Stable Variable Selection Method with Shrinkage Regression Applied to the Selection of Genetic Variants Associated with Alzheimer’s Disease

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper proposes a multi-stage method for addressing the problem of variable selection instability in datasets with high dimensionality compared to the number of samples.

My main concern is that it is not evident from the manuscript why the proposed methodology leads to a more stable variable selection. The authors should present and discuss (at least as a summary in the "Discussion" section) the mechanisms that allow their method to perform better compared to the traditional penalization techniques.

Author Response

In the attachment, follow the response to both reviewers.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

•              Consider providing a brief definition of Genome-Wide Association Studies (GWAS) for readers who may not be familiar with the term.

•              Explain the significance of identifying genetic factors associated with AD in more detail. How does it contribute to our understanding of the disease and potential treatments?

•              Ensure consistency in citation style throughout the introduction.

•              Define technical terms like "penalization techniques" and "genetic variants" for readers who may not be familiar with them.

•              Consider adding a sentence or two to engage the reader at the beginning of the introduction. Why is the topic important or relevant?

•              Conclude the introduction by summarizing the main points and reiterating the significance of your work in addressing the challenges mentioned.

•              Ensure precise language and terminology throughout the section to avoid ambiguity.

•              Define any technical terms or abbreviations upon first use for clarity.

•              Provide a brief rationale or justification for each step or method used in the study to help the reader understand the reasoning behind your choices.

•              Ensure that technical details, such as the software and packages used, are accurately described.

•              Include any specific settings or parameters used in the analysis for reproducibility.

•              Provide justification for the thresholds or criteria used in data filtering and variable selection (e.g., why 5% missing rate or minor allele frequency lower than 5% were chosen).

•              Clearly explain the rationale for comparing the proposed method with traditional logistic regression and how it contributes to evaluating the effectiveness of your approach.

 

 

Comments on the Quality of English Language

•              Some sentences are long and complex, which might make them difficult to understand. Consider breaking them down for clarity.

•              Check for any grammatical errors, such as missing articles or prepositions.

Author Response

In the attachment, follow the response to both reviewers.

Author Response File: Author Response.pdf