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Braun TD, Schifano ED, Finkelstein-Fox L, Park CL, Conboy LA, Deshpande R, Riley KE, Lazar SW. Yoga participation associated with changes in dietary patterns and stress: A pilot study in stressed adults with poor diet. Complement Ther Clin Pract 2021; 45:101472. [PMID: 34530181 PMCID: PMC8898640 DOI: 10.1016/j.ctcp.2021.101472] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/18/2021] [Accepted: 08/03/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND PURPOSE Stress contributes to dietary patterns that impede health. Yoga is an integrative stress management approach associated with improved dietary patterns in burgeoning research. Yet, no research has examined change in dietary patterns, body mass index (BMI), and stress during a yoga intervention among stressed adults with poor diet. MATERIALS AND METHODS Objectively-measured BMI and a battery of self-report questionnaires were collected at four time points during and following a 12-week yoga intervention (N = 78, 71% women, mean BMI = 25.69 kg/m2±4.59) - pre-treatment (T1), mid-treatment (6 weeks; T2), post-treatment (12 weeks; T3), and at 3-month follow-up (24 weeks; T4). RESULTS T1 to T3 fruit and vegetable intake, BMI, and stress significantly declined in the overall sample. Reduction in vegetable intake was no longer significant after accounting for reductions in caloric intake, and reduction in caloric intake remained significant after accounting for reductions in stress. CONCLUSION Findings may be interpreted as yoga either encouraging or adversely impacting healthy dietary patterns (i.e., minimizing likelihood of future weight gain vs. decreasing vegetable intake and overall caloric intake among individuals who may not need to lose weight, respectively). Continued research is warranted, utilizing causal designs.
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Affiliation(s)
- Tosca D Braun
- Department of Psychological Sciences, University of Connecticut, USA; Department of Psychiatry and Human Behavior, The Alpert Medical School of Brown University, Providence, RI, USA; Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA.
| | | | | | - Crystal L Park
- Department of Psychological Sciences, University of Connecticut, USA.
| | - Lisa A Conboy
- Beth Israel Deaconess Medical Center, Harvard Medical School, USA; New England School of Acupuncture, Massachusetts College of Pharmacy and Health Sciences, USA.
| | - Rina Deshpande
- Department of Psychiatry, Massachusetts General Hospital, USA.
| | - Kristen E Riley
- Department of Clinical Psychology, Graduate School of Applied and Professional Psychology, Rutgers University, USA.
| | - Sara W Lazar
- Department of Psychiatry, Massachusetts General Hospital, USA; Department of Psychology, Harvard Medical School, USA.
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2
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Xue Y, Yan J, Schifano ED. Simultaneous monitoring for regression coefficients and baseline hazard profile in Cox modeling of time-to-event data. Biostatistics 2021; 22:756-771. [PMID: 31985009 DOI: 10.1093/biostatistics/kxz064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 11/26/2019] [Accepted: 12/13/2019] [Indexed: 11/12/2022] Open
Abstract
The Cox model is the most popular tool for analyzing time-to-event data. The nonparametric baseline hazard function can be as important as the regression coefficients in practice, especially when prediction is needed. In the context of stochastic process control, we propose a simultaneous monitoring method that combines a multivariate control chart for the regression coefficients and a profile control chart for the cumulative baseline hazard function that allows for data blocks of possibly different censoring rates and sample sizes. The method can detect changes in either the parametric or the nonparametric part of the Cox model. In simulation studies, the proposed method maintains its size and has substantial power in detecting changes in either part of the Cox model. An application in lymphoma survival analysis in which patients were enrolled by 2-month intervals in the Surveillance, Epidemiology, and End Results program identifies data blocks with structural model changes.
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Affiliation(s)
- Yishu Xue
- Department of Statistics, University of Connecticut, 215 Glenbrook Rd. U-4120, Storrs, CT 06269-4120, USA
| | - Jun Yan
- Department of Statistics, University of Connecticut, 215 Glenbrook Rd. U-4120, Storrs, CT 06269-4120, USA
| | - Elizabeth D Schifano
- Department of Statistics, University of Connecticut, 215 Glenbrook Rd. U-4120, Storrs, CT 06269-4120, USA
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3
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Sofer T, Lee J, Kurniansyah N, Jain D, Laurie CA, Gogarten SM, Conomos MP, Heavner B, Hu Y, Kooperberg C, Haessler J, Vasan RS, Cupples LA, Coombes BJ, Seyerle A, Gharib SA, Chen H, O'Connell JR, Zhang M, Gottlieb DJ, Psaty BM, Longstreth WT, Rotter JI, Taylor KD, Rich SS, Guo X, Boerwinkle E, Morrison AC, Pankow JS, Johnson AD, Pankratz N, Reiner AP, Redline S, Smith NL, Rice KM, Schifano ED. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion. HGG Adv 2021; 2. [PMID: 34337551 PMCID: PMC8321319 DOI: 10.1016/j.xhgg.2021.100040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.
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Affiliation(s)
- Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ben Heavner
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Yao Hu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ramachandran S Vasan
- Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA.,Framingham Heart Study, Framingham, MA, USA
| | - L Adrienne Cupples
- Framingham Heart Study, Framingham, MA, USA.,Department of Biostatistics, Boston University, Boston, MA, USA
| | - Brandon J Coombes
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Amanda Seyerle
- Division of Pharmaceutical Outcomes and Policy, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jeffrey R O'Connell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Man Zhang
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA.,Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, MA, USA.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | | | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA.,Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Nicholas L Smith
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
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4
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Affiliation(s)
- Jing Wu
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, CT
| | | | - Jun Yan
- Department of Statistics, University of Connecticut, Storrs, CT
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5
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Schifano ED, Jeong H, Deshpande V, Dey DK. Fully and empirical Bayes approaches to estimating copula-based models for bivariate mixed outcomes using Hamiltonian Monte Carlo. TEST-SPAIN 2021. [DOI: 10.1007/s11749-020-00705-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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6
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7
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Xue Y, Wang H, Yan J, Schifano ED. An online updating approach for testing the proportional hazards assumption with streams of survival data. Biometrics 2019; 76:171-182. [PMID: 31424095 DOI: 10.1111/biom.13137] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 08/07/2019] [Indexed: 11/28/2022]
Abstract
The Cox model-which remains the first choice for analyzing time-to-event data, even for large data sets-relies on the proportional hazards (PH) assumption. When survival data arrive sequentially in chunks, a fast and minimally storage intensive approach to test the PH assumption is desirable. We propose an online updating approach that updates the standard test statistic as each new block of data becomes available and greatly lightens the computational burden. Under the null hypothesis of PH, the proposed statistic is shown to have the same asymptotic distribution as the standard version computed on an entire data stream with the data blocks pooled into one data set. In simulation studies, the test and its variant based on most recent data blocks maintain their sizes when the PH assumption holds and have substantial power to detect different violations of the PH assumption. We also show in simulation that our approach can be used successfully with "big data" that exceed a single computer's computational resources. The approach is illustrated with the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program. The proposed test promptly identified deviation from the PH assumption, which was not captured by the test based on the entire data.
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Affiliation(s)
- Yishu Xue
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - HaiYing Wang
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Jun Yan
- Department of Statistics, University of Connecticut, Storrs, Connecticut
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8
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Wu J, Chen MH, Schifano ED, Ibrahim JG, Fisher JD. A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials. Stat Med 2019; 38:5565-5586. [PMID: 31691322 DOI: 10.1002/sim.8379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 09/02/2019] [Accepted: 09/05/2019] [Indexed: 11/08/2022]
Abstract
In longitudinal clinical trials, it is common that subjects may permanently withdraw from the study (dropout), or return to the study after missing one or more visits (intermittent missingness). It is also routinely encountered in HIV prevention clinical trials that there is a large proportion of zeros in count response data. In this paper, a sequential multinomial model is adopted for dropout and subsequently a conditional model is constructed for intermittent missingness. The new model captures the complex structure of missingness and incorporates dropout and intermittent missingness simultaneously. The model also allows us to easily compute the predictive probabilities of different missing data patterns. A zero-inflated Poisson mixed-effects regression model is assumed for the longitudinal count response data. We also propose an approach to assess the overall treatment effects under the zero-inflated Poisson model. We further show that the joint posterior distribution is improper if uniform priors are specified for the regression coefficients under the proposed model. Variations of the g-prior, Jeffreys prior, and maximally dispersed normal prior are thus established as remedies for the improper posterior distribution. An efficient Gibbs sampling algorithm is developed using a hierarchical centering technique. A modified logarithm of the pseudomarginal likelihood and a concordance based area under the curve criterion are used to compare the models under different missing data mechanisms. We then conduct an extensive simulation study to investigate the empirical performance of the proposed methods and further illustrate the methods using real data from an HIV prevention clinical trial.
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Affiliation(s)
- Jing Wu
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, Rhode Island
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | | | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jeffrey D Fisher
- Department of Psychology and Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, Connecticut
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9
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Bar H, Schifano ED. Differential variation and expression analysis. Stat (Int Stat Inst) 2019. [DOI: 10.1002/sta4.237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Haim Bar
- Department of Statistics University of Connecticut Storrs CT 06269
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10
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Cilhoroz BT, Schifano ED, Panza GA, Ash GI, Corso L, Chen M, Deshpande V, Zaleski A, Farinatti P, Santos LP, Taylor BA, O'Neill RJ, Thompson PD, Pescatello LS. FURIN variant associations with postexercise hypotension are intensity and race dependent. Physiol Rep 2019; 7:e13952. [PMID: 30706700 PMCID: PMC6356167 DOI: 10.14814/phy2.13952] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 09/05/2018] [Accepted: 09/17/2018] [Indexed: 12/16/2022] Open
Abstract
FURIN is a proprotein convertase subtilisin/kexin enzyme important in pro-renin receptor processing, and FURIN (furin, paired basic amino acid cleaving enzyme) variants are involved in multiple aspects of blood pressure (BP) regulation. Therefore, we examined associations among FURIN variants and the immediate blood pressure (BP) response to bouts of aerobic exercise, termed postexercise hypotension (PEH). Obese (30.9 ± 3.6 kg m-2 ) Black- (n = 14) and White- (n = 9) adults 42.0 ± 9.8 year with hypertension (139.8 ± 10.4/84.6 ± 6.2 mmHg) performed three random experiments: bouts of vigorous (VIGOROUS) and moderate (MODERATE) intensity cycling and control. Subjects were then attached to an ambulatory BP monitor for 19 h. We performed deep-targeted exon sequencing with the Illumina TruSeq Custom Amplicon kit. FURIN genotypes were coded as the number of minor alleles (#MA) and selected for additional statistical analysis based upon Bonferonni or Benjamini-Yekutieli multiple testing corrected P-values under time-adjusted linear models for 19 hourly BP measurements. After VIGOROUS over 19 h, as FURIN #MA increased in rs12917264 (P = 2.4E-04) and rs75493298 (P = 6.4E-04), systolic BP (SBP) decreased 30.4-33.7 mmHg; and in rs12917264 (P = 1.6E-03) and rs75493298 (P = 9.7E-05), diastolic BP (DBP) decreased 17.6-20.3 mmHg among Blacks only. In addition, after MODERATE over 19 h in FURIN rs74037507 (P = 8.0E-04), as #MA increased, SBP increased 20.8 mmHg among Blacks only. Whereas, after MODERATE over the awake hours in FURIN rs1573644 (P = 6.2E-04), as #MA increased, DBP decreased 12.5 mmHg among Whites only. FURIN appears to exhibit intensity and race-dependent associations with PEH that merit further exploration among a larger, ethnically diverse sample of adults with hypertension.
