Background: Informant discrepancies have been reported between parent and adolescent measures of depressive disorders and suicidality. We aimed to examine the concordance between adolescent and parent ratings of depressive disorder using both clinical interview and questionnaire measures and assess multi-informant and multi-method approaches to classification.Method: Within the context of assessment of eligibility for a randomized clinical trial, 50 parent–adolescent pairs (mean age of adolescents = 15.0 years) were interviewed separately with a structured diagnostic interview for depression, the KID-SCID. Adolescent self-report and parent-report versions of the Strengths and Difficulties Questionnaire, the Short Mood and Feelings Questionnaire and the Depressive Experiences Questionnaire were also administered. We examined the diagnostic concordance rates of the parent vs. adolescent structured interview methods and the prediction of adolescent diagnosis via questionnaire methods.Results: Parent proxy reporting of adolescent depression and suicidal thoughts and behavior is not strongly concordant with adolescent report. Adolescent self-reported symptoms on depression scales provide a more accurate report of diagnosable adolescent depression than parent proxy reports of adolescent depressive symptoms. Adolescent self-report measures can be combined to improve the accuracy of classification. Parents tend to over report their adolescent’s depressive symptoms while under reporting their suicidal thoughts and behavior.Conclusion: Parent proxy report is clearly less reliable than the adolescent’s own report of their symptoms and subjective experiences, and could be considered inaccurate for research purposes. While parent report would still be sought clinically where an adolescent refuses to provide information, our findings suggest that parent reporting of adolescent suicidality should be interpreted with caution. The classification of psychiatric disorders is an area of controversy and particularly challenging in child and adolescent mental health. Accurate diagnostic classification can be used to predict benefit from treatment, predict duration, or severity of disorder and is central to etiological research. Researchers conducting clinical trials need to make use of valid measures for diagnostic classification to examine both eligibility and intervention outcomes . EligibiIn this paper, we examine a multi-informant and multi-method approach to diagnosing depressive disorders in adolescents. We draw on data from a clinical trial (The Family Options Study) for adolescent depression in which adolescents (12–18 years of age) needed to meet diagnostic criteria on a semi-structured interview for a depressive disorder for entry to the trial . This stEstablishing valid diagnostic classification for the purposes of determining eligibility for a clinical trial, or to determine response to treatment, encounters many specific challenges when conducting mental health research with children and adolescents . ChildreThe diagnosis of depression is particularly significant given its high population prevalence and increasing incidence across adolescence . DrawingDiagnostic interviews are the preferred method used to establish clinical diagnosis given these approaches are designed to investigate the specific diagnostic features listed in the Diagnostic and Statistical Manual of Mental Disorders. Several standardized structured and semi-structured interviews are available for the evaluation of mental disorders in children older than 7 years and some measures have been adapted for assessment of preschool aged children . These iHowever, notwithstanding the recommendation for multi-informant approaches, clinical practice guidelines also warn that measures of adolescent depression have low parent-child agreement . Parent’In the present study we hypothesized, firstly, that single informant models will show modest concordance between parent and adolescent reports of adolescent’s depressive and suicidal symptoms. Our second hypothesis was that adolescent questionnaire measures of depressive symptoms would more accurately reflect adolescent diagnosis than parent reports on adolescent symptoms. Our third hypothesis was that in a multi-measures approach tested within a multivariate model, adolescent self-reports would have relatively greater importance than parent proxy report in distinguishing amongst adolescents with current major depressive episodes (MDE) compared to those without MDE. In order to compare adolescent and parent self report in a multivariate model we undertook discriminant functional analysis.The Family Options trial aimed to measure the relative efficacy of two intervention programs aimed at treating adolescents who present with a unipolar mood disorder, here defined as major or minor adolescent depression  and 36 female (72.0%). Age ranged from 12 to 19 years, with an average age of 15.0 years (SD = 1.57). Assessments of the parent and adolescent were done within 1 week of each other, and are therefore considered to be observing the same behavior over the same time period. The KID-SCID was conducted with adolescents with sufficient modules to establish a diagnosis of Major Depressive Disorder (MDD), however, the parent version of the KID-SCID used only the depressive episodes module rather than all modules necessary to establish the presence of a mood disorders. Hence, only the MDE module of the adolescent KID-SCID was utilized in this study, so that the adolescent and parent KID-SCID diagnoses were directly comparable. The study protocol excluded adolescents who met the diagnostic criteria for Psychotic Disorders, Autism, and Major Substance Abuse. The primary inclusion criterion was meeting a Diagnosis of Major or Minor Depression or Dysthymia on the KID-SCID.The KID-SCID is a semi-structured instrument designed to generate childhood DSM 4th Edition DSM-IV;  diagnoseWithin the KID-SCID MDE module there is a specific question about suicidality (with follow up prompts). The participant is asked to focus on the worst 2 weeks in the past month when the most severe lowered mood, irritable mood and/or anhedonia were experienced. Adolescent participants were then asked: “During that (2 week period)...were things so bad that you were thinking a lot about death, or that you would be better off dead?” “What about thinking of hurting yourself?” (IF YES) “Did you do anything to hurt yourself?” The parent was also asked the same item about their adolescent offspring with modified wording, as follows: “During that (2 week period)...were things so bad that your young person (use their name) told you or others they were thinking a lot about death, or that they would be better off dead?” “What about thinking of hurting themselves?” (IF YES) “Did they do anything to hurt themselves?” Responses to these questions were then evaluated with regard to whether they meet the DSM-IV criteria of “recurrent thoughts of death (not just fear of dying), recurrent suicidal ideation without a specific plan, or a suicide attempt or a specific plan for committing suicide” and coded as present or not present.The SDQ consists of 25 items , and hasThe Short Mood and Feelings Questionnaire (SMFQ) consists of 13 items, and is based on the 34-item Moods and Feelings Questionnaire. Higher scores reflect more depressive symptoms . The queThe Depressive Experiences Questionnaire for adolescents (DEQ-A shortened) is a 20-item questionnaire designed to assess personality characteristics associated with depressive symptoms , and wasn = 40 for parent and n = 35 for adolescent. To test the first and second hypotheses, sensitivity and specificity scores were calculated to determine the predictive ability of the diagnostic tools used. The diagnostic power of a test needs to be determined by both the sensitivity and specificity. If the sensitivity is low, then true positives will be missed, but if the specificity is low, then there is little power to determine true negatives. How the sensitivity and specificity are assessed depends on the context of the testing scenario, and how important each of these is in relation to the requirements of the specific case . There was no missing data for the adolescent and parent KID-SCIDs because their completion was a requirement for entry to the trial. There was one case of missing data for an adolescent’s demographic variables, and 15 cases of missing data for the adolescent questionnaires, due to the adolescent having been screened with the KID-SCID as not being eligible for the study or not having returned the questionnaires. Accordingly, for all the questionnaires, we have fic case . Kappa sfic case . Cronbacfic case .Education level ranged from having completed year 6–12 of schooling, with a median of year 9. The adolescent’s primary parent was female in 88% of cases and male in 12% of cases, which did not permit analysis of mother vs. father reporting. The parent’s age range was 33–57 years old, with an average age of 47 years (SD = 5.58). 