Title: The Causal Impact of Genetically Predicted Inflammatory Bowel Disease on
Extraintestinal Manifestations: A Mendelian Randomization Study
Version: 3 Date: 19 Sep 2024
Reviewer's report:
Dear colleuge scientist,
Thank you for this study and all the work.
The study strengths is well described with the use of Mendelian Randomization, this MR is a
powerful tool for inferring causal relationships between exposures (e.g., genetically predicted
IBD) and outcomes (e.g., EIMs). This approach is useful in overcoming confounding biases that
typically affect observational studies.
Utilizing GWAS data for both exposure (IBD, UC, and CD) and outcomes (EIMs) strengthens
the study by leveraging large datasets that improve statistical power. The study reports robust
associations with specific EIMs (erythema nodosum, uveitis, PSC, and spondyloarthritis),
providing clear evidence of causality in genetically predicted cases of IBD. Including UC and
CD subgroup analyses is important because it dissects the different impacts these diseases may
have on EIMs.
Attention should be paid to the following points: Did the authors ensure that the selected
SNPs satisfy the key MR assumptions (e.g., independence, exclusivity, relevance)? A lack of
clarity in describing SNP selection criteria or violating these assumptions could weaken the
causal inference. I suggest asking for more details on how they addressed potential pleiotropy
or horizontal pleiotropy, where the selected SNPs influence the outcome via other pathways
unrelated to the exposure.
The study examines a range of EIMs. However, since multiple outcomes were tested, did the
authors adjust for multiple comparisons (e.g., Bonferroni correction or false discovery rate
control)? The p-values in the results are small, but the study would benefit from clarity on
whether such adjustments were made to reduce the risk of Type I errors (false positives).
Even with MR, establishing causality can be complex. Did the study adequately discuss potential
limitations of the MR method, including weak instrument bias, population stratification, or
canalization (changes in the relationship between genotype and phenotype over time)? Any
mention of sensitivity analyses (e.g., MR-Egger regression or heterogeneity tests) would be
valuable to assess the robustness of the findings.
While the associations are statistically significant, it would be important for the authors to offer
insights into possible biological mechanisms linking IBD to these specific EIMs. For example,
why might IBD be causally related to uveitis but not episcleritis or scleritis? Mechanistic
hypotheses could enhance the study's impact.
The study uses data from GWAS, which are often conducted on specific populations (e.g.,
European ancestry). Did the authors address the generalizability of their findings to other
ethnicities or populations? Differences in genetic architectures between populations could limit
the applicability of the results to more diverse groups.
The use of fixed-effects models in combining MR outcomes from different data sources suggests
an assumption that all included studies are estimating the same underlying effect size. However,
if there was heterogeneity between the studies, a random-effects model may have been more
appropriate. Did the authors explore heterogeneity between studies, and how was this handled?
Try to keep the discussion more to the point. With 7 pages it is a bit to long and misses good
take home messaged.
It would be helpful for the authors to conduct or report additional sensitivity analyses, such as
leave-one-out analyses, to ensure that individual SNPs are not driving the associations.
The abstract would benefit from including a conclusion or implications of the findings. What
are the clinical or public health implications of identifying causal relationships between IBD
and these EIMs?
Title: The Causal Impact of Genetically Predicted Inflammatory Bowel Disease on
Extraintestinal Manifestations: A Mendelian Randomization Study
Version: 3 Date: 19 Sep 2024
Reviewer's report:
Dear colleuge scientist,
Thank you for this study and all the work.
The study strengths is well described with the use of Mendelian Randomization, this MR is a
powerful tool for inferring causal relationships between exposures (e.g., genetically predicted
IBD) and outcomes (e.g., EIMs). This approach is useful in overcoming confounding biases that
typically affect observational studies.
Utilizing GWAS data for both exposure (IBD, UC, and CD) and outcomes (EIMs) strengthens
the study by leveraging large datasets that improve statistical power. The study reports robust
associations with specific EIMs (erythema nodosum, uveitis, PSC, and spondyloarthritis),
providing clear evidence of causality in genetically predicted cases of IBD. Including UC and
CD subgroup analyses is important because it dissects the different impacts these diseases may
have on EIMs.
Attention should be paid to the following points: Did the authors ensure that the selected
SNPs satisfy the key MR assumptions (e.g., independence, exclusivity, relevance)? A lack of
clarity in describing SNP selection criteria or violating these assumptions could weaken the
causal inference. I suggest asking for more details on how they addressed potential pleiotropy
or horizontal pleiotropy, where the selected SNPs influence the outcome via other pathways
unrelated to the exposure.
The study examines a range of EIMs. However, since multiple outcomes were tested, did the
authors adjust for multiple comparisons (e.g., Bonferroni correction or false discovery rate
control)? The p-values in the results are small, but the study would benefit from clarity on
whether such adjustments were made to reduce the risk of Type I errors (false positives).
