Review Reports

Response to Reviewer X Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions corrections highlighted in the resubmitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Must be improved

Thank you for your comments and suggestions, which have been revised in full and explained in the point-by-point response.

Are all the cited references relevant to the research?

Must be improved

Is the research design appropriate?

Must be improved

Are the methods adequately described?

Must be improved

Are the results clearly presented?

Must be improved

Are the conclusions supported by the results?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

1.     Abstract

Comments 1: The description of the plant material is unclear. It is not obvious whether the 585 recombinants represent individual plants, replicated observations, or distinct lines.

Response 1: Thank you for your careful comment. We revised as “In this study, a total of 588 accessions, including two parents and their 586 F₂:₄ recombinant individuals originated by via the single seed descent (SSD) method, were used to explore the genetic diversity of 17 phenotypic traits. ” And the each recombinant presents one individual, no additional repeats.

·       Comments 2: The population structure should be briefly explained.

Response 2: It’s a good idea. The population were structured by the single seed descent (SSD) method, and we revised it. In addition, the explicit flowchart of constructing process as Figure 1 in the part of “2.1. Materials”.

Comments 3: The conclusions, especially those based on PCA and clustering, are stated too strongly and should be toned down.

Response 3: Thanks for your constructive suggestions. We have toned down overstated conclusions and revised as “this study provides useful information and breeding materials for the utilization and innovation of pepper germplasm resources as well as genetic improvement of pepper”.

Comments 4: Statements about genetic diversity should be more cautious.

Response 4: Thank you for the cautious attitude to the genetic diversity. We have revised as “Genetic diversity analysis can contribute to compare the relationships between different germplasm resources” at the first sentence.

Comments 5: The phrase that recombinants “exceeded parental ranges” should be clarified; this likely refers to transgressive segregation and could be explicitly stated.

Response 5: Thanks for your excellent and well-defined reviews. We have   replaced the previous sentence as “The results indicated that most traits of the recombinants represented continuous distribution and transgressive segregation, with their minimum and maximum values exceeding the parental ranges”.

2.     Introduction

·       Comments 1: The Introduction provides general background, but it does not clearly explain why the chosen analyses (PCA, clustering, diversity indices) are appropriate for this study.

Response 1: We highly appreciate this constructive and insightful comment, and we fully agree with this valuable opinion. In the revised manuscript, we have supplemented detailed explanations in Lines 57–77 of the Introduction section to explicitly elaborate the rationality and applicability of adopting diversity index analysis, principal component analysis (PCA), and cluster analysis in this study. Specifically, the diversity index can quantitatively evaluate the magnitude of phenotypic variation and the degree of genetic differentiation among individual lines; PCA can effectively reduce the dimensionality of multi-trait phenotypic data and reveal the overall variation structure of the research population; cluster analysis is capable of classifying individual lines based on their comprehensive phenotypic similarity. The combined application of these three analytical methods is particularly suitable for systematically dissecting the phenotypic variation, trait correlation, and population differentiation characteristics of the artificially constructed F_2:4 segregating population in the present study.

Comments 2: The research objective is not clearly stated and should be added at the end of the Introduction. The manuscript later refers to an “alternative objective” (line 229), which suggests that the study aims are not clearly defined from the outset. All main objectives should be clearly formulated at the end of the Introduction.

Response 2: We sincerely thank the reviewer for pointing out this issue and fully appreciate this valuable suggestion. We have supplemented the research objectives in Lines 110–116 at the end of the Introduction, which is described as follows: “This study analyzed the phenotypic variation characteristics and segregation patterns of major agronomic traits in the pepper F_2:4 recombinant individuals, clarified the correlations among different agronomic traits, and elucidated the genetic differentiation characteristics of the population via multivariate statistical analysis. It laid a solid foundation for the innovation of pepper germplasm resources, further enriched pepper breeding materials, and provided strong support for the development of subsequent pepper breeding work”. Meanwhile, the ambiguous and inconsistent descriptions of alternative research objectives in the original manuscript have been completely removed.

Comments 3: Some methodological descriptions are included in the Introduction but would be more appropriate for the Materials and Methods section. For example, the description of the population structure and experimental setup (lines 74–81) should be moved. The Introduction should focus on background and rationale rather than procedural details.

Response 3: We highly appreciate the reviewer’s valuable comments and fully accept this constructive suggestion. We have moved the methodological contents from the last paragraph of the Introduction to Section 2 (Materials and Methods). The relocated original text is presented as follows: “The analysis of genetic diversity can not only discover the genetic relationship, but also clarify the correlation and the separation tendency among traits. In our previous work on the germplasm resources, a recombinant inbred population with 586 off-springs was constructed from the cross of inbred line C152 (dwarf plant, purple flower, short fruit, purple-black pericarp) and A37 (tall plant, white flower, long fruit, green pericarp). To explore the segregation, diversity and correlation of these polar difference traits in the offsprings, a total of 17 phenotype traits of the parents, F₁ and their F_2:4 recombinant individuals and analyzed, which will be beneficial for further understanding the genetics of these traits and reasonably utilizing the germplasm resources in breeding in pepper”.

Comments 4: Although the Introduction provides a broad overview of pepper diversity, the specific knowledge gap is not clearly defined. While previous studies are cited, the novelty of this study is not clearly articulated (e.g., use of a specific F_2:4 population, focus on trait combinations, or breeding relevance). This should be stated more explicitly.

