CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels

Round 1

Reviewer 1 Report

 

In this study, Li et al., developed a semantic segmentation model to accurately segment maize kernels and their internal structures. Six traits of kernel structure were used for the genome-wide association analysis. A total of 26 significant SNP loci were 17 associated, and 62 candidate genes were identified. This work is innovative and useful valuable to apply to other crops.

 

Please consider the following comments:

 

1. The innovation and research significance as well as limitation in this study should be pointed out in the discussion section.

2. Please note the full name of the abbreviation when it first appears such as RMSE

3. Is “kernel” instead of “maize” more appropriate used in table 1.

4. In method section, which programming language was used to construct the pipeline of U-Net model? Is coding script open to public or in-house?

5. More interpretation need to added to explain these six kernel structure traits (EM_Volume, EM_Ratio, EN_Volume, EN_Ratio, C_Volume and C_Ratio) were selected as key kernel traits for GWAS.

6. Related advanced application about GWAS on crop could be referred and cited (such as https://doi.org/10.1186/s12864-021-07391-x and https://doi.org/10.1186/s12870-020-02603-0).

7. Three protein interaction networks interacting with each other should be highlighted in figure 8.

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Please find my comments on attached file.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf