Validation of Molecular Markers Significant for Flowering Time, Plant Lodging, Stem Geometry Properties, and Raffinose Family Oligosaccharides in Pea (Pisum sativum L.)
by
, , , ,
Magdalena Gawłowska
1,*
,
Lesław Lahuta
2,
Lech Boros
3,
Aneta Sawikowska
1,4,5,
Pankaj Kumar
1
,
Michał Knopkiewicz
6,
Zygmunt Kaczmarek
1 and
Wojciech Święcicki
1 1
Institute of Plant Genetics, Polish Academy of Sciences, 34 Strzeszynska Street, 60-479 Poznan, Poland
2
Department of Plant Physiology, Genetics, and Biotechnology, University of Warmia and Mazury, 10-719 Olsztyn, Poland
3
Institute of Plant Breeding and Acclimatization, NRI, 05-870 Radzików, Poland
4
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
5
Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
6
Project Support Center, Adam Mickiewicz University, ul. Wieniawskiego 1, 61-712 Poznań, Poland
*
Author to whom correspondence should be addressed.
Agriculture 2022, 12(8), 1125; https://doi.org/10.3390/agriculture12081125
Submission received: 14 June 2022
/
Revised: 24 July 2022
/
Accepted: 26 July 2022
/
Published: 29 July 2022
(This article belongs to the Special Issue Advances in Genetics and Molecular Breeding of Crops)
Abstract
:The field pea (Pisum sativum L.) is studied as an important grain legume used in both human and animal feed. DNA markers can contribute to the rapid breeding of novel pea cultivars. This study aimed to identify such molecular markers as the number of days to the beginning of flowering, plant lodging, and stem geometry. Phenotypic measurements were recorded during the field trials. Qualitative and quantitative analyses of soluble carbohydrates (e.g., monosaccharides, sucrose, and raffinose family oligosaccharides) in the pea seeds were performed. A t-test was used to detect the significance of markers associated with each trait. Fifteen markers that were significant for thirteen traits were identified in this analysis. The same markers were identified for verbascose concentration in 2013 and 2014 and stem-wall thickness in 2014 and 2015. Our marker for the number of days to the beginning of flowering (AB141) was 4 cM from the AB64 marker, which was identified as a marker linked to days to 50% bloom. We found a negative correlation between lodging score at the end of flowering and stem diameter in the middle (2015, −0.40) of this study set of pea lines. Although similar correlations were detected in the Carneval × MP1401 population, the correlation between lodging at maturity and diameter in the middle and upper stem sections was positive. In markers validation, particularly for polygenic traits, a statistical analysis of the observed characters is an important step for a division of the trait values into a bimodal distribution.
1. Introduction
The pea is one of the most important grain legumes (global acreage is (9.7 Mt) (FAOSATAT 2020). The main advantages are the atmospheric N-fixation by symbiotic root bacteria (nitrogen fertilizers are not required) and high protein content in seeds [1]. The dry pea is used in soups such as dal, and processing green peas, which are canned or frozen, are used as a green vegetable. Forage peas are used for animal feed, cover crops, or green manures. [2]. Great progress has been achieved in pea breeding, mostly by using the gene le (shortened stem) and af (the leaf changed into tendrils) to reduce plant lodging, resulting in higher yield [3]. The application of molecular tools can significantly improve plant breeding. DNA markers used to estimate and select breeding materials based on different plant characteristics contribute to the rapid breeding [4,5]. Marker utility depends on its reliability and repeatability in different genetic backgrounds [6]. The most promising candidates for marker-assisted selection (MAS) are markers anchored in the gene sequence; however, in most cases, tightly linked markers are used [7], especially in the pea, which as an inbreeding crop and has high levels of Linkage Disequilibrium, making it difficult to find causal mutations. MAS is routinely applied in crop-breeding programs. While multiple molecular markers linked to some genes of interest have been identified, MAS continues to be extensively exploited to improve selection efficiency for economically valued traits. Marker validation can be quite easy for monogenic traits or traits with a generally accepted threshold between high and low trait values. The accuracy of the marker band and trait values has been previously reported [8]. MAS references in pea are mostly concerned with pathogen resistance [9,10]. In such cases, the distribution of resistance values is considered bimodal (resistant or susceptible lines). One of the studies performed a comparative analysis between the genotype and the phenotype for the selection of resistance/susceptibility. The study reported a 74% accuracy in the detection of the presence of the respective alleles when compared to the symptomology and ELISA test results [10]. Complications arise when normally distributed quantitative characters without a clear-cut border between high and low character values are considered. Markers have been reported to be significantly associated with Ascochyta blight scores in field experiments involving the pea [11]. Markers linked to lodging resistance were evaluated in pea plants from eight crosses [12]. The mean lodging scores were comparatively assessed among four plant groups and were based on the combination of the presence or absence of the A001 and A004 marker alleles (t-test) [12]. In addition, specific markers linked to major genes have been developed to select against trypsin inhibitors in pea seeds [13]. The averages for the two distinct groups, highlighted for marker patterns and TIA concentrations, were found to be significantly different (Fisher’s test) [13]. Another diagnostic marker, PsMlo, associated with powdery mildew resistance and boron tolerance, and linked markers associated with salinity tolerance across a diverse set of pea germplasms were described by Javid et al. [14]. However, the study group observed trait values with a bimodal distribution and validated the marker–trait correlation based on the percentage of accuracy.
Genomic selection appears to be a promising approach for the prediction of days to flowering and 1000 seed weight. A subset of 331SNPs genotyped in a reference collection of 372 pea accessions was used [15]. Increasing the marker coverage of the genome by using the newly developed GenoPea 13.2 K SNP array may further improve the prediction accuracy [15].
The markers for the present study were selected from our own earlier experiments. The genetic determination of seed oligosaccharide concentration and the trait-linked marker from Wt10245 × Wt11238 mapping population were presented by Gawlowska et al. [16]. However, most of the identified markers were non-sequence-defined or morphological. Therefore, significant markers were indicated by the QTL analysis in Carneval × MP1401 (Gawłowska, not published).
In this study, additional agronomic traits were also considered: the number of days to the beginning of flowering (DTF), stem geometry parameters, and plant lodging. Trait-linked markers were presented by Gawłowska et al. [3]. The aim was to validate the selected markers. Moreover, their usefulness in pea breeding selection was statistically assessed.
The hypothesis was that indicated markers, linked to quantitative characters, will be useful in selection of chosen plant materials.
2. Materials and Methods
2.1. Plant Materials
Twenty-five pea cultivars from the current Polish Register and nine cultivars from German Norddeutsche Pflanzenzucht Hans-Georg Lembke and KWS Lochow GmbH were selected for analysis. The pedigrees of the cultivars are listed in Table 1. All cultivars were white-flowered with no anthocyanin an afila leaf morphology. Only the cultivar named “Set” had normal leaves.
2.2. Genotyping
Total genomic DNA was extracted from the leaf tissues by using a DNeasy Plant Mini Kit (Qiagen, Hilden, Germany). Simple sequence repeat (SSR) markers (AB83, AB141, AC74, AA135, AA81, AA170, and AD135) [17] and legume markers (Leg65 and Leg 194) [18] were amplified by using GoTaq® Flexi DNA Polymerase (Promega, Madison, WI, USA; M8295), following the manufacturer’s protocol. The PsCam marker (PsCam962) was described by Tayeh et al. [19]. GLIP primers (Pis_GEN_9_3_1, Pis_GEN_16, mtmt_EST_03378_02_1, and Pis_GEN_25) were prepared by using the the 6th Framework Program of the European Union GLIP (Grain Legumes Integrated Project) [20]. This information is available at https://cordis.europa.eu/project/id/506223/reporting/pl (accessed on 28 June 2022). The primers described by Gilpin et al. [21] were used (P393 and P482). The SCAR markers A001 and A004 were previously identified in the (Carneval × MP1401) population by Tar’an et al. [22]. The marker OPG9b is a random amplified polymorphic DNA marker (Operon) with a product size of 580 bp.
The PCR reaction conditions used for SSR and sequence-defined marker analyses in a total reaction volume of 10 μL, as well as the thermal cycling protocol, were as follows: 1 × GoTaq Flexi Buffer (Promega), 1.5 mM MgCl2 (Promega), 1 mM dNTPs (ThermoFisher Scientific, Waltham, MA, USA), primer 1 (1 μM), primer 2 (1 μM), 0.6 U GoTaq DNA Polymerase (Promega), and 25 ng DNA template. The thermal cycling protocol comprised 94 °C for 4 min, then 35 cycles of 94 °C for 30 s, appropriate temperature for 1 min, 72 °C for 1 min, and then 72 °C for 5 min and 12 °C ∞. Mapping was performed by using JoinMap 3.0 [23]. The annealing temperature or PCR profile and the restriction enzymes used for polymorphism detection are presented in Supplementary Table S1.
Electrophoresis of the 6-phosphogluconate dehydrogenase (PGD) isoenzyme (extracted from fresh leaf material) was performed on 11% starch gels [24]. Buffor system H was used as described by Cardy et al. [25]. Electrophoresis was performed at 5 °C for 4–5 h. The staining for the enzyme activity was performed according to the procedure described by Wolko and Święcicki [26].
2.3. Sanger Sequencing
Sanger sequencing (BigDye Terminator v.3.1 Cycle Sequencing Kit, Applied Biosystems, Waltham, MA, USA) of the PCR products of the chosen SSR, PsCam, legume, and Operon markers were performed [27]. The appropriate Operon marker band was cut from the agarose gel before sequencing, purified by using the QIAquick Gel Extraction Kit (Qiagen), and re-amplified by using the OPG9b primer. The identification and physical localization of the sequences were performed according to the pea cultivar Cameor genome (version 1.1), using BLASTn analysis with a minimum e-value of 1 × 10−18, bit score of 98, word size of 11, gap existence cost of 1, gap elongation cost of 1, minimum nucleotide match score of 1, and nucleotide mismatch score of −1 [28].
