基于液体相芯片的陆地棉种质遗传评价与杂交策略（Genetic Evaluation and Crossing Strategy of Upland Cotton Germplasm Based on a Liquid-Phase Chip）

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26 October 2026

15 Pages

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1

Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Ministry of Agriculture and Rural Affairs, Shihezi 832000, China

2

Institute of Modern Seed Industry, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

Author to whom correspondence should be addressed.

Abstract

Upland cotton (Gossypium hirsutum L.) dominates Xinjiang’s cotton production, but the narrow genetic base of modern cultivars limits further yield gains. Here, 356 Xinjiang upland cotton accessions (184 northern, 172 southern) were genotyped at 280 elite trait locus–trait combinations (270 unique markers) using a liquid-phase chip. Pairwise kinship was inferred with the KING-robust estimator and cross-validated by IBS similarity, population structure was examined by PCA, and a marker-weighted comprehensive elite genotype index (S) was constructed. The overall missing rate was 0.43%, and five identical and 101 near-identical accession pairs were detected. PC1 and PC2 explained only 9.81% and 7.29% of the variation, with substantial regional overlap. Index S ranged from 0.39 to 0.57, led by Tkuo, Y80, Ba 44435, Ziyu R040068 and Youzhi 5; salt tolerance (0.07), lint yield (0.14), seed index (0.19) and fiber strength (0.22) showed the lowest carriage of the marker-defined elite alleles scored here. After excluding close relatives, 62,252 crosses (98.52%) satisfied the constraint, and 30 crosses (19 inter-regional) were recommended, the best being Ziyu TY112 × Tkuo (joint coverage U = 0.65). These parents and crosses provide candidates for parental selection, pending field validation.

1. Introduction

More than 90% of China’s cotton is now produced in Xinjiang, and the yield and fiber quality of upland cotton (Gossypium hirsutum L.) cultivars grown there rank among the highest worldwide. Further yield gains, however, are constrained by a narrow genetic base: genomic analyses indicate that the genetic composition of Chinese upland cotton cultivars is highly concentrated, with roughly 85% of the elite allelic variation derived from a small number of early-introduced germplasm accessions, leaving limited raw variation available for breeding [1,2,3]; cultivars bred in Xinjiang are further derived from a handful of backbone parents, and DNA fingerprinting studies have revealed high genetic similarity among cultivars together with cases of synonymy [4,5]. Given that the germplasm inventory can hardly be expanded in the short term, whether breeders can accurately identify usable elite allelic variation within existing materials and avoid the redundancy caused by crossing close relatives bears directly on breeding efficiency [6].

The tools for germplasm evaluation have advanced with genotyping technology. Early SSR-based diversity analyses provided a first sketch of the genetic composition of Chinese upland cotton germplasm [7], and solid-phase SNP arrays such as CottonSNP63K and CottonSNP80K subsequently supported large-scale germplasm studies at higher throughput [8,9]. Liquid-phase chips, which are based on genotyping by target sequencing (GBTS), have emerged over recent years: their loci can be customized on demand, genotyping costs are low, and no specific platform is required. In cotton, a 40 K liquid-phase chip [10] and a 10 K breeding chip integrating thousands of functional loci [11] have been released. Unlike randomly distributed background markers, functional-locus chips are designed around validated marker–trait associations and read out the genotypic value of target traits directly in breeding materials, so that evaluation shifts from descriptions of population structure to judgements of breeding value [11].

Germplasm evaluation ultimately feeds into decisions on parental crossing. Classical core collection theory aims to retain maximal genetic diversity with a minimal sample size [12,13], and its criterion is genetic representativeness; breeding practice cares more about the carriage and complementarity of elite alleles, and the two criteria rank the same set of materials differently [14,15,16]. Crossing is also subject to several constraints. Parents should be sufficiently unrelated to avoid inbreeding depression, yet maximizing marker-based genetic distance cannot substitute for trait complementarity, because the association between marker genetic distance and heterosis is moderate and varies with trait and marker type [17,18]. Genomic selection shows promise for early-generation prediction [19,20,21], but it offers limited direct support for the upstream decision of which parents to select and which crosses to make. Integrating functional-locus genotypes, kinship constraints and quantified allelic complementarity into a single crossing framework would make parental selection more operational.

In this study, 356 upland cotton accessions from Xinjiang (184 from northern Xinjiang and 172 from southern Xinjiang) were genotyped with a liquid-phase chip at 280 elite trait locus–trait combinations (270 unique markers). After data quality control, pairwise kinship was inferred with the KING-robust algorithm [22] and cross-validated by IBS genetic similarity, and population structure was examined by principal component analysis. A comprehensive elite genotype index weighted by marker number was constructed, and crossing schemes were formulated on the basis of joint elite allele coverage after excluding close relatives and highly concordant genotypes, with the aim of providing directly usable candidate parents and cross lists for parental selection and germplasm innovation in upland cotton.

