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Don J, Schork AJ, Glusman G, Rappaport N, Cummings SR, Duggan D, Raju A, Hellberg KLG, Gunn S, Monti S, Perls T, Lapidus J, Goetz LH, Sebastiani P, Schork NJ. The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank. GeroScience 2024; 46:3911-3927. [PMID: 38451433 PMCID: PMC11226417 DOI: 10.1007/s11357-024-01107-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.
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Affiliation(s)
- Janith Don
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Andrew J Schork
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | | | | | - Steve R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - David Duggan
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Anish Raju
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Kajsa-Lotta Georgii Hellberg
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | - Sophia Gunn
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stefano Monti
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Thomas Perls
- Department of Medicine, Section of Geriatrics, Boston University, Boston, MA, USA
| | - Jodi Lapidus
- Department of Biostatistics, Oregon Health & Science University, Portland, OR, USA
| | - Laura H Goetz
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Veterans Affairs Loma Linda Health Care, Loma Linda, CA, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine and Data Intensive Study Center, Boston, MA, USA
| | - Nicholas J Schork
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Lu X, Xie T, van Faassen M, Kema IP, van Beek AP, Xu X, Huo X, Wolffenbuttel BHR, van Vliet-Ostaptchouk JV, Nolte IM, Snieder H. Effects of endocrine disrupting chemicals and their interactions with genetic risk scores on cardiometabolic traits. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169972. [PMID: 38211872 DOI: 10.1016/j.scitotenv.2024.169972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
Ubiquitous non-persistent endocrine disrupting chemicals (EDCs) have inconsistent associations with cardiometabolic traits. Additionally, large-scale genome-wide association studies (GWASs) have yielded many genetic risk variants for cardiometabolic traits and diseases. This study aimed to investigate the associations between a wide range of EDC exposures (parabens, bisphenols, and phthalates) and 14 cardiometabolic traits and whether these are moderated by their respective genetic risk scores (GRSs). Data were from 1074 participants aged 18 years or older of the Lifelines Cohort Study, a large population-based biobank. GRSs for 14 cardiometabolic traits were calculated based on genome-wide significant common variants from recent GWASs. The concentrations of 15 EDCs in 24-hour urine were measured by isotope dilution liquid chromatography tandem mass spectrometry technology. The main effects of trait-specific GRSs and each of the EDC exposures and their interaction effects on the 14 cardiometabolic traits were examined in multiple linear regression. The present study confirmed significant main effects for all GRSs on their corresponding cardiometabolic trait. Regarding the main effects of EDC exposures, 26 out of 280 EDC-trait tests were significant with explained variances ranging from 0.43 % (MMP- estimated glomerular filtration rate (eGFR)) to 2.37 % (PrP-waist-hip ratio adjusted body mass index (WHRadjBMI)). We confirmed the association of MiBP and MBzP with WHRadjBMI and body mass index (BMI), and showed that parabens, bisphenol F, and many other phthalate metabolites significantly contributed to the variance of WHRadjBMI, BMI, high-density lipoprotein (HDL), eGFR, fasting glucose (FG), and diastolic blood pressure (DBP). Only one association between BMI and bisphenol F was nominally significantly moderated by the GRS explaining 0.36 % of the variance. However, it did not survive multiple testing correction. We showed that non-persistent EDC exposures exerted effects on BMI, WHRadjBMI, HDL, eGFR, FG, and DBP. However no evidence for a modulating role of GRSs was found.
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Affiliation(s)
- Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands; Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Martijn van Faassen
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ido P Kema
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - André P van Beek
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Xijin Xu
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Xia Huo
- Laboratory of Environmental Medicine and Developmental Toxicology, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 510632, Guangdong, China
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands.
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Clapp Sullivan ML, Schwaba T, Harden KP, Grotzinger AD, Nivard MG, Tucker-Drob EM. Beyond the factor indeterminacy problem using genome-wide association data. Nat Hum Behav 2024; 8:205-218. [PMID: 38225407 PMCID: PMC10922726 DOI: 10.1038/s41562-023-01789-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/20/2023] [Indexed: 01/17/2024]
Abstract
Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.
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Affiliation(s)
| | - Ted Schwaba
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michel G Nivard
- Department of Biological Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
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4
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Jain PR, Burch M, Martinez M, Mir P, Fichna JP, Zekanowski C, Rizzo R, Tümer Z, Barta C, Yannaki E, Stamatoyannopoulos J, Drineas P, Paschou P. Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation. BMC Genom Data 2023; 24:70. [PMID: 37986041 PMCID: PMC10662565 DOI: 10.1186/s12863-023-01168-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023] Open
Abstract
Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Myson Burch
- Department of Computer Sciences, Purdue University, West Lafayette, IN, USA
| | - Melanie Martinez
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Pablo Mir
- Unidad de Trastornos del Movimiento, Instituto de Biomedicina de Sevilla (IBiS). Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Jakub P Fichna
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Neurogenetics and Functional Genomics, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | - Cezary Zekanowski
- Department of Neurogenetics and Functional Genomics, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | - Renata Rizzo
- Child and Adolescent Neurology and Psychiatry, Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Zeynep Tümer
- Department of Clinical Genetics, Kennedy Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Csaba Barta
- Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Evangelia Yannaki
- Hematology Department- Hematopoietic Cell Transplantation Unit, Gene and Cell Therapy Center, George Papanikolaou Hospital, Thessaloniki, Greece
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Stamatoyannopoulos
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Medicine, Division of Oncology, University of Washington, Seattle, WA, USA
| | - Petros Drineas
- Department of Computer Sciences, Purdue University, West Lafayette, IN, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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5
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John M, Lencz T. Potential application of elastic nets for shared polygenicity detection with adapted threshold selection. Int J Biostat 2023; 19:417-438. [PMID: 36327464 PMCID: PMC10154439 DOI: 10.1515/ijb-2020-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.
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Affiliation(s)
- Majnu John
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY
- Departments of Psychiatry and of Mathematics, Hofstra University, Hempstead, NY
| | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY
- Departments of Psychiatry and of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
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6
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Zhang N, Chen Y, Li C, Qin X, He D, Wei W, Zhao Y, Cai Q, Shi S, Chu X, Wen Y, Jia Y, Zhang F. A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection. Microbes Infect 2023; 25:105170. [PMID: 37315735 PMCID: PMC10259091 DOI: 10.1016/j.micinf.2023.105170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Previous studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now. METHODS In this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender. RESULTS In whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: β = 0.1361, P = 0.0142; Hospitalized/Unscreened: β = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: β = -0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group. CONCLUSION Our study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.
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Affiliation(s)
- Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China; Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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7
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van Kippersluis H, Biroli P, Dias Pereira R, Galama TJ, von Hinke S, Meddens SFW, Muslimova D, Slob EAW, de Vlaming R, Rietveld CA. Overcoming attenuation bias in regressions using polygenic indices. Nat Commun 2023; 14:4473. [PMID: 37491308 PMCID: PMC10368647 DOI: 10.1038/s41467-023-40069-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/11/2023] [Indexed: 07/27/2023] Open
Abstract
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.
