Genetic Loci Determining Drought Resistance of Potato reveled by Genome-wide Association Study (GWAS)

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Here, we report a high-quality genomic variation database by whole-genome resequencing of 230 potato individuals. Through phylogenetic population structure analysis, we uncover that the breeding of potatoes was international interaction, not independent. Selective-sweep analysis detected 560 drought resistance response related genes, including ZFP, MYB and ERF transcription factors. Furthermore, based on three different models, the genome-wide association studies for drought resistance identified a set of candidate genes, such as MYB, WRKY and ERF, PP2A, UGT, E3 ubiquitin ligase, ZFP, etc., some crucial candidate genes were identified by different models at the same time. Among them, 15 candidates were identified both by GWAS and selective-sweep analysis, significant SNP 4:1861996 in the exon region of LBR (late blight resistance protein) harboring different genotype with different drought resistance. Our study provides important insights into the genetic basis of drought resistance, and will facilitate the cultivation of drought-resistant potato. Biological sciences/Plant sciences/Plant stress responses/Drought Biological sciences/Plant sciences Biological sciences/Plant sciences/Natural variation in plants Biological sciences/Plant sciences/Plant stress responses Potato Drought-resistance Genome resequencing Selective sweep Genome-wide association study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Drought is one of the bottlenecks restricting the sustainable development of agriculture in the world, which having important effects on the growth, development, as well as physiological and metabolic processes 1 . The mechanism of drought resistance is complex and affected by physiological and biochemical factors as well as the control of multiple gene pairs. The process of plant responses to drought stress involved in signaling molecules, such as hormones, transcription factors and stress response genes, as well as pretranscription epigenetic regulation, which encodes proteins with protective effects against drought stress 2 . Therefore, understanding the molecular mechanisms of plant response to drought stress (classical genetic regulation and epigenetic regulation) has important implications for improving plant drought resistance 3 . Plant response to drought stress is a complex biological process, which affected by multiple genes related to drought resistance and stress signaling pathways 4 . Abscisic acid (ABA) plays the key role in response to drought by activating stress response genes and regulating stomatal conductance 5 . In recent years, studies have shown that the main ABA signaling pathway also interacts with other signaling factors during drought stress response, brassinosteroids also modulate drought response through signaling components related to the ABA response pathway 6 , 7 . Signal cascades in the drought stress response network are activated by TF (transcription factor), such as MYB, ERF, WRKY, ZFP and so on 8 , which have been shown to improve drought tolerance in plants. In addition, drought stress signals can regulate the expression of different drought-induced genes independently of ABA pathways 9 . What’s more, epigenetic regulation is also an important factor affecting plant response to drought stress, which mediated by complex interactions among DNA methylation, histone modification and chromatin remodeling 10 , 11 . Potato is one of the most important food crops in the world, with its high yield and rich nutritional value, has played a positive role in solving the world food crisis and poverty problem 12 . However, drought stress often results in potato production. Drought stress can affect plant photosynthesis rate and harvest index, if potato suffered in short-term high-intensity drought under the tuber expansion period, the potato tuber will be abnormal 1 . Therefore, the study of potato drought resistance mechanism has great value to improve potato drought resistance and promote the development of potato industry. Recently, the whole genome resequencing and genome-wide association study (GWAS) have been used to detected candidate genes related to stress response. For example, using 10 drought-related physiological and anatomical traits and SNPs, 78 new markers × trait associations were identified in coast redwood through GWAS analysis, and the candidate genes involved in metabolic, stress, and signaling pathways 13 . A total of 38 QTL were reported through the GWAS of traits under well-watered and water deficit treatments at tuber initiation stage in 104 diploid potato accessions 14 . In maize, eight candidate genes for drought tolerance associated with eight significant SNPs were identified, including trehalase, AP2/EREB160 transcription factor, and glutathione S-transferase, which might be directly or indirectly involved in drought resistance 15 . Besides, the GWAS detected 17 and 20 QTL regions associated with traits under rainfed and drought conditions in winter wheat 16 . In general, a growing body of research now shows that GWAS is an excellent method to detect the genetic footprint of drought stress, which can greatly promote the understanding and improving drought tolerance in plants. Here, to further explore candidate genes for drought resistance in plants, we sequenced a diversity panel of 230 potato individuals with different drought resistance. Using 9 traits under normal watering conditions and 7 traits under drought conditions, GWAS has been performed to identify loci potentially associated with drought resistance. Several genomic loci and candidate genes are identified in this study, which provide a new resource for further molecular breeding and studies of potato drought resistance. Materials and Methods Plant material A total of 230 potato individuals were used for re-sequencing, including 87 potato individuals from CIP (international potato center) and 143 potato individuals from different Chinese potato research institutes (CPRI). Among them, 15 potato individuals were local domesticated species while the rest of them were artificially improved species. Phenotypic data measurement and analysis The seedlings of all potato individuals were planted in the same experimental field (Zhangjiakou, Hebei, China at 41°16′ N, 114°72′ E). The drought resistance experiment was carried out in the awning, which was divided into two areas, namely the drought treatment area and irrigation area. The potato individuals were grown in a randomized design, with the plant density of 65 cm × 30 cm, and 10 plants were planted in each row with three replicates. The field management were carried out equally for the two areas, including fertilizer application, pest control, weed management, etc. Differently, the irrigated area was irrigated at the stage of seedling, tuber formation, tuber growth and starch accumulation, and the irrigation amount reached 70% of the maximum water holding capacity of the field. The drought treatment area-controlled watering from squaring stage to harvest, when the water content naturally decreased by 30% of the maximum water, conducted statistics of related phenotypic data. Under normal watering conditions, 9 traits were collected, including ChC (chlorophyll content), PlH (plant height), StM (stem number), BrN (branch number), PPN (number of tubers per plant), PPW (weight of tuber per plant), RWC (relative water content), MSD (moisture saturation deficit) and WRR (water retention rate). Under drought conditions, 7 traits were collected, including ChC, PlH, BrN, PPW, RWC, VMF (variety membership function) and SSI (stress susceptibility index). The frequency distribution analysis and correlation analysis of each phenotypic traits were performed using SPSS. DNA isolation and sequencing Genomic DNA of the 230 potato individuals were extracted from leaves using the CTAB method. After evaluating the quality and quantity of the DNA samples, the paired-end genomic libraries with insert sizes of 450–500 bp were constructed using the Illumina TruSeq DNA Sample Prep kit following the manufacturer’s instructions. Then, the libraries were sequenced on the Illumina NovaSeq6000 platform in Beijing Biomics Biotech Co. Ltd. SNP calling The quality of the original paired-end reads obtained by sequencing was assessed and filtered, and the quality of sequencing data was counted. Then, the clean reads were compared with the potato reference genome using BWA (ver. 0.7.10-r789) 17 . The SNP calling was performed by adding the allele information of re-sequenced accessions to the SNP dataset using GATK 18 , the annotation of SNPs and Indels were performed by ANNOVAR 19 . The detection of structural variation (SV) was performed by Breakdancer 20 , and the detection of copy number variation (CNV) was performed by CNVnator 21 . According to the standard of missing data rate 0.05, the SNPs were firstly filtered. Then, the distribution of the results of each type of variation on the whole genome was shown using circos diagram 22 . Population analysis For the population genetics analysis, SNPs of all individuals were filtered with a missing data rate 0.05, a total of 1,484,530 high-quality SNPs were retained for downstream population analysis. In this study, a neighbor-joining tree using the software PHYLIP was constructed 23 , and the online tools iTQL ( https://itol.embl.de/iTOL ) was used to color the tree. Principal component analysis (PCA) was performed with EIGENSOFT 24 . Then, all the high-quality SNPs were used for the population structure assessment, which was performed with ADMIXTURE software 25 . Using the Haploview software 26 , genome-wide linkage disequilibrium (LD) decay distance was estimated. To measure the genetic similarity between individuals, the SPAGedi software was used to generate the relative kinship matrix (K) 27 . Identification of selective sweeps To identify genomic regions affected by drought stress, we selected drought susceptible (DS) potatoes and drought resistant (DR) potatoes as two different groups. For selective sweep detection, we first measured the level of nucleotide diversity (π) within 40-kb sliding windows with a step size of 5 kb in DS and DR groups using VCFtools 28 , and the ratio of genetic diversity by comparing the DS and DR groups (π DS /π DR ) was calculated. And genome-wide F ST were calculated for DS and DR groups with VCFtools, using 40-kb sliding windows with a step size of 5 kb. Potential selective sweeps were identified with the top 5% largest F ST and π DS /π DR values. Besides, based on the annotation information of potato reference genome, the genes located in the selective regions were detected as selected genes. Finally, the GO and KEGG analysis was performed by AgriGO analysis toolkit and KEGG databases 29 , respectively. Genome-wide association study In this study, SNPs with the MAF > 0.05 and miss rate < 10% were selected for the GWAS. Firstly, associations between SNPs and the phenotypic traits under normal watering conditions and drought conditions were detected using general linear model (GLM). Then, to control the false positives, the mixed linear model (MLM) also was used to detected the association sites using TASSEL V3.0 software 30 , which taking K and Q matrices into account. At the same time, the GWAS also be performed by FarmCPU (Fixed and random model Circulating Probability Unification) 31 . The Manhattan and quantile-quantile plots were created to show the results of GWAS analysis. P-value threshold was estimated by the Bonferroni test (1/N, N = total SNPs), where n is the effective number of independent SNPs 32 . Finally, we collected candidate genes in the upstream and downstream of significant SNPs according to the LD decay distance. RNA extraction and quantitative RT-PCR analysis The tissue culture seedling of Qingshu9, a variety with strong drought resistance, was used as the experimental material for quantitative RT-PCR experiment. The shoot and root of the seedings were collected after 0h and 24h treated with 20% PEG. To verify the expression levels of important candidate genes, total RNA was extracted using RNAprep Pure Plant Kit (TIANGEN, Beijing, China). A total of 1 ug RNA was used to synthesize cDNA according to the instructions of a PrimeScript RT Reagent Kit (TaKaRa, China). And the qRT-PCR reactions were performed through the SYBR-Green PrimeScript RT-PCR Kit (Takara) following the manufacturer’s instructions. EF1-α was used as the reference gene for qRT-PCR, the original data were processed by the 2 −△△Ct method 33 . Results Genome re-sequencing and SNP calling In this study, a total of 230 potato individuals were used for re-sequencing, among them, 87 individuals were collected from CIP (international potato center) and 143 individuals were coming from different Chinese potato research