2. Materials and Methods

2.1. Plant Materials and Genotype Data

A panel of 356 upland cotton accessions from Xinjiang, comprising 184 from northern Xinjiang and 172 from southern Xinjiang, was analyzed, including lines bred by individual breeding institutions and introduced cultivars (lines). All accessions were grown during the 2025 growing season, with the 184 northern Xinjiang accessions planted in Shihezi and the 172 southern Xinjiang accessions planted in Tiemenguan, and leaf tissue was sampled for DNA extraction at the squaring stage (bud stage). Each accession was genotyped with the Zheda Mianxin 41 K single-nucleotide-polymorphism (SNP) liquid-phase chip based on genotyping by target sequencing (GBTS) [10]. Published marker–trait association information was used to annotate the elite trait loci into four states: elite homozygous (Elite), heterozygous (Het), unfavorable homozygous (Bad) and missing (Missing), yielding 280 locus–trait combinations (270 unique markers, 10 of which were associated with multiple traits). The 36 original trait codes included synonyms of the same trait [e.g., GOT(LP) and LP both denote lint percent; Sympodia/Sympodial and FBN both denote fruiting branch number; the wilt series and VW both denote Verticillium wilt resistance] and six composite index traits formed as ratios of two traits (BW/FL boll weight–length index, FS/FL strength–length index, SI/FL seed index–length index, LP/SI lint percent–seed index, SI/LP seed index–lint percent index and LI/LP lint index–lint percent index), which were merged into 28 traits. These traits cover plant height (PH), fruiting branch number (FBN), boll number (BN), boll weight (BW), lint percent (LP), seed index (SI), lint yield (SCY) and fiber quality [fiber length (FL), fiber strength (FS), fiber uniformity (FU), fiber elongation (FE) and micronaire (FM)], as well as the stress-resistant traits Verticillium wilt resistance (VW), salt tolerance (SALT) and heat tolerance (HEAT). Cultivar and line names are romanized from Chinese throughout this article.

2.2. Genotype Coding and Quality Control

The Elite, Het and Bad states were coded as elite allele dosages of 2, 1 and 0, respectively, and Missing was treated as missing data. Two scales are used in the analyses below, the raw dosage, which ranges from 0 to 2, and the normalized carrier rate, obtained by dividing the dosage by two and ranging from 0 to 1. All accession-level and trait-level carrier rates and all indices (Ek, S and U) are reported on the normalized 0–1 scale. Per accession, the numbers of missing and heterozygous markers were counted; per marker, the missing rate, elite allele frequency and minor allele frequency (MAF) were calculated. The elite allele frequency was estimated as p = the sum of dosages divided by twice the number of non-missing samples. Data analysis was performed in Python 3.13.

2.3. Kinship Inference

Pairwise kinship was inferred with the KING-robust algorithm [22]. Based on the expected difference in shared locus states, the algorithm estimates pairwise kinship coefficients φ without prior population allele frequencies, producing a within-family (φ W) and a between-family (φ B) estimator; the kinship coefficient was taken as the larger of the two. Classification thresholds followed the study by Manichaikul et al. [22]: φ > 0.354 for duplicate/twin pairs, 0.177 < φ ≤ 0.354 for first-degree, 0.0884 < φ ≤ 0.177 for second-degree and 0.0442 < φ ≤ 0.0884 for third-degree relatives. Because most accessions are highly inbred lines (overall heterozygosity of only 6.96%) and the denominator of the KING estimator depends on the number of heterozygous loci, its absolute values may be poorly calibrated in this panel. Genome-wide IBS genetic similarity and pairwise Hamming distances (the number of loci with discordant genotypes) were therefore computed as cross-validation. Close-relative exclusion applied a dual criterion: KING kinship coefficient φ ≥ 0.0884 (second-degree or closer) or Hamming distance ≤ 2. The Hamming threshold of two loci was set to absorb technical and biological noise while remaining far below the divergence between independently bred materials: across the 63,190 pairs the median Hamming distance was 74 loci (P5–P95: 40–108) and only 106 pairs (0.17%) differed at two loci or fewer, which, with 277.9 shared loci on average, corresponds to a genotype concordance of at least 99.28%. This is stricter than the concordance observed in cotton between materials of known close relationship, where sister lines, backcross derivatives sharing a recurrent parent and plants from the same progeny row reached 91.33–98.49% similarity and a threshold of 90% was proposed for identifying such material [23]; concordance above 99% is therefore unlikely to arise from ordinary close pedigree relationships and points instead to duplicated or synonymous accessions. Genotypic concordance alone does not, however, establish redundancy: in the USDA National Cotton Germplasm Collection, pairs identified as putative duplicates from high identity-by-state values were not consistently identical when compared in the field, and the authors concluded that redundancy cannot be ascertained from genotypic or phenotypic profiles alone [8]. The pairs reported here are consequently treated as candidates for consolidation rather than confirmed duplicates (Section 3.5).

Because the absolute calibration of φ is uncertain in this panel, two checks were added to establish how far this affects the exclusion step. First, the five genotype-identical pairs returned φ = 0.500 with an IBS similarity of 1.000, the theoretical maximum expected for duplicate material, and 91.1% of the 101 near-identical pairs (Hamming distance of 1–2) exceeded the duplicate threshold of 0.354, so the estimator orders the most extreme pairs as expected even though its absolute values are shifted. Second, the kinship threshold was varied across the full range of standard degrees of relationship, from 0.0442 to 0.354: the number of admissible crosses changed only from 61,933 to 62,920, and 18 to 20 of the 20 highest-ranked recommended crosses were identical to those reported in Section 3.5, so the recommendations are not sensitive to the exact cut-off. A genome-wide SNP data set is not available for these accessions, so the thresholds could not be recalibrated against genome-wide estimates; this is restated as a limitation in Section 4.