2.4. Phenotyping
Field experiments were conducted in 2013, 2014, and 2015, in Radzików, near Warsaw (20.63° E, 52.23° N). The 2013 and 2014 experiments examined oligosaccharide concentration analysis from seeds, and the 2014 and 2015 experiments examined the agronomic traits. Pea lines were established in four-row 0.5 m2 microplots in a randomized complete block design with three replications. Rows were spaced 20 cm apart, with 5 cm in-row spacing (100 plants m−2) to assess susceptibility to lodging. Typical cultivation practices and treatments were used [29].
The data were recorded for the beginning of flowering (number of days from sowing to the beginning of flowering, DTF, BBCH61, 10% flowers open), plant height (H, cm), and plant lodging. Plant lodging was assessed at the end of blossoming (second term, Lodg2) and plant maturity (third term, Lodg3) on a scale of 1–9 (where 1 = strongly lodged plants and 9 = non-lodged plants). The plant height (cm) was measured as the distance from the soil surface to the top of the plant at maturity. Stem geometry parameters related to lodging were estimated. The diameter (Dia) and thickness of the stem wall (Thic) (mm) were measured between the 2nd and 4th internodes (at the bottom of the stem, B), at the 9th internode (in the middle of the stem, M), and below the first generative node (upper part, T). The diameter and thickness of the stem walls were measured by using digital calipers [3].
Qualitative and quantitative analyses of soluble carbohydrates in the seeds (monosaccharides, myo-inositol, sucrose, galactinol, and raffinose family oligosaccharides, such as raffinose, stachyose, and verbascose) were carried out by using gas chromatography [30]. The seeds were homogenized in a mixer mill, and soluble carbohydrates were extracted. The TMS derivatives of soluble carbohydrates were analyzed through a high-resolution gas chromatography on a ZEBRON ZB-1 capillary column, using a GC210 gas chromatograph (Shimadzu, Kyoto, Japan). The carbohydrates were quantified by using the following standards: glucose, fructose, myo-inositol, sucrose, raffinose, stachyose (purchased from Sigma, St. Louis, MO, USA), verbascose (Megazyme, Gatton, Australia), and galactinol (Research Industries, Lower Hutt, New Zealand). The carbohydrate content was calculated from the standard curve of the appropriate component. The results are presented as mg g−1 dry weight (DW). All data are presented as the mean of three independent replicates. Analyses were conducted at Warmia–Mazury University (Poland) [31].
One- and two-factor analyses of variance were used to test the hypothesis that differences exist between pea cultivars in terms of their agronomic characteristics. The Gabriel procedure [32] was used to divide the set of pea cultivars into homogenous groups. Although in the case of some traits the division into several (3–7) groups was significant, we considered only two homogeneous groups with high and low values for a given trait. The hypothesis regarding the lack of genotype × year interaction (GxY) was rejected based on the value of the F statistic, which is higher than the critical value at the significance level of p = 0.01.
An independent two-sample t-test for the significance of the differences between the two groups, separated by marker alleles, was also performed for each trait across all cultivars. Analysis of variance (ANOVA) was performed by using allelic groups as sources of variation. Significant differences between the means of the allelic groups were indicated for p < 0.05, p < 0.01, and p < 0.001. Measures of the central value (arithmetic mean) and dispersion (range, coefficient of variation, and CV%) of the distribution of pea cultivars were also calculated. Statistical analyses were performed by using the Genstat 21st Edition (VSN Int. http://www.genstat.co.uk (accessed on 2 April 2020)).
3. Results
3.1. Days to Flowering, Lodging, and Stem Geometry Parameters in Pea Cultivars
In the field trials of 34 pea cultivars, the DTF, plant height, lodging, stem diameter, and stem wall thickness were recorded. The studied cultivars differed in terms of DTF. The cultivars were found to flower 58 to 68 days after the sowing date in 2014 and 2015, respectively. In 2014, the earliest flowering cultivars were Navarro, Gregor, Brylant, Cysterski, and Santana (55 days), and the latest were Mentor, Batuta, and Mecenas (61 days). In 2015, the earliest flowering cultivar was Cysterski (67 days), while the latest was Batuta (70 days). On average, more cultivars flowered earlier in 2014 than in 2015 (Table 2).
Plant lodging values at the end of flowering were 7.6 in 2014 and 8.1 in 2015. The lodging index at maturity was, on average, 5.3 in 2014 and 6.8 in 2015. The lodging of the cultivars was lower in 2014 by 6% at the end of flowering, and 22% at maturity than that in 2015. The cultivars Batuta, Mecenas, Medal, Tarchalska, Santana, Casablanca, and Salamanca were characterized by a lower susceptibility to lodging (5.5) than the cultivars Cysterski, Kavalir, Lasso, Set, Terno, and Wenus (5.0) in 2014. In 2015, the cultivars Cysterski, Mentor, Batuta, Mecenas, Medal, and Santana were characterized by a lower susceptibility to lodging (above 8.0) than the cultivars Terno, Goplik, Brutus, and Dymek (below 5.0). Throughout the years, the cultivars Batuta, Mecenas, Medal, and Santana were found to be the most resistant to lodging, whereas the cultivar Terno was found to be the most susceptible
The differences observed in stem diameter, particularly in the wall thickness of a particular stem segment, were small (Table 3). The variation coefficients of these parameters for the cultivars examined in 2015 were also small but higher than those in 2014 (5.3–9.8% in 2014 and 8.8–14.0% in 2015).
Statistical analyses were conducted to select cultivars with high and low values of DTF, plant height, lodging, and stem geometry. The plants were divided into homogenous groups (four significantly different groups in terms of DTF in 2014 and two in 2015; three significantly different groups in terms of stem diameter in the middle and upper parts in 2015; two significantly different groups in terms of plant height in both years; two significantly different groups in terms of lodging in 2015; and two significantly different groups in terms of stem diameter at the bottom and stem wall thickness in 2015). Dividing cultivars into homogenous groups in 2014 was possible only for DTF and plant height and for all described characters in 2015. The division of the two homogenous groups was also included. Two-way ANOVA showed a lack of genotype × year interaction for three characteristics: stem wall thickness at the bottom and upper parts of the stem and stem diameter at the bottom part of the stem (Table 2).
3.2. Soluble Carbohydrates in Seeds of Pea Cultivars
The concentrations of soluble carbohydrates differed widely between the years (Table 4). The lowest concentration of fructose was found in the seeds of cultivar Brylant in both years of plant cultivation. The lowest concentrations of raffinose and stachyose were found in the cultivar Boruta. The highest raffinose content was observed in the seeds of cultivar Zekon. A significant genotype × year interaction was common for all traits for total RFO concentration (raffinose family of oligosaccharides) and respective carbohydrates.
Although for some traits, the division into several (3–7) groups was significant, we considered only two homogeneous groups with high and low values for a given trait (Table 5). We wanted to obtain the division of the trait value closest to the bimodal one.
The total concentration of the RFOs in 2014 was approximately 13% higher than that in 2013 (80.5 mg g−1 versus 70.4 mg g−1). The raffinose concentration was 25% higher in 2014 (16.2 mg g−1) than that in 2013 (12.1 mg g−1), and the stachyose concentration was approximately 14% higher in 2014 than in 2013 (33.7 and 29.1 mg g−1, respectively). The verbascose concentration increased by approximately 27% in 2014 compared to that in 2013 (35 mg g−1 in 2014 versus 25 mg g−1 in 2013). The difference in RFO content calculated as a percentage of total soluble carbohydrates (TSCs) was 3% between the years (68% in 2013 and 71% in 2014).
The ranking of the cultivars based on the value of a given characteristic changed over the two years. However, nine cultivars were classified as having a low total RFO concentration in both years (Batuta, Medal, Tarchalska, Brylant, Casablanca, Boruta, Madonna, Akord, and Alvesta), and seven cultivars had high RFO concentrations (Profi, Salamanca, Starter, Brutus, Baryton, Zekon, and Lasso). Fifteen cultivars (Set, Turkus, Dymek, Mecenas, Salamanca, Gregor, Akord, Santana, Profi, Baryton, La Mancha, Lasso, Wenus, Kavalier, and Starter) were classified as having a high RFO content (% of TSCs) and 11 cultivars (Zekon, Bohun, Goplik, Alvesta, Brutus, Boruta, Ramrod, Medal, Tarchalska, Brylant, and Cysterski) were classified as having a low RFO content (% of TSCs).
A total of 29 cultivars appeared to be in the same groups in both years based on their verbascose concentration (16 in the high-concentration group = Akord, Alvesta, Baryton, Boruta, Brylant, Cysterski, Ezop, Goplik, Medal, Mecenas, Salamanca, Santana, Set, Terno, Turkus, and Wenus; and 13 cultivars in the low-concentration group = La Mancha, Gregor, Kavalier, Lasso, Starter, Merlin, Zekon, Madonna, Profi, Brutus, Bohun, Navarro, and Tarchalska). Based on the stachyose concentration, eight cultivars were in the same groups in both years (one cultivar in the high-concentration group = Merlin; and seven cultivars in the low-concentration group = Boruta, Goplik, Wenus, Turkus, Batuta, Ezop, and Santana). Ten cultivars were found to have a low concentration of raffinose in both years (Boruta, Wenus, Ezop, Brylant, Turkus, Batuta, Tarchalska, Goplik, Akord, and Santana), and 12 cultivars had high concentrations of raffinose (Madonna, Merlin, La Mancha, Terno, Start, Bohun, Dymek, Kavalir, Lasso, Gregor, Navarro, and Zekon). In addition, there was a genotype × year interaction. The cultivars were further separated into groups with high and low RFO concentrations. This was necessary to validate the usefulness of the MAS markers.