18 parents (47.4%) were married or in a de facto relationships, and 18 (47.4%) were divorced/separated or single parents. Two parents (5.3%) were divorced or separated and had re-partnered with the adolescent’s current step-parent. The mean yearly income for the family as a whole was the $50,000–80,000 per year income bracket, with 17 families (45.9%) reporting earnings of less than $50,000 per annum; seven of these families reported an annual income of less than $20,000 per year. 11 of the parents (27.5%) had completed up to year 10 of secondary school. Five parents (12.5%) had completed year 12, nine (22.5%) had completed a TAFE diploma or certificate, 10 (25%) had completed a university bachelor degree, and 5 (12.5%) had completed a postgraduate university degree.Table 1. We found that the adolescent’s KID-SCID-MDE showed high sensitivity (0.86) but only moderate specificity (0.38). Alternatively, when the adolescent’s KID-SCID was taken as the “gold standard,” the sensitivity for the parent proxy account based on the KID-SCID of their child’s depressive symptoms was much lower at 0.66, but the specificity was improved at 0.67.First, we examined the sensitivity and specificity of measures with respect to the parent KID-SCID as the “gold standard” or reference point as shown in Table 1, the adolescent’s self-reported SMFQ better predicted the adolescent KID-SCID outcome than it predicted the parent’s KID-SCID outcome. The parent SMFQ also better predicted the adolescent KID-SCID as compared to the parent KID SCID. This suggests that the adolescent reported KID-SCID is more closely related to the SMFQ scales than the parent reported version of the KID-SCID. A similar trend is seen for the parent version of the SMFQ, as it also better predicted the adolescent KID-SCID compared with the parent KID-SCID, as shown in Table 1. Notably, when the adolescent self-reported via the SMFQ, it provided a good prediction of their own response to the SCID interview , while the parent report via the SMFQ predicted their own report of their adolescent’s symptoms via the KID-SCID but with reduced specificity .We then examined our three questionnaire measures of depressive symptoms in relation to the diagnosis of MDE by both the parent and adolescent diagnostic interviews. As presented in Table 1. The parent SDQ ES scale diagnosed 38 cases out of 40 cases, and hence the very high sensitivity (0.94 and 0.96), and very low specificity of the scale . This suggests that parents over-reported depressive symptoms using the SDQ when compared to results derived from either the adolescent or parent KID-SCIDs. Convergence calculations indicate that the adolescent measures when predicting adolescent KID-SCID outcomes had the highest convergence, with the results classified as poor.The SDQ-ES scale performed well when completed by the adolescent but seemed to invite over-reporting by parents. The adolescent SDQ ES scale performed well when predicting the adolescent KID-SCID diagnosis with high sensitivity (0.86) and a much improved specificity of 0.57. The adolescent version predicted the parent KID-SCID less well, as shown in Table 2. When the adolescent’s response to the suicide item on the KID-SCID was taken as the “gold standard” or reference point, the sensitivity for the parent’s response to this same item was only moderate at 0.57, but the specificity was high at 0.82. Notably only 40% of parents considered their child to be suicidal and, assuming the adolescent self-reports were accurate, this suggests that parents may under-report, or be less aware of their adolescent’s suicidal thoughts and behavior. When the parent response of the suicide item was used as the “gold standard,” the adolescent showed a higher sensitivity (0.87) and only a moderate specificity (0.47).We then examined the performance of our variance measures in relation to the adolescent’s report of suicidal ideas and behavior; which were self-reported by two thirds of our sample. These findings are reported in Overall, the sensitivity of the questionnaires on depressive symptoms in relation to the adolescent and parent suicidality items were high, ranging from 0.83 to 0.96. The specificity scores, however, were very low, ranging from 0.05 to 0.17 (with the exception of the adolescent SDQ). This indicates that the depressive symptoms questionnaires do not perform well in correctly identifying cases where the adolescent is not suicidal. While it is often the case that high depressive symptoms are accompanied by suicidal thoughts and sometimes behaviors, our findings here suggest that depressive symptom scales are likely to be over-inclusive if thought of as good proxies to identify an adolescent in danger of suicide. Direct questions to adolescent on their own suicidality perform with greater accuracy and thus appear to be preferable. Likewise, convergence statistics indicated poor convergence between the adolescent and parent KID-SCID suicide items and the diagnostic scales.2(7) = 17.31, p = 0.016, Wilk’s lambda = 0.56, canonical r = 0.67] which accounts for 44.9% of variance in the model. Coefficients, standardized discriminant function and structure coefficient for diagnosis and no diagnosis groups on the adolescent SCID are shown in Table 3.To determine the extent to which the Parent SCID plus the questionnaire data  could be used to identify adolescents who were diagnosed as having a MDE on the Adolescent SCID , these variables were entered into a discriminant function analysis. When the variables were entered together, results revealed a function that correctly classified 92.9% of adolescents with a diagnosis, and 85.7% of the adolescents with no diagnosis. The overall classification was 91.4% , which were all adolescent scales, were tested as a discrete model. When these three adolescent variables were entered together, the resulting discriminant function correctly classified 85.7% of the adolescents with a diagnosis, and 71.4% adolescents with no diagnosis, with the same overall classification of 82.9% [χIn this paper we considered whether the use of parent proxy reporting and interview measures adds to the evaluation of adolescent depression. Parent proxy reporting is often assumed to make an important contribution to evaluation studies of adolescent mental health and is recommended in clinical guidelines . In lineThe adolescent questionnaire diagnostic scales have high sensitivity and moderate specificity when predicting adolescent KID-SCID MDE outcomes. The parent questionnaire diagnostic scales had high sensitivity but lower specificity. The adolescent-report questionnaires more accurately reported adolescent depression than the parent report questionnaires. We also found that when parents used the Strength and Difficulties questionnaire to rate depression within a clinical sample, they tend to overstate the degree and severity of their adolescent’s depressive symptoms.With regard to the assessment of suicidality, both adolescent and parent questionnaires examining depressive symptoms have very low specificity in identifying suicidality. We also found that, when compared to the adolescent’s self rating of their suicidality, where two thirds meet the DSM criteria, less than half of the parents agreed that their children were suicidal. This is a particularly important finding since it suggests that parents may underreport adolescent suicidal thoughts and behavior and that some parents are either poor judges or not aware of that their adolescents are suicidal.For a clinical trial, it is particularly important that the measures used have a high degree of sensitivity so that participants who meet diagnostic criteria are appropriately identified. This is the most important aspect of classification in a clinical trial since it speaks to the validity of the trial’s claim to treat the target disorder. A lower specificity score will result in a participant being included in a trial but may not meet full diagnostic criteria and this would impact on the ability of the trial to determine intervention efficacy.Our findings are consistent with previous studies that have found that parent reports of an adolescent’s subjective experiences such as depressive symptoms are less accurate . PotentiIn line with our third hypothesis, discriminant functional analysis also showed that parent reporting performed poorly whereas adolescent reported data performed very well in predicting adolescent diagnosis on the KID SCID. The optimal combination of measures for classification emerged as adolescent reported SDQ (ES), DEQ-A Dependency and SMFQ which showed a high degree of classificatory accuracy in the ability to discern the presence and absence of diagnosis. These findings are inconsistent with current clinical guidelines on the assessment of adolescent depression but consistent with numerous previous findings of poor concordance of parent and youth reports. Of high significance for research designs, an approach assessing adolescents with both questionnaire and structured interview methods for classification proved to be more effective. These findings for adolescent depression are distinct from other recommendations for multi-informant assessment of other mental disorders. For example, Thompson recently reported that the combination of parent and adolescent report using the “atypicality” scale of the Behavior Assessment System for Children, 2nd Edition (BASC-2) significantly improved prediction of structured interview for psychosis-risk syndromes (SIPS) over either single-informant scale. The study concluded that a multi-informant approach may help identify adolescents at risk for psychosis, particularly if used in conjunction with adolescent self-report .Within a research context, the degree of discordance identified in the current study is concerning. Accurate assessment of inclusion criteria and evaluation of clinically significant improvement is reliant on accurate classification. One of the major practical considerations in running a clinical trial is the degree of participant burden imposed with lengthy measures, and this has been reported to be particularly troublesome for adolescents .There are a number of limitations within the current study that should be noted. In addition to the small sample size, the data is derived from a clinical study where adolescents suspected of having depressive disorders are referred for treatment. As such the findings may not generalize to less specific clinical settings. Other modes of assessing concordance could have been undertaken. Interestingly, This study provides further evidence of discrepancies between parent and adolescent reporting, with particular relevance to the clinical and research assessment of adolescent depression and suicidality. The parent proxy KID-SCID has low concordance with the adolescent’s clinical interview and is not as good a predictor of adolescent depression. Parent proxy report is less reliable but in a context where an adolescent refuses to provide information, it could be used to provide clinically relevant information. Parent report of adolescent depression and suicidal thoughts and intensions is of a lesser quality than that of the adolescent’s own report of the symptoms and subjective experiences. These are significant findings to consider within standard clinical assessment as well as within clinical research trials.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.


