Genetic Basis and Molecular Breeding Strategies for Processing Quality in Chestnut (Castanea spp.)

Abstract

Processing quality in chestnut (Castanea spp.) is a complex trait jointly determined by fruit development, postharvest metabolic changes, and responses to processing. However, its genetic basis and regulatory networks remain poorly understood. This review provides an integrated framework linking product-specific processing requirements with their biochemical basis, candidate genes, and molecular breeding strategies. Starch composition and fine structure primarily determine cooked texture, storage hardening, and digestibility; starch degradation and sugar metabolism affect sweetness and thermally induced flavor formation; and phenolic substrates, together with oxidative enzymes, determine browning potential and color stability. We review the biochemical basis underlying these traits and summarize candidate genes and regulatory pathways involved in starch synthesis and structural modification, starch-to-sugar conversion, enzymatic browning, flavor formation, and the accumulation of nutritional and bioactive compounds. Nevertheless, stable quantitative trait loci, favorable haplotypes, and causal genes associated with chestnut processing quality remain insufficiently validated. Future research should develop product-oriented, standardized phenotyping systems and integrate multi-environment genetic analyses, multi-omics network dissection, marker-assisted selection, genomic selection, and gene editing to elucidate the genetic mechanisms underlying chestnut processing quality and enable the precision breeding of processing-specific cultivars.

1. Introduction

Chestnut (Castanea spp.) is an important woody food crop and economic forest species that serves simultaneously as a nut crop, a starch source, and a functional food resource [1,2]. As consumption shifts from seasonal fresh consumption toward ready-to-eat, standardized, and diversified processed products, the chestnut industry is expanding from the traditional fresh nut market to the supply of raw materials for food processing. According to FAOSTAT, China produced approximately 1.529 million tonnes of chestnuts in 2024, accounting for about 72.9% of the global production of 2.098 million tonnes and ranking first worldwide [3], thereby providing an abundant raw-material base for the processing industry. Unlike high-fat nuts such as walnuts, hazelnuts, and almonds, chestnut kernels are characterized by high starch and low lipid contents, with starch being the predominant carbohydrate in kernel dry matter [4,5]. In addition to starch, chestnut kernels contain moderate amounts of protein and dietary fiber, together with minerals, vitamins, and phenolic compounds [1,2,4,5]. Chestnuts are now widely processed into sugar-roasted, steamed, boiled, and sterilized ready-to-eat kernels, as well as candied chestnuts, chestnut paste, chestnut flour, baking ingredients, and other starch-based foods, offering substantial potential for value addition [6,7].

As processing expands, variation in raw materials and inadequate processing suitability increasingly lead to inconsistent product quality. During processing and storage, chestnuts are prone to textural deterioration, kernel browning, flavor loss, and changes in digestibility [6,8]. These changes are jointly determined by the initial kernel composition, ripening and postharvest metabolism, processing conditions, and their interactions. Starch granule architecture and crystalline features, the amylose-to-amylopectin ratio, and molecular order affect water uptake and swelling, gelatinization, gel formation, retrogradation, and digestion, thereby determining product texture and digestibility [9,10,11]. Soluble sugars, including sucrose, glucose, fructose, and maltose, determine sweetness and also serve as precursors for the Maillard reaction and caramelization, thereby affecting product color and flavor [8,12]. Phenolic substrates, the activities of polyphenol oxidase (PPO) and peroxidase (POD), and cellular redox status jointly regulate enzymatic browning and thereby affect product color [13,14]. Postharvest starch degradation and sugar accumulation alter the starch-to-sugar ratio and water distribution, which in turn affect sweetness and mealy–glutinous texture [15]. Therefore, genetic dissection of these product-dependent traits and the establishment of standardized evaluation systems are prerequisites for breeding processing-specific cultivars.

Current chestnut breeding programs mainly target yield, nut size, maturity, adaptability, stress resistance, and fresh-eating quality, whereas the breeding of cultivars specifically suited to sugar roasting, steaming or boiling, ready-to-eat kernels, candying, flour production, and baking remains limited [16,17]. Chestnut is characterized by a long juvenile phase, complex genetic background, high heterozygosity, slow generation turnover, and strong genotype-by-environment interactions. Processing quality is further governed by multiple genes and metabolic pathways and is also affected by maturity, storage, and processing conditions; it therefore generally shows continuous variation typical of complex quantitative traits [16,18,19]. Evaluation based only on mature-stage phenotypes and a single processing test cannot reliably identify superior genotypes in early generations or distinguish genetic effects from environmental or processing effects [19,20].