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Affiliation(s)
- Hans van Kippersluis
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
- Tinbergen Institute, Amsterdam, The Netherlands.
| | - Pietro Biroli
- Department of Economics, University of Bologna, Bologna, Italy
| | - Rita Dias Pereira
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Titus J Galama
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Center for Social and Economic Research, University of Southern California, Los Angeles, CA, USA
| | - Stephanie von Hinke
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Economics, University of Bristol, Bristol, UK
| | - S Fleur W Meddens
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Statistics Netherlands, The Hague, The Netherlands
| | - Dilnoza Muslimova
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Eric A W Slob
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge, UK
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| | - Ronald de Vlaming
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelius A Rietveld
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
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8
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Yoon N, Cho YS. Development of a Polygenic Risk Score for BMI to Assess the Genetic Susceptibility to Obesity and Related Diseases in the Korean Population. Int J Mol Sci 2023; 24:11560. [PMID: 37511320 PMCID: PMC10380444 DOI: 10.3390/ijms241411560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Hundreds of genetic variants for body mass index (BMI) have been identified from numerous genome-wide association studies (GWAS) in different ethnicities. In this study, we aimed to develop a polygenic risk score (PRS) for BMI for predicting susceptibility to obesity and related traits in the Korean population. For this purpose, we obtained base data resulting from a GWAS on BMI using 57,110 HEXA study subjects from the Korean Genome and Epidemiology Study (KoGES). Subsequently, we calculated PRSs in 13,504 target subjects from the KARE and CAVAS studies of KoGES using the PRSice-2 software. The best-fit PRS for BMI (PRSBMI) comprising 53,341 SNPs was selected at a p-value threshold of 0.064, at which the model fit had the greatest R2 score. The PRSBMI was tested for its association with obesity-related quantitative traits and diseases in the target dataset. Linear regression analyses demonstrated significant associations of PRSBMI with BMI, blood pressure, and lipid traits. Logistic regression analyses revealed significant associations of PRSBMI with obesity, hypertension, and hypo-HDL cholesterolemia. We observed about 2-fold, 1.1-fold, and 1.2-fold risk for obesity, hypertension, and hypo-HDL cholesterolemia, respectively, in the highest-risk group in comparison to the lowest-risk group of PRSBMI in the test population. We further detected approximately 26.0%, 2.8%, and 3.9% differences in prevalence between the highest and lowest risk groups for obesity, hypertension, and hypo-HDL cholesterolemia, respectively. To predict the incidence of obesity and related diseases, we applied PRSBMI to the 16-year follow-up data of the KARE study. Kaplan-Meier survival analysis showed that the higher the PRSBMI, the higher the incidence of dyslipidemia and hypo-HDL cholesterolemia. Taken together, this study demonstrated that a PRS developed for BMI may be a valuable indicator to assess the risk of obesity and related diseases in the Korean population.
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Affiliation(s)
- Nara Yoon
- Department of Biomedical Science, Hallym University, Chuncheon 24252, Republic of Korea
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon 24252, Republic of Korea
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9
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Douville NJ, Larach DB, Lewis A, Bastarache L, Pandit A, He J, Heung M, Mathis M, Wanderer JP, Kheterpal S, Surakka I, Kertai MD. Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury. Front Genet 2023; 14:1094908. [PMID: 37124606 PMCID: PMC10133500 DOI: 10.3389/fgene.2023.1094908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
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Affiliation(s)
- Nicholas J. Douville
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Daniel B. Larach
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anita Pandit
- Center for Statistical Genetics and Precision Health Initiative, University of Michigan, Ann Arbor, MI, United States
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Heung
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan P. Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Miklos D. Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
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10
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Zhang N, Qi X, Chang H, Li C, Qin X, Wei W, Cai Q, He D, Zhao Y, Shi S, Chu X, Wen Y, Jia Y, Zhang F. Combined effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression: A cross-sectional study based on UK Biobank. J Med Virol 2023; 95:e28726. [PMID: 37185864 DOI: 10.1002/jmv.28726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 03/23/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023]
Abstract
Infection-induced perturbation of immune homeostasis could promote psychopathology. Psychiatric sequelae have been observed after previous coronavirus outbreaks. However, limited studies were conducted to explore the potential interaction effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression. In this study, first, polygenic risk scores (PRS) were calculated for eight COVID-19 clinical phenotypes using individual-level genotype data from the UK Biobank. Then, linear regression models were developed to assess the effects of COVID-19 PRS, C-reactive protein (CRP), systemic immune inflammation index (SII), and their interaction effects on the Generalized Anxiety Disorder-7 (GAD-7, 104 783 individuals) score and the Patient Health Questionnaire-9 (PHQ-9, 104 346 individuals) score. Several suggestive interactions between inflammation factors and COVID-19 clinical phenotypes were detected for PHQ-9 score, such as CRP/SII × Hospitalized/Not_Hospitalized in women group and CRP × Hospitalized/Unscreened in age >65 years group. For GAD-7 score, we also found several suggestive interactions, such as CRP × Positive/Unscreened in the age ≤65 years group. Our results suggest that not only COVID-19 and inflammation have important effects on anxiety and depression but also the interactions of COVID-19 and inflammation have serious risks for anxiety and depression.
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Affiliation(s)
- Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xin Qi
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hong Chang
- Shaanxi Provincial Institute for Endemic Disease Control, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
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11
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Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform 2023; 24:6972298. [PMID: 36611240 DOI: 10.1093/bib/bbac600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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Affiliation(s)
- Joel Defo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
| | - Denis Awany
- South African Tuberculosis Vaccine Initiative (SATVI), University of Cape Town, 7925, South Africa
| | - Raj Ramesar
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
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12
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Soremekun C, Machipisa T, Soremekun O, Pirie F, Oyekanmi N, Motala AA, Chikowore T, Fatumo S. Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. PLoS One 2023; 18:e0280344. [PMID: 36809439 PMCID: PMC9942994 DOI: 10.1371/journal.pone.0280344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/27/2022] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Liver disease is any condition that causes liver damage and inflammation and may likely affect the function of the liver. Vital biochemical screening tools that can be used to evaluate the health of the liver and help diagnose, prevent, monitor, and control the development of liver disease are known as liver function tests (LFT). LFTs are performed to estimate the level of liver biomarkers in the blood. Several factors are associated with differences in concentration levels of LFTs in individuals, such as genetic and environmental factors. The aim of our study was to identify genetic loci associated with liver biomarker levels with a shared genetic basis in continental Africans, using a multivariate genome-wide association study (GWAS) approach. METHODS We used two distinct African populations, the Ugandan Genome Resource (UGR = 6,407) and South African Zulu cohort (SZC = 2,598). The six LFTs used in our analysis were: aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, and albumin. A multivariate GWAS of LFTs was conducted using the exact linear mixed model (mvLMM) approach implemented in GEMMA and the resulting P-values were presented in Manhattan and quantile-quantile (QQ) plots. First, we attempted to replicate the findings of the UGR cohort in SZC. Secondly, given that the genetic architecture of UGR is different from that of SZC, we further undertook similar analysis in the SZC and discussed the results separately. RESULTS A total of 59 SNPs reached genome-wide significance (P = 5x10-8) in the UGR cohort and with 13 SNPs successfully replicated in SZC. These included a novel lead SNP near the RHPN1 locus (lead SNP rs374279268, P-value = 4.79x10-9, Effect Allele Frequency (EAF) = 0.989) and a lead SNP at the RGS11 locus (lead SNP rs148110594, P-value = 2.34x10-8, EAF = 0.928). 17 SNPs were significant in the SZC, while all the SNPs fall within a signal on chromosome 2, rs1976391 mapped to UGT1A was identified as the lead SNP within this region. CONCLUSIONS Using multivariate GWAS method improves the power to detect novel genotype-phenotype associations for liver functions not found with the standard univariate GWAS in the same dataset.
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Affiliation(s)
- Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Nashiru Oyekanmi
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- * E-mail:
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13
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Odintsova VV, Hagenbeek FA, van der Laan CM, van de Weijer S, Boomsma DI. Genetics and epigenetics of human aggression. HANDBOOK OF CLINICAL NEUROLOGY 2023; 197:13-44. [PMID: 37633706 DOI: 10.1016/b978-0-12-821375-9.00005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Abstract
There is substantial variation between humans in aggressive behavior, with its biological etiology and molecular genetic basis mostly unknown. This review chapter offers an overview of genomic and omics studies revealing the genetic contribution to aggression and first insights into associations with epigenetic and other omics (e.g., metabolomics) profiles. We allowed for a broad phenotype definition including studies on "aggression," "aggressive behavior," or "aggression-related traits," "antisocial behavior," "conduct disorder," and "oppositional defiant disorder." Heritability estimates based on family and twin studies in children and adults of this broadly defined phenotype of aggression are around 50%, with relatively small fluctuations around this estimate. Next, we review the genome-wide association studies (GWAS) which search for associations with alleles and also allow for gene-based tests and epigenome-wide association studies (EWAS) which seek to identify associations with differently methylated regions across the genome. Both GWAS and EWAS allow for construction of Polygenic and DNA methylation scores at an individual level. Currently, these predict a small percentage of variance in aggression. We expect that increases in sample size will lead to additional discoveries in GWAS and EWAS, and that multiomics approaches will lead to a more comprehensive understanding of the molecular underpinnings of aggression.