Response 4: We totally agree with your comments and have made targeted revisions accordingly. In Lines 98–110 of the Introduction, we have supplemented the specific research gaps in current pepper genetic diversity studies and clarified that systematic investigations on artificially constructed segregating populations, especially pepper F_2:4 recombinant individuals segregating populations, are still inadequate. Meanwhile, we have explicitly illustrated the research novelty of this study. The artificially developed F_2:4 recombinant individuals used in this study exhibits abundant trait segregation and recombination variations. It serves as a core experimental population for exploring elite germplasm variants, dissecting genetic laws of agronomic traits, and analyzing trait correlations, thereby exerting an irreplaceable effect on crop germplasm innovation and directional breeding improvement.

3.Materials and Methods

Comments 1：The description of plant material is unclear and potentially misleading. The manuscript states that 588 materials were used, including two parental lines, an F1 hybrid, and 585 “recombinant offspring (F_2:4)”. However, it is not clear whether these represent individual plants, replicated plots, or distinct recombinant inbred lines. The structure and development of the F2:4 population are not sufficiently described.

Response 1：We accept the reviewer’s constructive comments and have revised the manuscript accordingly. In Lines 120–126 of the Materials and Methods section, we have corrected and clearly defined the total number of experimental materials, supplemented and improved the detailed descriptions of plant materials and population construction, and additionally provided a pedigree diagram illustrating the development of the pepper recombinant population. Specifically, we clarified that this study used a total of 588 materials, including two parental lines and 586 recombinant individuals plants. All F_2:4 individuals represent independent recombinant lines rather than plants from replicated plots, which eliminates potential ambiguity in material interpretation.

Comments 2: The authors should clarify: how the F_2:4 lines were generated, how many independent lines were evaluated, whether these represent stabilized recombinant inbred lines, how segregation and homozygosity were managed.

Response 2: We highly appreciate your valuable comment. We have added supplementary explanations in Lines 120–126 of the Materials and Methods section. The F_2:4 recombinant individuals in this study was developed via the single seed descent (SSD) method, consisting of a total of 586 independent lines. During population generation and selection, only one single plant was reserved for seed propagation in each generation. Although this population has not yet achieved complete homozygosity, it harbors sufficient genetic recombination information to meet the research requirements of the present study. Therefore, further purification of the population is unnecessary for the current research objectives.

Comments 3: Terminology such as “materials”, “offspring”, and “recombinants” should be used consistently throughout the manuscript.

Response 3: We appreciate this valuable comment. We have systematically revised and unified the relevant professional terms throughout the manuscript, and uniformly replaced the inconsistent expressions including **materials**, **offspring** and **recombinants** with **recombinant individuals** to ensure consistent terminology usage in the full text.

Experimental design:

Comments 1: Insufficient information is provided on replication, randomization, and experimental layout. The apparent lack of replication raises concerns about statistical robustness.

Response 1: Thank you for pointing this out. We have clarified that the F_2:4 recombinant individuals was generated through the single seed descent (SSD) method with 586 recombinant individuals. Although the population has not yet reached complete homozygosity and stability, it carries sufficient genetic recombination information and is suitable for the current germplasm evaluation and utilization study; further purification to achieve complete homozygosity may not be necessary.

Comments 2: It is unclear whether traits were measured on single plants or multiple replicates.

Response 2: We greatly appreciate your valuable comment. Phenotypic traits of all materials were measured on single individual plants for each line. We have supplemented and clarified this point in Lines 146–148 of the Materials and Methods section in the revised manuscript.

Comments 3：The number of plants per line is not specified, which is important for interpreting variability.

Response 3：We appreciate your valuable comment. Phenotypic traits of all materials were measured on a single representative plant per line. We have supplemented and clarified this point in Lines 146–148 of the Materials and Methods section in the revised manuscript.

Statistical analysis:

Comments 1：Although the authors state that data were standardized prior to PCA using SPSS, the exact procedure (e.g., z-score standardization) is not clearly described.

·       Response 1：Thanks for your useful comment. We have supplemented the description that Z-score standardization was performed using SPSS prior to principal component analysis (PCA), and this content has been added in Lines 165–175 of the revised manuscript.

·       Comments 2：Given that several traits are ordinal (categorical scores), the treatment of these variables in PCA should be clarified and justified. PCA assumes continuous variables, so its application to mixed data types should be justified.

·       Response 2：We appreciate your comment. Prior to principal component analysis, we performed Z-score standardization on the mixed data to eliminate dimensional differences before further analysis. The relevant description has been stated in Lines 198–200 of the revised manuscript.

Comments 3：It is not stated whether PCA was based on a correlation or covariance matrix.

Response 3：Thank you for this suggestion. PCA in this study was strictly performed based on the phenotypic correlation matrix, and we have supplemented this description in Lines 198–200 of the revised manuscript.

·       Comments 4：The clustering procedure is insufficiently described (distance metric, linkage method), and the choice of method is not justified.

·       Response 4：We highly appreciate this constructive suggestion. We have supplemented and refined the detailed description of the clustering procedure in Lines 190–196 of the revised manuscript. Specifically, based on the standardized phenotypic data, Euclidean distance was used as the distance metric, and the UPGMA method was adopted for systematic cluster analysis of parental lines and recombinant inbred lines with Origin 2021. Meanwhile, R-type cluster analysis was performed by the average linkage method based on the Pearson correlation matrix.

·       Comments 5：No cluster validation is provided, so the robustness of the group structure remains unclear.

Response 5：Thank you for your valuable comment. Since the individuals of the F_2:4 recombinant individuals in this study have not yet reached complete homozygosity, the cluster analysis is only exploratory in nature, and therefore no cluster validation was performed.

Comments 6: There is no information on how missing data or outliers were handled.

Response 6: Thanks for pointing out this issue. All accessions with missing data or outliers were directly excluded from the analysis to ensure the accuracy and reliability of the results. This has been described in Lines 154–156 of the revised manuscript.

·       4 Results:

·       Comments 1: The Results section mainly reports PCA and clustering outputs without sufficient context or validation. The authors should better link the results to the study objectives and clarify the biological or breeding relevance of each analysis.

Response 1: We highly appreciate this valuable and constructive comment. We acknowledge that the original Results section mainly presented the statistical outputs of PCA and cluster analysis, with insufficient contextual introduction and result interpretation. According to your suggestion, we have revised our Results section carefully. We have added necessary contextual descriptions before PCA and clustering analyses to connect these analyses with our research purpose. We have also supplemented the biological and breeding implications of the obtained trait grouping and correlation results, to explain the practical value of these multivariate analysis results for pepper germplasm evaluation and trait selection. These revisions make our results more targeted and logically coherent.

·       Comments 2: PCA results are presented, but interpretation is limited. Although six principal components are reported (Table 6), the biological meaning is only briefly discussed. The authors should more clearly explain how trait loadings support the interpretation of each component (e.g., why PC1 reflects plant growth and PC2 reflects color-related traits).

Response 2: We appreciate the valuable and constructive comment. Based on the trait loading matrix, we have supplemented and refined the biological interpretation of all six principal components in the revised manuscript (Lines 385–399). The key representative traits and their corresponding loading values for each principal component were clearly clarified to provide solid data support for biological interpretation.

Specifically, PC1 represents plant growth and plant architecture, as core vegetative traits including plant height, leaf blade length, primary stem height, plant canopy diameter, and leaf blade width showed high loading values (0.650–0.795). PC2 mainly reflects plant color characteristics, which is strongly supported by the high loadings of internode anthocyanin pigmentation, immature fruit color, and leaf color (0.578–0.775). The original descriptions of PC3, PC4, PC5, and PC6 were too brief and have been fully supplemented in this revision. PC3 is characterized by extremely high loadings of fruit attitude (0.894) and peduncle attitude (0.865), representing the flower and fruit attachment habits. PC4 is dominated by transverse fruit diameter (0.600) and reflects fruit width characteristics. PC5 comprehensively reflects fruit morphology and peel appearance based on the loadings of pericarp surface rugosity, mature fruit color, and longitudinal fruit diameter. PC6 is dominated by lamina transverse section morphology (0.747) and represents leaf structural characteristics.

In summary, all principal components were systematically interpreted based on loading data, which effectively improves the integrity of the PCA results.

Comments 3: The cumulative variance explained (62.97%) is moderate, meaning that a substantial proportion of variation remains unexplained. This limitation should be acknowledged, and overinterpretation of PCA results should be avoided.

Response 3: We appreciate this valuable reminder. We have supplemented relevant descriptions in the revised manuscript (Lines 378–380). We clearly stated that the cumulative variance contribution rate of 62.97% is only a moderate explanatory level, indicating that some phenotypic variation remains unexplained. Therefore, we only interpreted the results objectively and strictly avoided over-interpretation, and this analytical limitation has been fully acknowledged in the manuscript.

·       Comments 4: Clustering results (three groups and subgroups) are presented, but there is no evidence that these groupings are stable. Some form of support or at least acknowledgment of the exploratory nature of clustering is needed.

Response4: We fully agree with your professional suggestion. This study focuses on analyzing the phenotypic variation characteristics of the segregating population, and the clustering analysis based on multiple phenotypic indicators is essentially an exploratory phenotypic classification method. Therefore, we have clearly stated in Lines 329–332 of the revised manuscript that the clustering analysis in this study was performed merely as an exploratory classification based on phenotypic variation.

·       Comments 5：Group descriptions are largely narrative. Quantitative comparisons (e.g., means or ranges of key traits per group) would strengthen the interpretation.

Response 5: Thank you very much for your valuable suggestion. We fully agree that adding quantitative comparisons of key traits among groups could further enrich the interpretation of the results. In the revised manuscript, we have carefully polished and optimized the narrative descriptions of each group to make the grouping characteristics more explicit. Given the existing data arrangement of this study, we will not add additional quantitative comparison analysis in the current revision. We will fully take your constructive comment into account and carry out relevant quantitative comparisons among groups in our future research.

·       Comments 6：Statements such as “genetic tendency toward one parent” are not sufficiently supported. The authors should clarify the basis for this conclusion or rephrase it more cautiously.

Response 6: Thank you for pointing out this issue, and we fully agree with this comment. Accordingly, we have revised the absolute descriptions regarding the genetic tendency toward a single parent and replaced the relevant statement with “inclined to female parent phenotypic traits” in Line 357~358 of the revised manuscript.

Comments 7：The biological interpretation of clusters appears partly speculative and should be toned down unless supported by stronger evidence.

