Genome-Wide Association Studies for Key Agronomic and Quality Traits in Potato (Solanum tuberosum L.)

Abstract

Deciphering the genetic mechanisms underlying key agronomic and quality traits in potato (Solanum tuberosum L.) is essential for advancing varietal improvement. Phenotypic instability in early clonal generations and inbreeding depression, coupled with the complexity of tetrasomic inheritance, pose significant challenges in constructing mapping populations for the genetic dissection of complex traits. Genome-wide association studies (GWASs) offer an efficient method to establish trait–genome associations by analyzing genetic recombination and mutation events in natural populations. This review systematically examines the application of GWASs in identifying agronomic traits in potato, such as plant architecture, yield components, tuber shape, and resistance to early and late blight and nematodes, as well as quality traits including dry matter, starch, and glycoalkaloid content. Some key chromosomal hotspots identified through GWASs include chromosome 5 associated with tuber yield, starch content, and late blight resistance; chromosome 4 and 10 associations with tuber shape and starch content; chromosomes 1, 9, and 11 associated with plant height, tuber number, glycoalkaloid content, and pest resistance. It elucidates the advantages and limitations of GWASs for genetic loci identification in this autotetraploid crop, providing theoretical insights and a reference framework for the precise localization of key genetic loci and the discovery of underlying genes using GWASs.

1. Introduction

Potato (Solanum tuberosum L.) is the world’s largest non-cereal crop, cultivated over an area of approximately 17.8 million hectares and serving as a staple food for over one billion people [1]. Potato tubers are rich in starch, vitamins, proteins, and mineral elements, playing a crucial role in maintaining nutritional balance and promoting human health [2]. Global climate change, including drought, extreme temperatures, soil salinization, and pests and diseases in potato-producing regions, negatively impacts both yield and quality [3]. In response to these challenges, breeding programs for potato are strategically designed to enhance traits critical for agricultural productivity, consumer preferences, and global food security. These programs focus on improving yield, resistance to biotic and abiotic stresses, and nutritional quality [4]. Yield improvement involves developing varieties with higher tuber specific gravity, better harvest indices, and the capacity to produce more tubers per plant, while addressing factors such as plant morphology and growth duration [5]. Efforts to enhance disease and pest resistance target genes that confer protection against key pathogens like Phytophthora infestans and potato virus Y (PVY), as well as pests such as the Colorado potato beetle. Given the increasing impact of climate change, breeding strategies also prioritize traits that improve abiotic stress tolerance, including water-use efficiency, cold hardiness, and heat tolerance. Additionally, nutritional quality is addressed by increasing levels of essential vitamins, minerals, and bioactive compounds, including carotenoids, antioxidants, and dietary fiber. To meet evolving market demands, breeding programs also aim to improve processing and storage characteristics, focusing on traits that reduce acrylamide formation during frying and enhance resistance to sprouting and bruising [6,7]. To further advance these breeding efforts and effectively tackle the challenges faced by the potato industry, it is crucial to elucidate the mechanisms underlying the formation of key agronomic and quality traits. This understanding will be instrumental in establishing a molecular breeding technical system, ultimately enabling the development of high-yield and high-quality new varieties that address the growing demands for nutrition and health.

Conventional breeding remains the primary method for developing new potato varieties and selecting elite varieties for use in molecular breeding [8]. It primarily relies on the selection of agronomic traits through a planned breeding process, including parental selection, hybridization, clonal selection, and breeding line evaluation, which can take more than a decade to produce new varieties through standardized procedures [9]. As a tetraploid, the common cultivated potato exhibits a narrow genetic background among varieties due to the limited genetic diversity, which restricts the development of breakthrough new varieties [10]. However, the genetic resources from primitive cultivated and wild species of potato, including resistance to biotic and abiotic stresses, diverse starch compositions, high vitamin and protein content, and unique flavor substances, are vital for developing breakthrough new varieties [11].

The evolution of the potato genome has been shaped by natural and artificial selection, leading to the development of diverse cultivated varieties with improved agronomic traits. Originating in the Andean region of South America, wild relatives like Solanum chacoense, Solanum phureja, and Solanum commersonii contributed valuable genetic traits, including disease resistance and stress tolerance. Domestication, which began 8000 to 10,000 years ago, involved selecting tetraploid genotypes from diploid ancestors through hybridization and introgression, incorporating beneficial alleles related to tuber development, disease resistance, and environmental adaptation [11]. Key events such as polyploidization produced modern cultivated tetraploids (Solanum tuberosum), with larger tubers, reduced bitterness, and adaptability to various climates. Comparative genomics has revealed extensive heterozygosity and structural variation in the potato genome, with gene flow and introgression further enriching its genetic diversity. These insights are crucial for breeding programs aiming to improve disease resistance, yield, and adaptability, utilizing the genetic potential of wild relatives to meet global food security challenges [8]. Previous researchers have developed methodologies based on conventional and biotechnological techniques using primitive cultivated and wild species, such as 2n gamete utilization, recombination breeding, and introgressive breeding, successfully transferring genes resistant to late blight, wilt, and cold-induced sweetening into common cultivated varieties [12,13,14]. Although the conventional breeding of potato has made significant progress, the phenotypic selection for major agronomic traits is influenced by narrow-sense heritability, selection differential, and selection intensity [15]. Phenotypic selection faces challenges such as a lack of precision and reliability in phenotypic identification, insufficient methods for identifying traits, and difficulties in accurately assessing the contributions of genotype and environment to phenotype, which results in low selection accuracy and efficiency [16]. In autotetraploid potato, the complexity of gene interactions makes it more challenging to effectively select and aggregate the best alleles in conventional breeding [17].

Molecular marker-assisted selection (MAS) has been widely used in breeding programs for various crops [18]. It utilizes molecular markers to select genotypes associated with target traits, which helps improve breeding efficiency. This method is less influenced by environmental factors or growth and development stages, thereby shortening the breeding cycle, improving breeding efficiency, and addressing some difficulties in trait identification and screening found in conventional breeding methods [19]. However, in autotetraploid potato, molecular marker-assisted selection can be prone to issues such as incorrect molecular markers and detection errors, leading to false-positive results. So far, the molecular markers such as restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNA (RAPD), and simple-sequence repeats (SSRs), which have been useful in other crops, face limitations in potato breeding due to challenges such as marker specificity and detection accuracy [15,20,21]. Genome-wide association studies (GWASs) have become an important method for identifying genetic variants associated with complex traits across the entire genome due to their higher resolution and broader allele frequency spectrum [22,23,24]. In potato, constructing complex genetic mapping populations is particularly challenging [25]. Hybrid offspring cannot display stable phenotypes in the early generations, and differences in phenotypes among materials decrease after about six generations [26]. Severe inbreeding depression also limits the development of highly homozygous lines within the population [27]. These challenges limit the effectiveness of methods designed to precisely locate trait-related genes by controlling the genetic background of mapping populations in potato. GWASs can directly utilize the historical genetic recombination and mutation events accumulated over hundreds of generations in natural potato populations. This allows for precise localization of trait-associated loci and genes, thereby enhancing the accuracy of quantitative trait loci (QTL) mapping and narrowing the confidence interval. The differences in mapping populations directly determine the mapping resolution and the detection efficacy of genetic loci [28].

GWASs have yielded significant insights into the genetic basis of fruit traits, disease resistance, and quality characteristics in Solanaceae crops such as tomato (Solanum lycopersicum), pepper (Capsicum annuum), and eggplant (Solanum melongena). In tomatoes, GWAS research has identified key genetic loci controlling fruit size and shape, located on chromosomes 6 and 10, which are significantly associated with genes involved in crucial metabolic pathways such as cell division and hormone regulation, including OVATE and SUN. These genes influence fruit morphology by regulating cell expansion and tissue morphogenesis [29]. Additionally, research on tomato disease resistance has pinpointed important loci related to late blight resistance, particularly the NBS-LRR class resistance genes on chromosome 6, providing potential molecular markers for disease-resistant breeding [30]. Furthermore, studies on tomato fruit quality have identified regulatory genes for sugar metabolism and pigment synthesis, such as UGP, Psy1, and MYB, which are closely linked to loci on chromosomes 1, 2, 3, and 10, regulating sugar content and fruit color [29,31]. Similarly, a GWAS in peppers has revealed important genes associated with fruit traits and quality. The shape of pepper fruits is significantly associated with loci on chromosomes 2, 5, and 10, with nearby genes like DDB1 and GAUT regulating fruit development through cell division [32]. Capsaicin content, which determines pungency, is controlled by genes such as KAS and Pun1 on chromosomes 3 and 7, involved in capsaicin biosynthesis [33]. Disease resistance research in peppers has also identified multiple single-nucleotide polymorphism (SNP) loci associated with late blight resistance, primarily on chromosomes 3 and 7, co-locating with R genes (such as NBS-LRR class genes), offering new targets for disease-resistant breeding [34]. Pepper fruit color is related to carotenoid and anthocyanin synthesis genes, such as Psy and MYB, located on chromosomes 1 and 6, which regulate fruit coloration [35]. In eggplants, the application of GWASs has greatly advanced the understanding of genetic factors underlying fruit traits, disease resistance, and quality characteristics. Key genetic loci controlling fruit shape, size, and color have been identified. For instance, Liu et al. discovered multiple significant loci associated with fruit size and shape, mainly on chromosomes 1, 8, and 10, which are related to genes such as expansin and cytokinin oxidase that regulate cell expansion and development [36]. These genes significantly impact fruit development by modulating cell division and growth. Cericola et al. identified several crucial loci associated with fruit color, particularly on chromosome 10, which are closely linked to genes like MYB involved in carotenoid and anthocyanin synthesis, affecting pigment accumulation and fruit color [37]. Additionally, the gene Psy involved in lycopene synthesis is also closely associated with changes in eggplant fruit color. Moreover, nutritional quality traits of eggplants, such as antioxidant activity and sugar content, have also been extensively studied through GWASs. Ro et al. demonstrated that antioxidant activity is associated with multiple loci on chromosomes 2 and 11, which are related to genes like PPO and PAL that regulate the synthesis of polyphenolic compounds [38]. These genes control the accumulation of polyphenolic compounds in eggplants, playing a significant role in enhancing antioxidant capacity. Overall, the GWAS has proven to be a powerful tool for elucidating the genetic basis of key traits in various Solanaceae crops. Building on these advances, similar approaches in potato have the potential to significantly enhance our understanding of its complex genetic architecture and improve breeding strategies.