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Affiliation(s)
- Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Camiel M van der Laan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Steve van de Weijer
- Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands.
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14
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Salvatore JE, Larsson Lönn S, Sundquist J, Kendler KS, Sundquist K. Social genetic effects for drug use disorder among spouses. Addiction 2022; 118:880-889. [PMID: 36494088 DOI: 10.1111/add.16108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022]
Abstract
AIMS Preclinical and human studies suggest that a social partner's genotype may be associated with addiction-related outcomes. This study measured whether spousal genetic makeup is associated with risk of developing drug use disorder (DUD) during marriage and whether the risk associated with a spouse's genotype could be disentangled from potentially confounding rearing environmental effects. DESIGN Univariable and multivariable logistic regression analyses. SETTING Sweden. PARTICIPANTS Men and women born between 1960 and 1990 and in opposite-sex first marriages before age 35 (n = 294 748 couples). MEASUREMENTS Outcome was DUD diagnosis (inclusive of opioids, sedatives/hypnotics/anxiolytics, cocaine, cannabis, amphetamine and other psychostimulants, hallucinogens, other drugs of abuse and combinations thereof) obtained from legal, medical and pharmacy registries. The focal predictor was family genetic risk scores for DUD (FGRS-DUD), which were inferred from diagnoses in first- through fifth-degree relatives and weighted by degree of genetic sharing. FGRS-DUD were calculated separately for each partner in a couple. FINDINGS Marriage to a spouse with a high FGRS-DUD was associated with increased risk of developing DUD during marriage, ORmales = 1.68 (95% CI = 1.50, 1.88) and ORfemales = 1.35 (1.16, 1.56), above and beyond the risk associated with one's own FGRS-DUD. The risk associated with a spouse's FGRS-DUD remained statistically significant after covarying for parental education. As indicated by a series of null interaction effects, there was no evidence that the risk associated with a spouse's FGRS-DUD differed depending on whether the spouse was DUD-affected, probands' probable contact with in-laws and whether the spouse was raised by his/her biological parents or in another home. CONCLUSIONS There is relatively robust evidence that a person's risk for developing drug use disorder is associated with the genetic makeup of the person's spouse.
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Affiliation(s)
- Jessica E Salvatore
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Sara Larsson Lönn
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.,Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
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15
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Su J, Kuo SIC, Trevino A, Barr PB, Aliev F, Bucholz K, Chan G, Edenberg HJ, Kuperman S, Lai D, Meyers JL, Pandey G, Porjesz B, Dick DM. Examining social genetic effects on educational attainment via parental educational attainment, income, and parenting. JOURNAL OF FAMILY PSYCHOLOGY : JFP : JOURNAL OF THE DIVISION OF FAMILY PSYCHOLOGY OF THE AMERICAN PSYCHOLOGICAL ASSOCIATION (DIVISION 43) 2022; 36:1340-1350. [PMID: 35666911 PMCID: PMC9733825 DOI: 10.1037/fam0001003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Higher parental educational attainment is associated with higher offspring educational attainment. In this study, we incorporated genotypic and phenotypic information from fathers, mothers, and offspring to disentangle the genetic and socioenvironmental pathways underlying this association. Data were drawn from a sample of individuals of European ancestry from the collaborative study on the genetics of alcoholism (n = 4,089; 51% female). Results from path analysis indicated that paternal and maternal educational attainment genome-wide polygenic scores were associated with offspring educational attainment, above and beyond the effect of offspring education polygenic score. Parental educational attainment, income, and parenting behaviors served as important socioenvironmental pathways that mediated the effect of parental education polygenic score on offspring educational attainment. Our study highlights the importance of using genetically informed family studies to disentangle the genetic and socioenvironmental pathways underlying parental influences on human development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Jinni Su
- Department of Psychology, Arizona State University
| | - Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University
| | | | - Peter B. Barr
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University
| | - Fazil Aliev
- Rutgers Addiction Research Center, Rutgers University
| | | | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine
- Department of Psychiatry, University of Iowa
| | | | | | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University
| | - Gayathri Pandey
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University
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16
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Qin X, Pan C, Cai Q, Zhao Y, He D, Wei W, Zhang N, Shi S, Chu X, Zhang F. Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression. Brain Behav Immun Health 2022; 26:100557. [PMID: 36457826 PMCID: PMC9706134 DOI: 10.1016/j.bbih.2022.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/01/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022] Open
Abstract
Background Gut microbiome and inflammatory bowel disease (IBD) are implicated in the development of depression, but the effect of their interactions on the risk of depression remains unclear. We aim to analyze the effect of interactions between gut microbiome and IBD on the risk of depression, and explore candidate genes involving the interactions. Methods Using the individual genotype and depression traits data from the UK Biobank, we calculated the polygenetic risk scores (PRS) of 114 gut microbiome, ulcerative colitis (UC), Crohn's disease (CD), and total IBD (CD + UC) respectively. The effects of interactions between gut microbiome and IBD on depression were assessed through a linear regression model. Moreover, for observed significant interactions between gut microbiome PRS and IBD PRS, PLINK software was used to test pair-wise single nucleotide polymorphisms (SNPs) interaction of corresponding gut microbiome PRS and IBD PRS on depression. Results We found 64 candidate interactions between gut microbiome and IBD on four phenotypes of depression, such as F_Lachnospiraceae (RNT) × (CD + UC) for patient health questionnaire-9 (PHQ-9) score (P = 1.48 × 10-3), F_Veillonellaceae (HB) × UC for self-reported depression (P = 2.83 × 10-3) and P_Firmicutes (RNT) × CD for age at first episode of depression (P = 8.50 × 10-3). We observed interactions of gut-microbiome-associated SNPs × IBD-associated SNPs, such as G_Alloprevotella (HB)-associated rs147650986 (GPM6A) × IBD-associated rs114471990 (QRICH1) (P = 2.26 × 10-4). Conclusion Our results support the effects of interactions between gut microbiome and IBD on depression risk, and reported several novel candidate genes for depression.
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Key Words
- ASD, Autism spectrum disorders
- CD, Crohn's disease
- CI, Confidence interval
- CNS, Central nervous system
- Depression
- ENS, Enteric nervous system
- ER, Endoplasmic reticulum
- FGFP, Flemish gut flora project
- GWAS, Genome-wide associations study
- Gut microbiome
- HB, Hurdle binary
- HPA, Hypothalamic-pituitary-adrenal
- HRC, Haplotype reference consortium
- IBD, Inflammatory bowel disease
- Inflammatory bowel disease (IBD)
- LD, Linkage disequilibrium
- PCs, Principal components
- PHQ-9, Patient health questionnaire-9
- PNT, Rank normal transformed
- PRS, Polygenetic risk scores
- QC, Quality control
- SCFAs, Short-chain fatty acids
- SCZ, Schizophrenia
- SNPs, Single nucleotide polymorphisms
- TDI, Townsend deprivation index
- UC, Ulcerative colitis
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Affiliation(s)
- Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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17
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Uddin MJ, Hjorthøj C, Ahammed T, Nordentoft M, Ekstrøm CT. The use of polygenic risk scores as a covariate in psychological studies. METHODS IN PSYCHOLOGY 2022. [DOI: 10.1016/j.metip.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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18
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Mowry BJ, Periyasamy S. Genome‐Wide Association Studies in Schizophrenia. ELS 2022:1-14. [DOI: 10.1002/9780470015902.a0025337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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19
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Cuevas AG, Mann FD, Krueger RF. The weight of childhood adversity: evidence that childhood adversity moderates the impact of genetic risk on waist circumference in adulthood. Int J Obes (Lond) 2022; 46:1875-1882. [PMID: 35931810 DOI: 10.1038/s41366-022-01191-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 06/26/2022] [Accepted: 07/07/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE The present study tested the interactive effects of childhood adversity and polygenic risk scores for waist circumference (PRS-WC) on waist circumference (WC). Consistent with a diathesis-stress model, we hypothesize that the relationship between PRS-WC and WC will be magnified by increasing levels of childhood adversity. METHODS Observational study of 7976 adults (6347 European Americans and 1629 African Americans) in the Health and Retirement Study with genotyped data. PRS-WC were calculated by the HRS administrative core using the weighted sum of risk alleles based on a genome-wide association study conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Childhood adversity was operationalized using a sum score of three traumatic events that occurred before the age of 18 years. RESULTS There was a statistically significant interaction between PRS-WC and childhood adversity for European Americans, whereby the magnitude of PRS-WC predicting WC increased as the number of adverse events increased. CONCLUSIONS This study supports the idea of the interactive effects of genetic risks and childhood adversity on obesity. More epidemiological studies, particularly with understudied populations, are needed to better understand the roles that genetics and childhood adversity play on the development and progression of obesity.