3.3. Trait Correlation
A negative correlation between plant height and lodging score was observed (9 = non-lodging; 1 = lodging) (−0.70, 2015). It means that, as the plant height increases, the lodging increases. The strongest correlation observed in 2014 was between the lodging score at the end of flowering and the diameter of the middle stem segment (−0.40). The strongest correlations observed in 2015 were for lodging in both terms of DTF (0.38) (Table 6).
3.4. Closely Linked Markers
Molecular analyses of the studied cultivars revealed 14 closely linked molecular markers (AC74, AA135, AA81, AB141, AB83, PsCam962, Pis_GEN_9_3_1, Pis_GEN_16, mtmt_3378, P393, A001, A004, Leg_65, and OPG9b) and one isozyme (Pgd-p). Three of these elements (AC74, AA135, and Pgd-p) were close to the oligosaccharide concentration QTLs. Oligosaccharide concentration markers were selected based on the previous investigations of QTLs in Carneval × MP1401 (not published, Figure 1) and Wt10245 × Wt11238 populations [16]. Four markers (AB141, Leg_65, Pis_GEN_9_3_1, and Pis_GEN_16) were linked to DTF. Eleven markers were found to be significant for plant height, lodging, and stem geometry.
The respective total oligosaccharide concentrations measured in the seed dry weight (mg g−1) and as a percentage of TSC are presented within parentheses. The markers were resolved on a 2% agarose gel against a size ladder presented on the left edge well (L). The accession order was as follows: 1. Batuta (71.0, 0.65), 2. Mentor (72.0, 0.69), 3. Casablanca (63.0, 0.70), 4. Bohun (72.7, 0.71), 5. Merlin (76.3, 0.77), 6. La Manche (71.5, 0.68), 7. Akord (79.4, 0.71), 8. Lasso (70.8, 0.65), 9. Mecenas (61.0, 0.63), 10. Baryton (74.8, 0.74), 11. Medal (71.3, 0.7), 12. Alvesta (79.3, 0.68), 13. Brylant (67.3, 0.68), 14. Set (74.0, 0.71), 15. Starter (65.6, 0.69), 16. Cysterski (68.0, 0.64), 17. Tarchalska (60.5, 0.70), 18. Salamanca (74.3, 0.65), 19. Boruta (66.2, 0.68), 20. Profi (68.2, 0.66), 21. Santana (87.1, 0.73), 22. Brutus (84.9, 0.72), 23. Ramrod (69.3, 0.70), 24. Gregor (65.6, 0.74), 25. Goplik (69.2, 0.69), 26. Wenus (69.0, 0.77), 27. Kavalir (68.6, 0.77), 28. Zekon (74.8, 0.68), 29. Dymek (71.7, 0.74), 30. Terno (75.3, 0.71), 31. Navarro (75.2, 0.71), 32. Ezop (67.1, 0.72), 33. Turkus (64.6, 0.72), 34. Madonna (77.6, 0.72), C. Carneval (75, 0.74), and MP. MP1401 (73, 0.71).
The linked markers in the 34 pea cultivars were determined by using ANOVA. The cultivars were divided into two groups based on allelic variants I or II of each marker (Figure 2). Markers that divided the cultivars into significantly different groups are listed in Table 7.
Eight markers had significant (p < 0.05) associations with the DTF, lodging, and stem geometry parameters in 2014. Six markers had significant (p < 0.05) associations with the DTF, lodging, and stem geometry parameters in 2015. Furthermore, the T-test indicated that three markers and one marker had significant (p < 0.05) associations with oligosaccharide concentration scores in 2013 and 2014, respectively. For example, alleles of the marker AA135 divided cultivars into two significantly different groups in terms of the percentage of RFO content (% of TSCs) in 2013. The isozymic marker Pgd-p grouped cultivars according to fructose concentrations. AC74 was linked to the concentrations of verbascose and RFOs (Figure 2).
PsCam962, AA81, AB141, mtmt_3378, and AB83 were significant stem geometry markers in 2014. PsCam962, AB141, Pis_GEN_9_3_1, A001, A004, and P393 were significant stem geometry markers in 2015. PsCam962 and AB141 markers were significant in both years (Table 7). A001, P393, AB141, and Pis_GEN9_3_1 were found to be significantly associated with lodging.
In QTL Cartographer, the sign of the additive effect shows the effect of the parent you declared as female (based on the software code rules). Therefore, if the additive effect of a locus is negative, the allele coming from “mother” reduces the value, regardless of the mother being more tolerant or susceptible.
4. Discussion
The study examined important agronomic traits in pea, such as days to flowering (DTF), stem geometry parameters, plant lodging, and seed oligosaccharide concentration. The aim of this study was to validate the linked markers. Marker validation can be quite easy with traits controlled by a single gene. The most complicated case is when we consider quantitative characters with a normal distribution, without a clear-cut border between high and low character values. The usefulness of these markers for pea breeding selection was statistically assessed.
4.1. The Variability of the Agronomic Traits
In our study, the cultivars were found to flower by 58 and 68 days from the sowing date in 2014 and 2015, respectively. According to Gali et al. [33] the days to flowering varied from 49 to 59. Huang et al. [34] stated that days to flowering varied significantly based on locations and year.
We found a negative correlation between lodging score at the end of flowering (9 = non-lodging; 1 = lodging) and mid-stem diameter (2015, −0.40) of our set of pea lines. While similar correlations were detected in the Carneval × MP1401 population, they were found to be positive [3]. Hence, we confirmed that pea lodging resistance is a complex and quantitative characteristic related to stem geometry and length, plant architecture, and leaf type [3]. The most common cause of lodging is the combined action of wind and rain [35]. The estimation of lodging resistance in field could be, therefore, rather difficult (even for afila and short stem pea). However, we identified four cultivars (Batuta, Mecenas, Medal, and Santana) as being the most resistant to lodging, and one cultivar was the most susceptible to lodging (Terno); it was the most susceptible one in both 2013 and 2014.
According to Zhang [36], there may be a balance between factors, such as plant height, stem stiffness, and lodging performance. A few studies have emphasized the existence of this balance in crop production and breeding. For example, Obraztsov and Amelin [37] claimed that the optimum stem length for lodging resistance in pea plants is 60–90 cm. Therefore, lodging resistance may be positively correlated with the stem length within a certain range.
In our study, the strongest positive correlations were observed in 2015 for lodging at the end of blossoming score (second term, Lodg2) and plant maturity score (third term, Lodg3) with DTF (0.35 and 0.38). It means that lodging decreases and the number of days to flowering increases. Zhang [36] also reported that lodging was correlated with days to flowering and days to maturity in three out of eight populations, out of which it was negative in two populations and positive in one.
The difference in the concentration of TSC, as well RFO, in the pea seeds of each cultivar between the years of cultivation could be due to different environmental conditions during plant vegetation, as has been documented for the peanut (Arachis hypogaea L.) [38], soybean (Glycine max L. Merr.) [39], lentil (Lens culinaris Medikus subsp. culinaris) [40], and chickpea (Cicer arietinum L.) [41]. Our study showed that the months of April and July had similar temperatures during 2013 and 2015; however, these months were warmer in 2014 (by 2.2 °C in April and 1.4 °C in July) (Supplementary Figure S1). The level of precipitation was lower in 2015 than in 2014 (by 6.2 mm in April, 31.2 mm in May, 35.8 mm in June, and 41.6 mm in July) (Supplementary Figure S1). The highest level of precipitation was observed in 2013, except in July, when the precipitation level was the lowest (Supplementary Figure S1). It is possible that there was a shortage of water in the collection phase of reserve materials, which usually results in a weaker accumulation of the main reserve materials, with the simultaneous accumulation of larger amounts of RFOs at lower precipitation [42,43,44]. The accumulation of higher amounts of RFOs in the seeds has also been reported to intensify when they prematurely enter the desiccation phase [42,44], possibly owing to abscisic acid [45]. While information about soluble carbohydrates in pea seeds is valuable for breeders, variance can make the selection of these traits difficult.
Environmental conditions influence seed development and composition. However, as we concluded earlier for mapping populations (2002 and 2004), there was a lack of genotype × year interaction [16]. Genotype × year interaction was defined as the unequal response of the average value of the genotype trait for weather conditions and the pressure of diseases and pests in different years [46]. The occurrence of G × Y interactions depend on both environmental conditions and the set of genotypes tested. If the weather conditions and differences between genotypes were similar over the years examined, the G × Y interaction may not have been statistically significant. In our study, regardless of the differences in the final concentration of sugars in mature pea seeds, an important additional trait of cultivars seems to be the occurrence of verbascose or stachyose as the predominant oligosaccharide within the RFO fraction. Among the 34 tested cultivars, the predominance of stachyose or verbascose was maintained in 30 cultivars throughout both the years of cultivation (data not shown). A previous study on the chickpea (Cicer arietinum L.), revealed a significant impact (p ≤ 0.001) (ANOVA) of genotype, environment, and their interaction on RFO concentration in seeds of both desi- and kabuli-types [41]. Piotrowicz-Cieślak [47] reported that temperature conditions affect the content of six soluble carbohydrates in maturing seeds of yellow lupin (Lupinus luteus L.). Seeds that matured at a constant temperature (26 °C) accumulated more raffinose (by 100%) and less stachyose (by 45%) and verbascose (by 24%) than seeds that matured at the optimum temperature regime. Carbohydrate levels were also altered by drought. The accumulation of RFO decreases under drought stress [47]. Sucrose and hexose levels increased, whereas starch levels decreased, suggesting the induction of starch hydrolysis and sucrose synthesis [48]. In developing field bean (Vicia faba L. var. minor) seeds, the reduction in starch accumulation observed during drought stress coincided with an increase in the level of RFOs [44]. The effects of genotype × year interactions are unpredictable and may not be useful for plant breeding programs. However, this should be assessed in future breeding programs. Such interactions are useful for predicting the repeatability of variation in the genotype trait value and the risk of failure in the production of a registered cultivar [46]. Although the impact of the environment (location and year) and the genotype × year interaction on seed traits was statistically significant in our study, we identified nine cultivars with low total RFO concentrations in both years (Batuta, Medal, Tarchalska, Brylant, Casablanca, Boruta, Madonna, Akord, and Alvesta) and seven cultivars with high RFO concentrations (Profi, Salamanca, Starter, Brutus, Baryton, Zekon, and Lasso). From the breeder’s standpoint, the relative proportion of genetic effects is the most important factor in deciding whether data from a specific location or year should be used to select genotypes with the desired traits [34].
