll nectary distributions differed most frequently among these paired accessions, followed closely by differences in ratings for quantity of stem hair Table . One paiG. hirsutum accessions from the United States and other countries was non-discriminatory for both marker types  were polymorphic. A key difference between the two studies is that a DNA bulk of 10 plants was used to obtain SSR data from individual accessions, whereas DNA from a single individual was used in the current study to obtain SNP data. One expected difference due to sampling technique was that SSR data should reveal larger genetic distances among heterogeneous accessions  than one would expect to see in the improved accessions, which are more homogeneous. We compared the genetic relationships among these accessions for the two marker types using NTSYS. The PCoA plots revealed an obvious separation of improved from wild accessions along the horizontal axis for both marker systems Fig. . The relpes Fig. . When SNons Fig. . When co509 Fig. . TherefoG. hirsutum accessions. While a relatively smaller number of SNPs derived from wild accessions were also included on the array, thus the “true” similarity with these individuals was skewed based on the source of the SNPs chosen for the array. Due to the ability to detect unique novel differences, particularly between the wild accessions, the SSR data set was less skewed and behaved more “realistically” or closer to “true”. While the SSRs and SNP array produce different similarity values, the technologies both provide methods for determining relationships between individuals.The array structure affected the calculated relationships among wild and cultivated accessions observed in the SNP marker system in a manner different from the SSRs. In Fig. p-values were analyzed versus expected ratios for each phenotype, and it was observed that the relatedness calculated using genetic similarities was efficient in correcting for population structure .Three traits were analyzed for the set of 195 accessions common with Hinze et al.  using thure Fig. . CorrectG. hirsutum draft genome sequence [G. raimondii scaffold_13_30556525 which does not align to any other Gossypium draft sequence [G. hirsutum D01 (Chromosome 15) [Arabidopsis thaliana protein AT2G24960.2.These two most highly significant loci were both derived from BAC-associated SNPs . The marsequence . There asequence , and i22some 15) . The cloGossypium species utilizing the recently developed CottonSNP63K array and a large panel of 390 Gossypium samples comprising 292 improved cultivars of G. hirsutum and small sets of non-improved G. hirsutum and other Gossypium species. This data allowed us to appraise the utility of the array for evaluating genetic diversity and population structure, and to conduct SNP-based characterization and analyses on a significant portion of the USDA National Cotton Germplasm Collection. The observed patterns of diversity among Gossypium species and within G. hirsutum agreed with expectations based on previous studies with molecular markers [G. hirsutum from other species and distinguished the wild from improved types of G. hirsutum. Within improved types of G. hirsutum, the observed clustering may be a sign of slight ascertainment bias as has been noted in other SNP studies [G. hirsutum. We found the array to be effective for grouping germplasm using PCoA and STRUCTURE analyses.The development of next generation sequencing technologies and the resulting detection of thousands of markers, primarily SNPs, have increased the power available to analyze diversity in germplasm collections , 55, 56, markers , 10. The studies , 63 withG. hirsutum and between cultivated G. hirsutum types versus non-cultivated germplasm, including several diploid and tetraploid species [G. hirsutum cultivars, and between improved genotypes from wild forms of G. hirsutum and other species. Thus, the SNP array presents a facile means of identifying several thousand or more SNPs between most pairwise combinations of G. hirsutum genotypes. Given that the intraspecific linkage maps average about 3,500 cM [We identified a subset of 192 improved and wild cotton accessions that were run in a previous SSR analysis and on the CottonSNP63K array, and a comparison of grouping and discrimination revealed generally similar patterns for the SSR- versus SNP-based distributions. Moreover, combining the SSR data with the SNP dataset resulted in plots without marked changes, indicating that either technology is sufficient for determining relationships between samples. It is nonetheless noteworthy that the CottonSNP63K array and cluster file were primarily designed to discriminate among cultivated types of  species . The abi3,500 cM , the expG. hirsutum species. This suggests that this array will likely have high utility for discrimination and association studies within the primarily cultivated cotton species. Specifically, discrimination