-->PONE-D-25-65645-->-->Genetic Diversity and Yield Trait Associations in Soybean Germplasm Using SSR Markers-->-->PLOS One

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Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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-->5. Review Comments to the Author

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Reviewer #1: Reviewer report

The manuscript presents a solid analysis of genetic diversity and SSR-based marker-trait associations in soybean germplasm evaluated across two environments. The study is technically sound and the results are clearly presented. However, the novelty is limited, and several methodological issues require clarification and discussion. Addressing these points would improve the rigor and interpretability of the findings.

Comments

1. While this manuscript does a solid job using SSR markers to map soybean genetic diversity, it’s currently missing a clear 'hook.' Since high-density SNP data is now the standard, the authors really need to highlight what makes this specific study unique. What does this research tell us that previous SSR studies missed? Strengthening the Introduction to clearly state this novelty would make the paper much more compelling (Lines 11-31).

2. Since the phenotypic data comes from just the 2024 season, it’s difficult to tell how much these yield traits fluctuate due to the environment. Because yield is so sensitive to outside factors, the results would be much more robust if we could see trait stability over time. At the very least, the authors should be upfront about this limitation in the discussion and explain how it might influence their conclusions (Lines 87-96).

3. The two test sites are geographically close and share similar weather patterns, I’m wondering if they actually provided enough environmental contrast for the study. It would be helpful if the authors could clarify how distinct these sites really are - otherwise, it becomes difficult to accurately interpret the genotype × environment interactions. A bit more detail here would help justify the experimental design. (Lines 87-92).

4. DNA was extracted from pooled leaf samples per accession. While this approach reduces intra-accession variation, it may also mask within-accession heterogeneity. The rationale and potential consequences of this strategy should be discussed. (Lines 97-100).

5. Using 20 SSR markers to characterize 150 accessions provides a relatively thin look at the genome. With such low marker density, there’s a risk that we’re missing a lot of the genetic landscape, which could make the diversity estimates and association results less reliable. The authors should explicitly address how this limited coverage might impact the strength of their conclusions. (Lines 123-129).

6. The transition from the STRUCTURE analysis to the TASSEL MLM needs more detail. Specifically, the authors should explain how they incorporated the Q and K matrices into their model. Providing the exact configuration used will help clarify how they controlled for population stratification and relatedness. (Lines 161-168; 271-273).

7. The authors set the significance threshold at P < 0.05, but they didn't account for multiple testing. When you're looking at several traits across multiple markers, the odds of finding 'significant' hits by pure chance go way up. To make these associations more believable, the authors should either justify why a raw p-value is sufficient here or, better yet, apply a correction like FDR or Bonferroni to filter out potential false positives. (Lines 170-172).

8. Although the analyses suggest a possible relationship between genetic clustering and geographic origin, this aspect is not explored in depth. The authors are encouraged to examine this relationship more explicitly, for example by assessing whether geographic origin meaningfully contributes to the observed population structure or whether extensive admixture weakens this association. Clarifying this point would substantially strengthen the population genetic interpretation. (Lines 231-238).

9. The UPGMA, PCoA, and STRUCTURE analyses reveal largely consistent patterns. To improve readability, the Results section could be streamlined by reducing repetitive descriptions and instead highlighting the complementary insights provided by each analytical approach. (Lines 231-268).

10. To get a better sense of how well the PCoA represents the actual data, it would be really helpful if the authors could include the percentage of variance explained by the first three principal coordinates. Without those numbers, it’s hard for the reader to tell how much of the genetic story is actually being captured by the plot and how much is just 'noise. (Lines 243-251).

11. Most associated markers account for only a small proportion of the phenotypic variation (around 2–6%). A more critical discussion of their biological significance and practical value for breeding would strengthen the interpretation of these results. (Lines 287-296).

12. Several sections of the Discussion repeat similar points about genetic diversity and the effectiveness of SSR markers. Condensing these passages would improve clarity and allow greater focus on interpretation rather than repetition. (Lines 298-358).

13. The authors report moderate values for PIC, gene diversity, and effective allele number, which is a good start. However, to really understand what these numbers mean for this specific population, it would be helpful to see them compared to previous soybean diversity studies. Placing these results in the context of earlier SSR-based research would clarify whether this germplasm is more or less diverse than what's typically seen in the field. (Lines 341-345).