2.4. Population Structure Analysis

After removing monomorphic markers, 266 polymorphic markers were retained. Missing genotypes were imputed with expected dosages and standardized by elite allele frequency before principal component analysis (PCA). The first two principal components were extracted, and the score distributions of accessions from the two regions were compared.

2.5. Comprehensive Elite Genotype Index

For each accession, the elite allele carrier rate in trait k, Ek, was computed as the mean elite allele dosage across the loci of that trait (missing loci excluded from both the numerator and the denominator) divided by two, so that Ek ranges from 0 to 1; Ek was then weighted by wk, the proportion of markers for trait k among all valid markers (renormalized over the traits valid for each accession), to construct the comprehensive elite genotype index S = ΣwkEk. Because Ek is normalized to the 0–1 scale, S also ranges from 0 to 1 and reflects the weighted mean level of elite allele carriage across all elite traits.

Weighting by marker number makes S the mean carrier rate over all scored loci, so that every functional locus contributes equally. The alternative of weighting traits equally was not adopted because marker numbers per trait are highly uneven, from 34 loci for lint percent down to a single locus for eight traits, including salt tolerance, seed cotton yield, heat tolerance, first fruiting branch position, lint index and CLCuV resistance; equal trait weights would give each of these single-locus traits the same 3.57% weight as lint percent, and because a single locus yields a carrier rate of only 0, 0.5 or 1, the index would then be driven by coarse, high-variance terms. Sensitivity to this choice was tested by recomputing S under two alternative schemes, equal weights per trait and weights proportional to the square root of the marker number: rank correlation with the reported S was ρ = 0.49 and 0.85, and 6 and 16 of the top 20 accessions were shared, respectively. Because U is defined at the trait level it inherits the same weighting, so the ranking of crosses was additionally recomputed with a marker-level coverage index that involves no trait weights at all, namely the mean over shared loci of the higher parental elite allele dose divided by two: across the 62,252 candidate crosses this index correlated with U at ρ = 0.785, and 27 of the 40 parents entering the 20 highest-ranked recommended crosses were recovered, but only 1 of the 20 specific combinations was identical. The identity of the candidate parents is therefore robust to the weighting scheme, whereas the ranking of individual combinations is not, and the recommended list should be read with this uncertainty in mind.

2.6. Parental Crossing Strategy

For all pairwise combinations of the 356 accessions (63,190 in total), pairs with second-degree or closer kinship (KING φ ≥ 0.0884) or highly concordant genotypes (Hamming distance ≤ 2) were excluded first. For the remaining crosses, the joint elite allele coverage U = Σwk·max(E1k, E2k), where E1k and E2k are the normalized carrier rates defined in Section 2.5 so that U also lies on the 0–1 scale (with weights renormalized over traits having data for at least one parent) and the complementarity gain Comp = U − (S1 + S2)/2 were calculated. Crosses were ranked by U in descending order, and parents were deduplicated in the recommended scheme (each parent participates in at most one cross, preventing a few top-ranked accessions from occupying all recommendations), yielding 30 recommended crossing schemes.

3. Results

3.1. Genotype Data Quality

The overall missing rate across the 356 accessions at 280 locus–trait combinations was 0.43%, with at most five missing markers in any single accession, indicating good data quality (Figure 1a); each accession carried 19.42 heterozygous markers on average (Figure 1b). The elite allele frequency per marker showed a bimodal distribution (Figure 1c). After removing 14 monomorphic markers (MAF = 0), 71 polymorphic markers still had MAF below 0.05, suggesting that directional selection has acted on these elite trait markers and that low-frequency elite alleles account for a certain proportion. Among the 28 traits, lint percent (LP) had the most markers (34), followed by fiber elongation (FE, 30), seed index (SI, 28), fiber strength (FS, 27) and fiber length (FL, 24) (Figure 2a).

Figure 1. Quality assessment of genotype data. (a) Distribution of missing markers per accession. (b) Distribution of heterozygous markers per accession. (c) Distribution of elite allele frequency per marker.

Figure 2. Marker composition and population carrier rates of elite traits. (a) Number of markers per trait. (b) Mean elite allele carrier rate per trait.

3.2. Kinship

The median IBS genetic similarity across all accession pairs was 0.78 (P5–P95: 0.68–0.88). Five pairs of accessions with identical genotypes were detected (Table 1). Among them, the northern Xinjiang accession Jingkang 1 was identical to both Hexin Ta 2 and Tahe 2 Xiaoling (the latter two differing from each other at one marker), suggesting the presence of synonymous or possibly duplicated accessions. The only inter-regional pair among the five comprised the northern Xinjiang line CF Ta 2 Tigao and the southern Xinjiang cultivar Xinluzhong 84; the two were identical at all shared markers yet come from different cotton regions, and their names share no correspondence. Because part of the panel was introduced from other breeding institutions, this pair likely reflects synonymy arising during germplasm exchange more than a chance identity between independently bred cultivars; its exact correspondence remains to be verified against pedigree records and should be screened in parent selection. In addition, 101 pairs of highly concordant accessions with Hamming distances of 1–2 were detected, giving 106 pairs in total with Hamming distance ≤ 2. Combining the KING estimator (φ ≥ 0.0884, second-degree or closer) with genotype concordance, 938 close or highly concordant pairs were identified, comprising five identical pairs, 101 highly concordant pairs, 164 KING duplicate-level pairs, 250 first-degree pairs and 418 s-degree pairs. The KING kinship matrix and the IBS similarity matrix showed consistent overall patterns with clear block-diagonal structure (Figure 3).

