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Tsai YT, Hrytsenko Y, Elgart M, Usman T, Chen ZZ, Wilson JG, Gerszten R, Sofer T. A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks. HGG Adv 2024:100304. [PMID: 38720460 DOI: 10.1016/j.xhgg.2024.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 10/30/2023] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 05/21/2024] Open
Abstract
Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.
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Affiliation(s)
- Yi-Ting Tsai
- Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA
| | - Tahir Usman
- Department of Medicine, Harvard Medical School, Boston, MA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Zsu-Zsu Chen
- Department of Medicine, Harvard Medical School, Boston, MA; Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA
| | - James G Wilson
- Department of Medicine, Harvard Medical School, Boston, MA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert Gerszten
- Department of Medicine, Harvard Medical School, Boston, MA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA.
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2
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJ, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Der Ida Chen Y, Sofer T. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. medRxiv 2023:2023.12.13.23299909. [PMID: 38168328 PMCID: PMC10760279 DOI: 10.1101/2023.12.13.23299909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - 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
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D. Mitchel
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M. Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C. Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 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
| | - Matthew Moll
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, West Roxbury, MA, USA
| | - Myriam Fornage
- 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
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ramon Casanova
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark, DK
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O’Connor
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital
| | - 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
| | - Yii Der Ida Chen
- 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
| | | | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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Elgart M, Zhang Y, Zhang Y, Yu B, Kim Y, Zee PC, Gellman MD, Boerwinkle E, Daviglus ML, Cai J, Redline S, Burk RD, Kaplan R, Sofer T. Anaerobic pathogens associated with OSA may contribute to pathophysiology via amino-acid depletion. EBioMedicine 2023; 98:104891. [PMID: 38006744 PMCID: PMC10709109 DOI: 10.1016/j.ebiom.2023.104891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 03/06/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND The human microbiome is linked to multiple metabolic disorders such as obesity and diabetes. Obstructive sleep apnoea (OSA) is a common sleep disorder with several metabolic risk factors. We investigated the associations between the gut microbiome composition and function, and measures of OSA severity in participants from a prospective community-based cohort study: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). METHODS Bacterial-Wide Association Analysis (BWAS) of gut microbiome measured via metagenomics with OSA measures was performed adjusting for clinical, lifestyle and co-morbidities. This was followed by functional analysis of the OSA-enriched bacteria. We utilized additional metabolomic and transcriptomic associations to suggest possible mechanisms explaining the microbiome effects on OSA. FINDINGS Several uncommon anaerobic human pathogens were associated with OSA severity. These belong to the Lachnospira, Actinomyces, Kingella and Eubacterium genera. Functional analysis revealed enrichment in 49 processes including many anaerobic-related ones. Severe OSA was associated with the depletion of the amino acids glycine and glutamine in the blood, yet neither diet nor gene expression revealed any changes in the production or consumption of these amino acids. INTERPRETATION We show anaerobic bacterial communities to be a novel component of OSA pathophysiology. These are established in the oxygen-poor environments characteristic of OSA. We hypothesize that these bacteria deplete certain amino acids required for normal human homeostasis and muscle tone, contributing to OSA phenotypes. Future work should test this hypothesis as well as consider diagnostics via anaerobic bacteria detection and possible interventions via antibiotics and amino-acid supplementation. FUNDING Described in methods.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yuan Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Bing Yu
- Human Genetics Centre, The University of Texas Health Science Centre at Houston, Houston, TX, USA; Human Genome Sequencing Centre, Baylor College of Medicine, Houston, TX, USA
| | - Youngmee Kim
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Phyllis C Zee
- Department of Neurology and Sleep Medicine Centre, Northwestern University, Chicago, IL, USA
| | - Marc D Gellman
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Eric Boerwinkle
- Human Genetics Centre, The University of Texas Health Science Centre at Houston, Houston, TX, USA; Human Genome Sequencing Centre, Baylor College of Medicine, Houston, TX, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Jianwen Cai
- Collaborative Studies Coordinating Centre, University of North Carolina at Chapel Hill, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA; Fred Hutchinson Cancer Research Centre, Division of Public Health Sciences, Seattle, WA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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4
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Tsai YT, Hrytsenko Y, Elgart M, Tahir U, Chen ZZ, Wilson JG, Gerszten R, Sofer T. A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks. medRxiv 2023:2023.10.24.23297474. [PMID: 37961678 PMCID: PMC10635196 DOI: 10.1101/2023.10.24.23297474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.