The increasing availability of high-quality reference genomes, pan-genomes, population resequencing data, transcriptomes, and metabolomes has provided a foundation for mining genetic variation, mapping quality-related loci, dissecting regulatory pathways, and identifying candidate genes [21,22,23,24,25]. Marker-assisted selection (MAS) and genomic selection (GS) can move part of the selection process to the seedling stage, thereby reducing the cost of evaluating complex quality traits in perennial woody crops [20,26]. Nevertheless, current chestnut research still focuses mainly on genomic resource development, differential expression analysis, and candidate gene screening [15,23,27,28,29]. Standardized phenotypes, stable quantitative trait loci (QTLs), favorable haplotypes, and validated causal genes remain scarce for traits such as cooked texture, color, flavor, and nutritional functionality. A complete pipeline linking processing quality requirements to precise breeding has not yet been established in chestnut.

Focusing on the breeding of processing-specific chestnut cultivars, this review synthesizes product-specific quality requirements, their biochemical and genetic bases, and relevant molecular breeding strategies. Relevant literature published up to 2026 was searched in Web of Science Core Collection, Scopus, PubMed, Google Scholar, and CNKI using combinations of “Castanea” or “chestnut” with terms related to processing quality, starch and sugar metabolism, texture, browning, flavor, nutritional components, postharvest metabolism, genomics, multi-omics, QTL, GWAS, marker-assisted selection, genomic selection, and gene editing. Relevant peer-reviewed studies were included, whereas duplicate and marginally relevant records were excluded. Direct evidence from Castanea species was prioritized; evidence from other horticultural crops was used only to illustrate conserved mechanisms or transferable approaches not yet verified in chestnut. Genes supported only by pathway homology, differential expression, or co-expression were treated as candidate rather than causal genes. The review integrates these evidence levels into a product-oriented framework for improving chestnut processing quality through molecular breeding.

2. Core Target Traits of Chestnut Processing Quality and Their Biochemical Basis

Different processed chestnut products have distinct requirements for kernel texture, color, flavor, nutritional functionality, and processing stability (Figure 1). According to processing methods, chestnut products can be grouped into sugar-roasted or other roasted products, steamed, boiled, and ready-to-eat products, candied products and chestnut paste, flour and baking ingredients, and functional products. Sugar-roasted products emphasize sweetness, caramel-like and nutty aromas, moderate nut size, and a mealy–glutinous texture. Steamed, boiled, canned, and sterilized ready-to-eat kernels require a high proportion of intact kernels, limited browning, and good texture retention after heat treatment. Candied chestnuts and chestnut paste require uniform syrup penetration, a fine texture, and stable color. Chestnut flour and baking ingredients depend more strongly on starch content, the amylose-to-amylopectin ratio, gelatinization and retrogradation properties, and digestibility.

Table 1. Core quality requirements, key biochemical basis, and cultivar-level evidence for different processed chestnut products.

Product Category	Core Quality Requirements	Key Biochemical Basis	Representative Cultivars	References
Sugar-roasted and roasted products	High sweetness; moderate nut size; pronounced caramel-like and nutty aromas; mealy–glutinous texture with moderate hardness; uniform golden color.	Content and conversion of soluble sugars, particularly sucrose; amylose and amylopectin composition, starch gelatinization, and partial starch degradation; Maillard reaction and caramelization; formation of volatile aldehydes, alcohols, furans, and related compounds.	In a comparative evaluation of different cultivars, ‘Yanshan Duanzhi’ and ‘Dabanhong’ showed favorable performance under roasting and sand-frying, while ‘Zipo’ showed favorable performance under roasting.	[17,30,31]
Steamed, boiled, and ready-to-eat products (including frozen and canned products)	High intact-kernel rate and low breakage; good texture retention after cooking or sterilization; limited browning and good storage stability.	Starch gelatinization, gel-network formation, and retrogradation behavior of amylose and amylopectin; polyphenol content, PPO activity, and related oxidation reactions.	In a comparative evaluation of different cultivars, ‘Zipo’ showed favorable performance under steaming.	[17,30,31]
Candied products and chestnut paste	Uniform syrup penetration and uptake; homogeneous and smooth mouthfeel with little coarse-fiber sensation or graininess; suitable viscosity, cohesiveness, and spreadability; stable color.	Syrup penetration and water migration; starch composition and degree of gelatinization; cell-wall and dietary-fiber composition and fragmentation behavior; enzymatic and non-enzymatic browning.	‘Martainha’, ‘Longal’, and ‘Judia’ showed good potential for candied products under the tested conditions.	[32]
Flour and baking-ingredient products	High starch content and an appropriate amylose-to-amylopectin ratio; favorable gelatinization, water absorption, and water-holding capacity; low retrogradation tendency; stable flour-processing performance.	Starch content and amylose/amylopectin composition; granule and crystalline structures; gelatinization temperature, peak viscosity, and setback; interactions among starch, water, and other components.	Chestnut flours from ‘Balestrera’ and ‘Rossera’ showed good performance for gluten-free baked foods and fresh pasta under the tested conditions.	[33]
Functional products	High resistant-starch content, a low starch digestion rate, and a low estimated glycemic index (eGI); good retention of bioactive compounds such as polyphenols and maintenance of antioxidant activity.	Formation and retention of resistant starch; starch digestion kinetics; retention of polyphenols, flavonoids, vitamin C, and other bioactive compounds.	‘Dabanhong’ was used in studies of modified low-eGI chestnut flour; this does not establish intrinsic low-eGI characteristics of the cultivar.	[11,34,35]

Note: Cultivar suitability is described according to the available evidence and should not be generalized beyond the evaluated conditions.

summarizes the core quality requirements and representative cultivar characteristics for the major categories of processed chestnut products.