Response 7: We greatly appreciate the reviewer’s valuable and professional comments. We have revised the descriptions of clustering analysis and clarified that the phenotypic clustering in this study is merely an exploratory analysis. The detailed revisions are listed as follows:

Lines 329–332: The original sentence “providing direct evidence for the screening of excellent germplasms” was deleted, and the new statement “this clustering analysis is an exploratory classification based only on observed germplasm differences, without inferring potential underlying genetic regulatory mechanisms” was added. Only the objective fact that the clustering results reflect the cumulative differences in multiple agronomic traits among different lines was retained.

Lines 342–343: The original sentence “subgroup division is beneficial for the targeted screening of germplasm materials” was deleted, and the new statement “subgroup classification further refines the subtle trait differences within major groups” was added to avoid overwinter pretation of the clustering results.

Lines 344–351: The original sentence “Subgroup I-A can be used as materials for dwarf and dense-planting breeding, and Subgroup I-B has reference value for the improvement of pepper fruit peel traits” was deleted. Only the intuitive and objective phenotypic characteristics of each subgroup were retained.

Lines 352–362: The original sentences “provides intermediate germplasm materials for plant type optimization breeding” and “can be used as important germplasm resources for pepper fruit color improvement” were deleted. Only the phenotypic traits of the germplasms were objectively presented.

Lines 363–370: The original sentence “this group can be utilized for the breeding of high-biomass and high-yield pepper varieties” was deleted. The summary section only objectively illustrates the phenotypic analytical value of the clustering results.

We sincerely thank the reviewer for the careful guidance and valuable suggestions.

Comments 8: Some correlations are statistically significant but weak in magnitude. The difference between statistical significance and biological relevance should be clearly distinguished.

Response 8: Thank you very much for the professional comments. We fully agree that statistical significance does not equate to practical biological and breeding value. We have supplemented and revised the relevant content in the Results section to clearly distinguish statistical significance from practical biological relevance. The detailed modifications are listed as follows:

The third paragraph of the correlation analysis in the Results section (Lines 300–301). A core sentence was newly added to clarify the essential difference between statistical significance and practical breeding value: statistical significance does not fully correspond to practical biological and breeding value. This revision provides a rigorous logical basis for result interpretation and standardizes the analysis of subsequent correlation results.

Description of Cluster II correlation analysis in the Results section (Lines 303–307). Targeted explanations were supplemented for the two trait pairs in Cluster II that reached extremely significant levels but exhibited very low correlation coefficients (transverse diameter of fruit vs. node pubescence density, transverse diameter of fruit vs. fruit shape of apex). We clearly stated that although these correlations met the statistical significance standard, the practical phenotypic associations were weak. The results objectively indicated that fruit appearance and leaf morphological traits are relatively independent in phenotypic variation and can be improved separately in breeding practice, avoiding overinterpretation of statistically significant but weak correlations.

Summary of correlation results in the Results section (Lines 312–314). A comprehensive summary of weak significant correlations was supplemented. We clarified that most trait pairs with extremely significant correlations presented low correlation magnitudes, indicating that such statistically significant associations have limited practical reference value for phenotypic evaluation and trait selection.

Through the above revisions, we have clearly distinguished statistical significance from practical biological relevance in the phenotypic correlation analysis. We sincerely appreciate the reviewer’s valuable guidance.

5. Discussion

·       Comments 1：The discussion largely repeats the Results instead of providing deeper interpretation. The authors should focus on biological meaning and implications for breeding.

Response 1: We appreciate the valuable comments from the reviewer. We have removed the repetitive descriptions of raw experimental results in the Discussion section and supplemented in-depth interpretations of biological mechanisms and systematic discussions on breeding implications throughout the revised manuscript.

Detailed Modifications:

• Lines 425-429: Newly added content: "However, phenotypic variation is jointly determined by genetic and environmental factors, and phenotypic-based diversity evaluation can only reflect superficial crop variation rather than stable genetic differences. Therefore, the results of this study are exploratory".

• Lines 428-440: Deleted the absolute conclusion: "provides a reliable material basis for quantitative trait locus (QTL) mapping and genetic dissection", and supplemented discussions on relevant biological mechanisms and breeding limitations.

• Lines 452-455: Deleted the overoptimistic breeding statement: "provides the possibility for screening breakthrough breeding materials", and improved the rational discussion on the limitations of breeding application.

Comments 2: PCA and clustering are treated as definitive evidence, while they are exploratory methods. Interpretations should be more cautious.

Response 2: We fully agree with this comment. We have revised the absolute and definitive descriptions regarding principal component analysis (PCA) and cluster analysis in the Discussion section. We have clearly clarified that these two methods are only exploratory statistical tools and cannot be used as definitive evidence for judging population genetic differences.

Detailed Modifications:

• Lines 474-478: New Notably, principal component analysis only reflects superficial phenotypic variation patterns and cannot serve as a definitive basis for population genetic differentiation. All PCA results in this study can only be regarded as preliminary phenotypic references for germpl.

• Lines 496-498: Newly added content "The clustering results are only exploratory phenotypic analysis outcomes. The phenotypic similarity reflected by clustering cannot be equated with true genetic similarity, and the grouping results are susceptible to environmental conditions and phenotypic measurement errors.

·       Comments 3: Conclusions about genetic diversity and breeding value are too strong. Identifying promising materials based solely on these analyses may be premature and should be toned down.