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Affiliation(s)
- Yi-Ting Tsai
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Usman Tahir
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Zsu-Zsu Chen
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA
| | - James G Wilson
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert Gerszten
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
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Zhang Y, Elgart M, Granot-Hershkovitz E, Wang H, Tarraf W, Ramos AR, Stickel AM, Zeng D, Garcia TP, Testai FD, Wassertheil-Smoller S, Isasi CR, Daviglus ML, Kaplan R, Fornage M, DeCarli C, Redline S, González HM, Sofer T. Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos. EBioMedicine 2023; 87:104393. [PMID: 36493726 PMCID: PMC9732133 DOI: 10.1016/j.ebiom.2022.104393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Sleep phenotypes have been reported to be associated with cognitive ageing outcomes. However, there is limited research using genetic variants as proxies for sleep traits to study their associations. We estimated associations between Polygenic Risk Scores (PRSs) for sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnoea (OSA) and measures of cogntive ageing in Hispanic/Latino adults. METHODS We used summary statistics from published genome-wide association studies to construct PRSs representing the genetic basis of each sleep trait, then we studied the association of the PRSs of the sleep phenotypes with cognitive outcomes in the Hispanic Community Healthy Study/Study of Latinos. The primary model adjusted for age, sex, study centre, and measures of genetic ancestry. Associations are highlighted if their p-value <0.05. FINDINGS Higher PRS for insomnia was associated with lower global cognitive function and higher risk of mild cognitive impairment (MCI) (OR = 1.20, 95% CI [1.06, 1.36]). Higher PRS for daytime sleepiness was also associated with increased MCI risk (OR = 1.14, 95% CI [1.02, 1.28]). Sleep duration PRS was associated with reduced MCI risk among short and normal sleepers, while among long sleepers it was associated with reduced global cognitive function and with increased MCI risk (OR = 1.40, 95% CI [1.10, 1.78]). Furthermore, adjustment of analyses for the measured sleep phenotypes and APOE-ε4 allele had minor effects on the PRS associations with the cognitive outcomes. INTERPRETATION Genetic measures underlying insomnia, daytime sleepiness, and sleep duration are associated with MCI risk. Genetic and self-reported sleep duration interact in their effect on MCI. FUNDING Described in Acknowledgments.
