ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR)

Parisa Riahi; Anoshirvan Kazemnejad; Shayan Mostafaei; Akira Meguro; Nobuhisa Mizuki; Amir Ashraf-Ganjouei; Ali Javinani; Seyedeh Tahereh Faezi; Farhad Shahram; Mahdi Mahmoudi

doi:10.1371/journal.pone.0227997

Peer Review History

Original SubmissionOctober 14, 2019
12 Nov 2019 Decision Letter - Zezhi Li, Editor PONE-D-19-28263 ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model Based Multifactor Dimension Reduction Algorithm (MB-MDR) PLOS ONE Dear Dr Mahmoudi, 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. We would appreciate receiving your revised manuscript by Dec 27 2019 11:59PM. When you are 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. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. 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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 #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ******** 5. 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 #1: Behçet’s disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in the Asian countries. Riahi et al ’s manuscript investigate interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Modelbased multifactor dimensionality reduction (MB-MDR). They have included 748 BD patients and 776 healthy controls. Their results indicated that TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β=12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third order (β=12.74 and β=12.73, respectively). In general, their results support their statement. However, there are still some issue need to be resolved. 1. It will be easy to understand the manuscript if the author explain how they define genotype. 2. It will be interesting if the author explain difference between MB-MDR and other analysis methods? 3. I am wondering what is the advantage of MB-MDR compared with other methods when they analyze data? 4. I am wondering if the author will get similar results(interactions of ERAP1 SNPs) by using other analysis method? Reviewer #2: Comments for “ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model Based Multifactor Dimension Reduction Algorithm (MB-MDR)” In this study, Parisa, et al. evaluated the potential synergistic and antagonism effect of ERAP1 SNPs on patients with Behçet’s disease (BD) by using a new method MB-MDR. The analysis results are based on a considerable number of cases (748) and healthy controls (776), which could comprehensively assess the correlation between ERAP1 SNPs and disease occurrence in the area surveyed, thus, it supplies a way to predict the risk of disease. While the mechanism of Behçet’s disease is uncertain and multiple factors could contribute to this disease, animal models for this disease is unavailable at present, acquisition of the statistics from a clinical sample is the only way we approach this disease. Therefore, our analysis methods largely determine the reliability of statistic. This study provides the data calculated by the “mbmdr” R package version 3.5.1, the result could be insufficiently supported due to the following three major concerns. 1. Although this method MB-MDR is powerful, it doesn’t means its results are more reliable than other methods. Because the data are confront with other research data, for example, conclusions from Kirino, et al. 2013 and your former conclusion in Mahmoudi, et al. 2018. 2. Since DB is a chronic disease caused by many factors, evaluation of the synergism of SNPs in individual gene could not meaningful, while the synergism of SNPs between or among multiple genes could be more reliable. 3. In this study, there are only tables, which are not direct-viewing diagrams, especially for the Table 2, if authors draw a diagram showing the main and interaction effects, it could be better for understanding by the readers. Reviewer #3: 1. In the Introduction section, I would like to the author of this paper to give a more through description of how ERAP1 gene polymorphisms are associated with HLA-B51. 2. In the Discussion section, the author should discuss the advantage of MB-MDR method ******* 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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Revision 1