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Affiliation(s)
- Adolfo G Cuevas
- Department of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, NY, USA.
| | - Frank D Mann
- Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
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20
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The interaction of early life factors and depression-associated loci affecting the age at onset of the depression. Transl Psychiatry 2022; 12:294. [PMID: 35879288 PMCID: PMC9314326 DOI: 10.1038/s41398-022-02042-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10-3) and males (β = 1.421, P = 7.64 × 10-4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10-8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10-6) and KIRREL3 (rs535603, P = 4.84 × 10-6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.
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21
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Cheng B, Yang X, Cheng S, Li C, Zhang H, Liu L, Meng P, Jia Y, Wen Y, Zhang F. A large-scale polygenic risk score analysis identified candidate proteins associated with anxiety, depression and neuroticism. Mol Brain 2022; 15:66. [PMID: 35870967 PMCID: PMC9308259 DOI: 10.1186/s13041-022-00954-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/09/2022] [Indexed: 11/10/2022] Open
Abstract
Psychiatric disorders and neuroticism are closely associated with central nervous system, whose proper functioning depends on efficient protein renewal. This study aims to systematically analyze the association between anxiety / depression / neuroticism and each of the 439 proteins. 47,536 pQTLs of 439 proteins in brain, plasma and cerebrospinal fluid (CSF) were collected from recent genome-wide association study. Polygenic risk scores (PRS) of the 439 proteins were then calculated using the UK Biobank cohort, including 120,729 subjects of neuroticism, 255,354 subjects of anxiety and 316,513 subjects of depression. Pearson correlation analyses were performed to evaluate the correlation between each protein and each of the mental traits by using calculated PRSs as the instrumental variables of protein. In general population, six correlations were identified in plasma and CSF such as plasma protease C1 inhibitor (C1-INH) with neuroticism score (r = - 0.011, P = 2.56 × 10- 9) in plasma, C1-INH with neuroticism score (r = -0.010, P = 3.09 × 10- 8) in CSF, and ERBB1 with self-reported depression (r = - 0.012, P = 4.65 × 10- 5) in CSF. C1-INH and ERBB1 may induce neuroticism and depression by affecting brain function and synaptic development. Gender subgroup analyses found that BST1 was correlated with neuroticism score in male CSF (r = - 0.011, P = 1.80 × 10- 5), while CNTN2 was correlated with depression score in female brain (r = - 0.013, P = 6.43 × 10- 4). BST1 and CNTN2 may be involved in nervous system metabolism and brain health. Six common candidate proteins were associated with all three traits (P < 0.05) and were confirmed in relevant proteomic studies, such as C1-INH in plasma, CNTN2 and MSP in the brain. Our results provide novel clues for revealing the roles of proteins in the development of anxiety, depression and neuroticism.
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Affiliation(s)
- Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China. .,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China.
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22
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Liu L, Meng Q, Weng C, Lu Q, Wang T, Wen Y. Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data. PLoS Comput Biol 2022; 18:e1010328. [PMID: 35839250 PMCID: PMC9328574 DOI: 10.1371/journal.pcbi.1010328] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/27/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022] Open
Abstract
Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods. Accurate disease risk prediction is an essential step towards precision medicine. Deep learning models have achieved the state-of-the-art performance for many prediction tasks. However, they generally suffer from the curse of dimensionality and lack of biological interpretability, both of which have greatly limited their applications to the prediction analysis of whole-genome sequencing data. We present here an explainable deep transfer learning model for the analysis of high-dimensional genomic data. Our proposed method can detect predictive genes that harbor genetic variants with both linear and non-linear effects via the proposed group-wise feature importance score. It can also efficiently and accurately model disease risk based on the detected predictive genes using the proposed transfer-learning based network architecture. Our proposed method is built at the gene level, and thus is much more biologically interpretable. It is also computationally efficiently and can be applied to whole-exome sequencing data that have millions of potential predictors. Through both simulation studies and the analysis of whole-exome data obtained from the Alzheimer’s Disease Neuroimaging Initiative, we have demonstrated that our method can efficiently detect predictive genes and it has better prediction performance than many existing methods.
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Affiliation(s)
- Long Liu
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Qingyu Meng
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Cherry Weng
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Qing Lu
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
| | - Tong Wang
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi, China
- * E-mail: (TW); (YW)
| | - Yalu Wen
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Statistics, University of Auckland, Auckland, New Zealand
- * E-mail: (TW); (YW)
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23
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Pettit RW, Amos CI. Linkage Disequilibrium Score Statistic Regression for Identifying Novel Trait Associations. CURR EPIDEMIOL REP 2022. [DOI: 10.1007/s40471-022-00297-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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24
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Chimusa ER, Defo J. Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis. Front Genet 2022; 13:838518. [PMID: 35664319 PMCID: PMC9159898 DOI: 10.3389/fgene.2022.838518] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e−14) and myometrial relaxation and contraction (p = 3e−08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.
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25
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Tanner S, Thomson S, Drummond K, O’Hely M, Symeonides C, Mansell T, Saffery R, Sly PD, Collier F, Burgner D, Sugeng EJ, Dwyer T, Vuillermin P, Ponsonby AL. A Pathway-Based Genetic Score for Oxidative Stress: An Indicator of Host Vulnerability to Phthalate-Associated Adverse Neurodevelopment. Antioxidants (Basel) 2022; 11:antiox11040659. [PMID: 35453345 PMCID: PMC9030597 DOI: 10.3390/antiox11040659] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/16/2022] [Accepted: 03/23/2022] [Indexed: 01/12/2023] Open
Abstract
The developing brain is highly sensitive to environmental disturbances, and adverse exposures can act through oxidative stress. Given that oxidative stress susceptibility is determined partly by genetics, multiple studies have employed genetic scores to explore the role of oxidative stress in human disease. However, traditional approaches to genetic score construction face a range of challenges, including a lack of interpretability, bias towards the disease outcome, and often overfitting to the study they were derived on. Here, we develop an alternative strategy by first generating a genetic pathway function score for oxidative stress (gPFSox) based on the transcriptional activity levels of the oxidative stress response pathway in brain and other tissue types. Then, in the Barwon Infant Study (BIS), a population-based birth cohort (n = 1074), we show that a high gPFSox, indicating reduced ability to counter oxidative stress, is linked to higher autism spectrum disorder risk and higher parent-reported autistic traits at age 4 years, with AOR values (per 2 additional pro-oxidant alleles) of 2.10 (95% CI (1.12, 4.11); p = 0.024) and 1.42 (95% CI (1.02, 2.01); p = 0.041), respectively. Past work in BIS has reported higher prenatal phthalate exposure at 36 weeks of gestation associated with offspring autism spectrum disorder. In this study, we examine combined effects and show a consistent pattern of increased neurodevelopmental problems for individuals with both a high gPFSox and high prenatal phthalate exposure across a range of outcomes, including high gPFSox and high DEHP levels against autism spectrum disorder (attributable proportion due to interaction 0.89; 95% CI (0.62, 1.16); p < 0.0001). The results highlight the utility of this novel functional genetic score and add to the growing evidence implicating gestational phthalate exposure in adverse neurodevelopment.
