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Hasan M, Joslin S, Chemaly M, Liang B. Anonic Silicon Hydrogels Affect the Concentration of Proteins in Tears during Wear. Curr Eye Res 2024; 49:242-251. [PMID: 38146606 DOI: 10.1080/02713683.2023.2294702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 12/03/2023] [Indexed: 12/27/2023]
Abstract
PURPOSE The objective of this study was to quantitatively assess the concentration of human tear proteins in patients wearing contact lenses of various ionicities and determine whether differences were related to the incidence of corneal infiltrative events (CIE). METHODS 24 subjects (samples) were randomly selected for spectral count analysis to obtain protein concentrations using LCMS analysis. The subjects were neophyte and ametropic with ages between 18 and 40; 6 wore control lenses, 8 wore TestLens1, and 10 wore TestLens2. 16 subjects experienced CIEs during the study. RESULTS A pairwise multiple hypothesis test identified 7 proteins that significantly differed in concentration between TestLens1 and control, and 11 proteins that differed between TestLens2 and control. Of the 12 unique proteins, 9 were at increased concentration and 3 were at lower concentration in the tears of test lens wearers compared to the control lens group. Bootstrap clustering confirmed these findings, showing 3 similar clusters to the original sample groups which separated people wearing control lenses from those wearing TestLens1 or TestLens2 with 83% accuracy and between TestLens1 and TestLens2 with 45% accuracy. Permutation testing identified 5 proteins that had significantly changed in concentration between people wearing TestLens2 and Control lenses. There was no difference in protein concentrations between those subjects who experienced a CIE and those who did not. CONCLUSION Wearing contact lenses of different ionicities can affect the concentration of proteins in the tear film. The current study did not find any associations of the concentration of proteins with CIEs. Future tests with increased sample size are needed to establish any relations between these changes and clinical performance.
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Heller R, Krieger A, Rosset S. Optimal multiple testing and design in clinical trials. Biometrics 2023; 79:1908-1919. [PMID: 35899317 DOI: 10.1111/biom.13726] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023]
Abstract
A central goal in designing clinical trials is to find the test that maximizes power (or equivalently minimizes required sample size) for finding a false null hypothesis subject to the constraint of type I error. When there is more than one test, such as in clinical trials with multiple endpoints, the issues of optimal design and optimal procedures become more complex. In this paper, we address the question of how such optimal tests should be defined and how they can be found. We review different notions of power and how they relate to study goals, and also consider the requirements of type I error control and the nature of the procedures. This leads us to an explicit optimization problem with objective and constraints that describe its specific desiderata. We present a complete solution for deriving optimal procedures for two hypotheses, which have desired monotonicity properties, and are computationally simple. For some of the optimization formulations this yields optimal procedures that are identical to existing procedures, such as Hommel's procedure or the procedure of Bittman et al. (2009), while for other cases it yields completely novel and more powerful procedures than existing ones. We demonstrate the nature of our novel procedures and their improved power extensively in a simulation and on the APEX study (Cohen et al., 2016).
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Affiliation(s)
- Ruth Heller
- Department of Statistics and Operations Research, Tel-Aviv University, Tel Aviv, Israel
| | - Abba Krieger
- Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Saharon Rosset
- Department of Statistics and Operations Research, Tel-Aviv University, Tel Aviv, Israel
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3
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Fithian W, Lei L. Conditional calibration for false discovery rate control under dependence. Ann Stat 2022. [DOI: 10.1214/21-aos2137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- William Fithian
- Department of Statistics, University of California, Berkeley
| | - Lihua Lei
- Department of Statistics, Stanford University
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4
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Rosset S, Heller R, Painsky A, Aharoni E. Optimal and maximin procedures for multiple testing problems. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Saharon Rosset
- Department of Statistics and Operations Research Tel‐Aviv University Tel‐Aviv Israel
| | - Ruth Heller
- Department of Statistics and Operations Research Tel‐Aviv University Tel‐Aviv Israel
| | - Amichai Painsky
- Department of Industrial Engineering Tel‐Aviv University Tel‐Aviv Israel
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Zhou H, Zhang X, Chen J. Covariate adaptive familywise error rate control for genome-wide association studies. Biometrika 2021; 108:915-931. [PMID: 34803516 DOI: 10.1093/biomet/asaa098] [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: 02/17/2020] [Indexed: 11/12/2022] Open
Abstract
The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.
