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Dreyfuss JM, Djordjilović V, Pan H, Bussberg V, MacDonald AM, Narain NR, Kiebish MA, Blüher M, Tseng YH, Lynes MD. ScreenDMT reveals DiHOMEs are replicably inversely associated with BMI and stimulate adipocyte calcium influx. Commun Biol 2024; 7:996. [PMID: 39143411 PMCID: PMC11324735 DOI: 10.1038/s42003-024-06646-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Activating brown adipose tissue (BAT) improves systemic metabolism, making it a promising target for metabolic syndrome. BAT is activated by 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), which we previously identified to be inversely associated with BMI and which directly improves metabolism in multiple tissues. Here we profile plasma lipidomics from 83 people and test which lipids' association with BMI replicates in a concordant direction using our novel tool ScreenDMT, whose power and validity we demonstrate via mathematical proofs and simulations. We find that the linoleic acid diols 12,13-diHOME and 9,10-diHOME are both replicably inversely associated with BMI and mechanistically activate calcium influx in mouse brown and white adipocytes in vitro, which implicates this signaling pathway and 9,10-diHOME as candidate therapeutic targets. ScreenDMT can be applied to test directional mediation, directional replication, and qualitative interactions, such as identifying biomarkers whose association is shared (replication) or opposite (qualitative interaction) across diverse populations.
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Affiliation(s)
- Jonathan M Dreyfuss
- Bioinformatics & Biostatistics Core, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Vera Djordjilović
- Department of Economics, Ca' Foscari University of Venice, Cannaregio 873, Venice, Italy
| | - Hui Pan
- Bioinformatics & Biostatistics Core, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital, Leipzig, Germany
| | - Yu-Hua Tseng
- Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
| | - Matthew D Lynes
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA.
- Department of Medicine, MaineHealth, Portland, ME, USA.
- Roux Institute at Northeastern University, Portland, ME, USA.
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2
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He Y, Song PXK, Xu G. Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis. J R Stat Soc Series B Stat Methodol 2024; 86:411-434. [PMID: 38746015 PMCID: PMC11090400 DOI: 10.1093/jrsssb/qkad129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 05/16/2024]
Abstract
Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific fields. Testing for the mediation effect (ME) is greatly challenged by the fact that the underlying null hypothesis (i.e. the absence of MEs) is composite. Most existing mediation tests are overly conservative and thus underpowered. To overcome this significant methodological hurdle, we develop an adaptive bootstrap testing framework that can accommodate different types of composite null hypotheses in the mediation pathway analysis. Applied to the product of coefficients test and the joint significance test, our adaptive testing procedures provide type I error control under the composite null, resulting in much improved statistical power compared to existing tests. Both theoretical properties and numerical examples of the proposed methodology are discussed.
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Affiliation(s)
- Yinqiu He
- Department of Statistics, University of Wisconsin, Madison, WI, USA
| | - Peter X K Song
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Gongjun Xu
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
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3
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Dai R, Zheng C. False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach. Biometrics 2023; 79:3497-3509. [PMID: 36854821 PMCID: PMC10460825 DOI: 10.1111/biom.13848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/17/2023] [Indexed: 03/02/2023]
Abstract
False discovery rate (FDR) controlling procedures provide important statistical guarantees for replicability in signal identification based on multiple hypotheses testing. In many fields of study, FDR controling procedures are used in high-dimensional (HD) analyses to discover features that are truly associated with the outcome. In some recent applications, data on the same set of candidate features are independently collected in multiple different studies. For example, gene expression data are collected at different facilities and with different cohorts, to identify the genetic biomarkers of multiple types of cancers. These studies provide us with opportunities to identify signals by considering information from different sources (with potential heterogeneity) jointly. This paper is about how to provide FDR control guarantees for the tests of union null hypotheses of conditional independence. We present a knockoff-based variable selection method (Simultaneous knockoffs) to identify mutual signals from multiple independent datasets, providing exact FDR control guarantees under finite sample settings. This method can work with very general model settings and test statistics. We demonstrate the performance of this method with extensive numerical studies and two real-data examples.
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Affiliation(s)
- Ran Dai
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, U.S.A
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4
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Dreyfuss JM, Djordjilovic V, Pan H, Bussberg V, MacDonald AM, Narain NR, Kiebish MA, Blüher M, Tseng YH, Lynes MD. ScreenDMT reveals linoleic acid diols replicably associate with BMI and stimulate adipocyte calcium fluxes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548737. [PMID: 37503007 PMCID: PMC10369939 DOI: 10.1101/2023.07.12.548737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Activating brown adipose tissue (BAT) improves systemic metabolism, making it a promising target for metabolic syndrome. BAT is activated by 12, 13-dihydroxy-9Z-octadecenoic acid (12, 13-diHOME), which we previously identified to be inversely associated with BMI and which directly improves metabolism in multiple tissues. Here we profile plasma lipidomics from a cohort of 83 people and test which lipids' association with BMI replicates in a concordant direction using our novel tool ScreenDMT, whose power and validity we demonstrate via mathematical proofs and simulations. We find that the linoleic acid diols 12, 13-diHOME and 9, 10-diHOME both replicably inversely associate with BMI and mechanistically activate calcium fluxes in mouse brown and white adipocytes in vitro, which implicates this pathway and 9, 10-diHOME as candidate therapeutic targets. ScreenDMT can be applied to test directional mediation, directional replication, and qualitative interactions, such as identifying biomarkers whose association is shared (replication) or opposite (qualitative interaction) across diverse populations.