2.1. Starch Properties and the Formation of Textural Quality

2.1.1. Starch Composition and Fine Structure

The amylose-to-amylopectin ratio, amylopectin chain-length distribution, and molecular order of chestnut starch are key intrinsic determinants of cooked texture and processing suitability. Highly glutinous cooked kernels generally have a lower amylose content and exhibit higher local molecular order and relative crystallinity. During cooking, their solubilized starch contains a lower proportion of B1 chains and a higher proportion of B2 chains, suggesting that an appropriate amylopectin chain-length profile and ordered structure favor a soft, viscoelastic texture with moderate adhesiveness [36]. Higher amylose content promotes molecular reassociation and gel-network formation after gelatinization and increases retrogradation during cooling and storage, resulting in a firmer texture, greater hardness, and lower adhesiveness [9,37,38]. However, mealiness and friability are also affected by total starch content, moisture content and distribution, cell-wall structure, and cooking method [8,39,40]. Therefore, the amylose-to-amylopectin ratio and the fine structure of amylopectin are important indicators of chestnut textural quality and processing suitability. Lower amylose content and an appropriate amylopectin architecture may favor the soft and glutinous texture required by some roasted and paste products, whereas flour and baking applications may require a different balance between gel strength and thermal stability [33,41].

2.1.2. Starch Gelatinization and Retrogradation

Starch gelatinization and retrogradation directly determine texture formation, thermal-processing stability, and storage quality in chestnut products. During heating, starch granules absorb water and swell, crystalline structures disintegrate, and part of the starch molecules leach out, increasing system viscosity. During cooling, the molecular chains reassociate to form a gel network [41,42]. Peak viscosity reflects water absorption, swelling, and thickening during gelatinization; breakdown reflects stability under continued heating and shear; and final viscosity and setback reflect gel formation and molecular reassociation during cooling [9,10,43,44]. Product requirements differ. Sugar-roasted, boiled or steamed, sterilized ready-to-eat kernels require moderate gelatinization, good thermal stability, and a low retrogradation tendency, in order to limit softening during high-temperature treatment and hardening during storage, and achieve a mealy–glutinous texture [17,30,31,39]. Chestnut paste and flour-based products require a balance among thickening, gelatinization, spreadability, and resistance to retrogradation [32,33,41,42,43,44]. Gelatinization temperature, peak viscosity, breakdown, final viscosity, and setback should therefore be treated as a combined set of breeding phenotypes.

2.2. Browning Reactions and Control of Color Quality

Browning is a key process governing the formation and stability of color in chestnut products and includes both enzymatic and non-enzymatic reactions. Enzymatic browning occurs mainly during shelling, cutting, comminution, insufficient blanching, and postharvest storage. Tissue damage disrupts the compartmental separation of phenolic substrates and oxidative enzymes, allowing phenolics to be converted into quinones by PPO and POD in the presence of oxygen; subsequent polymerization produces brown or black pigments and decreases kernel lightness and yellowness [13]. For ready-to-eat, canned, and candied kernels, raw materials with low browning potential should combine low substrate oxidation potential, appropriate oxidase activity, and strong antioxidant capacity. Postharvest treatments, such as hot-water treatment, can reduce quality deterioration during storage [45]. However, treatment effects should be distinguished from genetically determined variation in intrinsic browning potential.

Non-enzymatic browning occurs mainly under the high-temperature, low-moisture conditions used for sugar roasting, baking, and related processes. Reducing sugars and amino compounds participate in the Maillard reaction, while sugars can also undergo caramelization, producing golden-yellow to light-brown coloration and generating furans, pyrazines, and other compounds associated with caramel-like and roasted-nut aromas [8,12,30]. Excessive browning reactions, however, cause overly dark color, texture hardening, and undesirable odors, and may promote the formation of heat-induced by-products such as acrylamide and 5-hydroxymethylfurfural [46]. Ready-to-eat, canned, and candied products therefore prioritize low enzymatic browning, whereas sugar-roasted and baked products require an appropriate, rather than minimal, browning potential. Evaluation should integrate measurements of reducing sugars, free amino acids, and heat-reaction products.