by the array is best for US and Australian cultivars as they are the source of the SNPs used in development of the array. In the present study, we have shown that the SNP genotype information obtained using CottonSNP63K array has enabled GWAS to identify loci associated with a seed trait. In our analysis of cottonseed nutritional traits, four SNPs were found significantly associated with protein content, with two located in Chromosome 2, a member of the At subgenome. In a recent association mapping study of seed oil and protein in cotton, 228 SSR markers were used to genotype 180 elite G. hirsutum cultivars [SNPs have quickly become the preferred marker system for measuring genetic diversity in plants due to the potential to identify a substantial number of SNPs throughout the plant genome and therefore have greater power when associating that information with phenotypic traits of interest. Only recently have SNPs become utilized for genotyping by the cotton research community. To be successful, SNPs need to be dense and polymorphic within the population of interest. The SNPs on the array displayed polymorphism across the range of samples tested. Over 60% of the SNPs in this dataset were found to be polymorphic in the economically important ultivars . The SSRultivars , none coWhile multiple markers with significant associations were identified with seed protein content, no significant associations were found for seed oil content or seed index. Detection of significant marker associations is dependent upon statistical power to detect linkage disequilibrium of the marker with the causative allele for the phenotype. Statistical power in GWAS can be influenced by many factors including trait architecture and effect size, number of samples in the population, number of markers, distribution of markers, statistical method utilized, etc. , 66. It t subgenome that affects leaf shape in cotton; the findings were supported by similarity to genes regulating leaf morphology in other plant species. A separate study targeting SNPs for genetic male sterility (ms) in cotton [ms5 on chromosome 12 (A12) and ms6 on chromosome 26 (D12). Similarly, Ellis et al. [Cbd resistance locus as was obtained from screening an F2:3 population. These results demonstrate that genotyping and GWAS can be effectively conducted with SNPs in cotton utilizing the array genotyping platform. As with many quantitative traits, statistical power necessary for identification of all loci responsible for a trait needs to be high to detect the small effect of numerous loci. In instances where a large proportion of the overall phenotype is explained by large numbers of loci, genomic selection utilizing the complete genotypes obtained by the array may provide a promising future avenue of research. An additional option would be to increase power by adding additional samples, as has been done with human and other crop analyses. For example, extensive application of an analogous array to the USDA Soybean Germplasm Collection enabled synthesis of haplotype block maps that are expected to lead to the discovery of many SNP-trait associations for economically important characteristics [In addition to the GWAS performed here, an additional analysis using the CottonSNP63K array, followed by a “targeted” approach of a specific set of SNPs rather than genome-wide SNPs was recently published. Zhu et al.  identifin cotton  successfs et al.  used theeristics . The conG. hirsutum cultivars by comparing representative cultivars from the NCGC and worldwide cotton researchers. Because breeders often use a limited range of material, assessing the relatedness of cultivars can assist with selecting more distantly related lines to maximize variability in breeding programs. Recent SNP-based reports show a lower diversity in cotton relative to other plant species. A diversity study in soybeans (Glycine spp.) showed Chinese landraces of G. max L. (HE = 0.338) with a slightly higher diversity than inbreds (HE = 0.313) [Vitis spp.) germplasm collection had higher diversity in the primary cultivated species of V. vinifera L. ssp. sativa (HE = 0.345) than the wild form, V. vinifera L. ssp. sylvestris (HE = 0.266) [ca. 5,200 polymorphic SNPs between the two samples (Table We assessed the utility of the CottonSNP63K array data for distinguishing among = 0.313) . Though = 0.266) . Our cur= 0.266) , 25 haveWhile the current study is one of the first looking primarily at US germplasm, SNPs have been tested to determine their suitability in the examination of distinctness, uniformity and stability (DUS) for cotton varieties in China . DUS exaThe integration of genomic data into germplasm collection documentation systems and its combination with taxonomic and phenotypic data will impact how these genetic resources are conserved as well as how they are used by plant breeders. In the current study, eight pairs of genotypes with high IBS