Table 1. Accession pairs with identical genotypes. Cultivar names are romanized from Chinese.

Figure 3. Heatmaps of genetic relatedness. (a) KING-robust kinship coefficients; (b) IBS genetic similarity. Accessions were ordered by hierarchical clustering on IBS distance, with regional origins indicated by the top strips.

3.3. Population Structure

PC1 and PC2 explained 9.81% and 7.29% of the total genetic variation, respectively. Accessions from northern and southern Xinjiang overlapped substantially in the PC1-PC2 plane. The difference in mean PC1 scores between regions was statistically significant (Welch’s t-test, p = 0.004) but small in magnitude (Cohen’s d = 0.31), with regional origin explaining only 2.31% of the PC1 variance (Figure 4b), indicating weak differentiation and closely related genetic backgrounds between the two regional pools. Because PC1 and PC2 together account for only 17.10% of the total variation, the regional comparison was repeated on the genome-wide IBS similarity matrix, which does not depend on the PCA representation. Mean IBS similarity was 0.7702 within northern Xinjiang, 0.7950 within southern Xinjiang and 0.7795 between regions; the pooled within-region-minus-between-region difference was 0.0023, which was detectable in a permutation test with 1000 replicates (p = 0.001) but accounted for only 0.03% of the variance in pairwise similarity, and mean similarity within northern Xinjiang was slightly lower than that between regions, indicating that the regional labels do not correspond to a consistent genetic grouping in this panel.

Figure 4. Principal component analysis of population structure. (a) PC1-PC2 scatter plot. (b) Comparison of PC1 scores between regions.

The remaining 82.90% of the variation is spread over many further axes rather than concentrated in a second strong structure: the first 10 principal components account for 43.27% and 20 components for 58.20%, and 49 components are needed to reach 80%, a decay pattern typical of largely independent loci with little linkage-driven structure. Regional origin is associated with several of these axes, explaining 2.31% of the variance on PC1, 1.57% on PC2, 2.66% on PC4 and 2.60% on PC5, and 11.15% summed over the first 10 components (Mann–Whitney p = 0.0070 on PC1 and 0.0015 on PC2); the regional signal is therefore weak but not confined to PC1-PC2, while the bulk of the variation reflects differences among individual accessions. For parental selection this means that regional origin is a poor proxy for genetic distance, which is consistent with the inter-regional crosses in Section 3.5 having been selected on joint coverage and IBS distance rather than on regional labels.

3.4. Genetic Evaluation of Elite Alleles

The comprehensive elite genotype index S ranged from 0.39 to 0.57 with a median of 0.45 (Figure 5a). The top five accessions were Tkuo (0.571, southern Xinjiang), Y80 (0.537, northern Xinjiang), Ba 44435 (0.534, southern Xinjiang), Ziyu R040068 (0.528, northern Xinjiang) and Youzhi 5 (0.525, southern Xinjiang) (Figure 5b, Table 2), which can serve as core parents for pyramiding elite alleles. Across traits, fiber uniformity (FU, 0.85), first fruiting branch position (FFBP, 0.85) and lint percent (0.75) showed the highest mean carrier rates among single traits, whereas salt tolerance (0.07), lint yield (SCY, 0.14), seed index (0.19) and fiber strength (0.22) showed the lowest (Figure 2b); these four traits had the lowest carriage of the marker-defined elite alleles scored here and are candidate priorities for introgression, subject to the limitations on locus coverage discussed in Section 4.

Figure 5. Comprehensive evaluation of elite genotypes. (a) Distribution of index S. (b) Top 15 accessions ranked by index S.

Table 2. Top 15 accessions ranked by comprehensive elite genotype index. Cultivar names are romanized from Chinese.

3.5. Parental Crossing Strategy

After excluding pairs with second-degree or closer kinship or highly concordant genotypes, 62,252 crosses (98.52% of all 63,190 combinations) satisfied the kinship constraint. Ranking by joint coverage U with parent deduplication yielded 30 recommended crossing schemes (Figure 6b; the top 10 crosses are listed in Table 3), of which 19 were inter-regional northern × southern Xinjiang crosses (63.33%). The cross with the highest joint coverage was Ziyu TY112 (northern Xinjiang) × Tkuo (southern Xinjiang) (U = 0.65, complementarity gain 0.12), followed by Ziyu R040068 (northern Xinjiang) × Zhongmian 96A (southern Xinjiang) (U = 0.63, complementarity gain 0.13). The IBS genetic distances between parents of the recommended crosses ranged from 0.25 to 0.41 with a median of 0.30, corresponding to the 92nd percentile of all candidate crosses (Figure 6a), indicating that the recommended parents are genetically distant relative to the candidate pool, which reflects the relatedness constraint imposed during selection and is not a demonstrated reduction in inbreeding depression, which was not evaluated here.

Figure 6. Parental crossing strategy. (a) IBS genetic distance versus joint elite allele coverage of candidate crosses. (b) Top 30 recommended crosses.

Table 3. Recommended parental combinations (top 10 crosses). N, Northern Xinjiang; S, Southern Xinjiang. Cultivar names are romanized from Chinese.