In recent years, GWASs have been widely applied in the genetic mechanism research of agronomic traits such as plant morphology, yield components, tuber shape, resistance to late blight and early blight, and nematode resistance in potato. They have also been used to study quality traits such as dry matter, starch, and glycoalkaloid content. Through these studies, a large number of markers and genes closely associated with target traits have been identified, providing rich genetic resources for potato breeding. GWASs have already achieved some success in potato breeding. For instance, a GWAS identified marker loci chr11_1259552 and chr11_1772869, which are tightly linked to the Sen3 locus conferring resistance to potato wart disease. Based on these loci, KASP markers were developed and employed to evaluate 56 germplasm resources for resistance, successfully identifying 15 clones with potential resistance to potato wart [39]. In another GWAS focused on chip-processing traits, the HSI2-like gene was mapped as being associated with chip color, and molecular markers were developed from adjacent loci c1_8019 and c1_8020. Among these, KASP_c1_8019 exhibited superior genotyping performance, enabling the successful identification of potato varieties with excellent chip-processing quality [40]. The integration of GWAS data with multi-omics data such as transcriptomics, proteomics, and metabolomics has provided opportunities to study the association between phenotypic variation and RNA, proteins, or metabolites. This includes metabolite genome-wide association studies (mGWASs) [41], qualitative trait genome-wide association studies (QT-GWASs) [42], homoeologous locus-based genome-wide association studies (hGWASs) [43], and expression genome-wide association studies (eGWASs) [44]. In addition, genomic selection using high-density SNP markers obtained through GWASs has also improved breeding efficiency. Compared with pedigree-based selection, genomic selection does not require locating trait-controlling gene loci or field phenotyping, and it has an advantage in accelerating genetic gain per unit time [15]. Genomic selection breeding has achieved good results in predicting traits such as potato yield, number of tubers, frying color, dry matter content, starch content, late blight resistance, and scab resistance. These traits, which are controlled by small-effect QTLs, have low heritability and are not easily identified in early generations. However, the accuracy of genomic selection prediction is affected by the genetic relationship between training and test populations, population size, chromosome ploidy, genome heterozygosity, linkage disequilibrium decay, the number of molecular markers, and the heritability of target traits [45].

To conclude, the aim of this review is to provide a comprehensive overview of the application of GWASs in potato research, with a focus on identifying key genetic loci associated with agronomic and quality traits. By highlighting recent advancements and key findings, this review seeks to offer insights into the genetic mechanisms underlying complex traits such as yield, disease resistance, and tuber quality. The ultimate goal is to demonstrate how these genetic insights can be harnessed to refine and enhance potato breeding strategies. Such advancements are expected to contribute significantly to the development of improved potato varieties with better performance, resilience, and adaptability, thereby addressing the challenges of food security and promoting sustainable agricultural practices.

2. Overview of Steps for Conducting GWASs

GWASs in potato follow a series of methodologically rigorous steps. A genetically diverse and phenotypically representative population is initially selected to encompass the genetic variation pertinent to the traits of interest. Phenotyping is performed with high precision to accurately capture trait variations. This is followed by genotyping, which identifies genetic variants in the population essential for pinpointing trait-associated markers. Population stratification is meticulously controlled to eliminate potential confounders that might distort association signals. Association analysis is conducted using robust statistical methods to identify loci significantly correlated with the traits of interest. Once a locus is identified, linkage disequilibrium (LD) analysis is used to refine the association and identify candidate genes within high-LD regions. These candidate genes are then validated using functional genomics techniques, such as gene overexpression, gene silencing, and CRISPR-mediated genome editing. Concurrently, molecular markers linked to confirmed genetic variants are developed for marker-assisted selection in breeding programs (Figure 1).

2.1. Population Selection, Phenotyping, and Genotyping

In GWASs, the size of the population and phenotypic variation are key factors that ensure the applicability and accuracy of the association results. The selected population should extensively cover genetic diversity to enhance the statistical power and increase the probability of detecting SNPs associated with traits [46]. In a genome-wide association study on potato starch content, 39 association SNPs were identified when analyzing a population of 90 samples, while increasing the population size to 184 samples resulted in the identification of 127 SNPs [40]. This demonstrates the advantage of large population sizes in enhancing detection capabilities. However, the expansion of population size also brings challenges such as the increased sequencing costs and the collection of phenotypic data. Concurrently, the degree of phenotypic variation within the population is an important factor affecting GWAS results. The genetic and environmental heterogeneity of the target trait must be considered during association analysis, and selecting a population with greater phenotypic variation can enhance detection efficiency [47]. When the genetic diversity of the target population is limited, integrating populations with higher genetic diversity can enhance a GWAS’s detection capability for quantitative trait nucleotides (QTNs). In an association study on late blight resistance conducted on 380 potato accessions, five resistance markers were initially identified. By introducing native Andean cultivars (S. tuberosum groups Andigena, Phureja, and Stenotomum) and wild species (S. acaule and S. bulbocastanum), the number of resistance markers increased to 16, significantly improving the ability of the GWAS to detect resistance markers for late blight [48]. However, attention must be paid to the fixation index (F_st) and expected heterozygosity (H_e) of QTNs during the process of increasing population genetic diversity, as a higher F_st may indicate significant genetic differentiation between populations, affecting the interpretation of results [49]. Populations with higher F_st are more likely to successfully detect SNPs due to their higher genetic variability [50]. Therefore, populations with low F_st and high H_e are generally more effective in capturing genetic variation within the population, thereby improving the statistical power of GWASs. When designing GWASs, factors such as sample size, genetic diversity, and phenotypic variation should be balanced during population selection to optimize study design and ensure the scientific value and practicality of the results.

For population genotyping, the common methods include SNP arrays [51], targeted exonic sequencing [52], reduced representation genome sequencing [53], and whole-genome resequencing [54]. While the first three methods have been effectively utilized in potato genotyping, leading to the identification and validation of numerous trait-associated loci, whole-genome resequencing has increasingly become the preferred approach in GWASs. This shift is attributable to its reduced cost and superior coverage, which provide more comprehensive genomic information [55,56,57]. Whole-genome resequencing provides variant information across the entire genome, including SNPs, small insertions or deletions (indels ≤ 50 bp), and structural variations (SVs > 50 bp), which helps to more comprehensively understand genetic variation [58]. High-coverage whole-genome resequencing improves the accuracy of variant detection, especially in low-frequency variations and complex genotype regions [59]. In addition, whole-genome resequencing data combined with population genetic analysis can reveal the genetic structure, history, and selection signals of the population, providing a deeper perspective for understanding the genetic basis of traits [60]. The accuracy of genotyping is heavily dependent on the quality of the reference genome. Studies by Scheben et al. emphasize the critical role of a high-quality reference genome in ensuring completeness, continuity, and representativeness, which are essential for accurately pinpointing SNPs and other genetic variations [24]. A high-quality reference genome not only ensures precise genotyping but also minimizes both false positives and false negatives, thereby enhancing genotype-calling accuracy [61].

Due to technological and cost constraints, the sequencing efforts were limited to diploid genomes, leading to the publication of several diploid potato reference genome assemblies. These include the doubled haploid Solanum tuberosum group phureja DM 1-3516 R44 (727 Mb) [61], the diploid wild species Solanum commersonii (830 Mb) [62], the self-compatible diploid wild species Solanum chacoense M6 (882 Mb) [63], and the doubled haploid Solanum tuberosum group Tuberosum RH89-039-16 (1.67 Gb), with genetic backgrounds of common cultivars such as Katahdin, Chippewa, and Primura [64]. These diploid reference genome assemblies provide a comprehensive view of genetic variation, thus enhancing the accuracy and resolution of genotyping [63]. However, the polyploid nature of the potato genome, marked by high heterozygosity and repetitive sequences, means that diploid potato reference genome assemblies have inherent limitations in addressing the genomic complexities of tetraploid potato [65]. Advancements in third-generation sequencing technologies, including HiFi reads from the Pacific Biosciences Sequel II system and ultra-long reads from the Oxford Nanopore system, have opened new avenues for assembling the complex genomes of tetraploid potato [66]. Currently, successfully assembled reference genomes of common cultivated tetraploid potato include Otava (3.1 Gb) [67], Qingshu No. 9 (2.67 Gb) [65], Cooperation 88 (3.15 Gb) [68], Atlantic (2.65 Gb), and Castle Russet (2.50 Gb) [69]. These newly assembled reference genomes not only provide genomic information closer to that of common cultivated varieties but also enhance the accuracy and applicability of genotyping in tetraploid potato varieties, thus offering valuable resources for genetic research and molecular breeding. In conclusion, the comprehensive reference genome information for potato is now more accessible than ever. The database hosted at the University of Georgia, accessible at https://spuddb.uga.edu/ (accessed on 10 July 2024), provides a central repository for almost all the reference genomes mentioned above, serving as a valuable resource for researchers and breeders working with potato genetics and breeding. The reference genome of Qingshu No. 9 is available at the following website: https://ngdc.cncb.ac.cn/biosample/browse/SAMC490813 (accessed on 22 March 2022).