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Affiliation(s)
| | | | - Gregory A. Panza
- Department of KinesiologyUniversity of ConnecticutStorrsConnecticut
- Department of Preventive CardiologyHartford HospitalHartfordConnecticut
| | | | - Lauren Corso
- Department of KinesiologyUniversity of ConnecticutStorrsConnecticut
| | - Ming‐Hui Chen
- Department of StatisticsUniversity of ConnecticutStorrsConnecticut
| | - Ved Deshpande
- Department of StatisticsUniversity of ConnecticutStorrsConnecticut
| | - Amanda Zaleski
- Department of KinesiologyUniversity of ConnecticutStorrsConnecticut
- Department of Preventive CardiologyHartford HospitalHartfordConnecticut
| | - Paulo Farinatti
- Department of Physical Activity SciencesRio de Janeiro State UniversityRio de JaneiroBrazil
| | - Lucas P. Santos
- Department of Medical SciencesFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Beth A. Taylor
- Department of KinesiologyUniversity of ConnecticutStorrsConnecticut
- Department of Preventive CardiologyHartford HospitalHartfordConnecticut
| | - Rachel J. O'Neill
- Institute for Systems GenomicsUniversity of ConnecticutStorrsConnecticut
- Department of Molecular and Cell BiologyUniversity of ConnecticutStorrsConnecticut
| | - Paul D. Thompson
- Department of Preventive CardiologyHartford HospitalHartfordConnecticut
| | - Linda S. Pescatello
- Department of KinesiologyUniversity of ConnecticutStorrsConnecticut
- Institute for Systems GenomicsUniversity of ConnecticutStorrsConnecticut
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11
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Greenberg J, Braun TD, Schneider ML, Finkelstein-Fox L, Conboy LA, Schifano ED, Park C, Lazar SW. Is less more? A randomized comparison of home practice time in a mind-body program. Behav Res Ther 2018; 111:52-56. [PMID: 30312895 DOI: 10.1016/j.brat.2018.10.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 10/01/2018] [Accepted: 10/03/2018] [Indexed: 01/24/2023]
Abstract
Home practice is a major component of mind-body programs, yet little is known about how to optimize the amount of prescribed home practice in order to achieve an effective "dose" of practice while minimizing participant burden. This study tested how varying the amount of home practice in a mind-body program impacts compliance and stress reduction, and whether prescribing a flexible home practice schedule increases compliance. Eighty-four stressed participants undergoing a 12-week yoga program were randomized to low, medium, and high home practice conditions. The medium condition allowed participants the flexibility to choose one of two amounts of practice each day. The low practice group exhibited the highest compliance (91%) compared to the medium and low practice groups (∼60%), but exhibited the lowest total practice time, and did not significantly reduce stress. The high practice group was the only group to achieve significant stress-reduction, which was maintained 12 weeks post program. Prescribing a flexible home practice schedule did not increase compliance. Results suggest that prescribing higher practice doses may maximize practice time and symptom reduction despite lower compliance.
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Affiliation(s)
- Jonathan Greenberg
- Department of Psychiatry, Massachusetts General Hospital, USA; Harvard Medical School, USA.