Premature flowering or bolting is an undesirable characteristic that causes severe sugar yield losses and interferes with harvesting. Vernalization is a prerequisite for the floral induction, achieved by exposure to low temperatures for 10–14 weeks. This process is also controlled by other environmental factors, such as long daylight photoperiods and a combination of genetic factors. The objective of this study was the identification of new genetic polymorphisms linked to bolting tendency in sugar beet.p-value = 1.246∙10−13). The association between SNP183 and bolting tendency was then confirmed in an independent population of 730 plants from 11 breeding lines . SNP183 is located in the intron of Bv_22330_orky, a sugar beet homolog of a matrix metalloproteinase (MMP) gene that could be implied in flowering in Arabidopsis thaliana.Two pollinators characterized by low and high bolting tendency were subjected to RAD-sequencing in order to detect discriminating SNPs between lines. 6,324 putative SNPs were identified. Of these, 192 were genotyped in a set of 19 pollinators, each comprising bolted and non-bolted individuals, for a total of 987 samples. Among the 192 candidate SNPs, the strongest overall association was found for SNP183 on chromosome 6  contains supplementary material, which is available to authorized users. Beta vulgaris L.) it is critical to gain a better understanding of the biological processes behind the switch from vegetative growth to floral induction [B locus, mapped on chromosome 2 [B locus (BB) initiate bolting under long day conditions whereas plants carrying recessive alleles in the homozygous state (bb) need vernalization for floral induction. Environmental and genetic factors strongly influence heterozygous plants (Bb) that show a more complex behaviour [Bb plants bolting without vernalization show a delay in bolting time compared to BB individuals [B locus was recently found to correspond to the BOLTING TIME CONTROL 1 (BTC1) gene. Biennial plants, which do not flower without a period of vernalization, carry a partial loss of function BTC1 allele. A second locus (B2) mapped on chromosome 9 and acting epistatically with the B locus was also associated with bolting behaviour. BvBBX19, encoding a DOUBLE B-BOX TYPE ZINC FINGER protein B-box transcription factor was found to underlie the B2 locus [For an effective genetic improvement of sugar beet , CONSTANS (CO), and FLOWERING LOCUS T (FT) are key genes controlling flowering. Similar genes also exist in sugar beet: BvFL1 on chromosome 6 [BvCOL1 on chromosome 2 [BvFT1 and BvFT2 on chromosomes 9 and 4, respectively [BvFT1 and BvFT2 are major regulators of bolting in beet [B and B2 locus genes BTC1 and BvBBX19 [FLC-like gene BvFL1 is a floral repressor. Its expression is down regulated during a prolonged cold period under long daylight condition [CO-like gene BvCOL1 reinforces the late flowering phenotype [FLC-like and CO-like genes suggests a partial evolutionary conservation in the regulation of floral transition between Arabidopsis and sugar beet [Given the known complexity of floral regulation in model species it is likely that additional genes influence bolting behavior in sugar beet . In Arabmosome 6 , BvCOL1 mosome 2 , and BvFectively . BvFT1 a in beet  and act  BvBBX19 , 16. Theondition . Similarhenotype . The fungar beet .B locus.Due to the highly complex interactions between genotype and environment, initial progress in bolting resistance was obtained by selecting varieties specific for the climates where they would be grown . SelectiThe use of molecular markers can facilitate the detection of unfavorable alleles linked to the bolting tendency, allowing for earlier and more precise selection of non-bolters. Single Nucleotide Polymorphisms (SNPs) are ideal markers for this kind of work since they are spread throughout the genome and represent 90 % of sequence variation among plants . SNP marIn this paper we suggest the identification of a new putative locus involved in the genetic determination of bolting tendency in sugar beets. Two sugar beet pollinators, P1 and P2, characterized respectively by early- and late-bolting habit were subjected to RAD-SNP discovery. 192 SNPs were selected for further SNP association analysis. These SNPs were genotyped on a set of 19 pollinators, each comprising bolted and non-bolted individuals, for a total of 987 samples. The association between SNP genotypes and bolting tendency was tested by fitting one SNP at a time in a logistic regression model. A SNP marker associated with bolting tendency was located on chromosome 6. This SNP was then tested in an independent sugar beet population. The novel associated polymorphism provides further indication of the polygenic nature of bolting tendency in sugar beet.http://bvseq.molgen.mpg.de) to exclude SNPs with nearby flanking polymorphisms within 50 bp. A total of 192 polymorphic SNP between bulks, randomly distributed within and across all chromosomes, were selected for the SNP association analysis. The array of 192 SNPs used in this study along with their corresponding sequences are available as Additional file RAD sequencing of the two DNA bulks, including (respectively) 4 non-bolted P1 and four bolted P2 plants, produced 96,822,109 raw reads of which 81,031,436 (84 %) were of high quality (longer than 100 nt) with an average length of 103.26 nt. RAD paired end sequence assembly was created using the P1 reads. Sequences from the P2 bulk were aligned to reference assembly for P1 using Bowtie (parameter: bowtie -f –v1). The aligned reads revealed a total of 288,843 (~150× coverage) unique consensus RAD tags common between the two bulks. The SNP discovery pipeline highlighted a total of 6,324 SNPs. Contigs were aligned to the sugar beet reference genome . Table 192 SNPs were genotyped on 987 samples from 19 pollinators each comprising both non-bolted and bolted individual plants. The relationship between SNP genotypes and bolting phenotypes was modeled with logistic regression. Among the 192 candidate SNPs, the only significant association was found for SNP183 on chromosome 6  and blasted on NCBI. The resulted NCBI ID was XM_010697593.1. Bv_22330_orky gene and it was not mapped in any gene known to be involved in bolting . As shown in Methods, SNP183 does not cosegregate with the BTC1 locus on chromosome 2. In addition, though both on chromosome 6, SNP183 and BvFL1 are on different (not anchored) scaffolds . Further studies are needed to clarify if SNP183 and BvFL1 could co-segregate.SNP183 was mapped in the sequence of the single intron present in the P = 4.4∙10−7), while the TT genotype was significantly higher in the non-bolting group   Table . The twoBv_22330_orky gene sequence is shown in Fig. The location of SNP183 along the Bv_22330_orky encodes a putative Matrix Metalloproteinase (MMP) causing late flowering and early senescence in Arabidopsis thaliana. In sugar beet, four genes are annotated as MMPs gelatinase A based on the recently annotated genome [Bv5_099660_fneg, Bv1u_021120_ykma, Bv_22320_wuom and Bv_22330_orky.Bv_22330_orky were found in Arabidopsis thaliana by BLASTP homology searches, as already reported in Golldack et al. [Bv_22320_wuom and Bv_22330_orky with 100 % bootstrap support.Five MMPs similar to k et al. ). We conP = 0.0062).The SNP183 was genotyped in 730 individual plants from 11 breeding lines. A TaqMan assay was developed to discriminate rapidly and reliably between the C and T alleles at SNP183 locus. The frequency of the dominant C allele was 66 % in the bolting group and 46 % in the non-bolting group. Based on these results, individual plants carrying the C allele associated to high bolting tendency were discarded from the breeding program. The association between SNP genotype and bolting behavior was tested with a logistic regression model and was mildly significant  programs to select for bolting resistance in sugar beets. MAS approaches to the reduction of bolting tendency are highly desirable in sugar beet breeding, since they are more efficient, faster, and often more reliable and less expensive than phenotypic selection, and allow to breed for complex traits like resistance to bolting. Bolting tendency is a complex trait controlled by environmental and developmental cues and multiple genetic loci . The intBv_22330_orky. While this gene may play a role in bolting control, which has not been previously reported in sugar beet, the SNP183 may actually be in linkage disequilibrium with neighbouring genes associated to bolting tendency. Besides being a marker linked to a gene involved in bolting behaviour, SNP183 -though less likely- could actually have a biological role itself: it can be a silent informative mutation that modifies splicing, if located in the donor/acceptor splice site; or it could affect the micro RNA binding.SNP183 was mapped to the intron sequence of the sugar beet gene Bv_22330_orky was found to code for a matrix metalloproteinase (MMP). MMPs are a family of zinc and calcium dependent proteases and are divided into three subfamilies: gelatinases, collagenases and stromelysins [Arabidopsis, MMPs is a family of proteins that could be implied in flowering [Bv_22330_orky, led to the loss of the first 220 bp. This is also found in rice, where in duplicated blocks, DNA segment loss occurred with high frequency [melysins . Human Mmelysins . The divlowering  and, as lowering . In tobalowering . In sugarequency .BTC1 are able to discriminate between the annual or biennial habit of sugar beet [BTC1) affect bolting tendency in cultivated biennial sugar beets. Therefore, a next challenge is the discovery of additional DNA polymorphisms associated with this trait. As a first specimen of such polymorphism, SNP183 on chromosome 6 can be used -together with other- polymorphisms as a tool to improve selection efficiency and accelerate the development of novel sugar beet varieties displaying low-bolting tendency.Today, molecular markers are used to evaluate sugar beet germplasm only for the presence of annual bolters . Severalgar beet . HoweverOur study provides indication for the association of a DNA polymorphism on chromosome 6 with bolting tendency in sugar beet. The results support the polygenic nature of flowering control in sugar beet confirming the importance of previously reported QTLs. The SNP183, together with other associated polymorphisms, could assist breeding programs aimed at developing germplasm with low bolting tendency. Further studies on this gene will provide new insights into genetic mechanisms of bolting, which are needed to breed for bolting resistance in sugar beet.The plant material used in this study was provided by the Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova . For SNP discovery, two sugar beet pollinators, P1 and P2, characterized respectively by early- and late-bolting habit, were subjected to RAD-sequencing. The majority