2.2. Population Stratification and Linkage Disequilibrium Analysis

Population stratification is a key factor contributing to false positives by causing differences in allele frequencies between subpopulations. Stratification may arise from geographical isolation, historical migrations, and population admixture, which can affect the genetic structure of populations [70]. To mitigate the impact of population stratification on association analysis results, researchers have developed various statistical methods to analyze and correct population structure. Genomic control (GC) is a commonly used method that adjusts p-value thresholds to account for biases introduced by population structure [71]. Structured association (SA) improves the accuracy of association analyses by utilizing genetic structure information of populations [72]. Principal component analysis (PCA) and multi-dimensional scaling (MDS) can reveal genetic differences between samples, helping to identify and correct potential population stratification [73,74]. Nonmetric multi-dimensional scaling (NMDS) provides a way to visualize genetic differences between samples without considering the original distance metric [73]. Furthermore, the unified mixed-model approach (Q + K model) combines SA with kinship-based corrections, providing a comprehensive consideration of population structure and relationships between individuals [75,76]. Applying these methods reduces the impact of population stratification on association analysis results, thereby enhancing the accuracy and reliability of the research. Linkage disequilibrium (LD) is a critical concept in population genetics that describes the non-random association of alleles and plays a foundational role in association analysis [77]. The level of LD can provide valuable insights into population history, genetic structure, and selection pressures [78]. However, LD within populations is influenced by several factors, including population stratification, genetic drift, artificial domestication, and natural selection. These factors can create LD between non-causal alleles and target trait genes, leading to false-positive results in association analyses [79].

2.3. GWAS Analysis Models

The core task of GWASs is to use statistical models to assess the association between molecular markers, such as SNPs, and phenotypic traits to identify and quantify QTLs affecting complex traits [80]. GWAS models are categorized into single-locus models and multi-locus models. Single-locus models, due to their computational simplicity and ease of implementation, have been widely used in early association analyses [81]. These models primarily include the general linear model (GLM) and the mixed linear model (MLM). GLM is suitable for analyzing independent genetic effects that conform to a normal distribution. In contrast, the MLM further considers population structure and kinship relationships by introducing random effects, thereby reducing the risk of false-positive associations [82]. The widespread application of the MLM is attributed to its effectiveness in controlling biases in population structure, particularly in dealing with false positives caused by population stratification and kinship relationships [83]. To enhance the analytical efficiency and statistical power of the MLM, researchers have developed several advanced models. These include efficient mixed-model association expedited (EMMAX) [84], the compressed mixed linear model (CMLM) [85], factored spectrally transformed linear mixed models (Fast-LMMs) [86], and genome-wide efficient mixed-model association (GEMMA) [87]. These models enhance the detection of rare and low-frequency variations through different mathematical transformations and algorithm optimizations while accelerating computational speed. However, they face several challenges in GWASs. Firstly, to control the issue of multiple testing across the entire genome, p-values usually need to be strictly corrected, such as with the Bonferroni correction. This adjustment may lead to the omission of some loci truly associated with phenotypes if they do not meet the corrected significance threshold, resulting in false negatives [88]. Secondly, single-locus models cannot capture the cumulative effects or epistasis effects between multiple loci, which may limit a comprehensive understanding of the genetic structure of complex traits [89]. Additionally, the site filtering criteria in GWASs, such as filtering out loci with a minor allele frequency (MAF) less than 0.05, may exclude variations that, although low in frequency, may have significant impacts on specific traits [88]. This filtering strategy might ignore an essential part of genetic diversity, particularly rare variations that have adaptive significance in specific populations or environments.

To address the limitations of single-locus models in traditional GWASs, such as the problem of false negatives caused by multiple testing corrections and the insufficiency in assessing multi-gene effects, researchers have developed multi-locus models. These models can consider all loci simultaneously without the need for multiple testing corrections [90]. Multi-locus models enhance the detection capability for small-effect loci, which are often difficult to detect by single-locus models in the genetic structure of complex traits [89]. Currently, the main multi-locus models include the multi-locus mixed model (MLMM) [91], fixed and random model circulating probability unification (FarmCPU) [92], multi-locus random-SNP-effect mixed linear model (mrMLM) [90], and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK), which is an optimized version of FarmCPU [70]. These models provide a variety of genomic data analysis methods, allowing researchers to choose the most appropriate model based on the research purpose and data characteristics. A key advantage of multi-locus models is their ability to detect rare variations, which may be ignored in single-locus models due to MAF threshold filtering [93,94]. At the same time, multi-locus models can assess the interaction between loci, that is, epistasis effects, which cannot be achieved in single-locus models and are crucial for understanding the genetic basis of complex traits [95]. Although multi-locus models may be more computationally complex and resource-intensive than single-locus models, the computational efficiency and feasibility of these models are continuously improving with the development of computational technology [90]. In addition, the integrated use of multi-locus and single-locus models can provide more comprehensive genetic information and enhance the detection capability for complex trait genetic loci, especially in traits that cannot be fully explained by single-locus models [96,97,98]. The integration of single-locus and multi-locus models is of great significance for enhancing the detection capability of GWASs for complex trait genetic loci, providing a powerful tool for in-depth understanding of the genetic structure of complex traits.

3. GWASs for Key Agronomic Traits in Potato

For a long time, one of the important goals of potato breeding has been to improve the adaptability, yield, and quality of potato by improving key agronomic traits such as plant morphology, maturity period, yield components, and tuber phenotype. Plant morphology affects the efficiency of photosynthesis and resistance to diseases [99]. The maturity period determines the growth cycle and adaptability of the crop [100]. Yield components are directly related to the final yield [101]. Tuber phenotype impacts market acceptance and consumer preferences [102]. In addition, as a globally important food crop, potato inevitably face challenges from pests, diseases, and adverse conditions during growth, which can severely impact yield and quality [103]. This section will focus on the application of GWASs in deciphering the genetic mechanisms of these key agronomic traits and providing new perspectives and methods for the genetic basis research of these complex traits (

Table 1. Application of GWASs in the research of potato agronomic traits.