4.2. Evaluation of Closely Linked Markers
Sari et al. [49] reported that flowering in peas is controlled by five loci: the late flowering (Lf) locus prevents flowering on long and short days (chr6LG2), the high response (Hr) locus supports early flowering in short days and reduces the photoperiod response (Chr5LG3), the sterile nodes (Sn) locus recognizes a response to the photoperiod (chr7LG7), and the early (E) locus induces early flowering in some genetic backgrounds (chr1LG6). The Die Neutralis (Dne) locus is known to reduce the photoperiod response and supports early flowering in short days (Chr5LG3) [47]. Fondevilla et al. [50] described QTL dfII.1 connected with DF days to 50% bloom in LG2 near P484. In our map, the distance between Leg65 and P482 was 29 Mbp (AA18-Leg65 6 cM at the Wt10245 × Wt11238 map and AA18-P482 5 cM at the Carneval × MP1401 map). Fondevilla et al. [50] reported no homology between their dfII.1 near P482 and QTL flo1, as detected by Prioul et al. [51]. The distance between the DTF QTL (Leg65) and Lf gene was 23 Mbp [28] (Supplementary Table S1). According to Weller and Ortega [52], the HR locus (Chr5LG3) is homologous to ELF3, and the Dne locus (Chr5LG3) is homologous to ELF4. The Spanish QTL dfIII.1 is located in the region of the AB64 marker [50]. The DTF marker (AB141) was 4 cM away from the AB64 marker [1]. However, the AB141 marker was located distantly from other reported flowering loci (333 Mbp from ELF4 and 496 Mbp from ELF3) [53,54,55] (Supplementary Table S1). The marker Pis_GEN_9_3_1 (chr3LG5) was located 62 Mbp from QTLs of DTF detected (PsC12850p269) in Gali et al. [33]. Jiang et al. [56] reported 22 markers associated with DTF that were detected by association mapping. Their marker PsC19344p128 was located 10 Mbp from our DTF marker AB141 (Chr5LG3) [28]. Locus E (chr1LG6) was 10 cM away from the Gsn2 marker [57]. Our DTF marker, Pis_GEN_16, was 118 Mbp from the Gsn2 marker.
The markers in Gawłowska et al. [3] were confirmed in this study. The markers connected to the stem diameter were P393, PsCam962, and A004. Markers P393 and A004 also divided the pea cultivars according to their stem diameter, stem wall thickness, and lodging. Markers A004, AA81, and AB141 have been linked to plant height [3]. In our study, the markers AB141 and AA81 were associated with the stem diameter, stem wall thickness, and lodging. Marker AB83, which is significant for lodging [3], was found to be significant for stem diameter in our study.
A t-test was used to estimate the significance of differences between marker classes similar to Zhang report [36]. The A001 and A004 markers were more useful than A002 in MAS for selecting lodging resistance in pea. Considering quantitative traits, such as yield and lodging resistance, the identification of markers determining the high percentage of variation in the examined trait is important. Because quantitative traits are unstable under different conditions, repetitions in many environments and years are necessary to reduce the impact of environmental variability on QTL marker identification. Thus, combining two or more MAS markers may yield a better result. Phenotypic selection is a better solution when QTLs are responsible for a low percentage of phenotypic variability. MAS is suitable when markers are tightly linked to target traits and express a high proportion of phenotypic variation [36]. Keller et al. [58] pointed out that lodging resistance should be increased by indirectly selecting the height and stiffness of plants together with MAS for QTLs that do not coincide with other QTLs for morphological traits.
Dumont et al. [59] mapped two QTLs of sugar concentration in the LG5 and LG6 Champagne × Terese populations. The raffinose and glucose concentration QTLs occurred on Chr3LG5 (AA475-AA163) and Chr1LG6 near AD159. The QTL of sucrose concentration on the Wt10245 × Wt11238 map linked with the marker OPG9B (Chr3LG5) was located 7.7 cM from AB146 [3,16], and the distance between markers AB146 and AA163 was 29.1 cM [1]. Marker AC74 Chr1LG6, linked with our study’s oligosaccharide concentration QTLs, was located 37 cM above the marker AD159 (the raffinose concentration QTL), according to the Aubert et al. [60] map, and 20 cM above AD51, according to Burstin et al. [15]. Marker afp14f Chr1LG6 (marker mt0082 from the Wt10245 × Wt11238 map, linked with galactinol concentration [16]), according to the physical sequences, was located at 23 Mbp by Dumont et al. [59] QTL RafT2.b was located near the AD159 marker (the concentration of raffinose QTL) and 42.8 cM from Sus3 marker.
Fowler et al. [61] observed that raffinose accumulation and degradation in sucrose and galactose delayed floral initiation in barley, and this could be correlated with an increase in resistance to cold temperatures (the reproductive state is the most sensitive to frost). Dumont et al. [59] confirmed an increase in the raffinose content in seedlings during cold acclimation. In our study, DTF was significantly associated with PisGen16 in Chr1LG6. DTF (2014) was correlated with total RFO (2014).
5. Conclusions
The usefulness of molecular markers in MAS depends on many factors, such as a close linkage between a marker and a gene or a genome region controlling a given character, a clear border between high- and low-trait values, the mode of inheritance of a trait, and the influence of the environment on trait expression.
In markers validation, particularly for polygenic traits, a statistical analysis of the observed characters is an important step for a division of the trait values into a bimodal distribution.
Fifteen markers were selected for 13 traits, which divided cultivars into groups that were clearly differentiated: total RFO concentration, percentage of RFO in TSCs, fructose and verbascose content, thickness of stem wall in the middle and the top of the stem, stem diameter, plant height, DTF, and lodging in the second and third terms. Only two markers—verbascose content (AC74) and stem wall thickness in the middle part of the stem (PsCam962)—were identified as being repetitive in both years of investigation.
Although only two markers were found to be repetitive in the years studied, the MAS studies need to be continued because most of the agricultural features are quantitative/polygenic.
Supplementary Materials
The following are available online at www.mdpi.com/article/10.3390/agriculture12081125/s1, Figure S1: Weather data in Radzików in 2013, 2014, and 2015; Table S1: The thermal cycling protocols, annealing temperatures and restriction enzymes used to visualize polymorphism of the markers; Table S2: Physical localization of the markers linked with QTL. References [12,16,20,28,33,50,53,54,55,56,57,59,62,63,64,65,66] are cited in the Supplementary Materials.
Author Contributions
Conceptualization, M.G. and W.Ś.; validation, W.Ś.; formal analysis, A.S. and Z.K.; investigation, M.G., L.L., L.B., P.K. and M.K.; resources, L.B.; writing—original draft preparation, M.G. and W.Ś.; writing—review and editing, M.G., L.L., L.B., A.S., P.K., M.K., Z.K. and W.Ś.; supervision, W.Ś.; project administration, M.G.; funding acquisition, W.Ś. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Opening National and Regional Programs for Transnational Collective Research between SME Associations and Research Organizations (CORNET 15th) [Innovative Protein Products from Sustainably Grown Legumes for Poultry Nutrition (ProLegu)].