values (putative duplicates based on genotype) were grown in the field and phenotypic descriptors were measured. Results of genotype and phenotype comparisons were mixed. In some cases, cultivars with a highly similar DNA profile for this set of SNP markers also had identical phenotypes, while in other cases, cultivars with a highly similar DNA profile had multiple phenotypic differences. The observed phenotypic differences are too strong to be due to natural environmental effects. It must be noted that unusual observations may be due to the possibility of human errors in any assessments, whether they be based on genotype or phenotype. Therefore, a researcher desiring to use a similar approach to remove highly similar individuals from their lines may wish to verify a small number of markers on independent DNA isolations with simplex SNP marker assays in the lab to ensure that a mistake was not made in sampling prior to more extensive field-based assessment. Based on our data at this time, we assert that any potential duplicates or redundancies within a collection cannot be ascertained solely by genotype or phenotype profiles, but that SNP and/or SSR data can bring greater power for identification of redundancy, solely due to the number of “markers or traits” one can look at. Often with phenotypic characterization, one is only working with 50–75 traits at most. SNP markers may be more appropriate to distinguish among closely related individuals while SSR markers can easily estimate diversity among less related germplasm. When comparing SSR and SNP markers in cotton cultivars, the study of Kuang et al. [With increasing SNP discovery projects and the development of the CottonSNP63K array to assay thousands of SNPs, it is anticipated that SNP markers will play an increasingly important role in cotton genetics, germplasm conservation, and breeding applications. In this study, we provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be useful as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton.


Myriophyllum spicatum L.) can hybridize with the related North American native species northern watermilfoil (M. sibiricum Kom.). Hybrid watermilfoil (M. spicatum × M. sibiricum) populations have higher fitness and reduced sensitivity to some commonly used aquatic herbicides, making management more difficult. There is growing concern that management practices using herbicides in lakes with mixed populations of watermilfoil species may further select for hybrid individuals due to the difference in herbicide sensitivity. Accurate and cost-effective identification of rare hybrid individuals within populations is therefore critical for herbicide management decisions. Here we describe KASP assays for three SNPs in the ITS region to genotype individuals from both parental watermilfoil species and their hybrid, using synthesized plasmids containing the respective sequences as positive controls. Using KASP we genotyped 16 individuals from one lake and 23 individuals from a second lake, giving a highly accurate picture of Myriophyllum species distribution dynamics. We identified one hybrid individual among 16 samples from one lake, a discovery rate of <10%. Discriminant analysis showed that while a single SNP was generally sufficient for genotyping an individual, using multiple SNPs increased the reliability of genotyping. In the future, the ability to genotype many samples will provide the ability to identify the presence of rare individuals, such as a less common parental species or the inter-specific hybrid. Lakes with complex species distribution dynamics, such as a low proportion of hybrids, are where herbicide application must be carefully chosen so as not to select for the more vigorous and less herbicide-sensitive hybrid individuals.The invasive aquatic plant Eurasian watermilfoil ( Myriophyllum spicatum L.) was introduced to the United States from Asia during the 1940s  (M. spicatum × M. sibiricum) populations appear to have higher fitness, manifested as a faster and more aggressive growth rate than either parental species in both laboratory and field conditions  . In the nditions . Some hynditions . There iM. spicatum, M. sibiricum, and M. spicatum × M. sibiricum hybrid individuals using morphological characteristics have been proposed. Morphological characteristics, while sufficient to distinguish between M. spicatum and M. sibiricum, are no longer reliable once hybrid individuals are present, as the hybrid characteristics are often intermediate between the two species   .M. spicatum and M. sibiricum  274 or 551 of the ITS amplicon, respectively. By eliminating the cloning and sequencing for species identification with the PCR-RFLP assay, Myriophyllum species distribution dynamics.Single nucleotide polymorphism genotyping in these species has been performed using several methods. Originally, the ITS region was amplified via polymerase chain reaction (PCR), the PCR