Traits	Population Origin	Population Size	Planting Environments	Number of Loci	Chromosome Location of Key Loci	References
Plant height	Tetraploid	370	Two years, one location	92	1, 5	Zhao et al., 2023 [104]
Number of main stems	Tetraploid	251	Two years, four locations	7	2, 4, 5	Han et al., 2023 [105]
Stem color	Tetraploid	466	Eight years, three locations	2	4, 7	Berdugo-Cely et al., 2017 [106]
Growth habit	Tetraploid	162	Two years, three locations	2	5	Massa et al., 2018 [107]
Leaf shape	Tetraploid	237	Two years, one location	9	1	Zia et al., 2020 [108]
Number of leaflets	Tetraploid	237	Two years, one location	3	3, 4, 8	Zia et al., 2020 [108]
Canopy cover	Tetraploid	189	Two years, two locations	8	1, 2, 3	Ospina Nieto et al., 2021 [109]
Flower color	Tetraploid	466	Eight years, three locations	13	3, 7	Berdugo-Cely et al., 2017 [106]
	Tetraploid	110	Four years, two locations	1	10	Rak et al., 2017 [110]
	Tetraploid	237	Two years, one location	1	4	Zia et al., 2020 [108]
Stamen and pistil length	Tetraploid	237	Two years, one location	5	2, 5	Zia et al., 2022 [111]
Self-fertility	Diploid	164	Two years, one location	1	3	Kaiser et al., 2021 [112]
Maturity	Diploid	98	Two years, one location	1	5	Manrique-Carpintero et al., 2015 [113]
	Tetraploid	156	Two years, one location	2	5	Massa et al., 2015 [114]
	Diploid	110	Two years, one location	1	5	Braun et al., 2017 [115]
	Tetraploid	162	Two years, three locations	1	5	Massa et al., 2018 [107]
	Tetraploid	188	Two years, one location	2	4, 5	Mengist et al., 2018 [116]
	Tetraploid	237	Two years, one location	3	5, 9	Zia et al., 2020 [108]
	Tetraploid	586	Two years, one location	3	4, 5, 6	Caraza-Harter and Endelman, 2022 [117]
	Tetraploid	241	Two years, one location	3	5	Xue et al., 2022 [118]
	Tetraploid	384	Four years, one location	6	5	Pandey et al., 2023 [119]
Yield	Diploid	98	Two years, one location	3	2, 5, 12	Manrique-Carpintero et al., 2015 [113]
	Tetraploid	110	Four years, two locations	6	2, 5	Rak et al., 2017 [110]
	Tetraploid	290	Two years, two locations	192	4	Campbell et al., 2023 [120]
	Tetraploid	192	Three years, two locations	6	5, 8, 9	Yousaf et al., 2023 [121]
Average tuber weight	Diploid	98	Two years, one location	6	4, 5	Manrique-Carpintero et al., 2015 [113]
	Diploid	110	Two years, one location	2	1	Braun et al., 2017 [115]
	Tetraploid	110	Four years, two locations	6	5	Rak et al., 2017 [110]
	Tetraploid	103	Three years, two locations	3	5	Aliche et al., 2019 [122]
	Tetraploid	192	Three years, two locations	20	4	Yousaf et al., 2023 [121]
Tuber number per plant	Diploid	98	Two years, one location	2	5	Manrique-Carpintero et al., 2015 [113]
	Tetraploid	110	Four years, two locations	3	4, 5, 10	Rak et al., 2017 [110]
	Tetraploid	103	Three years, two locations	1	3	Aliche et al., 2019 [122]
	Tetraploid	251	Two years, four locations	13	1	Han et al., 2023 [105]
	Tetraploid	192	Three years, two locations	3	6, 7	Yousaf et al., 2023 [121]
Tuber shape	Diploid	186	Three years, one location	7	2, 10	Prashar et al., 2014 [123]
	Tetraploid	110	Four years, two locations	8	5, 6	Rak et al., 2017 [110]
	Tetraploid	466	Eight years, three locations	1	7	Berdugo-Cely et al., 2017 [106]
	Tetraploid	162	Two years, three locations	1	5	Massa et al., 2018 [107]
	Tetraploid	237	Two years, one location	3	4, 10	Zia et al., 2020 [108]
	Tetraploid	205	Two years, two locations	13	4, 10	Park et al., 2021 [124]
	Tetraploid	214	Three years, two locations	11	10	Pandey et al., 2022 [125]
	Tetraploid	370	Two years, one location	146	1, 2	Zhao et al., 2023 [126]
	Tetraploid	192	Three years, two locations	21	10	Yousaf et al., 2023 [121]
Tuber flesh color	Tetraploid	214	Three years, two locations	5	3	Pandey et al., 2022 [125]
Tuber skin color	Tetraploid	466	Eight years, three locations	6	7	Berdugo-Cely et al., 2017 [106]
	Tetraploid	237	Two years, one location	17	1, 8, 12	Zia et al., 2020 [108]
	Tetraploid	214	Three years, two locations	16	3, 11	Pandey et al., 2022 [125]
Eye depth	Diploid	186	Three years, one location	4	10	Prashar et al., 2014 [123]
	Tetraploid	214	Three years, two locations	3	3, 5, 10	Pandey et al., 2022 [125]
	Tetraploid	370	Two years, one location	53	5, 6	Zhao et al., 2023 [126]
Tuber skin russeting texture degree	Tetraploid	214	Three years, two locations	12	1, 4, 5, 12	Pandey et al., 2022 [125]
Tuber skin maturity	Tetraploid	586	Two years, one location	3	4, 5, 9	Caraza-Harter and Endelman, 2022 [117]
Dormancy and germination	Diploid	129	One year, one location	14	2, 3, 7, 11	Bisognin et al., 2018 [127]
Root diameter	Tetraploid	192	Two years, one location	9	9, 11, 12	Yousaf et al., 2023 [121]
Stolon diameter	Tetraploid	192	Three years, two locations	8	5, 6, 11	Yousaf et al., 2023 [121]
Stolon length	Tetraploid	192	Two years, one location	41	4, 6, 9	Yousaf et al., 2021 [128]
Stolon weight	Tetraploid	192	Three years, two locations	12	3, 4	Yousaf et al., 2023 [121]
Plant vigor	Diploid	98	Two years, one location	3	3, 5, 10	Manrique-Carpintero et al., 2015 [113]
Nitrogen use efficiency	Tetraploid	88	Two years, two locations	77	3, 5, 6	Getahun et al., 2022 [129]
Early blight resistance	Tetraploid	162	Two years, three locations	1	5	Massa et al., 2018 [107]
	Tetraploid	80	Three years, one location	33	1, 5, 11	Odilbekov et al., 2020 [130]
	Tetraploid	241	Two years, one location	9	5	Xue et al., 2022 [118]
Late blight resistance	Tetraploid	103	Six years, two locations	3	7, 9	Lindqvist-Kreuze et al., 2014 [131]
	Tetraploid	156	Two years, one location	2	9	Massa et al., 2015 [114]
	Tetraploid	184	Two years, two locations	17	5	Mosquera et al., 2016 [132]
	Tetraploid	94	Two years, one location	6	1, 4, 5, 8	Santa et al., 2018 [133]
	Diploid	150	Three years, one location	16	3, 12	Juyo Rojas et al., 2019 [134]
	Tetraploid	284	Two years, one location	964	1, 4, 9	Wang et al., 2021 [135]
	Diploid/tetraploid	380	Four years, three locations	30	3, 9	Lindqvist-Kreuze et al., 2021 [48]
	Tetraploid	222	Three years, one location	7	11	Sood et al., 2023 [136]
Bacterial wilt resistance	Diploid	94	One year, one location	5	1, 10, 11	Habe et al., 2019 [137]
Verticillium wilt resistance	Tetraploid	162	Two years, three locations	1	5	Massa et al., 2018 [107]
Common scab resistance	Diploid	110	Two years, one location	2	11	Braun et al., 2017 [115]
	Tetraploid	143	Three years, one location	3	2, 4, 12	Yuan et al., 2019 [138]
	Tetraploid	198	Two years, one location	3	1, 2	Kaiser et al., 2020 [139]
	Tetraploid	165	Twelve years, two locations	6	1	Koizumi et al., 2021 [140]
	Tetraploid	151	Four years, one location	7	1, 3, 6, 10	Zorrilla et al., 2021 [141]
Wart resistance	Tetraploid	133	One year, one location	67	12	Obidiegwu et al., 2015 [142]
	Tetraploid	215	One year, one location	186	11	Bartkiewicz et al., 2018 [143]
	Tetraploid	330	Public database	70	11	Prodhomme et al., 2020 [144]
	Tetraploid	569	Public database	333	10, 11, 12	Prodhomme et al., 2020 [145]
Tuber end rot resistance	Diploid	98	Two years, one location	4	1, 3, 5, 6	Manrique-Carpintero et al., 2015 [113]
Cyst nematode resistance	Tetraploid	222	Three years, one location	20	3, 10	Sood et al., 2023 [136]
Potato tuber moth resistance	Tetraploid	94	Two years, one location	14	2, 7, 12	Santa et al., 2018 [133]
Colorado potato beetle resistance	Diploid	136	Two years, one location	11	2	Kaiser et al., 2021 [112]
Tuber bruising resistance	Tetraploid	158	One year, one location	5	1, 7, 8	Angelin-Bonnet et al., 2023 [146]
Drought tolerance	Diploid	104	One year, one location	38	3, 11, 12	Diaz et al., 2021 [147]
Hollow heart tolerance	Tetraploid	151	Four years, one location	5	3, 5, 6, 12	Zorrilla et al., 2021 [141]

).

3.1. Plant Morphology

Morphological characteristics of potato plants are key indicators for identifying germplasm resources and evaluating growth and development, which are crucial for guiding breeding practices and optimizing cultivation management. A comprehensive assessment of traits such as plant height, growth habit, leaf shape, number of leaflets, number of main stems, stem color, and flower color can reflect the genetic background of plants and indicate its adaptability and production potential [148]. In a GWAS of 370 tetraploid potato materials for plant height, clean reads of each sample were aligned to the tetraploid Qingshu No. 9 reference genome using the BWA software (version 0.7.10-r789), and subsequent variant calling was performed to identify SNPs across the genome. Ninety-two significant association loci were detected, with a notable locus located in the 38.83 Mb to 38.92 Mb region on chromosome 5, where 35 candidate genes were identified. These genes are mainly enriched in plant hormone signal transduction and metabolic pathways, including GID1 and PIF3 involved in the gibberellin metabolic pathway and BSK and CYCD3 involved in the brassinosteroid metabolic pathway [104]. The density of main stems affects the number of lateral branches, tuber size, yield, and leaf senescence traits in potato. Compared to the actual tuber planting density in the field, the density of main stems can more accurately reflect the planting density of potato [149]. A GWAS of the number of main stems in potato over 2 years and eight locations identified a total of seven associated loci on chromosomes 2, 4, and 5. These loci correspond to genes encoding E3 ubiquitin protein ligase RGLG2, cytochrome P450, COP9 signalosome, gibberellin 3-β-dioxygenase, and galacturonosyltransferase [105]. In the association analysis of plant height and the number of main stems, loci related to genes regulating plant signal transduction and hormone metabolism were identified. Particularly notable are loci associated with gibberellin metabolism genes, such as rna25450, PIF3, and GID1, which offer potential applications. Gibberellins, key plant hormones, promote stem elongation, germination, and flowering, making them highly relevant to potato cultivation [150]. Rna25450 is potentially involved in regulating gibberellin production or response, while PIF3, a transcription factor involved in light-mediated responses, may modulate gibberellin signaling, affecting stem elongation [151]. GID1, a gibberellin receptor, is crucial for initiating signaling cascades that lead to cell elongation and increased plant height, as well as lateral stem development from axillary buds, influencing the number of main stems [152]. This research deepens the understanding of the genetic mechanisms underlying plant height and stem number and provides insights for developing new molecular markers. The growth habits, leaf shape, and number of leaflets of potato have an important impact on photosynthetic utilization and field management [153]. In the GWAS of 162 tetraploid potato materials, genotyping was performed using the Infinium 8303 Potato Array, and two loci related to plant growth habits were detected at 15 cM to 16 cM on chromosome 5 [107]. Nine loci related to leaf shape were identified on chromosomes 1 and 7. The genes screened from these loci mainly encode ring finger proteins, GTP ring hydrolase II, ATP-binding proteins, DNA-binding proteins, amino acid transport proteins, and PS60 proteins. These proteins play a role in regulating leaf development and may provide potential targets for improving leaf shape to enhance photosynthesis and stress resistance. In addition, three loci for the number of leaflets were detected on chromosomes 3, 4, and 8, among which the locus Solcap_snp_c2_2183 on chromosome 8 is related to the gene encoding L-pyrroline-5-carboxylate synthase (P5CS) [108]. P5CS is a key enzyme in the proline biosynthesis pathway in plants, responsible for converting glutamate into L-pyrroline-5-carboxylate (P5C), which is the rate-limiting step in proline production. Proline is thought to play a regulatory role in cell proliferation, differentiation, and tissue development [154]. P5CS may indirectly influence cell division and expansion during potato organ development by modulating proline levels. For example, during tuber formation and enlargement, P5CS may help maintain appropriate proline levels to promote cell differentiation and expansion, thereby supporting normal tuber development [155]. By regulating proline synthesis, P5CS may affect the balance of plant hormones, influencing key processes such as tuber formation, leaf development, and flower differentiation. Two loci related to stem color were also detected on chromosomes 4 and 7 [106]. The genetic basis of stem color is not fully clear, it may be related to the plant’s response to environmental factors such as light exposure and nutrient supply. Although the existing GWAS results have revealed multiple genes related to these traits involved in plant morphogenesis, the associated loci have not yet been verified. Therefore, future research needs to verify these loci and explore their specific roles in trait regulation.