Response 3: Thank you for the valuable comment. We have revised all definitive statements regarding population genetic diversity and germplasm breeding potential into preliminary and tentative academic descriptions to avoid overinterpretation and ensure rigorous conclusion interpretation.

Detailed Modifications:

• Lines 438-439: Deleted the definitive statement: "provides a reliable material basis", and revised it to: "may provide a potential material basis for subsequent QTL mapping, while its definite breeding superiority cannot be confirmed".

• Lines 452-455: Supplemented a restrictive statement: "These superior phenotypes were only observed under a single environmental condition, and their practical breeding value remains to be verified", which weakens the definitive judgment on breeding potential.

• Lines 551-555: Revised the original statement: "excellent breeding germplasms were screened" to: "the excellent germplasms screened in this study represent only preliminary results and are merely for reference in subsequent breeding research", clarifying the exploratory nature of the germplasm screening results.

·       Comments 4: Key limitations should be clearly acknowledged, especially unclear population structure (F_2:4), lack of replication, and limitations of the statistical approaches.

Response 4：We greatly appreciate the reviewer’s valuable suggestion. We have duly acknowledged and systematically elaborated the major limitations of this study in the revised Discussion section. Specifically, we have clarified that the F_2:4 population used in our study had not reached complete homozygosity and presented unstable genetic structure, which might bring confounding influences on phenotypic evaluation and genetic analysis. We also explicitly stated that the absence of sufficient biological and multi-environment replicates reduced the stability of phenotypic data and restricted the extrapolation of our results. Moreover, we emphasized that principal component analysis and cluster analysis were merely exploratory statistical methods, which could only interpret phenotypic correlations rather than infer genetic causality. The inherent limitations of conventional statistical models in robustness and applicability were also pointed out. In addition, we have supplemented that the present work was a preliminary exploratory study. Corresponding improvements, including constructing higher-generation homozygous populations, arranging multi-environment replicates, and adopting more precise analytical models, have been prospected in future research.

Comments 5: Environmental effects are mentioned but not sufficiently integrated into interpretation of results.

Response 5: We appreciate this valuable suggestion. We have incorporated the environmental interference effects into the interpretation of multiple results, including phenotypic variation, transgressive segregation, principal component analysis, and cluster analysis, and systematically improved the correlation analysis between our experimental findings and environmental factors.

Detailed Modifications:

Lines 425-429: Supplemented a restrictive statement regarding environmental effects: "However, phenotypic variation is jointly determined by genetic and environmental factors, and phenotypic-based diversity evaluation can only reflect superficial crop variation rather than stable genetic differences. Therefore, all results of this study should be interpreted cautiously and are only exploratory conclusions.".

Lines 452-455: Added a description of environmental limitations: "This phenomenon preliminarily indicates the potential of this population to generate novel phenotypic variation, but these superior variations were only observed under a single experimental condition, and their practical breeding value remains to be verified"; meanwhile, the absolute breeding conclusion was deleted:"provides the possibility for screening breakthrough materials for breeding".

Lines 474-478: Supplemented the explanation of environmental interference: "Importantly, this phenotypic clustering result is only an exploratory analysis and is susceptible to environmental influences. Phenotypic similarity cannot be equated with true genetic similarity, and clustering results cannot be directly used to judge the genetic relationships among lines.".

Comments 6: A clearer distinction between statistical patterns and biological interpretation is needed (e.g., correlations or clusters should not be directly interpreted as genetic relationships).

·       Response 6: This comment is very helpful for improving our manuscript. We have strictly distinguished phenotypic statistical patterns from authentic biological and genetic mechanisms in the revised manuscript and corrected the inappropriate interpretation of directly equating phenotypic statistical results to genetic relationships.

·       Detailed Modifications:

·       Lines 474-478: Added statements to distinguish statistical results from genetic mechanisms: “Notably, these observed phenotypic correlations are only statistical patterns rather than direct genetic evidence. Statistical association does not equal genetic causation (Niles, 1922), and such phenotypic associations are susceptible to environmental interference and cannot be directly interpreted as genetic linkage or causal genetic regulatory relationships”.

·       Lines 506-509: Added limitations of cluster analysis results: “Phenotypic similarity cannot be equated with true genetic similarity, and clustering grouping results cannot be directly used to judge the genetic relationships among lines”.

·       Comments 7: The current conclusions give the impression of definitive findings, while the analyses are exploratory and should be interpreted with caution.

Response 7: We greatly appreciate this valuable comment. We have revised all absolute and definitive conclusions throughout the manuscript and replaced them with prudent and objective exploratory statements.

Detailed Modifications:

Lines 438-440: Deleted the definitive statement: “provides a reliable material basis for quantitative trait locus (QTL) mapping and genetic dissection”, and replaced it with a prudent expression: “Such wide segregation may provide a potential material basis for subsequent QTL mapping, but cannot guarantee definite breeding superiority”.

Lines 520-523: Weakened the absolute genetic conclusion by adding a restrictive statement: “Nevertheless, the maternal phenotypic bias observed in our study is only a statistical phenotypic pattern. Due to the lack of molecular verification and multi-environment repetition, this phenomenon cannot be concluded as a stable genetic rule of this population”.

Lines452-455: Weakened the overconfident conclusion on breeding value by supplementing a prudent description: “These lines show good phenotypic performance under the single experimental condition of this study. However, it is premature to confirm their breeding value. Multi-environment identification and molecular verification are required before applying these materials to practical pepper breeding”.