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank Norddeutsche Pflanzenzucht Hans-Georg Lembke and KWS Lochow GmbH, Polish breeding companies and Polish Pisum Gene Bank for providing pea seeds for the analyses. The authors gratefully acknowledge Jamin Schmitzger (Washington State University, USA) for constructive feedback and language correction.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Bourion, V.; Rizvi, S.; Fournier, S.; Larambergue, H.; Galmiche, F.; Marget, P.; Duc, G.; Burstin, J. Genetic Dissection of Nitrogen Nutrition in Pea through a Qtl Approach of Root, Nodule, and Shoot Variability. Theor. Appl. Genet. 2010, 121, 71–86. [Google Scholar] [CrossRef]
- Robinson, G.H.J.; Domoney, C. Perspectives on the Genetic Improvement of Health- and Nutrition-Related Traits in Pea. Plant Physiol. Biochem. 2021, 158, 353–362. [Google Scholar] [CrossRef]
- Gawłowska, M.; Knopkiewicz, M.; Święcicki, W.; Boros, L.; Wawer, A. Quantitative Trait Loci for Stem Strength Properties and Lodging in Two Pea Bi-Parental Mapping Populations (Pisum sativum L.). Crop Sci. 2021, 61, 1682–1697. [Google Scholar] [CrossRef]
- Mohan, M.; Nair, S.; Bhagwat, A.; Krishna, T.; Yano, M.; Bhatia, C.; Sasaki, T. Genome Mapping, Molecular Markers and Marker-Assisted Selection in Crop Plants. Mol. Breed. 1997, 3, 87–103. [Google Scholar] [CrossRef]
- Collard, B.C.; Mackill, D.J. Marker-Assisted Selection: An Approach for Precision Plant Breeding in the Twenty-First Century. Philos. Trans. R. Soc. B Biol. Sci. 2008, 363, 557–572. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Renshaw, D.; Yang, H.; Yan, G. Development of a Co-Dominant DNA Marker Tightly Linked to Gene Tardus Conferring Reduced Pod Shattering in Narrow-Leafed Lupin (Lupinus Angustifolius L.). Euphytica 2010, 176, 49–58. [Google Scholar] [CrossRef]
- Masouleh, A.K.; Waters, D.L.; Reinke, R.F.; Henry, R.J. A High-Throughput Assay for Rapid and Simultaneous Analysis of Perfect Markers for Important Quality and Agronomic Traits in Rice Using Multiplexed Maldi-Tof Mass Spectrometry. Plant Biotechnol. J. 2009, 7, 355–363. [Google Scholar] [CrossRef] [PubMed]
- Kroc, M.; Czepiel, K.; Wilczura, P.; Mokrzycka, M.; Święcicki, W. Development and Validation of a Gene-Targeted Dcaps Marker for Marker-Assisted Selection of Low-Alkaloid Content in Seeds of Narrow-Leafed Lupin (Lupinus angustifolius L.). Genes 2019, 10, 428. [Google Scholar] [CrossRef] [PubMed]
- Mc Phee, K.E.; Inglis, D.A.; Gundersen, B.; Coyne, C.J. Mapping Qtl for Fusarium Wilt Race 2 Partial Resistance in Pea (Pisum sativum). Plant Breed. 2012, 131, 300–306. [Google Scholar] [CrossRef]
- Smýkal, P.; Šafářová, D.; Navrátil, M.; Dostalová, R. Marker Assisted Pea Breeding: Eif4e Allele Specific Markers to Pea Seed-Borne Mosaic Virus (Psbmv) Resistance. Mol. Breed. 2010, 26, 425–438. [Google Scholar] [CrossRef]
- Jha, A.B.; Gali, K.K.; Banniza, S.; Warkentin, T.D. Validation of Snp Markers Associated with Ascochyta Blight Resistance in Pea. Can. J. Plant Sci. 2019, 99, 243–249. [Google Scholar] [CrossRef]
- Zhang, C.; Tar’an, B.; Warkentin’, T.; Tullu, A.; Bett, K.E.; Vandenberg, B.; Somers, D.J. Selection for Lodging Resistance in Early Generations of Field Pea by Molecular Markers. Crop Sci. 2006, 46, 321–329. [Google Scholar] [CrossRef]
- Page, D.; Aubert, G.; Duc, G.; Welham, T.; Domoney, C. Combinatorial Variation in Coding and Promoter Sequences of Genes at the Tri Locus in Pisum sativum Accounts for Variation in Trypsin Inhibitor Activity in Seeds. Mol. Genet. Genom. 2002, 267, 359–369. [Google Scholar] [CrossRef]
- Javid, M.; Rosewarne, G.M.; Sudheesh, S.; Kant, P.; Leonforte, A.; Lombardi, M.; Kennedy, P.R.; Cogan, N.O.; Slater, A.T.; Kaur, S. Validation of Molecular Markers Associated with Boron Tolerance, Powdery Mildew Resistance and Salinity Tolerance in Field Peas. Front. Plant Sci. 2015, 6, 917. [Google Scholar] [CrossRef]
- Burstin, J.; Salloignon, P.; Chabert-Martinello, M.; Magnin-Robert, J.-B.; Siol, M.; Jacquin, F.; Chauveau, A.; Pont, C.; Aubert, G.; Delaitre, C.; et al. Genetic Diversity and Trait Genomic Prediction in a Pea Diversity Panel. BMC Genom. 2015, 16, 105. [Google Scholar] [CrossRef] [PubMed]
- Gawlowska, M.; Lahuta, L.; Święcicki, W.; Krajewski, P. Variability in the Oligosaccharide Concentration in Seeds of the Mapping Population of Pea (Pisum sativum L). Czech. J. Genet. Plant Breed. 2014, 50, 157–162. [Google Scholar] [CrossRef]
- Loridon, K.; Mc Phee, K.; Morin, J.; Dubreuil, P.; Pilet-Nayel, M.; Aubert, G.; Rameau, C.; Baranger, A.; Coyne, C.; Lejeune-Henaut, I. Microsatellite Marker Polymorphism and Mapping in Pea (Pisum sativum L.). Theor. Appl. Genet. 2005, 111, 1022–1031. [Google Scholar] [CrossRef]
- Fredslund, J.; Schauser, L.; Madsen, L.H.; Sandal, N.; Stougaard, J. Prifi: Using a Multiple Alignment of Related Sequences to Find Primers for Amplification of Homologs. Nucleic Acids Res. 2005, 33, W516–W520. [Google Scholar] [CrossRef]
- Tayeh, N.; Aluome, C.; Falque, M.; Jacquin, F.; Klein, A.; Chauveau, A.; Berard, A.; Houtin, H.; Rond, C.; Kreplak, J.; et al. Development of Two Major Resources for Pea Genomics: The Genopea 13.2k Snp Array and a High-Density, High-Resolution Consensus Genetic Map. Plant J. 2015, 84, 1257–1273. [Google Scholar] [CrossRef]
- Święcicki, W.; Gawłowska, M.; Bednarowicz, M.; Knopkiewicz, M. Localization of the Common Markers on the Pea Maps Wt10245 X Wt11238, Carneval X Mp1401 and P665 X Messire (Pisum sativum L.). Sci. Med. 2012, 3, 229–234. [Google Scholar]
- Gilpin, B.J.; McCallum, J.A.; Frew, T.J.; Timmerman-Vaughan, G.M. A Linkage Map of the Pea (Pisum sativum L.) Genome Containing Cloned Sequences of Known Function and Expressed Sequence Tags (Ests). Theor. Appl. Genet. 1997, 95, 1289–1299. [Google Scholar] [CrossRef]
- Tar’an, B.; Warkentin, T.; Somers, D.J.; Miranda, D.; Vandenberg, A.; Blade, S.; Woods, S.; Bing, D.; Xue, A.; DeKoeyer, D.; et al. Quantitative Trait Loci for Lodging Resistance, Plant Height and Partial Resistance to Mycosphaerella Blight in Field Pea (Pisum sativum L.). Theor. Appl. Genet. 2003, 107, 1482–1491. [Google Scholar] [CrossRef] [PubMed]
- Joinmap Version 3.0. Software for the Calculation of Genetic Linkage Maps; Plant Research International B.V.: Wageningen, The Netherlands, 2001.
- Gottlieb, L.D. Enzyme Differentiation and Phylogeny in Clarkia franciscana, C. rubicunda and C. amoena. Evolution 1973, 27, 205–214. [Google Scholar] [CrossRef]
- Cardy, B.; Stuber, C.; Goodman, M. Techniques for Starch Gel Electrophoresis of Enzymes from Maize (Zea mays L.); North Carolina State University: Raleigh, NC, USA, 1980. [Google Scholar]
- Wolko, B.; Święcicki, W.K. The Application of Electrophoretic Methods of Isozymes Separation for Genetical Characterization of Pea Cultivars. Genet. Pol. 1987, 19, 89–99. [Google Scholar]
- Sanger, F.; Nicklen, S.; Coulson, A.R. DNA Sequencing with Chain-Terminating Inhibitors. Proc. Natl. Acad. Sci. USA 1977, 74, 5463–5467. [Google Scholar] [CrossRef]
- Kreplak, J.; Madoui, M.A.; Capal, P.; Novak, P.; Labadie, K.; Aubert, G.; Bayer, P.E.; Gali, K.K.; Syme, R.A.; Main, D.; et al. A Reference Genome for Pea Provides Insight into Legume Genome Evolution. Nat. Genet. 2019, 51, 1411–1422. [Google Scholar] [CrossRef]
- Strażyński, P.; Mrówczyński, M.; Horoszkiewicz-Janka, J.; Kozłowski, J.; Księżak, J.; Matyjaszczyk, E.; Osiecka, A.; Kierzek, R.; Krawczyk, R.; Korbas, M.; et al. Metodyka Integrowanej Ochrony Grochu Siewnego Dla Producentów; Strażyński, P., Mrówczyński, M., Eds.; Instytut Ochrony Roślin–Państwowy Instytut Badawczy: Poznań, Poland, 2014; ISBN 978-83-64655-01-2. (In Polish) [Google Scholar]
- Gawłowska, M.; Święcicki, W.; Lahuta, L.; Kaczmarek, Z. Raffinose Family Oligosaccharides in Seeds of Pisum Wild Taxa, Type Lines for Seed Genes, Domesticated and Advanced Breeding Materials. Genet. Resour. Crop Evol. 2017, 64, 569–579. [Google Scholar] [CrossRef]
- Lahuta, L.B. Biosynthesis of Raffinose Family Oligosaccharides and Galactosyl Pinitols in Developing and Maturing Seeds of Winter Vetch (Vicia villosa Roth.). Acta Soc. Bot. Pol. 2006, 75, 219–227. [Google Scholar] [CrossRef]
- Gabriel, K.R. A Procedure for Testing the Homogeneity of All Sets of Means in Analysis of Variance. Biometrics 1964, 20, 459–477. [Google Scholar] [CrossRef]