The color and shape of potato flowers are important traits for distinguishing germplasm differences and affect the hybridization fruit set rate [156]. Several studies have identified loci related to flower color on chromosomes 1, 3, 4, 5, 6, 7, 10, and 12 [106,108,110]. However, the results of these studies are inconsistent and cannot be verified against each other, likely due to differences in the size and genetic background of populations. This inconsistency indicates the complexity of flower color association analysis. A GWAS of 136 diploid potato materials for pistil and stamen length detected five loci related to stamen length on chromosomes 1, 5, and 8. These loci are associated with genes involved in mitochondrial respiratory chain complex IV assembly, chloroplast mRNA processing, and protein phosphorylation functions. Five loci related to pistil length were detected on chromosomes 2, 4, 7, and 12, with genes encoding aldehyde dehydrogenase, F-box protein, galactosyltransferase, and mitochondrial processing peptidase subunit α identified [111]. Due to the inhibitory effect of S-RNase on pollen tube growth, potato exhibits a significant phenomenon of self-incompatibility [157]. Therefore, identifying new genes that regulate self-fertility is particularly important. In the association analysis of self-fertility in 164 diploid potato materials, the locus solcap_snp_c1_13698 located near Sli on chromosome 12 was detected. This locus is co-located with the gene encoding the ovule receptor-like kinase (ORLK) [112]. ORLK plays a crucial role in potato fertility by regulating several key processes in reproduction. It influences the development of female reproductive organs, including ovules, and impacts their maturation and functionality [158]. ORLK is involved in signal transduction pathways that modulate gene expression related to ovule development, hormone signaling, and cell differentiation. This regulation ensures proper pollen reception and fertilization, thus affecting seed formation. Additionally, ORLK helps ovules withstand environmental stress, thereby maintaining reproductive capacity under adverse conditions. It also impacts seed development and quality, influencing seed size, quality, and embryo development [159]. During evolution, the morphological structure of pistils and stamens may affect the extension of pollen tubes, thereby influencing self-fertilization. Although the two studies identified loci related to pistil and stamen length and self-fertility on chromosome 12, the long distance between the loci indicates there is no significant association. Thus, the association analysis results of flower morphology provide new perspectives and directions for understanding the genetic mechanisms of self-fertility.

3.2. Tuber Phenotype

The morphological characteristics of potato tubers, including shape, eye depth, skin color, and flesh color, are key indicators of their commercial value [160]. The Ro locus located on chromosome 10 has been identified as the major locus controlling potato tuber shape [123,161]. Additionally, the loci related to tuber shape have been found on chromosomes 4 and 6, with genes encoding gibberellin oxidase 1, ribosomal protein S6 kinase, and serine/threonine protein kinase identified nearby [108,162]. Multiple significant loci associated with tuber shape have been detected on chromosomes 1 and 2, and fifty candidate genes have been identified within a 60 kb region upstream and downstream of these loci [126]. These genes are mainly involved in metabolic pathways such as starch and sucrose metabolism, arginine and proline metabolism, sphingolipid biosynthesis, amino sugar and nucleotide sugar metabolism, and glycosaminoglycan degradation [126]. Although the major genes controlling potato tuber shape have been identified, GWASs have also discovered new loci associated with tuber shape, indicating the complexity of its regulation. In particular, the PLATZ transcription factor identified in this study may influence tuber development through various mechanisms, including the regulation of gene expression related to tuber development, which affects cell division and expansion. PLATZ transcription factors can also modulate plant hormone signaling, such as auxins and gibberellins, which are crucial for tuber growth and shape formation [163]. Additionally, PLATZ transcription factors impact tuber morphology and size by affecting cell cycle regulation, responding to environmental stress, regulating nutrient distribution, and interacting with other transcription factor networks. These multiple regulatory mechanisms make PLATZ transcription factors key regulators for optimizing potato tuber morphology, and this finding further confirms the reliability of the GWAS results. These findings deepen the understanding of the genetic basis of tuber shape formation and provide an important reference for developing new molecular markers. Fifty-three significant loci associated with potato eye depth were detected on chromosomes 5 and 6, and seventy-four candidate genes were identified. These include genes encoding hydrolases, methyltransferases, and genes involved in auxin transport and signaling pathways, such as CYPs [126]. Many studies have also detected loci associated with eye depth on chromosome 10 and speculated that these loci may be related to the Ro gene [123,164]. An analysis group of 214 tetraploid potato materials was constructed. Samples were genotyped using the Illumina 22K SNP Potato Array (GGP Potato V3) on the Illumina iScan (Illumina Inc., San Diego, CA, USA). After quality control and variant calling, genome-wide association analysis was performed to identify significant loci. Five loci associated with flesh color and sixteen loci associated with skin color were detected. The loci related to anthocyanin content in the flesh were identified on chromosomes 1 and 11, and two loci near 43.9 Mb and 48.5 Mb on chromosome 3 were identified near the Bch gene encoding β-carotene hydroxylase [125]. Other studies have also revealed the loci related to skin color scattered on various chromosomes, potentially related to genes encoding MYBR domain transcription factors, alternative oxidases, and lycopene ε-cyclases [106,108,125,165,166]. The Y gene on chromosome 3 and the P gene on chromosome 11 have been confirmed to regulate the production of carotenoids and anthocyanins in potato, respectively. The association analysis detected the loci at corresponding positions on chromosomes 3 and 11, helping to better understand the genetic basis of pigment formation in potato. Two loci related to tuber skin maturity were identified on chromosomes 5 and 6, with the locus on chromosome 5 co-located with the StCDF1 locus [117].

3.3. Root and Stolon Phenotype

The root and stolon are key organs for nutrient absorption and yield formation in potato [167]. A GWAS was performed on the root system and stolon traits of 192 tetraploid potato materials, detecting nine loci related to total root length, root surface area, root volume, and root fresh weight on chromosomes 4, 6, 7, 9, 11, and 12. Simultaneously, forty-one loci related to stolon length, the total number of stolons, and stolon branching were detected on chromosomes 1, 2, 3, 9, 11, and 12. These loci are associated with root signal transduction, transcription and post-transcriptional gene regulation, the sucrose synthase transport protein gene family, and PVY resistance genes [128]. In subsequent studies, eight loci related to root diameter were identified on chromosomes 5, 6, and 11, and nine closely related loci for stolon fresh weight were identified on chromosome 4, co-located with the gene encoding pectin methylesterase QUA2 [121]. Pectin methylesterase QUA2 plays a critical role in potato root and stolon development by modulating pectin metabolism, which affects cell wall properties. Through its enzymatic action, QUA2 alters pectin methylation, influencing cell wall rigidity and porosity [168]. This modification impacts cell division and expansion, thereby affecting the growth patterns of roots and stolons. Additionally, QUA2′s role in pectin dynamics affects how roots and stolons respond to environmental stimuli and interact with other growth regulators, such as auxins and cytokinins [169]. By ensuring appropriate structural integrity and support, QUA2 is essential for optimal root and stolon development, contributing to overall plant performance. Both studies were association analyses of the main traits of the root and stolon under mist culture conditions, with associated loci distributed across multiple chromosomes, indicating that these traits are controlled by multiple genes.