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Affiliation(s)
- Samuel Tanner
- Developing Brain Division, The Florey Institute for Neuroscience and Mental Health, Parkville, VIC 3052, Australia; (S.T.); (S.T.); (K.D.)
| | - Sarah Thomson
- Developing Brain Division, The Florey Institute for Neuroscience and Mental Health, Parkville, VIC 3052, Australia; (S.T.); (S.T.); (K.D.)
| | - Katherine Drummond
- Developing Brain Division, The Florey Institute for Neuroscience and Mental Health, Parkville, VIC 3052, Australia; (S.T.); (S.T.); (K.D.)
| | - Martin O’Hely
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- School of Medicine, Deakin University, Geelong, VIC 3216, Australia
| | - Christos Symeonides
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- The Minderoo Foundation, Perth, WA 6000, Australia
| | - Toby Mansell
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
| | - Richard Saffery
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
| | - Peter D. Sly
- Children’s Health Research Centre, University of Queensland, South Brisbane, QLD 4101, Australia;
- WHO Collaborating Centre for Children’s Health and Environment, South Brisbane, QLD 4104, Australia
| | - Fiona Collier
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- School of Medicine, Deakin University, Geelong, VIC 3216, Australia
- Barwon Health, Geelong, VIC 3216, Australia
| | - David Burgner
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3052, Australia
| | - Eva J. Sugeng
- Department of Environment and Health, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands;
| | - Terence Dwyer
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford OX3 9DU, UK
| | - Peter Vuillermin
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- School of Medicine, Deakin University, Geelong, VIC 3216, Australia
- Barwon Health, Geelong, VIC 3216, Australia
| | - Anne-Louise Ponsonby
- Developing Brain Division, The Florey Institute for Neuroscience and Mental Health, Parkville, VIC 3052, Australia; (S.T.); (S.T.); (K.D.)
- Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne, Parkville, VIC 3052, Australia; (M.O.); (C.S.); (T.M.); (R.S.); (F.C.); (D.B.); (T.D.); (P.V.)
- Correspondence:
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26
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Wolf HM, Romero R, Strauss JF, Hassan SS, Latendresse SJ, Webb BT, Tarca AL, Gomez-Lopez N, Hsu CD, York TP. Study protocol to quantify the genetic architecture of sonographic cervical length and its relationship to spontaneous preterm birth. BMJ Open 2022; 12:e053631. [PMID: 35301205 PMCID: PMC8932269 DOI: 10.1136/bmjopen-2021-053631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION A short cervix (cervical length <25 mm) in the midtrimester (18-24 weeks) of pregnancy is a powerful predictor of spontaneous preterm delivery. Although the biological mechanisms of cervical change during pregnancy have been the subject of extensive investigation, little is known about whether genes influence the length of the cervix, or the extent to which genetic factors contribute to premature cervical shortening. Defining the genetic architecture of cervical length is foundational to understanding the aetiology of a short cervix and its contribution to an increased risk of spontaneous preterm delivery. METHODS/ANALYSIS The proposed study is designed to characterise the genetic architecture of cervical length and its genetic relationship to gestational age at delivery in a large cohort of Black/African American women, who are at an increased risk of developing a short cervix and delivering preterm. Repeated measurements of cervical length will be modelled as a longitudinal growth curve, with parameters estimating the initial length of the cervix at the beginning of pregnancy, and its rate of change over time. Genome-wide complex trait analysis methods will be used to estimate the heritability of cervical length growth parameters and their bivariate genetic correlation with gestational age at delivery. Polygenic risk profiling will assess maternal genetic risk for developing a short cervix and subsequently delivering preterm and evaluate the role of cervical length in mediating the relationship between maternal genetic variation and gestational age at delivery. ETHICS/DISSEMINATION The proposed analyses will be conducted using deidentified data from participants in an IRB-approved study of longitudinal cervical length who provided blood samples and written informed consent for their use in future genetic research. These analyses are preregistered with the Center for Open Science using the AsPredicted format and the results and genomic summary statistics will be published in a peer-reviewed journal.
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Affiliation(s)
- Hope M Wolf
- Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, Virginia, USA
| | - Roberto Romero
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, U.S. Department of Health and Human Services, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
- Detroit Medical Center, Detroit, Michigan, USA
| | - Jerome F Strauss
- Department of Obstetrics and Gynecology, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
- Department of Obstetrics and Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Sonia S Hassan
- Office of Women's Health, Wayne State University, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Physiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Shawn J Latendresse
- Department of Psychology and Neuroscience, Baylor University, Waco, Texas, USA
| | - Bradley T Webb
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, North Carolina, USA
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, Virginia, USA
| | - Adi L Tarca
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, U.S. Department of Health and Human Services, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Computer Science, Wayne State University College of Engineering, Detroit, Michigan, USA
| | - Nardhy Gomez-Lopez
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, U.S. Department of Health and Human Services, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Chaur-Dong Hsu
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, U.S. Department of Health and Human Services, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Physiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Timothy P York
- Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, Virginia, USA
- Department of Obstetrics and Gynecology, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
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Alathari BE, Cruvinel NT, da Silva NR, Chandrabose M, Lovegrove JA, Horst MA, Vimaleswaran KS. Impact of Genetic Risk Score and Dietary Protein Intake on Vitamin D Status in Young Adults from Brazil. Nutrients 2022; 14:1015. [PMID: 35267990 PMCID: PMC8912678 DOI: 10.3390/nu14051015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 01/27/2023] Open
Abstract
Given the relationship between vitamin D deficiency (VDD) and adverse outcomes of metabolic diseases, we investigated the interplay of dietary and genetic components on vitamin D levels and metabolic traits in young adults from Brazil. Genetic analysis, dietary intake, and anthropometric and biochemical measurements were performed in 187 healthy young adults (19−24 years). Genetic risk scores (GRS) from six genetic variants associated with vitamin D (vitamin D-GRS) and 10 genetic variants associated with metabolic disease (metabolic-GRS) were constructed. High vitamin D-GRS showed a significant association with low 25(OH)D concentrations (p = 0.001) and high metabolic-GRS showed a significant association with high fasting insulin concentrations (p = 0.045). A significant interaction was found between vitamin D-GRS and total protein intake (g/day) (adjusted for non-animal protein) on 25(OH)D (pinteraction = 0.006), where individuals consuming a high protein diet (≥73 g/d) and carrying >4 risk alleles for VDD had significantly lower 25(OH)D (p = 0.002) compared to individuals carrying ≤4 risk alleles. Even though our study did not support a link between metabolic-GRS and vitamin D status, our study has demonstrated a novel interaction, where participants with high vitamin D-GRS and consuming ≥73 g of protein/day had significantly lower 25(OH)D levels. Further research is necessary to evaluate the role of animal protein consumption on VDD in Brazilians.
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Affiliation(s)
- Buthaina E. Alathari
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK; (B.E.A.); (J.A.L.)
- Department of Food Science and Nutrition, Faculty of Health Sciences, The Public Authority for Applied Education and Training, P.O. Box 14281, AlFaiha 72853, Kuwait
| | - Nathália Teixeira Cruvinel
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia 74690-900, Brazil; (N.T.C.); (N.R.d.S.)
| | - Nara Rubia da Silva
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia 74690-900, Brazil; (N.T.C.); (N.R.d.S.)
| | - Mathurra Chandrabose
- Department of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading RG6 6ES, UK;
| | - Julie A. Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK; (B.E.A.); (J.A.L.)
- Institute for Food, Nutrition and Health, University of Reading, Reading RG6 6AH, UK
- Institute of Cardiovascular and Metabolic Research, University of Reading, Reading RG6 6AA, UK
| | - Maria A. Horst
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia 74690-900, Brazil; (N.T.C.); (N.R.d.S.)
| | - Karani S. Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK; (B.E.A.); (J.A.L.)