| | - Tosca D Braun
- Department of Psychological Sciences, University of Connecticut, USA
| | | | | | - Lisa A Conboy
- Harvard Medical School, USA; Beth Israel Deaconess Medical Center, USA
| | | | - Crystal Park
- Department of Psychological Sciences, University of Connecticut, USA
| | - Sara W Lazar
- Department of Psychiatry, Massachusetts General Hospital, USA; Harvard Medical School, USA
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12
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Cilhoroz BT, Panza GA, Schifano ED, Ash GI, Corso LM, Chen MH, Deshpande V, Zaleski A, Farinatti P, Taylor BA, O’Neill RJ, Thompson PD, Pescatello LS. FURIN Variant Associations with Postexercise Hypotension are Ethnicity and Intensity Dependent. Med Sci Sports Exerc 2018. [DOI: 10.1249/01.mss.0000535988.19797.e9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Wu J, Ibrahim JG, Chen MH, Schifano ED, Fisher JD. Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials. Stat Sin 2018; 28:1929-1963. [PMID: 30595637 DOI: 10.5705/ss.202016.0319] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or nonignorable. In this article, we develop a new probit model for longitudinal binary response data. It resolves a challenging issue for estimating the variance of the random effects, and substantially improves the convergence and mixing of the Gibbs sampling algorithm. We show that when improper uniform priors are specified for the regression coefficients of the joint multinomial model via a sequence of one-dimensional conditional distributions for the missing data indicators under nonignorable missingness, the joint posterior distribution is improper. A variation of Jeffreys prior is thus established as a remedy for the improper posterior distribution. In addition, an efficient Gibbs sampling algorithm is developed using a collapsing technique. Two model assessment criteria, the deviance information criterion (DIC) and the logarithm of the pseudomarginal likelihood (LPML), are used to guide the choices of prior specifications and to compare the models under different missing data mechanisms. We report on extensive simulations conducted to investigate the empirical performance of the proposed methods. The proposed methodology is further illustrated using data from an HIV prevention clinical trial.
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Affiliation(s)
- Jing Wu
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, Chapel Hill, NC, USA
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | | | - Jeffrey D Fisher
- Department of Psychology and Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, USA
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14
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Pescatello LS, Schifano ED, Ash GI, Panza GA, Corso LML, Chen MH, Deshpande V, Zaleski A, Cilhoroz B, Farinatti P, Taylor BA, O'Neill RJ, Thompson PD. Deep-targeted sequencing of endothelial nitric oxide synthase gene exons uncovers exercise intensity and ethnicity-dependent associations with post-exercise hypotension. Physiol Rep 2017; 5:e13510. [PMID: 29180482 PMCID: PMC5704084 DOI: 10.14814/phy2.13510] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 10/28/2017] [Accepted: 10/30/2017] [Indexed: 12/17/2022] Open
Abstract
In previous studies, we found an endothelial nitric oxide synthase gene (NOS3) variant rs2070744 associated with the ambulatory blood pressure (BP) response following bouts of moderate and vigorous intensity acute exercise, termed post-exercise hypotension (PEH). In a validation cohort, we sequenced NOS3 exons for associations with PEH Obese (30.9 ± 3.6 kg.m-2) African American (n = 14) [AF] and Caucasian (n = 9) adults 42.0 ± 9.8 years with hypertension (139.8 ± 10.4/84.6 ± 6.2 mmHg) performed three random experiments: bouts of vigorous and moderate intensity cycling and control. Subjects were attached to an ambulatory BP monitor for 19 h. We performed deep-targeted exon sequencing with the Illumina TruSeq Custom Amplicon kit. Variant genotypes were coded as number of minor alleles (#MA) and selected for additional statistical analysis based upon Bonferonni or Benjamini-Yekutieli multiple testing-corrected P-values under time-adjusted linear models for 19 hourly BP measurements for each subject. After vigorous intensity over 19 h, among NOS3 variants passing multiple testing thresholds, as the #MA increased in rs891512 (P = 6.4E-04), rs867225 (P = 6.5E-04), rs743507 (P = 2.6E-06), and rs41483644 (P = 2.4E-04), systolic (SBP) decreased from 17.5 to 33.7 mmHg; and in rs891512 (P = 9.7E-05), rs867225 (P = 2.6E-05), rs41483644 (P = 1.6E-03), rs3730009 (P = 2.6E-04), and rs77325852 (P = 5.6E-04), diastolic BP decreased from 11.1 mmHg to 20.3 mmHg among AF only. In contrast, after moderate intensity over 19 h in NOS3 rs3918164, as the #MA increased, SBP increased by 16.6 mmHg (P = 2.4E-04) among AF only. NOS3 variants exhibited associations with PEH after vigorous, but not moderate intensity exercise among AF only. NOS3 should be studied further for its effects on PEH in a large, ethnically diverse sample of adults with hypertension to confirm our findings.
