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Article

Genome-Wide Association Study of Agronomic and Physiological Traits Related to Drought Tolerance in Potato

by
Alba Alvarez-Morezuelas
*,
Leire Barandalla
,
Enrique Ritter
and
Jose Ignacio Ruiz de Galarreta
NEIKER-Basque Institute for Agricultural Research and Development, Basque Research and Technology Alliance (BRTA), Campus Agroalimentario de Arkaute, 01192 Arkaute, Spain
*
Author to whom correspondence should be addressed.
Plants 2023, 12(4), 734; https://doi.org/10.3390/plants12040734
Submission received: 30 December 2022 / Revised: 2 February 2023 / Accepted: 4 February 2023 / Published: 7 February 2023
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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Abstract

:
Potato (Solanum tuberosum L.) is often considered a water-sensitive crop and its production can be threatened by drought events, making water stress tolerance a trait of increasing interest. In this study, a panel of 144 tetraploid potato genotypes was evaluated for two consecutive years (2019 and 2020) to observe the variation of several physiological traits such as chlorophyll content and fluorescence, stomatal conductance, NDVI, and leaf area and circumference. In addition, agronomic parameters such as yield, tuber fresh weight, tuber number, starch content, dry matter and reducing sugars were determined. GGP V3 Potato array was used to genotype the population, obtaining a total of 18,259 high-quality SNP markers. Marker-trait association was performed using GWASpoly package in R software and Q + K linear mixed models were considered. This approach allowed us to identify eighteen SNP markers significantly associated with the studied traits in both treatments and years, which were related to genes with known functions. Markers related to chlorophyll content and number of tubers under control and stress conditions, and related to stomatal conductance, NDVI, yield and reducing sugar content under water stress, were identified. Although these markers were distributed throughout the genome, the SNPs associated with the traits under control conditions were found mainly on chromosome 11, while under stress conditions they were detected on chromosome 4. These results contribute to the knowledge of the mechanisms of potato tolerance to water stress and are useful for future marker-assisted selection programs.

1. Introduction

Climate change is causing negative effects on crop production, both through biotic stresses and abiotic stresses such as temperature stress, drought and salinity [1]. The impact of climate change on crop yield and quality will vary depending on the area and crop system [2]. In Spain, crops are mostly grown using artificial irrigation systems, which optimize the limited water available. However, the availability of water resources has been decreasing in recent years and in the future, it will be necessary to increase the amount of irrigation or even to irrigate in rainfed areas. Therefore, it will be essential to cultivate more water-efficient materials [3].
Potato (Solanum tuberosum L.) is one of the most important crops in the world with an annual production of 359 million tons of tubers (FAOSTAT, 2020). It is a highly-valued crop as it can grow in a wide range of environments, is very versatile in terms of uses, is a short-duration crop, and 85% of its biomass is edible [4,5]. Potatoes are relatively water-efficient and compared to other crops, produce more calories per unit of water used [6,7]. However, this crop also has a high irrigation requirement and it is considered a drought-sensitive crop. The drought susceptibility of potatoes is associated with their shallow and sparse root system, but canopy development and variety also play an important role in water stress tolerance [4,8]. Drought is one of the main factors limiting yield, particularly in susceptible crops such as potatoes. If potato crops are not adapted to water stress, a loss ofyield between 18% and 32% is estimated for the year 2050, although with adaptation there would be a reduction of between 9 and 18% for the period 2040–2069 [9]. It is difficult to estimate the global yield loss due to water stress alone, as other abiotic stresses such as temperature, solar radiation or salinity are closely related. However, some studies have reported a decrease between 15% and 91% in potato yield under water stress conditions [10,11,12].
Potato breeding activities in recent years have focused on searching for regions of the genome related to tuber quality traits [13,14], agronomically-important traits [15], floral traits [16], root and stolon traits [17], and nitrogen use efficiency [18], and markers have also been developed for applying in marker-assisted selection for resistance to some diseases such as common scab [19,20] or Phytophthora infestans [21,22].
Drought tolerance is a complex trait which depends on several factors such as the duration of the stress, the severity of the drought, and the developmental stage of the plant. Stress in the early growth stage is considered the most harmful [23,24]. From a physiological point of view, survival or recovery is the major objective in plant stress tolerance, but from an agricultural point of view, crop yield is the trait that determines crop drought tolerance [25]. Yield decrease is mainly associated with inhibition of photosynthesis, decrease in stomatal conductance to prevent water loss through transpiration, and reduction of leaf area [11,24,26,27].
Breeding for drought tolerance is challenging and absolutely essential under the expected climatic changes that could lead to more frequent periods of low water supply. Genetic basis of drought tolerance is complex, but there are tools such as the DroughtDB database which collects genes of interest for drought stress in plants and helps us understand the mechanisms of tolerance [28]. Although an enormous amount of knowledge has been gained about drought tolerance in recent years, we are still far from understanding all the underlying mechanisms and signaling pathways involved [25]. Water stress tolerance traits are polygenic and affected by several minor alleles. Therefore, a deeper understanding of the loci and alleles involved is needed.
Traditional potato breeding has certain difficulties due to the heterozygous nature of tetraploid potatoes, and furthermore, allelic combinations and genetic effects become even more complex when dealing with quantitative polygenic traits such as water stress tolerance [29]. The collection of accurate phenotypic data for the traits of interest in the study population is a major challenge, as these assays should be multi-year and multi-environment and should have a sufficient number of genotypes population under study [30].
The potato genome is comprised of 12 chromosomes and has an average size of approximately 840 Mbp. For a few years now, the complete genome sequence is available and allowed the development of Single Nucleotide Polymorphisms (SNP) arrays by the potato community [31]. Several generations of SNP arrays were generated, building on the original Infinium 8303 SNP array [32]. In recent years, advances in sequencing have been developed, sequencing costs have decreased, and the number of reads has increased [33].
Association mapping, also known as linkage disequilibrium (LD) mapping, is a powerful tool for the association of a phenotype with a genotype and the identification of causal genes/loci [34]. One of the most attractive aspects of association mapping is that it is not necessary to establish mapping families, and instead historical recombination events can be explored at the population level [35,36]. The absence of biparental crosses for identifying QTL makes association mapping easier and less expensive [37].
In this study, we have performed Genome Wide Association Studies (GWAS) with the aim of identifying QTLs associated with physiological and agronomic traits of interest for potato breeding under water stress and unstressed conditions, in order to accelerate the selection processes in potato breeding programs.

2. Results

2.1. Phenotypic Data Analysis

Analysis of variance (ANOVA) showed highly significant differences for all traits between genotypes, between treatments and interactions between genotypes and treatments (G × T) in both years (Table 1). Descriptive statistics for the traits are provided in the Supplementary Table S1.
The correlation of physiological and yield-related variables between control and stressed samples was studied (Figure 1). Yield is one of the most important traits when looking for tolerance to abiotic stresses. We saw that the yields under control conditions and under water stress conditions were correlated with more or less the same traits, especially with number and weight of tubers under both control and drought conditions. Yield_C and Yield_D was also correlated with most of the physiological parameters and the highest correlations occurred 70 days after planting (DAP). All correlations were positive, except for FLUOR 50, FLUOR 70, dry matter and starch.