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Affiliation(s)
- Jonathan M. Dreyfuss
- Bioinformatics & Biostatistics Core, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Vera Djordjilovic
- Department of Economics, Ca’ Foscari University of Venice, Cannaregio 873, Venice, Italy
| | - Hui Pan
- Bioinformatics & Biostatistics Core, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital, Leipzig, Germany
| | - Yu-Hua Tseng
- Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
| | - Matthew D. Lynes
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- Department of Medicine, MaineHealth, Portland, ME, USA
- Roux Institute at Northeastern University, Portland, ME, USA
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5
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Lyu P, Li Y, Wen X, Cao H. JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies. Bioinformatics 2023; 39:btad366. [PMID: 37279733 PMCID: PMC10279524 DOI: 10.1093/bioinformatics/btad366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
MOTIVATION Replicability is the cornerstone of scientific research. The current statistical method for high-dimensional replicability analysis either cannot control the false discovery rate (FDR) or is too conservative. RESULTS We propose a statistical method, JUMP, for the high-dimensional replicability analysis of two studies. The input is a high-dimensional paired sequence of p-values from two studies and the test statistic is the maximum of p-values of the pair. JUMP uses four states of the p-value pairs to indicate whether they are null or non-null. Conditional on the hidden states, JUMP computes the cumulative distribution function of the maximum of p-values for each state to conservatively approximate the probability of rejection under the composite null of replicability. JUMP estimates unknown parameters and uses a step-up procedure to control FDR. By incorporating different states of composite null, JUMP achieves a substantial power gain over existing methods while controlling the FDR. Analyzing two pairs of spatially resolved transcriptomic datasets, JUMP makes biological discoveries that otherwise cannot be obtained by using existing methods. AVAILABILITY AND IMPLEMENTATION An R package JUMP implementing the JUMP method is available on CRAN (https://CRAN.R-project.org/package=JUMP).
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Affiliation(s)
- Pengfei Lyu
- Department of Statistics, Florida State University, 600 W College AVE, Tallahassee, FL 32306, United States
| | - Yan Li
- School of Mathematics, Jilin University, 2699 Qianjin ST, Changchun, Jilin 130012, China
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Hongyuan Cao
- Department of Statistics, Florida State University, 600 W College AVE, Tallahassee, FL 32306, United States
- School of Mathematics, Jilin University, 2699 Qianjin ST, Changchun, Jilin 130012, China
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6
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Wang J, Gui L, Su WJ, Sabatti C, Owen AB. Detecting multiple replicating signals using adaptive filtering procedures. Ann Stat 2022. [DOI: 10.1214/21-aos2139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Jingshu Wang
- Department of Statistics, The University of Chicago
| | - Lin Gui
- Department of Statistics, The University of Chicago
| | - Weijie J. Su
- Department of Statistics and Data Science, University of Pennsylvania
| | | | - Art B. Owen
- Department of Statistics, Stanford University
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7
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Yue Y, Hu YJ. A new approach to testing mediation of the microbiome at both the community and individual taxon levels. Bioinformatics 2022; 38:3173-3180. [PMID: 35512399 PMCID: PMC9191207 DOI: 10.1093/bioinformatics/btac310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/28/2022] [Accepted: 05/02/2022] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results. RESULTS We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa. AVAILABILITY AND IMPLEMENTATION Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ye Yue
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Yi-Juan Hu
- To whom correspondence should be addressed.
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8
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Djordjilović V, Hemerik J, Thoresen M. On optimal two-stage testing of multiple mediators. Biom J 2022; 64:1090-1108. [PMID: 35426161 PMCID: PMC9544827 DOI: 10.1002/bimj.202100190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/18/2021] [Accepted: 11/28/2021] [Indexed: 11/27/2022]
Abstract
Mediation analysis in high‐dimensional settings often involves identifying potential mediators among a large number of measured variables. For this purpose, a two‐step familywise error rate procedure called ScreenMin has been recently proposed. In ScreenMin, variables are first screened and only those that pass the screening are tested. The proposed data‐independent threshold for selection has been shown to guarantee asymptotic familywise error rate. In this work, we investigate the impact of the threshold on the finite‐sample familywise error rate. We derive a power maximizing threshold and show that it is well approximated by an adaptive threshold of Wang et al. (2016, arXiv preprint arXiv:1610.03330). We illustrate the investigated procedures on a case‐control study examining the effect of fish intake on the risk of colorectal adenoma. We also apply our procedure in the context of replicability analysis to identify single nucleotide polymorphisms (SNP) associated with crop yield in two distinct environments.