products were cloned, and multiple clones were sequenced to determine their genotype . This prRecent advances in SNP genotyping provide more cost-effective and accurate results than PCR-RFLP. Currently, the Kompetitive Allele Specific PCR (KASP) assay is a common technique for genotyping SNPs. This assay is used in several fields of study, including plant breeding, disease identification, and species identification . KASP isMyriophyllum species distribution dynamics, and increasing the likelihood of detecting a rare hybrid individual. KASP assay design is very flexible, as useful SNPs are not limited to available restriction enzyme recognition sites, and primers can even cover stretches of sequence containing multiple SNPs by incorporating degenerate or mixed bases into the primer sequence. KASP assays are quantitative and therefore amenable to statistical analysis, such as assigning probabilities to genotyping calls. Data from multiple SNP genotyping assays can be integrated into a single model, increasing the robustness of species diagnostics.Kompetitive Allele Specific PCR genotyping has several advantages compared to other genotyping assays. KASP assays are more convenient, as thels can be genotyped simultaneously on a standard 96-well plate, giving a much more accurate view of the Myriophyllum species distribution dynamics in each case. Discriminant analysis showed that while a single SNP was generally sufficient for genotyping an individual, using multiple SNPs increased the reliability of genotyping.Here we describe KASP assays for three SNPs in the ITS region to genotype individuals from both parental watermilfoil species and their hybrid, using synthesized plasmids containing the appropriate sequences as positive controls. Using these three KASP assays, we genotyped 23 and 16 individuals, respectively, from two lakes, giving a highly accurate picture of M. spicatum biotypes and known inter-specific watermilfoil hybrid (M. spicatum × M. sibiricum) biotypes (eight biotypes each) were harvested from aquaponics cultures maintained in the CSU Weed Research lab. Unknown Myriophyllum individuals were collected from the following two lakes in northern Colorado: Rainbow Lake located at 40.506758, -104.989224, and Walleye Lake at 40.505680, -104.982883. Individual stems  were collected from each lake by rake throws. A single leaf from a stem was used for extraction and therefore a tissue sample is expected to represent a unique individual. Tissue samples were stored in sealed bags with damp paper towels at 4°C until DNA extraction.Several previously identified g for 5 min. The upper phase was transferred to a new tube, to which 500 μL of chloroform:isoamyl alcohol (24:1) was added. The samples were again centrifuged at 10,000 × g for 5 min. The upper phase was transferred to a new tube and nucleic acids were precipitated using 0.1 volumes of 3 M sodium acetate and 2.5 volumes of 100% ethanol. Samples were precipitated at 4°C for 15 min and then centrifuged at 15,000 × g for 15 min. The resulting pellets were re-suspended in 50 μL of sterilized water. DNA concentrations and quality were assessed using a spectrophotometer . Samples were subsequently diluted to 5 ng/μL for use in all KASP assays.DNA was extracted from 50 mg of watermilfoil leaf tissue using a modified CTAB method . All steFigure 1 and Table 1). The complete oligonucleotides were synthesized by GenScript  in the pUC57-Kan plasmid.Two plasmids were designed as positive controls for the KASP assay. Plasmid inserts consisted of the sequence within the ITS region complementary to the genotyping primers, with all inter-primer sequence removed (E. coli cells using a standard heat transformation protocol (provided by GenScript) and selection on LB plates using 50 μg/mL kanamycin. Individual colonies were transferred to a numbered patch plate and allowed to grow at 37°C for 16 h.Control plasmids were transformed into DH5α E. coli colony and used to inoculate 1 mL of LB containing 50 μg/mL kanamycin. After incubating for 16 h at 37°C with shaking, the E. coli cultures were pelleted by centrifugation at 8,000 × g. DNA was extracted from the pellets using Qiaprep Spin Miniprep kit  according to the manufacturer’s recommendations. DNA concentrations and quality were assessed using a NanoDrop 2000 spectrophotometer. Extracted plasmids were subsequently diluted to 5 pg/μL for use in all KASP assays. A 1:1 mixture of the diluted plasmids was used in KASP assays to simulate an inter-specific hybrid.DNA was extracted from cultures grown from ten colonies on each patch plate. A toothpick was dipped into the Table 2). For each primer set, the forward primer for M. spicatum was appended at the 5′ end with the sequence complementary to the HEX fluorophore quencher, while the forward primer for M. sibiricum was appended at the 5′ end with the sequence complementary to the FAM fluorophore quencher. The forward primers for