2.3. Sugar Metabolism, Volatile Compounds, and Flavor Formation

Sugar metabolism determines the natural sweetness of chestnuts and provides precursors for flavor formation during thermal processing. Sucrose is the predominant soluble sugar in chestnut, whereas glucose, fructose, and maltose occur at lower concentrations [47,48]. These sugars differ in sweetness intensity and thermal reactivity, thereby contributing differently to sweetness and heat-induced flavor formation [49]. At maturity, high-sugar cultivars may contain approximately 1.5 times as much soluble sugar as low-sugar cultivars, a difference associated with enhanced expression of genes involved in starch degradation and sucrose synthesis during late kernel development [47]. Postharvest cold storage can promote the conversion of starch into soluble sugars, increasing sweetness and influencing subsequent Maillard reactions by altering the reducing-sugar profile [15].

The characteristic aroma of chestnut is formed mainly during thermal processing. Different processing methods alter the degradation and conversion of sugars, amino acids, and lipids and consequently produce distinct volatile profiles. Aldehydes, alcohols, ketones, esters, furans, and other heterocyclic compounds provide an important chemical basis for distinguishing the flavor profiles generated by different processing methods [8,12,30,31]. Sugar roasting and baking promote the Maillard reaction, caramelization, Strecker degradation, and lipid oxidation, thereby enhancing caramel-like, nutty, and roasted aromas [49]. Steaming, boiling, and sterilization mainly affect the release, transformation, and retention of existing volatiles [12,31]. Raw materials intended for sugar roasting must therefore balance sweetness, reducing-sugar and amino-acid composition, and thermal-reaction potential, whereas steamed, canned, and ready-to-eat products require greater retention of sweetness and aroma, together with effective control of oxidative off-flavors.

2.4. Functional Components and Nutritional Quality

The nutritional functionality of chestnut can be evaluated from the perspectives of starch digestibility and antioxidant activity. The proportions of rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS), together with the ordered structure of starch, affect in vitro digestion rates and the eGI. Pullulanase debranching combined with heat-moisture treatment reduced the eGI of chestnut flour from 67.3 to 49.1–53.0 [34]. Plasma-jet treatment combined with lipase hydrolysis reduced the eGI from 62.07 to 44.87 and promoted the formation of amylose and starch–lipid complexes [50]. These results demonstrate that processing modifications can regulate starch digestibility, but they do not establish that the cultivars used possess a stable, genetically determined low eGI. Steaming, boiling, and sugar roasting also modify digestibility through starch gelatinization and disruption of crystalline structures [51]. Screening for functional cultivars should therefore compare genotypes under standardized processing conditions and evaluate native resistant-starch content separately from processing-induced effects.

In addition to starch, chestnut contains polyphenols, flavonoids, tannins, vitamins, and other bioactive components [1]. Their contents and retention during processing are jointly affected by genotype, maturity, and processing conditions. Moderate heat treatment may increase some antioxidant indices by disrupting cellular structures and releasing bound phenolics, whereas excessive heating causes losses of vitamins, carotenoids, and some free phenolic compounds [52,53,54]. Functional product development should therefore evaluate the baseline content of bioactive compounds in raw materials, their retention during processing, and their bioaccessibility in the final product.

3. Genetic Basis and Candidate Genes of Processing-Quality Traits

Most chestnut processing-quality traits are complex quantitative traits jointly controlled by multiple genes and environmental factors. QTL mapping and genome-wide association studies (GWAS) using molecular marker data provide important approaches for dissecting their genetic architecture and identifying loci associated with phenotypic variation. These loci can then be integrated with transcriptomic and metabolomic data to prioritize candidate genes and regulatory networks, followed by functional validation. However, stable QTLs directly associated with processing-quality traits remain scarce in chestnut. The increasing availability of chromosome-scale reference genomes, pan-genomes, and population resequencing datasets in Castanea provides important genomic resources for genetic variation analysis, locus discovery, and candidate gene identification. In addition, genome-wide simple sequence repeat (SSR) markers provide complementary molecular tools for germplasm characterization and genetic analysis [22,23,24]. In chestnut, GWAS has identified loci associated with horticultural traits such as nut weight and leaf length. A study of 151 Chinese chestnut accessions identified 45 significant associations and functionally validated CmAP2 and CmCIB1, demonstrating the feasibility of population-association mapping for candidate gene discovery in chestnut [25]. By contrast, genetic mapping of processing-quality traits remains limited, and current evidence is derived mainly from genomic and multi-omics analyses. For example, SBE has been identified as a hub candidate gene for amylopectin synthesis [29]. Transcriptomic studies have identified candidate genes associated with sugar accumulation [47,48], whereas integrated omics and genome analyses have revealed candidate structural genes and transcription factors associated with polyphenol and anthocyanin accumulation [28,55]. Accordingly, candidate genes are discussed according to the major processing-quality traits with which they are associated.