of P1 plants started to bolt 5 weeks from sowing while P2 plants started to bolt much later (at 15 weeks) after vernalization and in long-daylight conditions. Both P1 and P2 pollinators carrying the allele for biennial habit at the BTC1 locus in the homozygous state .For SNP association analysis, 19 sugar beet pollinators segregating for bolting tendency were evaluated. Approximately 1000 seeds per pollinator were sown early  in a randomized block design at the Experimental Farm of the University of Padova. As expected, several plants for each pollinator died due to cold stress during the early seedling stage. The surviving plants were inspected every week for onset of bolting until June 30, 2013. Every week plants showing stem elongation were scored as bolting individuals while plants that did not show stem elongation were classified as non-bolting individuals . A leaf http://bvseq.molgen.mpg.de) were retained.High-quality genomic DNA, from the parental lines (P1 and P2) used for discovery of markers, was extracted from leaf tissue following the procedure described by Stevanato et al. . DNA sam−1, added to 2.5 μl of TaqMan OpenArray Genotyping Master Mix in a 384 well-plate. Samples from 384 well plate were loaded in the Open Array plate using the AccuFill system. The association between SNP genotypes and bolting tendency was tested by fitting one SNP at a time in a logistic regression model. A logit link function was used in a generalised linear model of the following form:A set of 192 randomly distributed SNPs was selected for SNP association analysis. These SNPs were tested on a set of 19 pollinators, each comprising bolted and non-bolted individuals, for a total of 987 samples. Genotyping was performed using the Quant Studio 12 K Flex Real-Time PCR System and Open Array technology . The PCR reaction was prepared using 2.5 μl of genomic DNA, at a concentration of 10 ng μllogit(p(xi)) is the log-odds of the probability p for plant i of having either high or low bolting tendency; μ is the overall trait mean, populationk and SNPj are the fixed effects of plant population k (19 classes) and SNP locus j, with zij an indicator variable for the genotype of plant i at locus j .where The detected SNP-bolting association was tested in an independent annual beet population. The SNP183 was genotyped in 730 individual plants from 11 breeding lines. A TaqMan assay was developed to discriminate rapidly and reliably between the C and T alleles at SNP183 locus. All 730 plants were subjected to long photoperiod (16 h light / 8 h darkness) and 20.8 % of the plants started to bolt from two weeks after sowing (bolting group), while 79.2 % of plants did not show bolting behavior (non-bolting group). The association between SNP183 and bolting in the validation population was tested with the same logistic regression model used in the discovery population (see Equation (Amino acid sequences were aligned with ClustalW  and phylAll supporting data are included as additional files.


Gene-based SNPs were identified and mapped in pea using five recombinant inbred line populations segregating for traits of agronomic importance.Pisum sativum L.) is one of the world’s oldest domesticated crops and has been a model system in plant biology and genetics since the work of Gregor Mendel. Pea is the second most widely grown pulse crop in the world following common bean. The importance of pea as a food crop is growing due to its combination of moderate protein concentration, slowly digestible starch, high dietary fiber concentration, and its richness in micronutrients; however, pea has lagged behind other major crops in harnessing recent advances in molecular biology, genomics and bioinformatics, partly due to its large genome size with a large proportion of repetitive sequence, and to the relatively limited investment in research in this crop globally. The objective of this research was the development of a genome-wide transcriptome-based pea single-nucleotide polymorphism (SNP) marker platform using next-generation sequencing technology. A total of 1,536 polymorphic SNP loci selected from over 20,000 non-redundant SNPs identified using deep transcriptome sequencing of eight diverse Pisum accessions were used for genotyping in five RIL populations using an Illumina GoldenGate assay. The first high-density pea SNP map defining all seven linkage groups was generated by integrating with previously published anchor markers. Syntenic relationships of this map with the model legume Medicago truncatula and lentil (Lens culinaris Medik.) maps were established. The genic SNP map establishes a foundation for future molecular breeding efforts by enabling both the identification and tracking of introgression of genomic regions harbouring QTLs related to agronomic and seed quality traits.Pea (The online version of this article (doi:10.1007/s00122-014-2375-y) contains supplementary material, which is available to authorized users. Pisum sativum L., 2n = 2x = 14) belongs to the Fabaceae family of flowering plants which includes other important agricultural crops such as soybean, chickpea, lentil, alfalfa, peanut, and common bean. Being a rich source of protein, polysaccharides and providing slowly digestible starch, soluble sugars, fiber, minerals, and vitamins, pea plays an important role in meeting human nutritional needs  genomic markers useful in providing a framework for genetic map development and in assessing genetic diversity, germplasm characterization and have also been used in mapping of quantitative trait loci (QTLs). However, since they are usually not part of the expressed portion of the genome, they are likely to provide, at most, indirect information on functional genes  technology  have become the marker of choice due to their abundance and the availability of high-throughput screening techniques. In humans, their frequency is about one SNP in every 1000 bp while in soybean on average one SNP occurs in every 200–300 bp, with intronic regions showing three times higher frequency than exonic regions  populations.The objective of this research was the development of a transcriptome-based SNP marker resource in pea. We used targeted 3′end cDNA sequencing of eight Pisum sativum cultivars  and two wild accessions P651 (P. fulvum), PI 358610 (P. sativum ssp. abyssinicum)  is a land race collected in Ethiopia (http://www.ars-grin.gov/cgi-bin/npgs/html/taxon.pl?28663). CDC Bronco  were not parents of any of the RIL populations but were included in this research to increase genetic diversity. A single plant of each genotype was selfed to produce enough seed for tissue collection and library preparation. Leaf, stem, flowers and developing seeds were collected from plant materials grown in a growth chamber with 16 h light/8 h dark with day temperature 23 °C and night temperature 15 °C. Additional samples were collected from etiolated seedlings grown on filter paper soaked with sterile water in sealed Petri plates with no light. Five RIL populations, derived from diverse parents, were used for genetic mapping. All except Pop9 are from the University of Saskatchewan field pea breeding program and were developed for the purpose of mapping QTLs for traits of importance including disease resistance and nutritional value. These populations were developed by selfing using the single seed descent method from the F2 generation, then seeds from single F7 plants were bulked. Summarized RIL population parentage and their origins are provided in Supplementary Table S1. In total 586 RILs from five populations were used for mapping. DNA was extracted using a modified CTAB method  to remove smaller fragments (<250 bp). The 3′-fragments of cDNA were recovered using DynBeads M-270 streptavidin (Invitrogen) and then ligated with A-adaptor. Roche 454 Titanium sequencing of the titrated single strand DNA libraries was carried out following the procedure described by Margulies et al. (Five kinds of tissue samples were collected from each accession including leaf (2 week old), stem (before flowering), etiolated seedlings (1 week old), flower (mixed stages) and developing seeds (mixed stages). RNA extraction, 3′-cDNA library construction, and 454 Roche Titanium sequencing were carried out exactly as described in Sharpe et al. . Brieflys et al.  with modA de novo assembly of the CDC Bronco 454 reads was performed using NGen (DNAStar) software as described in Sharpe et al.  with a fP. sativum and P. fulvum accessions was carried out using the software QDD  as described in Sharpe et al.  using thMedicago and soybean genomes with BLAT . The SNPs were selected to assess a variety of different levels of sequence read depth, variable allele frequency, the presence or absence of SNPs in sequence flanking targeted SNPs, and marker quality score (Illumina assay design tool (ADT) score). ADT score predicts success information, validation status, and minor allele frequencies. Single SNP validation was carried out using in-house developed KASP assays as described by Sharpe et al.  on the sMedicago, a filtered set of 2,646 SNPs, where such position duplicates had been removed, was selected. From these, all SNPs associated with a GoldenGate assay ADT score below 0.4 were removed leaving a total of 2,594 SNPs for final selection based upon observed allelic variation amongst the six P. sativum cultivars and a minimal amount of missing data. In total 1,245 SNPs showed a polymorphism among the cultivars, but 138 of these had large amounts of missing data leaving 1,107 selected for the GoldenGate Oligo Pool Assay (OPA) synthesis. From the remaining 1,349 SNPs that did not show any differences among the cultivars, but were polymorphic between either one or both of the P. sativum ssp. abyssinicum and P. fulvum accessions and the reference CDC Bronco, a set of additional markers was chosen. Starting with missing data from just one genotype, additional markers were chosen that had the highest ADT score and a reasonably low amount of missing data (up to four genotypes with missing data). This resulted in 429 additional markers providing a final set of 1,536 SNPs represented on the pea GoldenGate array .From the 8,822 target SNPs identified in 4,194 contigs and selected for potential array development, an additional filtering step was employed to remove potentially heterozygous SNPs by identifying those SNPs that showed less than 100 % of the reads matching either the reference or the alternate allele . These SNPs were removed to avoid potential issues with assay reliability for genotyping in targeted RILs. Sequence data for the contigs surrounding the SNPs were checked for the number of base pairs to the end of the contig, to the next SNP, or to the closest splice site. Those with less than 60 bp in this flanking region were eliminated since they cannot be used for Illumina GoldenGate arrays . All remaining candidate SNPs were submitted to