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Affiliation(s)
- Linda S Pescatello
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Institute for Systems Genomics, University of Connecticut, Storrs, Connecticut
| | | | - Garrett I Ash
- School of Nursing, Yale University, New Haven, Connecticut
| | - Gregory A Panza
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Lauren M L Corso
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Ved Deshpande
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Amanda Zaleski
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Burak Cilhoroz
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
| | - Paulo Farinatti
- Department of Physical Activity Sciences, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - Beth A Taylor
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Rachel J O'Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, Connecticut
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut
| | - Paul D Thompson
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
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15
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Wu J, de Castro M, Schifano ED, Chen MH. Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood. J Stat Theory Pract 2017; 12:23-41. [PMID: 29805335 DOI: 10.1080/15598608.2017.1299058] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In medical studies, the monotone partial likelihood is frequently encountered in the analysis of time-to-event data using the Cox model. For example, with a binary covariate, the subjects can be classified into two groups. If the event of interest does not occur (zero event) for all the subjects in one of the groups, the resulting partial likelihood is monotone and consequently, the covariate effects are difficult to estimate. In this article, we develop both Bayesian and frequentist approaches using a data-dependent Jeffreys-type prior to handle the monotone partial likelihood problem. We first carry out an in-depth examination of the conditions of the monotone partial likelihood and then characterize sufficient and necessary conditions for the propriety of the Jeffreys-type prior. We further study several theoretical properties of the Jeffreys-type prior for the Cox model. In addition, we propose two variations of the Jeffreys-type prior: the shifted Jeffreys-type prior and the Jeffreys-type prior based on the first risk set. An efficient Markov-chain Monte Carlo algorithm is developed to carry out posterior computation. We perform extensive simulations to examine the performance of parameter estimates and demonstrate the applicability of the proposed method by analyzing real data from the SEER prostate cancer study.
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Affiliation(s)
- Jing Wu
- Department of Statistics, University of Connecticut, Storrs, Connecticut, USA
| | - Mário de Castro
- Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, São Carlos, SP, Brazil
| | | | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, Connecticut, USA
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16
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Hollenbach JP, Schifano ED, Hammel C, Cloutier MM. Exposure to secondhand smoke and asthma severity among children in Connecticut. PLoS One 2017; 12:e0174541. [PMID: 28362801 PMCID: PMC5375151 DOI: 10.1371/journal.pone.0174541] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 03/10/2017] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE To determine whether secondhand smoke (SHS) exposure is associated with greater asthma severity in children with physician-diagnosed asthma living in CT, and to examine whether area of residence, race/ethnicity or poverty moderate the association. METHODS A large childhood asthma database in CT (Easy Breathing) was linked by participant zip code to census data to classify participants by area of residence. Multinomial logistic regression models, adjusted for enrollment date, sex, age, race/ethnicity, area of residence, insurance type, family history of asthma, eczema, and exposure to dogs, cats, gas stove, rodents and cockroaches were used to examine the association between self-reported exposure to SHS and clinician-determined asthma severity (mild, moderate, and severe persistent vs. intermittent asthma). RESULTS Of the 30,163 children with asthma enrolled in Easy Breathing, between 6 months and 18 years old, living in 161 different towns in CT, exposure to SHS was associated with greater asthma severity (adjusted relative risk ratio (aRRR): 1.07 [1.00, 1.15] and aRRR: 1.11 [1.02, 1.22] for mild and moderate persistent asthma, respectively). The odds of Black and Puerto Rican/Hispanic children with asthma being exposed to SHS were twice that of Caucasian children. Though the odds of SHS exposure for publicly insured children with asthma were three times greater than the odds for privately insured children (OR: 3.02 [2.84,3,21]), SHS exposure was associated with persistent asthma only among privately insured children (adjusted odds ratio (aOR): 1.23 [1.11,1.37]). CONCLUSION This is the first large-scale pragmatic study to demonstrate that children exposed to SHS in Connecticut have greater asthma severity, clinically determined using a systematic approach, and varies by insurance status.