15 Dec 2019 Author Response Manuscript ID: PONE-D-19-28263 entitled “ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR)” Dear academic editor, Zezhi Li, We are grateful to the reviewers and the editorial board of PLOS ONE, for their constructive criticisms on our paper. We revised the manuscript accordingly. The modifications are given as point-by-point responses to the comments of the reviewers. All changes in the manuscript are highlighted in yellow color. Also, all authors declare that they are agreed on the revision. We wish to thank the comments and hope that the revised version of the manuscript may be now suitable for publication. Yours sincerely, Anoshirvan Kazemnejad: Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, P.O.Box: 14115-111, Tehran, IR, Iran. E-mail address: kazem_an@modares.ac.ir Mahdi Mahmoudi: Rheumatology Research Center, Tehran University of Medical Sciences, P.O. Box: 14117-13137, Tehran, IR. E-mail address: mahmoudim@tums.ac.ir Comments to the Author 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 #1: Behçet’s disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. Riahi et al.’s manuscript investigates interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Model-based multifactor dimensionality reduction (MB-MDR). They have included 748 BD patients and 776 healthy controls. Their results indicated that the TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β=12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third-order (β=12.74 and β=12.73, respectively). In general, their results support their statement. However, there is still some issue that needs to be resolved. 1. It will be easy to understand the manuscript if the author explains how they define genotype. Response: Thanks for your comment. We have added the explanation of genotypes to the method section. 2. It will be interesting if the author explains the difference between MB-MDR and other analysis methods? Response: As mentioned at the end of the introduction section, conventional approaches (e.g., stepwise logistic regression) used for the analysis of genomic data are oversimplified and usually cannot consider all possible significant interactions between multiple polymorphisms. For example, logistics regression for identifying the most important of interactions between multiple polymorphisms is not an appropriate method in a large number of dichotomous variables (or SNPs), small sample size, and sparse data (1, 2). In logistic regression for high dimensional and sparse data, parameter estimation is a costly and non-accurate procedure that introduces large standard errors because sample sizes are too small compared to the order of interaction size. As a consequence, many false positives are generated when dealing with such data (3). In other words, logistic regression performs poorly when there is a dimensionality problem (4). With no ‘best’ statistical approach available, full combinatorial approaches (e.g., Multifactor Dimensionality Reduction) may be optimal for detecting SNPs interactions. MDR approach is now a reference in the epistasis and SNPs interactions detection field. No parameters are estimated (i.e., nonparametric), and no assumptions are made on the genetic model (i.e., model-free) under this supervised classification approach. However, MDR suffers from some major drawbacks, including that crucial interactions could be missed owing to pooling too many cells together or that proposed MDR analyses will only reveal at most one significant epistasis model, the selection being based on computationally demanding cross-validation and permutation strategies (5). To overcome the aforementioned hurdles, Model-Based MDR is a flexible framework to detect gene–gene or SNP-SNP interactions. Besides, MB-MDR has adequate power, even the presence of error sources or noise/ genotyping errors, missing genotypes, phenotypic mixtures, and genetic heterogeneity (5). The mentioned details were added in the introduction section. 