The 106 putative duplicate and near-duplicate pairs were examined for consistency with accession records. Forty-one of the pairs occurred between the two regional groups, and 30 accessions appeared in more than one pair, one of them in as many as 14, a pattern consistent with the repeated recirculation of a small number of elite lines within the breeding program. Complete pedigree records were not available for these accessions, so the pairs could be checked only at the level of accession names, and 79 of the 106 showed no correspondence in name; genotypic evidence alone does not establish whether a pair represents the same line maintained under different names, a pair of sister lines, or a labeling error during sampling or genotyping. These pairs are therefore reported as candidates for consolidation, and confirmation against pedigree and seed-source records remains necessary before any of the accessions concerned is used as a parent.

4. Discussion

The pattern of elite allele carriage in this panel was highly uneven across traits: traits long subjected to intense selection, such as lint percent, first fruiting branch position and fiber uniformity, had carrier rates above 0.75, whereas salt tolerance (0.07), lint yield (0.14), seed index (0.19) and fiber strength (0.22) were markedly low. This pattern is consistent with the selection history of upland cotton. A genomic analysis of Chinese cultivars bred over 70 years showed that nearly half of the loci associated with yield and fiber quality have pleiotropic effects, with widespread trade-offs and linkage drag among traits [1]; multi-environment association analyses by Zhu et al. [24] and the identification of loci favorable to both yield and quality by Wang et al. [25] likewise indicate that elite alleles for lint yield and fiber strength occur at limited frequencies in breeding populations and are difficult to combine. For salt tolerance, association analyses at both the germination and seedling stages have detected numerous small-effect loci with clear differentiation of salt-tolerant alleles among germplasm [26,27]; the carrier rate of only 0.07 in this panel suggests that elite salt-tolerant alleles are rare among the main Xinjiang breeding materials, although this inference rests on a single salt-tolerant locus in the present panel and requires phenotypic confirmation. Accordingly, the accessions identified here as carriers of elite alleles for these traits are candidates as donor parents for targeted improvement, subject to confirmation of the corresponding phenotypes.

Methodologically, this study examined the behavior of the KING-robust estimator on inbred line materials. The estimator is built on the difference in shared heterozygous states between two individuals, requires no prior population allele frequencies and is robust to population structure [22], but its derivation assumes Hardy–Weinberg equilibrium. The accessions analyzed here are highly inbred lines with an overall heterozygosity of only 6.96% and about 19 heterozygous markers per accession on average; the resulting shrinkage of the estimator’s denominator inflates its variance, and its absolute values depart from theoretical calibration, with a median kinship coefficient of −3.41 for unrelated pairs where values should cluster near zero. This study therefore did not rely on KING values alone for kinship classification, but used genome-wide IBS similarity and Hamming distances as cross-evidence and applied the dual exclusion criterion of φ ≥ 0.0884 or Hamming distance ≤ 2; all five genotype-identical pairs were verified individually. Future work should re-estimate kinship from genome-wide SNPs to obtain well-calibrated coefficients and classification thresholds.

The pronounced overlap of northern and southern Xinjiang accessions on the first two principal components (PC1 explaining only 9.81% of the variation) suggests that regional labels capture little of the genetic differentiation within this panel. This reading is supported by the IBS-based comparison in Section 3.3, in which regional origin accounted for 0.03% of the variance in pairwise similarity, and it should be read with the caveat that PC1 and PC2 together explain only 17.10% of the total variation and that the conclusion rests on the marker set of this chip. This result is unsurprising: parent exchange among Xinjiang breeding institutions is frequent, breeding objectives such as early maturity and machine picking are highly consistent, and long-term directional selection has homogenized the cultivar gene pools of the two cotton regions [4,28,29]; studies of elite U.S. upland cotton and race stock accessions similarly show that traditional ecological-region classification explains limited genetic differentiation [30]. For crossing practice, the intuition that inter-regional crosses are inherently superior does not hold; the genetic value of a cross should be quantified pair by pair. The value of the 19 inter-regional crosses in our recommendations was measured directly by joint coverage and IBS genetic distance rather than by regional labels.

The crossing recommendations of this study are centered on the joint elite allele coverage U of the two parents, which represents the expectation that progeny retain, for each trait, at least the higher of the two parents’ elite allele carriage levels (trait-level complementarity), implicitly assuming that elite alleles are retained dominantly in progeny; the complementarity gain Comp measures how far parental complementarity exceeds the parental mean. Compared with the prediction of heterosis from molecular genetic distance [17,18], this index targets the functional loci of breeding-goal traits directly and acts through allelic complementarity rather than overdominance. Parent deduplication in the recommendations allows the 30 crosses to cover a broader range of parental lineages and facilitates parallel validation of multiple crosses, preventing a few high-index accessions from occupying all crosses. Because U is computed from marker-based trait-level carriage statistics, its dominance–complementarity assumption has not been verified in the field, and it does not incorporate phenotypic stability, general combining ability or specific combining ability; the final crosses should be validated in multi-year, multi-location field trials before large-scale use [31].

A field validation of the recommended crosses is planned to test the assumptions behind U directly. The top-ranked crosses will be made and their segregating generations evaluated at Shihezi in northern Xinjiang and Tiemenguan in southern Xinjiang over consecutive seasons, with the target traits scored in the field and the retention of the parental elite alleles tracked at the corresponding loci. This design measures whether elite alleles are in fact retained at the higher parental level after segregation and selection, and it provides the phenotypic data needed to relate marker-inferred carriage to field performance and to estimate combining ability, neither of which could be assessed from the single-season genotypic data reported here.