institutes (CPRI), which having different drought-resistance (Fig. 1 ). Through the whole genome resequencing, 17.40 billion paired-end reads were generated, and the average sequencing depth was 9.8× (Table S1 and Figure S1 ). After calling and filtering, a total of 3,986,623 single nucleotide polymorphisms (SNPs) (missing data rate 0.05) and 1,130,197 Indels were obtained (Table 1 , Table S2 ~ 4 and Figure S2 ). Table 1 Summary of the whole-genome variations from the re-sequencing of 230 potato individuals. SNPs Indels Exonic 382,746 Exonic 26,998 Intergenic 2,564,208 Intergenic 681,423 Intronic 532,439 Intronic 200,358 Downstream 162,257 Downstream 69,200 Upstream 167,014 Upstream 68,515 Upstream/downstream 20,028 Upstream/downstream 10,720 Splicing 836 Splicing 746 3'UTR 91,589 3'UTR 44,888 5'UTR 62,015 5'UTR 26,599 Stop gain 2,921 Stop gain 643 Stop loss 570 Stop loss 107 Ts 2,469,746 Insertion 9,858 Tv 1,516,877 Deletion 16,390 Nonsynonymous 164,488 Frame-shift 16,249 Synonymous 218,258 Non-frame-shift 999 We found that 2,564,208 SNPs were in intergenic and 915,185 SNPs were in genes, and 382,746 SNPs occurred in exonic (9.6%). Within coding regions, nonsynonymous SNPs (164,488) were less than synonymous SNPs (218,258), and the ratio of nonsynonymous-to-synonymous is 0.74 (Table 1 ). A total of 26,998 (2.4%) Indels were in exonic, and the number of Insertion and Deletion were 9,858 and 16,390, respectively (Table 1 and Figure S2 ). Besides, we analyzed the whole genome SNP mutation types and the ratio of transition/transversion (Ts/Tv) is 1.63 (Table S3 and Figure S3). What’s more, 5,756 CNVs (copy number variants) and 6,486 SVs (structure variations) were identified in this study (Table S5). Finally, all the variations generating from re-sequencing were shown by a circos image (Fig. 2 ). Phylogenetic relationship and population structure To better understand the phylogenetic relationship of the 230 potato individuals, a total of 1,484,530 high-quality SNPs (missing data rate 0.05) were used to constructed the neighbor-joining (NJ) tree. We found that the NJ tree did not separate the potatoes from CIP and CPRI, which indicated that there might be more communication of potatoes from China and abroad during the breeding of potato (Fig. 3 A). Principal-component analysis (PCA) reflected the similar result of NJ tree (Fig. 3 B and Figure S4). We also performed population structure analysis of 230 potato individuals, which also supported above opinion, the potatoes from CIP and CPRI had the same genetic background (Fig. 3 C, Table S6 and Figure S5). Thus, it seems that the breeding of potatoes was international interaction, not independent. Relative kinship and linkage disequilibrium The relative kinship of 230 potato individuals were evaluated, the result indicated that the relative kinship among each potato individuals re-sequenced was relatively weak (Fig. 4 A), which showing that the experimental materials were suitable for genome-wide association analysis and had little influence on the results of subsequent association analysis. Then, the high-quality SNPs of potato individuals were employed to estimate the linkage disequilibrium (LD) extent, which is crucial to GWAS analysis. In this study, the decay of LD reached half of maximum average r 2 at 35 kb across all chromosomes for the 230 potato individuals (Fig. 4 B). Phenotypic variation A total of 16 phenotypic traits, among them, 9 traits were investigated under normal watering conditions, including ChC (chlorophyll content), PlH (plant height), StM (stem number), BrN (branch number), PPN (number of tubers per plant), PPW (weight of tuber per plant), RWC (relative water content), MSD (moisture saturation deficit) and WRR (water retention rate) (Table S7), which having be detected abundant variation (Figure S6). Then, the correlation analysis of 9 phenotypic traits under normal watering conditions were performed, which shown that the correlation coefficients between some traits were relatively high (Figure S7), such as PlH and PPW (r = 0.55), PlH and PPN (r = 0.43), RWC and MSD (r = -0.94). What’s more, 7 traits under drought conditions also were investigated, including ChC, PlH, BrN, PPW, RWC, VMF (variety membership function) and SSI (stress susceptibility index) (Table S8), abundant variation was detected (Figure S8). The correlation coefficient between PlH and PPW (r = 0.53) also was higher, and there was a significant negative correlation between VMF and SSI (r = -0.71) (Figure S9). Selective sweep analysis To identify the potential selective signatures related to drought-resistance in potato, we selected potatoes with significant differences in drought resistance as two groups based on the phenotypic traits under drought conditions, one was drought susceptible (DS) potato, and the other was drought resistant (DR) potato. Then, the selective sweep analyses were performed by population fixation statistics (F ST ) and nucleotide diversity between the DS group and DR group (π DS /π DR ) in 40-kb sliding windows (a step of 5 kb). The windows with the top 5% of F ST as well as π DS /π DR were considered as the selective sweeps (Fig. 5 ). A total of 680 common selective sweeps were identified between the DS group and DR group in this study (Table S9), and 560 annotated genes located in the selective-sweep regions (Table S10), which could potentially play important roles in the drought resistance of potato. To further understand the functions of the 560 genes, we performed gene ontology analysis (GO) and KEGG enrichment analysis. The result of GO analysis indicated that these genes related to multiple biological process, cellular component, and molecular function (Figure S10), and many genes were involved in “Plant hormone signal transduction”, “Plant-pathogen interaction”, “Neurotrophin signaling pathway” and “Toll-like receptor signaling pathway” through KEGG analysis (Fig. 6 ). The genes encoding ZFP protein, E3 ubiquitin ligase, auxin-responsive protein, MYB and ERF transcription factor were found in the selective regions (Table S10), which revealed that the genes identified through selective sweep analysis probably play an important role in the drought resistance of potato. Genome-wide association analysis for traits under drought conditions Using the high-quality SNPs and phenotypic data under drought conditions, we further detected the genes related to drought resistance through GWAS analysis. In this study, three different statistical models were used to detected significantly association signals, including generalized linear model (GLM), mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU). Based on the LD decay distance (about 35 Kb), the candidate genes were searched on downstream and upstream of the significantly association SNPs. The Manhattan plots and quantile-quantile plots of GWAS for 7 traits under drought conditions were displayed in Figure S11-S16, and the detailed information about candidate genes identified by three models were summarized in Table S11-S13. Through three models, a set of significant SNPs were detected, and the number of the significant SNPs and associated genes were statistics (Table 2 ). For branch number (BrN), GLM and FarmCPU models identified 8334 and 9 significant SNPs, respectively. We found that the threshold value of the GWAS of BrN by GLM model was too looser. To reduce the false positives, we detected associated genes with a strict threshold value (top 0.01), and 193 genes associated with 55 significant SNPs were identified in this study (Table S11). one SNP (1:72674289) was the common loci, the related candidate gene PGSC0003DMG400000063 encoded a pollen-specific leucine-rich repeat extensin-like protein (Fig. 7 B). Our result shown that the most of the accessions carrying 1:72674289-GG had higher BrN than the accessions carrying 1:72674289-AA (Fig. 7 C). Three models identified 267, 7 and 43 associated loci of chlorophyll content (ChC), respectively, which detected 699, 20 and 156 candidate genes, 8 common genes were identified both by GLM and MLM models which encoding ERF transcription factor, auxin-responsive protein, embryonic abundant protein, etc., among them, two genes encoding unknown proteins also were identified by FarmCPU model (Figure S12). What’s more, the GWAS identified 280, 13 and 14 significant SNPs associated with plant height (PlH) using GLM, MLM and FarmCPU models (Figure S13). All the 13 significant SNPs identified by MLM model also were detected through GLM model, and 44 genes were found according to the common SNPs, which encoding serine/threonine-protein kinase, UDP-glycosyltransferase, F-box protein, calcineurin B-like protein and so on, and 5 genes were identified through three models at the same time. Table 2 Summary of the significant sites and associated genes of traits in different models. Conditions Traits Number of significant SNPs Number of associated genes Normal watering conditions GLM MLM FarmCPU GLM& MLM GLM& FarmCPU MLM& FarmCPU GLM MLM FarmCPU GLM& MLM GLM& FarmCPU MLM& FarmCPU BrN 7243 16 11 6 2 0 5176 107 36 51 8 0 ChC 82 9 23 6 1 0 274 44 97 30 8 0 MSD 38 3 44 3 5 1 143 11 134 11 10 6 PlH 401 19 23 16 17 2 925 80 72 59 43 6 PPN 158 30 23 26 0 0 369 118 151 98 0 0 PPW 3465 0 60 0 33 0 3123 0 125 0 29 0 RWC 0 3 44 0 0 1 0 10 133 0 0 6 StM 70 3 19 3 1 0 145 9 75 9 5 0 WRR 314 20 34 11 14 5 536 113 120 50 27 21 Drought conditions BrN 8334 0 9 0 1 0 5390 0 37 0 1 0 ChC 267 7 43 3 5 1 699 20 156 8 8 2 PlH 280 13 14 13 7 2 612 44 67 44 50 5 PPW 1419 0 33 0 19 0 2633 0 104 0 60 0 RWC 0 0 33 0 0 0 0 0 148 0 0 0 SSI 38 12 29 10 2 1 130 50 182 40 3 4 VMF 64 13 70 12 21 5 206 98 189 83 39 23 Note : BrN: branch number, ChC: chlorophyll content, MSD: moisture saturation deficit, PlH: plant height, PPN: number of tubers per plant, PPW: weight of tuber per plant. RWC: relative water content, StM: stem number, WRR: water retention rate, SSI: stress susceptibility index, VMF: variety membership function. For weight of tuber per plant (PPW), 1419 and 33 significant loci were detected through GLM and FarmCPU models (Figure S14), while MLM model didn’t detect significant loci, and 19 common significant loci associated with 60 genes, including WRKY and MYB transcription factor, proline-rich receptor-like protein kinase, calcium-dependent protein kinase and so on. Our further study identified one interesting genetic variation (2:4478353) (Fig. 7 D), the accessions carrying 2:4478353-AA were shown to have higher weight of tuber per plant (PPW) than the accessions carrying 2:4478353-GG (Fig. 7 E). The significant SNP 2:4478353 was in the promoter region of the candidate gene PGSC0003DMG400004427 , which encoded a Proline-rich receptor-like protein kinase (PERK). In the analysis of relative water content (RWC), only FarmCPU model detected 33 significant SNPs (Figure S15), and 148 genes located in the downstream and upstream of significant SNPs. What’s more, a total of 38, 12 and 29 significant SNPs were identified through the analysis of stress susceptibility index (SSI) using GLM, MLM and FarmCPU models, respectively (Figure S16). And 8:36470394 was the common significant SNP (Fig. 7 f) locating in the genomic region of the candidate gene PGSC0003DMG400002204 , which encoded a pollen-specific protein. The further analysis indicated that the accessions carrying 8:36470394-GG had higher stress susceptibility index (SSI) than the accessions carrying 8:36470394-AA (Fig. 7 G). We also performed genome-wide association analysis for variety membership function (VMF), almost all significant SNPs detected by MLM model also were detected by GLM model (Fig. 8 A, B, C). And 83 common associated genes were identified through the above two methods, among them, 21 genes also were the associated genes identified by FarmCPU model, which encoding serine/threonine-protein phosphatase 2A (PP2A), salicylate carboxymethyl transferase, abscisic acid hydroxylase, AAA-ATPase, E3 ubiquitin-protein ligase, zinc finger protein (ZFP) and so on. The significant SNP (3:826504) was the common loci located in the genomic region of the related candidate gene PGSC0003DMG400013420 , which encoded an ABC transporter (Fig. 7 H). Our result shown that the accessions carrying 3:826504-GG had higher variety membership function (VMF) than the accessions carrying 3:826504-AA (Fig. 7 I). In addition, we found that 15 genes were identified associated with variety membership function (VMF) both of GLM model and MLM model, as well as located in selective regions (Table 3 ), which encoding serine/threonine-protein kinase, NAC domain-containing protein, adenylate isopentenyl transferase and so on. Among them, one gene encoded LBR (late blight resistance protein) was identified by all the three models, and the significant SNP 4:1861996 located in the exon region of LBR, and the genetic region of LBR had significantly F ST value (Fig. 8 D). Most interestingly, the different genotype of 4:1861996 