- Institute for Food, Nutrition and Health, University of Reading, Reading RG6 6AH, UK
- Institute of Cardiovascular and Metabolic Research, University of Reading, Reading RG6 6AA, UK
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Bioengineered Efficacy Models of Skin Disease: Advances in the Last 10 Years. Pharmaceutics 2022; 14:pharmaceutics14020319. [PMID: 35214050 PMCID: PMC8877988 DOI: 10.3390/pharmaceutics14020319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/24/2021] [Accepted: 01/25/2022] [Indexed: 12/19/2022] Open
Abstract
Models of skin diseases, such as psoriasis and scleroderma, must accurately recapitulate the complex microenvironment of human skin to provide an efficacious platform for investigation of skin diseases. Skin disease research has been shifting from less complex and less relevant 2D (two-dimensional) models to significantly more relevant 3D (three-dimensional) models. Three-dimensional modeling systems are better able to recapitulate the complex cell–cell and cell–matrix interactions that occur in vivo within skin. Three-dimensional human skin equivalents (HSEs) have emerged as an advantageous tool for the study of skin disease in vitro. These 3D HSEs can be highly complex, containing both epidermal and dermal compartments with integrated adnexal structures. The addition of adnexal structures to 3D HSEs has allowed researchers to gain more insight into the complex pathology of various hereditary and acquired skin diseases. One method of constructing 3D HSEs, 3D bioprinting, has emerged as a versatile and useful tool for generating highly complex HSEs. The development of commercially available 3D bioprinters has allowed researchers to create highly reproducible 3D HSEs with precise integration of multiple adnexal structures. While the field of bioengineered models for study of skin disease has made tremendous progress in the last decade, there are still significant efforts necessary to create truly biomimetic skin disease models. In future studies utilizing 3D HSEs, emphasis must be placed on integrating all adnexal structures relevant to the skin disease under investigation. Thorough investigation of the intricate pathology of skin diseases and the development of effective treatments requires use of highly efficacious models of skin diseases.
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Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review. BMC Med Res Methodol 2022; 22:21. [PMID: 35034628 PMCID: PMC8761268 DOI: 10.1186/s12874-022-01504-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/03/2022] [Indexed: 01/03/2023] Open
Abstract
Background Mendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems. Methods We conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement. Results Only 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions. Conclusions We found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01504-0.
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Liu D, Bu D, Li H, Wang Q, Ding X, Fang X. Intestinal metabolites and the risk of autistic spectrum disorder: A two-sample Mendelian randomization study. Front Psychiatry 2022; 13:1034214. [PMID: 36713927 PMCID: PMC9877426 DOI: 10.3389/fpsyt.2022.1034214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/22/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Observational studies have reported a strong association between autistic spectrum disorder (ASD) and intestinal metabolites. However, it is unclear whether this correlation is causally or violated by confounding or backward causality. Therefore, this study explored the potential causal relationship between intestinal metabolites and dependent metabolites on ASD. METHODS We used a two-sample Mendelian random analysis and selected variants closely related to intestinal flora-dependent metabolites as instrumental variables. MR-Egger, inverse variance weighted (IVW), MR-PRESSO, maximum likelihood, and weighted median were performed to reveal their causal relationships. Ten metabolites were studied, which included trimethylamine-N-oxide, betaine, carnitine, choline, glutamate, kynurenine, phenylalanine, serotonin, tryptophan, and tyrosine. Sensitivity tests were also performed to evaluate the robustness of the MR study. RESULTS The IVW method revealed that serotonin may increase the ASD risk (OR 1.060, 95% CI: 1.006-1.118), while choline could decrease the ASD risk (OR 0.925, 95% CI: 0.868-0.988). However, no definite causality was observed between other intestinal metabolites (e.g., trimethylamine-N-oxide, betaine, and carnitine) with ASD. Additionally, neither the funnel plot nor the MR-Egger test showed horizontal pleiotropy, and the MR-PRESSO test found no outliers. Cochran's Q test showed no significant heterogeneity among the studies, suggesting the robustness of the study. CONCLUSION Our study found potential causality from intestinal metabolites on ASD. Clinicians are encouraged to offer preventive measures to such populations.
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Affiliation(s)
- Deyang Liu
- Department of Rehabilitation Medicine, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Dengyin Bu
- Department of Psychiatric Medicine, Xiangyang Central Hospital, Hubei University of Arts and Science, Xiangyang, China
| | - Hong Li
- Department of Rehabilitation Medicine, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Qingsong Wang
- Department of Rehabilitation Medicine, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xudong Ding
- Department of Rehabilitation Medicine, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiaolu Fang
- Department of Clinical Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
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Kloeve-Mogensen K, Rohde PD, Twisttmann S, Nygaard M, Koldby KM, Steffensen R, Dahl CM, Rytter D, Overgaard MT, Forman A, Christiansen L, Nyegaard M. Polygenic Risk Score Prediction for Endometriosis. FRONTIERS IN REPRODUCTIVE HEALTH 2021; 3:793226. [PMID: 36303976 PMCID: PMC9580817 DOI: 10.3389/frph.2021.793226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/09/2021] [Indexed: 12/19/2022] Open
Abstract
Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10−7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10−11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10−5), infiltrating (OR = 1.66, p = 2.7× 10−9), and peritoneal (OR = 1.51, p = 2.6 × 10−3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10−16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.
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Affiliation(s)
- Kirstine Kloeve-Mogensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Simone Twisttmann
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | | | - Rudi Steffensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Christian Møller Dahl
- Department of Business and Economics, University of Southern Denmark, Odense, Denmark
| | - Dorte Rytter
- Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Axel Forman
- Department of Gynecology and Obstetrics, Aarhus University Hospital, Skejby, Denmark
| | - Lene Christiansen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- *Correspondence: Mette Nyegaard
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32
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Damena D, Agamah FE, Kimathi PO, Kabongo NE, Girma H, Choga WT, Golassa L, Chimusa ER. Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways. Front Genet 2021; 12:676960. [PMID: 34868193 PMCID: PMC8639191 DOI: 10.3389/fgene.2021.676960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.
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Affiliation(s)
- Delesa Damena
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Peter O Kimathi
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Ntumba E Kabongo
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Hundaol Girma
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Wonderful T Choga
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Lemu Golassa
- Aklilu Lema Institute of Pathobiology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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Neale ZE, Kuo SIC, Dick DM. A systematic review of gene-by-intervention studies of alcohol and other substance use. Dev Psychopathol 2021; 33:1410-1427. [PMID: 32602428 PMCID: PMC7772257 DOI: 10.1017/s0954579420000590] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.
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Affiliation(s)
- Zoe E. Neale
- Department of Psychology, Virginia Commonwealth University
| | | | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University
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34
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Chen Y, Meng P, Cheng S, Jia Y, Wen Y, Yang X, Yao Y, Pan C, Li C, Zhang H, Zhang J, Zhang Z, Zhang F. Assessing the effect of interaction between C-reactive protein and gut microbiome on the risks of anxiety and depression. Mol Brain 2021; 14:133. [PMID: 34481527 PMCID: PMC8418706 DOI: 10.1186/s13041-021-00843-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/24/2021] [Indexed: 12/31/2022] Open
Abstract
Cumulative evidence shows that gut microbiome can influence brain function and behavior via the inflammatory processes. However, the role of interaction between gut dysbiosis and C-reactive protein (CRP) in the development of anxiety and depression remains to be elucidated. In this study, a total of 3321 independent single nucleotide polymorphism (SNP) loci associated with gut microbiome were driven from genome-wide association study (GWAS). Using individual level genotype data from UK Biobank, we then calculated the polygenetic risk scoring (PRS) of 114 gut microbiome related traits. Moreover, regression analysis was conducted to evaluate the possible effect of interaction between gut microbiome and CRP on the risks of Patient Health Questionnaire-9 (PHQ-9) (N = 113,693) and Generalized Anxiety Disorder-7 (GAD-7) (N = 114,219). At last, 11 candidate CRP × gut microbiome interaction with suggestive significance was detected for PHQ-9 score, such as F_Ruminococcaceae (β = - 0.009, P = 2.2 × 10-3), G_Akkermansia (β = - 0.008, P = 7.60 × 10-3), F_Acidaminococcaceae (β = 0.008, P = 1.22 × 10-2), G_Holdemanella (β = - 0.007, P = 1.39 × 10-2) and O_Lactobacillales (β = 0.006, P = 1.79× 10-2). 16 candidate CRP × gut microbiome interaction with suggestive significance was detected for GAD-7 score, such as O_Bacteroidales (β = 0.010, P = 4.00× 10-4), O_Selenomonadales (β = - 0.010, P = 1.20 × 10-3), O_Clostridiales (β = 0.009, P = 2.70 × 10-3) and G_Holdemanella (β = - 0.008, P = 4.20 × 10-3). Our results support the significant effect of interaction between CRP and gut microbiome on the risks of anxiety and depression, and identified several candidate gut microbiomes for them.