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Affiliation(s)
- Yuan Zhang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Michael Elgart
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ariana M Stickel
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Tanya P Garcia
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Fernando D Testai
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine at Chicago, Chicago, IL, USA
| | | | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles DeCarli
- Department of Neurology, Alzheimer's Disease Center, University of California, Davis, Sacramento, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Hector M González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Center, University of California, San Diego, La Jolla, CA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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6
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Elgart M, Goodman MO, Isasi C, Chen H, Morrison AC, de Vries PS, Xu H, Manichaikul AW, Guo X, Franceschini N, Psaty BM, Rich SS, Rotter JI, Lloyd-Jones DM, Fornage M, Correa A, Heard-Costa NL, Vasan RS, Hernandez R, Kaplan RC, Redline S, Sofer T. Correlations between complex human phenotypes vary by genetic background, gender, and environment. Cell Rep Med 2022; 3:100844. [PMID: 36513073 PMCID: PMC9797952 DOI: 10.1016/j.xcrm.2022.100844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/11/2022] [Accepted: 11/09/2022] [Indexed: 12/15/2022]
Abstract
We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Medicine, Harvard Medical School, Boston, MA, USA,Corresponding author
| | - Matthew O. Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carmen Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 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
| | - 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
| | - Paul S. de Vries
- 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
| | - Huichun Xu
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ani W. Manichaikul
- Center for Public Health Genomics, University of Virginia, 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
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, 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
| | | | - Myriam Fornage
- 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,Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nancy L. Heard-Costa
- Boston University and National Heart Lung and Blood Institute’s Framingham Heart Study, Framingham, MA, USA,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Ramachandran S. Vasan
- Boston University and National Heart Lung and Blood Institute’s Framingham Heart Study, Framingham, MA, USA,Preventive Medicine & Epidemiology, and Cardiovascular Medicine, Medicine, Boston University School of Medicine, and Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Ryan Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA,Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Medicine, Harvard Medical School, Boston, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Corresponding author
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7
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Zhang Y, Elgart M, Kurniansyah N, Spitzer BW, Wang H, Kim D, Shah N, Daviglus M, Zee PC, Cai J, Gottlieb DJ, Cade BE, Redline S, Sofer T. Genetic determinants of cardiometabolic and pulmonary phenotypes and obstructive sleep apnoea in HCHS/SOL. EBioMedicine 2022; 84:104288. [PMID: 36174398 PMCID: PMC9515437 DOI: 10.1016/j.ebiom.2022.104288] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/24/2022] [Accepted: 09/08/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Obstructive Sleep Apnoea (OSA) often co-occurs with cardiometabolic and pulmonary diseases. This study is to apply genetic analysis methods to explain the associations between OSA and related phenotypes. METHODS In the Hispanic Community Healthy Study/Study of Latinos, we estimated genetic correlations ρg between the respiratory event index (REI) and 54 anthropometric, glycemic, cardiometabolic, and pulmonary phenotypes. We used summary statistics from published genome-wide association studies to construct Polygenic Risk Scores (PRSs) representing the genetic basis of each correlated phenotype (ρg>0.2 and p-value<0.05), and of OSA. We studied the association of the PRSs of the correlated phenotypes with both REI and OSA (REI≥5), and the association of OSA PRS with the correlated phenotypes. Causal relationships were tested using Mendelian Randomization (MR) analysis. FINDINGS The dataset included 11,155 participants, 31.03% with OSA. 22 phenotypes were genetically correlated with REI. 10 PRSs covering obesity and fat distribution (BMI, WHR, WHRadjBMI), blood pressure (DBP, PP, MAP), glycaemic control (fasting insulin, HbA1c, HOMA-B) and insomnia were associated with REI and/or OSA. OSA PRS was associated with BMI, WHR, DBP and glycaemic traits (fasting insulin, HbA1c, HOMA-B and HOMA-IR). MR analysis identified robust causal effects of BMI and WHR on OSA, and probable causal effects of DBP, PP, and HbA1c on OSA/REI. INTERPRETATION There are shared genetic underpinnings of anthropometric, blood pressure, and glycaemic phenotypes with OSA, with evidence for causal relationships between some phenotypes. FUNDING Described in Acknowledgments.