had different drought resistance (Fig. 8 E). The 69 potato individuals carrying 4:1861996-AA had significantly better drought resistance than the individuals carrying 4:1861996-AG, and the most of the individuals with 4:1861996-AA were drought resistant (DR) potatoes, while the individuals with 4:1861996-AG were drought susceptible (DS) potatoes. To better understand the function of candidate genes identified by both the GWAS analysis and selective sweep analysis, the expression levels of 15 important candidate genes were verified by quantitative real time PCR (qRT-PCR) in the materials with stronger drought resistance. Based on the qRT-PCR results, we found that the expression levels of most of the candidate genes were up-regulated after drought treatment for 24 h. Besides, we found that some genes are up-regulated expression in both the shoot and root, such as PGSC 0003DMG400005975 , PGSC0003DMG400035020 , PGSC0003DMG400005970 and so on (Fig. 9 ). But there were also some genes that were only up-regulated expression in the shoot, while the up-regulated expression levels in the root were not obvious, such as PGSC0003DMG400005978 and PGSC0003DMG400002468 , which suggested that some candidate genes might take part drought response in different tissues. Table 3 Summary of the candidate genes associated with VMF identified by GLM and MLM model. Gene ID Chromosome SNP Start End P value (GLM model) P value (MLM model) Gene function PGSC0003DMG400035020 4 1530957 1550680 1559669 5.00E-06 6.31E-06 Unknown PGSC0003DMG400006008 4 1530957 1541367 1542478 5.00E-06 6.31E-06 Adenylate isopentenyl transferase PGSC0003DMG400005978 4 1709353 1694262 1695176 7.12E-06 9.19E-06 Unknown PGSC0003DMG400040065 4 1709353 1677219 1677845 7.12E-06 9.19E-06 NAC domain-containing protein PGSC0003DMG400005975 4 1709353 1734305 1737409 7.12E-06 9.19E-06 Serine/threonine-protein kinase PGSC0003DMG400006000 4 1709353 1730627 1733353 7.12E-06 9.19E-06 R-like protein kinase PGSC0003DMG400005976 4 1709353 1710526 1718731 7.12E-06 9.19E-06 Chaperone protein dnaJ PGSC0003DMG400006002 4 1709353 1708936 1709799 7.12E-06 9.19E-06 Unknown PGSC0003DMG400005977 4 1709353 1700475 1701857 7.12E-06 9.19E-06 Unknown PGSC0003DMG400005970 4 1861996 1858431 1862567 1.33E-06 5.45E-06 Late blight resistance protein PGSC0003DMG400040027 0 32343039 32368061 32368494 3.5418E-06 7.72E-06 Adenylate isopentenyl transferase PGSC0003DMG400045608 0 32343039 32366289 32366594 3.5418E-06 7.72E-06 Adenylate isopentenyl transferase PGSC0003DMG400002468 0 32343039 32361429 32363751 3.5418E-06 7.72E-06 Pentatricopeptide repeat-containing protein PGSC0003DMG400002467 0 32343039 32354306 32356707 3.5418E-06 7.72E-06 Geraniol 8-hydroxylase PGSC0003DMG400002470 0 32343039 32350218 32351019 3.5418E-06 7.72E-06 Geraniol 8-hydroxylase Genome-wide association analysis for traits under normal watering conditions In this study, we also performed genome-wide association analysis for 9 traits under normal watering conditions using three different statistical models (GLM, MLM and FarmCPU). And the Manhattan plots and quantile-quantile plots were displayed in Supplemental Figure S17-S23, and the detailed information about candidate genes identified by three models were summarized in Table S14-S16. We found that the most of the significant SNPs identified by MLM model also were identified by GLM model (Table 2 ). When the analysis of chlorophyll content (ChC), 6 common significant SNPs were detected (Fig. 10 A), which identified 30 associated genes, including zinc finger protein, ERF transcription factor, glycosyltransferase, late blight resistance protein and so on. The significant SNP (4:8470510) was the common loci located in the genomic region of the related candidate gene PGSC0003DMG400023619 , which encoded an ethylene-responsive transcription factor (Fig. 10 C). Our result shown that the accessions carrying 4:8470510-GG had higher chlorophyll content (ChC) than the accessions carrying 4:8470510-AA (Fig. 10 D). For moisture saturation deficit (MSD), all the three significant SNPs detected through MLM model also were detected through GLM model (Fig. 10 B), and 11 genes were the common associated genes, which encoding serine/threonine-protein phosphatase, serine/threonine-protein kinase, ERF transcription factor and so on, among them, 6 genes also were identified through FarmCPU model. The common significant SNP 7:50992935 (Fig. 10 E) locating in the promoter region of the candidate gene PGSC0003DMG400017294 , which encoded an unknown protein. The further analysis indicated that the accessions carrying 7:50992935-CC had higher moisture saturation deficit (MSD) than the accessions carrying 8:36470394-AA (Fig. 10 F). What’s more, a total of 59 common genes were identified by the analysis of plant height (PlH) using GLM model and MLM model, and 6 genes were also identified by FarmCPU model. What’s more, a total of 158, 30 and 23 significant SNPs were identified through the analysis of number of tubers per plant (PPN) using GLM, MLM and FarmCPU models, respectively, and only GLM and MLM models detected 26 common significant SNPs, which associated with 98 genes encoding protein phosphatase 2A (PP2A), ERF, MYB and bHLH transcription factor, zinc finger protein (ZFP) and so on. Through the GWAS of water retention rate (WRR), 21 common associated genes were identified by three models at the same time, which encoded to leucine-rich repeat receptor-like protein kinase, WRKY transcription factor, xyloglucan glycosyltransferase, UDP-glycosyltransferase and so on. Discussion Recently, GWAS has been a fast and effective approach to study the quantitative traits and detect the natural variation of plant, which has been successfully applied to define the associated loci and make help to the breeding of adaptation and yield improvement 34 . To dissect the genetic footprint for drought resistance in potato, GWAS and selective sweep analysis were performed. To ensure the reliability of the results of genome-wide association analysis, three different association models were used for GWAS. However, many significant loci were above the significance threshold when the association analysis of BrN and PPW using GLM model, which demonstrated that there might be false positives in the results of GLM model. The result shown that the most of the significant SNPs identified through MLM model were also detected by GLM model, which effectively reduced the occurrence of false positives, as described in other studies 35 , 36 . But when we used MLM model, the GWAS of BrN, PPW, RWC under drought conditions and PPW under normal watering conditions did not detect significant associated SNPs, which indicated that the result of the MLM model might have false negatives 31 . Because of considering the effect of kinship relatedness among individuals, the MLM model might be too strict, and the quantile-quantile plots of the GWAS results also indicated this phenomenon. Meanwhile, FarmCPU model also were used in this study, some traits did not have significant loci in other models, but had significant loci in FarmCPU model, such as RWC, indicating that FarmCPU model was an effective association analysis tool 31 . Overall, to get reliable significant associated results, multiple models should be used together when the association analysis of complex traits. Based on the different association models, reliable significant loci were identified by multiple models at the same time, important candidate genes might play crucial roles in the drought resistant, such as transcription factors, ZFP protein, E3 ubiquitin ligase, auxin-responsive protein and so on. The GWAS results indicated that MYB, WRKY and ERF transcription factors might be associated with drought stress tolerance of potato. Previous studies have shown that GbMYB5 gene enhanced the drought tolerance of transgenic tobacco and cotton, suggesting that GbMYB5 was involved in drought stress response 37 . WRKY transcription factor, a DNA-binding protein binding w-box, is involved in drought stress response in many studies 38 . Arabidopsis WRKY46, WRKY54, and WRKY70 transcription factors are involved in drought responses 39 . GhWRKY59 is an important transcription factor to improve drought resistance of cotton, which regulates the expression of drought response gene GhDREB2 and improves drought resistance of cotton by phosphorylation of MAP3K15 40 . Previous GWAS of drought-resistance traits also detected WRKY transcription factor 41 , which revealed that WRKY family members may be play a key role in regulating drought-resistance of potato. And the study of a WRKYe-27 gene shown that the gene was up-regulated under drought stress in potato 42 . Other studies have shown that ERFs can be induced by biological and abiotic stresses and participate in the regulation of plant response to environmental stress 43 . TaERF1 and TaERF3 could enhance the response to salt and drought stress in wheat 44 . Therefore, we regarded the MYB, WRKY and ERF genes as candidate genes associated with drought resistance of potato. What’s more, we identified one candidate gene encoding abscisic acid (ABA) hydroxylase, and ABA play important roles in plant response to drought stress 45 . And the study indicated that ABA can relate to glucosyl ester (GE) through the UDP-glucosyltransferases (UGT) 46 . In our study, the candidate genes encoding UGT also were identified, which might play an important role in ABA homeostasis and regulated the response to drought stress of plant 47 . We also detected candidate genes encoding E3 ubiquitin-protein ligase, which might take part in the ABA signaling pathway through ubiquitin-mediated degradation 48 . What’s more, ABA signaling regulates the plasma membrane transporters by CDPKs 49 , so the candidate genes identified in this study, which encoding calcium-dependent protein kinase (CDPK) and calcineurin B-like protein (CBL), might be involved in ABA- and Ca 2+ -mediated pathway under drought stress. Interestingly, the current association study identified one significant SNP 4:1861996, which harboring different genotype with different drought resistance, and located in the exon region of LBR (late blight resistance protein). This result indicated that the genes related to late blight resistance might also play important roles in the response to drought stress, and the changes of genotypes may have important effects on drought resistance of potato, most importantly, the potential genetic variation can be used to breed potato with enhanced drought tolerance. Conclusions To conclude, the genetic footprint of drought resistance was dissected through selective sweep analysis and genome-wide association analysis. A total of 560 drought resistance response related genes were detected through selective-sweep analysis, including ZFP protein, E3 ubiquitin ligase, auxin-responsive protein, MYB and ERF transcription factors. Based on three association analysis models, a set of candidate genes were identified, some of them were important candidate genes associated with multiple models, which had high credibility, such as MYB, WRKY and ERF transcription factors, PP2A, UGT, E3 ubiquitin ligase, ZFP, etc. Most importantly, 15 drought-resistance related candidate genes were identified by GWAS and selective-sweep analysis, significant SNP 4:1861996 in the exon region of LBR (late blight resistance protein) harboring different genotype with different drought resistance. Overall, the candidate genes identified in this study might be instrumental in developing drought-resistant germplasm in potato. Declarations Declaration of competing interest The authors declare that they have no personal relationships and no conflict of interest exits in this manuscript. Funding information This research was funded by Key R & D projects of Hebei Province “Optimization of potato distant hybridization breeding technology and selection of new potato varieties with water-saving, drought-resistance, high yield and good quality”, grant number 20326319D; and by China Agriculture Research System of MOF and MARA. Author Contribution Jiang Yin and Yan Wang conceived and designed the experiments. Kuan Wang and Lei Liu provided Methodology, Software; Kuan Wang, Lei Liu, Lei Wang and Yan Feng performed the validation and formal analysis. Lipan Qi and Benchi Ma performed the investigation. Jiang Yin and Yan Wang provided the funding. Jiepan Chen and Xuechen Gong performed the data curation; Kuan Wang and Lei Liu were responsible for writing; Jiang Yin and Yan Wang were responsible for writing—review & editing. All authors reviewed the manuscript. Acknowledgement Thanks to Beijing Biomics Biotech Co., Ltd. for the help in data analysis. Data Availability The re-sequencing sequences underlying this study have been deposited in NCBI database under BioProject accession number: PRJNA802642 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA802642?reviewer=fg98t7vakiq01qnp42icgj6bat). And other data are provided in the manuscript. References Monneveux, P.; Ramírez, D.; Khan, M.A. Raymundo R.M, Loayza H, Quiroz R. Drought and heat tolerance evaluation in potato ( Solanum tuberosum L.). Potato Res . 57 , 225-247 (2014). Marco, F.; Bitrián M.; Carrasco, P.; Rajam, M.V.; Alcázar, R.; Tiburcio, A.F. Genetic engineering strategies for abiotic stress tolerance in plants. Plant biology and biotechnology Publisher, Springer, New Delhi. , pp579-60 (2015). Banerjee, A.; Roychoudhury, A. Epigenetic regulation during salinity and drought stress in plants: Histone modifications and DNA methylation. Plant Gene . 11 , 19-204 (2017). Golldack, D.; Li, C.; Mohan, H.; Probst, N. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4634456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326998221,"identity":"ea3a7c84-3649-40d3-9e32-05465f3f9e68","order_by":0,"name":"Kuan Wang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Kuan","middleName":"","lastName":"Wang","suffix":""},{"id":326998222,"identity":"f964c483-c0d1-49df-b19c-dc85aa8d53a3","order_by":1,"name":"Jiepan Chen","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Jiepan","middleName":"","lastName":"Chen","suffix":""},{"id":326998223,"identity":"0dc9485d-37b3-4d70-b9ff-2534b939b8ec","order_by":2,"name":"Lei Liu","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":326998224,"identity":"a583e970-fa0e-4c48-984e-4f4b0e37ea72","order_by":3,"name":"Benchi Ma","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Benchi","middleName":"","lastName":"Ma","suffix":""},{"id":326998225,"identity":"1a7f9cbf-df18-42d1-a867-b3323a71fbc4","order_by":4,"name":"Lei Wang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":326998226,"identity":"0c9a6b10-9e80-49e5-ad1a-d2121de1eebe","order_by":5,"name":"Yan Feng","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Feng","suffix":""},{"id":326998227,"identity":"4e303a1e-d6cd-41f8-bee8-8d304f6a6998","order_by":6,"name":"Lipan Qi","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Lipan","middleName":"","lastName":"Qi","suffix":""},{"id":326998228,"identity":"5757bcf2-a10d-46ef-9851-012f39105b2a","order_by":7,"name":"Xuechen Gong","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xuechen","middleName":"","lastName":"Gong","suffix":""},{"id":326998229,"identity":"104b1444-690d-4427-98ca-fbd119ec6831","order_by":8,"name":"Jiang Yin","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Yin","suffix":""},{"id":326998230,"identity":"e4f0885a-d1a6-4c19-8e53-42d0b748270f","order_by":9,"name":"Yan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBADOX4JBjYGBgMwx4AoLcaSM0jVkrjhBkgLAxFaDI6fPfyap+YO4+bbzccefCi4k9jA3rxNgqHmDm4tZ/LSrHmOPWM2u3Ms3XCGwbPEBp5jZRIMx57h1GJ2IMfMmIftMJvZjRwzaR6Dw4kNEjlmEowNh3FrOf8GqOXfYR7jGfnfpP+AtMi/IaDlRo7xY962wxIGEjls0gxgW3jwa7G/8caMcW7fYQOJG2nmhj0Gh43beNKKLRKO4dYi2Z9j/OHNt8P1/TOSnz348eewbD/74Y03PtTg1gIEbFI8KFwQkYBPAwMD88cf+BWMglEwCkbBSAcAGwBZUh41PDIAAAAASUVORK5CYII=","orcid":"","institution":"Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-25 07:39:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4634456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4634456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60711148,"identity":"ab393b69-2009-4b35-a587-d326219358fb","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5526342,"visible":true,"origin":"","legend":"\u003cp\u003eThe tubers of drought susceptible and resistant potatoes under normal watering and drought condition.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/1cb1e60cfed48ddfe590a6d3.png"},{"id":60711149,"identity":"2e020ae9-28f6-4ab6-ab45-f6b1918263a7","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3085982,"visible":true,"origin":"","legend":"\u003cp\u003eVariation across the chromosomes by circos. From the outside to the inside are chromosomes, SNP density, Indel density, CNV, Insertion (INS), Deletion (DEL), Inversion (INV).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/3f6161684d2208a5e6f0338e.png"},{"id":60711150,"identity":"9dd2e35b-2a0a-41f9-8e2a-096dd3434563","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6567514,"visible":true,"origin":"","legend":"\u003cp\u003eThe genetic structure of the 230 potato individuals re-sequenced. (\u003cstrong\u003eA\u003c/strong\u003e) The neighbor-joining phylogenetic tree constructed using the SNPs from re-sequencing data. CPRI means the potato individuals from Chinese potato research institutes, and CIP means the potato individuals from international potato center; (\u003cstrong\u003eB\u003c/strong\u003e) Principal component analysis (PCA) of the 230 potato individuals; (\u003cstrong\u003eC\u003c/strong\u003e) Population structure analysis of the 230 potato individuals estimated by ADMIXTURE, given different number of groups (K = 2, 3 or 4). The y axis quantifies subgroup membership, and the x axis shows the potato individuals.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/101fe2be74a7f05a0ba5ea33.png"},{"id":60711154,"identity":"ac21d130-5114-4584-bed6-590544ebf45f","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5503633,"visible":true,"origin":"","legend":"\u003cp\u003eThe heat-map of relative kinship and linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e). (\u003cstrong\u003eA\u003c/strong\u003e) The heat-map of relative kinship in potato population re-sequenced; (\u003cstrong\u003eB\u003c/strong\u003e) Linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e) for the potato species, using the 230 potato individuals.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/98d697854d561022f3d1214f.png"},{"id":60711157,"identity":"542b970b-b3ec-4027-9b65-ff0137d3adad","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7207826,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide distribution of selective sweeps in potato. (\u003cstrong\u003eA\u003c/strong\u003e) Selective sweeps in drought resistant potato compared with drought susceptible potato. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e scores are plotted across the potato chromosomes; (\u003cstrong\u003eB\u003c/strong\u003e) The selective regions with the top 5% of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR \u003c/sub\u003e(the blue points).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/181d415cba50718c0eeae75c.png"},{"id":60711158,"identity":"f9f7e5a3-82cc-4e2a-8357-7582a4e9b053","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3326987,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG annotation of genes\u003cstrong\u003e \u003c/strong\u003elocated in the selected regions (with the top 5% of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e) between drought resistant potatoes and drought susceptible potatoes.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/7db12c662bbac0fcf5597ce2.png"},{"id":60711155,"identity":"2ece3a2b-4c51-4ad9-bb46-e867abd89921","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3006682,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide association analysis identified significant SNPs. (\u003cstrong\u003eA\u003c/strong\u003e) Genome-wide association analysis of branch number (BrN) using the GLM model. (\u003cstrong\u003eB\u003c/strong\u003e) Local Manhattan plot of BrN on chromosome 1. (\u003cstrong\u003eC\u003c/strong\u003e) Box plot of the BrN based on the different genotypes of 1:72674289. (\u003cstrong\u003eD\u003c/strong\u003e) Local Manhattan plot of PPW (weight of tuber per plant) on chromosome 2. (\u003cstrong\u003eE\u003c/strong\u003e) Box plot of the PPW based on the different genotypes of 2:4478353. (\u003cstrong\u003eF\u003c/strong\u003e) Local Manhattan plot of SSI (stress susceptibility index) on chromosome 8. (\u003cstrong\u003eG\u003c/strong\u003e) Box plot of the SSI based on the different genotypes of 8:36470394. (\u003cstrong\u003eH\u003c/strong\u003e) Local Manhattan plot of VMF on chromosome 3. (\u003cstrong\u003eI\u003c/strong\u003e) Box plot of the VMF (variety membership function) based on the different genotypes of 3:826504.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/0926804386e3d2cec99fd960.png"},{"id":60711159,"identity":"c91e985e-7720-4e17-b585-bd18b98e606c","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6774145,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis of SNP 4:1861996 associated with variety membership function (VMF). (\u003cstrong\u003eA\u003c/strong\u003e) Manhattan plot of GWAS for variety membership function (VMF) using GLM model; (\u003cstrong\u003eB\u003c/strong\u003e) Manhattan plot of GWAS for variety membership function (VMF) using MLM model; (\u003cstrong\u003eC\u003c/strong\u003e) Manhattan plot of GWAS for variety membership function (VMF) using FarmCPU model; (\u003cstrong\u003eD\u003c/strong\u003e) The selective signals on chromosome 4; (\u003cstrong\u003ee\u003c/strong\u003e) Box plots of variety membership function (VMF) with different genotype of 4:1861996.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/f3f461e3096991a0d2cfa8b1.png"},{"id":60711160,"identity":"e734594e-e7bd-4409-b982-c861654f407b","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":664667,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression levels of candidate genes by quantitative RT-PCR. Error bars were used to express the standard deviation of three biological replications.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/201d753bb89ec68e7acab0cf.png"},{"id":60711153,"identity":"b662b2f9-2797-44e4-ae24-23e52a7426e3","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2463013,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide association study (GWAS) on chlorophyll content (ChC) and moisture saturation deficit (MSD) under normal watering conditions. (\u003cstrong\u003eA\u003c/strong\u003e) Manhattan plots of GWAS for chlorophyll content (ChC) using GLM, MLM and FarmCPU models, respectively. (\u003cstrong\u003eB\u003c/strong\u003e) Manhattan plots of GWAS for moisture saturation deficit (MSD) using GLM, MLM and FarmCPU models, respectively. (\u003cstrong\u003eC\u003c/strong\u003e) Local Manhattan plot of ChC on chromosome 4. (\u003cstrong\u003eD\u003c/strong\u003e) Box plot of the ChC based on the different genotypes of 4:8470510. (\u003cstrong\u003eE\u003c/strong\u003e) Local Manhattan plot of MSD on chromosome 7. (\u003cstrong\u003eF\u003c/strong\u003e) Box plot of the MSD based on the different genotypes of 7:50992935.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/6c81219f3ad8ca9c7fc06f23.png"},{"id":73251364,"identity":"a6048a98-35d6-4c83-b4b1-5d981363f991","added_by":"auto","created_at":"2025-01-08 08:02:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17321499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/042bb230-c078-473a-9eee-164bb6f3edbe.pdf"},{"id":60711156,"identity":"b6e2a4dc-40bb-4e53-83ca-1f48583e4d21","added_by":"auto","created_at":"2024-07-19 20:17:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10367493,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2024615.docx","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/20d9a315c5a0d3e6745c0846.docx"},{"id":60711151,"identity":"3fe0e4f1-2f02-4940-8185-ce7d383d0cd8","added_by":"auto","created_at":"2024-07-19 20:17:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2203726,"visible":true,"origin":"","legend":"","description":"","filename":"TableS116.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4634456/v1/442df2dabbf44c5ea8d1cc30.