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Affiliation(s)
- Jessica P. Hollenbach
- Asthma Center, Connecticut Children’s Medical Center, Hartford, Connecticut, United States of America
- Department of Pediatrics, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
- * E-mail:
| | - Elizabeth D. Schifano
- Department of Statistics, University of Connecticut, Storrs, Connecticut, United States of America
| | - Christopher Hammel
- University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
| | - Michelle M. Cloutier
- Asthma Center, Connecticut Children’s Medical Center, Hartford, Connecticut, United States of America
- Department of Pediatrics, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
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17
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Sofer T, Schifano ED, Christiani DC, Lin X. Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies. Biometrics 2017; 73:1210-1220. [PMID: 28346824 DOI: 10.1111/biom.12680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 01/01/2017] [Accepted: 02/01/2017] [Indexed: 11/29/2022]
Abstract
We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.
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Affiliation(s)
- Tamar Sofer
- Department of Biostatistics, University of Washington, Seattle, Washington 98105, U.S.A
| | - Elizabeth D Schifano
- Department of Statistics, University of Connecticut, Storrs, Connecticut 06269, U.S.A
| | - David C Christiani
- Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A
| | - Xihong Lin
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A
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18
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Pescatello LS, Schifano ED, Ash GI, Panza GA, Lamberti L, Chen MH, Deshpande V, Zaleski A, Farinatti P, Taylor BA, Thompson PD. Deep-targeted exon sequencing reveals renal polymorphisms associate with postexercise hypotension among African Americans. Physiol Rep 2016; 4:e12992. [PMID: 27940662 PMCID: PMC5064144 DOI: 10.14814/phy2.12992] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 09/14/2016] [Accepted: 09/15/2016] [Indexed: 12/23/2022] Open
Abstract
We found variants from the Angiotensinogen-Converting Enzyme (ACE), Angiotensin Type 1 Receptor (AGTR1), Aldosterone Synthase (CYP11B2), and Adducin (ADD1) genes exhibited intensity-dependent associations with the ambulatory blood pressure (BP) response following acute exercise, or postexercise hypotension (PEH). In a validation cohort, we sequenced exons from these genes for their associations with PEH Obese (30.9 ± 3.6 kg m-2) adults (n = 23; 61% African Americans [AF], 39% Caucasian) 42.0 ± 9.8 years with hypertension (139.8 ± 10.4/84.6 ± 6.2 mmHg) completed three random experiments: bouts of vigorous and moderate intensity cycling and control. Subjects wore an ambulatory BP monitor for 19 h. We performed deep-targeted exon sequencing using the Illumina TruSeq Custom Amplicon kit. Variant genotypes were coded as number of minor alleles (#MA) and selected for further statistical analysis based upon Bonferonni or Benjamini-Yekutieli multiple testing corrected p-values under time adjusted linear models for 19 hourly BP measurements per subject. After vigorous intensity over 19 h among ACE, AGTR1, CYP11B2, and ADD1 variants passing multiple testing thresholds, as the #MA increased, systolic (SBP) and/or diastolic BP decreased 12 mmHg (P = 4.5E-05) to 30 mmHg (P = 6.4E-04) among AF only. In contrast, after moderate intensity over 19 h among ACE and CYP11B2 variants passing multiple testing thresholds, as the #MA increased, SBP increased 21 mmHg (P = 8.0E-04) to 22 mmHg (P = 8.2E-04) among AF only. In this replication study, ACE, AGTR1, CYP11B2, and ADD1 variants exhibited associations with PEH after vigorous, but not moderate intensity exercise among AF only. Renal variants should be explored further with a multi-level "omics" approach for associations with PEH among a large, ethnically diverse sample of adults with hypertension.