2.2. Population Structure Analysis and Linkage Disequilibrium

STRUCTURE software revealed that the study population was formed by two subpopulations of 133 and 11 genotypes respectively, since the obtained delta K value was 2 (Supplementary Figure S1a). The probabilistic assignment of each genotype to belong to one of the assigned groups was also performed for deriving the corresponding values of the Q matrix (Supplementary Figure S1b). These results indicate that there was genetic diversity in the population with different structural dimensions, which was also considered for the association analysis. A genetic distance matrix was performed between all genotypes to evaluate the genetic diversity and it was observed that the highest value between two varieties was 0.4, the minimum value was 0.26 and the mean value was 0.37.
Linkage disequilibrium (LD) decay was determined using the filtered SNP data. In our study, the genetic distance between markers was calculated as the point of intersection between the half decay r2 value of the genome and the smoothing spline regression model fitted to LD decay (Supplementary Figure S2).

2.3. Genome-Wide Association Analysis

The total of 31,190 markers were filtered to ensure the quality of the SNPs, removing markers with a missing value rate higher than 10% and those with a minor allele frequency below 0.05, obtaining 18,259 SNP markers. These SNP markers provide a genome-wide coverage along the 12 chromosomes of tetraploid potatoes (Table 2).
The association mapping was performed with kinship correction to minimize false positive associations. The Q + K model was used with the 18,259 high-quality SNP markers and the panel of 144 accessions. The results of the Q-Q plots indicate that the observed −log10(P) values are in accordance with the expected −log10(P) values (Supplementary Figure S3).
The results of the association analysis are presented as marker-trait associations to get an overall impression of the effect of water stress in our population. In this study, eighteen QTLs were identified above the Bonferroni threshold. Five of these QTLs were associated with two of the traits measured under control conditions, while the rest were associated with traits measured in plants under water stress.
Two SNP markers associated with chlorophyll content measured at 70 DAP were found, one on chromosome 6 (PotVar0039950) and the other on chromosome 11 (solcap_snp_c2_15287). Two other QTLs were also found on chromosome 11, which in this case were associated with the number of tubers in control plants (solcap_snp_c2_37217 and ST4.03ch11_2070850). The marker solcap_snp_c2_15676, located on chromosome 5, was also associated with this trait (Table 3, Figure 2).
If we observe the physiological parameters under drought conditions we can see that most of the associations occurred with measurements taken at 70 DAP. Two QTLs associated with Normalized Difference Vegetation Index (NDVI) were found, both on chromosome 4 (solcap_snp_c2_43735 and PotVar0113919). The marker solcap_snp_c2_45637 on chromosome 1 was also found to be associated with stomatal conductance and the marker PotVar0039950 on chromosome 6 was associated with leaf chlorophyll content. Although almost all associations were found in measurements taken at 70 DAP, the NDVI was also associated with one marker (solcap_snp_c1_6462) in the first stress phase, at 50 DAP (Table 3, Figure 2).
One of the most important parameters when assessing stress tolerance is the maintenance of crop yield. In this case we saw that the marker solcap_snp_c2_26653, located on chromosome 8, was associated with yield under water stress conditions, and that it is co-localized with the osmotin gene (Soltu.DM.08G027260.1). The markers PotVar0064470 and solcap_snp_c2_55085, located on chromosomes 10 and 11 respectively, were associated with tuber number under stress conditions. The trait for which the most associated QTLs were found was the content of reducing sugars under drought conditions. One of them (solcap_snp_c1_3746) was found on chromosome 2, while the other four were located on chromosome 4, and three of them (solcap_snp_c2_55785, solcap_snp_c2_55783, solcap_snp_c2_55775) co-localized with the same gene, leucine-rich receptor-like protein kinase family protein (Table 3, Figure 2).

3. Discussion

Thanks to new massive sequencing techniques and the development of chips such as the GGP Potato 35K array used in this study, we can obtain a global view of the genome and select regions and genes of interest related to the desired trait [32,38]. The traits evaluated in this work have complex inheritance patterns that make the task of existing mapping technologies to detect the underlying genetics even more difficult. Different studies have analysed the heritability of yield and its components under control and water stress conditions. These traits under control conditions have a fairly acceptable heritability of around 0.7 [39], but it is not very clear how water stress affects the heritability of these traits. In some studies, it drops to 0.06 [40], while in other studies this decrease was much lower [41].
When analysing multiple tests one must address the problem of false positives, so it is important to adjust the p-value of each marker when performing the statistical analysis [42]. In our study, we can observe that the FDR values are higher than the Bonferroni p-values. The Bonferroni correction is the most commonly used in association studies, but this method is very strict and can sometimes fail to identify important associations, so the FDR correction is usually used [43,44].
In this study, QTLs related to chlorophyll content measured at 70 DAP and tuber number under control conditions were identified. These two parameters also showed a significant positive correlation, indicating that the amount of chlorophyll in the leaves of the plants has an effect on the number of tubers.
When plants are under water stress, one of the tolerance mechanisms is the inhibition of photosynthesis, and as a consequence, chlorophyll content decreases. Chlorophyll content was significantly associated with two SNPs, solcap_snp_c2_15287 and PotVar0039950. The solcap_snp_c2_15287 (Soltu.DM.11G023130) was co-localized with a gene encoding for a “P-loop containing nucleoside triphosphate hydrolases superfamily protein”, which is a type of hydrolase that catalyses the hydrolysis of the beta-gamma phosphate bond of a bound nucleoside triphosphate (NTP), and the obtained energy from this reaction is used to make conformational changes in other molecules [45]. In an assay on water-stressed Arabidopsis, they found an association between two P-loop-containing nucleoside triphosphate genes and proline content, which is closely related to plant response to drought [46]. Another study in rice showed that a new DEAD-box helicase ATP-binding protein (OsABP), a kind of P-loop containing nucleoside triphosphate hydrolase, was upregulated in response to multiple abiotic stresses, including NaCl, dehydration, ABA, and blue and red light [47].
The PotVar0039950 marker was found to be associated with the SPAD70 trait under both control and water stress conditions. This marker co-localizes with a “Radical SAM superfamily protein” gene (Soltu.DM.06G028800.1) and is located on chromosome 6. Radical SAM is a designation for a superfamily of enzymes that are involved in numerous processes, such as enzyme activation, post-transcriptional and post-translational modifications, lipid metabolism, or biosynthesis of antibiotics and natural products [48]. In a previous study in Sonneratia apetala they found that the SAMS1 gene was related to this group of proteins and indicated that SAMS1 enhanced the plant’s cold resistance by enhancing the biosynthesis of S-adenosyl-L-methionine (SAM). In addition, SAMS1 is also involved in ethylene biosynthesis, which is closely related to the plant’s response to drought stress [49].
Normalized Difference Vegetation Index was associated with PotVar0113919 marker, which co-localized with the ascorbate peroxidase gene (Soltu.DM.04G030200.1). Ascorbate peroxidase (APX) is an enzyme essential for protecting chloroplasts and other parts of the cell from damage caused by reactive oxygen species, and its production increases when plants are exposed to unfavorable environmental conditions [50]. The expression of APX encoding genes is modulated by those environmental stimuli, such as drought [51]. Other studies in cowpea and wheat showed in sensitive cultivars an increase in APX transcripts in response to water stress [52,53]. Likewise in potato, an increase in ascorbate peroxidase activity was observed under drought and heat stress treatments in three of the four tested varieties [54]. In our study, this gene is associated with NDVI70 under stress conditions, similar to another study where ascorbate peroxidase concentrations were correlated with photosynthesis, Fv/Fm and chlorophyll parameters [55].
The increase in the yield under water deficit was associated with solcap_snp_c2_26653 on chromosome 8 and is co-localized with the osmotin gene (Soltu.DM.08G027260.1). Osmotin is a multifunctional protein. Its overexpression induces abiotic stress tolerance, lowering the osmotic potential under stress [56]. Studies in cotton and tomato showed that the overexpression of the osmotin gene had a protective role and enhances drought stress tolerance [57,58]. Increases in leaf expansion, chlorophyll and relative water content were observed due to overexpression of osmotin in transgenic sesame plants and were fully recovered after rewatering [59].
Tuber numbers under stress conditions were associated with two SNP markers, PotVar0064470 and solcap_snp_c2_55085. PotVar0064470 was co-localized with an “Alternative oxidase family protein” gene (Soltu.DM.11G001000.2). Alternative oxidase (AOX) activity is important for maintaining photosynthetic electron transport under stress, and also helps plants cope with excess energy under drought, by avoiding the over-reduction of chloroplast electron carriers [60,61]. During severe or prolonged mild drought stress in Nicotiana tabacum, the amount of AOX protein was important for maintaining the photosynthetic rate and improving growth during prolonged water deficit [62]. In our study, the number of tubers was significantly correlated with photosynthesis-related parameters such as chlorophyll content or NDVI, which confirms the protective function of AOX. Also associated with tuber number was solcap_snp_c2_55085, which co-localized with “Transketolase” gene. Transketolase (TK) is an enzyme that participates in both the pentose phosphate pathway in all organisms and the Calvin cycle of photosynthesis [63]. In a study with wheat plants, the decrease in transketolase level suggested the suppression of the two pathways in the leaves of drought-stressed plants [64]. However, in studies with transgenic rice, co-overproduction of Rubisco and transketolase did not improve photosynthesis [65].
The content of reducing sugars under drought conditions was associated with five QTLs. One of these QTLs was solcap_snp_c2_25284 and co-localized with sucrose transporter (Soltu.DM.04G031670.1). Cellular accumulation of soluble sugars during drought stress influences the expression of sugar transporters [66], which is in agreement with the results obtained in our study. In potato, some studies have also analyzed the export of sucrose from the source to the leaves by analyzing the expression of genes related to sucrose transporters (SWEETs and SUTs), which are involved in stress response [24,67]. The markers solcap_snp_c2_55785, solcap_snp_c2_55783, and solcap_snp_c2_55775 were on chromosome 4 and are co-localized with the same gene, leucine-rich receptor-like protein kinase family protein (Soltu.DM.04G031690.1). Studies in rice showed that overexpression of LRK, which encodes a leucine-rich receptor-like kinase, increased drought tolerance [68,69]. A potato gene, StLRPK1, encoding a protein belonging to leucine-rich repeat receptor-like kinases was identified, and the results suggest that StLRPK1 may participate in the responses against environmental stresses in potato, which is in accordance with our results [70].
In this study we found markers associated with the evaluated physiological traits. Other authors have previously reported QTLs and genomic regions associated with chlorophyll content, chlorophyll fluorescence and NDVI in water stress assays in other populations, indicating that these results are robust [71,72,73]. For yield-related parameters, we found markers related to yield, tuber number and reducing sugar content as in previous studies reporting QTLs associated with these traits [74,75,76].
One additional, important aspect to consider is the validation of the significant SNP markers by expression analyses in control and water stress conditions using RT-qPCR in more sensitive and more tolerant genotypes. This aspect will be considered in a follow-up publication.