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Loci Determining Drought Resistance of Potato reveled by Genome-wide Association Study (GWAS)","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDrought is one of the bottlenecks restricting the sustainable development of agriculture in the world, which having important effects on the growth, development, as well as physiological and metabolic processes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The mechanism of drought resistance is complex and affected by physiological and biochemical factors as well as the control of multiple gene pairs. The process of plant responses to drought stress involved in signaling molecules, such as hormones, transcription factors and stress response genes, as well as pretranscription epigenetic regulation, which encodes proteins with protective effects against drought stress \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the molecular mechanisms of plant response to drought stress (classical genetic regulation and epigenetic regulation) has important implications for improving plant drought resistance \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePlant response to drought stress is a complex biological process, which affected by multiple genes related to drought resistance and stress signaling pathways \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Abscisic acid (ABA) plays the key role in response to drought by activating stress response genes and regulating stomatal conductance \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In recent years, studies have shown that the main ABA signaling pathway also interacts with other signaling factors during drought stress response, brassinosteroids also modulate drought response through signaling components related to the ABA response pathway \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Signal cascades in the drought stress response network are activated by TF (transcription factor), such as MYB, ERF, WRKY, ZFP and so on \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, which have been shown to improve drought tolerance in plants. In addition, drought stress signals can regulate the expression of different drought-induced genes independently of ABA pathways \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. What\u0026rsquo;s more, epigenetic regulation is also an important factor affecting plant response to drought stress, which mediated by complex interactions among DNA methylation, histone modification and chromatin remodeling \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePotato is one of the most important food crops in the world, with its high yield and rich nutritional value, has played a positive role in solving the world food crisis and poverty problem \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, drought stress often results in potato production. Drought stress can affect plant photosynthesis rate and harvest index, if potato suffered in short-term high-intensity drought under the tuber expansion period, the potato tuber will be abnormal \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Therefore, the study of potato drought resistance mechanism has great value to improve potato drought resistance and promote the development of potato industry.\u003c/p\u003e \u003cp\u003eRecently, the whole genome resequencing and genome-wide association study (GWAS) have been used to detected candidate genes related to stress response. For example, using 10 drought-related physiological and anatomical traits and SNPs, 78 new markers \u0026times; trait associations were identified in coast redwood through GWAS analysis, and the candidate genes involved in metabolic, stress, and signaling pathways \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A total of 38 QTL were reported through the GWAS of traits under well-watered and water deficit treatments at tuber initiation stage in 104 diploid potato accessions \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In maize, eight candidate genes for drought tolerance associated with eight significant SNPs were identified, including trehalase, AP2/EREB160 transcription factor, and glutathione S-transferase, which might be directly or indirectly involved in drought resistance \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Besides, the GWAS detected 17 and 20 QTL regions associated with traits under rainfed and drought conditions in winter wheat \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In general, a growing body of research now shows that GWAS is an excellent method to detect the genetic footprint of drought stress, which can greatly promote the understanding and improving drought tolerance in plants.\u003c/p\u003e \u003cp\u003eHere, to further explore candidate genes for drought resistance in plants, we sequenced a diversity panel of 230 potato individuals with different drought resistance. Using 9 traits under normal watering conditions and 7 traits under drought conditions, GWAS has been performed to identify loci potentially associated with drought resistance. Several genomic loci and candidate genes are identified in this study, which provide a new resource for further molecular breeding and studies of potato drought resistance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 230 potato individuals were used for re-sequencing, including 87 potato individuals from CIP (international potato center) and 143 potato individuals from different Chinese potato research institutes (CPRI). Among them, 15 potato individuals were local domesticated species while the rest of them were artificially improved species.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic data measurement and analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe seedlings of all potato individuals were planted in the same experimental field (Zhangjiakou, Hebei, China at 41\u0026deg;16\u0026prime; N, 114\u0026deg;72\u0026prime; E). The drought resistance experiment was carried out in the awning, which was divided into two areas, namely the drought treatment area and irrigation area. The potato individuals were grown in a randomized design, with the plant density of 65 cm \u0026times; 30 cm, and 10 plants were planted in each row with three replicates. The field management were carried out equally for the two areas, including fertilizer application, pest control, weed management, etc. Differently, the irrigated area was irrigated at the stage of seedling, tuber formation, tuber growth and starch accumulation, and the irrigation amount reached 70% of the maximum water holding capacity of the field. The drought treatment area-controlled watering from squaring stage to harvest, when the water content naturally decreased by 30% of the maximum water, conducted statistics of related phenotypic data. Under normal watering conditions, 9 traits were collected, including ChC (chlorophyll content), PlH (plant height), StM (stem number), BrN (branch number), PPN (number of tubers per plant), PPW (weight of tuber per plant), RWC (relative water content), MSD (moisture saturation deficit) and WRR (water retention rate). Under drought conditions, 7 traits were collected, including ChC, PlH, BrN, PPW, RWC, VMF (variety membership function) and SSI (stress susceptibility index). The frequency distribution analysis and correlation analysis of each phenotypic traits were performed using SPSS.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA isolation and sequencing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenomic DNA of the 230 potato individuals were extracted from leaves using the CTAB method. After evaluating the quality and quantity of the DNA samples, the paired-end genomic libraries with insert sizes of 450\u0026ndash;500 bp were constructed using the Illumina TruSeq DNA Sample Prep kit following the manufacturer\u0026rsquo;s instructions. Then, the libraries were sequenced on the Illumina NovaSeq6000 platform in Beijing Biomics Biotech Co. Ltd.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSNP calling\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe quality of the original paired-end reads obtained by sequencing was assessed and filtered, and the quality of sequencing data was counted. Then, the clean reads were compared with the potato reference genome using BWA (ver. 0.7.10-r789) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The SNP calling was performed by adding the allele information of re-sequenced accessions to the SNP dataset using GATK \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, the annotation of SNPs and Indels were performed by ANNOVAR \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The detection of structural variation (SV) was performed by Breakdancer \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and the detection of copy number variation (CNV) was performed by CNVnator \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. According to the standard of missing data rate\u0026thinsp;\u0026lt;\u0026thinsp;20% and minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05, the SNPs were firstly filtered. Then, the distribution of the results of each type of variation on the whole genome was shown using circos diagram \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePopulation analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the population genetics analysis, SNPs of all individuals were filtered with a missing data rate\u0026thinsp;\u0026lt;\u0026thinsp;10% and minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05, a total of 1,484,530 high-quality SNPs were retained for downstream population analysis. In this study, a neighbor-joining tree using the software PHYLIP was constructed \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and the online tools iTQL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/iTOL\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/iTOL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to color the tree. Principal component analysis (PCA) was performed with EIGENSOFT \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Then, all the high-quality SNPs were used for the population structure assessment, which was performed with ADMIXTURE software \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Using the Haploview software \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, genome-wide linkage disequilibrium (LD) decay distance was estimated. To measure the genetic similarity between individuals, the SPAGedi software was used to generate the relative kinship matrix (K) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of selective sweeps\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify genomic regions affected by drought stress, we selected drought susceptible (DS) potatoes and drought resistant (DR) potatoes as two different groups. For selective sweep detection, we first measured the level of nucleotide diversity (π) within 40-kb sliding windows with a step size of 5 kb in DS and DR groups using VCFtools \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and the ratio of genetic diversity by comparing the DS and DR groups (π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e) was calculated. And genome-wide \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e were calculated for DS and DR groups with VCFtools, using 40-kb sliding windows with a step size of 5 kb. Potential selective sweeps were identified with the top 5% largest \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e values. Besides, based on the annotation information of potato reference genome, the genes located in the selective regions were detected as selected genes. Finally, the GO and KEGG analysis was performed by AgriGO analysis toolkit and KEGG databases \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, SNPs with the MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and miss rate\u0026thinsp;\u0026lt;\u0026thinsp;10% were selected for the GWAS. Firstly, associations between SNPs and the phenotypic traits under normal watering conditions and drought conditions were detected using general linear model (GLM). Then, to control the false positives, the mixed linear model (MLM) also was used to detected the association sites using TASSEL V3.0 software \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which taking K and Q matrices into account. At the same time, the GWAS also be performed by FarmCPU (Fixed and random model Circulating Probability Unification) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The Manhattan and quantile-quantile plots were created to show the results of GWAS analysis. P-value threshold was estimated by the Bonferroni test (1/N, N\u0026thinsp;=\u0026thinsp;total SNPs), where n is the effective number of independent SNPs \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Finally, we collected candidate genes in the upstream and downstream of significant SNPs according to the LD decay distance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and quantitative RT-PCR analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe tissue culture seedling of Qingshu9, a variety with strong drought resistance, was used as the experimental material for quantitative RT-PCR experiment. The shoot and root of the seedings were collected after 0h and 24h treated with 20% PEG. To verify the expression levels of important candidate genes, total RNA was extracted using RNAprep Pure Plant Kit (TIANGEN, Beijing, China). A total of 1 ug RNA was used to synthesize cDNA according to the instructions of a PrimeScript RT Reagent Kit (TaKaRa, China). And the qRT-PCR reactions were performed through the SYBR-Green PrimeScript RT-PCR Kit (Takara) following the manufacturer\u0026rsquo;s instructions. EF1-α was used as the reference gene for qRT-PCR, the original data were processed by the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e method \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenome re-sequencing and SNP calling\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, a total of 230 potato individuals were used for re-sequencing, among them, 87 individuals were collected from CIP (international potato center) and 143 individuals were coming from different Chinese potato research institutes (CPRI), which having different drought-resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through the whole genome resequencing, 17.40\u0026nbsp;billion paired-end reads were generated, and the average