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Affiliation(s)
- Yuan Zhang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Michael Elgart
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian W. Spitzer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Doyoon Kim
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Neomi Shah
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Phyllis C. Zee
- Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel J. Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Corresponding author at: Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
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8
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Elgart M, Lyons G, Romero-Brufau S, Kurniansyah N, Brody JA, Guo X, Lin HJ, Raffield L, Gao Y, Chen H, de Vries P, Lloyd-Jones DM, Lange LA, Peloso GM, Fornage M, Rotter JI, Rich SS, Morrison AC, Psaty BM, Levy D, Redline S, Sofer T. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. Commun Biol 2022; 5:856. [PMID: 35995843 PMCID: PMC9395509 DOI: 10.1038/s42003-022-03812-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [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] [Received: 07/19/2021] [Accepted: 08/05/2022] [Indexed: 01/03/2023] Open
Abstract
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models. Combining a standard polygenic risk score (PRS) as a feature in a machine learning model increases the percentage variance explained for those traits, helping to account for non-linearities or interaction effects in genetics-based prediction models.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Genevieve Lyons
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Santiago Romero-Brufau
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 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
| | - Henry J Lin
- 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
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Yan Gao
- The Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, 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
| | - Paul de Vries
- 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
| | | | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Myriam Fornage
- 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.,Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 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
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, 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
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA.,The Framingham Heart Study, Framingham, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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9
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Zhang Y, Elgart M, Kurniansya N, Spitzer B, Wang H, Shah N, Daviglus M, Phylis Z, Cai J, Gottlieb D, Cade B, Redline S, Sofer T. 0034 Genetic Determinants of Cardiometabolic and Pulmonary Traits and Obstructive Sleep Apnea in the Hispanic Community Health Study/Study of Latinos. Sleep 2022. [DOI: 10.1093/sleep/zsac079.033] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Obstructive sleep apnea (OSA) often co-occurs with other health outcomes. The respiratory event index (REI), often used to define OSA, is similarly correlated with several health phenotypes. Genetic data provide an opportunity to explain the nature of these associations.
Methods
We used data from the Hispanic Community Healthy Study/Study of Latinos (HCHS/SOL) to estimate genetic correlations (i.e., the correlation between phenotypes that is due to genetic effects) between OSA severity as measured by the REI and 56 anthropometric, glycemic, cardiometabolic, and pulmonary traits. Genetically-correlated traits (>0.2 and p-value<0.05) were carried forward for additional analysis. Using summary statistics from published genome-wide association studies (GWAS), we constructed Polygenic Risk Scores (PRSs) representing the genetic basis of each correlated trait and OSA, and studied their associations with the genetically-correlated traits, REI and OSA. OSA was defined as REI5. When a PRS for a correlated-trait was associated (p-value<0.05) with REI/OSA or vice versa, we used GWAS summary statistics to test causal relationships using Mendelian Randomization (MR) analysis. We further estimated correlated-trait PRS associations with REI and OSA in subgroups of individuals with and without obesity (BMI>30).
Results
The dataset included 11,155 participants (mean age: 46.2 (SD =13.8) years; 41.1% males) from the baseline HCHS/SOL exam who underwent home sleep apnea testing. 30.65% had OSA. 22 traits were genetically correlated with REI. Without BMI adjustment, the PRSs of BMI, waist-to-hip ratio (WHR), diastolic blood pressure (DBP), pulse pressure (PP), HbA1c, triglycerides (TG), FEV1/FVC and insomnia were significantly associated with REI/OSA. The associations of WHR, DBP, PP, HbA1c and insomnia PRSs and REI/OSA remained in BMI adjusted analysis. In obesity-stratified analysis, PRS of BMI, WHR and DBP were associated with REI/OSA in individuals with obesity, while PRSs of FEV1/FVC, HbA1c, insomnia, PP, TG, and WHR were associated with REI/OSA in individuals without obesity. MR analysis identified robust causal effect of increased BMI on OSA, and suggestive causal effects of WHR, DBP, and PP on OSA.
Conclusion
Our results support shared genetic basis of anthropometric traits, blood pressure traits, and insomnia with OSA, with potential differences in disease mechanisms in individuals with and without obesity.
Support (If Any)
Funding: R35HL135818, R21HL145425.
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Affiliation(s)
- Yuan Zhang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Michael Elgart
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Nuzulul Kurniansya
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Brian Spitzer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Neomi Shah
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medi-cine at Mount Sinai
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago
| | - Zee Phylis
- Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Feinberg School of Medicine
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Daniel Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital
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Abstract
Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
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11
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Fuks G, Elgart M, Amir A, Zeisel A, Turnbaugh PJ, Soen Y, Shental N. Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling. Microbiome 2018; 6:17. [PMID: 29373999 PMCID: PMC5787238 DOI: 10.1186/s40168-017-0396-x] [Citation(s) in RCA: 115] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 12/25/2017] [Indexed: 05/02/2023]
Abstract
BACKGROUND Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA. RESULTS This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis. CONCLUSIONS SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).