4. Materials and Methods

4.1. Plant Material and Location

A total of 144 tetraploid potato genotypes belonging to Solanum tuberosum ssp. tuberosum were used in this study, representing a wide range of parents used in breeding programs. The field experiments were performed in the facilities of NEIKER research center in Spain (42°51′05.7″ N, 2°37′13.2″ W) during the years 2019 and 2020.

4.2. Experimental Design

The trials were conducted from May to September in both years, and the climatic conditions at the experimental field in terms of average maximum and minimum temperature; humidity and total precipitation are shown in Table 4. The experimental design in each year included two blocks, irrigated (control) and non-irrigated treatments. In each block the genotypes were planted in a completely randomized experimental design with two replicates of five plants each, at a distance of 0.30 m between plants and 0.75 m between rows. The irrigation strategy in the case of the irrigated control field was based on the weekly replenishment of the accumulated water deficit from the third week of June onwards. For the estimation of the doses of each of the irrigations, a daily soil water balance was calculated using the FAO56 dual coefficient model [77] and the meteorological data recorded by the Arkaute weather station belonging to the EUSKALMET network.

4.3. Phenotypic Data Collection

Four physiological traits were measured in each genotype at two different dates, 50 days after planting (DAP) and 70 DAP. The chlorophyll content (CC) was measured using a SPAD-502 chlorophyll meter (Konica Minolta, Osaka, Japan) in the last fully-expanded leaf in three plants of each replicate and treatment. Photochemical efficiency of PSII was measured in leaves exposed to light (Fv’/Fm’) using a fluorimeter (FluorPen FP 100, Photon Systems Instruments, Drasov, Czech Republic), likewise in the last fully-expanded leaf in three plants of each replicate and treatment. Stomatal conductance (gs, mmol H2O m−2 s−1) was measured using a porometer (Leaf Porometer, Decagon Devices, Pullman, Washington, EEUU) in the last fully-expanded leaf in one plant of each replicate and treatment. Normalized difference vegetation index (NDVI) was measured in each replicate using a Rapidscan (RapidScan CS-45, Holland Scientific, Lincoln, EEUU). Plants were scanned from 0.5 m above the crop canopy in five plants of each replicate along the row direction. Three leaves per replicate and variety were collected at 70 DAP from each of the treatments to estimate leaf area and leaf circumference values using ImageJ software v.1.8.0.
Plants were harvested at 127 DAP in 2019 and 126 DAP in 2020 to allow late cultivars to complete their cycle. The whole experiment was harvested at once and total yield, tuber number per plant and average tuber fresh weight was assessed in each replicate. The five plants from each replicate were harvested together and the total value was divided by five to get yield and tuber number for each plant. Tuber weight was calculated as yield/tuber number. Dry matter content was measured in two replicates of each variety and treatment. Tubers were weighed immediately after harvest (FW). After 72 h at 80 °C, they were weighed again to obtain the dry weight (DW). The starch content was calculated with the following formula [78]:
Starch = ( DW FW ∗   100 − 6.0313 ) ∗ 10
The determination of reducing sugars content present in the samples was assessed by spectrophotometry based on the reduction of dinitrosalicylic acid [79]. Two replicates per variety and treatment were analyzed. The potatoes were peeled and mashed into a homogeneous juice. A total of 0.3 g of the mixture was weighed and 1 ml of distilled water and 2 mL of dinitrosalicylic acid were added. Then the samples were heated at 100 °C in a water bath with stirring for 10 min. Afterwards, the samples were diluted with distilled water and the absorbance was measured in the UV-VIS spectrophotometer at 546 nm. The percentage of reducing sugars was calculated as follows:
% reducing   sugars = ( absorbance − 0.00385 ) ∗ 1.07893
Analysis of variance (ANOVA) was performed on the data of both years for each parameter using Rstudio v.2022.12.0 (R Core Team, 2017) and the mean values of all traits were used to calculate the marker-trait associations.

