{"title":"Comment on ‘Systematic Druggable Genome-Wide Mendelian Randomization Identifies Therapeutic Targets for Sarcopenia’ by Yin et al.—The Author's Reply","authors":"Kang-Fu Yin, Yong-Ping Chen","doi":"10.1002/jcsm.13652","DOIUrl":null,"url":null,"abstract":"<p>We appreciate the attention and feedback from Liu et al. [<span>1</span>] on our study. We highly value their comments and would like to address some misunderstandings and provide additional background information through the following points.</p>\n<p>Firstly, Liu et al. mentioned that colocalisation analysis following Mendelian randomisation (MR) analysis might introduce irrelevant pleiotropic effects by violating the exclusion restriction assumption, thereby not strengthening the MR results. We believe this viewpoint may stem from an incomplete understanding of the principles of two-sample MR and Bayesian colocalisation analysis. Two-sample MR extracts strictly valid instrumental variables (IVs), selecting those SNPs that are significantly associated with the exposure variable. These SNPs need to satisfy the exclusion restriction assumption, meaning they influence the outcome variable only through the exposure variable and not through other pathways [<span>2</span>]. The purpose of Bayesian colocalisation analysis is to determine whether a genetic variant simultaneously affects multiple phenotypes. Colocalisation analysis typically identifies SNPs that are significantly associated with multiple phenotypes, i.e., shared SNPs [<span>3</span>]. This means that the IVs selected for MR analysis do not necessarily coincide with the shared SNPs identified by colocalisation, thus avoiding the violation of the exclusion restriction assumption. Even if there is a coincidence, after rigorous IV selection and pleiotropy methods such as MR-Egger regression and MR-PRESSO evaluation, the research results remain robust and interpretable [<span>4</span>]. Colocalisation analysis is an important complement to cis-MR studies, used to evaluate the validity of the IV assumption [<span>4</span>]. The lack of colocalisation analysis in cis-MR studies may lead to false-positive results similar to candidate gene studies, which have now largely been abandoned due to their irreproducible findings [<span>5</span>].</p>\n<p>Secondly, Liu et al. mentioned irrelevant horizontal pleiotropy and relevant horizontal pleiotropy in their comments. Regarding irrelevant horizontal pleiotropy, we have already employed common MR methods to address pleiotropy in our article, such as MR-Egger regression and MR-PRESSO. MR-Egger regression detects and corrects pleiotropy by estimating the intercept term, while MR-PRESSO reduces pleiotropic effects by identifying and excluding outliers. However, Liu et al. suggested that pleiotropy might still exist but did not provide specific evidence or reasons. We believe that every methodological approach has its limitations, including the CAUSE (Causal Analysis Using Summary Effect estimates) method suggested by Liu et al. for future pleiotropy identification. Although the CAUSE method performs well in identifying pleiotropy, it also has a high false-positive rate and cannot fully explain the shared genetic components between two traits of interest [<span>6</span>]. Relevant horizontal pleiotropy refers to a SNP affecting the outcome variable through multiple pathways, which is a common issue in complex trait studies. We agree that this is a topic requiring further research, but it does not undermine the robustness of our current findings [<span>2, 4</span>]. We have employed various methods to detect and correct pleiotropy in our study to ensure the reliability of the results.</p>\n<p>Lastly, our study applied cis-MR analysis and used Bayesian colocalisation results for sensitivity analysis to support our conclusions. This approach has been supported in previous literature [<span>4, 7-11</span>] and was also recommended in Liu et al.’s comments. Therefore, we believe our results are robust.</p>\n<p>Regarding the CAUSE method [<span>6</span>] and the latest TWAS (Transcriptome-Wide Association Study) [<span>12, 13</span>] algorithms mentioned by Liu et al., these methods may represent future research directions but still require further public validation.</p>\n<p>Once again, we thank Liu et al. for their valuable comments. We hope that our responses above can clarify some misunderstandings and look forward to further discussion and exchange.</p>","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"1 1","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cachexia, Sarcopenia and Muscle","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jcsm.13652","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We appreciate the attention and feedback from Liu et al. [1] on our study. We highly value their comments and would like to address some misunderstandings and provide additional background information through the following points.
Firstly, Liu et al. mentioned that colocalisation analysis following Mendelian randomisation (MR) analysis might introduce irrelevant pleiotropic effects by violating the exclusion restriction assumption, thereby not strengthening the MR results. We believe this viewpoint may stem from an incomplete understanding of the principles of two-sample MR and Bayesian colocalisation analysis. Two-sample MR extracts strictly valid instrumental variables (IVs), selecting those SNPs that are significantly associated with the exposure variable. These SNPs need to satisfy the exclusion restriction assumption, meaning they influence the outcome variable only through the exposure variable and not through other pathways [2]. The purpose of Bayesian colocalisation analysis is to determine whether a genetic variant simultaneously affects multiple phenotypes. Colocalisation analysis typically identifies SNPs that are significantly associated with multiple phenotypes, i.e., shared SNPs [3]. This means that the IVs selected for MR analysis do not necessarily coincide with the shared SNPs identified by colocalisation, thus avoiding the violation of the exclusion restriction assumption. Even if there is a coincidence, after rigorous IV selection and pleiotropy methods such as MR-Egger regression and MR-PRESSO evaluation, the research results remain robust and interpretable [4]. Colocalisation analysis is an important complement to cis-MR studies, used to evaluate the validity of the IV assumption [4]. The lack of colocalisation analysis in cis-MR studies may lead to false-positive results similar to candidate gene studies, which have now largely been abandoned due to their irreproducible findings [5].