Within breeding workflows, the core parents, carriers of deficient-trait alleles and candidate cross lists produced here can be used directly in current molecular breeding systems. Functional-locus liquid-phase chips are already used for parent selection and marker-assisted selection at many breeding institutions, and genomic selection for traits such as lint percent and fiber length has reached practical value [11,20]; the locus genotype data from this study can serve as input for training populations and, combined with designed breeding strategies such as MAGIC, enable targeted pyramiding of elite alleles [1]. As the cost of liquid-phase chips falls and genotyping throughput rises [10], the functional-locus-linked workflow of evaluation, crossing and validation is expected to become routine in upland cotton germplasm innovation.

Several limitations apply to these results. The carrier rates are conditioned on the composition of the locus panel, and marker numbers per trait were uneven, ranging from 34 for lint percent to a single locus for salt tolerance, seed cotton yield, heat tolerance, first fruiting branch position, lint index and CLCuV resistance; the low carrier rates for salt tolerance and lint yield therefore rest on limited evidence and should be read as indicative rather than as a demonstration of genetic deficiency at the phenotypic level. The Elite and Bad states were further assigned from published marker–trait associations, most of which were detected in other panels and environments, so the direction and magnitude of allele effects may not transfer completely to the materials analyzed here. The kinship coefficients are not calibrated in absolute terms, and no genome-wide SNP data set was available with which to recalibrate the classification thresholds, so the exclusions rest on the dual criterion of Section 2.3 and on the threshold-sensitivity check reported there. The indices S and U depend on a marker–count weighting, and the ranking of individual crosses is shown in Section 2.5 to be sensitive to that choice, so the recommended list should be treated as a set of candidates rather than as a fixed ranking. All accessions were grown in a single season at two locations and no phenotypic data were collected, so the relationship between marker-inferred elite allele carriage and field performance has not been quantified; the dominance–complementarity assumption behind U and the retention of elite alleles in progeny on which it depends remain to be tested in the field. Pedigree records were not available for the putative duplicate and near-duplicate pairs, which were therefore checked only at the level of accession names. The recommended crosses are consequently ranked candidates for field validation rather than finished crossing decisions.

5. Conclusions

Using 280 elite trait locus–trait combinations (270 unique markers) on a liquid-phase chip, we completed the genetic evaluation and crossing strategy construction for 356 upland cotton accessions from Xinjiang. The overall genotype missing rate was 0.43%, indicating reliable data quality; genetic differentiation between regions was weak, but five pairs of genotype-identical and 101 pairs of highly concordant accessions were present, and synonymous materials should be screened during parent selection. The comprehensive elite genotype index S ranged from 0.39 to 0.57, with Tkuo (0.571), Y80 (0.537), Ba 44435 (0.534), Ziyu R040068 (0.528) and Youzhi 5 (0.525) ranking in the top five as candidate core parents; salt tolerance, lint yield, seed index and fiber strength showed the lowest carriage of the marker-defined elite alleles scored here, subject to phenotypic confirmation. Under the close-relative exclusion constraint, 30 crossing schemes were recommended (19 inter-regional), with the best cross Ziyu TY112 × Tkuo reaching a joint elite allele coverage of 0.65. These core parents, deficiency-trait carriers and recommended crosses provide candidates for parental selection, germplasm innovation and molecular design breeding in upland cotton, and their actual breeding value requires confirmation in multi-year, multi-location field trials.

Author Contributions

Conceptualization, S.X. and H.L.; methodology, S.X.; software, S.X.; formal analysis, S.X.; data curation, Y.L.; investigation, S.Y. and B.X.; writing—original draft preparation, S.X.; writing—review and editing, Q.M., J.L., X.N. and H.L.; visualization, S.X.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project “Breeding of Early-maturing, Widely-adaptive and High-quality Machine-picked Cotton Varieties” (grant number 2023A02003); the Molecular Breeding Technology Research and Design Breeding of Major Crops of Xinjiang Production and Construction Corps (grant number NCG202504); the Key Areas Science and Technology Program of Xinjiang Production and Construction Corps (grant number 2024AB001); the Modern Agricultural Industry Technology System of Xinjiang Uygur Autonomous Region (grant number XJARS-03-21); and the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region (grant number 2024A02002-5-5).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The genotype data presented in this study have been deposited in the OMIX database of the China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/omix, accessed on 1 October 2026), under accession number OMIX002667.

Conflicts of Interest

The authors declare no conflicts of interest.