4.4. DNA Extraction and Genotyping

Genomic DNA was extracted from 144 fresh potato leaves using innuPREP Plant DNA Kit (Analytik Jena, Jena, Germany) following the manufacturer’s instructions. DNA concentration and quality were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltman, MA, USA). The extracted DNA was sent to Neogen (Scotland, UK) for genotyping with the GGPv3 Potato 35K array. The software Genome Studio (Illumina, San Diego, CA, USA) was used for genotype calling, scoring four alleles per locus. The total set of markers obtained was filtered to ensure the quality of the SNPs, removing markers with a missing value rate higher than 10% and those with a minor allele frequency below 0.05.

4.5. Population Structure, Linkage Disequilibrium and GWAS Study

The population structure matrix (Q-matrix) was analyzed using K-values ranging from 1 to 10 for the entire population with 18,259 SNP markers with Structure v.2.4 software [80]. Three independent analyses were performed for each K-value. In this analysis, the length of the burn-in period was 100,000, with 100,000 MCMC replications after burn-in. The optimal value of K was identified using a previously-developed method based on delta K (∆K) [81] in the Structure Harvester website [82]. The relationship between genotypes and the genetic diversity in the population was calculated from the SNP marker data using TASSEL software [83].
Linkage Disequilibrium was estimated for high-quality SNPs after filtering using TASSEL software [83]. The pairwise squared allele-frequency correlations (r2) between SNP markers were calculated with a sliding window of 50 SNPs. These results were plotted against physical distance and an internal trend line was drawn as a non-linear logarithmic regression curve to estimate LD decay using R [84].
Association mapping analysis was performed with the phenotype and genotype data using the statistical package GwasPoly [85] developed for R software (R Core Team, 2017). The mixed model was used to perform association analysis with correction for kinship (K) and for sub-populations (Q). To correct for multiple testing, we used the 5% Bonferroni threshold (−log10(P) = 5.01).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12040734/s1. Table S1: Mean, standard deviation and phenotypic variance of 144 tetraploid potato varieties. Figure S1: (a) Delta K values over 10 runs and (b) bar plot displaying Q values obtained from STRUCTURE software in a population of 144 potato genotypes with a delta K = 2. Figure S2: Linkage disequilibrium (LD) decay plot between r2 and genetic distance. Figure S3: Q-Q plots for all traits evaluated under control and drought stress conditions in 144 potato varieties. Numbers 1 to 12 refer to each of the 12 potato chromosomes, 0 refers to control markers that are not associated with any chromosome, and 13 refers to the chloroplast.