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Affiliation(s)
- Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Yao Yao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 71006, China.
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West BT, Little RJ, Andridge RR, Boonstra PS, Ware EB, Pandit A, Alvarado-Leiton F. ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS. Ann Appl Stat 2021; 15:1556-1581. [PMID: 35237377 PMCID: PMC8887878 DOI: 10.1214/21-aoas1453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.
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Affiliation(s)
- Brady T. West
- Survey Research Center, Institute for Social Research, University of Michigan,
| | - Roderick J. Little
- Department of Biostatistics, School of Public Health, University of Michigan,
| | | | - Philip S. Boonstra
- Department of Biostatistics, School of Public Health, University of Michigan,
| | - Erin B. Ware
- Survey Research Center, Institute for Social Research, University of Michigan,
| | - Anita Pandit
- Department of Biostatistics, School of Public Health, University of Michigan,
| | - Fernanda Alvarado-Leiton
- Michigan Program in Survey and Data Science, Institute for Social Research, University of Michigan
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Wen C, Ba H, Pan W, Huang M. Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants. Bioinformatics 2021; 36:5214-5222. [PMID: 32683450 DOI: 10.1093/bioinformatics/btaa650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 05/22/2020] [Accepted: 07/14/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The association analysis between genetic variants and imaging phenotypes must be carried out to understand the inherited neuropsychiatric disorders via imaging genetic studies. Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics. RESULTS To address these challenges in both statistics and computation, we propose a novel co-sparse reduced-rank regression model that identifies complex correlations in a dimensional reduction manner. We developed an iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty. The simulation studies showed that our method achieved accurate and stable performance in parameter estimation and variable selection. In real application, the proposed approach successfully detected several novel Alzheimer's disease-related genetic variants and regions of interest, which indicate that our method may be a valuable statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION The R package csrrr, and the code for experiments in this article is available in Github: https://github.com/hailongba/csrrr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Canhong Wen
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Hailong Ba
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Wenliang Pan
- Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Meiyan Huang
- School of Biomedical Engineering, Guangzhou 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
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Huang M, Lai H, Yu Y, Chen X, Wang T, Feng Q. Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease. Med Image Anal 2021; 73:102189. [PMID: 34343841 DOI: 10.1016/j.media.2021.102189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/30/2021] [Accepted: 07/16/2021] [Indexed: 01/01/2023]
Abstract
Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.
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Affiliation(s)
- Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Haoran Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Yuwei Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Xiumei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Tao Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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Wang CA, Attia JR, Lye SJ, Oddy WH, Beilin L, Mori TA, Meyerkort C, Pennell CE. The interactions between genetics and early childhood nutrition influence adult cardiometabolic risk factors. Sci Rep 2021; 11:14826. [PMID: 34290306 PMCID: PMC8295375 DOI: 10.1038/s41598-021-94206-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/29/2021] [Indexed: 12/20/2022] Open
Abstract
It is well established that genetics, environment, and interplay between them play a crucial role in adult disease. We aimed to evaluate the role of genetics, early life nutrition, and the interaction between them, on optimal adult health. As part of a large international consortium (n ~ 154,000), we identified 60 SNPs associated with both birthweight and adult disease. Utilising the Raine Study, we developed a birthweight polygenic score (BW-PGS) based on the 60 SNPs and examined relationships between BW-PGS and adulthood cardiovascular risk factors, specifically evaluating interactions with early life nutrition. Healthy nutrition was beneficial for all individuals; longer duration of any breastfeeding was particularly associated with lower BMI and lower Systolic Blood Pressure in those with higher BW-PGS. Optimal breastfeeding offers the greatest benefit to reduce adult obesity and hypertension in those genetically predisposed to high birthweight. This provides an example of how precision medicine in early life can improve adult health.
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Affiliation(s)
- Carol A Wang
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia.,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - John R Attia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia.,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Stephen J Lye
- Alliance for Human Development, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Wendy H Oddy
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Lawrence Beilin
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley, WA, Australia
| | - Trevor A Mori
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley, WA, Australia
| | | | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia. .,Hunter Medical Research Institute, Newcastle, NSW, Australia.
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Slunecka JL, van der Zee MD, Beck JJ, Johnson BN, Finnicum CT, Pool R, Hottenga JJ, de Geus EJC, Ehli EA. Implementation and implications for polygenic risk scores in healthcare. Hum Genomics 2021; 15:46. [PMID: 34284826 PMCID: PMC8290135 DOI: 10.1186/s40246-021-00339-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/11/2021] [Indexed: 12/15/2022] Open
Abstract
Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.
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Affiliation(s)
- John L Slunecka
- Avera Institute for Human Genetics, Avera McKennan & University Health Center, Sioux Falls, SD, USA.
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan & University Health Center, Sioux Falls, SD, USA
| | - Brandon N Johnson
- Avera Institute for Human Genetics, Avera McKennan & University Health Center, Sioux Falls, SD, USA
| | - Casey T Finnicum
- Avera Institute for Human Genetics, Avera McKennan & University Health Center, Sioux Falls, SD, USA
| | - René Pool
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan & University Health Center, Sioux Falls, SD, USA
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Sund ER, van Lenthe FJ, Avendano M, Raina P, Krokstad S. Does urbanicity modify the relationship between a polygenic risk score for depression and mental health symptoms? Cross-sectional evidence from the observational HUNT Study in Norway. J Epidemiol Community Health 2021; 75:420-425. [PMID: 32581065 PMCID: PMC8053322 DOI: 10.1136/jech-2020-214256] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/26/2020] [Accepted: 05/30/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND Research suggests that genetic predisposition for common mental disorders may be moderated by the environment. This study examines whether a polygenic risk score (PRS) for depression is moderated by the level of residential area urbanicity using five symptoms of poor mental health as outcomes. METHODS The study sample consisted of 41 198 participants from the 2006-2008 wave of the Norwegian HUNT study. We created a weighted PRS for depression based on 99 variants identified in a recent genome -wide association study. Participants were classified into urban or rural place of residence based on wards that correspond to neighbourhoods. Mixed effects logistic regression models with participants nested in 477 neighbourhoods were specified. RESULTS A SD increase in PRS for depression was associated with a small but statistically significant increase in the odds of anxiety, comorbid anxiety and depression and mental distress. Associations for depression were weaker and not statistically significant. Compared with urban residents, rural resident had higher odds for reporting poor mental health. Genetic propensity for depression was higher for residents of urban than rural areas, suggesting gene-environment correlation. There was no sign of effect modification between genetic propensity and urbanicity for depression, anxiety, comorbid anxiety and depression, or mental distress. CONCLUSION The PRS predicted small but significant odds of anxiety, comorbid anxiety and depression and mental distress, but we found no support for a differential effect of genetic propensity in urban and rural neighbourhoods for any of the outcomes.