3.1. Genetic Evidence for Starch Metabolism and Textural Quality

Chestnut starch quality is jointly regulated by substrate supply, starch synthesis and structural remodeling, and starch degradation during maturation and postharvest storage (Figure 2). Based on conserved starch-biosynthetic pathways and chestnut transcriptomic evidence, genes encoding sucrose synthase (SUS), phosphoglucomutase (PGM), and the glucose-6-phosphate translocator (GPT) are considered candidates associated with carbon allocation and precursor supply, whereas genes encoding ADP-glucose pyrophosphorylase (AGPase) are associated with ADP-glucose synthesis. The gene encoding granule-bound starch synthase I (GBSS1) is a candidate associated with amylose synthesis, whereas genes encoding soluble starch synthase (SS), starch branching enzyme (SBE), and debranching enzymes such as isoamylase (ISA) are potentially associated with chain elongation, branch formation, and structural remodeling of amylopectin [29,56]. Transcriptomic analyses of developing kernels have associated the expression of AGP2, AGP3, GBSS1, SS1, SS3, SBE2.1, SBE2.2, ISA1, ISA2, ISA3, PHO, and related genes with starch accumulation. Their expression differs according to developmental stage, cultivar, and pollination combination, indicating the joint effects of development and genetic background [56,57,58]. Co-expression network analysis further identified SBE as a hub gene in amylopectin synthesis [29]. At the transcriptional level, CmbZIP13 and CmbZIP35 can bind to the promoters of CmISA2 and CmSBE1_2, respectively, indicating that basic leucine zipper (bZIP) transcription factors may contribute to variation in starch fine structure by regulating genes involved in starch branching and debranching [59]. During maturation and cold storage, the expression of genes encoding glucan water dikinase (GWD), α-amylase (AMY), β-amylase (BMY), starch phosphorylase (PHO), and related genes has been associated with starch degradation and sugar accumulation [15,47]. These findings define a candidate network, but the causal effects of these genes on amylose proportion, amylopectin chain-length distribution, gelatinization and retrogradation, and cooked texture require confirmation through analyses of allelic variation and functional validation.

3.2. Genetic Evidence for Enzymatic Browning and Color Quality

Enzymatic browning in chestnut kernels is jointly affected by the supply of phenolic substrates, PPO and POD activities, cellular compartment integrity, and redox status [13,14] (Figure 3). The chestnut PPO gene family has been characterized, and three family members have been identified. The chestnut PPO gene family has been characterized, with three family members identified. Expression analyses across cultivars detected CmPPO1; however, PPO activity alone did not directly explain differences in browning degree [60]. A recent genome-wide analysis identified 98 class III peroxidase (CmPRX) genes in C. mollissima; however, the study focused on alkaline-stress responses, and their involvement in kernel browning remains unvalidated [61]. Studies of the spatiotemporal expression patterns and functional divergence of PPO family members in banana, pecan, and walnut indicate that individual PPO genes may make distinct contributions in different tissues and at different stages of browning [62,63,64].

In addition to oxidase genes, genes in the phenylpropanoid and flavonoid pathways, including phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate: CoA ligase (4CL), chalcone synthase (CHS), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), and anthocyanidin reductase (ANR), may indirectly affect browning potential by regulating the types and concentrations of phenolic substrates [28]. Studies of polyphenol metabolism in chestnut have identified candidate structural genes and transcription factors potentially associated with polyphenol accumulation, including MYB and basic helix–loop–helix (bHLH) transcription factors. Heterologous expression of the flavonol synthase gene CmFLS has provided preliminary evidence for its role in regulating flavonol accumulation [28].

3.3. Genetic Evidence for Sugar Accumulation and Flavor Formation

Chestnut sweetness is jointly regulated by starch degradation, sucrose synthesis and cleavage, and sugar transport (Figure 4). Transcriptomic comparisons of cultivars with contrasting sugar contents identified differentially expressed genes such as BAM, INV (invertase), PGK (phosphoglycerate kinase), and MDH1 (malate dehydrogenase I), indicating that cultivar differences in sugar accumulation are associated with starch conversion and glycolysis [48]. During late development, high-sugar cultivars show higher expression of genes involved in starch degradation or the transport of degradation products, including GWD, PWD (phosphoglucan water dikinase), LDA (limit dextrinase), ISA, PHS1 (α-glucan phosphorylase), and MEX1 (maltose excess I) [47]. During cold storage, AMY, BMY, GLGP (glycogen phosphorylase), SPS (sucrose-phosphate synthase), INV, HK (hexokinase), BGLB (β-glucosidase), and related genes show differential expression associated with starch degradation, sucrose accumulation, and hexose conversion, implicating these genes in cold-induced sweetening in chestnut [15]. Transporters such as MEX1 and glucose transporters (GLTs) may mediate the subcellular transport of maltose and glucose, but the functions of sucrose transporters (SUTs), Sugars Will Eventually be Exported Transporters (SWEETs), and monosaccharide transporters (MSTs) in source–sink transport and kernel sugar accumulation in chestnut remain poorly characterized.