Author Contributions

A.A.-M.: data curation, formal analysis, investigation, methodology, writing original draft, writing-reviewing and editing. L.B.: investigation, methodology and writing-reviewing. E.R.: data curation, software, supervision. J.I.R.d.G.: conceptualization, resources, writing-reviewing, funding acquisition, investigation, project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by MCIN/AEI/10.13039/501100011033, grant number PID2019-109790RR-C2 and the Basque Government.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to Ufuk Demirel (Niğde Ömer Halisdemir Üniversitesi) for his help in association mapping analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pareek, A.; Dhankher, O.P.; Foyer, C.H. Mitigating the Impact of Climate Change on Plant Productivity and Ecosystem Sustainability. J. Exp. Bot. 2020, 71, 451–456. [Google Scholar] [CrossRef] [PubMed]
  2. Malhi, G.S.; Kaur, M.; Kaushik, P. Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review. Sustainability 2021, 13, 1318. [Google Scholar] [CrossRef]
  3. Daccache, A.; Keay, C.; Jones, R.J.A.; Weatherhead, E.K.; Stalham, M.A.; Knox, J.W. Climate Change and Land Suitability for Potato Production in England and Wales: Impacts and Adaptation. J. Agric. Sci. 2012, 150, 161–177. [Google Scholar] [CrossRef]
  4. Nasir, M.W.; Toth, Z. Effect of Drought Stress on Potato Production: A Review. Agronomy 2022, 12, 635. [Google Scholar] [CrossRef]
  5. Lutaladio, N.B.; Castaldi, L. Potato: The Hidden Treasure. J. Food Compos. Anal. 2009, 22, 491–493. [Google Scholar] [CrossRef]
  6. Sun, S.; Wang, Y.; Wang, F.; Liu, J.; Luan, X.; Li, X.; Zhou, T.; Wu, P. Alleviating Pressure on Water Resources: A New Approach Could Be Attempted. Sci. Rep. 2015, 5, 14006. [Google Scholar] [CrossRef]
  7. Hill, D.; Nelson, D.; Hammond, J.; Bell, L. Morphophysiology of Potato (Solanum tuberosum) in Response to Drought Stress: Paving the Way Forward. Front. Plant Sci. 2021, 11, 597554. [Google Scholar] [CrossRef]
  8. Zarzyńska, K.; Boguszewska-Mańkowska, D.; Nosalewicz, A. Differences in Size and Architecture of the Potato Cultivars Root System and Their Tolerance to Drought Stress. Plant Soil Environ. 2017, 63, 159–164. [Google Scholar] [CrossRef]
  9. Hijmans, R.J. The Effect of Climate Change on Globar Potato Production. Am. J. Potato Res. 2003, 80, 271–279. [Google Scholar] [CrossRef]
  10. Aliche, E.B.; Oortwijn, M.; Theeuwen, T.P.J.M.; Bachem, C.W.B.; Visser, R.G.F.; van der Linden, C.G. Drought Response in Field Grown Potatoes and the Interactions between Canopy Growth and Yield. Agric. Water Manag. 2018, 206, 20–30. [Google Scholar] [CrossRef]
  11. Gervais, T.; Creelman, A.; Li, X.Q.; Bizimungu, B.; De Koeyer, D.; Dahal, K. Potato Response to Drought Stress: Physiological and Growth Basis. Front. Plant Sci. 2021, 12, 698060. [Google Scholar] [CrossRef]
  12. Obidiegwu, J.E.; Bryan, G.J.; Jones, H.G.; Prashar, A. Coping with Drought: Stress and Adaptive Responses in Potato and Perspectives for Improvement. Front. Plant Sci. 2015, 6, 542. [Google Scholar] [CrossRef]
  13. Pandey, J.; Scheuring, D.C.; Koym, J.W.; Vales, M.I. Genomic Regions Associated with Tuber Traits in Tetraploid Potatoes and Identification of Superior Clones for Breeding Purposes. Front. Plant Sci. 2022, 13, 952263. [Google Scholar] [CrossRef]
  14. Schreiber, L.; Nader-Nieto, A.C.; Schönhals, E.M.; Walkemeier, B.; Gebhardt, C. SNPs in Genes Functional in Starch-Sugar Interconversion Associate with Natural Variation of Tuber Starch and Sugar Content of Potato (Solanum tuberosum L.). G3 Genes Genomes Genet. 2014, 4, 1797–1811. [Google Scholar] [CrossRef]
  15. Li, Y.; Colleoni, C.; Zhang, J.; Liang, Q.; Hu, Y.; Ruess, H.; Simon, R.; Liu, Y.; Liu, H.; Yu, G.; et al. Genomic Analyses Yield Markers for Identifying Agronomically Important Genes in Potato. Mol. Plant 2018, 11, 473–484. [Google Scholar] [CrossRef]
  16. Zia, M.A.B.; Demirel, U.; Nadeem, M.A.; Çaliskan, M.E. Genome-Wide Association Study Identifies Various Loci Underlying Agronomic and Morphological Traits in Diversified Potato Panel. Physiol. Mol. Biol. Plants 2020, 26, 1003–1020. [Google Scholar] [CrossRef]
  17. Yousaf, M.F.; Demirel, U.; Naeem, M.; Çalışkan, M.E. Association Mapping Reveals Novel Genomic Regions Controlling Some Root and Stolon Traits in Tetraploid Potato (Solanum tuberosum L.). 3 Biotech 2021, 11, 174. [Google Scholar] [CrossRef]
  18. Nieto, C.A.O.; Van Bueren, E.T.L.; Allefs, S.; Vos, P.G.; Van Der Linden, G.; Maliepaard, C.A.; Struik, P.C. Association Mapping of Physiological and Morphological Traits Related to Crop Development under Contrasting Nitrogen Inputs in a Diverse Set of Potato Cultivars. Plants 2021, 10, 1727. [Google Scholar] [CrossRef]
  19. Koizumi, E.; Igarashi, T.; Tsuyama, M.; Ogawa, K.; Asano, K.; Kobayashi, A.; Sanetomo, R.; Hosaka, K. Association of Genome-Wide SNP Markers with Resistance to Common Scab of Potato. Am. J. Potato Res. 2021, 98, 149–156. [Google Scholar] [CrossRef]
  20. Yuan, J.; Bizimungu, B.; De Koeyer, D.; Rosyara, U.; Wen, Z.; Lagüe, M. Genome-Wide Association Study of Resistance to Potato Common Scab. Potato Res. 2020, 63, 253–266. [Google Scholar] [CrossRef]
  21. Mosquera, T.; Alvarez, M.F.; Jiménez-Gómez, J.M.; Muktar, M.S.; Paulo, M.J.; Steinemann, S.; Li, J.; Draffehn, A.; Hofmann, A.; Lübeck, J.; et al. Targeted and Untargeted Approaches Unravel Novel Candidate Genes and Diagnostic SNPs for Quantitative Resistance of the Potato (Solanum tuberosum L.) to Phytophthora Infestans Causing the Late Blight Disease. PLoS ONE 2016, 11, e0156254. [Google Scholar] [CrossRef] [PubMed]
  22. Wang, F.; Zou, M.; Zhao, L.; Li, H.; Xia, Z.; Wang, J. Genome-Wide Association Analysis of Late Blight Resistance Traits in Potato Germplasm Resources. Res. Sq. BMC Plant Biol. 2020, 12, 1–19. [Google Scholar]
  23. Plich, J.; Boguszewska-Mańkowska, D.; Marczewski, W. Relations Between Photosynthetic Parameters and Drought-Induced Tuber Yield Decrease in Katahdin-Derived Potato Cultivars. Potato Res. 2020, 63, 463–477. [Google Scholar] [CrossRef]
  24. Aliche, E.B.; Theeuwen, T.P.J.M.; Oortwijn, M.; Visser, R.G.F.; van der Linden, C.G. Carbon Partitioning Mechanisms in POTATO under Drought Stress. Plant Physiol. Biochem. 2020, 146, 211–219. [Google Scholar] [CrossRef] [PubMed]
  25. Krannich, C.T.; Maletzki, L.; Kurowsky, C.; Horn, R. Network Candidate Genes in Breeding for Drought Tolerant Crops. Int. J. Mol. Sci. 2015, 16, 16378–16400. [Google Scholar] [CrossRef]
  26. Dahal, K.; Li, X.Q.; Tai, H.; Creelman, A.; Bizimungu, B. Improving Potato Stress Tolerance and Tuber Yield under a Climate Change Scenario—A Current Overview. Front. Plant Sci. 2019, 10, 563. [Google Scholar] [CrossRef]
  27. Pinheiro, C.; Chaves, M.M. Photosynthesis and Drought: Can We Make Metabolic Connections from Available Data? J. Exp. Bot. 2011, 62, 869–882. [Google Scholar] [CrossRef]