Thank you again for this constructive suggestion.

References

·       Comments 1: The cited references are generally relevant. Approximately 33.33% were published within the last 5 years and 76.19% within the last 10 years, indicating reasonable coverage of recent literature. The average reference age is 8.54 years (10.81 years including the oldest reference from 1922). No self-citations were identified. Overall, the reference list is appropriate, although the manuscript would benefit from stronger integration of recent key studies in the discussion.

Response 1: Thank you very much for your valuable comments. We have carefully revised the Discussion section and supplemented relevant recent literature, including the reference below: [42] Singh A, Singh A, Barb J, et al. Genetic variation and germplasm usage[J]. Crop Improvement, 2023.

Response to Reviewer X Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions corrections highlighted in the resubmitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

Thank you for your comments and suggestions, which have been revised in full and explained in the point-by-point response.

Are all the cited references relevant to the research?

Must be improved

Is the research design appropriate?

Must be improved

Are the methods adequately described?

Must be improved

Are the results clearly presented?

Can be improved

Are the conclusions supported by the results?

Must be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: All the traits discussed in the manuscript are quantitative traits and transverse segregation with individuals above parental performances are obvious. Can the authors genotype and add dendrograms generated form the molecular data?

Response 1: Thank you for pointing this out. We agree with this comment. However, due to environmental factors, some plants failed to emerge normally, resulting in missing data. Therefore, it is not possible to supplement the molecular experiments and related analyses.

Comments 2: Authors used term seven quantitative traits and ten qualitative traits. In the context of plant breeding qualitative traits are the traits that have categories and controlled by major genes. In the discussion, authors mentioned that all these traits are controlled by QTLs. Instead of labelling them as quantitative traits and ten qualitative traits, use categorical and non-categorical traits. Categorical traits are groups and non-categorical traits are measured traits.

Response 2: Thank you for pointing this out. We agree with this comment. Therefore, We have revised terminology to categorical traits and non-categorical (measured) traits throughout the manuscript.

Comments 3: To get most of the results excel were used. Reproducibility is very low and can the authors use some reliable software such as Mathlab, GenAlex etc.

Response 3: Thank you for pointing this out. We agree with this comment. However, we only used Excel for preliminary data processing. For data calculation, statistical analysis and graphing, we adopted reliable software such as SPSS and Origin to ensure the accuracy and repeatability of the results. In future research, we will further optimize our analytical methods and use the recommended software to improve the reliability of our results.

Comments 4: 588 materials how many F₁ hybrids? I have noticed  in the abstract 586  recombinant offsprings (F_2:4) were mentioned , However, in the materials and methods, 588 individuals are mentioned with 585 recombinant offsprings (F_2:4). Can you please these number discrepancies?

Response 4: Thank you for pointing this out. We fully agree with this comment. We sincerely apologize for the negligence in the writing process. In this study, a total of 588 pepper materials were used for the experiment. Two pepper lines with significantly different phenotypic traits were selected as parental materials: the female parent C152 (dwarf plant, purple flower, and purple-black pericarp) and the male parent A37 (tall plant, white flower, and green pericarp). An F_2:4 recombinant individuals containing 586 individual plants was developed via the single seed descent (SSD) method. We have revised and supplemented the above detailed information in Lines 120–126 of the Materials and Methods section in the revised manuscript.

Comments 5: What is plastic grass house? What are the environmental conditions and how do you regulate the environmental conditions in there?

Response 5: Thank you very much for this valuable suggestion. All experimental materials in this study were sown and nursed uniformly in January 2024 and transplanted to plastic greenhouses in Xigu District, Lanzhou City, Gansu Province, China, in March 2024. The experimental site is approximately 1600 m above sea level, and the soil type is the loessal soil with flat terrain and uniform soil fertility. All plants were cultivated and managed under uniform environmental conditions, with drip irrigation applied to regulate water and fertilizer supply. The corresponding detailed information has been supplemented in Sections 2.2 Field Conditions and 2.3 Experimental Design and Cultivation Management of the Materials and Methods section in the revised manuscript.

Comments 6: Can you give the geo-location using longitudes and latitudes?

Response 6: Thank you for your valuable comment. We have supplemented the relevant content in Lines 130–133 of the Materials and Methods section. This experiment was carried out in Xigu District, Lanzhou City, Gansu Province, China, with the geographical coordinates of 103°18′–103°42′ E and 35°58′–36°15′ N.

Comments 7: How old the plants are when conducting the measurements?

Response 7: Thank you for pointing this out. All pepper materials used in this study are annual crops. All seedlings were transplanted in March (spring), and phenotypic trait data were uniformly collected in August. We have supplemented the above content in Line 143 of the Materials and Methods section in the revised manuscript.

Comments 8: Please mention the relevant software versions with the references

Response 8: Thank you for pointing this out. We have supplemented the software version information and corresponding references in Section 2.5 Statistical Analysis of the Materials and Methods. The software used in this study included SPSS 26.0 (IBM Corp., Armonk, NY, USA) and Origin 2021 (Origin Lab Corp., Northampton, MA, USA).

Comments 9: Can you specify the R-type clustering? Did you use R to generate the dendrograms? Or did you use unsupervised ML techniques?