14. The association results would be strengthened by a clearer comparison with previously published soybean GWAS or QTL studies on yield-related traits. (Lines 391-409).

15. While the manuscript reports stable SSR–trait associations, these markers are not linked to known genomic regions or candidate genes. Even a brief discussion placing them in a genomic context would strengthen the study. (Lines 403-420).

16. Terms such as “hundred-grain weight” and “hundred-seed weight” are used interchangeably. Please standardize terminology throughout. (Throughout manuscript).

17. The manuscript is generally well written, but some sentences – especially in the Discussion – are overly long and repetitive. Careful language editing would improve readability.

Reviewer #2: The authors used SSR markers to study genetic diversity and yield trait correlations in soybean germplasm. The study contains no new information. The manuscript needs multiple modifications.

Abstract

• The issue of the study is unavailable.

• Some scored data about the morphological traits should be included

• The abbreviated SSR should be defined in full name

• A brief conclusion should be written at the end of the abstract

Keywords

The words used for the creation of the title should not be used as the keywords

Introduction

• Recent references should be used

• The authors should provide some lines about genetic diversity methods.

• The methods of association between markers and quantitative traits should be stated

• The gap in the research should be highlighted.

• The hypothesis of the study should be determined

• Originality of the work should be added

Materials and Methods

• All methods used should be supported by the references.

• The manufacture of all materials and instruments should be defined

• All abbreviations should be written in full

Results and Discussion

• The significant status of each measurement should be stated

• The details of the results in the tables and figures must be documented.

• All captions should be improved

• The genetic diversity indices should be performed for populations.

• The gene flow and fixation index should be computed

• The discussion is poor and should be improved. There are a few repeated lines. The contributors should analyze the link between all researched features by explaining the minimum and maximum results. The writers should interpret the relationship between each of the parameters being studied.

Conclusions

• Conclusions should include the most important findings. Additional works should be included.

Reviewer #3: The study aims to evaluate the genetic diversity, and marker trait associations between SSR markers and yield related traits in 150 soybean accessions. The study utilized 20 pairs of SSR primers to genotype diverse soybean accessions. The study was conducted across two environmental conditions.

This study has following strengths:

1. The objective “understanding genetic diversity and identifying marker–trait associations in soybean” is clearly stated and highly relevant to breeding programs.

2. The authors specify the number of SSR primers (20), sample size (150 accessions), and the use of two environmental conditions.

3. The inclusion of multiple analytical methods (GLM, MLM, cluster analysis, PCoA, population structure analysis) shows methodological rigor.

4. The study provides detailed quantitative results (e.g., allele numbers, PIC values, polymorphism rates). Identifying 44 markers associated with yield traits and 6 consistently across environments is a strong highlight.

5. The intended application is clearly stated highlighting the practical value of the study.

Following are few suggestions:

1. Twelve key agronomic traits are mentioned in the abstract. Do add some specific details (what they are? Some description related to substantial phenotypic variation?).

2. Line 30: What you mean by “future conservation management” and “scientific breeding”? Can you replace these with some more familiar terms?

3. Line 34: Provide reference related to “biofuel production”.

4. Line 45 to 47: Simplify the statement “Substantial beneficial ……………..”

5. Line 48 and 51: Is using the term “divergent” or “diverse” right here?

6. Line 64: Please provide reference.

7. Line 68: Please provide reference.

8. Line 69: Please use some simple word instead of “corroborated”

9. Line 75: Use some other word instead of “safeguarding”.

10. Line 88 and 90: Please explain why these environments (E1 and E2) were chosen for this study? How these environments are different? Based on environmental factors, do they differ? Instead of using two similar types of environments, wasn’t it better to go for multi-year study?

11. In results and discussion, please emphasize on interpretations. For example, what do the high polymorphism rates imply? How does clustering across environments support your conclusions? A sentence tying the results to broader implications would strengthen the narrative.

Conclusion/Recommendations

1. Overall, the study presents promising findings. The article is concise and well-written.

2. Recommendation: Minor Revision

Reviewer #4: The research article entitled “Genetic Diversity and Yield Trait Associations in Soybean Germplasm Using SSR Markers” is generally well organized, and the research objectives, methodology, and results are presented in a logical sequence. Most sections are understandable, and the figures and tables appropriately support the text. The manuscript has presented valuable data on Chinese soybean germplasm. However, following comments should be addressed to improve the scientific rigor and clarity of the study.