  28. Alter, S.; Bader, K.C.; Spannagl, M.; Wang, Y.; Bauer, E.; Schön, C.C.; Mayer, K.F.X. DroughtDB: An Expert-Curated Compilation of Plant Drought Stress Genes and Their Homologs in Nine Species. Database 2015, 2015, bav046. [Google Scholar] [CrossRef]
  29. Naeem, M.; Demirel, U.; Yousaf, M.F.; Caliskan, S.; Caliskan, M.E.; Wehling, P. Overview on Domestication, Breeding, Genetic Gain and Improvement of Tuber Quality Traits of Potato Using Fast Forwarding Technique (GWAS): A Review. Plant Breed. 2021, 140, 519–542. [Google Scholar] [CrossRef]
  30. Byrne, S.; Meade, F.; Mesiti, F.; Griffin, D.; Kennedy, C.; Milbourne, D. Genome-Wide Association and Genomic Prediction for Fry Color in Potato. Agronomy 2020, 10, 90. [Google Scholar] [CrossRef]
  31. Xu, X.; Pan, S.; Cheng, S.; Zhang, B.; Mu, D.; Ni, P.; Zhang, G.; Yang, S.; Li, R.; Wang, J.; et al. Genome Sequence and Analysis of the Tuber Crop Potato. Nature 2011, 475, 189–195. [Google Scholar] [CrossRef] [Green Version]
  32. Felcher, K.J.; Coombs, J.J.; Massa, A.N.; Hansey, C.N.; Hamilton, J.P.; Veilleux, R.E.; Buell, C.R.; Douches, D.S. Integration of Two Diploid Potato Linkage Maps with the Potato Genome Sequence. PLoS ONE 2012, 7, e36347. [Google Scholar] [CrossRef]
  33. Visser, R.G.F.; Bachem, C.W.B.; Borm, T.; de Boer, J.; van Eck, H.J.; Finkers, R.; van der Linden, G.; Maliepaard, C.A.; Uitdewilligen, J.G.A.M.L.; Voorrips, R.; et al. Possibilities and Challenges of the Potato Genome Sequence. Potato Res. 2014, 57, 327–330. [Google Scholar] [CrossRef]
  34. Baldwin, S.J.; Dodds, K.G.; Auvray, B.; Genet, R.A.; Macknight, R.C.; Jacobs, J.M.E. Association Mapping of Cold-Induced Sweetening in Potato Using Historical Phenotypic Data. Ann. Appl. Biol. 2011, 158, 248–256. [Google Scholar] [CrossRef]
  35. Zhu, C.; Gore, M.; Buckler, E.S.; Yu, J. Status and Prospects of Association Mapping in Plants. Plant Genome 2008, 1, 5–20. [Google Scholar] [CrossRef]
  36. Korte, A.; Farlow, A. The Advantages and Limitations of Trait Analysis with GWAS: A Review. Plant Methods 2013, 9, 1. [Google Scholar] [CrossRef]
  37. Myles, S.; Peiffer, J.; Brown, P.J.; Ersoz, E.S.; Zhang, Z.; Costich, D.E.; Buckler, E. Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design. Plant Cell 2009, 21, 2194–2202. [Google Scholar] [CrossRef]
  38. Sharma, S.K.; MacKenzie, K.; McLean, K.; Dale, F.; Daniels, S.; Bryan, G.J. Linkage Disequilibrium and Evaluation of Genome-Wide Association Mapping Models in Tetraploid Potato. G3 Genes Genomes Genet. 2018, 8, 3185–3202. [Google Scholar] [CrossRef]
  39. Ozturk, G.; Yildirim, Z. Heritability Estimates of Some Quantitative Traits in Potatoes. Turkish J. F. Crops 2014, 19, 262–267. [Google Scholar] [CrossRef]
  40. Cabello, R.; Monneveux, P.; Bonierbale, M.; Khan, M.A. Heritability of Yield Components under Irrigated and Drought Conditions in Andigenum Potatoes. Am. J. Potato Res. 2015, 91, 492–499. [Google Scholar] [CrossRef]
  41. Rudack, K.; Seddig, S.; Sprenger, H.; Köhl, K.; Uptmoor, R.; Ordon, F. Drought Stress-Induced Changes in Starch Yield and Physiological Traits in Potato. J. Agron. Crop Sci. 2017, 203, 494–505. [Google Scholar] [CrossRef]
  42. Gupta, P.K.; Kulwal, P.L.; Jaiswal, V. Association Mapping in Crop Plants: Opportunities and Challenges; Elsevier: Amsterdam, The Netherlands, 2014; Volume 85, ISBN 9780128002711. [Google Scholar]
  43. Khlestkin, V.K.; Rozanova, I.V.; Efimov, V.M.; Khlestkina, E.K. Starch Phosphorylation Associated SNPs Found by Genome-Wide Association Studies in the Potato (Solanum tuberosum L.). BMC Genet. 2019, 20, 29. [Google Scholar] [CrossRef] [PubMed]
  44. Wang, K.; Li, M.; Hakonarson, H. Analysing Biological Pathways in Genome-Wide Association Studies. Nat. Rev. Genet. 2010, 11, 843–854. [Google Scholar] [CrossRef] [PubMed]
  45. Kumar, V.; Wani, S.H.; Suprasanna, P.; Tran, L.S.P. Salinity Responses and Tolerance in Plants. In Salinity Responses and Tolerance in Plants; Springer: Berlin/Heidelberg, Germany, 2018; Volume 2, pp. 1–326. ISBN 9783319903187. [Google Scholar]
  46. Verslues, P.E.; Lasky, J.R.; Juenger, T.E.; Liu, T.W.; Nagaraj Kumar, M. Genome-Wide Association Mapping Combined with Reverse Genetics Identifies New Effectors of Low Water Potential-Induced Proline Accumulation in Arabidopsis. Plant Physiol. 2014, 164, 144–159. [Google Scholar] [CrossRef] [PubMed]
  47. Macovei, A.; Vaid, N.; Tula, S.; Tuteja, N. A New DEAD-Box Helicase ATP-Binding Protein (OsABP) from Rice Is Responsive to Abiotic Stress. Plant Signal. Behav. 2012, 7, 1138–1143. [Google Scholar] [CrossRef]
  48. Frey, P.A.; Hegeman, A.D.; Ruzicka, F.J. The Radical SAM Superfamily. Crit. Rev. Biochem. Mol. Biol. 2008, 43, 63–88. [Google Scholar] [CrossRef]
  49. Shen, Z.J.; Qin, Y.Y.; Luo, M.R.; Li, Z.; Ma, D.N.; Wang, W.H.; Zheng, H.L. Proteome Analysis Reveals a Systematic Response of Cold-Acclimated Seedlings of an Exotic Mangrove Plant Sonneratia Apetala to Chilling Stress. J. Proteom. 2021, 248, 104349. [Google Scholar] [CrossRef]
  50. Asada, K. Ascorbate Peroxidase—A Hydrogen Peroxide-scavenging Enzyme in Plants. Physiol. Plant. 1992, 85, 235–241. [Google Scholar] [CrossRef]
  51. Caverzan, A.; Passaia, G.; Barcellos Rosa, S.; Werner Ribeiro, C.; Lazzarotto, F.; Margis-Pinheiro, M. Plant Responses to Stresses: Role of Ascorbate Peroxidase in the Antioxidant Protection. Peroxidases Biochem. Charact. Funct. Potential Appl. 2013, 4, 142–158. [Google Scholar] [CrossRef]
  52. Sečenji, M.; Hideg, É.; Bebes, A.; Györgyey, J. Transcriptional Differences in Gene Families of the Ascorbate–Glutathione Cycle in Wheat during Mild Water Deficit. Plant Cell Rep. 2010, 29, 37–50. [Google Scholar] [CrossRef]
  53. D’Arcy-Lameta, A.; Ferrari-Iliou, R.; Contour-Ansel, D.; Pham-Thi, A.T.; Zuily-Fodil, Y. Isolation and Characterization of Four Ascorbate Peroxidase CDNAs Responsive to Water Deficit in Cowpea Leaves. Ann. Bot. 2006, 97, 133–140. [Google Scholar] [CrossRef]
  54. Demirel, U.; Morris, W.L.; Ducreux, L.J.M.; Yavuz, C.; Asim, A.; Tindas, I.; Campbell, R.; Morris, J.A.; Verrall, S.R.; Hedley, P.E.; et al. Physiological, Biochemical, and Transcriptional Responses to Single and Combined Abiotic Stress in Stress-Tolerant and Stress-Sensitive Potato Genotypes. Front. Plant Sci. 2020, 11, 169. [Google Scholar] [CrossRef]
  55. Alhoshan, M.; Zahedi, M.; Ramin, A.A.; Sabzalian, M.R. Effect of Soil Drought on Biomass Production, Physiological Attributes and Antioxidant Enzymes Activities of Potato Cultivars. Russ. J. Plant Physiol. 2019, 66, 265–277. [Google Scholar] [CrossRef]
  56. Bashir, M.A.; Silvestri, C.; Ahmad, T.; Hafiz, I.A.; Abbasi, N.A.; Manzoor, A.; Cristofori, V.; Rugini, E. Osmotin: A Cationic Protein Leads to Improve Biotic and Abiotic Stress Tolerance in Plants. Plants 2020, 9, 992. [Google Scholar] [CrossRef]