References

1. Li, Y.Q.; Si, Z.F.; Wang, G.P.; Shi, Z.L.; Chen, J.W.; Qi, G.A.; Jin, S.K.; Han, Z.G.; Gao, W.H.; Tian, Y.; et al. Genomic insights into the genetic basis of cotton breeding in China. Mol. Plant2023, 16, 662–677. [Google Scholar] [CrossRef] [Scilit] [PubMed]
2. Fang, L.; Wang, Q.; Hu, Y.; Jia, Y.H.; Chen, J.D.; Liu, B.L.; Zhang, Z.Y.; Guan, X.Y.; Chen, S.Q.; Zhou, B.L.; et al. Genomic analyses in cotton identify signatures of selection and loci associated with fiber quality and yield traits. Nat. Genet.2017, 49, 1089–1098. [Google Scholar] [CrossRef] [Scilit] [PubMed]
3. Ma, Z.Y.; He, S.P.; Wang, X.F.; Sun, J.L.; Zhang, Y.; Zhang, G.Y.; Wu, L.Q.; Li, Z.K.; Liu, Z.H.; Sun, G.F.; et al. Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat. Genet.2018, 50, 803–813. [Google Scholar] [CrossRef] [Scilit] [PubMed]
4. Li, J.H.; Jin, Y.L.; Qiao, Y.Q.; Xu, Z.; Hu, J.H.; Zhang, X.Y.; Sun, J.; Xue, F. Construction of DNA fingerprinting and genetic diversity analysis of 150 Xinjiang upland cotton varieties. Mol. Plant Breed.2022, 20, 2983–3001. [Google Scholar]
5. Wang, X.Y.; Li, X.Y.; Gong, Z.L.; Wang, J.D.; Fan, L.P.; Zheng, J.Y.; Liang, Y.J.; Guo, J.P.; Mamat, M.; Ai, X.T. Construction of DNA fingerprinting and genetic diversity analysis of Xinjiang upland cotton based on SSR markers. Cotton Sci.2018, 30, 308–315. [Google Scholar]
6. Han, Z.G.; Chen, H.; Cao, Y.W.; He, L.; Si, Z.F.; Hu, Y.; Lin, H.; Ning, X.Z.; Li, J.L.; Ma, Q.; et al. Genomic insights into genetic improvement of upland cotton in the world’s largest growing region. Ind. Crops Prod.2022, 183, 114929. [Google Scholar] [CrossRef] [Scilit]
7. Chen, G.; Du, X.M. Genetic diversity of source germplasm of upland cotton in China as determined by SSR marker analysis. Acta Genet. Sin.2006, 33, 733–745. [Google Scholar] [CrossRef] [Scilit] [PubMed]
8. Hinze, L.L.; Hulse-Kemp, A.M.; Wilson, I.W.; Zhu, Q.-H.; Llewellyn, D.J.; Taylor, J.M.; Spriggs, A.; Fang, D.D.; Ulloa, M.; Burke, J.J.; et al. Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K array. BMC Plant Biol.2017, 17, 37. [Google Scholar] [CrossRef] [Scilit] [PubMed]
9. Cai, C.P.; Zhu, G.Z.; Zhang, T.Z.; Guo, W.Z. High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. BMC Genom.2017, 18, 654. [Google Scholar] [CrossRef] [Scilit] [PubMed]
10. Si, Z.F.; Jin, S.K.; Li, J.Y.; Han, Z.G.; Li, Y.Q.; Wu, X.N.; Ge, Y.X.; Fang, L.; Zhang, T.Z.; Hu, Y. The design, validation, and utility of the ‘ZJU CottonSNP40K’ liquid chip through genotyping by target sequencing. Ind. Crops Prod.2022, 188, 115629. [Google Scholar] [CrossRef] [Scilit]
11. Zhang, X.Y.; Sun, Z.W.; Cao, M.Y.; Wang, H.Z.; Gu, Q.S.; Yang, J.; Tang, S.X.; Ke, H.F.; Zhang, D.M.; Wu, L.Q.; et al. MianxinNo.1: A modern cultivar genome-informed SNP array based on cGPS technology for genomics-assisted breeding in cotton. Theor. Appl. Genet.2026, 139, 208. [Google Scholar] [CrossRef] [Scilit] [PubMed]
12. Frankel, O.H. Genetic perspectives of germplasm conservation. In Genetic Manipulation: Impact on Man and Society; Arber, W., Illmensee, K., Peacock, W.J., Starlinger, P., Eds.; Cambridge University Press: Cambridge, UK, 1984; pp. 161–170. [Google Scholar]
13. Brown, A.H.D. Core collections: A practical approach to genetic resources management. Genome1989, 31, 818–824. [Google Scholar] [CrossRef] [Scilit]
14. Han, P.; Tian, X.M.; Wang, Y.; Huang, C.; Ma, Y.Z.; Zhou, X.F.; Yu, Y.; Zhang, D.W.; Xu, H.J.; Cao, Y.; et al. Construction of a core germplasm bank of upland cotton (Gossypium hirsutum L.) based on phenotype, genotype and favorable alleles. Genet. Resour. Crop Evol.2022, 69, 2399–2411. [Google Scholar] [CrossRef] [Scilit]
15. Zhang, X.; Liu, Y.; Qian, Y.Y.; Wang, G.E.; Wang, H.J.; Jiao, X.F.; Xie, H.; Cui, S.F.; Li, J.L. Genetic diversity analysis and primary core germplasm construction of upland cotton (Gossypium hirsutum L.). Acta Agric. Boreali-Sin.2023, 38, 26–33. [Google Scholar]
16. Li, C.Q.; Dong, C.G.; Zhao, H.H.; Wang, J.; Du, L.; Ai, N.J. Identification of superior parents with high fiber quality using molecular markers and phenotypes based on a core collection of upland cotton (Gossypium hirsutum L.). Mol. Breed.2022, 42, 30. [Google Scholar] [CrossRef] [Scilit] [PubMed]