Major Comments:

1. The field study is only for one year with two locations. Multi-year or multi-location data would strengthen study, and authors should acknowledge this limitation.

2. As the study is comprehensive, however there is need to highlight novel markers associations and the special germplasm. The authors should emphasize how their findings extend beyond previously published soybean SSR studies.

3. PVE values for SSR markers are relatively low (2–6%), authors should discuss the implications for breeding and whether these loci are sufficiently informative for MAS. For instance, in table S6, PVE ranges from 3.22% to 3.90% for the trait plant height indicating that each SSR marker explains only a small fraction of variation in plant height. While this is expected for complex quantitative traits, the manuscript does not adequately discuss the limited practical utility of such low-effect markers for marker-assisted selection. This limitation should be explicitly acknowledged.

4. The supplementary tables report nominal P-values (<0.05), but there is no indication that multiple testing correction (e.g., Bonferroni or FDR) was applied. Given the large number of markers–trait tests typically performed, the absence of correction raises concerns about inflated Type I error.

5. Several markers identified by GLM (are not confirmed by MLM, suggesting potential false positives due to population structure effects. Authors should be cautious in reporting such association and they should emphasize MLM-validated markers. e.g., Satt302 and Satt278 Trait: Pods per Plant

6. Needs to recheck Table S1 critically about the total germplasm, 150 0r 148, as some lines such as NF867 and HH705 are repeated. If the accessions are repeated, then write the paper accordingly.

Minor Comments:

• Need to add more columns in Table S1 such as “Type” (e.g., landrace, improved variety), accession numbers, collection years, or source institutions. Such information is often expected in germplasm tables to allow reproducibility and traceability.

• Some germplasm names include spaces, hyphens, or inconsistent capitalization, which needs uniformity which improves professionalism.

• Table S2: Position” is ambiguous. Does it refer to chromosomal position? Need to clarify.

• Table S2: There is a typo in row 5 (Annealing Temperature: 561 ℃), which is clearly impossible. Such errors reduce confidence in the data.

• Needs uniformity, “Sat_385” and “Satt220.” why some use underscores, some do not? Need to re-check the completed paper

• Table S2: Make sure the formatting is consistent (some have extra spaces, e.g., “58.2 ℃”).

• Table S3: Some numeric entries, e.g., Hundred-seed weight in E2 (24.94366197), are reported with excessive decimal places. Needs rounding to 2–3 decimal places in all other tables too

• Table S3: Need to mention the unit of some agronomic trait such plant height, cm? stem diameter, cm or mm? Units should be included for all the traits in other tables too such as S4

• Table S4: The sample IDs (e.g., 38.00, 30.00) are confusing—are these, averages of specific samples? A clear explanation in a footnote is necessary.

• The term “Interpretation rate (%)” should be replaced with “Phenotypic variance explained (PVE in all the relevant tables.

• While two environments were used, the discussion could more explicitly address environment-specific vs. stable associations, particularly for markers detected in both E1 and E2.

• Decimal precision is inconsistent in all the supplementary tables (e.g., P values reported with varying numbers of digits). For instance, in S15, (some have many decimals, e.g., 0.048112905) which is unnecessary; 3–4 decimal places are sufficient.

• Need to write either 100-seed weight or hundred-grain weight

• Needs to highlight markers detected across both environments and models (GLM and MLM) in all the relevant tables

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Reviewer #1: Yes: Alibek Zatybekov

Reviewer #2: Yes: Nawroz Tahir

Reviewer #3: No

Reviewer #4: No

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<div>PONE-D-25-65645R1-->-->Genetic Diversity and Yield Trait Associations in Soybean Germplasm Using SSR Markers-->-->PLOS One

Dear Dr. Zong,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 10 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

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Reviewer #5: (No Response)

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #5: Partly

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #5: No

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-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #5: No

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Reviewer #5: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #5: This manuscript presents an interesting study evaluating 150 soybean genotypes using both phenotypic traits and genetic markers across two distinct environmental conditions. The research is highly relevant within the context of crop breeding and aligns well with the economic and agricultural significance of this crop. However, the study also exhibits several flaws, discrepancies, and conceptual inconsistencies that are of fundamental importance to its scientific contribution and overall validity. These critical issues must be thoroughly addressed and resolved before the manuscript can be considered for publication.