3. I am wondering what is the advantage of MB-MDR compared with other methods when they analyze data? Response: As mentioned in the response of the previous comment, Model-Based MDR is a flexible framework to detect gene–gene or SNP-SNP interactions with adequate power even the presence of error sources or noise/ genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity (5). Please see the before comment’s answer. 4. I am wondering if the author will get similar results (interactions of ERAP1 SNPs) by using other analysis methods? Response: Based on the previous comment’s answer, logistic regression has a high computational burden and non-accurate estimations with large standard errors. However, the results of MDR were reported as follow: Figure: Entropy-based network of interactions between12 ERAP1 SNPs. For example, the results of entropy in MDR: Attribute H(A) H(A\|C) I(A; C) --------- ---- ------ ------ Y 1 1 0 rs1065407 1.4731 1.4196 0.0534 rs2287987 0.9805 0.8194 0.1611 rs30187 1.518 1.4962 0.0218 rs10050860 0.8635 0.6771 0.1864 rs27044 1.2802 1.2662 0.0139 rs26653 1.486 1.4505 0.0354 rs27434 1.425 1.4107 0.0142 rs469876 1.1953 1.1863 0.009 rs17481856 0.7089 0.7075 0.0014 rs28096 1.4858 1.4455 0.0402 rs13167972 1.5345 1.5059 0.0285 Attribute A Attribute B H(AB) H(AB\|C) I(A;B) IG(A;B;C) I(AB;C) ----------- ----------- ----- ------- ------ -------- ------- rs1065407 Y 0.9889 0.9489 1.4842 -0.0136 0.0399 rs2287987 Y 0.7689 0.606 1.2116 0.0018 0.1629 rs2287987 rs1065407 0.7778 0.61 1.6757 -0.0467 0.1678 rs30187 Y 0.9376 0.9314 1.5804 -0.0157 0.0062 rs30187 rs1065407 1 0.9377 1.9911 -0.0131 0.0622 rs30187 rs2287987 0.7807 0.6112 1.7178 -0.0134 0.1695 rs10050860 Y 0.7659 0.5795 1.0976 0 0.1864 rs10050860 rs1065407 0.7659 0.5795 1.5706 -0.0534 0.1864 rs10050860 rs2287987 0.7748 0.6087 1.0691 -0.1813 0.1662 rs10050860 rs30187 0.7659 0.5795 1.6156 -0.0218 0.1864 rs27044 Y 0.9936 0.9899 1.2865 -0.0103 0.0037 rs27044 rs1065407 0.9906 0.9463 1.7626 -0.0231 0.0443 rs27044 rs2287987 0.7893 0.6335 1.4713 -0.0192 0.1558 rs27044 rs30187 0.9361 0.9293 1.8621 -0.029 0.0068 rs27044 rs10050860 0.7659 0.5795 1.3777 -0.0139 0.1864 rs26653 Y 0.9103 0.8887 1.5757 -0.0139 0.0216 rs26653 rs1065407 1 0.9277 1.959 -0.0165 0.0723 rs26653 rs2287987 0.7893 0.6335 1.6771 -0.0407 0.1558 rs26653 rs30187 0.9856 0.9697 2.0184 -0.0414 0.0159 rs26653 rs10050860 0.7659 0.5795 1.5835 -0.0354 0.1864 rs26653 rs27044 0.9863 0.962 1.7799 -0.0251 0.0243 rs27434 Y 0.9986 0.9914 1.4263 -0.0071 0.0072 rs27434 rs1065407 0.9996 0.9406 1.8984 -0.0087 0.059 rs27434 rs2287987 0.7921 0.643 1.6133 -0.0262 0.1491 rs27434 rs30187 0.9974 0.9891 1.9456 -0.0278 0.0083 rs27434 rs10050860 0.7659 0.5795 1.5225 -0.0142 0.1864 rs27434 rs27044 0.9974 0.9891 1.7078 -0.0199 0.0083 rs27434 rs26653 0.9961 0.9849 1.9149 -0.0385 0.0112 rs469876 Y 0.9551 0.9505 1.2402 -0.0045 0.0045 rs469876 rs1065407 0.9964 0.9549 1.6719 -0.0209 0.0415 rs469876 rs2287987 0.7807 0.6297 1.395 -0.0191 0.151 rs469876 rs30187 0.9753 0.9324 1.738 0.012 0.0429 rs469876 rs10050860 0.7659 0.5795 1.2929 -0.009 0.1864 rs469876 rs27044 0.8712 0.8636 1.6042 -0.0153 0.0076 rs469876 rs26653 0.9796 0.9206 1.7017 0.0145 0.059 rs469876 rs27434 1 0.9813 1.6203 -0.0046 0.0186 rs17481856 Y 0.9812 0.9812 0.7277 -0.0014 0 rs17481856 rs1065407 0.9889 0.9489 1.1931 -0.0149 0.0399 rs17481856 rs2287987 0.7807 0.6379 0.9087 -0.0196 0.1428 rs17481856 rs30187 0.9974 0.9901 1.2296 -0.0159 0.0073 rs17481856 rs10050860 0.7659 0.5795 0.8065 -0.0014 0.1864 rs17481856 rs27044 0.9936 0.9899 0.9955 -0.0116 0.0037 rs17481856 rs26653 0.8441 0.8053 1.3509 0.002 0.0388 rs17481856 rs27434 0.9977 0.99 1.1362 -0.008 0.0077 rs17481856 rs469876 0.9999 0.9919 0.9043 -0.0023 0.0081 rs28096 Y 0.9643 0.9297 1.5214 -0.0056 0.0346 rs28096 rs1065407 0.9993 0.9376 1.9595 -0.0319 0.0617 rs28096 rs2287987 0.7807 0.6209 1.6855 -0.0415 0.1598 rs28096 rs30187 0.9725 0.929 2.0313 -0.0186 0.0435 rs28096 rs10050860 0.7659 0.5795 1.5833 -0.0402 0.1864 rs28096 rs27044 0.9665 0.9292 1.7995 -0.0168 0.0373 rs28096 rs26653 0.9949 0.9359 1.9768 -0.0166 0.059 rs28096 rs27434 0.9706 0.9324 1.9402 -0.0163 0.0382 rs28096 rs469876 0.9842 0.9494 1.6968 -0.0144 0.0348 rs28096 rs17481856 0.9643 0.9297 1.2304 -0.007 0.0346 