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Affiliation(s)
- Garold Fuks
- Departments of Physics of Complex Systems, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Michael Elgart
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Amnon Amir
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093 USA
| | - Amit Zeisel
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, 10 Karolinska Institutet, S-171 77 Stockholm, Sweden
| | - Peter J. Turnbaugh
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143 USA
| | - Yoav Soen
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Noam Shental
- Department of Computer Science, The Open University of Israel, 43107 Ra’anana, Israel
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12
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Elgart M, Soen Y. Microbiome-Germline Interactions and Their Transgenerational Implications. Bioessays 2017; 40:e1700018. [DOI: 10.1002/bies.201700018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 10/30/2017] [Indexed: 01/16/2023]
Affiliation(s)
| | - Yoav Soen
- Biomolecular Sciences; Rehovot Israel
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13
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Elgart M, Stern S, Salton O, Gnainsky Y, Heifetz Y, Soen Y. Impact of gut microbiota on the fly's germ line. Nat Commun 2016; 7:11280. [PMID: 27080728 PMCID: PMC4835552 DOI: 10.1038/ncomms11280] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [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] [Received: 09/17/2015] [Accepted: 03/09/2016] [Indexed: 12/20/2022] Open
Abstract
Unlike vertically transmitted endosymbionts, which have broad effects on their host's germ line, the extracellular gut microbiota is transmitted horizontally and is not known to influence the germ line. Here we provide evidence supporting the influence of these gut bacteria on the germ line of Drosophila melanogaster. Removal of the gut bacteria represses oogenesis, expedites maternal-to-zygotic-transition in the offspring and unmasks hidden phenotypic variation in mutants. We further show that the main impact on oogenesis is linked to the lack of gut Acetobacter species, and we identify the Drosophila Aldehyde dehydrogenase (Aldh) gene as an apparent mediator of repressed oogenesis in Acetobacter-depleted flies. The finding of interactions between the gut microbiota and the germ line has implications for reproduction, developmental robustness and adaptation. The gut microbiota can play various roles in the host's physiology, but is not known to influence the germ line. Here, Elgart et al. show that certain extracellular gut bacteria can affect oogenesis and embryo development in the fruit fly.
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Affiliation(s)
- Michael Elgart
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Shay Stern
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Orit Salton
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yulia Gnainsky
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yael Heifetz
- Department of Entomology, The Hebrew University, Rehovot 76100, Israel
| | - Yoav Soen
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
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14
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Soen Y, Knafo M, Elgart M. A principle of organization which facilitates broad Lamarckian-like adaptations by improvisation. Biol Direct 2015; 10:68. [PMID: 26631109 PMCID: PMC4668624 DOI: 10.1186/s13062-015-0097-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [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] [Received: 05/08/2015] [Accepted: 11/18/2015] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND During the lifetime of an organism, every individual encounters many combinations of diverse changes in the somatic genome, epigenome and microbiome. This gives rise to many novel combinations of internal failures which are unique to each individual. How any individual can tolerate this high load of new, individual-specific scenarios of failure is not clear. While stress-induced plasticity and hidden variation have been proposed as potential mechanisms of tolerance, the main conceptual problem remains unaddressed, namely: how largely non-beneficial random variation can be rapidly and safely organized into net benefits to every individual. PRESENTATION OF THE HYPOTHESIS We propose an organizational principle which explains how every individual can alleviate a high load of novel stressful scenarios using many random variations in flexible and inherently less harmful traits. Random changes which happen to reduce stress, benefit the organism and decrease the drive for additional changes. This adaptation (termed 'Adaptive Improvisation') can be further enhanced, propagated, stabilized and memorized when beneficial changes reinforce themselves by auto-regulatory mechanisms. This principle implicates stress not only in driving diverse variations in cells tissues and organs, but also in organizing these variations into adaptive outcomes. Specific (but not exclusive) examples include stress reduction by rapid exchange of mobile genetic elements (or exosomes) in unicellular, and rapid changes in the symbiotic microorganisms of animals. In all cases, adaptive changes can be transmitted across generations, allowing rapid improvement and assimilation in a few generations. TESTING THE HYPOTHESIS We provide testable predictions derived from the hypothesis. IMPLICATIONS OF THE HYPOTHESIS The hypothesis raises a critical, but thus far overlooked adaptation problem and explains how random variation can self-organize to confer a wide range of individual-specific adaptations beyond the existing outcomes of natural selection. It portrays gene regulation as an inseparable synergy between natural selection and adaptation by improvisation. The latter provides a basis for Lamarckian adaptation that is not limited to a specific mechanism and readily accounts for the remarkable resistance of tumors to treatment.