Line 81–82: In the supplementary material, I noticed that most loci are not stable across the two environments. I highly recommend removing this specific part of the sentence.

Lines 102–104: The authors state that they collected 3 leaves per plant. However, it is unclear from how many total plants the leaves were sampled. Were they collected from every replication of the samples and across both environments? If so, the total volume of leaf tissue would be far too large for the commercial extraction kit used. Furthermore, how did this fit into a 5 mL tube, and how can the authors guarantee that taking a tiny 30 mg subsample from such a large bulk mass provides a truly representative sample? There is a lack of clarity here that can be easily resolved.

Line 107: Given that you have three plant replications, please specify how the three random plants were selected. I assume three plants from each environment were used for morphological measurements.

Line 122: Please clarify how many samples were used and from which specific part of the plant they were taken.

Line 122: Shannon-Weaver diversity index – There is no reference, formula, or software package cited for this method. It is unclear whether a software tool was used or if it was calculated based on a previously described protocol in literature. Please add a formal reference or the exact formula to ensure clarity and reproducibility.

Line 165: The software version for iTOL is missing.

Line 179: Phenotypic variation explained (R2) – This specific abbreviation appears only in the Materials and Methods section, after which it is abruptly changed to "PVE". This inconsistency must be standardized throughout the manuscript.

Line 142: The text states a fixed annealing temperature of 50°C. However, Supplementary Figure 2 indicates that the annealing temperatures for each primer actually vary from 40.3°C to 60.5°C. Please resolve this discrepancy.

Figure 2: Figure 2 presents data for only 40 individuals. While I fully understand the technical impossibility of running all samples on a single gel for direct comparison, I strongly recommend adding the remaining gels for at least one marker. If not included as a main figure, this data should be provided as supplementary material. This will allow the reader to gain a clear understanding of how the scoring and sizing of alleles were standardized and compared across separate gels.

Line 265: Supplementary material regarding the Q-values is missing. Since these values are directly utilized in the association analysis, providing this data is essential. Looking at Figure 5, it can be inferred that there are accessions with a Q-value < 0.6, which implies a mixed origin (admixture). However, this is not discussed in the text. Are all your accessions at Q > 0.6? Without a data table, this cannot be verified. Please provide the supplementary data and discuss how many accessions fall into this admixture category. Additionally, Figures 5b and 5c are sufficient; Figure 5a appears redundant.

Line 281: It is mathematically and conceptually impossible for the General Linear Model (GLM) and the Mixed Linear Model (MLM) to yield identical p-values for the same 20 loci. The MLM model, by definition, introduces the Kinship matrix (K) as a random effect to control for population stratification, which inherently reduces the statistical power (resulting in higher p-values) and lowers the individual PVE of the markers compared to an uncorrected GLM. The identical metrics reported by the authors strongly suggest either a major data formatting error or that the MLM multi-locus correction was not executed properly in the software. The authors must provide the exact log outputs and original multi-model comparison charts to clear this discrepancy.

Lines 309–327: This section contains excessive redundancies that hinder the reader's ability to comprehend your claims and conclusions. Reading the phrase "genetic diversity" fifteen times causes the core message to get lost in the text. I recommend introducing the official abbreviation for the index—Shannon-Weaver diversity index (H')—early in the Materials and Methods and within the tables to reduce text density. Crucially, you must clarify that you are discussing a diversity index calculated from phenotypic characteristics, not molecular ones. The current phrasing is highly confusing. Please shorten the paragraph, remove repetitive statements, and rewrite this section using precise terminology. Furthermore, your interpretation is logically flawed: you draw direct conclusions about genetic variation and breeding potential solely from morphological variation. It would be far more logical to move these conclusions to the section where you discuss the molecular-derived indices—gene diversity index (H) and Shannon information index (I). As your own data demonstrates, morphology is heavily influenced even by subtle environmental variations, resulting in different clusters and different marker associations across the two environments.

Lines 337–360: There is severe confusion, repetition, and data overlap in this section. References 27 and 29 repeat the exact same data points within the citations, and reference 29 is not even related to soybean research. This paragraph requires a complete rewrite.