3.4. Yield Components

Potato yield is the most important trait in breeding, usually measured by the number of tubers per plant, average tuber weight, and commercial rate [170]. A GWAS on the yield components of 192 tetraploid potato materials identified three loci related to the number of tubers per plant on chromosomes 6 and 7, with the genes encoding NTGP 4, GTP ring hydrolase, and endo-1,3-glucanase [121]. In another GWAS involving 251 tetraploid potato materials, a total of thirteen loci related to the number of tubers per plant were detected. One locus, SNP pos415937 on chromosome 6, is associated with the gene rna 25637, which encodes the transcription factor bHLH113 related to plant growth and development, abiotic stress, seed germination, and flower morphogenesis. However, the locus SNP pos821510, which shows better repeatability in multiple environments, is located on chromosome 9 and co-located with the gene rna 25774, which encodes the auxin response protein IAA13 [105]. Additionally, a GWAS in other diploid and tetraploid populations detected loci associated with the number of tubers per plant on chromosomes 3, 4, 5, and 10 [110,113,122]. Twenty SNPs related to average tuber weight were detected on chromosomes 3, 4, 6, and 7, with eight loci concentrated on chromosome 4, co-located with the gene encoding inositol pentakisphosphate 2-kinase, which plays an important role in the biosynthesis of phytate [121]. Other studies have also located loci associated with average tuber weight on chromosomes 4 and 6, particularly on chromosome 5, where several significant loci associated with average tuber weight were found. Most of these loci are close to the major maturity gene StCDF1 [110,113,122,171]. StCDF1 may be involved in regulating the potato’s response to photoperiod, thereby influencing the timing of flowering and tuber formation, which in turn affects the growth cycle and duration of tuber development. StCDF1 may interact with hormones such as gibberellins and phytochromes, which are critical for promoting cell division and elongation, potentially impacting tuber size and number. Furthermore, StCDF1 could play a role in the potato’s response to environmental stresses, such as temperature fluctuations and water scarcity, thereby affecting its growth and development [172]. Additionally, six loci related to yield per plant were detected on chromosomes 5, 8, and 9, with genes encoding F-box protein At 3g23880-like, carotenoid cleavage dioxygenase 4, diaminopimelate epimerase, and isoamylase isoform two identified near these loci [121]. Loci associated with yield components were detected on all chromosomes, confirming that these are complex traits controlled by multiple genes. The genes controlling these yield component traits are primarily involved in the regulation of plant hormone signaling, cell cycle, and nutrient accumulation. Research on the genetic mechanisms of these traits should start at the whole-genome level and comprehensively consider the interactions between various loci and genes. Significantly improving yield traits by intervening in a specific locus is difficult, and using genomic selection methods to improve potato yield may be a more effective approach.

3.5. Maturity

Breeders typically assess the maturity period of potato by observing the growth status of the above-ground plant (apical canopy growth, leaf senescence, and time to ridge senescence), as well as the formation and harvest index of tubers [173]. QTLs related to maturity have been found on all 12 chromosomes [174], and the major gene StCDF1 related to maturity was identified on chromosome 5. The StCDF1 locus not only positively regulates the initiation of tuberization through StSP6A but also interacts with the photoperiod gene StFKF1 to delay tuberization [171]. In a GWAS of the maturity period of a tetraploid potato population of 586 individuals, two closely associated loci were detected. Among them, the SNP PotVar0079081 on chromosome 5 explained 33% of the variation, and the distance between this locus and StCDF1 was 49 kb [117]. In other studies, the determined loci related to maturity were also near StCDF1 on chromosome 5 [107,113,114,115,116,118]. Additionally, an associated locus, Solcap_snp_c2_1023, on chromosome 9 was detected, co-located with a gene encoding glycosyltransferase involved in polysaccharide synthesis [108]. Most loci associated with plant maturity in the aforementioned studies are near StCDF1 on chromosome 5, but differences exist in the distances between the identified loci and StCDF1. This situation highlights the limitations of GWASs, which cannot accurately locate the genes controlling traits based on significant associated loci due to the influence of the analysis population and analysis methods. Therefore, the screened molecular markers related to maturity still need to be tested in more populations to verify their accuracy.

3.6. Dormancy and Germination

Dormancy and germination are essential stages for the normal reproduction of potato tubers, making the study of the genetic mechanisms behind these processes significant for breeding new varieties with optimal dormancy periods [175]. A GWAS performed on 192 diploid potato samples identified loci on chromosomes 2, 3, 5, and 7 that affect both tuber dormancy and germination. Genes controlling the metabolism of gibberellins and cytokinins, such as POTH1, BEL11, and BEL34, were identified on chromosomes 5 and 11. Additionally, a gene involved in ABA biosynthesis, encoding 9-cis-epoxycarotenoid dioxygenase (NCED), was identified on chromosome 5. NCED influences potato tuber dormancy primarily through its role in ABA biosynthesis. By regulating ABA levels, NCED helps maintain dormancy under unfavorable conditions and facilitates dormancy release when conditions improve. This regulation is essential for ensuring that tubers sprout at the appropriate time, thereby optimizing their growth and development [127]. These findings reveal the crucial regulatory roles of plant hormones in the dormancy and germination of potato tubers, where cytokinins and gibberellins control the release from dormancy, and gibberellins and auxins participate in subsequent bud development. The identified loci are primarily involved in the synthesis and signaling pathways of these hormones. Furthermore, a locus related to sucrose metabolism was found on chromosome 2 and has been confirmed to regulate tuber dormancy and germination [176]. These results demonstrated the effectiveness of GWASs in deciphering the genetic mechanisms of tuber dormancy and germination traits in potato.

3.7. Disease and Pest Resistance

Late blight, caused by Phytophthora infestans (Mont.) de Bary, is a significant oomycete disease affecting potato production. Multiple late blight resistance Rpi genes have been identified on potato chromosomes 1, 4, 5, 6, 7, 8, 9, 10, and 11 [177]. However, Rpi genes often rapidly lose their resistance efficacy under the evolutionary pressure of the late blight pathogen, making the screening of new resistance markers an important demand in the breeding for late blight resistance in potato. A GWAS on late blight resistance in 284 tetraploid potato materials identified nine hundred sixty-four associated loci on chromosomes 1, 3, 4, 5, 6, 7, 8, and 11. Fourteen candidate genes were identified within a 20 kb range of the candidate loci, encoding chitinase 1, protein kinases, serine–threonine protein phosphatases, and nucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins, among which NBS-LRR proteins are the most critical for late blight tolerance [135]. Additionally, genes encoding zinc finger proteins and ABC transporters were identified on chromosomes 1 and 3, which are related to potato hypersensitive response and jasmonic acid signaling. Loci detected on chromosome 5 are also related to the maturity gene StCDF1 [133]. These loci identified on multiple chromosomes are closely related to potato late blight resistance, not only associated with immune response and tolerance but also revealing a possible complex relationship between late blight resistance and maturity. Early blight is an important disease of potato caused by Alternaria solani [178]. In an association analysis of early blight resistance in 241 tetraploid materials, nine associated loci were detected on chromosomes 2, 3, 5, 7, 8, and 12. Four loci on chromosome 5 are all related to maturity loci [118]. Other studies have also found that early blight resistance and maturity are located at the same loci [107]. In another study, early-blight-resistance-related loci were detected on chromosomes 1, 6, 10, and 11 [130].

Common scab disease, caused by Streptomyces scabies, is a major soil-borne disease affecting potato [179]. Association analysis of resistance to common scab in 143 tetraploid samples has identified three associated loci on chromosomes 2, 4, and 12 [138]. Two loci on chromosome 1 were detected, which are co-located with genes encoding extension proteins and jasmonic acid–amino acid synthase [139]. Additionally, significant associations with common scab have been detected on chromosomes 1, 3, 5, 6, 10, and 11 [140,141]. Wart disease, caused by Synchytrium endobioticum, is a quarantine disease affecting potato [180]. The resistance gene locus Sen1 for P1 type wart disease has been identified on chromosome 11 [142,143]. In a GWAS of 330 tetraploid samples, genotyping was performed using the 20 K SNP array for potato. After quality control and variant calling, genome-wide association analysis was conducted to identify significant loci. Twelve loci associated with resistance to P1 type wart disease were detected, all located between 0.78 Mb and 4.35 Mb on chromosome 11, including the verified resistant locus Sen1. Additionally, a specific marker, PotVar0067008, associated with Sen1 was identified, with a false-positive rate of 4% and a false-negative rate of 24% [145]. However, the closest associated locus, PotVar0067008, for wart disease resistance Sen1 still has probabilities of false positives and negatives, possibly due to the low or uneven coverage of the SNP chip used in the experiment, making it unable to detect closer and more associated loci. Moreover, recombination events may occur between PotVar0067008 and Sen1 in the chromosomes of the validation population, reducing the identification power of the associated locus for wart disease. Subsequent association analysis of resistance to P2, P6, and P18 types of wart disease identified two key loci, co-located with the locus Sen4 on chromosome 12 and the locus Sen5 on chromosome 5 [144].

The potato tuber moth, cyst nematode, and Colorado potato beetle are major quarantine pests of potato [181]. A GWAS was performed on 222 tetraploid potato materials infected by two types of cyst nematodes (G. pallida and G. rostochiensis), and twenty associated loci for cyst nematode resistance were detected, mainly concentrated on chromosomes 5, 10, and 11, which may be related to response regulators [136]. In another study, a GWAS for resistance to the Colorado potato beetle in 136 diploid materials detected all associated loci on chromosome 2, which are related to foliar leptine glycoalkaloid metabolism [112]. Fifteen loci associated with resistance to the potato tuber moth were detected on chromosomes 2, 3, 4, 6, 7, 9, 10, and 12, with loci on chromosome 2 related to genes encoding glycoalkaloid synthesis and loci on chromosome 7 related to the gene encoding the auxin response transcription activator ARF19 [133]. In the aforementioned studies, most experimental materials did not show significant resistance to pests, which undoubtedly increased the difficulty of screening specific resistance loci. However, GWAS successfully identified a series of loci associated with plant response to biotic stress genes, and in the association analysis of resistance to the Colorado potato beetle and the potato tuber moth, loci related to glycoalkaloid metabolism were identified.