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Affiliation(s)
- Huijuan Zhou
- Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China
| | - Xianyang Zhang
- Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Mayo Clinic, 200 First St. SW, Rochester, Minnesota 55905, U.S.A
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6
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Ignatiadis N, Huber W. Covariate powered cross‐weighted multiple testing. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12411] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - Wolfgang Huber
- European Molecular Biology Laboratory Heidelberg Germany
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7
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Velten B, Huber W. Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes. Biostatistics 2019; 22:348-364. [PMID: 31596468 PMCID: PMC8036004 DOI: 10.1093/biostatistics/kxz034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 06/27/2019] [Accepted: 08/14/2019] [Indexed: 12/18/2022] Open
Abstract
Penalization schemes like Lasso or ridge regression are routinely used to regress a response of interest on a high-dimensional set of potential predictors. Despite being decisive, the question of the relative strength of penalization is often glossed over and only implicitly determined by the scale of individual predictors. At the same time, additional information on the predictors is available in many applications but left unused. Here, we propose to make use of such external covariates to adapt the penalization in a data-driven manner. We present a method that differentially penalizes feature groups defined by the covariates and adapts the relative strength of penalization to the information content of each group. Using techniques from the Bayesian tool-set our procedure combines shrinkage with feature selection and provides a scalable optimization scheme. We demonstrate in simulations that the method accurately recovers the true effect sizes and sparsity patterns per feature group. Furthermore, it leads to an improved prediction performance in situations where the groups have strong differences in dynamic range. In applications to data from high-throughput biology, the method enables re-weighting the importance of feature groups from different assays. Overall, using available covariates extends the range of applications of penalized regression, improves model interpretability and can improve prediction performance.
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Affiliation(s)
- Britta Velten
- Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
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Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. Nat Commun 2019; 10:3433. [PMID: 31366926 PMCID: PMC6668431 DOI: 10.1038/s41467-019-11247-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 07/03/2019] [Indexed: 12/31/2022] Open
Abstract
Multiple hypothesis testing is an essential component of modern data science. In many settings, in addition to the p-value, additional covariates for each hypothesis are available, e.g., functional annotation of variants in genome-wide association studies. Such information is ignored by popular multiple testing approaches such as the Benjamini-Hochberg procedure (BH). Here we introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% more associations than BH at the same false discovery rate. We prove that AdaFDR controls false discovery proportion and show that it makes substantially more discoveries while controlling false discovery rate (FDR) in extensive experiments. AdaFDR is computationally efficient and allows multi-dimensional covariates with both numeric and categorical values, making it broadly useful across many applications.
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10
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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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12
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Lei L, Fithian W. AdaPT: an interactive procedure for multiple testing with side information. J R Stat Soc Series B Stat Methodol 2018. [DOI: 10.1111/rssb.12274] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Lihua Lei
- University of California; Berkeley USA
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13
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Dobriban E. Weighted mining of massive collections of [Formula: see text]-values by convex optimization. INFORMATION AND INFERENCE : A JOURNAL OF THE IMA 2018; 7:251-275. [PMID: 29930799 PMCID: PMC5998655 DOI: 10.1093/imaiai/iax013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Accepted: 10/05/2017] [Indexed: 06/08/2023]
Abstract
Researchers in data-rich disciplines-think of computational genomics and observational cosmology-often wish to mine large bodies of [Formula: see text]-values looking for significant effects, while controlling the false discovery rate or family-wise error rate. Increasingly, researchers also wish to prioritize certain hypotheses, for example, those thought to have larger effect sizes, by upweighting, and to impose constraints on the underlying mining, such as monotonicity along a certain sequence. We introduce Princessp, a principled method for performing weighted multiple testing by constrained convex optimization. Our method elegantly allows one to prioritize certain hypotheses through upweighting and to discount others through downweighting, while constraining the underlying weights involved in the mining process. When the [Formula: see text]-values derive from monotone likelihood ratio families such as the Gaussian means model, the new method allows exact solution of an important optimal weighting problem previously thought to be non-convex and computationally infeasible. Our method scales to massive data set sizes. We illustrate the applications of Princessp on a series of standard genomics data sets and offer comparisons with several previous 'standard' methods. Princessp offers both ease of operation and the ability to scale to extremely large problem sizes. The method is available as open-source software from github.com/dobriban/pvalue_weighting_matlab (accessed 11 October 2017).