Even with MR, establishing causality can be complex. Did the study adequately discuss potential
limitations of the MR method, including weak instrument bias, population stratification, or
canalization (changes in the relationship between genotype and phenotype over time)? Any
mention of sensitivity analyses (e.g., MR-Egger regression or heterogeneity tests) would be
valuable to assess the robustness of the findings.
While the associations are statistically significant, it would be important for the authors to offer
insights into possible biological mechanisms linking IBD to these specific EIMs. For example,
why might IBD be causally related to uveitis but not episcleritis or scleritis? Mechanistic
hypotheses could enhance the study's impact.
The study uses data from GWAS, which are often conducted on specific populations (e.g.,
European ancestry). Did the authors address the generalizability of their findings to other
ethnicities or populations? Differences in genetic architectures between populations could limit
the applicability of the results to more diverse groups.
The use of fixed-effects models in combining MR outcomes from different data sources suggests
an assumption that all included studies are estimating the same underlying effect size. However,
if there was heterogeneity between the studies, a random-effects model may have been more
appropriate. Did the authors explore heterogeneity between studies, and how was this handled?
Try to keep the discussion more to the point. With 7 pages it is a bit to long and misses good
take home messaged.
It would be helpful for the authors to conduct or report additional sensitivity analyses, such as
leave-one-out analyses, to ensure that individual SNPs are not driving the associations.
The abstract would benefit from including a conclusion or implications of the findings. What
are the clinical or public health implications of identifying causal relationships between IBD
and these EIMs?
Please review this paper.
user
Title: The Causal Impact of Genetically Predicted Inflammatory Bowel Disease on
Extraintestinal Manifestations: A Mendelian Randomization Study
Version: 3 Date: 19 Sep 2024
Reviewer's report:
Dear colleuge scientist,
Thank you for this study and all the work.
The study strengths is well described with the use of Mendelian Randomization, this MR is a
powerful tool for inferring causal relationships between exposures (e.g., genetically predicted
IBD) and outcomes (e.g., EIMs). This approach is useful in overcoming confounding biases that
typically affect observational studies.
Utilizing GWAS data for both exposure (IBD, UC, and CD) and outcomes (EIMs) strengthens
the study by leveraging large datasets that improve statistical power. The study reports robust
associations with specific EIMs (erythema nodosum, uveitis, PSC, and spondyloarthritis),
providing clear evidence of causality in genetically predicted cases of IBD. Including UC and
CD subgroup analyses is important because it dissects the different impacts these diseases may
have on EIMs.
Attention should be paid to the following points: Did the authors ensure that the selected
SNPs satisfy the key MR assumptions (e.g., independence, exclusivity, relevance)? A lack of
clarity in describing SNP selection criteria or violating these assumptions could weaken the
causal inference. I suggest asking for more details on how they addressed potential pleiotropy
or horizontal pleiotropy, where the selected SNPs influence the outcome via other pathways
unrelated to the exposure.
The study examines a range of EIMs. However, since multiple outcomes were tested, did the
authors adjust for multiple comparisons (e.g., Bonferroni correction or false discovery rate
control)? The p-values in the results are small, but the study would benefit from clarity on
whether such adjustments were made to reduce the risk of Type I errors (false positives).
Even with MR, establishing causality can be complex. Did the study adequately discuss potential
limitations of the MR method, including weak instrument bias, population stratification, or
canalization (changes in the relationship between genotype and phenotype over time)? Any
mention of sensitivity analyses (e.g., MR-Egger regression or heterogeneity tests) would be
valuable to assess the robustness of the findings.
While the associations are statistically significant, it would be important for the authors to offer
insights into possible biological mechanisms linking IBD to these specific EIMs. For example,
why might IBD be causally related to uveitis but not episcleritis or scleritis? Mechanistic
hypotheses could enhance the study's impact.
The study uses data from GWAS, which are often conducted on specific populations (e.g.,
European ancestry). Did the authors address the generalizability of their findings to other
ethnicities or populations? Differences in genetic architectures between populations could limit
the applicability of the results to more diverse groups.
The use of fixed-effects models in combining MR outcomes from different data sources suggests
an assumption that all included studies are estimating the same underlying effect size. However,
if there was heterogeneity between the studies, a random-effects model may have been more
appropriate. Did the authors explore heterogeneity between studies, and how was this handled?
Try to keep the discussion more to the point. With 7 pages it is a bit to long and misses good
take home messaged.
It would be helpful for the authors to conduct or report additional sensitivity analyses, such as
leave-one-out analyses, to ensure that individual SNPs are not driving the associations.
The abstract would benefit from including a conclusion or implications of the findings. What
are the clinical or public health implications of identifying causal relationships between IBD
and these EIMs?
assistant