  57. Parkhi, V.; Kumar, V.; Sunilkumar, G.; Campbell, L.M.; Singh, N.K.; Rathore, K.S. Expression of Apoplastically Secreted Tobacco Osmotin in Cotton Confers Drought Tolerance. Mol. Breed. 2009, 23, 625–639. [Google Scholar] [CrossRef]
  58. Goel, D.; Singh, A.K.; Yadav, V.; Babbar, S.B.; Bansal, K.C. Overexpression of Osmotin Gene Confers Tolerance to Salt and Drought Stresses in Transgenic Tomato (Solanum lycopersicum L.). Protoplasma 2010, 245, 133–141. [Google Scholar] [CrossRef]
  59. Hakim; Ullah, A.; Hussain, A.; Shaban, M.; Khan, A.H.; Alariqi, M.; Gul, S.; Jun, Z.; Lin, S.; Li, J.; et al. Osmotin: A Plant Defense Tool against Biotic and Abiotic Stresses. Plant Physiol. Biochem. 2018, 123, 149–159. [Google Scholar] [CrossRef]
  60. Bartoli, C.G.; Gomez, F.; Gergoff, G.; Guiamét, J.J.; Puntarulo, S. Up-Regulation of the Mitochondrial Alternative Oxidase Pathway Enhances Photosynthetic Electron Transport under Drought Conditions. J. Exp. Bot. 2005, 56, 1269–1276. [Google Scholar] [CrossRef]
  61. Sunil, B.; Saini, D.; Bapatla, R.B.; Aswani, V.; Raghavendra, A.S. Photorespiration Is Complemented by Cyclic Electron Flow and the Alternative Oxidase Pathway to Optimize Photosynthesis and Protect against Abiotic Stress. Photosynth. Res. 2019, 139, 67–79. [Google Scholar] [CrossRef]
  62. Selinski, J.; Scheibe, R.; Day, D.A.; Whelan, J. Alternative Oxidase Is Positive for Plant Performance. Trends Plant Sci. 2018, 23, 588–597. [Google Scholar] [CrossRef]
  63. Chapagain, S.; Park, Y.C.; Kim, J.H.; Jang, C.S. Oryza Sativa Salt-Induced RING E3 Ligase 2 (OsSIRP2) Acts as a Positive Regulator of Transketolase in Plant Response to Salinity and Osmotic Stress. Planta 2018, 247, 925–939. [Google Scholar] [CrossRef] [PubMed]
  64. Liu, H.; Sultan, M.A.R.F.; Liu, X.L.; Zhang, J.; Yu, F.; Zhao, H.X. Physiological and Comparative Proteomic Analysis Reveals Different Drought Responses in Roots and Leaves of Drought-Tolerant Wild Wheat (Triticum boeoticum). PLoS ONE 2015, 10, e0121852. [Google Scholar] [CrossRef] [PubMed]
  65. Suzuki, Y.; Kondo, E.; Makino, A. Effects of Co-Overexpression of the Genes of Rubisco and Transketolase on Photosynthesis in Rice. Photosynth. Res. 2017, 131, 281–289. [Google Scholar] [CrossRef] [PubMed]
  66. Kaur, H.; Manna, M.; Thakur, T.; Gautam, V.; Salvi, P. Imperative Role of Sugar Signaling and Transport during Drought Stress Responses in Plants. Physiol. Plant. 2021, 171, 833–848. [Google Scholar] [CrossRef]
  67. Chen, Y.; Li, C.; Yi, J.; Yang, Y.; Lei, C.; Gong, M. Transcriptome Response to Drought, Rehydration and Re-Dehydration in Potato. Int. J. Mol. Sci. 2020, 21, 159. [Google Scholar] [CrossRef]
  68. Kang, J.; Li, J.; Gao, S.; Tian, C.; Zha, X. Overexpression of the Leucine-Rich Receptor-like Kinase Gene LRK2 Increases Drought Tolerance and Tiller Number in Rice. Plant Biotechnol. J. 2017, 15, 1175–1185. [Google Scholar] [CrossRef]
  69. Ouyang, S.Q.; Liu, Y.F.; Liu, P.; Lei, G.; He, S.J.; Ma, B.; Zhang, W.K.; Zhang, J.S.; Chen, S.Y. Receptor-like Kinase OsSIK1 Improves Drought and Salt Stress Tolerance in Rice (Oryza sativa) Plants. Plant J. 2010, 62, 316–329. [Google Scholar] [CrossRef]
  70. Wu, T.; Tian, Z.; Liu, J.; Xie, C. A Novel Leucine-Rich Repeat Receptor-like Kinase Gene in Potato, StLRPK1, Is Involved in Response to Diverse Stresses. Mol. Biol. Rep. 2009, 36, 2365–2374. [Google Scholar] [CrossRef]
  71. Khan, M.A.; Saravia, D.; Munive, S.; Lozano, F.; Farfan, E.; Eyzaguirre, R.; Bonierbale, M. Multiple QTLs Linked to Agro-Morphological and Physiological Traits Related to Drought Tolerance in Potato. Plant Mol. Biol. Report. 2015, 33, 1286–1298. [Google Scholar] [CrossRef]
  72. Díaz, P.; Sarmiento, F.; Mathew, B.; Ballvora, A.; Vásquez, T.M. Genomic Regions Associated with Physiological, Biochemical and Yield-Related Responses under Water Deficit in Diploid Potato at the Tuber Initiation Stage Revealed by GWAS. PLoS ONE 2021, 16, e0259690. [Google Scholar] [CrossRef]
  73. Anithakumari, A.M.; Nataraja, K.N.; Visser, R.G.F.; van der Linden, C.G. Genetic Dissection of Drought Tolerance and Recovery Potential by Quantitative Trait Locus Mapping of a Diploid Potato Population. Mol. Breed. 2012, 30, 1413–1429. [Google Scholar] [CrossRef] [Green Version]
  74. Rak, K.; Bethke, P.C.; Palta, J.P. QTL Mapping of Potato Chip Color and Tuber Traits within an Autotetraploid Family. Mol. Breed. 2017, 37, 15. [Google Scholar] [CrossRef]
  75. Massa, A.N.; Manrique-Carpintero, N.C.; Coombs, J.J.; Zarka, D.G.; Boone, A.E.; Kirk, W.W.; Hackett, C.A.; Bryan, G.J.; Douches, D.S. Genetic Linkage Mapping of Economically Important Traits in Cultivated Tetraploid Potato (Solanum tuberosum L.). G3 Genes Genomes Genet. 2015, 5, 2357–2364. [Google Scholar] [CrossRef]
  76. Tagliotti, M.E.; Deperi, S.I.; Bedogni, M.C.; Huarte, M.A. Genome-Wide Association Analysis of Agronomical and Physiological Traits Linked to Drought Tolerance in a Diverse Potatoes (Solanum tuberosum) Panel. Plant Breed. 2021, 140, 654–664. [Google Scholar] [CrossRef]
  77. Allen, R.G.; Pereira, L.S.; Smith, M.; Raes, D.; Wright, J.L. FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions. J. Irrig. Drain. Eng. 2005, 131, 2–13. [Google Scholar] [CrossRef]
  78. Müller, K.; Cervenkova, I. Die Ermittlung Des Stärke- Und Trockensubstanzgehaltes in Kartoffelknollen Nach Bestimmung Des Unterwassergewichtes an Hand Modifizierter Tabellenwerte. Starch Stärke 1978, 30, 12–20. [Google Scholar] [CrossRef]
  79. Lindsay, H. A Colorimetric Estimation of Reducing Sugars in Potatoes with 3,5-Dinitrosalicylic Acid. Potato Res. 1973, 16, 176–179. [Google Scholar] [CrossRef]
  80. Pritchard, J.K. Documentation for Structure Software: Version 2.2; Department of Human Genetics University of Chicago: Chicago, IL, USA, 2007. [Google Scholar]
  81. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the Number of Clusters of Individuals Using the Software STRUCTURE: A Simulation Study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef]
  82. Earl, D.A.; vonHoldt, B.M. Structure Harvester: A Website and Program for Visualizing STRUCTURE Output and Implementing the Evanno Method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  83. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for Association Mapping of Complex Traits in Diverse Samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef]
  84. Remington, D.L.; Thornsberry, J.M.; Matsuoka, Y.; Wilson, L.M.; Whitt, S.R.; Doebley, J.; Kresovich, S.; Goodman, M.M.; Buckler, E.S., IV. Structure of Linkage Disequilibrium and Phenotypic Associations in the Maize Genome. Proc. Natl. Acad. Sci. USA 2001, 98, 11479–11484. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Rosyara, U.R.; De Jong, W.S.; Douches, D.S.; Endelman, J.B. Software for Genome-Wide Association Studies in Autopolyploids and Its Application to Potato. Plant Genome 2016, 9, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Plants 12 00734 g001 550