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Affiliation(s)
- Edgar Dobriban
- Department of Statistics, The Wharton School, University of Pennsylania, USA
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14
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Ignatiadis N, Klaus B, Zaugg J, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods 2016; 13:577-80. [PMID: 27240256 PMCID: PMC4930141 DOI: 10.1038/nmeth.3885] [Citation(s) in RCA: 316] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 05/03/2016] [Indexed: 11/25/2022]
Abstract
Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (http://www.bioconductor.org/packages/IHW). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.
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Affiliation(s)
| | - Bernd Klaus
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Judith Zaugg
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Wolfgang Huber
- European Molecular Biology Laboratory, Heidelberg, Germany
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15
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Fortney K, Dobriban E, Garagnani P, Pirazzini C, Monti D, Mari D, Atzmon G, Barzilai N, Franceschi C, Owen AB, Kim SK. Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity. PLoS Genet 2015; 11:e1005728. [PMID: 26677855 PMCID: PMC4683064 DOI: 10.1371/journal.pgen.1005728] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Accepted: 11/16/2015] [Indexed: 11/20/2022] Open
Abstract
We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity. These results indicate that several disease variants may be depleted in centenarians versus the general population. Next, we used iGWAS to harness information from 14 meta-analyses of disease and trait GWAS to identify longevity loci in two studies of long-lived humans. In a standard GWAS analysis, only one locus in these studies is significant (APOE/TOMM40) when controlling the false discovery rate (FDR) at 10%. With iGWAS, we identify eight genetic loci to associate significantly with exceptional human longevity at FDR < 10%. We followed up the eight lead SNPs in independent cohorts, and found replication evidence of four loci and suggestive evidence for one more with exceptional longevity. The loci that replicated (FDR < 5%) included APOE/TOMM40 (associated with Alzheimer’s disease), CDKN2B/ANRIL (implicated in the regulation of cellular senescence), ABO (tags the O blood group), and SH2B3/ATXN2 (a signaling gene that extends lifespan in Drosophila and a gene involved in neurological disease). Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits, including coronary artery disease and Alzheimer’s disease. iGWAS provides a new analytical strategy for uncovering SNPs that influence extreme longevity, and can be applied more broadly to boost power in other studies of complex phenotypes. Longevity is a complex phenotype, and few genetic variants that affect lifespan have been identified. However, aging and disease are closely related, and a great deal is known about the genetic basis of disease risk. Here, we show using genome-wide association studies (GWAS) of longevity and disease that there is an overlap between loci involved in longevity and loci involved in several diseases, such as Alzheimer’s disease and coronary artery disease. We then develop a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from 14 large studies of disease and disease-related traits in order to narrow the search for SNPs associated with longevity. Using iGWAS, we found eight SNPs that are significant in our discovery cohorts, and we were able to validate four of these in replication studies of long-lived subjects. Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits. Beyond the study of human longevity, iGWAS can be applied to boost statistical power in any GWAS of a target phenotype by using larger GWAS of genetically-related conditions.
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Affiliation(s)
- Kristen Fortney
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
- Department of Genetics, Stanford University, Stanford, California, United States of America
| | - Edgar Dobriban
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine Experimental Pathology, University of Bologna, Bologna, Italy
- Center for Applied Biomedical Research, St. Orsola-Malpighi University Hospital, Bologna, Italy
| | - Chiara Pirazzini
- Department of Experimental, Diagnostic and Specialty Medicine Experimental Pathology, University of Bologna, Bologna, Italy
- Interdepartmental Centre "L. Galvani" CIG, University of Bologna, Bologna, Italy
| | - Daniela Monti
- Department of Clinical, Experimental and Biomedical Sciences, University of Florence, Florence, Italy
| | - Daniela Mari
- Department of Medical Sciences, University of Milan, Milan, Italy
- Geriatric Unit, IRCCS Ca' Grande Foundation, Maggiore Policlinico Hospital, Milan, Italy
| | - Gil Atzmon
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Claudio Franceschi
- Department of Experimental, Diagnostic and Specialty Medicine Experimental Pathology, University of Bologna, Bologna, Italy
- IRCCS, Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - Art B. Owen
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Stuart K. Kim
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
- Department of Genetics, Stanford University, Stanford, California, United States of America
- * E-mail:
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