M. sibiricum at SNPs 118 and 478 spanned sequences containing SNPs that discriminate between sub-populations, which required the use of degenerate bases in the primers. Degenerate bases are indicated according to the universal IUPAC code  region ; Table 2PAC code . The forM. spicatum forward primer, 18 μL of the M. sibiricum forward primer, 45 μL of the common reverse primer, and 69 μL of 10 mM Tris-HCl, pH 8.3. KASP master mixes were made for each SNP assay with 432 μL LGC Genomics Master Mix  and 11.88 μL of the appropriate primer master mix.A primer master mix including forward and reverse primers for a single SNP was made according to the KASP assay manufacturer’s recommendations . After all primers were re-suspended in Tris-HCl, pH 8.3, at 100 μM, a primer master mix was assembled with 18 μL of the Kompetitive Allele Specific PCR reactions were assembled in a 96-well plate with 4 μL of master mix and either 4 μL water (no-template control), 4 μL genomic DNA at 5 ng/μL, or 4 μL of plasmid DNA at 5 pg/μL. Reactions were performed in a Bio-Rad CFX Connect  according to the following standard KASP PCR program: activation at 94°C for 15 min, then 10 touchdown cycles of 94°C for 20 s (denaturing), 61–55°C for 60 s , 23°C for 30 s (to permit accurate plate reading), followed by 26 cycles of 94°C for 20 s, 55°C for 60 s, 23°C for 30 s. Fluorescence was tracked in real-time with plate reads at the end of every amplification cycle. Fluorescence data from the cycle showing the greatest distinction between clusters without any background amplification was used for genotyping, which was determined to be cycles 22–24 of the amplification phase.x,y) coordinate of the control (plasmid) hybrid and either the control M. sibiricum or M. spicatum clusters, then drawing a line from that point to the origin . Additionally, a zone of “no amplification” was defined as 30% based on the maximum fluorescence observed in no-template controls. The 30% cut-off was selected because technical variation at low fluorescence values is more likely to influence genotyping calls than at high fluorescence values, and based on the observation that none of our no-template controls exceeded 30%. A quarter circle at 30% around the axis intercept was used to define this zone. Genotypes were assigned to unknown samples based on the sector of the plot where their fluorescence values occurred. If a sample fell within the bounds of a zone it was assigned that genotyping call.Due to slight variations in maximum fluorescence and fluorescence in the no-template controls between plates, HEX and FAM fluorescence for each data point were transformed as a percentage of the maximum fluorescence for each fluorophore within a plate. Maximum fluorescence is defined as the highest FAM or HEX signal from any reaction in a 96-well plate. Cutoffs for genotyping calls on unknown samples were drawn by calculating the point halfway between the mean  was assigned a genotype based on its zone, linear discriminant analysis was performed in JMP 12.2  to evaluate the probability of that individual having its assigned genotype. Genotyping results from each SNP were first assessed independently, then using all three SNPs combined to provide more robust probabilities.M. sibiricum and the invasive M. spicatum species as well as inter-specific hybrids. Our KASP primers utilize the previously identified SNPs at base pairs 118, 363, and 478 of the ITS region (Table 2). We tested the primer sets on plasmids containing known sequences, on known lab biotypes of M. spicatum and hybrids, and on unknown Myriophyllum individuals harvested from two lakes in northern Colorado. We assigned genotypes manually, and then measured the reliability of the genotyping calls using discriminant analysis to assign probabilities to calls from each SNP individually as well as using all three SNPs together.We developed three KASP primer sets that distinguish between the native Figure 1) to serve as positive controls for the KASP-PCR. Plasmid controls were ideal because they allow for rapid generation of DNA of a known genotype and eliminate the need to maintain both species of Myriophyllum as well as the inter-specific hybrid in hydroponic culture as positive genotyping controls.We developed two plasmids . SNP 118 had a very clear M. sibiricum cluster, but the M. spicatum and the 1:1 synthetic hybrids were relatively close to each other, due to increased FAM fluorescence for the M. spicatum samples . However, there was no overlap between the M. spicatum and the synthetic hybrid sample clusters. SNP 363 and SNP 478 show obvious separation of the fluorescence signal from each of the three possible genotypes, with the M. spicatum plasmids having almost exclusively HEX signal, M. sibiricum plasmids having almost exclusively FAM signal, and the 1:1 mixture of each genotype having both HEX and FAM signals . No plasmid had an ambiguous call or fell below the 