sequencing depth was 9.8\u0026times; (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). After calling and filtering, a total of 3,986,623 single nucleotide polymorphisms (SNPs) (missing data rate\u0026thinsp;\u0026lt;\u0026thinsp;20% and minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and 1,130,197 Indels were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u0026thinsp;~\u0026thinsp;4 and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the whole-genome variations from the re-sequencing of 230 potato individuals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIndels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e382,746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,564,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e681,423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e532,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200,358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69,200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68,515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpstream/downstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpstream/downstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSplicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSplicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44,888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStop gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStop gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStop loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStop loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,469,746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsertion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,516,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonsynonymous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164,488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrame-shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynonymous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218,258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-frame-shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe found that 2,564,208 SNPs were in intergenic and 915,185 SNPs were in genes, and 382,746 SNPs occurred in exonic (9.6%). Within coding regions, nonsynonymous SNPs (164,488) were less than synonymous SNPs (218,258), and the ratio of nonsynonymous-to-synonymous is 0.74 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 26,998 (2.4%) Indels were in exonic, and the number of Insertion and Deletion were 9,858 and 16,390, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Besides, we analyzed the whole genome SNP mutation types and the ratio of transition/transversion (Ts/Tv) is 1.63 (Table S3 and Figure S3). What\u0026rsquo;s more, 5,756 CNVs (copy number variants) and 6,486 SVs (structure variations) were identified in this study (Table S5). Finally, all the variations generating from re-sequencing were shown by a circos image (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic relationship and population structure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo better understand the phylogenetic relationship of the 230 potato individuals, a total of 1,484,530 high-quality SNPs (missing data rate\u0026thinsp;\u0026lt;\u0026thinsp;10% and minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were used to constructed the neighbor-joining (NJ) tree. We found that the NJ tree did not separate the potatoes from CIP and CPRI, which indicated that there might be more communication of potatoes from China and abroad during the breeding of potato (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Principal-component analysis (PCA) reflected the similar result of NJ tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Figure S4). We also performed population structure analysis of 230 potato individuals, which also supported above opinion, the potatoes from CIP and CPRI had the same genetic background (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table S6 and Figure S5). Thus, it seems that the breeding of potatoes was international interaction, not independent.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelative kinship and linkage disequilibrium\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe relative kinship of 230 potato individuals were evaluated, the result indicated that the relative kinship among each potato individuals re-sequenced was relatively weak (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), which showing that the experimental materials were suitable for genome-wide association analysis and had little influence on the results of subsequent association analysis. Then, the high-quality SNPs of potato individuals were employed to estimate the linkage disequilibrium (LD) extent, which is crucial to GWAS analysis. In this study, the decay of LD reached half of maximum average r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e at 35 kb across all chromosomes for the 230 potato individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 16 phenotypic traits, among them, 9 traits were investigated under normal watering conditions, including ChC (chlorophyll content), PlH (plant height), StM (stem number), BrN (branch number), PPN (number of tubers per plant), PPW (weight of tuber per plant), RWC (relative water content), MSD (moisture saturation deficit) and WRR (water retention rate) (Table S7), which having be detected abundant variation (Figure S6). Then, the correlation analysis of 9 phenotypic traits under normal watering conditions were performed, which shown that the correlation coefficients between some traits were relatively high (Figure S7), such as PlH and PPW (r\u0026thinsp;=\u0026thinsp;0.55), PlH and PPN (r\u0026thinsp;=\u0026thinsp;0.43), RWC and MSD (r = -0.94). What\u0026rsquo;s more, 7 traits under drought conditions also were investigated, including ChC, PlH, BrN, PPW, RWC, VMF (variety membership function) and SSI (stress susceptibility index) (Table S8), abundant variation was detected (Figure S8). The correlation coefficient between PlH and PPW (r\u0026thinsp;=\u0026thinsp;0.53) also was higher, and there was a significant negative correlation between VMF and SSI (r = -0.71) (Figure S9).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSelective sweep analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify the potential selective signatures related to drought-resistance in potato, we selected potatoes with significant differences in drought resistance as two groups based on the phenotypic traits under drought conditions, one was drought susceptible (DS) potato, and the other was drought resistant (DR) potato. Then, the selective sweep analyses were performed by population fixation statistics (F\u003csub\u003eST\u003c/sub\u003e) and nucleotide diversity between the DS group and DR group (π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e) in 40-kb sliding windows (a step of 5 kb). The windows with the top 5% of F\u003csub\u003eST\u003c/sub\u003e as well as π\u003csub\u003eDS\u003c/sub\u003e/π\u003csub\u003eDR\u003c/sub\u003e were considered as the selective sweeps (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A total of 680 common selective sweeps were identified between the DS group and DR group in this study (Table S9), and 560 annotated genes located in the selective-sweep regions (Table S10), which could potentially play important roles in the drought resistance of potato.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further understand the functions of the 560 genes, we performed gene ontology analysis (GO) and KEGG enrichment analysis. The result of GO analysis indicated that these genes related to multiple biological process, cellular component, and molecular function (Figure S10), and many genes were involved in \u0026ldquo;Plant hormone signal transduction\u0026rdquo;, \u0026ldquo;Plant-pathogen interaction\u0026rdquo;, \u0026ldquo;Neurotrophin signaling pathway\u0026rdquo; and \u0026ldquo;Toll-like receptor signaling pathway\u0026rdquo; through KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The genes encoding ZFP protein, E3 ubiquitin ligase, auxin-responsive protein, MYB and ERF transcription factor were found in the selective regions (Table S10), which revealed that the genes identified through selective sweep analysis probably play an important role in the drought resistance of potato.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association analysis for traits under drought conditions\u003c/h2\u003e \u003cp\u003eUsing the high-quality SNPs and phenotypic data under drought conditions, we further detected the genes related to drought resistance through GWAS analysis. In this study, three different statistical models were used to detected significantly association signals, including generalized linear model (GLM), mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU). Based on the LD decay distance (about 35 Kb), the candidate genes were searched on downstream and upstream of the significantly association SNPs. The Manhattan plots and quantile-quantile plots of GWAS for 7 traits under drought conditions were displayed in Figure S11-S16, and the detailed information about candidate genes identified by three models were summarized in Table S11-S13.\u003c/p\u003e \u003cp\u003eThrough three models, a set of significant SNPs were detected, and the number of the significant SNPs and associated genes were statistics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For branch number (BrN), GLM and FarmCPU models identified 8334 and 9 significant SNPs, respectively. We found that the threshold value of the GWAS of BrN by GLM model was too looser. To reduce the false positives, we detected associated genes with a strict threshold value (top 0.01), and 193 genes associated with 55 significant SNPs were identified in this study (Table S11). one SNP (1:72674289) was the common loci, the related candidate gene \u003cem\u003ePGSC0003DMG400000063\u003c/em\u003e encoded a pollen-specific leucine-rich repeat extensin-like protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Our result shown that the most of the accessions carrying 1:72674289-GG had higher BrN than the accessions carrying 1:72674289-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Three models identified 267, 7 and 43 associated loci of chlorophyll content (ChC), respectively, which detected 699, 20 and 156 candidate genes, 8 common genes were identified both by GLM and MLM models which encoding ERF transcription factor, auxin-responsive protein, embryonic abundant protein, etc., among them, two genes encoding unknown proteins also were identified by FarmCPU model (Figure S12). What\u0026rsquo;s more, the GWAS identified 280, 13 and 14 significant SNPs associated with plant height (PlH) using GLM, MLM and FarmCPU models (Figure S13). All the 13 significant SNPs identified by MLM model also were detected through GLM model, and 44 genes were found according to the common SNPs, which encoding serine/threonine-protein kinase, UDP-glycosyltransferase, F-box protein, calcineurin B-like protein and so on, and 5 genes were identified through three models at the same time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the significant sites and associated genes of traits in different models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eNumber of significant SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c15\" namest=\"c10\"\u003e \u003cp\u003eNumber of associated genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ewatering conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLM\u0026amp;\u003c/p\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGLM\u0026amp;\u003c/p\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMLM\u0026amp;\u003c/p\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eGLM\u0026amp;\u003c/p\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eGLM\u0026amp;\u003c/p\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMLM\u0026amp;\u003c/p\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e 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colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eDrought conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cb\u003eNote\u003c/b\u003e: BrN: branch number, ChC: chlorophyll content, MSD: moisture saturation deficit, PlH: plant height, PPN: number of tubers per plant, PPW: weight of tuber per plant. RWC: relative water content, StM: stem number, WRR: water retention rate, SSI: stress susceptibility index, VMF: variety membership function.