rs13167972 Y 0.8817 0.8593 1.6528 -0.0062 0.0224 rs13167972 rs1065407 0.9906 0.9487 2.0169 -0.0401 0.0418 rs13167972 rs2287987 0.7865 0.6323 1.7285 -0.0354 0.1542 rs13167972 rs30187 0.9771 0.9337 2.0754 -0.007 0.0434 rs13167972 rs10050860 0.7659 0.5795 1.632 -0.0285 0.1864 rs13167972 rs27044 0.8993 0.8746 1.9153 -0.0177 0.0247 rs13167972 rs26653 0.9155 0.8517 2.105 -0.0002 0.0638 rs13167972 rs27434 0.9779 0.9376 1.9815 -0.0025 0.0403 rs13167972 rs469876 0.9138 0.8916 1.816 -0.0154 0.0221 rs13167972 rs17481856 0.8712 0.8496 1.3722 -0.0083 0.0216 rs13167972 rs28096 0.9945 0.9455 2.0257 -0.0197 0.049 Reviewer #2: Comments for “ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR)” In this study, Parisa et al. evaluated the potential synergistic and antagonism effect of ERAP1 SNPs on patients with Behçet’s disease (BD) by using a new method MB-MDR. The analysis results are based on a considerable number of cases (748) and healthy controls (776), which could comprehensively assess the correlation between ERAP1 SNPs and disease occurrence in the area surveyed; thus, it supplies a way to predict the risk of disease. While the mechanism of Behçet’s disease is uncertain and multiple factors could contribute to this disease, animal models for this disease is unavailable at present, acquisition of the statistics from a clinical sample is the only way we approach this disease. Therefore, our analysis methods largely determine the reliability of the statistic. This study provides the data calculated by the “mbmdr” R package version 3.5.1, the result could be insufficiently supported due to the following three major concerns. Response: Thanks for your comments. The mentioned corrections were done. 1. Although this method MB-MDR is powerful, it doesn’t mean its results are more reliable than other methods. Because the data are confronted with other research data, for example, conclusions from Kirino et al. 2013 and your former conclusion in Mahmoudi et al. 2018. Response: In general, MB-MDR has different results compared to other algorithms in the mentioned studies because of the different model’s assumptions and methodology. However, typical approaches perform poorly when there is a dimensionality problem for identifying interactions in genetics studies. On the other hand, MDR reduces dimensions by converting a high-dimensional model to a one-dimensional one (4). Nevertheless, MDR as a non-parametric algorithm suffers from some major drawbacks, including that critical interactions could be missed owing to pooling too many cells together or that proposed MDR analyses will only reveal the most significant epistasis model based on computationally demanding cross-validation and permutation strategies (5). Parametric algorithm (MB-MDR) tend to have particular model assumptions, which lead to our ability to determine statistical significance under such assumptions. In a non-parametric test (MDR), we often have fewer assumptions to evaluate, but also differences in how statistical significance is determined. For example, if we run a non-parametric test, such as MDR, there is no P-value table. Based on the permutation test in cross-validation of both MDR, MB-MDR, both algorithms considered as appropriate and reference methods in the epistasis and SNPs interactions detection field (5). 2. Since DB is a chronic disease caused by many factors, evaluation of the synergism of SNPs in an individual gene could not be meaningful, while the synergism of SNPs between or among multiple genes could be more reliable. Response: Thank you for your valuable comment. As you have mentioned, there are many factors contributing to BD’s pathophysiology, including various genes and also multiple environmental variables. Indeed, evaluating these factors, especially the gene-gene interactions, would shed more light on the underlying mechanisms of BD. However, this article is the starting point where we explored the application of a robust statistical method to investigate the interactions between ERAP1 SNPs in BD. Undoubtedly, our team is determined to test such powerful methods to evaluate the synergism of different genes’ SNPs in BD and other multifactor rheumatic diseases in future studies. 