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Affiliation(s)
- Yoav Soen
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, 76100, Israel.
| | - Maor Knafo
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, 76100, Israel.
| | - Michael Elgart
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, 76100, Israel.
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15
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Elgart M, Snir O, Soen Y. Stress-mediated tuning of developmental robustness and plasticity in flies. Biochim Biophys Acta 2014; 1849:462-6. [PMID: 25134463 DOI: 10.1016/j.bbagrm.2014.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 07/31/2014] [Accepted: 08/02/2014] [Indexed: 11/30/2022]
Abstract
Organisms have to be sufficiently robust to environmental and genetic perturbations, yet plastic enough to cope with stressful scenarios to which they are not fully adapted. How this apparent conflict between robustness and plasticity is resolved at the cellular and whole organism levels is not clear. Here we review and discuss evidence in flies suggesting that the environment can modulate the balance between robustness and plasticity. The outcomes of this modulation can vary from mild sensitizations that are hardly noticeable, to overt qualitative changes in phenotype. The effects could be at both the cellular and whole organism levels and can include cellular de-/trans-differentiation ('Cellular reprogramming') and gross disfigurements such as homeotic transformations ('Tissue/whole organism reprogramming'). When the stress is mild enough, plastic changes in some processes may prevent drastic changes in more robust traits such as cell identity and tissue integrity. However, when the stress is sufficiently severe, this buffering may no longer be able to prevent such overt changes, and the resulting phenotypic variability could be subjected to selection and might assist survival at the population level. This article is part of a Special Issue entitled: Stress as a fundamental theme in cell plasticity.
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Affiliation(s)
- M Elgart
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - O Snir
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Y Soen
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel.
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16
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Fridmann-Sirkis Y, Stern S, Elgart M, Galili M, Zeisel A, Shental N, Soen Y. Delayed development induced by toxicity to the host can be inherited by a bacterial-dependent, transgenerational effect. Front Genet 2014; 5:27. [PMID: 24611070 PMCID: PMC3933808 DOI: 10.3389/fgene.2014.00027] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 01/25/2014] [Indexed: 11/20/2022] Open
Abstract
Commensal gut bacteria in many species including flies are integral part of their host, and are known to influence its development and homeostasis within generation. Here we report an unexpected impact of host–microbe interactions, which mediates multi-generational, non-Mendelian inheritance of a stress-induced phenotype. We have previously shown that exposure of fly larvae to G418 antibiotic induces transgenerationally heritable phenotypes, including a delay in larval development, gene induction in the gut and morphological changes. We now show that G418 selectively depletes commensal Acetobacter species and that this depletion explains the heritable delay, but not the inheritance of the other phenotypes. Notably, the inheritance of the delay was mediated by a surprising trans-generational effect. Specifically, bacterial removal from F1 embryos did not induce significant delay in F1 larvae, but nonetheless led to a considerable delay in F2. This effect maintains a delay induced by bacterial-independent G418 toxicity to the host. In line with these findings, reintroduction of isolated Acetobacter species prevented the inheritance of the delay. We further show that this prevention is partly mediated by vitamin B2 (Riboflavin) produced by these bacteria; exogenous Riboflavin led to partial prevention and inhibition of Riboflavin synthesis compromised the ability of the bacteria to prevent the inheritance. These results identify host–microbe interactions as a hitherto unrecognized factor capable of mediating non-Mendelian inheritance of a stress-induced phenotype.