- Gali, K.K.; Liu, Y.; Sindhu, A.; Diapari, M.; Shunmugam, A.S.K.; Arganosa, G.; Daba, K.; Caron, C.; Lachagari, R.V.; Tar’an, B.; et al. Construction of High-Density Linkage Maps for Mapping Quantitative Trait Loci for Multiple Traits in Field Pea (Pisum sativum L.). BMC Plant Biol. 2018, 18, 172. [Google Scholar] [CrossRef]
- Huang, S.; Gali, K.K.; Tar2019an, B.; Warkentin, T.D.; Bueckert, R.A. Pea Phenology: Crop Potential in a Warming Environment. Crop Sci. 2017, 57, 1540–1551. [Google Scholar] [CrossRef]
- Niu, L.; Feng, S.; Ding, W.; Li, G. Influence of Speed and Rainfall on Large-Scale Wheat Lodging from 2007 to 2014 in China. PLoS ONE 2016, 11, e0157677. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C. Implementation of Marker-Assisted Selection for Lodging Resistance in Pea Breeding. Ph.D. Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 2004. [Google Scholar]
- Obraztsov, A.S.; Amelin, A.V. Ideotype of Pea Plants in Relation to Their Resistance to Lodging in the South of the Non-Chernozem Zone of the Rsfsr. Sel’skokhozyaĭstvennaya Biol. 1990, 1, 83–89. [Google Scholar]
- Pattee, H.E.; Isleib, T.G.; Giesbrecht, F.G.; Mc Feeters, R.F. Investigations into Genotypic Variations of Peanut Carbohydrates. J. Agric. Food Chem. 2000, 48, 750–756. [Google Scholar] [CrossRef] [PubMed]
- Jaureguy, L.M.; Chen, P.; Scaboo, A.M. Heritability and Correlations among Food-Grade Traits in Soybean. Plant Breed. 2011, 130, 647–652. [Google Scholar] [CrossRef]
- Tahir, M.; Vandenberg, A.; Chibbar, R.N. Influence of Environment on Seed Soluble Carbohydrates in Selected Lentil Cultivars. J. Food Compos. Anal. 2011, 24, 596–602. [Google Scholar] [CrossRef]
- Gangola, M.P.; Khedikar, Y.P.; Gaur, P.M.; Båga, M.; Chibbar, R.N. Genotype and Growing Environment Interaction Shows a Positive Correlation between Substrates of Raffinose Family Oligosaccharides (Rfo) Biosynthesis and Their Accumulation in Chickpea (Cicer arietinum L.) Seeds. J. Agric. Food Chem. 2013, 61, 4943–4952. [Google Scholar] [CrossRef]
- Górecki, R.; Lahuta, L.; Jones, A.; Hedley, C. Soluble Sugars in Maturing Pea Seeds of Different Lines in Relation to Desiccation Tolerance. In Seed Biology: Advances and Applications; Black, M., Bradford, K., Vásquez-Ramos, J., Eds.; CAB International: Wallingford, UK, 2000; pp. 67–74. [Google Scholar]
- Górecki, R.J.; Fordonski, G.; Halmajan, H.; Horbowicz, M.; Jones, R.G.; Lahuta, L. Seed Physiology and Biochemistry. In Carbohydrates in Grain Legume Seeds: Improving Nutritional Quality and Agronomic Characteristics; Hedley, C.L., Ed.; CAB International: Norwich, UK, 2001; pp. 117–143. ISBN 08519946. [Google Scholar]
- Lahuta, L.B.; Łogin, A.; Rejowski, A.; Socha, A.; Zalewski, K. Influence of Water Deficit on the Accumulation of Sugars in Developing Field Bean (Vicia faba var. minor.) Seeds. Seed Sci. Technol. 2000, 28, 93–100. [Google Scholar]
- Lahuta, L.; Górecki, R.J.; Gojło, E.; Horbowicz, M. Effect of Exogenous Abscisic Acid on Accumulation of Raffinose Family Oligosaccharides and Galactosyl Cyclitols in Tiny Vetch Seeds (Vicia hirsuta [L.] S.F. Gray). Acta Soc. Bot. Pol. 2004, 73, 277–283. [Google Scholar] [CrossRef]
- Mądry, W.; Talbot, M.B.; Ukalski, K.; Drzazga, T.; Iwanska, M. Podstawy Teoretyczne Znaczenia Efektów Genotypowych I Interakcyjnych W Hodowli Roślin Na Przykładzie Pszenicy Ozimej. Biuletyn Instytutu Hodowli i Aklimatyzacji Roślin 2006, 240/241, 13–32. [Google Scholar]
- Piotrowicz-Cieślak, A.I. Contents of Soluble Carbohydrates in Yellow Lupin Seeds Maturated at Various Temperatures. Acta Physiol. Plant 2006, 28, 349–356. [Google Scholar] [CrossRef]
- Lemoine, R.; La Camera, S.; Atanassova, R.; Dedaldechamp, F.; Allario, T.; Pourtau, N.; Bonnemain, J.L.; Laloi, M.; Coutos-Thevenot, P.; Maurousset, L.; et al. Source-to-Sink Transport of Sugar and Regulation by Environmental Factors. Front. Plant Sci. 2013, 4, 272. [Google Scholar] [CrossRef] [PubMed]
- Sari, H.; Sari, D.; Eker, T.; Toker, C. De Novo Super-Early Progeny in Interspecific Crosses Pisum sativum L. × P. fulvum Sibth. Et Sm. Sci. Rep. 2021, 11, 19706. [Google Scholar] [CrossRef] [PubMed]
- Fondevilla, S.; Almeida, N.F.; Satovic, Z.; Rubiales, D.; Vaz Patto, M.C.; Cubero, J.I.; Torres, A.M. Identification of Common Genomic Regions Controlling Resistance to Mycosphaerella Pinodes, Earliness and Architectural Traits in Different Pea Genetic Backgrounds. Euphytica 2011, 182, 43–52. [Google Scholar] [CrossRef]
- Prioul, S.; Frankewitz, A.; Deniot, G.; Morin, G.; Baranger, A. Mapping of Quantitative Trait Loci for Partial Resistance to Mycosphaerella Pinodes in Pea (Pisum Sativum L.), at the Seedling and Adult Plant Stages. Theor. Appl. Genet. 2004, 108, 1322–1334. [Google Scholar] [CrossRef] [PubMed]
- Weller, J.L.; Ortega, R. Genetic Control of Flowering Time in Legumes. Front. Plant. Sci. 2015, 6, 207. [Google Scholar] [CrossRef]
- Weller, J.L.; Liew, L.C.; Hecht, V.F.G.; Rajandran, V.; Laurie, R.E.; Ridge, S.; Wenden, B.; Schoor, J.K.V.; Jaminon, O.; Blassiau, C.; et al. A Conserved Molecular Basis for Photoperiod Adaptation in Two Temperate Legumes. Proc. Natl. Acad. Sci. USA 2012, 109, 21158–21163. [Google Scholar] [CrossRef]
- Tafesse, E.G.; Gali, K.K.; Lachagari, V.B.R.; Bueckert, R.; Warkentin, T.D. Genome-Wide Association Mapping for Heat and Drought Adaptive Traits in Pea. Genes 2021, 12, 1897. [Google Scholar] [CrossRef]
- Liew, L.C.; Hecht, V.; Laurie, R.E.; Knowles, C.L.; Vander Schoor, J.K.; Macknight, R.C.; Weller, J.L. Die Neutralis and Late Bloomer 1 Contribute to Regulation of the Pea Circadian Clock. Plant Cell 2009, 21, 3198–3211. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Y.; Diapari, M.; Bueckert, R.A.; Tar’an, B.; Warkentin, T.D. Population Structure and Association Mapping of Traits Related to Reproductive Development in Field Pea. Euphytica 2017, 213, 215. [Google Scholar] [CrossRef]
- Weeden, N.; Ellis, T.; Timmerman-Vaughan, G.; Swiecicki, W.; Rozov, S.; Berdnikov, V. A Consensus Linkage Map for Pisum sativum. Pisum Genet. 1998, 30, 1–4. [Google Scholar]
- Keller, M.; Karutz, C.; Schmid, E.J.; Stamp, P.; Winzeler, M.; Keller, B.; Messmer, M.M. Quantitative Trait Loci for Lodging Resistance in a Segregating Wheat×Spelt Population. Theor. Appl. Genet. 1999, 98, 1171–1182. [Google Scholar] [CrossRef]
- Dumont, E.; Fontaine, V.; Vuylsteker, C.; Sellier, H.; Bodele, S.; Voedts, N.; Devaux, R.; Frise, M.; Avia, K.; Hilbert, J.-L. Association of Sugar Content Qtl and Pql with Physiological Traits Relevant to Frost Damage Resistance in Pea under Field and Controlled Conditions. Theor. Appl. Genet. 2009, 118, 1561–1571. [Google Scholar] [CrossRef] [PubMed]
- Aubert, G.; Morin, J.; Jacquin, F.; Loridon, K.; Quillet, M.; Petit, A.; Rameau, C.; Lejeune-Henaut, I.; Huguet, T.; Burstin, J. Functional Mapping in Pea, as an Aid to the Candidate Gene Selection and for Investigating Synteny with the Model Legume Medicago Truncatula. Theor. Appl. Genet. 2006, 112, 1024–1041. [Google Scholar] [CrossRef] [PubMed]
- Fowler, D.B.; Breton, G.; Limin, A.E.; Mahfoozi, S.; Sarhan, F. Photoperiod and Temperature Interactions Regulate Low-Temperature-Induced Gene Expression in Barley. Plant. Physiol. 2001, 127, 1676–1681. [Google Scholar] [CrossRef] [PubMed]
- Gawłowska, M.; Święcicki, W. The fa2 gene and molecular markers mapping in the gp segment of the Pisum linkage group V. J. Appl. Genet. 2016, 57, 317–322. [Google Scholar] [CrossRef]
- Berbel, A.; Ferrándiz, C.; Hecht, V.; Dalmais, M.; Lund, O.S.; Sussmilch, F.C.; Taylor, S.A.; Bendahmane, A.; Ellis, T.H.N.; Beltrán, J.P.; et al. VEGETATIVE1 is essential for development of the compound inflorescence in pea. Nat. Commun. 2012, 3, 797. [Google Scholar] [CrossRef] [PubMed]
- Hecht, V.; Foucher, F.; Ferrándiz, C.; Macknight, R.; Navarro, C.; Morin, J.; Vardy, M.E.; Ellis, N.; Beltrán, J.P.; Rameau, C.; et al. Conservation of Arabidopsis flowering genes in model legumes. Plant Physiol. 2015, 137, 1420–1434. [Google Scholar] [CrossRef]
- Knopkiewicz, M.; Gawłowska, M.; Święcicki, W. Poszukiwanie polimorficznych markerów zdefiniowanych sekwencyjnie w populacji grochu Carneval x MP1401. Searching for polymorphic sequence-defined markers in the pea. Fragm. Agron. 2012, 29, 87–94. [Google Scholar]
- Sindhu, A.; Ramsay, L.; Sanderson, L.A.; Stonehouse, R.; Li, R.; Condie, J.; Shunmugam, A.S.; Liu, Y.; Jha, A.B.; Diapari, M.; et al. Gene-based SNP discovery and genetic mapping in pea. Theor. Appl. Genet. 2014, 127, 2225–2241. [Google Scholar] [CrossRef]
Figure 1.