Research on aroma-related genes currently focuses mainly on analyses of volatile compounds after processing. The terpene synthase (TPS) gene family has been characterized in chestnut, and members involved in sesquiterpene synthesis have been functionally studied. Their confirmed roles, however, are mainly related to insect defense, and their contribution to the processing aroma of edible kernels has not been demonstrated [65]. In other fruits, LOX (lipoxygenase), ADH (alcohol dehydrogenase), AAT (alcohol acyltransferase), and related genes participate in the formation of lipid-derived volatiles [66], but their roles in chestnut processing flavor remain inferred from pathway homology. Future studies should integrate transcriptomic data from developing kernels, metabolomic profiles of non-volatile precursors, volatile profiles generated by thermal processing, and sensory evaluations. Genotype-metabolite association analyses and functional validation will be required to distinguish candidate genes merely associated with volatile accumulation from causal genes with direct regulatory functions.

3.4. Genetic Evidence for Nutritional Quality and Functional Components

The genetic basis of nutritional functionality in chestnut primarily involves starch digestibility and the formation of antioxidant compounds. Candidate pathways associated with functional components are summarized in Figure 5. The proportions of resistant starch and slowly digestible starch are influenced by amylose content, amylopectin chain-length distribution, crystalline structure, and starch–lipid complexes. Upstream determinants may include genes involved in starch synthesis and structural modification, such as GBSS1, SS, SBE, and ISA. Developmental transcriptomic analyses and genomic co-expression networks have identified multiple candidate genes associated with starch synthesis and highlighted SBE as a putative hub candidate associated with amylopectin synthesis [29,56,67]. However, stable, direct effects of these genes on resistant-starch content or eGI have not been demonstrated in genetic populations, and these genes should therefore not be designated as “low-eGI genes.”

Chestnut polyphenols, flavonoids, and tannins are produced mainly through the phenylpropanoid and flavonoid pathways. Integrated metabolomic and transcriptomic analyses have identified candidate genes, including PAL, C4H, 4CL, CHS, CHI (chalcone isomerase), F3H (flavanone 3-hydroxylase), F3′H (flavonoid 3′-hydroxylase), FLS (flavonol synthase), DFR, LAR (leucoanthocyanidin reductase), LDOX, and ANR, together with regulatory and modifying factors such as MYB, bHLH, ethylene response factors (ERFs), and UDP-dependent glycosyltransferases (UGTs). Heterologous expression of CmFLS promotes flavonol accumulation and provides preliminary functional evidence [27,28]. By contrast, molecular research on vitamin C biosynthesis in chestnut kernels remains limited. GME (GDP-D-mannose 3′,5′-epimerase), GGP (GDP-L-galactose phosphorylase), GPP (L-galactose-1-phosphate phosphatase), GalDH (L-galactose dehydrogenase), and GLDH (L-galactono-1,4-lactone dehydrogenase) can currently be regarded only as pathway-based candidate genes [68].

Representative candidate genes and regulators associated with major processing-quality traits, together with their current evidence status, are summarized in

Table 2. Evidence status of representative candidate genes and regulators for chestnut processing-quality traits.

Processing-Quality Trait	Representative Candidate Genes/Regulators	Evidence Status	References
Starch metabolism and textural quality	AGP2/3, GBSS1, SS1/3, SBE, ISA; CmbZIP13, CmbZIP35	Transcriptomic/co-expression evidence; Y1H promoter-binding evidence for CmbZIP13–CmISA2 and CmbZIP35–CmSBE1_2.	[29,56,59]
Enzymatic browning and color quality	CmPPO family; CmPRX family; PAL, C4H, 4CL, CHS; MYB/bHLH factors	PPO/PRX gene-family identification and multi-omics association; individual roles in kernel browning remain largely unvalidated, with additional PPO evidence mainly comparative.	[28,60,61,62,63,64]
Sugar accumulation and flavor formation	BAM, GWD, PWD, MEX1, AMY, BMY, SPS, INV; LOX, ADH, AAT	Transcriptomic/physiological association for sugar traits; LOX/ADH/AAT evidence is comparative.	[15,47,48,66]
Nutritional quality and functional components	CmFLS; MYB/bHLH/ERF/UGT factors; GME, GGP, GPP, GalDH, GLDH	Integrated multi-omics evidence; heterologous functional evidence for CmFLS; vitamin C candidates remain pathway-based and unvalidated in Castanea.	[27,28,68]

Note: The genes and regulators listed are representative rather than exhaustive; evidence from non-Castanea species is considered comparative.

.

Overall, the strength of evidence supporting candidate genes remains uneven across processing-quality traits. Most candidates are supported primarily by transcriptomic, co-expression, or multi-omics associations, whereas CmbZIP13/CmbZIP35 and CmFLS have additional promoter-binding or heterologous functional evidence. Because few candidates have been independently validated, their causal effects on processing phenotypes remain unresolved. These candidates should therefore be regarded as priorities for further validation rather than confirmed breeding targets.

4. Applicability and Strategies of Molecular Breeding Technologies for Improving Processing Quality

Translating candidate gene discoveries into breeding applications requires adequate genetic and functional validation. The applicability of major molecular breeding approaches to chestnut processing-quality improvement is summarized in Figure 6.