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Affiliation(s)
- Yael Fridmann-Sirkis
- Department of Biological Chemistry, Weizmann Institute of Science Rehovot, Israel
| | - Shay Stern
- Department of Biological Chemistry, Weizmann Institute of Science Rehovot, Israel
| | - Michael Elgart
- Department of Biological Chemistry, Weizmann Institute of Science Rehovot, Israel
| | - Matana Galili
- Department of Biological Chemistry, Weizmann Institute of Science Rehovot, Israel
| | - Amit Zeisel
- Department of Physics of Complex Systems, Weizmann Institute of Science Rehovot, Israel
| | - Noam Shental
- Department of Computer Science, The Open University Raanana, Israel
| | - Yoav Soen
- Department of Biological Chemistry, Weizmann Institute of Science Rehovot, Israel
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Amir A, Zeisel A, Zuk O, Elgart M, Stern S, Shamir O, Turnbaugh PJ, Soen Y, Shental N. High-resolution microbial community reconstruction by integrating short reads from multiple 16S rRNA regions. Nucleic Acids Res 2013; 41:e205. [PMID: 24214960 PMCID: PMC3905898 DOI: 10.1093/nar/gkt1070] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The emergence of massively parallel sequencing technology has revolutionized microbial profiling, allowing the unprecedented comparison of microbial diversity across time and space in a wide range of host-associated and environmental ecosystems. Although the high-throughput nature of such methods enables the detection of low-frequency bacteria, these advances come at the cost of sequencing read length, limiting the phylogenetic resolution possible by current methods. Here, we present a generic approach for integrating short reads from large genomic regions, thus enabling phylogenetic resolution far exceeding current methods. The approach is based on a mapping to a statistical model that is later solved as a constrained optimization problem. We demonstrate the utility of this method by analyzing human saliva and Drosophila samples, using Illumina single-end sequencing of a 750 bp amplicon of the 16S rRNA gene. Phylogenetic resolution is significantly extended while reducing the number of falsely detected bacteria, as compared with standard single-region Roche 454 Pyrosequencing. Our approach can be seamlessly applied to simultaneous sequencing of multiple genes providing a higher resolution view of the composition and activity of complex microbial communities.
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Affiliation(s)
- Amnon Amir
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel, Toyota Technological Institute at Chicago, Chicago, IL 60637, USA, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA, Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel, Microsoft Research, Cambridge, MA 02142, USA, FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA and Department of Mathematics and Computer Science, The Open University of Israel, Raanana 43537, Israel
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18
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Farhy C, Elgart M, Shapira Z, Oron-Karni V, Yaron O, Menuchin Y, Rechavi G, Ashery-Padan R. Pax6 is required for normal cell-cycle exit and the differentiation kinetics of retinal progenitor cells. PLoS One 2013; 8:e76489. [PMID: 24073291 PMCID: PMC3779171 DOI: 10.1371/journal.pone.0076489] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Accepted: 08/27/2013] [Indexed: 11/19/2022] Open
Abstract
The coupling between cell-cycle exit and onset of differentiation is a common feature throughout the developing nervous system, but the mechanisms that link these processes are mostly unknown. Although the transcription factor Pax6 has been implicated in both proliferation and differentiation of multiple regions within the central nervous system (CNS), its contribution to the transition between these successive states remains elusive. To gain insight into the role of Pax6 during the transition from proliferating progenitors to differentiating precursors, we investigated cell-cycle and transcriptomic changes occurring in Pax6 (-) retinal progenitor cells (RPCs). Our analyses revealed a unique cell-cycle phenotype of the Pax6-deficient RPCs, which included a reduced number of cells in the S phase, an increased number of cells exiting the cell cycle, and delayed differentiation kinetics of Pax6 (-) precursors. These alterations were accompanied by coexpression of factors that promote (Ccnd1, Ccnd2, Ccnd3) and inhibit (P27 (kip1) and P27 (kip2) ) the cell cycle. Further characterization of the changes in transcription profile of the Pax6-deficient RPCs revealed abrogated expression of multiple factors which are known to be involved in regulating proliferation of RPCs, including the transcription factors Vsx2, Nr2e1, Plagl1 and Hedgehog signaling. These findings provide novel insight into the molecular mechanism mediating the pleiotropic activity of Pax6 in RPCs. The results further suggest that rather than conveying a linear effect on RPCs, such as promoting their proliferation and inhibiting their differentiation, Pax6 regulates multiple transcriptional networks that function simultaneously, thereby conferring the capacity to proliferate, assume multiple cell fates and execute the differentiation program into retinal lineages.