Lines 361–377: The authors spend considerable effort trying to justify the discrepancies between molecular and phenotypic clustering, as well as the lack of correlation with geographical origin. However, a non-overlapping pattern between neutral molecular markers (SSR) and adaptive phenotypic traits is a widely accepted norm in plant genetics, not an anomaly. More importantly, the authors completely miss the opportunity to explain this phenomenon using their own Population Structure and GLM/MLM data. Please add a discussion regarding how much of the investigated material falls into the genetic admixture (mixed origin) category, and whether these cultivars and lines share a common genetic base. Furthermore, the phrase "unfavorable cultivation conditions" is inappropriate in this context; please justify or remove it. Your locus association data varies heavily between the two environments despite the similar conditions, indicating a substantial Genotype-by-Environment interaction—this must be discussed here. Finally, your cluster analysis overlaps with the structure analysis, showing 3 main groups, which indicates a shared genetic background, with the majority of the material falling into a single group.

Addressing this will also help you eliminate the superficial remarks in the subsequent lines. On lines 379–380, the authors state that UPGMA clustering is "prone to human-induced biases". UPGMA is a deterministic, distance-based mathematical algorithm that operates without human intervention. The authors must explain what "human biases" they are referring to. On line 381, the authors state that STRUCTURE analysis yields "more distinct phenotypic characteristics of the clusters". This is fundamentally incorrect. STRUCTURE is a genotype-based Bayesian clustering method that uses allele frequencies. Genetic admixture and complex population stratification actually increase the risk of false-positive marker-trait associations. This is precisely why mixed linear models (MLM) using Q (structure) and K (kinship) matrices are required.

Conclusion (Line 430): The authors use the term "genome-wide SSR linkage mapping". This is conceptually incorrect. Linkage mapping strictly requires bi-parental mapping populations to track recombination frequencies. Since this study utilizes a natural population panel of 150 independent cultivars, the correct terminology is Association mapping (or Association analysis). The authors must replace "linkage mapping" with "association mapping" throughout the manuscript to maintain scientific accuracy.

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Reviewer #5: No

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<div>PONE-D-25-65645R2-->-->Genetic Diversity and Yield Trait Associations in Soybean Germplasm Using SSR Markers-->-->PLOS One

Dear Dr. Zong,

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Reviewer #5: (No Response)

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Reviewer #5: Partly

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Reviewer #5: No

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Reviewer #5: No

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Reviewer #5: Yes

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Reviewer #5: Reviewer Report (Round 2)The manuscript has been significantly improved, and the authors have correctly addressed most of the recommendations provided. I have only two remaining recommendations at this stage: Regarding my previous comment about the identical statistical metrics between the GLM and MLM models: To mathematically support their explanation and results regarding the overlapping p-values, I highly recommend that the authors include the corresponding Quantile-Quantile plots (QQ-plots) for both GLM and MLM in the Supplementary Material. Even providing a representative subset of these plots would be highly beneficial. A QQ-plot is the ideal diagnostic tool to perfectly visualize this overlap and will provide the readers with a clear understanding of the minimal kinship correction effect on these specific loci.

Regarding my comment on Lines 361–377 (Discrepancies in clustering and G X E interaction): It appears that the authors made a technical oversight during the revision process by pasting the exact same response from the previous question, thereby leaving this comment unaddressed. I strongly advise the authors to revisit this point carefully. Addressing the expected discrepancy between neutral SSRs and adaptive traits, interpreting the genetic admixture from their data, and discussing the apparent Genotype-by-Environment (G X E) interaction will greatly enrich their Discussion section and ensure it is fully substantiated by their actual findings. Additionally, upon reviewing the newly provided population structure table, I noticed a mathematical inconsistency in the Q-matrix coefficients. For several accessions, such as individuals 141, 133, 124, and 117, the membership probabilities across the genetic groups do not sum up to 1.00 (100%). By definition, the rows of a Q-matrix must sum to exactly one. I kindly request the authors to double-check their source data and correct these formatting or calculation errors to ensure the validity of the structure covariates used in the models.

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Reviewer #5: No

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Genetic Diversity and Yield Trait Associations in Soybean Germplasm Using SSR Markers

PONE-D-25-65645R3

Dear Dr. Zong,

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Academic Editor

PLOS One