Abstract:Mediation analysis has been a powerful tool to identify factors mediating the association between exposure variables and outcomes. It has been applied to various genomic applications with the hope to gain novel insights into the underlying mechanism of various diseases. Given the high-dimensional nature of epigenetic data, recent effort on epigenetic mediation analysis is to first reduce the data dimension by applying high-dimensional variable selection techniques, then conducting testing in a low dimensional … Show more
“…Instead, there may be only a few DNA methylation CpG sites regulating the expression level of a gene. Under this circumstance, a sparse relationship between DNA methylation CpG sites and the expression level should be modeled [ 51 , 107 ]. Furthermore, IUSMMT may be sub-optimal if the effect sizes of DNA methylation CpG sites located within the gene have the same direction (e.g., all are positive or negative).…”
Section: Discussionmentioning
confidence: 99%
“…We subsequently determined the existence of mediation effect to detect candidate genes with potentially mediating roles by adherence to the principle of intersection-union test (IUT) [46][47][48][49][50][51][52], in which the maximum of the two P-values (denoted by P max ) in the two tests above is…”
Section: Introductionmentioning
confidence: 99%
“…Although it is conceptually straightforward, this naïve IUT-based approach is oftentimes extremely conservative especially in large scale mediation effect tests due to its composite null nature [ 43 , 53 ], which can be equivalently expressed as a combination of three disjoint component null hypotheses. To correct the intrinsic conservativeness of IUT, we estimated the proportion for each component null hypothesis and constructed a novel null distribution for P max by fitting a three-component mixture null distribution [ 54 ], which, in contrast to the naïve uniform null distribution of P max assumed in previous literature [ 51 , 52 , 55 ], can lead to a desirable control of family-wise error rate (FWER) or false discovery rate (FDR). We thus refer to our proposed approach framework as the intersection-union survival mixture-adjusted mediation test (IUSMMT).…”
Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.
“…Instead, there may be only a few DNA methylation CpG sites regulating the expression level of a gene. Under this circumstance, a sparse relationship between DNA methylation CpG sites and the expression level should be modeled [ 51 , 107 ]. Furthermore, IUSMMT may be sub-optimal if the effect sizes of DNA methylation CpG sites located within the gene have the same direction (e.g., all are positive or negative).…”
Section: Discussionmentioning
confidence: 99%
“…We subsequently determined the existence of mediation effect to detect candidate genes with potentially mediating roles by adherence to the principle of intersection-union test (IUT) [46][47][48][49][50][51][52], in which the maximum of the two P-values (denoted by P max ) in the two tests above is…”
Section: Introductionmentioning
confidence: 99%
“…Although it is conceptually straightforward, this naïve IUT-based approach is oftentimes extremely conservative especially in large scale mediation effect tests due to its composite null nature [ 43 , 53 ], which can be equivalently expressed as a combination of three disjoint component null hypotheses. To correct the intrinsic conservativeness of IUT, we estimated the proportion for each component null hypothesis and constructed a novel null distribution for P max by fitting a three-component mixture null distribution [ 54 ], which, in contrast to the naïve uniform null distribution of P max assumed in previous literature [ 51 , 52 , 55 ], can lead to a desirable control of family-wise error rate (FWER) or false discovery rate (FDR). We thus refer to our proposed approach framework as the intersection-union survival mixture-adjusted mediation test (IUSMMT).…”
Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.
“…Finally, HIMA evaluates the significance of the mediation effect using P max, s = max( , P as ) and adjusts for multiple comparisons through Bonferroni correction. HIMA has been recently extended to survival outcomes [96] and to yield unbiased mediator on outcome effects with debiased Lasso [159] . Fig.…”
Section: Mediation Analysis Approaches In the Presence Of High-dimensional Mediatorsmentioning
“…For example, environmental exposures can lead to epigenetic changes, affecting disease risk downstream. Testing mediation effects between exposures and corresponding epigenetic changes can help identify mechanisms that mediate the exposure–outcome relationship ( VanderWeele, 2016 ; Gao et al, 2019 ). Unfortunately, mediation analysis does have its limitations, the use of mediation requires a large sample size, oftentimes unavailable in epigenetic studies.…”
Section: Challenges and Current Limitations Of Social Epigenomic Studmentioning
Social epigenomics has emerged as an integrative field of research focused on identification of socio-environmental factors, their influence on human biology through epigenomic modifications, and how they contribute to current health disparities. Several health disparities studies have been published using genetic-based approaches; however, increasing accessibility and affordability of molecular technologies have allowed for an in-depth investigation of the influence of external factors on epigenetic modifications (e.g., DNA methylation, micro-RNA expression). Currently, research is focused on epigenetic changes in response to environment, as well as targeted epigenetic therapies and environmental/social strategies for potentially minimizing certain health disparities. Here, we will review recent findings in this field pertaining to conditions and diseases over life span encompassing prenatal to adult stages.
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