Response 9: Thank you for pointing out this issue. We have supplemented the detailed procedure of R-type cluster analysis in Lines 194–200 of the revised manuscript. The detailed description is as follows: “In this study, the raw data of 17 pepper phenotypic traits were firstly preprocessed by Z-score standardization. SPSS 26.0 was used to perform Pearson correlation analysis to calculate the pairwise correlation coefficients and corresponding P-values among phenotypic traits, and the Pearson correlation matrix was generated accordingly. Subsequently, based on the Z-score standardized data, R-type cluster analysis was performed using the average linkage method in Origin 2021 (Origin Lab Corp., Northampton, MA, USA), and the clustering dendrogram and correlation heatmap were constructed and visualized.” Notably, no R programming language or unsupervised machine learning algorithms were applied throughout the whole analysis process. All statistical analyses and cluster procedures in this study were performed using traditional and classical statistical methods.

Comments 10: For clear presentation please put the tables (1-5) in landscape format.

Response 10: Thank you for pointing this out. We agree with this comment. Therefore, we have arranged Tables 1–5 in landscape format.

Comments 11: In the dendrogram how male parent can appeared in both Group I and Group III. Where is the female parent (line 182-184)

Response 11: We appreciate your valuable comment. We fully agree with this suggestion. The revision has been made in Lines 341–343 of the manuscript, and the corrected text is as follows: Group I contains female parent C152; Group III contains male parent A37.

Comments 12: Can you include the figure for PCA analysis? and how did you perform it. include int eh method section.

Response 12: Thank you very much for your constructive comment. In this study, we only performed principal component analysis (PCA) using SPSS software and obtained the corresponding PCA statistical tables, but did not construct PCA figures. We have added the detailed implementation procedure of PCA based on SPSS in the Materials and Methods section at Lines 202–204 of the revised manuscript, including data standardization, analysis parameters and output indexes.

Comments 13: What does this mean? (Error! Not a valid bookmark 216 self-reference.). (Line 216)

Response 13: We appreciate your valuable comment. We agree with this remark. Accordingly, we have corrected the error and properly cited the PCA table in Line 385 of the manuscript.

Comments 14: Most of the discussion was included the past literature without commenting them to the findings present in the manuscript. So, discussing the findings connecting with the available literature is recommended.

Response 14: This comment is very helpful for improving our manuscript. We sincerely appreciate your valuable suggestion. We have closely integrated all cited literature with our experimental results. By comparing with previous studies, we systematically analyzed the consistency, differences, and inherent limitations of our findings, which effectively avoids simple literature stacking and makes the discussion more targeted and rigorous.

Detailed Modifications:

First paragraph of Discussion: Newly added content: "However, phenotypic variation is jointly determined by genetic and environmental factors, and phenotypic-based diversity evaluation can only reflect superficial crop variation rather than stable genetic differences. Therefore, all results of this study should be interpreted cautiously and are only exploratory conclusions". The previous literature (Guo et al., 2023; Oladosu et al., 2021; Tian et al., 2024; Liu et al., 2022) was closely combined with our phenotypic results to verify the rationality of our evaluation method. Meanwhile, the limitations of this study were supplemented based on previous evidence, ensuring that all literature serves the interpretation of our own results.

Lines 435-448 (Population variation paragraph): Deleted content: "...provides a reliable material basis for quantitative trait locus (QTL) mapping and genetic dissection..."; Newly added content: "...Such wide segregation may provide a potential material basis for subsequent QTL mapping, but cannot guarantee definite breeding superiority....". The cited literature (Islam et al., 2025; Yang et al., 2024; Chithra et al., 2022) was no longer simply stacked. We compared the phenotypic variation characteristics of the pepper RIL population in this study with previously reported RIL results in soybean and maize, confirmed the general consistency of population variation patterns, and clarified that our findings are only preliminary phenotypic conclusions.

Lines 449-463 (Transgressive segregation paragraph): Deleted content: "...provides the possibility for screening breakthrough materials for breeding...."; Newly added content: "...This phenomenon preliminarily indicates the potential of this population to generate novel phenotypic variation, but these superior variations were only observed under a single experimental condition, and their practical breeding value remains to be verified....". Based on the reported genetic recombination and gene interaction mechanisms (Chauhan and Chandel., 2016; Koide et al., 2019), we specifically explained the formation of transgressive segregation and novel immature white fruit trait in our population, realizing one-to-one correspondence between literature evidence and our results.

Trait correlation paragraph: No content was deleted. Newly added content: "...Notably, these observed phenotypic correlations are only statistical patterns rather than direct genetic evidence. Statistical association does not equal genetic causation (Niles, 1922), and such phenotypic associations are susceptible to environmental interference and cannot be directly interpreted as genetic linkage or causal genetic regulatory relationships....". Combined with previous conclusions (Monson et al., 2022; Rowland et al., 2020; Cui et al., 2020), all literature was used to interpret our trait correlation results rather than providing redundant background introduction, eliminating the problem of pure literature stacking.

PCA analysis paragraph: Deleted redundant textbook-style content: "...PCA is a multivariate statistical analysis technique that can condense a large number of variables into a small number of principal components, which simplifies data analysis while retaining core information...."; Newly added content: "...Notably, principal component analysis is only an exploratory phenotypic statistical method and cannot provide definitive genetic evidence. Without multi-environment validation, the variation pattern observed in this study cannot be summarized as a stable genetic rule....". Core published literature (Pereira-Dias et al., 2019; Dutta et al., 2018; Luitel et al., 2018; Bedjaoui et al., 2022) was retained and compared with our PCA cumulative variation explanation rate and PC1 variation characteristics to conduct in-depth discussion of our results.