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Affiliation(s)
- Chen Farhy
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Michael Elgart
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Zehavit Shapira
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Varda Oron-Karni
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Orly Yaron
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yotam Menuchin
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Gideon Rechavi
- Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ruth Ashery-Padan
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Oron-Karni V, Farhy C, Elgart M, Marquardt T, Remizova L, Yaron O, Xie Q, Cvekl A, Ashery-Padan R. Dual requirement for Pax6 in retinal progenitor cells. Development 2008; 135:4037-4047. [PMID: 19004853 DOI: 10.1242/dev.028308] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Throughout the developing central nervous system, pre-patterning of the ventricular zone into discrete neural progenitor domains is one of the predominant strategies used to produce neuronal diversity in a spatially coordinated manner. In the retina, neurogenesis proceeds in an intricate chronological and spatial sequence, yet it remains unclear whether retinal progenitor cells (RPCs) display intrinsic heterogeneity at any given time point. Here, we performed a detailed study of RPC fate upon temporally and spatially confined inactivation of Pax6. Timed genetic removal of Pax6 appeared to unmask a cryptic divergence of RPCs into qualitatively divergent progenitor pools. In the more peripheral RPCs under normal circumstances, Pax6 seemed to prevent premature activation of a photoreceptor-differentiation pathway by suppressing expression of the transcription factor Crx. More centrally, Pax6 contributed to the execution of the comprehensive potential of RPCs: Pax6 ablation resulted in the exclusive generation of amacrine interneurons. Together, these data suggest an intricate dual role for Pax6 in retinal neurogenesis, while pointing to the cryptic divergence of RPCs into distinct progenitor pools.
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Affiliation(s)
- Varda Oron-Karni
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
| | - Chen Farhy
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
| | - Michael Elgart
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
| | - Till Marquardt
- European Neuroscience Institute, Developmental Neurobiology Laboratory, University of Göttingen Medical School/Max Planck Society, Grisebachstrasse 5, 37077 Göttingen, Germany
| | - Lena Remizova
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
| | - Orly Yaron
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
| | - Qing Xie
- Albert Einstein College of Medicine, Departments of Ophthalmology and Visual Sciences and Genetics, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Ales Cvekl
- Albert Einstein College of Medicine, Departments of Ophthalmology and Visual Sciences and Genetics, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Ruth Ashery-Padan
- Sackler Faculty of Medicine, Human Molecular Genetics and Biochemistry, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel
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21
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Abstract
Three patients with biopsy-confirmed erythroplasia of Queyrat were treated with topically applied fluorouracil. The lesions cleared completely, and recurrence-free follow-up periods ranged from 20 to 60 months. There were normal histological findings in posttreatment biopsy specimens in two of the patients. A literature review yielded five additional cases successfully treated with fluorouracil applied topically. Patients with histologically confirmed erythroplasia of Queyrat should be afforded treatment with topically applied fluorouracil, as results appear to be superior to those of surgical or radiological treatment, with less morbidity.
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