Illumina for assay design and a total of 7,229 SNPs were returned with ADT rank scores that indicate the likelihood of assays working based on an ideal score of 1 and acceptable to utilize with a minimum score of 0.4; preferential selection was given to ones scoring above 0.6 as recommended by Illumina. A filtered set of 3,106 SNPs, in which only the designed SNP marker with the highest ADT score per contig, was retained. Since some contigs overlapped at the same orthologous gene mapping position in http://www.illumina.com/technology/goldengate_genotyping_assay.ilmn). The PCR products generated were scanned for genotying using an Illumina HiScan . GenomeStudio software 2010.3  was used for data clustering and allele calls were visually inspected for errors in automatic allele calling and corrected where deemed necessary. Any calls that were not clearly one allele or the other were reported as missing data to avoid errors. The segregation data in individual RIL populations were subjected to Chi square test to determine deviations from balanced segregation ratios. The markers showing Chi square values more than five were not used, except in the PR-19 RIL population where a higher degree of segregation distortion was present and Chi square values were higher.A total of 586 RILs from five mapping populations (Table http://gmod.org/wiki/Cmap) was also utilized for the visualization of individual maps and to assess the congruency of marker positions and order among different genetic maps.Individual linkage maps were constructed. Maximum likelihood mapping was carried out using using JoinMap 4.0 .An assessment of the syntenic relationship between pea, http://knowpulse2.usask.ca/portal) is a repository of legume genetic and genomic data for pulse crop breeding. The assembled 454 sequencing data for the pea reference cultivar CDC Bronco is available with all polymorphic loci and associated markers (KASP and Illumina GoldenGate arrays) indicated. Potential homologues of a gene of interest can be identified using a BLAST interface based on sequence similarity. This allows for identification of existing markers associated with the gene of interest, as well as export information on specific loci in a variety of formats to aid in marker design. A comparative GBrowse  with a Medicago genomic backbone graphically displays sequence similarity-based homology between legume species providing an alternative approach to finding candidate markers. For an overview of all data made available through this project, visit the project page . All raw read data have been submitted to the NCBI-NIH Short Read Archive ; Alfetta (SRX555303); Nitouche SRX555304, Cooper (SRX555305), Striker (SRX555306), Orb (SRX555307), PI358610 (SRX555310) P651 (SRX555311)).KnowPulse . Of these, 6,701 (30.6 %) non-redundant contigs were identified with significant homology to segments of the sequenced genomes of Medicago or soybean. Of this subset, 4,066 (60.7 %) had significant hits to annotated genes in Medicago, 1,048 (15.6 %) had significant hits to un-annotated regions in Medicago. Of those with no significant hit in Medicago, 1,587 (23.7 %) had a significant hit to annotated genes within soybean. These contigs collectively represent 20,008 SNPs  of the 32 SNPs tested for validation failed across all genotypes (Supplementary Table S4). The other 27 SNPs were successfully separated by KASP assay, with 97 % successfully matching SNP calls with the 454 sequence data in each genotype. Three of the loci with errors were only incorrect in Nitouche and Orb, suggesting they could be heterozygous at these loci. Seven genotypes had no results in the 454 data, so represent new genotypic information for these cultivars. There were five instances of the KASP assays revealing the heterozygous nature of the cultivars, not seen in the 454 sequencing data which was derived from DNA extracted from a single plant. Details regarding the parameters for the selection of SNPs for the final pea GoldenGate array (Ps1536 OPA) are provided in the “Of the 20,008 SNPs, a set of 8,822 in 4,194 contigs could be utilized for GoldenGate SNP assay development based upon there being no other observable nucleotide variation flanking the target SNPs to compromise assay performance. This set of non-redundant and high-quality SNPs comprised of 5,667 transitions (64 %) and 3,079 transversions (36 %) with 76 SNPs that could not be classified due to ambiguity of bases in the reference. An average SNP frequency of 1 SNP per 667 bp in An in silico analysis of a non-redundant set of 29,725 CDC Bronco contigs for the presence of microsatellite repeats revealed 406 (1.4 %) contigs that contained such a repeat with 64 (16 %) of these with a potential polymorphism based upon the available sequence data for all genotypes (Supplementary Table S5).Five pea RIL populations Table  were genSingle-nucleotide polymorphism loci exhibiting a distorted segregation with parental allele frequency of between 0.1 and 0.3 were identified in all populations but mainly in PR-19 . However, the majority (90 %) of these distorted loci exhibited a balanced frequency greater than 0.3 in at least one of the other populations. All but 24 SNP markers from PR-19 with allele frequencies between 0.1 and 0.3 revealed a more balanced frequency in at least one of the other RIL populations. A total of 535 (57 %) loci identified as polymorphic in the parents of the PR-19 population (a cultivar × wild accession population) exhibited extreme segregation distortion with an allele frequency below 0.1 and these loci were not included for further analysis.The pair-wise comparisons of SNP markers shared by RIL populations are summarized in Table The size of the individual maps of the five mapping populations ranged from 358.02 to 691.89 cM with the total of mapped markers between 323 (PR-02) and 492 (Pop9). The Pop9 map also had 94 framework markers and the maximum marker interval distance was 13.51 cM. The analysis allowed the placement of 939 of the 1,009 polymorphic SNP loci to unique positions on the seven linkage groups (PsLGs) of the consensus map covering 771.6 cM . Unfortunately, it is not possible to establish if particular transcript contigs are associated with particular tissues since the methodology utilized did not provide an efficient format for sample indexing, however, it is possible, via comparative sequence analysis, to infer both the gene function and expression characteristics from the model legume genomes, such as Medicago  or the wild P. fulvum genotype (P651). The two wild accessions were included to broaden the genetic base of the germplasm being evaluated and thus to increase the chances for identifying SNPs. Pisum sativum subspp. abyssinicum is thought to have arisen in an independent domestication from Pisum sativum, as has Pisum fulvum. These initial SNPs were filtered to a smaller high-quality set of SNPs based upon read coverage/depth and low levels of ambiguity (<3 reads or <80 % concordance), as well as identified significant homology to either the model Medicago or soybean genomes. This identified a set of 20,008 high-quality SNPs across 6,707 annotated contigs. The significant decrease in the number of high-quality SNPs and contigs likely indicates two things; (1) even though the methodology enables targeted profiling of discrete regions of genes, its effect is diluted because of the presence of a large number of transcripts from a relatively small number of genes in any given tissue type; and (2) the 3′ end of genes can contain large stretches of untranslated regions (UTR) that can have poor levels of sequence homology even with quite closely related species. The set of SNPs selected for SNP assay development  exhibited ratios of transition SNPs (64 %) to transversion SNPs (36 %) and a SNP frequency in P. sativum cultivars (1 SNP per 667 bp) that closely reflects the types of nucleotide conversions and frequency seen in similar transcript studies in pea using 454 sequencing  did not amplify at all, and although an amount of assay failure is to be expected a subsequent analysis of these SNPs indicated that one of them had a flanking SNP in the assay design space and two of them were closely adjacent to exon/intron boundaries within the orthologous P. sativum cultivar Alfetta and the wild P. fulvum accession P651. The remaining populations each had between 22 % (PR-02) and 26 % (Pop9) polymorphic loci, the former being derived from a cross between two modern cultivars (Orb and CDC Striker) and the latter between a modern cultivar and a Chinese landrace (Cameor and China). The substantially greater number of polymorphic loci in the PR-19 cross likely reflects the very large degree of nucleotide diversity that exists in genic regions between the two species  in each population is expected based upon the observed failure rate for the GoldenGate 1,536 SNP assay format  was PR-19, derived from a cross between the P. sativum cross a relatively small proportion of loci (22–26 %) are polymorphic, but collectively a much larger proportion of loci can be mapped. In theory a much higher number of loci could have been mapped in PR-19 since 940 loci were identified as polymorphic between the parental lines, however, many of these loci (57 %) exhibited extreme segregation distortion  that was unique to this population. Of note, the majority of these loci revealed distortion skewed toward the cultivated parent genotype. The scale of this distortion is such that it indicates there were serious factors influencing the expected Mendelian ratios in the RIL population. It is possible that significant amounts of heterozygosity existed in the P. fulvum parent of the F1 such that an alternative undetectable allele is present at many loci and which resulted in the levels of distortion we observed. It is also possible that multiple gametophytic and/or genetic factors limited the representation of one parental allele over the other allele in a segregating population and there are many such reported examples in different crops  SNP assays that produced segregation data suitable for linkage analysis reflected the desire to design an array that would provide utility for genetic mapping across a broad range of crosses. The bi-allelic nature of the SNP assay format means that in any one All of the maps individually contained eight or more independent linkage groups, following the same observation in a single population described by Leonforte et al.  and indiMedicago genome . Together with other resources that have been recently developed Supplementary material 2 (DOCX 153 kb)Supplementary material 3 (DOCX 716 kb)Supplementary material 4 (DOCX 16 kb)Supplementary material 5 (XLSX 436 kb)Supplementary material 6 (XLSX 9 kb)Supplementary material 7 (DOCX 28 kb)Supplementary material 8 (XLSX 848 kb)Supplementary material 9 (XLSX 46 kb)Below is the link to the electronic supplementary material.