3.8. Drought and Bruising Resistance

An in-depth analysis of the genetic structure of drought response is essential for breeding drought-tolerant potato varieties and enhancing the drought tolerance of existing varieties. By conducting an association analysis of drought traits in a population containing 104 diploid materials, thirty-eight loci associated with the drought index were identified on chromosomes 1, 3, 4, and 12. Further analysis identified 38 candidate genes encoding transcription factors, antioxidant enzymes, sugar transport proteins, and heat shock proteins [147]. During harvesting and transportation, tubers are often subjected to mechanical impact, leading to discoloration of the tuber flesh, which affects the appearance and flavor of the tubers [182]. An association analysis of tuber blackspot bruising traits after impact in 158 tetraploid materials detected a total of five associated loci. Among these, two loci on chromosome 8 are close to three genes encoding polyphenol oxidase, including the gene POT32, which is related to the production of melanin [146]. The POT32 gene may regulate the expression of stress-related genes, enhancing the plant’s ability to withstand environmental stress. This includes the activation or inhibition of key transcription factors, affecting the plant’s response to drought, salinity, and other stress conditions [183]. POT32 may also increase the expression of antioxidant enzymes such as superoxide dismutase and catalase, improving the plant’s tolerance to oxidative stress by reducing the accumulation of reactive oxygen species and mitigating cellular damage caused by stress. Additionally, the POT32 gene may influence the balance of plant hormones like abscisic acid (ABA) and auxin, which play crucial roles in regulating plant growth and stress. These research results provide important clues for an in-depth analysis of the genetic mechanism of tuber bruising after impact, and also provide references for improving the impact resistance of tubers, enhancing the appearance and flavor of tubers.

4. GWASs for Key Quality Traits in Potato

Starch, sugar, and organic acids in potato tubers play a crucial role in determining their quality. Starch, as the primary energy storage molecule, is directly related to the mouthfeel and digestibility of potato-processing products [146]. The content and composition of sugar affect the low-temperature sweetening during tuber storage and the browning reaction during processing [2]. Organic acids in tubers, such as chlorogenic acid and fumaric acid, are not only involved in plant metabolic pathways but also significantly impact the flavor and nutritional value of potato [184]. GWASs have been used to reveal genetic loci controlling these key components in various potato populations, providing important clues for elucidating their biosynthetic pathways and regulatory networks (

Table 2. Application of GWAS in the research of potato quality traits.

Traits	Population Origin	Population Size	Planting Environments	Number of Loci	Chromosome Location of Key Loci	References
Specific gravity	Diploid	98	Two years, one location	2	5, 9	Manrique-Carpintero et al., 2015 [113]
	Tetraploid	205	Two years, two locations	11	1, 5	Park et al., 2021 [124]
Starch content	Tetraploid	264/184	Three years, two locations	117	1, 11, 12	Schonhals et al., 2017 [185]
	Tetraploid	90	One year, one location	15	4, 5, 11	Khlestkin et al., 2020 [186]
Starch granule morphology	Tetraploid	90	One year, one location	38	1, 2	Khlestkin et al., 2020 [186]
	Tetraploid	90	One year, one location	14	1	Khlestkin et al., 2019 [187]
Glucose content	Tetraploid	162	Two years, three locations	4	4, 5	Massa et al., 2018 [107]
Anthocyanin content	Diploid	96	One year, one location	7	1, 10	Parra-Galindo et al., 2019 [165]
	Diploid	96	One year, one location	22	1, 10	Parra-Galindo et al., 2021 [166]
Glycoalkaloid content	Diploid	136	Two years, one location	23	2, 6, 7	Kaiser et al., 2021 [112]
	Tetraploid	185	One year, one location	21	2, 7, 11	Levina et al., 2020 [188]
	Tetraploid	275	Two years, two locations	603	1, 11	Vos et al., 2022 [189]
Protein content	Tetraploid	277	Three years, four locations	12	5	Klaassen et al., 2019 [190]
Phenol content	Diploid/tetraploid	404	One year, one location	23	9	Berdugo-Cely et al., 2022 [191]
Ascorbic acid content	Diploid/tetraploid	404	One year, one location	11	2, 3, 4, 6	Berdugo-Cely et al., 2022 [191]
Fumarate content	Tetraploid	258	One year, one location	11	1, 4, 5	Toubiana et al., 2020 [192]
Chlorogenic acid content	Diploid	271	Two years, one location	18	4, 8, 10	Yang et al., 2021 [193]
Citric acid content	Tetraploid	162	Two years, three locations	1	5	Massa et al., 2018 [107]
Folate content	Diploid	94	One year, one location	42	4, 6	Bali et al., 2018 [194]
Phosphorus content	Tetraploid	90	Three years, one location	4	5	Khlestkin et al., 2022 [195]
Calcium content	Tetraploid	151	Four years, one location	6	1, 3, 7, 8	Zorrilla et al., 2021 [141]
Cadmium content	Tetraploid	188	Two years, one location	4	3, 5, 6, 7	Mengist et al., 2018 [116]
Zinc content	Tetraploid	188	Two years, one location	5	1, 3, 5	Mengist et al., 2018 [116]
Antioxidant activity	Diploid/tetraploid	404	One year, one location	50	3, 4, 6, 9	Berdugo-Cely et al., 2022 [191]
Cold-induced sweetening	Diploid	110	Two years, one location	2	4, 6	Braun et al., 2017 [115]
Chip color	Tetraploid	110	Four years, two locations	17	2, 3, 9	Rak et al., 2017 [110]
	Tetraploid	274	Three years, one location	30	4, 10	Byrne et al., 2020 [196]
	Tetraploid	393	Two years, one location	2	4, 10	Jo et al., 2023 [40]
	Tetraploid	184	Public database	22	3, 7	Levina et al., 2023 [197]
	Tetraploid	384	Four years, one location	12	1, 3, 7	Pandey et al., 2023 [119]
Bud-end and stem-end fry color	Tetraploid	162	Two years, three locations	7	4	Massa et al., 2018 [107]

). These findings have enriched our understanding of the mechanisms behind the formation of potato tuber quality and provided new strategies and tools for molecular-assisted breeding and quality improvement.

4.1. Starch

Starch is the primary storage carbohydrate in potatoes and has a decisive impact on their economic value and processing performance. Starch not only serves as the main energy source for potatoes but its content and quality directly affect processing characteristics, such as how starch granule size and distribution influence the texture and consistency of the final product. Additionally, starch plays a crucial role during potato storage; high-quality starch enhances the stability and adaptability of potatoes during prolonged storage [198]. Multiple studies highlight the significant role of chromosome 5 in regulating starch content and specific gravity. In a GWAS of specific gravity in 205 tetraploid materials, genotyping was performed using the Infinium 8303 Potato Array, and eleven loci associated with tuber specific gravity were identified on chromosomes 1 and 5. Six of these loci on chromosome 5 were concentrated in the region of 43.55 cM to 54.04 cM [124]. Similar results were observed in an independent GWAS for starch content in 90 potato varieties, where genotyping was performed using the Illumina 22K SNP Potato Array (GGP Potato V3). Two loci associated with genes encoding aldehyde dehydrogenase and phenylalanine ammonia-lyase were identified at 49.30 cM and 49.57 cM on chromosome 5, respectively [186]. Additionally, in a GWAS of a diploid potato population consisting of 98 individuals, genotyping was conducted using the Infinium 8303 Potato Array. A locus associated with tuber specific gravity was identified at 12.9 cM on chromosome 5 [113]. Although differences in the positions of the loci detected on chromosome 5 were observed between diploid and tetraploid populations, the results indicated the importance of chromosome 5 in the regulation of starch content, especially the region from 43.55 cM to 54.04 cM, which may provide important clues for explaining the genetic mechanism of starch formation and accumulation in tubers. The processing characteristics of potato starch are influenced by the crystallinity and morphology of starch granules [199]. In a GWAS of the starch morphology structure in 90 tetraploid potatoes, thirty-eight loci related to the aspect ratio, roundness, and Feret’s diameter of starch granules were identified, mainly distributed on chromosomes 1 and 2, and ultimately located in genes encoding proteins that regulate the circadian rhythm [186]. The phosphorus content in potato starch is also an important indicator affecting its processing quality. Through association analysis, fourteen loci related to the phosphorus content in starch were detected on chromosomes 1, 4, 5, 7, 8, 10, and 11, among which the locus on chromosome 5 may be related to the gene encoding glucan water dikinase, and genes encoding carotenoid cleavage dioxygenase 7 and malate dehydrogenase were selected on chromosome 1 [187]. In the association analysis of starch phosphorus content, it was found that the suppression of glucan water dikinase expression would lead to the production of low-molecular-weight branched starch and small cracks in starch granules [200], indicating that starch morphology may be related to the circadian rhythm-related metabolic process and starch phosphorylation process. It provides a valuable direction for further interpreting the complex mechanism of starch granule morphology formation.