4.1. Marker-Assisted Selection for Processing Quality

MAS is suitable for traits controlled by major genes or large-effect loci that have been validated across multiple environments. Molecular markers tightly linked to favorable alleles, particularly SNP-based markers or functional markers that have been validated, can be used for genotypic screening at the seedling stage, thereby reducing the number of individuals requiring long-term field and processing evaluations [26]. Genomic, population resequencing, and transcriptomic resources in Castanea provide a basis for candidate gene identification and marker development [23,69]. Trait-associated loci identified through QTL mapping or GWAS can provide a basis for marker development, but their application in MAS requires stable marker–trait associations and independent validation [25]. Genome-wide SSR markers are particularly suitable for germplasm identification, genetic diversity and kinship analyses, and core germplasm construction; however, their use in precise selection of processing-quality traits still requires stable marker–trait associations [24].

For processing quality, locus discovery can focus on starch synthesis, sugar metabolism, and browning potential. Nevertheless, candidate genes such as SBE, GBSS1, SUS, BAM, PAL, and FLS cannot be directly used as selection markers [27,29,70]. Functional variants should be developed into functional markers or favorable haplotypes only after showing stable associations with standardized phenotypes across multiple populations, years, and processing conditions and being validated in independent germplasm [70]. At present, MAS is better suited to a limited number of component traits with large effects and should not be used alone to select highly polygenic traits such as texture, color, flavor, and nutritional quality.

4.2. Genomic Selection and Prediction Models for Processing Quality

GS is more suitable for complex traits such as texture, color, flavor, and nutritional quality, which are controlled by many small-effect genes. Its core requirement is a training population with both genome-wide marker data and standardized processing phenotypes. Genomic best linear unbiased prediction (GBLUP), Bayesian, reproducing kernel Hilbert space (RKHS), or machine-learning (ML) models can then be developed, and seedlings that have not yet undergone processing evaluation can be selected based on genomic estimated breeding values (GEBVs) [20,71]. Studies in apples have shown that GS can achieve moderate to high prediction accuracy for quality traits such as firmness, soluble solids, and color, although model performance depends on training-population size, relatedness, heritability, and environmental effects [72,73]. For chestnut, prediction models must also account for maturity, storage treatment, and processing protocol; otherwise, models may capture technological differences rather than stable genetic effects.

No mature GS model for Chinese chestnut processing quality that has been validated in an independent population has yet been reported. American chestnut restoration programs demonstrate the value of integrating large-scale phenotyping, genotyping, and genomic prediction in long-generation Castanea breeding [74,75]. Future chestnut studies should establish training populations evaluated across years and locations under standardized postharvest and processing conditions. Multi-trait, multi-environment, and reaction-norm models should be prioritized to predict multiple quality indicators simultaneously, and model transferability should be assessed through cross-year and cross-population validation.

4.3. Gene Editing for Developing New Processing-Quality Germplasm

Gene editing is appropriate when causal genes and their target effects are well defined. It can directly generate desired variants while reducing the introduction of unfavorable linked genomic segments during conventional crossing. CRISPR/Cas9 editing targeting PDS was first achieved in European chestnut in 2021 [76], and a DNA-free editing method based on ribonucleoprotein delivery was established in 2022 [77]. In 2026, Cspmr4-knockout materials showed enhanced tolerance to Phytophthora root rot, indicating that gene editing in Castanea has advanced from a technical proof of concept to the improvement of functional traits [78].

No gene-edited chestnut line directly targeting processing quality has yet been reported with confirmed stable inheritance and validated product performance. Once causal relationships between target genes and processing-quality traits have been established, gene editing could provide an effective strategy for trait improvement. However, potential pleiotropic effects and product-specific consequences must be carefully considered. Editing starch-biosynthetic genes can substantially alter amylose content, amylopectin structure, gelatinization properties, and resistant-starch formation [79,80,81]. The effects of individual target genes should therefore be evaluated separately according to the quality requirements of different chestnut products. PPO editing may reduce browning while also altering pathogen responses, as demonstrated in potato [82]. The routine application of genome editing in chestnut remains constrained by the efficiency of transformation and plant regeneration; assessment of off-target mutations and chimerism, long-term evaluation in perennial plants, and validation under actual processing conditions are also essential before its application to processing-quality improvement.

These approaches are complementary rather than interchangeable. GWAS is used primarily for locus discovery, MAS for validated large-effect loci, GS for polygenic traits, and gene editing for experimentally supported causal genes. Their practical application in Castanea therefore depends on available phenotyping and genotyping resources, validation requirements, and, for gene editing, transformation and regeneration capacity.