Ananas comosus [L.] Merr.) is the third most important tropical fruit in the world after banana and mango. As a crop with vegetative propagation, genetic redundancy is a major challenge for efficient genebank management and in breeding. Using expressed sequence tag and nucleotide sequences from public databases, we developed 213 single nucleotide polymorphism (SNP) markers and validated 96 SNPs by genotyping the United States Department of Agriculture - Agricultural Research Service pineapple germplasm collection, maintained in Hilo, Hawaii. The validation resulted in designation of a set of 57 polymorphic SNP markers that revealed a high rate of duplicates in this pineapple collection. Twenty-four groups of duplicates were detected, encompassing 130 of the total 170 A cosmos accessions. The results show that somatic mutation has been the main source of intra-cultivar variations in pineapple. Multivariate clustering and a model-based population stratification suggest that the modern pineapple cultivars are comprised of progenies that are derived from different wild Ananas botanical varieties. Parentage analysis further revealed that both A. comosus var. bracteatus and A. comosus var. ananassoides are likely progenitors of pineapple cultivars. However, the traditional classification of cultivated pineapple into horticultural groups  was not well supported by the present study. These SNP markers provide robust and universally comparable DNA fingerprints; thus, they can serve as an efficient genotyping tool to assist pineapple germplasm management, propagation of planting material, and pineapple cultivar protection. The high rate of genetic redundancy detected in this pineapple collection suggests the potential impact of applying this technology on other clonally propagated perennial crops.Pineapple ( Ananas comosus (L.) Merr., is a perennial herbaceous fruit crop belonging to the family Bromeliaceae. The crop is cultivated in all tropical and subtropical regions and ranks third in production among noncitrus tropical fruits, following banana (including plantain) and mango. The annual worldwide production reached 21.9 million metric tons in 2012 and the top seven producers  jointly accounted for 90% of the global production .1 The pineapple plant is indigenous to South America.3 The putative center of origin is located in the Paraná–Paraguay River drainages between southern Brazil and Paraguay, based on the diversity distribution of related species and botanical varieties of pineapple in this region.4–6 However, the eastern part of the Guiana shield has also been hypothesized as the center of domestication for pineapple, based on the variation of chloroplast and nuclear DNA markers, the high level of phenotypic diversity, and the large number of primitive cultigens in this area.8 Pineapple was widely cultivated in tropical Americas before the arrival of Christopher Columbus, the first European to see this fruit, in 1493.9Pineapple, 4 Since then, there have been multiple introductions and exchanges of germplasm among the pineapple producing countries. 4 Although many landraces and traditional cultivars exist in the Americas, only a few cultivars have been dispersed to Asia and Africa for use in commercial production.10 About 70% of the world’s production comes from a single cultivar, Smooth Cayenne,10 which is a highly productive pineapple excellent for canning.11 The current fresh fruit pineapple market is largely comprised of two cultivars bred by the Pineapple Research Institute, CO-2 and MD-2.11 Developing new cultivars with desirable resistance and postharvest traits will depend on the available germplasm of this species. The United States Department of Agriculture (USDA) - Agricultural Research Service pineapple germplasm collection in Hilo, Hawaii, is one of the major collections in the world, along with the collections maintained by EMBRAPA/CNPMF in Cruz das Almas, Brazil, and by CIRAD-FLHOR in Martinique. As part of the ARS National Clonal Germplasm Repository for Tropical and Subtropical Fruit and Nut Crops, the collection at Hilo currently maintains over 180 accessions of pineapple cultivars and their wild relatives.The introduction of pineapple into Asia and the Pacific began with the Spaniards in the early sixteenth century and pineapple reached Africa in mid-sixteenth century.in vitro culture. Vegetative propagation has allowed the exchange of germplasm as clones among regions, countries, and continents. However, the exchange of vegetative planting materials has also resulted in problems for conservators of pineapple germplasm because records and labels of the cultivars have not always followed the same naming conventions, and accessions have limited information about their correct identity. Therefore, homonyms and synonyms are common among the names of pineapple cultivars and that restricts the sharing of information and materials among pineapple researchers and hampers the use of pineapple germplasm in breeding.12–14 Another major challenge for pineapple cultivar identification is that the protracted vegetative propagation has led to the accumulation of somatic mutations. Some mutations caused noticeable phenotypic effects and created intra-cultivar variation, which became the target of clonal selection.10 While these selected mutants are important in horticultural production, it is necessary to identify them so that breeders and genebank curators can efficiently conserve and use these genetic materials.As with many other tropical perennial crops, pineapple germplasm is almost exclusively maintained by vegetative propagation, by crowns, slips, suckers, or 14 Using isozyme markers, Aradhya et al. studied pineapple germplasm and found considerable variation within and between species of Ananas.15 In the Hawaii pineapple collection, they identified 66 distinct zymotypes that were able to differentiate all species and botanical varieties. Their results also suggested that, rather than genetic divergence due to reproductive isolating barriers, differentiation among the species of Ananas may be due to ecological isolation, and therefore may represent a species complex.The utilization of biochemical and DNA molecular markers for pineapple germplasm management has been recently reviewed.16–23 In spite of the significant progress in marker-assisted germplasm management over the last 20 years, cultivar identification in pineapple remains a challenging task. Using AFLP markers, Kato et al. characterized 148 A. comosus accessions maintained in the USDA pineapple collection in Hilo, Hawaii.20 They showed that a unique profile for major groups that had been classified by morphological traits, such as ‘Cayenne’, ‘Spanish’, and ‘Queen’, could not be established using AFLP-based DNA fingerprints. SSR markers likewise lacked congruence between phenotype and molecular marker-based classification in pineapple.23 Moreover, neither AFLP nor SSR are the most suitable marker tool for detection of duplicates in the pineapple germplasm.Both dominant DNA markers  and co-dominant markers  have been used to assist pineapple cultivar identification and germplasm management.Theobroma cacao, 2013),24 grapevine ,25 pummelo ,26 strawberry ,27 tea , and longan . Like other perennial horticulture crops, DNA fingerprinting that uses a small set of SNP markers is in great demand by the pineapple community for a broad range of research and field applications. These applications include, but are not limited to, identification of mislabeled accessions, parentage, and sibship analysis for quality control in breeding and seeds programs, and authentication and traceability to support the production of high-value clones for premium markets. Nonetheless, this most powerful tool for germplasm management has not been applied to pineapple germplasm management.Single nucleotide polymorphisms (SNPs) are the most abundant class of polymorphisms in plant genomes. Compared to SSR markers, SNP analysis can be done without requiring DNA separation by size and can, therefore, be automated in high-throughput assay formats. The diallelic nature of SNPs facilitates a much lower error rate in allele calling and promotes compatibility between laboratories. These advantages have resulted in the increasing use of SNPs as the markers of choice for accurate genotype identification and diversity analysis in perennial crops, as recently demonstrated in cacao , includes a more than 5,600 expressed sequence tags (ESTs) with 3,383 consensus sequences. The comprehensive sequence, bioinformatics, and functional classification of EST resources are available for text or sequence-based searches. A draft genome of A. comosus has been developed, which covers about 375 Mb (62%) of the estimated 526 Mb genome of this species.14 These readily available genomic resources provide opportunities for mining new markers to use for pineapple germplasm management and breeding. The objectives of the present study were to develop SNP markers through the data mining of ESTs and transcriptome data and to assess their potential application for pineapple cultivar identification. The results reported herein represent the