30% fluorescence threshold for any of the three SNPs. This test confirmed the utility of plasmids as internal positive controls for plant genotyping assays.The plasmid DNA performed consistently from assay to assay and allowed us to more accurately characterize unknown individuals in the KASP assay. For SNP 118, SNP 363, and SNP 478, we tested ten distinct Myriophyllum that are maintained in aquaponics culture at CSU. These biotypes were originally collected from various locations in North America (Table 3). The KASP results from all three SNP primer sets showed that eight of these biotypes clustered with the M. spicatum plasmid control, with high HEX signal and minimal FAM signal , while eight clustered with the 1:1 synthetic hybrid mixture of M. spicatum and M. sibiricum plasmid controls, with approximately equal HEX and FAM fluorescent signals  . The genotyping calls from the KASP assay matched the known genotypes of the samples exactly.We tested several biotypes of Table 3). Particularly for SNP118, several individuals had a reduced probability that the genotype was correct . However, when all three SNPs were considered together, the probability was 100% for each genotype call (Table 3). These results confirm that all three SNPs are strongly linked and co-inherited and therefore the three SNPs can be used together to provide accurate genotyping, as would be expected for SNPs in the ITS2 region.The probability that a genotype call was correct was calculated by performing discriminant analysis on the corrected fluorescence data for each SNP separately and for all three SNPs together (n = 23) and Walleye Lake (n = 16). For Rainbow Lake, all sampled individuals were identified as the invasive M. spicatum, as the fluorescence signal from all three SNPs for each individual was predominantly the HEX wavelength . Only the genotyping call of M. spicatum for plant 23 for SNP 118 was unsupported by the linear discrimination analysis (P = 0.085). The analysis assigned this call as “No amplification” with P = 0.856. However, when all three SNPs were considered together, a call of M. spicatum for Plant 23 was predicted with P = 1.0. Samples from Walleye Lake were identified as M. spicatum, M. sibiricum and inter-species hybrid genotypes, with 11 individuals showing predominantly HEX fluorescence and clustering with the M. spicatum plasmid controls, while four individuals  showed predominantly FAM fluorescence and clustered with the M. sibiricum plasmid controls . Additionally, one individual (plant 1) was identified as the hybrid genotype, as all three SNPs showed unambiguous dual HEX and FAM fluorescence and clustered with the artificial hybrid .We also tested our assay on wild, unknown individuals from two lakes in northern Colorado, Rainbow Lake  but 100% probability of a correct call when data from all three SNPs were considered simultaneously. The forward primers for both SNP 118 and SNP 478 used one degenerate base each; however, the calls for SNP 478 appear to be much more accurate than for SNP 118. The degenerate bases in each case were required to allow efficient amplification of ITS sequences, because SNPs exist in the forward primers that distinguish between different sub-populations of M. sibiricum, and M. spicatum are largely unknown, in part due to the cost and limited throughput of current genotyping methods. The genetic diversity of the two Myriophyllum species in the ITS region has been explored by M. spicatum, M. sibiricum and their interspecific hybrid, but not to distinguish within-species genetic variation.The ability to genotype dozens of individuals provides the ability to identify the presence of rare individuals, such as a less common parental species or the inter-specific hybrid. Lakes with complex species distribution dynamics, such as low proportion of hybrids, are where herbicide application must be carefully chosen so as not to select for the more vigorous and less herbicide-sensitive hybrid individuals. Only recently were hybrids suspected , and theMyriophyllum species distribution dynamics, testing the hypothesis that increased selection pressure from herbicide application favors hybrid individuals due to their decreased herbicide sensitivity. Lakes can be genotyped using KASP both before and after herbicide applications to quantify shifts in species distribution dynamics toward invasive M. spicatum or hybrid watermilfoil individuals.Monitoring has been conducted in some areas of North America e.g., , but theLake material was collected from two of the lakes at the Swift Ponds at Colorado Youth Outdoors: Rainbow Lake  and Walleye Lake .EP, MF, KK, SN, and TG designed the experiments; EP, MF, and KK performed the experiments; EP, MF, and TG analyzed the results; and EP, MF, KK, SN, and TG wrote and approved the manuscript.EP, MF, and KK have a patent pending on the genotyping method. The other 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.