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor weight of tuber per plant (PPW), 1419 and 33 significant loci were detected through GLM and FarmCPU models (Figure S14), while MLM model didn\u0026rsquo;t detect significant loci, and 19 common significant loci associated with 60 genes, including WRKY and MYB transcription factor, proline-rich receptor-like protein kinase, calcium-dependent protein kinase and so on. Our further study identified one interesting genetic variation (2:4478353) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), the accessions carrying 2:4478353-AA were shown to have higher weight of tuber per plant (PPW) than the accessions carrying 2:4478353-GG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The significant SNP 2:4478353 was in the promoter region of the candidate gene \u003cem\u003ePGSC0003DMG400004427\u003c/em\u003e, which encoded a Proline-rich receptor-like protein kinase (PERK). In the analysis of relative water content (RWC), only FarmCPU model detected 33 significant SNPs (Figure S15), and 148 genes located in the downstream and upstream of significant SNPs. What\u0026rsquo;s more, a total of 38, 12 and 29 significant SNPs were identified through the analysis of stress susceptibility index (SSI) using GLM, MLM and FarmCPU models, respectively (Figure S16). And 8:36470394 was the common significant SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef) locating in the genomic region of the candidate gene \u003cem\u003ePGSC0003DMG400002204\u003c/em\u003e, which encoded a pollen-specific protein. The further analysis indicated that the accessions carrying 8:36470394-GG had higher stress susceptibility index (SSI) than the accessions carrying 8:36470394-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eWe also performed genome-wide association analysis for variety membership function (VMF), almost all significant SNPs detected by MLM model also were detected by GLM model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, C). And 83 common associated genes were identified through the above two methods, among them, 21 genes also were the associated genes identified by FarmCPU model, which encoding serine/threonine-protein phosphatase 2A (PP2A), salicylate carboxymethyl transferase, abscisic acid hydroxylase, AAA-ATPase, E3 ubiquitin-protein ligase, zinc finger protein (ZFP) and so on. The significant SNP (3:826504) was the common loci located in the genomic region of the related candidate gene \u003cem\u003ePGSC0003DMG400013420\u003c/em\u003e, which encoded an ABC transporter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). Our result shown that the accessions carrying 3:826504-GG had higher variety membership function (VMF) than the accessions carrying 3:826504-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003eIn addition, we found that 15 genes were identified associated with variety membership function (VMF) both of GLM model and MLM model, as well as located in selective regions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which encoding serine/threonine-protein kinase, NAC domain-containing protein, adenylate isopentenyl transferase and so on. Among them, one gene encoded LBR (late blight resistance protein) was identified by all the three models, and the significant SNP 4:1861996 located in the exon region of LBR, and the genetic region of LBR had significantly \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e value (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Most interestingly, the different genotype of 4:1861996 had different drought resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). The 69 potato individuals carrying 4:1861996-AA had significantly better drought resistance than the individuals carrying 4:1861996-AG, and the most of the individuals with 4:1861996-AA were drought resistant (DR) potatoes, while the individuals with 4:1861996-AG were drought susceptible (DS) potatoes.\u003c/p\u003e \u003cp\u003eTo better understand the function of candidate genes identified by both the GWAS analysis and selective sweep analysis, the expression levels of 15 important candidate genes were verified by quantitative real time PCR (qRT-PCR) in the materials with stronger drought resistance. Based on the qRT-PCR results, we found that the expression levels of most of the candidate genes were up-regulated after drought treatment for 24 h. Besides, we found that some genes are up-regulated expression in both the shoot and root, such as PGSC\u003cem\u003e0003DMG400005975\u003c/em\u003e, \u003cem\u003ePGSC0003DMG400035020\u003c/em\u003e, \u003cem\u003ePGSC0003DMG400005970\u003c/em\u003e and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). But there were also some genes that were only up-regulated expression in the shoot, while the up-regulated expression levels in the root were not obvious, such as \u003cem\u003ePGSC0003DMG400005978\u003c/em\u003e and \u003cem\u003ePGSC0003DMG400002468\u003c/em\u003e, which suggested that some candidate genes might take part drought response in different tissues.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the candidate genes associated with VMF identified by GLM and MLM model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003e(GLM model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003e(MLM model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGene function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400035020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1530957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1550680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1559669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.31E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400006008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1530957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1541367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1542478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.31E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdenylate isopentenyl transferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400005978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1694262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1695176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400040065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1677219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1677845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNAC domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400005975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1734305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1737409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSerine/threonine-protein kinase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400006000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1730627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1733353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR-like protein kinase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400005976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1710526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1718731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChaperone protein dnaJ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400006002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1708936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1709799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400005977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1709353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1701857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400005970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1861996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1858431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1862567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.45E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLate blight resistance protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400040027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32343039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32368061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32368494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5418E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdenylate isopentenyl transferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400045608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32343039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32366289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32366594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5418E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdenylate isopentenyl transferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400002468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32343039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32361429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32363751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5418E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePentatricopeptide repeat-containing protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400002467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32343039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32354306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32356707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5418E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGeraniol 8-hydroxylase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSC0003DMG400002470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32343039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32350218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32351019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5418E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGeraniol 8-hydroxylase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association analysis for traits under normal watering conditions\u003c/h2\u003e \u003cp\u003eIn this study, we also performed genome-wide association analysis for 9 traits under normal watering conditions using three different statistical models (GLM, MLM and FarmCPU). And the Manhattan plots and quantile-quantile plots were displayed in Supplemental Figure S17-S23, and the detailed information about candidate genes identified by three models were summarized in Table S14-S16.\u003c/p\u003e \u003cp\u003eWe found that the most of the significant SNPs identified by MLM model also were identified by GLM model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the analysis of chlorophyll content (ChC), 6 common significant SNPs were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA), which identified 30 associated genes, including zinc finger protein, ERF transcription factor, glycosyltransferase, late blight resistance protein and so on. The significant SNP (4:8470510) was the common loci located in the genomic region of the related candidate gene \u003cem\u003ePGSC0003DMG400023619\u003c/em\u003e, which encoded an ethylene-responsive transcription factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Our result shown that the accessions carrying 4:8470510-GG had higher chlorophyll content (ChC) than the accessions carrying 4:8470510-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). For moisture saturation deficit (MSD), all the three significant SNPs detected through MLM model also were detected through GLM model (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), and 11 genes were the common associated genes, which encoding serine/threonine-protein phosphatase, serine/threonine-protein kinase, ERF transcription factor and so on, among them, 6 genes also were identified through FarmCPU model. The common significant SNP 7:50992935 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE) locating in the promoter region of the candidate gene \u003cem\u003ePGSC0003DMG400017294\u003c/em\u003e, which encoded an unknown protein. The further analysis indicated that the accessions carrying 7:50992935-CC had higher moisture saturation deficit (MSD) than the accessions carrying 8:36470394-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eWhat\u0026rsquo;s more, a total of 59 common genes were identified by the analysis of plant height (PlH) using GLM model and MLM model, and 6 genes were also identified by FarmCPU model. What\u0026rsquo;s more, a total of 158, 30 and 23 significant SNPs were identified through the analysis of number of tubers per plant (PPN) using GLM, MLM and FarmCPU models, respectively, and only GLM and MLM models detected 26 common significant SNPs, which associated with 98 genes encoding protein phosphatase 2A (PP2A), ERF, MYB and bHLH transcription factor, zinc finger protein (ZFP) and so on. Through the GWAS of water retention rate (WRR), 21 common associated genes were identified by three models at the same time, which encoded to leucine-rich repeat receptor-like protein kinase, WRKY transcription factor, xyloglucan glycosyltransferase, UDP-glycosyltransferase and so on.