first validation study of SNPs in pineapple, demonstrating the utility of a transcriptome as an approach for rapid development of a high-quality genotyping tool. These SNP markers, as well as the genotyping method, will be particularly useful for intellectual property rights in varietal protection, germplasm management, and pineapple breeding programs.Ample genomic resources have been developed for pineapple.Ananas spp. were downloaded from NCBI GenBank . Redundant entries were examined and excluded using the CD-HIT program with a 95% sequence similarity threshold. The FASTA-formatted files of pineapple were merged into a single data set for further data mining. Putative EST-SNPs were detected using the QualitySNP program.32 All of these selected clusters included a minimum of six EST sequences, whereas both the minimum redundancy threshold and minimal confidence score required by QualitySNP was set at three. In order to meet the requirements and constraints for primer design, all candidates for SNP markers with less than 50 nucleotides between two neighboring SNPs were removed. A subset of 96 identified SNP sequences was then chosen for design and manufacture of SNP assay.All available nucleotide sequences of http://www.ars.usda.gov/main/site_main.htm?modecode=20-40-05-10) were harvested and dried in silica gel. DNA was extracted from dried pineapple leaves with the DNeasy Plant Mini kit , which is based on the use of silica as an affinity matrix. The dry leaf tissue was placed in a 2-mL microcentrifuge tube with one quarter-inch ceramic sphere and 0.15 g garnet matrix . The leaf samples were disrupted by high-speed shaking in a TissueLyser II (Qiagen Inc.) at 30 Hz for 1 min. Lysis solution (DNeasy kit buffer AP1 containing 25 mg/mL polyvinylpolypyrrolidone), along with RNase A, was added to the powdered leaf samples and the mixture was incubated at 65 °C, as specified in the kit instructions. The remainder of the extraction method followed the manufacturer’s suggestions. DNA was eluted from the silica column with two washes of 50 µL buffer AE, which were pooled, resulting in 100 µL DNA solution. Using a NanoDrop spectrophotometer , DNA concentration was determined by absorbance at 260 nm. DNA purity was estimated by the 260:280 ratio and the 260:230 ratio.To evaluate the putative SNP markers for suitability of cultivar identification, we used a nanofluidic genotyping system and validated the SNPs for 170 pineapple accessions . The pin35 Using these primers, the isolated DNAs were subjected to Specific Target Amplification in order to enrich the SNP sequences of interest.34 Genotyping was performed on a nanofluidic 96.96 Dynamic Array IFC . This chip automatically assembles PCRs, enabling simultaneous testing of up to 96 samples with 96 SNP markers. The use of a 96.96 Dynamic Array IFC for SNP genotyping of human samples has been described by Wang et al.33 End-point fluorescent images of the 96.96 IFC were acquired on an EP1 imager (Fluidigm Corp.). The data were analyzed with Fluidigm Genotyping Analysis Software (Fluidigm Corp.).36Ninety-six putative SNP sequences were submitted to the Assay Design Group at Fluidigm Corp.  for design and manufacture of primers for a SNPtype genotyping panel. The assays were based on competitive allele-specific PCR, and they enable bi-allelic scoring of SNPs at specific loci . The Fluidigm SNPtype Genotyping Reagent Kit was used according to the manufacturer’s instructions.38Duplicate accessions were identified using pairwise multilocus matching among all individual samples. DNA samples that were fully matched at the genotyped SNP loci were declared the same cultivar or clones. The program GenAlEx 6.5  was used for computation.After duplicate identification, the redundant samples were removed and descriptive statistics for measuring the informativeness of the SNP markers were calculated based on the remaining distinctive cultivars. The key descriptive statistics included minor allele frequency, observed heterozygosity, expected heterozygosity, Shannon’s information index, and inbreeding coefficient. Computations were carried out using the same program.39 A dendrogram was generated from the resulting distance matrix using the NJ algorithm available in PHYLIP.41 The unrooted tree was visualized using the web-based tool Interactive Tree of Life v2 (http://itol.embl.de/).42A cluster analysis using the neighbor-joining (NJ) method was used to further examine the genetic relationship among accessions. Kinship coefficient was chosen as genetic distance measurement of shared ancestry among the individual accessions. The computation was executed using MICROSATELLITE ANALYZER .43 This algorithm attempted to identify genetically distinct subpopulations based on allele frequencies. The admixture model was applied and the number of clusters , indicating the number of subpopulations the program attempted to find, was set from 1 to 10. The analyses were carried out without assuming any prior information about the genetic group or geographic origin of the samples. Ten independent runs were assessed for each fixed number of clusters (K), each consisting of 1 × 106 iterations after a burn-in of 2 × 106 iterations. The ΔK value was used to detect the most probable number of clusters and the computation was performed using the online program STRUCTURE HARVESTER.44 Of the 10 independent runs, the one with the highest Ln Pr (X|K) value (log probability or log likelihood) was chosen and represented as bar plots.A model-based clustering algorithm implemented in the STRUCTURE software program was applied to the SNP data.ananassoides, bracteatus, and erectifolius are the putative progenitors for cultivated pineapples, we applied parentage analysis to verify the origin of the 53 accessions in A. comosus var. comosus  was calculated. Critical LOD scores were determined for the assignment of parentage to a group of individuals without knowing the maternity or paternity. Simulations were run for 10 000 cycles, assuming that 10% of candidate parents were sampled, a total of 90% of loci was typed with a 1% typing error rate. The most probable single mother (or father) for each offspring was identified on the basis of the critical difference in LOD scores (D) between the most likely and the next most likely candidate parent at greater than 95 or 80% confidence.46To test the hypothesis that vars. 47 using default parameters, was used to assemble sequences into 1793 contigs and 11 809 singlets with an average size of 3.59 sequences per contig, among which putative SNPs were detected in only 48 contigs using the QualitySNP program. Each of these selected clusters included a minimum of six EST sequences. In total, we obtained 213 putative SNPs, including 75C/T, 59A/G, 10A/T, 12A/C, 4T/G, 11C/G, 41 indel, and 1 high tri-allelic polymorphism. To select high-quality SNPs for validation, candidate SNP sites with at least 50 bp before and after the site were filtered. We calculated the number of all sequences in a cluster and the number containing the SNP type in this cluster. We then selected 96 SNPs for validation by genotyping the 170 pineapple accessions in the USDA-ARS pineapple collection.A total of 13 203 mRNA nucleotide and 5941 EST sequences from pineapple were gathered using methods previously described. After adapter removal, trimming, and quality control, 18 241 higher quality sequences were selected. The program CAP3,Out of the chosen 96 SNP markers, 80 were successful for genotyping. The failure of the remaining 16 SNPs was likely due to the sequence complexity or the presence of polymorphisms within the flanking sequences. However, among the 80 successful SNPs, 23 were monomorphic across the 170 pineapple samples . These monomorphic markers may have resulted from errors in transcriptome sequencing, which then led to the incorrect identification of SNP. It is also possible that some of these SNPs may correspond to rare alleles that were not present in the set of pineapple accessions we analyzed. A total of 57 polymorphic SNPs were retained for further analysis of this sample set. These 57 SNPs were reliably scored across the validation panel and thus were considered true SNPs. The flanking sequences of these 57 SNPs are listed in SNP profiles of the multiple accessions from the same pineapple cultivar showed that genotyping results were highly consistent . MultiloFrom each of the synonymous groups, only one accession was retained and used for subsequent diversity analysis. Among the 170 genotyped accessions, there were 64 accessions with a unique SNP profile. Descriptive statistics were then computed for the 57 polymorphic SNPs across the 64 pineapple accessions with a unique SNP profile and the result is presented in A. comosus vars. ananassoides, bracteatus, and erectifolius, as well as the hybrids derived from these related botanical varieties. Within this cluster, vars. ananassoides bracteatus and erectifolius are clearly separated. This cluster also included several cultivated pineapple clones, such as Bogota, Pina Lisa, and Criolla from Colombia, Bermuda from Barbados, Cayenne Lot 520 from Hawaii, Cabezona from Puerto Rico, and