5. A Molecular Design Breeding Strategy for Processing-Specific Cultivars

5.1. Standardized High-Throughput Phenotyping and AI-Assisted Evaluation of Processing Quality

Breeding processing-specific chestnut cultivars first requires a product-oriented, stable, and reproducible phenotyping system. Chestnut processing quality encompasses multidimensional traits, including texture, color, flavor, and nutritional functionality, and cannot be fully represented by a single compositional or sensory index [6]. In recent years, quality evaluation has expanded from conventional nutritional analysis to the integrated characterization of physicochemical indices, texture, flavor, and consumer sensory responses. Wang et al. established a comprehensive quality-evaluation model for 24 Yanshan chestnut cultivars [83]. Xu et al. used an electronic eye, electronic nose, electronic tongue, and GC–MS to differentiate the sensory profiles of chestnuts processed by different methods [12]. Kuang et al. combined check-all-that-apply (CATA) evaluation, physicochemical indices, and volatile analysis to identify key attributes affecting consumer preferences for frozen chestnuts [84].

For molecular breeding, product-specific indicators should be established according to product type. Sugar-roasted products should emphasize sweetness, color, mealy–glutinous texture, and characteristic aroma. Steamed, boiled, and ready-to-eat products should emphasize intact-kernel rate, texture retention after sterilization, and resistance to browning. Chestnut paste, candied products, and chestnut flour should be evaluated for starch composition, pasting and retrogradation properties, water absorption and retention, and textural fineness [32]. Machine vision, near-infrared spectroscopy, hyperspectral imaging, intelligent sensory systems, and flavoromics can increase phenotyping efficiency [85]. Random forests algorithms, support vector machines, and interpretable machine-learning methods can resolve nonlinear relationships among multiple indicators [86,87]. However, AI models must be based on standardized sampling, adequate sample sizes, and independent cross-year validation, and should report feature contributions and prediction uncertainty. Otherwise, high apparent accuracy may merely reflect differences among batches, production regions, or processing conditions.

5.2. A Processing-Quality-Oriented Roadmap for Molecular Design Breeding

Molecular design breeding of processing-specific chestnut cultivars should begin with product requirements and establish a closed-loop pipeline encompassing target-trait definition, standardized phenotyping, multi-omics genetic analysis, breeding decisions, and product validation [22,23,88,89] (Figure 7). First, core targets such as mealy–glutinous texture, resistance to retrogradation, sweetness, resistance to browning, characteristic flavor, and nutritional functionality should be defined according to the quality requirements of sugar-roasted, steamed, boiled, candied, paste, flour, and functional products. Second, starch composition and pasting parameters, sugar profiles, PPO/POD activities, browning indices, volatile compounds, and in vitro starch digestibility and digestion-resistance parameters should be converted into reproducible breeding phenotypes. Reference genomes, pan-genomes, population resequencing data, transcriptomes, and metabolomes should then be integrated to identify key loci, favorable haplotypes, and candidate regulatory networks. Digital technologies, artificial intelligence, and data-science approaches can facilitate the integration and analysis of high-dimensional multi-omics datasets, helping to identify complex genotype–phenotype relationships and prioritize breeding-relevant features [86,90]. Existing Castanea genomic and multi-omics data platforms provide a foundation for this process, but the lack of systematic associations between processing-quality phenotypes and genomic data remains the principal limitation.

After validation, breeding decisions should be matched to trait architecture. Stable major-effect variants can support MAS, genome-wide information can be used for GS of complex quantitative traits, and experimentally validated causal genes may be considered for gene editing [20,26,90]. Selected or edited materials should subsequently undergo standardized processing-quality evaluation and end-product validation [20].

6. Challenges and Perspectives

Chestnut processing quality is highly sensitive to maturity, environment, year-to-year variation, storage, and processing conditions. The lack of standardized sampling and evaluation currently limits the reproducibility of genetic mapping and prediction models. Harmonized maturity criteria, postharvest treatments, processing protocols, and quality-control materials across years and locations are therefore needed.

At the molecular level, stable processing-quality QTLs, favorable haplotypes, and validated causal genes remain scarce. Future studies should strengthen multi-environment QTL/GWAS analyses under standardized processing protocols, integrate transcriptomic and metabolomic data with structural phenotypes, and account for genotype-by-environment interactions in genomic prediction.

Translating candidate genes and associated loci into cultivars remains a major bottleneck. Marker–trait associations and genomic predictions require independent validation, whereas gene editing depends on improved transformation and regeneration systems and long-term evaluation. Addressing these constraints will be essential for converting current genomic discoveries into processing-specific cultivars.

7. Conclusions

Chestnut processing quality is a product-dependent, polygenic trait integrating starch properties, sugar metabolism, browning, flavor formation, and nutritional components. Genomic and multi-omics studies have identified numerous candidate genes and regulatory networks, but direct genotype-to-processing-phenotype validation remains limited. Progress therefore depends on coupling standardized product-oriented phenotyping with robust genetic evidence and selecting breeding strategies according to trait architecture. This evidence-based framework can support more precise development of processing-specific chestnut cultivars.