Secondly, Liu et al. mentioned irrelevant horizontal pleiotropy and relevant horizontal pleiotropy in their comments. Regarding irrelevant horizontal pleiotropy, we have already employed common MR methods to address pleiotropy in our article, such as MR-Egger regression and MR-PRESSO. MR-Egger regression detects and corrects pleiotropy by estimating the intercept term, while MR-PRESSO reduces pleiotropic effects by identifying and excluding outliers. However, Liu et al. suggested that pleiotropy might still exist but did not provide specific evidence or reasons. We believe that every methodological approach has its limitations, including the CAUSE (Causal Analysis Using Summary Effect estimates) method suggested by Liu et al. for future pleiotropy identification. Although the CAUSE method performs well in identifying pleiotropy, it also has a high false-positive rate and cannot fully explain the shared genetic components between two traits of interest [6]. Relevant horizontal pleiotropy refers to a SNP affecting the outcome variable through multiple pathways, which is a common issue in complex trait studies. We agree that this is a topic requiring further research, but it does not undermine the robustness of our current findings [2, 4]. We have employed various methods to detect and correct pleiotropy in our study to ensure the reliability of the results.
Lastly, our study applied cis-MR analysis and used Bayesian colocalisation results for sensitivity analysis to support our conclusions. This approach has been supported in previous literature [4, 7-11] and was also recommended in Liu et al.’s comments. Therefore, we believe our results are robust.
Regarding the CAUSE method [6] and the latest TWAS (Transcriptome-Wide Association Study) [12, 13] algorithms mentioned by Liu et al., these methods may represent future research directions but still require further public validation.
Once again, we thank Liu et al. for their valuable comments. We hope that our responses above can clarify some misunderstandings and look forward to further discussion and exchange.
我们感谢 Liu 等人[1]对我们研究的关注和反馈。首先,Liu 等人提到,在孟德尔随机化(Mendelian randomisation,MR)分析之后进行共定位分析,可能会因为违反排除限制假设而引入无关的多向效应,从而无法强化 MR 结果。我们认为这种观点可能源于对双样本 MR 和贝叶斯共定位分析原理的不完全理解。双样本 MR 提取严格有效的工具变量(IV),选择那些与暴露变量显著相关的 SNPs。这些 SNP 需要满足排除限制假设,即它们只通过暴露变量而不是其他途径影响结果变量[2]。贝叶斯共定位分析的目的是确定一个遗传变异是否同时影响多种表型。共定位分析通常确定与多种表型显著相关的 SNP,即共享 SNP [3]。这意味着被选作 MR 分析的 IV 不一定与通过共定位确定的共享 SNP 重合,从而避免了对排除限制假设的违反。即使存在重合,经过严格的IV选择和多向性方法(如MR-Egger回归和MR-PRESSO评估)后,研究结果仍具有稳健性和可解释性[4]。共定位分析是顺式磁共振研究的重要补充,用于评估 IV 假设的有效性[4]。顺式-磁共振研究中缺乏共定位分析可能会导致假阳性结果,这与候选基因研究类似,而候选基因研究由于其结果不可重复,目前已基本被放弃[5]。关于无关水平褶积,我们在文章中已经采用了常用的 MR 方法来解决褶积问题,如 MR-Egger 回归和 MR-PRESSO。MR-Egger 回归通过估计截距项来检测和纠正褶状效应,而 MR-PRESSO 则通过识别和排除异常值来减少褶状效应。不过,Liu 等人认为多向效应可能仍然存在,但没有提供具体的证据或原因。我们认为,每种方法都有其局限性,包括 Liu 等人建议的 CAUSE(使用摘要效应估计的因果分析)方法,该方法可用于未来的褶状效应识别。虽然 CAUSE 方法在识别褶状效应方面表现良好,但它的假阳性率也很高,而且不能完全解释两个相关性状之间的共有遗传成分[6]。相关的水平多效性是指一个 SNP 通过多种途径影响结果变量,这是复杂性状研究中的一个常见问题。我们同意这是一个需要进一步研究的课题,但这并不影响我们目前研究结果的稳健性[2, 4]。最后,我们的研究采用了顺式磁共振分析,并使用贝叶斯共定位结果进行敏感性分析,以支持我们的结论。这种方法得到了以往文献[4, 7-11]的支持,也是 Liu 等人评论中推荐的方法。关于刘等人提到的 CAUSE 方法[6]和最新的 TWAS(全转录组关联研究)[12, 13]算法,这些方法可能是未来的研究方向,但仍需要进一步的公开验证。我们再次感谢刘等人的宝贵意见。希望我们的上述回答能澄清一些误解,并期待进一步的讨论和交流。
期刊介绍:
The Journal of Cachexia, Sarcopenia, and Muscle is a prestigious, peer-reviewed international publication committed to disseminating research and clinical insights pertaining to cachexia, sarcopenia, body composition, and the physiological and pathophysiological alterations occurring throughout the lifespan and in various illnesses across the spectrum of life sciences. This journal serves as a valuable resource for physicians, biochemists, biologists, dieticians, pharmacologists, and students alike.