Trinidad from Trinidad. The proximity between these cultivars and the two related botanical varieties indicates that these cultivars are either selected or derived from vars. ananassoides and bracteatus. The two Bolivian accessions (N94-92 Short Fruit#1 and N94-92 Long Fruit#2) were labeled as Ananas species in their passport record data. The cluster result showed that they should be A. comosus var. ananassoides or hybrids derived from A. comosus var. ananassoides.The unrooted NJ tree grouped the 64 accessions into three main clusters . The cluA. comosus var.comosus, including several well-known cultivars such as Cayenne Hilo, Mauritius, and Antigua. Since these three cultivars represent the reference horticultural groups of ‘Cayenne’ (Cayenne Hilo) and ‘Queen’ (Mauritius and Antigua), respectively, their grouping here, in one main cluster, demonstrated that the differences used to designate membership to these two horticultural groups are relatively small, in comparison with the other botanical varieties. The third cluster includes 26 cultivated pineapples that formed a large and diverse group. Within this large cluster there are several important pineapple cultivars such as Criolla from Mexico, Montelirio from Guatemala, and Pernambuco and Manzana from Brazil. The majority of the accessions in this cluster seemed mainly cultivated in South America.The second cluster comprised of exclusively ananassoides were assigned to one Bayesian cluster, whereas the cultivated germplasm, as well as vars. bracteatus and erectifolius, were grouped in a different cluster. The F1 hybrid of Wild Brazil × Plot 520 was confirmed by analysis with STRUCTURE. In addition, several accessions were classified as hybrids of the two clusters, such as N94-92, F1 Ananassoides × Plot 435, Wild Brazil × Cayenne Lot 520, and Cb 32  to 14 accessions . A. comoanalysis . Accessianalysis . For exaanalysis .Despite substantial progress in genomics research on pineapple, advanced molecular tools to support germplasm management are not available. Developing SNP markers from transcriptome sequences has been considered an efficient strategy for non-model species. In the present study, we validated 96 SNP markers based on the transcriptome sequences of pineapple at various development stages and used them to genotype a diverse panel of cultivated and wild germplasm. We obtained a success rate of approximately 60% for marker validation, which demonstrated that this approach can serve as a shortcut for SNP development. As shown in the present study, even a small set of SNP markers can significantly improve accuracy and efficiency in germplasm management.48 But majority of the clones or synonymous groups have been less known to the pineapple community, such as Pernambuco vs. Sugar Loaf, Spanish Samoa vs. Natal, and Ruby vs. Los Banos. Identification of these clone groups will significantly facilitate the efficient exchange, conservation, and use of pineapple germplasm.Reliable identification of pineapple cultivars is invaluable for germplasm conservation and cultivar protection. In the present study, it has been demonstrated that the set of 57 SNP markers was effective for the assessment of genetic identity of pineapple germplasm. Results from multiple clones of the same cultivar showed 100% concordance, demonstrating that the nanofluidic system is a reliable platform for generating pineapple DNA fingerprints with high accuracy. The present result revealed a high rate of genetic redundancy in this pineapple collection. Some of the identified duplicates are well-documented synonymous cultivars. For example, the Cayenne cultivars are known to be derived from a few ancestral pineapple plants that originated from Cayenne, French Guiana.10 are the signature character for the cultivar group Smooth Cayenne. Such a mutation is difficult to detect when a small set of molecular markers are applied. Similar problems were found in fingerprinting projects dealing with other vegetative propagated crops such as bananas , 49 bread fruit (2015),50 and apple .51 More comprehensive genomic approaches, such as next-generation sequencing, would be needed to detect which genes or alleles had been changed, thereby causing the phenotypic variation. For this reason, the reduction of identified duplicates in pineapple germplasm genebank needs to be considered on a case-by-case basis. Characterization of phenotypic traits among the synonymous group members is still essential to complement DNA fingerprinting for genotype identification.However, caution is needed for the interpretation of genetic redundancy in pineapple. It is well known that somatic mutation is common in pineapple. Many phenotypic traits such as spiny leaves, fruit flesh color, acidity, and sugar content of fruit have been well documented. These somatic mutations are the major source of variation exploited for the selection of new cultivars. For example, the spiny or smooth leaf margins, caused by a single gene,A. comosus is a mostly self-incompatible diploid with 2n=2x=50 chromosomes.53 This species includes five botanical varieties of A. comosus: vars. comosus, ananassoides, parguazensis, erectifolius, and bracteatus, based on the revised classification of Coppens d’Eeckenbrugge and Leal.7 The present results show that out of the 170 Ananas accessions maintained in the USDA pineapple collection, there are only 64 distinctive genotypes. Clustering analysis and model-based stratification both showed that A. comosus var. ananassoides differs from A. comosus var. bracteatus, thus supporting the revised taxonomy system that classified A. comosus var. ananassoides and var. bracteatus as two different botanical varieties.7 However, accessions of var. erectifolius were found to have high similarity and grouping closely together with the accessions of var. bracteatus. This result differs with a previous report based on isozyme variation,15 which showed that A. comosus var. erectifolius did not have a distinctive isozyme profile, in comparison with the rest of the A. comosus var. comosus cultivars. Nonetheless, the present study only used two accessions of A. comosus var. erectifolius, which may be a bias in terms of the sample representation. Additional samples of A. comosus var. erectifolius from other genebanks need to be examined and a larger number of SNP markers need to be analyzed to clarify if the classification of A. comosus var. erectifolius should be revised.A. comosus var. ananassoides or A. comosus var. bracteatus, instead of with the rest of the A. comosus var. comosus accessions (A. comosus var. comosus) may be questionable. It would be appropriate to consider cultivated pineapple as a complex of different botanical varieties, with possible significant gene flow among them.The second observation is that several cultivated pineapple accessions  were grouped together with cessions  and 3. T55 Despite these horticultural groups having been adopted by many users, little investigation has been done to show a genetic basis to reinforce this categorization. Kato et al. examined the efficacy of the horticultural groups and reported that the classifications of ‘Cayenne’, ‘Spanish’ and ‘Queen’ were not well supported by AFLP analysis.20 Shoda et al. analyzed 31 pineapple accessions using SSR markers.22 Their results also showed disagreement between the horticultural type and the results of the SSR analysis. The current study showed that the ‘Cayenne’ cultivars have a distinguishable genetic identity, and most of the affiliated accessions were grouped in a single cluster. However, accessions in the other groups did not appear well clustered. For example, cultivars Mauritius and Antigua are two well-known reference cultivars in the ‘Queen’ group, but in the NJ tree , which are dispersed across different clusters as shown in the NJ tree, bracteatus , chili pepper (Capsicum spp.), potato (Solanum spp.), and cacao (T. cacao), as reviewed by Clement et al.57Parentage analysis showed that both vars. ultivars . This reacteatus . Ananas In conclusion, we conducted a study to develop a set of SNP markers for pineapple and employed them for fingerprinting the USDA’s pineapple collection, using a nanofluidic array. This approach enabled us to generate high-quality SNP profiles for the purpose of pineapple cultivar identification. This is a highly useful tool for genebank management, which will also lead to more efficient crop improvement and, furthermore, has the potential to protect intellectual property rights of breeders. Our result also generated significant insight regarding the origin and domestication of pineapple. Efforts to sequence multiple cultivars from the same synonymous groups with somaclonal mutations are underway, in order to gain a comprehensive understanding about the genetic basis for mutation-based changes in important agronomic traits. This information will be highly useful for verification of pineapple cultivars and will improve the efficiency of pineapple genebank operation. The high rate of genetic redundancy detected in this collection, also suggests the potential impact of applying this technology on other tropical perennial crops.