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Affiliation(s)
- Linda S Pescatello
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Institute for Systems Genomics, University of Connecticut, Storrs, Connecticut
| | | | - Garrett I Ash
- School of Nursing, Yale University, New Haven, Connecticut
| | - Gregory A Panza
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Lauren Lamberti
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Ved Deshpande
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Amanda Zaleski
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Paulo Farinatti
- Department of Physical Activity Sciences, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - Beth A Taylor
- Department of Kinesiology, University of Connecticut, Storrs, Connecticut
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
| | - Paul D Thompson
- Department of Preventive Cardiology, Hartford Hospital, Hartford, Connecticut
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19
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Abstract
We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.
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Affiliation(s)
| | - Jing Wu
- Department of Statistics, University of Connecticut
| | - Chun Wang
- Department of Statistics, University of Connecticut
| | - Jun Yan
- Department of Statistics, University of Connecticut
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20
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Sofer T, Schifano ED, Hoppin JA, Hou L, Baccarelli AA. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. ACTA ACUST UNITED AC 2013; 29:2884-91. [PMID: 23990415 DOI: 10.1093/bioinformatics/btt498] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
MOTIVATION DNA methylation is a heritable modifiable chemical process that affects gene transcription and is associated with other molecular markers (e.g. gene expression) and biomarkers (e.g. cancer or other diseases). Current technology measures methylation in hundred of thousands, or millions of CpG sites throughout the genome. It is evident that neighboring CpG sites are often highly correlated with each other, and current literature suggests that clusters of adjacent CpG sites are co-regulated. RESULTS We develop the Adjacent Site Clustering (A-clustering) algorithm to detect sets of neighboring CpG sites that are correlated with each other. To detect methylation regions associated with exposure, we propose an analysis pipeline for high-dimensional methylation data in which CpG sites within regions identified by A-clustering are modeled as multivariate responses to environmental exposure using a generalized estimating equation approach that assumes exposure equally affects all sites in the cluster. We develop a correlation preserving simulation scheme, and study the proposed methodology via simulations. We study the clusters detected by the algorithm on high dimensional dataset of peripheral blood methylation of pesticide applicators. AVAILABILITY We provide the R package Aclust that efficiently implements the A-clustering and the analysis pipeline, and produces analysis reports. The package is found on http://www.hsph.harvard.edu/tamar-sofer/packages/ CONTACT tsofer@hsph.harvard.edu
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Affiliation(s)
- Tamar Sofer
- Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA
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21
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Strawderman RL, Wells MT, Schifano ED. Hierarchical Bayes, maximum a posteriori estimators, and minimax concave penalized likelihood estimation. Electron J Stat 2013. [DOI: 10.1214/13-ejs795] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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22
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Schifano ED, Epstein MP, Bielak LF, Jhun MA, Kardia SLR, Peyser PA, Lin X. SNP set association analysis for familial data. Genet Epidemiol 2012; 36:797-810. [PMID: 22968922 DOI: 10.1002/gepi.21676] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Revised: 07/06/2012] [Accepted: 07/30/2012] [Indexed: 11/06/2022]
Abstract
Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
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Affiliation(s)
- Elizabeth D Schifano
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
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23
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Abstract
A linear model involving a mixture distribution is considered for the comparison of normalized microarray data from two treatment groups. Model fitting using an empirical Bayes approach has been shown to be both accurate and numerically stable. The posterior odds of treatment/gene interactions derived from the model involve shrinkage estimates of both the interactions and the gene-specific error variances, leading to powerful inference. We show that the same model can easily be fit under a fully Bayesian framework, allowing increased flexibility in terms of prior distributional assumptions and posterior inference.
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Affiliation(s)
- Haim Y Bar
- Department of Statistical Science, Cornell University, Ithaca
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24
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Schifano ED, Strawderman RL, Wells MT. Majorization-Minimization algorithms for nonsmoothly penalized objective functions. Electron J Stat 2010. [DOI: 10.1214/10-ejs582] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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