Figure 1. Pearson’s correlations between different physiological and agronomical traits under control (indicated with postfix “_C”) and water stress conditions (indicated with postfix “_D”) in a panel of 144 potato varieties.
Figure 1. Pearson’s correlations between different physiological and agronomical traits under control (indicated with postfix “_C”) and water stress conditions (indicated with postfix “_D”) in a panel of 144 potato varieties.
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Plants 12 00734 g002 550
Figure 2. Manhattan plots for the traits with significant SNPs associated under control and drought stress conditions in 144 potato varieties.
Figure 2. Manhattan plots for the traits with significant SNPs associated under control and drought stress conditions in 144 potato varieties.
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Table
Table 1. Analysis of variance between genotypes (G) and treatments (T) in 144 tetraploid potato varieties.
Table 1. Analysis of variance between genotypes (G) and treatments (T) in 144 tetraploid potato varieties.
TraitF Value
20192020
Genotype(G)Treatment(T)G × TGenotype(G)Treatment(T)G × T
SPAD_504.83 ***15.17 ***1.39 **8.57 ***5.51 *1.55 ***
NDVI_502.53 ***411.95 ***2.37 ***2.26 ***38.25 ***1.52 **
SC_503.89 ***293.64 ***3.95 ***6.61 ***275.80 ***2.18 ***
FLUOR_507.72 ***148.46 ***2.79 ***3.81 ***3.81 ns3.56 ***
SPAD_706.58 ***62.22 ***2.20 ***8.63 ***7.04 **1.56 ***
NDVI_704.30 ***360.85 ***2.58 ***2.89 ***61.99 ***1.75 ***
SC_704.49 ***322.74 ***3.76 ***4.23 ***140.85 ***1.72 ***
FLUOR_706.21 ***584.25 ***3.10 ***3.72 ***252.39 ***1.57 ***
Yield19.63 ***900.35 ***5.20 ***7.88 ***627.35 ***1.89 ***
TubNum11.63 ***352.54 ***1.98 ***6.25 ***17.57 ***1.54 ***
TubWeight8.97 ***859.01 ***2.62 ***6.24 ***541.72 ***1.95 ***
DryMatter1453.69 ***1200 ***802.88 ***1326.9 ***1200 ***530 ***
RS51.10 ***1200 ***30.74 ***32.97 ***72.53 ***18.85 ***
Starch1497.04 ***1200 ***853.63 ***1388.46 ***1200 ***558.55 ***
Area5.58 ***358.25 ***2.56 ***3.58 ***178.29 ***1.58 ***
Perim6.24 ***256.34 ***2.14 ***3.69 ***189.32 ***1.67 ***
*, **, *** Significant at p = 0.05, p = 0.01 and p = 0.001, respectively.
Table
Table 2. Number of SNPs per chromosome before and after filtering and size of each chromosome. CH01 to CH12 refers to each of the 12 potato chromosomes, CH00 are control markers that are not associated with any chromosome and CH13 refers to the chloroplast.
Table 2. Number of SNPs per chromosome before and after filtering and size of each chromosome. CH01 to CH12 refers to each of the 12 potato chromosomes, CH00 are control markers that are not associated with any chromosome and CH13 refers to the chloroplast.
ChromosomeNumber of SNPs
(Total)		Number of SNPs
(Filtered)		Chromosome Length (bps)
CH00464156
CH013958248688,663,952
CH023335191448,614,681
CH032919163762,190,286
CH042798161172,208,621
CH052538152052,070,158
CH062390146159,532,096
CH072457140756,760,843
CH082043123456,938,457
CH092204129661,540,751
CH101865106159,756,223
CH112249136145,475,667
CH121942109861,165,649
CH132817155,312
Total31,19018,259810,654,046
Table
Table 3. Significant SNPs associated with evaluated physiological and agronomical traits under control and drought stress conditions in 144 potato varieties.
Table 3. Significant SNPs associated with evaluated physiological and agronomical traits under control and drought stress conditions in 144 potato varieties.
TraitMarkerChrom.PositionRefAltEffectR2p-ValueFDRBiological Function
SPAD70_CPotVar0039950653985614CT−2.070.01832.14 × 10−20.0361Radical SAM superfamily protein
SPAD70_Csolcap_snp_c2_152871141743380AG−4.200.06817.05 × 10−60.0138P-loop containing nucleoside triphosphate hydrolases superfamily protein
TubNum_Csolcap_snp_c2_15676518718517GT−25.380.04380.00030.0222RNA-binding CRS1/YhbY (CRM) domain-containing protein
TubNum_Csolcap_snp_c2_37217111818959AG−32.800.00060.04860.05-
TubNum_CST4.03ch11_2070850112070850AT42.250.01530.03550.0416Di-glucose binding protein with Kinesin motor domain
NDVI50_Dsolcap_snp_c1_646222450782GT0.030.07442.53 × 10−60.0027Plant protein with unknown function
SPAD70_DPotVar0039950653985614CT−3.650.07124.25 × 10−60.0083Radical SAM superfamily protein
NDVI70_Dsolcap_snp_c2_43735464055406AG−0.070.00950.04320.0444GroES-like zinc-binding dehydrogenase family protein
NDVI70_DPotVar0113919464089292AG−0.070.00490.04610.0472Ascorbate peroxidase
SC70_Dsolcap_snp_c2_45637112022163AG−182.030.07293.26 × 10−60.0055Hypothetical protein
Yield_Dsolcap_snp_c2_26653854286889GT−0.750.0714.37 × 10−60.0111Osmotin
TubNum_DPotVar006447011787325GT−10.250.06142.03 × 10−50.0166Alternative oxidase family protein
TubNum_Dsolcap_snp_c2_550851020334943AG27.360.05863.23 × 10−50.0194Transketolase
RS_Dsolcap_snp_c1_374627050595CT0.140.0340.00160.025Cofactor assembly of complex C
RS_Dsolcap_snp_c2_25284465872176AG0.100.00410.02770.0388Sucrose transporter
RS_Dsolcap_snp_c2_55785465970953GT0.110.09520.010.0277Leucine-rich receptor-like protein kinase family protein
RS_Dsolcap_snp_c2_55783465971150AG0.110.09520.010.0305Leucine-rich receptor-like protein kinase family protein
RS_Dsolcap_snp_c2_55775465972399CT0.110.09520.010.0333Leucine-rich receptor-like protein kinase family protein
Table
Table 4. Maximum and minimum temperatures, humidity and precipitation in the experimental field for years 2019 and 2020.
Table 4. Maximum and minimum temperatures, humidity and precipitation in the experimental field for years 2019 and 2020.
Year 2019
14–31 MayJuneJulyAugust1–17 September
Max. temperature (°C)18.125.47.127.522.2
Min. temperature (°C)5.710.03.113.010.0
Humidity (%)79.970.32.373.575.1
Precipitation (L/m2)33.9172.124.730.5
Year 2020
26–31 MayJuneJulyAugust1–28 September
Max. temperature (°C)26.822.26.427.724.6
Min. temperature (°C)9.20.72.413.011.0
Humidity (%)68.37.23.272.371
Precipitation (L/m2)05.88.831.433.5
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MDPI and ACS Style

Alvarez-Morezuelas, A.; Barandalla, L.; Ritter, E.; Ruiz de Galarreta, J.I. Genome-Wide Association Study of Agronomic and Physiological Traits Related to Drought Tolerance in Potato. Plants 2023, 12, 734. https://doi.org/10.3390/plants12040734

AMA Style

Alvarez-Morezuelas A, Barandalla L, Ritter E, Ruiz de Galarreta JI. Genome-Wide Association Study of Agronomic and Physiological Traits Related to Drought Tolerance in Potato. Plants. 2023; 12(4):734. https://doi.org/10.3390/plants12040734

Chicago/Turabian Style

Alvarez-Morezuelas, Alba, Leire Barandalla, Enrique Ritter, and Jose Ignacio Ruiz de Galarreta. 2023. "Genome-Wide Association Study of Agronomic and Physiological Traits Related to Drought Tolerance in Potato" Plants 12, no. 4: 734. https://doi.org/10.3390/plants12040734

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