Comments 15: English is good. How ever some terminologies used in the manuscript were confusing need revising those terminologies. 

Response 15: We appreciate this valuable comment. We have systematically revised and unified the relevant professional terms throughout the manuscript, and uniformly replaced the inconsistent expressions including **materials**, **offspring** and **recombinants** with **recombinant individuals** to ensure consistent terminology usage in the full text.

Response to Reviewer X Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions corrections highlighted in the re-submitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

Thank you for your comments and suggestions, which have been revised in full and explained in the point-by-point response.

Are all the cited references relevant to the research?

Yes

Is the research design appropriate?

Yes

Are the methods adequately described?

Yes

Are the results clearly presented?

Yes

Are the conclusions supported by the results?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Lines 36-38: "climate change, pests and diseases, as well as the diversification of market demands" this is too broad. Focus specifically on pepper breeding challenges.

Response 1: Thanks for your careful review and valuable suggestion. We fully accept this comment. We have revised the manuscript, with the modified content presented in Lines 39–42. The original sentence "However, with the intensification of climate change, pests and diseases, as well as the diversification of market demands, the stress resistance, yield, and quality of traditional pepper varieties have become increasingly difficult to meet the requirements of modern agricultural production" has been replaced with "With the upgrading of consumer quality demand and the large-scale development of protected vegetable industry, higher requirements have been put forward for the yield potential, quality stability, stress resistance and comprehensive agronomic traits of pepper varieties".

Comments 2: Lines 75 and 86: The population size is reported as 585 offspring in line 75, but line 86 states 585 recombinant offspring (F_2:4). Line 11 states 586 F2:4 recombinants. Line 180 states 586 recombinants. Please unify the population size throughout the manuscript.

Response 2: We appreciate your valuable and constructive comment. We have uniformly described the population size in the manuscript as 586 F_2:4 recombinant individuals

Comments 3: Lines 180-184: The text states Group I contains the male parent and 319 recombinants, Group III contains male parent and 41 recombinants. This appears contradictory. Please clarify: is the male parent in both groups? Or should one be the female parent (C152)? Check Figure 1 legend and corresponding text.

Response 3: We appreciate your valuable comment. The female and male parents belong to different groups: Group I contains the female parent C152, while Group III contains the male parent A37. We have revised the relevant content in Lines 338–340 of the manuscript.

Comments 4: Line 216: Contains "Error! Not a valid bookmark self-reference." This needs to be corrected to properly reference the PCA table or figure.

Response 4: We greatly appreciate your valuable and constructive comment. We agree with this remark. Accordingly, we have corrected the error and properly cited the PCA table in Line 382 of the manuscript.

Comments 5: Table 3: The CV values for parents (e.g., longitudinal diameter of fruit: 1.08 = 108%) are extremely high, suggesting the parental data may have been calculated from only two individuals? Please clarify whether parental CVs are based on replicated measurements or single plants. CV > 100% is unusual for parental lines.

Response 5: Thank you for pointing out this issue. The coefficient of variation (CV) of the two parents was calculated based on individual plant data rather than data from replicated plots, which led to relatively higher CV values. We have supplemented the corresponding explanation in Lines 206–207 of the revised manuscript.

Comments 6: Figure 1: The R-type clustering dendrogram is shown, but the correlation heatmap or matrix visualization is mentioned but not clearly presented. Consider adding a correlation heatmap with significance markers as a separate figure or supplement

Response 6 : We thank the reviewer for this helpful comment. We have now included a revised correlation heatmap with significance markers (Figure 2) as a new figure in the manuscript, as shown below:

Figure 2. Heatmap of correlation analysis for 17 phenotypic traits

Comments 7: Line 217: Cumulative variance is 62.97% from six principal components. This is relatively low for phenotypic data. Consider discussing whether additional traits or environmental replication might increase explained variance.

Response 7: Thank you for pointing this out. We agree with this comment. The cumulative variance of 62.97% explained by six principal components may appear relatively low for phenotypic data. This is likely due to the abundant genetic variation and complex genetic basis of the phenotypic traits evaluated in this pepper population, as well as the inherent variability of field phenotypic investigation. Considering the rich diversity and complex genetic architecture of the tested traits, the current interpretation rate of variance is reasonable and acceptable for genetic diversity analysis. Therefore, we have retained the original description in the revised manuscript.

Comments 8: Lines 321-327: The claim that female parent genotype is retained at high frequency needs supporting evidence. Without molecular markers, you cannot distinguish between maternal inheritance, selection bias, or environmental effects. Please moderate this claim or add a caveat.

Response 8: We appreciate your valuable and constructive comment. We have moderated the original conclusion in Lines 563–565 of the manuscript and clarified that this conclusion is only derived from phenotypic data. Specifically, the original sentence “Traits for which the recombinant offspring clustered together with the female parent accounted for 57.34%, reflecting that the genetic background of the female parent is retained at a high frequency in the offspring” has been revised to “Traits for which the recombinant offspring clustered together with the female parent accounted for 57.34%, indicating a phenotypic tendency toward the female parent.”