Validation of AA135 and AC74 markers in selected pea cultivars from the Polish and German Gene Bank collection. *, **—Significance at p < 0.05, p < 0.01, respectively.
Figure 1.
Validation of AA135 and AC74 markers in selected pea cultivars from the Polish and German Gene Bank collection. *, **—Significance at p < 0.05, p < 0.01, respectively.
Figure 2.
Comparison of the mean soluble carbohydrate concentration score of markers and ANOVA classes across pea cultivars; *, **, and *** significance at p < 0.10, p < 0.05, and p < 0.01, respectively.
Figure 2.
Comparison of the mean soluble carbohydrate concentration score of markers and ANOVA classes across pea cultivars; *, **, and *** significance at p < 0.10, p < 0.05, and p < 0.01, respectively.
Table 1.
Analyzed pea cultivars from the Polish and German register.
| No. | Genotype Name Polish Register | Leaf Type | Pedigree | No. | Genotype Name German Register | Delivered by | Leaf Type |
|---|---|---|---|---|---|---|---|
| 1 | Akord | afila | (A × B) × B | 1 | Alvesta | KWS Lochow GmbH | afila |
| 2 | Baryton | afila | ua | 2 | Casablanca | KWS Lochow GmbH | afila |
| 3 | Batuta | afila | C × D/95 | 3 | La Mancha | KWS Lochow GmbH | afila |
| 4 | Bohun | afila | E × F | 4 | Santana | KWS Lochow GmbH | afila |
| 5 | Boruta | afila | G × H | 5 | Gregor | Pflanzenzucht Hans-Georg Lembke | afila |
| 6 | Brutus | afila | ua | 6 | Starter | Pflanzenzucht Hans-Georg Lembke | afila |
| 7 | Brylant | afila | ua | 7 | Salamanca | Pflanzenzucht Hans-Georg Lembke | afila |
| 8 | Cysterski | afila | ua | 8 | Navarro | Pflanzenzucht Hans-Georg Lembke | afila |
| 9 | Dymek | afila | I × J | 9 | Madonna | Pflanzenzucht Hans-Georg Lembke | afila |
| 10 | Ezop | afila | K ×L | ||||
| 11 | Goplik | afila | Ł/82 × M | ||||
| 12 | Kavalir | afila | ua | ||||
| 13 | Lasso | afila | ua | ||||
| 14 | Mecenas | afila | N × O | ||||
| 15 | Medal | afila | MERLIN × PIAST | ||||
| 16 | Mentor | afila | R × S | ||||
| 17 | Merlin | afila | T × U | ||||
| 18 | Profi | afila | ua | ||||
| 19 | Ramrod (Piast) | afila | Bred by Łagiewniki Breeding Station | ||||
| 20 | Set | normal | Line from map. pop. × LP1/90 | ||||
| 21 | Tarchalska | afila | ((W × Y) × Z) × Ż | ||||
| 22 | Terno | afila | ua | ||||
| 23 | Turkus | afila | Bred by Sobótka Breeding Station | ||||
| 24 | Wenus | afila | Ź × Ż | ||||
| 25 | Zekon | afila | ua |
Note: Letters from A–Ź are the codes for the parental lines; ua, unavailable; https://wyszukiwarka.ihar.edu.pl/pl (accessed on 28 June 2022).
Table 2.
Differentiation of pea cultivars based on DTF, plant height, lodging, stem diameter, and thickness of stem wall.
Table 2.
Differentiation of pea cultivars based on DTF, plant height, lodging, stem diameter, and thickness of stem wall.
| Character | 2014 | 2015 | GxY Interaction | ||||
|---|---|---|---|---|---|---|---|
| Mean | Range | CV% | Mean | Range | CV% | F Statistic | |
| DTF (days) | 58 | 53–64 | 1.4 | 68 | 68–71 | 0.6 | 6.3 ** |
| Plant height (cm) | 93.8 | 72–113 | 7.6 | 67.4 | 53–87 | 10.5 | 2.4 ** |
| Lodging 2nd (the end of flowering) | 7.6 | 6.3–8.7 | 9.0 | 8.1 | 4.7–9.0 | 7.5 | 2.9 ** |
| Lodging 3rd (maturity) | 5.3 | 5.3–5.5 | 13.8 | 6.8 | 3.7–8.5 | 13.5 | 2.7 ** |
| Stem wall thickness in the bottom of the stem (mm) | 0.5 | 0.39–0.53 | 8.9 | 0.4 | 0.33–0.50 | 10.1 | - |
| Stem wall thickness in the middle part of the stem (mm) | 0.4 | 0.37–0.54 | 9.4 | 0.4 | 0.37–0.53 | 10.4 | 1.9 ** |
| Stem wall thickness in the upper part of the stem (mm) | 0.4 | 0.35–0.50 | 9.8 | 0.4 | 0.35–0.57 | 14.0 | - |
| Stem diameter in the bottom of the stem (mm) | 3.7 | 3.17–4.19 | 5.3 | 2.9 | 2.52–3.38 | 8.8 | - |
| Stem diameter in the middle part of the stem (mm) | 4.9 | 4.23–5.36 | 5.9 | 4.0 | 3.19–4.94 | 10.6 | 2.0 ** |
| Stem diameter in the upper part of the stem (mm) | 4.7 | 3.95–5.35 | 6.6 | 3.7 | 2.77–4.70 | 11.7 | 2.03 ** |
Note: ** p < 0.01; GxY interaction—genotype × year interaction.
Table 3.
Mean values of DTF, lodging, and stem geometry of pea cultivar groups being homogenous.
| Character | Subgroup | 2014 | 2015 |
|---|---|---|---|
| DTF (days) | I | 60 | 68 |
| II | 56 | 66 | |
| Plant height (cm) | I | 103.8 | 73.6 |
| II | 87.9 | 59.2 | |
| Lodging 2nd (the end of flowering) | I | – | 8.5 |
| II | – | 6.6 | |
| Lodging 3rd (maturity) | I | – | 7.4 |
| II | – | 4.7 | |
| Stem wall thickness in the bottom part | I | – | 0.43 |
| II | – | 0.36 | |
| Stem wall thickness in the middle part | I | – | 0.47 |
| II | – | 0.40 | |
| Stem wall thickness in the upper part | I | – | 0.48 |
| II | – | 0.38 | |
| Stem diameter in the bottom part | I | – | 3.1 |
| II | – | 2.7 | |
| Stem diameter in the middle part | I | – | 4.4 |
| II | – | 3.7 | |
| Stem diameter in the upper part | I | – | 4.0 |
| II | – | 3.4 |
Note: “–” indicates no substantial differences.
Table 4.
Variation range of oligosaccharide concentrations in seeds of pea cultivars.
| Year | 2013 | 2014 | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Character | G × Y Interaction | ||||||||||||
| (mg g−1) DW | Cultivar with min. concentration | Mean | CV% | Cultivar with max. concentration | Mean | CV% | Cultivar with min. concentration | Mean | CV% | Cultivar with max. concentration | Mean | CV% | F statistic |
| TSCs 1 | Turkus | 84.2 | 0.9 | Zekon | 118.0 | 4.2 | Batuta | 101.5 | 12.2 | Navarro | 135.8 | 7.9 | 4.19 ** |
| Fructose | Brylant | 0.2 | 9.2 | Mentor | 0.5 | 8.1 | Brylant | 0.3 | 15.9 | Merlin | 0.7 | 7.0 | 2.52 ** |
| Sucrose | La Mancha | 22.0 | 2.8 | Goplik | 39.5 | 3.2 | Salamanca | 21.9 | 3.2 | Bohun | 48.0 | 6.7 | 6.91 ** |
| Raffinose | Boruta | 7.4 | 2.9 | Zekon | 20.0 | 4.0 | Boruta | 8.9 | 6.3 | Zekon | 23.2 | 3.6 | 4.43 ** |
| Stachyose | Boruta | 20.6 | 2.9 | Zekon | 40.1 | 5.6 | Boruta | 24.0 | 6.7 | Lasso | 48.3 | 3.3 | 2.30 ** |
| Verbascose | Kavalier | 15.3 | 3.5 | Mecenas | 36.1 | 3.9 | Merlin | 20.8 | 6.9 | Wenus | 50.9 | 3.5 | 11.56 ** |
| Total RFOs | Turkus | 58.4 | 1.2 | Mecenas | 81.7 | 3.6 | Batuta | 69.1 | 13.6 | Navarro | 97.6 | 4.8 | 5.86 ** |
| RFOs content (% of TSCs) | Tarchalska | 61.0 | 0.4 | Starter | 75.0 | 0.4 | Goplik | 66.0 | 1.2 | Salamanca | 77.0 | 0.05 | 24.25 ** |
Note: ** p < 0.01; 1 concentration of total soluble carbohydrates, pea cultivars with extreme values in both years are in bold font.
Table 5.
Mean values of soluble carbohydrates of pea cultivars in two homogenous groups according to the Gabriel procedure.
Table 5.
Mean values of soluble carbohydrates of pea cultivars in two homogenous groups according to the Gabriel procedure.
| Character (mg g−1 DW) | Genotype Group of High and Low Concentration | Mean Value | |
|---|---|---|---|
| 2013 (No. of Objects) | 2014 (No. of Objects) | ||
| TSCs | High | 106.6 (16) | 130.3 (08) |
| Low | 93.7 (18) | 112.6 (26) | |
| Fructose | High | 0.4 (20) | 0.6 (18) |
| Low | 0.3 (14) | 0.4 (16) | |
| Sucrose | High | 34.6 (11) | 38.3 (09) |
| Low | 26.3 (23) | 28.4 (25) | |
| Raffinose | High | 14.6 (24) | 19.1 (12) |
| Low | 9.6 (10) | 13.3 (22) | |
| Stachyose | High | 34.1 (16) | 40.2 (13) |
| Low | 24.1 (18) | 27.1 (21) | |
| Verbascose | High | 30.4 (19) | 41.7 (18) |
| Low | 20.5 (15) | 27.8 (16) | |
| Total RFOs | High | 75.2 (10) | 86.6 (23) |
| Low | 65.6 (24) | 74.4 (11) | |
| RFOs content (% of TSCs) | High | 71.2% (20) | 73.4% (19) |
| Low | 64.9% (14) | 67.7% (15) | |
Note: DW, dry weight; RFO, raffinose family of oligosaccharides; TSCs, total soluble carbohydrates.