17. Sarfraz, Z.; Iqbal, M.S.; Pan, Z.E.; Jia, Y.H.; He, S.P.; Wang, Q.L.; Qin, H.D.; Liu, J.H.; Liu, H.; Yang, J.; et al. Integration of conventional and advanced molecular tools to track footprints of heterosis in cotton. BMC Genom.2018, 19, 776. [Google Scholar] [CrossRef] [Scilit] [PubMed]
18. Geng, X.L.; Qu, Y.J.; Jia, Y.H.; He, S.P.; Pan, Z.E.; Wang, L.R.; Du, X.M. Assessment of heterosis based on parental genetic distance estimated with SSR and SNP markers in upland cotton (Gossypium hirsutum L.). BMC Genom.2021, 22, 123. [Google Scholar] [CrossRef] [Scilit] [PubMed]
19. Li, Z.T.; Liu, S.M.; Conaty, W.; Zhu, Q.H.; Moncuquet, P.; Stiller, W.; Wilson, I. Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods. Heredity2022, 129, 103–112. [Google Scholar] [CrossRef] [Scilit] [PubMed]
20. Islam, M.S.; Fang, D.D.; Jenkins, J.N.; Guo, J.; McCarty, J.C.; Jones, D.C. Evaluation of genomic selection methods for predicting fiber quality traits in upland cotton. Mol. Genet. Genom.2020, 295, 67–79. [Google Scholar] [CrossRef] [Scilit] [PubMed]
21. Billings, G.T.; Jones, M.A.; Rustgi, S.; Bridges, W.C.; Holland, J.B.; Hulse-Kemp, A.M.; Campbell, B.T. Outlook for implementation of genomics-based selection in public cotton breeding programs. Plants2022, 11, 1446. [Google Scholar] [CrossRef] [Scilit] [PubMed]
22. Manichaikul, A.; Mychaleckyj, J.C.; Rich, S.S.; Daly, K.; Sale, M.; Chen, W.M. Robust relationship inference in genome-wide association studies. Bioinformatics2010, 26, 2867–2873. [Google Scholar] [CrossRef] [Scilit] [PubMed]
23. Tian, H.; Zhou, Y.; Wang, Y.; Li, M.; Zhao, G.; Du, H.; Liu, J.; Geng, Z. Development of a liquid-phased probe array for upland cotton and its application in cultivar identification. Genes2026, 17, 8. [Google Scholar] [CrossRef] [Scilit] [PubMed]
24. Zhu, G.Z.; Hou, S.; Song, X.H.; Wang, X.; Wang, W.; Chen, Q.J.; Guo, W.Z. Genome-wide association analysis reveals quantitative trait loci and candidate genes involved in yield components under multiple field environments in cotton (Gossypium hirsutum). BMC Plant Biol.2021, 21, 250. [Google Scholar] [CrossRef] [Scilit] [PubMed]
25. Wang, P.P.; He, S.P.; Sun, G.F.; Pan, Z.E.; Sun, J.L.; Geng, X.L.; Peng, Z.; Gong, W.F.; Wang, L.R.; Pang, B.Y.; et al. Favorable pleiotropic loci for fiber yield and quality in upland cotton (Gossypium hirsutum). Sci. Rep.2021, 11, 15935. [Google Scholar] [CrossRef] [Scilit] [PubMed]
26. Yuan, Y.C.; Xing, H.X.; Zeng, W.G.; Xu, J.L.; Mao, L.L.; Wang, L.Y.; Feng, W.; Tao, J.C.; Wang, H.R.; Zhang, H.J.; et al. Genome-wide association and differential expression analysis of salt tolerance in Gossypium hirsutum L. at the germination stage. BMC Plant Biol.2019, 19, 394. [Google Scholar] [CrossRef] [Scilit] [PubMed]
27. Sun, Z.W.; Li, H.L.; Zhang, Y.; Li, Z.K.; Ke, H.F.; Wu, L.Q.; Zhang, G.Y.; Wang, X.F.; Ma, Z.Y. Identification of SNPs and candidate genes associated with salt tolerance at the seedling stage in cotton (Gossypium hirsutum L.). Front. Plant Sci.2018, 9, 1011. [Google Scholar] [CrossRef] [Scilit] [PubMed]
28. Wang, J.D.; Zhang, Z.L.; Gong, Z.L.; Liang, Y.J.; Ai, X.T.; Sang, Z.W.; Guo, J.P.; Li, X.Y.; Zheng, J.Y. Analysis of the genetic structure and diversity of upland cotton groups in different planting areas based on SNP markers. Gene2022, 809, 146042. [Google Scholar] [CrossRef] [Scilit] [PubMed]
29. Shi, J.B.; Zhang, Y.B.; Wang, N.; Xu, Q.H.; Huo, F.C.; Liu, X.H.; Yan, G.T. Genetic diversity and population structure analysis of upland cotton (Gossypium hirsutum L.) germplasm in China based on SSR markers. Genet. Resour. Crop Evol.2023, 70, 2671–2682. [Google Scholar] [CrossRef] [Scilit]
30. Zhu, L.L.; Tyagi, P.; Kaur, B.; Kuraparthy, V. Genetic diversity and population structure in elite U.S. and race stock accessions of upland cotton (Gossypium hirsutum). J. Cotton Sci.2019, 23, 38–47. [Google Scholar] [CrossRef] [Scilit]
31. Gnanasekaran, M.; Thiyagu, K. Gene action, combining ability and standard heterosis for seed cotton yield and fibre quality components in upland cotton. Electron. J. Plant Breed.2021, 12, 325–334. [Google Scholar]

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