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRecently, GWAS has been a fast and effective approach to study the quantitative traits and detect the natural variation of plant, which has been successfully applied to define the associated loci and make help to the breeding of adaptation and yield improvement \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. To dissect the genetic footprint for drought resistance in potato, GWAS and selective sweep analysis were performed.\u003c/p\u003e \u003cp\u003eTo ensure the reliability of the results of genome-wide association analysis, three different association models were used for GWAS. However, many significant loci were above the significance threshold when the association analysis of BrN and PPW using GLM model, which demonstrated that there might be false positives in the results of GLM model. The result shown that the most of the significant SNPs identified through MLM model were also detected by GLM model, which effectively reduced the occurrence of false positives, as described in other studies \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. But when we used MLM model, the GWAS of BrN, PPW, RWC under drought conditions and PPW under normal watering conditions did not detect significant associated SNPs, which indicated that the result of the MLM model might have false negatives \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Because of considering the effect of kinship relatedness among individuals, the MLM model might be too strict, and the quantile-quantile plots of the GWAS results also indicated this phenomenon. Meanwhile, FarmCPU model also were used in this study, some traits did not have significant loci in other models, but had significant loci in FarmCPU model, such as RWC, indicating that FarmCPU model was an effective association analysis tool \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Overall, to get reliable significant associated results, multiple models should be used together when the association analysis of complex traits.\u003c/p\u003e \u003cp\u003eBased on the different association models, reliable significant loci were identified by multiple models at the same time, important candidate genes might play crucial roles in the drought resistant, such as transcription factors, ZFP protein, E3 ubiquitin ligase, auxin-responsive protein and so on. The GWAS results indicated that MYB, WRKY and ERF transcription factors might be associated with drought stress tolerance of potato. Previous studies have shown that \u003cem\u003eGbMYB5\u003c/em\u003e gene enhanced the drought tolerance of transgenic tobacco and cotton, suggesting that \u003cem\u003eGbMYB5\u003c/em\u003e was involved in drought stress response \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. WRKY transcription factor, a DNA-binding protein binding w-box, is involved in drought stress response in many studies \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Arabidopsis WRKY46, WRKY54, and WRKY70 transcription factors are involved in drought responses \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. GhWRKY59 is an important transcription factor to improve drought resistance of cotton, which regulates the expression of drought response gene \u003cem\u003eGhDREB2\u003c/em\u003e and improves drought resistance of cotton by phosphorylation of MAP3K15 \u003csup\u003e40\u003c/sup\u003e. Previous GWAS of drought-resistance traits also detected WRKY transcription factor \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, which revealed that WRKY family members may be play a key role in regulating drought-resistance of potato. And the study of a \u003cem\u003eWRKYe-27\u003c/em\u003e gene shown that the gene was up-regulated under drought stress in potato \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Other studies have shown that ERFs can be induced by biological and abiotic stresses and participate in the regulation of plant response to environmental stress \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. TaERF1 and TaERF3 could enhance the response to salt and drought stress in wheat \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Therefore, we regarded the MYB, WRKY and ERF genes as candidate genes associated with drought resistance of potato.\u003c/p\u003e \u003cp\u003eWhat\u0026rsquo;s more, we identified one candidate gene encoding abscisic acid (ABA) hydroxylase, and ABA play important roles in plant response to drought stress \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. And the study indicated that ABA can relate to glucosyl ester (GE) through the UDP-glucosyltransferases (UGT) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In our study, the candidate genes encoding UGT also were identified, which might play an important role in ABA homeostasis and regulated the response to drought stress of plant \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We also detected candidate genes encoding E3 ubiquitin-protein ligase, which might take part in the ABA signaling pathway through ubiquitin-mediated degradation \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. What\u0026rsquo;s more, ABA signaling regulates the plasma membrane transporters by CDPKs \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, so the candidate genes identified in this study, which encoding calcium-dependent protein kinase (CDPK) and calcineurin B-like protein (CBL), might be involved in ABA- and Ca\u003csup\u003e2+\u003c/sup\u003e-mediated pathway under drought stress.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the current association study identified one significant SNP 4:1861996, which harboring different genotype with different drought resistance, and located in the exon region of LBR (late blight resistance protein). This result indicated that the genes related to late blight resistance might also play important roles in the response to drought stress, and the changes of genotypes may have important effects on drought resistance of potato, most importantly, the potential genetic variation can be used to breed potato with enhanced drought tolerance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo conclude, the genetic footprint of drought resistance was dissected through selective sweep analysis and genome-wide association analysis. A total of 560 drought resistance response related genes were detected through selective-sweep analysis, including ZFP protein, E3 ubiquitin ligase, auxin-responsive protein, MYB and ERF transcription factors. Based on three association analysis models, a set of candidate genes were identified, some of them were important candidate genes associated with multiple models, which had high credibility, such as MYB, WRKY and ERF transcription factors, PP2A, UGT, E3 ubiquitin ligase, ZFP, etc. Most importantly, 15 drought-resistance related candidate genes were identified by GWAS and selective-sweep analysis, significant SNP 4:1861996 in the exon region of LBR (late blight resistance protein) harboring different genotype with different drought resistance. Overall, the candidate genes identified in this study might be instrumental in developing drought-resistant germplasm in potato.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no personal relationships and no conflict of interest exits in this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThis research was funded by Key R \u0026amp; D projects of Hebei Province \u0026ldquo;Optimization of potato distant hybridization breeding technology and selection of new potato varieties with water-saving, drought-resistance, high yield and good quality\u0026rdquo;, grant number 20326319D; and by China Agriculture Research System of MOF and MARA.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiang Yin and Yan Wang conceived and designed the experiments. Kuan Wang and Lei Liu provided Methodology, Software; Kuan Wang, Lei Liu, Lei Wang and Yan Feng performed the validation and formal analysis. Lipan Qi and Benchi Ma performed the investigation. Jiang Yin and Yan Wang provided the funding. Jiepan Chen and Xuechen Gong performed the data curation; Kuan Wang and Lei Liu were responsible for writing; Jiang Yin and Yan Wang were responsible for writing\u0026mdash;review \u0026amp; editing. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThanks to Beijing Biomics Biotech Co., Ltd. for the help in data analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe re-sequencing sequences underlying this study have been deposited in NCBI database under BioProject accession number: PRJNA802642 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA802642?reviewer=fg98t7vakiq01qnp42icgj6bat). And other data are provided in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMonneveux, P.; Ram\u0026iacute;rez, D.; Khan, M.A. Raymundo R.M, Loayza H, Quiroz R. Drought and heat tolerance evaluation in potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.). \u003cem\u003ePotato Res\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 225-247 (2014).\u003c/li\u003e\n\u003cli\u003eMarco, F.; Bitri\u0026aacute;n M.; Carrasco, P.; Rajam, M.V.; Alc\u0026aacute;zar, R.; Tiburcio, A.F. Genetic engineering strategies for abiotic stress tolerance in plants. \u003cem\u003ePlant biology and biotechnology\u003c/em\u003e Publisher,\u003cem\u003e \u003c/em\u003eSpringer, New Delhi.\u003cstrong\u003e,\u003c/strong\u003e pp579-60 (2015). \u003c/li\u003e\n\u003cli\u003eBanerjee, A.; Roychoudhury, A. Epigenetic regulation during salinity and drought stress in plants: Histone modifications and DNA methylation. \u003cem\u003ePlant Gene\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 19-204 (2017).\u003c/li\u003e\n\u003cli\u003eGolldack, D.; Li, C.; Mohan, H.; Probst, N. Tolerance to drought and salt stress in plants: Unraveling the signaling networks. \u003cem\u003eFront. plant sci.\u003c/em\u003e\u003cstrong\u003e 5\u003c/strong\u003e, 151 (2014). \u003c/li\u003e\n\u003cli\u003eOsakabe, Y.; Yamaguchi-shinozaki, K.; Shinozaki, K.; Tran, L.P. ABA control of plant macro-element membrane transport systems in response to water deficit and high salinity. \u003cem\u003eNew Phytol.\u003c/em\u003e \u003cstrong\u003e202\u003c/strong\u003e, 35-49 (2014).\u003c/li\u003e\n\u003cli\u003eNolan, T.M.; Nemanja, V.; Liu, D.; Eugenia, R.; Yin, Y. 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Reactive oxygen species signaling and stomatal movement in plant responses to drought stress and pathogen attack. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Integr\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Plant Biol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 805-826 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Potato, Drought-resistance, Genome resequencing, Selective sweep, Genome-wide association study","lastPublishedDoi":"10.21203/rs.3.rs-4634456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4634456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDetecting the genetic footprint of drought resistance is important and imperative. Here, we report a high-quality genomic variation database by whole-genome resequencing of 230 potato individuals. Through phylogenetic population structure analysis, we uncover that the breeding of potatoes was international interaction, not independent. Selective-sweep analysis detected 560 drought resistance response related genes, including ZFP, MYB and ERF transcription factors. Furthermore, based on three different models, the genome-wide association studies for drought resistance identified a set of candidate genes, such as MYB, WRKY and ERF, PP2A, UGT, E3 ubiquitin ligase, ZFP, etc., some crucial candidate genes were identified by different models at the same time. Among them, 15 candidates were identified both by GWAS and selective-sweep analysis, significant SNP 4:1861996 in the exon region of LBR (late blight resistance protein) harboring different genotype with different drought resistance. Our study provides important insights into the genetic basis of drought resistance, and will facilitate the cultivation of drought-resistant potato.\u003c/p\u003e","manuscriptTitle":"Genetic Loci Determining Drought Resistance of Potato reveled by Genome-wide Association Study (GWAS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 20:17:10","doi":"10.21203/rs.3.rs-4634456/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"528b14c9-1396-447b-a15a-014f79dc1bbd","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34598361,"name":"Biological sciences/Plant sciences/Plant stress responses/Drought"},{"id":34598362,"name":"Biological sciences/Plant sciences"},{"id":34598363,"name":"Biological sciences/Plant sciences/Natural variation in plants"},{"id":34598364,"name":"Biological sciences/Plant sciences/Plant stress responses"}],"tags":[],"updatedAt":"2025-01-08T07:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 20:17:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4634456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4634456","identity":"rs-4634456","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}