3. In this study, there are only tables, which are not direct-viewing diagrams, especially for the Table 2, if authors draw a diagram showing the main and interaction effects, it could be better for understanding by the readers. Response: Thank you for the comment, the network based on the results of Table 2 and 3 were shown and added as Figure 1 in the results section. Figure 1: SNP-SNP entropy-based interaction network of 12 ERAP1 SNPs. Reviewer #3: 1. In the Introduction section, I would like the author of this paper to give a more thorough description of how ERAP1 gene polymorphisms are associated with HLA-B51. Response: Thank you for your comment. We have provided more explanation regarding the possible association of ERAP1 gene polymorphisms and HLA-B51 in the introduction section. 2. In the Discussion section, the author should discuss the advantage of the MB-MDR method Response: Thanks. The advantages of the MB-MDR method were added at the end of the discussion section. 1. Briggs F, Ramsay P, Madden E, Norris J, Holers V, Mikuls TR, et al. Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis. Genes and immunity. 2010;11(3):199. 2. Johnstone IM, Titterington DM. Statistical challenges of high-dimensional data. The Royal Society Publishing; 2009. 3. Niel C, Sinoquet C, Dina C, Rocheleau G. A survey about methods dedicated to epistasis detection. Frontiers in genetics. 2015;6:285. 4. Kim H, Jeong H-B, Jung H-Y, Park T, Park M. Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes. BioMed research international. 2019;2019. 5. John JMM, Van Lishout F, Van Steen K. Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. European Journal of Human Genetics. 2011;19(6):696. Attachments Attachment Submitted filename: Response to reviewers.doc https://doi.org/10.1371/journal.pone.0227997.r002
6 Jan 2020 Decision Letter - Zezhi Li, Editor ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR) PONE-D-19-28263R1 Dear Dr. Mahmoudi, We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements. Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication. Shortly after the formal acceptance letter is sent, an invoice for payment will follow. 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With kind regards, Zezhi Li, Ph.D., M.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed Reviewer #3: All comments have been addressed ******** 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 #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 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 #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 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 #1: Behçet’s disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in the Asian countries. Riahi et al ’s manuscript investigate interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Modelbased multifactor dimensionality reduction (MB-MDR). They have included 748 BD patients and 776 healthy controls. Their results indicated that TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β=12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third order (β=12.74 and β=12.73, respectively). In general, they answer my questions. Reviewer #2: (No Response) Reviewer #3: The authors of this study did a thorough revision work. The authors have addressed all the issues I was concerned during the initial submission. I am satisfied with their revision work. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No https://doi.org/10.1371/journal.pone.0227997.r003
Formally Accepted
24 Jan 2020 Acceptance Letter - Zezhi Li, Editor PONE-D-19-28263R1 ERAP1 polymorphisms interactions and their association with Behçet’s disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR) Dear Dr. Mahmoudi: I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. For any other questions or concerns, please email plosone@plos.org. Thank you for submitting your work to PLOS ONE. With kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Zezhi Li Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0227997.r004

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