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Affiliation(s)
- Vera Djordjilović
- Department of Economics, Ca' Foscari University of Venice, Dorsoduro, Venice, Italy
| | - Jesse Hemerik
- Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Magne Thoresen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Blindern, Oslo, Norway
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9
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Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges. Comput Struct Biotechnol J 2021; 19:3209-3224. [PMID: 34141140 PMCID: PMC8187160 DOI: 10.1016/j.csbj.2021.05.042] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 12/12/2022] Open
Abstract
Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor 48109, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor 48109, MI, USA
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10
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Luo X, Schwartz J, Baccarelli A, Liu Z. Testing cell-type-specific mediation effects in genome-wide epigenetic studies. Brief Bioinform 2021; 22:bbaa131. [PMID: 32632436 PMCID: PMC8138838 DOI: 10.1093/bib/bbaa131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 05/19/2020] [Accepted: 05/27/2020] [Indexed: 12/12/2022] Open
Abstract
Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418-24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.
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Affiliation(s)
- Xiangyu Luo
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Joel Schwartz
- Department of Environmental Health, Harvard University, Boston, MA, USA
| | - Andrea Baccarelli
- Leon Hess Professor in the Department of Environmental Health Sciences, Columbia University, New York City, NY, USA
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China
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11
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Hoang AT, Dickhaus T. Randomized p -values for multiple testing and their application in replicability analysis. Biom J 2021; 64:384-409. [PMID: 33464615 DOI: 10.1002/bimj.202000155] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 10/01/2020] [Accepted: 11/02/2020] [Indexed: 11/11/2022]
Abstract
We are concerned with testing replicability hypotheses for many endpoints simultaneously. This constitutes a multiple test problem with composite null hypotheses. Traditional p -values, which are computed under least favorable parameter configurations (LFCs), are over-conservative in the case of composite null hypotheses. As demonstrated in prior work, this poses severe challenges in the multiple testing context, especially when one goal of the statistical analysis is to estimate the proportion π 0 of true null hypotheses. Randomized p -values have been proposed to remedy this issue. In the present work, we discuss the application of randomized p -values in replicability analysis. In particular, we introduce a general class of statistical models for which valid, randomized p -values can be calculated easily. By means of computer simulations, we demonstrate that their usage typically leads to a much more accurate estimation of π 0 than the LFC-based approach. Finally, we apply our proposed methodology to a real data example from genomics.
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Affiliation(s)
- Anh-Tuan Hoang
- Institute for Statistics, University of Bremen, Bremen, Germany
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12
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Zhao SD, Nguyen YT. Nonparametric false discovery rate control for identifying simultaneous signals. Electron J Stat 2020. [DOI: 10.1214/19-ejs1663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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14
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Tony Cai T, Sun W, Wang W. Covariate‐assisted ranking and screening for large‐scale two‐sample inference. J R Stat Soc Series B Stat Methodol 2019. [DOI: 10.1111/rssb.12304] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Wenguang Sun
- University of Southern California Los Angeles USA
| | - Weinan Wang
- University of Southern California Los Angeles USA
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15
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Sampson JN, Boca SM, Moore SC, Heller R. FWER and FDR control when testing multiple mediators. Bioinformatics 2018; 34:2418-2424. [PMID: 29420693 PMCID: PMC6355102 DOI: 10.1093/bioinformatics/bty064] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 02/01/2018] [Accepted: 02/05/2018] [Indexed: 12/17/2022] Open
Abstract
Motivation The biological pathways linking exposures and disease risk are often poorly understood. To gain insight into these pathways, studies may try to identify biomarkers that mediate the exposure/disease relationship. Such studies often simultaneously test hundreds or thousands of biomarkers. Results We consider a set of m biomarkers and a corresponding set of null hypotheses, where the jth null hypothesis states that biomarker j does not mediate the exposure/disease relationship. We propose a Multiple Comparison Procedure (MCP) that rejects a set of null hypotheses or, equivalently, identifies a set of mediators, while asymptotically controlling the Family-Wise Error Rate (FWER) or False Discovery Rate (FDR). We use simulations to show that, compared to currently available methods, our proposed method has higher statistical power to detect true mediators. We then apply our method to a breast cancer study and identify nine metabolites that may mediate the known relationship between an increased BMI and an increased risk of breast cancer. Availability and implementation R package MultiMed on https://github.com/SiminaB/MultiMed. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute,
Rockville, MD, USA
| | - Simina M Boca
- Department of Oncology and Department of Biostatistics, Bioinformatics
& Biomathematics, Innovation Center for Biomedical Informatics, Georgetown University
Medical Center, Washington, DC, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute,
Rockville, MD, USA
| | - Ruth Heller
- Department of Statistics and Operations Research, Tel Aviv University,
Tel Aviv, Israel
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