Table 6.
Correlation among the number of days to flowering, lodging, and the stem geometry properties in 34 pea lines in 2014 and 2015.
Table 6.
Correlation among the number of days to flowering, lodging, and the stem geometry properties in 34 pea lines in 2014 and 2015.
| 2014 | DTF | H | Lodg2 | Lodg3 | BDiam | Mdiam | Tdiam | Bthic | Mthic | Tthic |
|---|---|---|---|---|---|---|---|---|---|---|
| DTF | 1 | |||||||||
| H | ns | 1 | ||||||||
| Lodg2 | ns | ns | 1 | |||||||
| Lodg3 | 0.26 (ns) | ns | 0.57 | 1 | ||||||
| BDiam | ns | ns | ns | ns | 1 | |||||
| Mdiam | ns | 0.48 | −0.40 | ns | 0.36 | 1 | ||||
| Tdiam | ns | ns | ns | ns | 0.34 | 0.70 | 1 | |||
| Bthic | ns | ns | ns | ns | ns | ns | ns | 1 | ||
| Mthic | ns | ns | ns | ns | ns | 0.34 | 0.31 (ns) | ns | 1 | |
| Tthic | ns | ns | ns | ns | ns | ns | 0.32 (ns) | ns | ns | 1 |
| 2015 | DTF | H | Lodg2 | Lodg3 | Bdiam | Mdiam | Tdiam | Bthic | Mthic | Tthic |
| DTF | 1 | |||||||||
| H | −0.32 (ns) | 1 | ||||||||
| Lodg2 | 0.35 | −0.64 | 1 | |||||||
| Lodg3 | 0.38 | −0.70 | 0.91 | 1 | ||||||
| Bdiam | ns | ns | ns | ns | 1 | |||||
| Mdiam | 0.37 | ns | ns | ns | 0.79 | 1 | ||||
| Tdiam | 0.35 | ns | ns | ns | 0.54 | 0.84 | 1 | |||
| Bthic | ns | ns | ns | ns | ns | ns | ns | 1 | ||
| Mthic | ns | ns | ns | ns | 0.47 | 0.48 | 0.37 | ns | 1 | |
| Tthic | 0.43 | ns | ns | ns | 0.53 | 0.73 | 0.62 | ns | 0.67 | 1 |
Note: DTF, the number of days to the beginning of flowering; H, plant height; Lodg2, plant lodging at full blossoming (second term); Lodg3, plant lodging at plant maturity (third term); Dia, diameter; Thic, stem wall thickness; B, lower stem; M, middle stem; T, upper stem, <0.3494; ns, non-significant at the 0.05 probability level.
Table 7.
Mean trait score of marker classes in the cultivar groups and T-test results between marker classes.
Table 7.
Mean trait score of marker classes in the cultivar groups and T-test results between marker classes.
| Character | Year | Linkage Group | Marker (Distance from QTL) | Mean Value of the Character for A Cultivar Group | F Statistics | Sign | |
|---|---|---|---|---|---|---|---|
| Allele I (mg g−1 DW) (No. of Objects) | Allele II (mg g−1 DW) (No. of Objects) | ||||||
| Fructose | 2013 | chr1LG6 | AC74 | 0.42 (02) | 0.34 (29) | 0.05 | * |
| 2013 | chr7LG7 | Pgd-p | 0.30 (07) | 0.35 (24) | 0.033 | ** | |
| Verbascose | 2013 | chr1LG6 | AC74 (1 cM) | 34.85 (02) | 25.17 (29) | <0.001 | *** |
| 2014 | chr1LG6 | AC74 (1 cM) | 40.55 (02) | 33.98 (29) | 0.021 | * | |
| Total RFO | 2013 | chr1LG6 | AC74 (5 cM) | 75.09 (02) | 68.70 (29) | 0.01 | ** |
| RFOs (% of TSCs) | 2013 | chr7LG7 | AA135 (5 cM) | 0.68 (18) | 0.70 (13) | 0.012 | * |
| Character | Year | Linkage Group | Marker (Distance from QTL) | Mean Value of the Character for A Cultivar Group | F Statistics | Sign | |
| Allele I (No. of Objects) | Allele II (No. of Objects) | ||||||
| Stem wall thickness (in the middle part) | 2014 | chr3LG5 | PsCam962 | 0.42 (12) | 0.45 (23) | 0.014 | * |
| 2015 | chr5LG3 | AB141 | 0.45 (13) | 0.42 (20) | 0.019 | * | |
| 2015 | chr3LG5 | PisGen9_3_1 | 0.42 (10) | 0.44 (24) | 0.036 | * | |
| 2015 | chr5LG3 | A001 | 0.44 (12) | 0.42 (22) | 0.040 | * | |
| 2015 | chr3LG5 | PsCam962 | 0.42 (12) | 0.44 (22) | 0.043 | * | |
| Stem wall thickness (top) | 2014 | chr3LG5 | AA81 | 0.45 (13) | 0.41 (14) | 0.004 | ** |
| 2014 | chr3LG5 | mtmt_EST_03378 | 0.40 (14) | 0.44 (19) | 0.001 | *** | |
| Stem diameter (in the bottom part) | 2015 | chr4LG4 | P393 | 2.86 (29) | 3.09 (05) | 0.004 | ** |
| Stem diameter (in the middle part) | 2015 | chr5LG3 | A004 | 4.14 (18) | 3.93 (16) | 0.009 | ** |
| 2015 | chr5LG3 | A001 | 3.99 (28) | 4.28 (06) | 0.006 | ** | |
| Stem diameter (in the upper part) | 2014 | chr5LG3 | AB141 | 4.54 (13) | 4.76 (20) | 0.035 | * |
| 2014 | chr3LG5 | AB83 | 4.47 (20) | 4.73 (09) | 0.027 | * | |
| 2015 | chr5LG3 | A004 | 3.82 (18) | 3.65 (16) | 0.038 | * | |
| 2015 | chr5LG3 | A001 | 3.69 (28) | 4.02 (06) | 0.002 | ** | |
| DTF | 2014 | chr6LG2 | Leg_65 | 60.00 (20) | 67.4 (09) | 0.005 | *** |
| 2014 | chr5LG3 | AB141 | 58.00 (13) | 59.00 (20) | 0.027 | * | |
| 2014 | chr3LG5 | PisGen9_3_1 | 58.00 (10) | 59.00 (24) | 0.030 | * | |
| 2014 | chr1LG6 | PisGen16 | 58.00 (15) | 59.00 (19) | 0.010 | ** | |
| Plant height | 2014 | chr3LG5 | OPG9b | 89.2 (07) | 95.0 (27) | 0.036 | * |
| Lodging in 2nd term (the end of flowering) | 2014 | chr5LG3 | AB141 | 7.41 (13) | 7.76 (20) | 0.028 | * |
| 2015 | chr3LG5 | PisGen9_3_1 | 8.57 (10) | 7.95 (24) | 0.008 | ** | |
| 2015 | chr5LG3 | A001 | 8.28 (28) | 7.39 (6) | <.001 | *** | |
| Lodging in 3rd term (maturity) | 2015 | chr3LG5 | PisGen9_3_1 | 7.35 (10) | 6.64 (24) | 0.024 | * |
| 2015 | chr5LG3 | A001 | 7.05 (28) | 5.83 (6) | 0.001 | ** | |
| 2015 | chr4LG4 | P393 | 6.97 (29) | 6.07 (5) | 0.026 | * | |
*, **, and *** significance at p < 0.05, p < 0.01 and p < 0.001, respectively.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Gawłowska, M.; Lahuta, L.; Boros, L.; Sawikowska, A.; Kumar, P.; Knopkiewicz, M.; Kaczmarek, Z.; Święcicki, W. Validation of Molecular Markers Significant for Flowering Time, Plant Lodging, Stem Geometry Properties, and Raffinose Family Oligosaccharides in Pea (Pisum sativum L.). Agriculture 2022, 12, 1125. https://doi.org/10.3390/agriculture12081125
AMA Style
Gawłowska M, Lahuta L, Boros L, Sawikowska A, Kumar P, Knopkiewicz M, Kaczmarek Z, Święcicki W. Validation of Molecular Markers Significant for Flowering Time, Plant Lodging, Stem Geometry Properties, and Raffinose Family Oligosaccharides in Pea (Pisum sativum L.). Agriculture. 2022; 12(8):1125. https://doi.org/10.3390/agriculture12081125
Chicago/Turabian StyleGawłowska, Magdalena, Lesław Lahuta, Lech Boros, Aneta Sawikowska, Pankaj Kumar, Michał Knopkiewicz, Zygmunt Kaczmarek, and Wojciech Święcicki. 2022. "Validation of Molecular Markers Significant for Flowering Time, Plant Lodging, Stem Geometry Properties, and Raffinose Family Oligosaccharides in Pea (Pisum sativum L.)" Agriculture 12, no. 8: 1125. https://doi.org/10.3390/agriculture12081125
APA StyleGawłowska, M., Lahuta, L., Boros, L., Sawikowska, A., Kumar, P., Knopkiewicz, M., Kaczmarek, Z., & Święcicki, W. (2022). Validation of Molecular Markers Significant for Flowering Time, Plant Lodging, Stem Geometry Properties, and Raffinose Family Oligosaccharides in Pea (Pisum sativum L.). Agriculture, 12(8), 1125. https://doi.org/10.3390/agriculture12081125
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