4.2. Sugar

The sugar content in potatoes has a significant impact on their quality, affecting not only the taste and flavor but also the processing characteristics and nutritional value. During processing, high levels of reducing sugars in potatoes can lead to the formation of acrylamide during high-temperature cooking, impacting food safety and taste. In cold storage, potatoes can undergo cold sweetening, where starch is converted into sugars, altering their flavor and texture and potentially making them less suitable for certain processing applications [2]. In a GWAS conducted on 162 tetraploid potato samples, genotyping was performed using the Infinium 8303 Potato Array. Further analysis revealed four loci associated with tuber glucose content on chromosomes 4, 5, 6, and 10. Within a 2 Mb region proximal to the locus on chromosome 4, genes encoding α-amylase, hexose transporter, invertase, F-box protein, and WD40 protein were identified, while the β-fructofuranosidase gene InvCD111 was identified on chromosome 10 [107]. The β-fructofuranosidase encoded by the InvCD111 gene plays a crucial role in the distribution of sugars and tuber development in potato tubers by catalyzing the hydrolysis of sucrose into glucose and fructose. This hydrolysis not only provides energy and structural precursors for the tubers but may also regulate the sugar content of the tubers by affecting the conversion rate of sucrose into glucose and fructose. Additionally, the activity of InvCD111 may indirectly influence the balance between starch and sugars under stress conditions such as cold storage, leading to the conversion of starch into sugars and affecting the cold-sweetening phenomenon in potatoes [201]. Cold-induced sweetening during storage, which affects the processing quality of potato, was significantly associated with two loci on chromosomes 4 and 6 in a GWAS of 110 diploid samples [115]. Despite using different ploidy levels, both studies utilized the Infinium 8303 Potato Array for genotyping and identified loci in close proximity on chromosomes 4 and 6, suggesting these loci may be tightly linked and are important regions for the development of molecular markers in the future. The Maillard reaction involving fructose, glucose, and free amino acids in tubers during frying is a primary cause of reduced fry color. Several studies evaluating and conducting GWASs on fry color have identified significant association loci on chromosomes 4 and 10, which are co-located with genes encoding UDP-glucose pyrophosphorylase, invertase, starch synthase, cell wall invertase, and other genes involved in starch and sucrose cleavage, synthesis, metabolism, and starch storage processes [40,196]. Additionally, the competitive allele-specific PCR (KASP) markers developed by Jo et al. have been effectively validated, providing strong technical support for screening varieties suitable for chip processing [40]. The application of these markers can more efficiently identify potato varieties with excellent processing qualities.

4.3. Glycoalkaloids

Glycoalkaloids in potatoes, while beneficial for plant defense, can impart bitterness and pose health risks due to their toxicity, necessitating careful breeding and agricultural practices to manage their levels for safe and palatable consumption [202]. In potato, glycoalkaloids primarily exist in the form of α-solanine and α-chaconine, and excessive glycoalkaloid content in tubers can be toxic to humans [203]. A GWAS performed on 275 tetraploid potato populations identified three loci (Sga1.1, Sga3.1, and Sga11.1) associated with glycoalkaloid content, which are co-located with known genes GAME6, GAME9, GAME11, SGT1, and SGT2 involved in glycoalkaloid synthesis [189]. Subsequently, two loci, Sgr7.1 and Sgr8.1, identified in a GWAS of the ratio of α-solanine to α-chaconine in 132 tetraploid potato materials, are associated with the genes SGT1 and SGT2. The study also validated Sga1.1, Sgr8.1, and Sga11.1 in a diploid F1 population [189]. It is particularly noteworthy that the study used AFLP technology to identify two new loci, Sga5.1 and Sga7.1, in a diploid mapping population that were not detected in the GWAS, possibly due to changes in population structure and insufficient coverage of the SNP chip. This result indicates the limitations of GWASs in detecting rare variations and structural variations. Additionally, three loci associated with β-chaconine were identified on chromosome 8 [188].

4.4. Mineral Elements

Mineral elements have a multi-faceted impact on potato quality traits. They not only enhance the nutritional value of potatoes, benefiting human health, but are also crucial for potato growth and development. Adequate mineral elements contribute to proper tuber development, increased disease resistance, improved taste and texture, and extended storage life [204]. Calcium in potato is mainly absorbed through the tuber, root, and stolon. A local deficiency of calcium can trigger cell death and tissue necrosis mechanisms, leading to discoloration and hollowing within the tubers [205,206]. An association analysis of calcium content in 151 breeding lines with Atlantic as the maternal parent identified six loci related to calcium content on chromosomes 1, 3, 4, 5, 7, and 8 and also identified five loci related to the incidence of hollow heart [141]. The content of cadmium and zinc in tubers is an important indicator for evaluating their nutritional quality [207,208]. To further investigate this trait, an association analysis population consisting of 188 parental lines was constructed, and genotyping was performed using the Infinium 8303 Potato Array to analyze the associations with cadmium and zinc content in tubers. A total of nine loci related to cadmium and zinc content in tubers were identified. A locus related to both cadmium and zinc content was found on chromosome 5, which is only 0.5 Mb away from the StCDF1 locus. This result suggests that the contents of cadmium and zinc in tubers may be associated with the maturity of potato, providing a new perspective for understanding the accumulation mechanism of mineral elements in tubers, and also helps to deepen the understanding of the accumulation of mineral elements in potato tubers, providing valuable references for the subsequent fine mapping of related genes [116].

4.5. Organic Acids

Chlorogenic acid is the most abundant phenolic compound in potato tubers and plays an important role in improving crop disease resistance. However, an excessive content of chlorogenic acid can affect the processing quality and flavor of tubers [209]. An association analysis population was constructed using 271 diploid materials from S. Stenotomum and S. Phureja, and a total of eighteen loci associated with chlorogenic acid content were detected on chromosomes 4, 8, and 10 [193]. In a GWAS of total phenolic content in tetraploid materials, two loci related to genes encoding glycosyltransferases were also found on chromosome 4 [191]. Fumaric acid plays an important role in maintaining intracellular ion balance, nutrient absorption, osmotic regulation, and antioxidation [210]. A GWAS of 258 tetraploid materials detected eleven loci associated with fumaric acid content in tubers, among which three were located within the genes encoding zinc finger protein ZAT2, RING domain protein, and a gene of unknown function [192].

5. Summary and Prospects

The potato, as the fourth most important food crop globally, presents a highly complex and heterozygous genome, which poses significant challenges to understanding the genetic mechanisms behind its traits. The high heterozygosity, inbreeding depression, and intricate genetic patterns make it crucial to employ modern molecular biology techniques to advance potato breeding and genetic improvement. GWASs have proven to be an effective strategy for exploring the genetic mechanisms of potato traits. By identifying loci associated with traits such as plant morphology, yield components, maturity, tuber shape, quality traits, disease resistance, insect resistance, and stress tolerance, GWASs provide a systematic approach to reveal the molecular genetic mechanisms underlying these traits. The assembly of high-quality reference genomes for both diploid and tetraploid potatoes has further facilitated the application of GWASs to study complex traits. Despite their advantages, GWASs have limitations, including high requirements for population size and phenotypic data, issues with population structure and kinship, limited detection of rare and structural variations, and potential false positives and negatives. Ongoing improvements in analytical methods, combined with other genetic approaches and experimental validations, will enhance the accuracy and utility of GWASs for molecular marker-assisted selection and genomic selection in potato breeding.

The results of various studies highlight the importance of the StCDF1 locus on chromosome 5 in regulating multiple key traits in potato, particularly those related to tuber maturity, mineral content, and stress responses. The co-location of loci related to tuber skin maturity with StCDF1, along with the identification of loci associated with cadmium and zinc content only 0.5 Mb away from this locus, suggests a strong link between tuber maturity and mineral accumulation. These findings provide valuable insights into the mechanisms of mineral element accumulation in potatoes, which could have significant implications for future breeding strategies. Additionally, StCDF1 has been implicated in regulating the potato’s response to photoperiod, influencing the timing of flowering and tuberization. This locus may also interact with hormones like gibberellins and phytochromes, potentially impacting tuber size and development by promoting cell division and elongation. The proximity of loci related to both tuber weight and late blight resistance near StCDF1 further supports its central role in potato growth and stress response, particularly under conditions such as temperature fluctuations and water scarcity. Despite these promising associations, the exact relationship between StCDF1 and these traits varies slightly across studies, with different distances reported between the identified loci and StCDF1. This highlights a limitation of GWASs, which may not always pinpoint the precise location of genes due to factors like population structure or analysis methods. Therefore, the molecular markers identified in these studies still require validation across a wider range of populations to confirm their accuracy and utility in breeding programs. Overall, the results emphasize the potential of StCDF1 as a major genetic locus that regulates multiple traits in potatoes, from maturity to mineral accumulation and disease resistance. Future research focusing on the fine mapping and functional characterization of this locus will be critical for its application in MAS and the development of improved potato varieties with enhanced productivity, quality, and resilience.

In addition, the integration of CRISPR gene-editing technology and multi-omics data provides a promising new direction for GWASs of potato traits. As the fourth most important food crop globally, the potato presents a highly complex and heterozygous genome, which poses significant challenges to genomic research. However, the efficiency and precision of CRISPR technology offer a powerful tool for validating candidate genes identified through GWASs. Traditional GWASs primarily rely on the statistical association of SNPs with target traits, enabling the identification of potential key loci. Nevertheless, it is often difficult to establish direct causal relationships between these loci and the biological traits of interest. CRISPR allows for the precise knockout or modification of these genomic loci, enabling researchers to directly assess their roles in controlling important traits such as disease resistance, tuber size, and starch content, thereby enhancing the practical utility of GWASs. The integration of multi-omics data, including transcriptomics, metabolomics, and proteomics, offers multi-dimensional insights into the complex traits of the potato. Various biological processes, such as growth, stress response, and metabolic regulation, are governed by intricate regulatory mechanisms that can be comprehensively elucidated through multi-omics analyses. By integrating transcriptomic and metabolomic data, researchers can better understand the molecular mechanisms underlying SNPs. For instance, transcriptomic data can reveal gene expression patterns associated with specific traits, while metabolomic data can identify the metabolic pathways influenced by these genes. This approach not only facilitates the accurate identification of functional genes associated with target traits but also provides precise targets for CRISPR-based editing. Looking forward, the combination of CRISPR and multi-omics data holds great potential for advancing research on potato traits. Through this integrative strategy, researchers can leverage GWASs to identify potential genetic variants and use CRISPR technology to rapidly validate the function of these variants, ultimately accelerating the genetic improvement of complex traits in potatoes. This method enhances the efficiency of functional gene screening and offers new tools and strategies for speeding up the breeding process.