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Affiliation(s)
- Erik Reidar Sund
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Frank J van Lenthe
- Department of Public Health, Erasmus MC, Rotterdam, Netherlands
- Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, Netherlands
| | - Mauricio Avendano
- Department of GLobal Health and Social Medicine, King's College London School of Social Science and Public Policy, London, UK
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, USA
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Canada
- Labarge Centre for Mobility in Aging, McMaster University, Hamilton, Canada
| | - Steinar Krokstad
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
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41
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Livingstone KM, Abbott G, Bowe SJ, Ward J, Milte C, McNaughton SA. Diet quality indices, genetic risk and risk of cardiovascular disease and mortality: a longitudinal analysis of 77 004 UK Biobank participants. BMJ Open 2021; 11:e045362. [PMID: 33795309 PMCID: PMC8023730 DOI: 10.1136/bmjopen-2020-045362] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/17/2020] [Accepted: 03/17/2021] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVES To examine associations of three diet quality indices and a polygenic risk score with incidence of all-cause mortality, cardiovascular disease (CVD) mortality, myocardial infarction (MI) and stroke. DESIGN Prospective cohort study. SETTING UK Biobank, UK. PARTICIPANTS 77 004 men and women (40-70 years) recruited between 2006 and 2010. MAIN OUTCOME MEASURES A polygenic risk score was created from 300 single nucleotide polymorphisms associated with CVD. Cox proportional HRs were used to estimate independent effects of diet quality and genetic risk on all-cause mortality, CVD mortality, MI and stroke risk. Dietary intake (Oxford WebQ) was used to calculate Recommended Food Score (RFS), Healthy Diet Indicator (HDI) and Mediterranean Diet Score (MDS). RESULTS New all-cause (n=2409) and CVD (n=364) deaths and MI (n=1141) and stroke (n=748) events were identified during mean follow-ups of 7.9 and 7.8 years, respectively. The adjusted HR associated with one-point higher RFS for all-cause mortality was 0.96 (95% CI: 0.94 to 0.98), CVD mortality was 0.94 (95% CI: 0.90 to 0.98), MI was 0.97 (95% CI: 0.95 to 1.00) and stroke was 0.94 (95% CI: 0.91 to 0.98). The adjusted HR for all-cause mortality associated with one-point higher HDI and MDS was 0.97 (95% CI: 0.93 to 0.99) and 0.95 (95% CI: 0.91 to 0.98), respectively. The adjusted HR associated with one-point higher MDS for stroke was 0.93 (95% CI: 0.87 to 1.00). There was little evidence of associations between HDI and risk of CVD mortality, MI or stroke. There was evidence of an interaction between diet quality and genetic risk score for MI. CONCLUSION Higher diet quality predicted lower risk of all-cause mortality, independent of genetic risk. Higher RFS was also associated with lower risk of CVD mortality and MI. These findings demonstrate the benefit of following a healthy diet, regardless of genetic risk.
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Affiliation(s)
- Katherine M Livingstone
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Victoria, Australia
| | - Gavin Abbott
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Victoria, Australia
| | - Steven J Bowe
- Deakin Biostatistics Unit, Deakin University, Geelong, Victoria, Australia
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Catherine Milte
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Victoria, Australia
| | - Sarah A McNaughton
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Victoria, Australia
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Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort. Nutrients 2021; 13:nu13041150. [PMID: 33807197 PMCID: PMC8067152 DOI: 10.3390/nu13041150] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 03/26/2021] [Accepted: 03/27/2021] [Indexed: 12/19/2022] Open
Abstract
Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.
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Schlomer GL, Cleveland HH, Feinberg ME, Murray JL, Vandenbergh DJ. Longitudinal Links between Adolescent and Peer Conduct Problems and Moderation by a Sensitivity Genetic Index. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2021; 31:189-203. [PMID: 33128845 DOI: 10.1111/jora.12592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The most extensively studied influence on adolescent conduct problem behaviors is peers, and the literature points to genetics as one source of individual differences in peer influence. The purpose of this study was to test the hypothesis that an environmental sensitivity genetic index comprised of DRD4, 5-HTTLPR, and GABRA2 variation would moderate the association between peer and adolescent conduct problems. Latent growth modeling was applied to PROSPER project longitudinal data from adolescents and their peers. Results showed the hypothesis was supported; adolescents with more copies of putative sensitivity alleles were more strongly influenced by their peers. The interaction form was consistent with differential susceptibility in follow-up analyses. Strengths and weaknesses of genetic aggregates for sensitivity research are discussed.
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Alathari BE, Aji AS, Ariyasra U, Sari SR, Tasrif N, Yani FF, Sudji IR, Lovegrove JA, Lipoeto NI, Vimaleswaran KS. Interaction between Vitamin D-Related Genetic Risk Score and Carbohydrate Intake on Body Fat Composition: A Study in Southeast Asian Minangkabau Women. Nutrients 2021; 13:nu13020326. [PMID: 33498618 PMCID: PMC7911469 DOI: 10.3390/nu13020326] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/17/2021] [Accepted: 01/20/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolic diseases have been shown to be associated with low vitamin D status; however, the findings have been inconsistent. Hence, the objective of our study was to investigate the relationship between vitamin D status and metabolic disease-related traits in healthy Southeast Asian women and examine whether this relationship was modified by dietary factors using a nutrigenetic study. The study included 110 Minangkabau women (age: 25–60 years) from Padang, Indonesia. Genetic risk scores (GRS) were constructed based on five vitamin D-related single nucleotide polymorphisms (SNPs) (vitamin D-GRS) and ten metabolic disease-associated SNPs (metabolic-GRS). The metabolic-GRS was significantly associated with lower 25-hydroxyvitamin D (25(OH)D) concentrations (p = 0.009) and higher body mass index (BMI) (p = 0.016). Even though the vitamin D-GRS had no effect on metabolic traits (p > 0.12), an interaction was observed between the vitamin D-GRS and carbohydrate intake (g) on body fat percentage (BFP) (pinteraction = 0.049), where those individuals who consumed a high carbohydrate diet (mean ± SD: 319 g/d ± 46) and carried >2 vitamin D-lowering risk alleles had significantly higher BFP (p = 0.016). In summary, we have replicated the association of metabolic-GRS with higher BMI and lower 25(OH)D concentrations and identified a novel interaction between vitamin D-GRS and carbohydrate intake on body fat composition.
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Affiliation(s)
- Buthaina E. Alathari
- Department of Food Science and Nutrition, Faculty of Health Sciences, The Public Authority for Applied Education and Training, Al Faiha 72853, Kuwait;
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK;
| | - Arif Sabta Aji
- Department of Public Health, Alma Ata Graduate School of Public Health, University of Alma Ata, Yogyakarta 55183, Indonesia;
- Department of Nutrition, Faculty of Health Sciences, University of Alma Ata, Yogyakarta 55183, Indonesia
| | - Utami Ariyasra
- Biomedical Science Department, Faculty of Medicine, Andalas University, West Sumatra 25172, Indonesia; (U.A.); (S.R.S.)
| | - Sri R. Sari
- Biomedical Science Department, Faculty of Medicine, Andalas University, West Sumatra 25172, Indonesia; (U.A.); (S.R.S.)
| | - Nabila Tasrif
- Public Health Department, Faculty of Medicine, Andalas University, West Sumatra 25172, Indonesia;
| | - Finny F. Yani
- Department of Child Health, Faculty of Medicine, Andalas University, West Sumatra 25172, Indonesia;
| | - Ikhwan R. Sudji
- Department of Medical Laboratory Technology, Faculty of Health Science, University Perintis, Padang 25586, Indonesia;
| | - Julie A. Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK;
| | - Nur I. Lipoeto
- Department of Nutrition, Faculty of Medicine, Andalas University, West Sumatra 25172, Indonesia;
| | - Karani S. Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Harry Nursten Building, Pepper Lane, Reading RG6 6DZ, UK;
- Correspondence:
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Beyond Haemostasis and Thrombosis: Platelets in Depression and Its Co-Morbidities. Int J Mol Sci 2020; 21:ijms21228817. [PMID: 33233416 PMCID: PMC7700239 DOI: 10.3390/ijms21228817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 11/18/2020] [Accepted: 11/19/2020] [Indexed: 12/11/2022] Open
Abstract
Alongside their function in primary haemostasis and thrombo-inflammation, platelets are increasingly considered a bridge between mental, immunological and coagulation-related disorders. This review focuses on the link between platelets and the pathophysiology of major depressive disorder (MDD) and its most frequent comorbidities. Platelet- and neuron-shared proteins involved in MDD are functionally described. Platelet-related studies performed in the context of MDD, cardiovascular disease, and major neurodegenerative, neuropsychiatric and neurodevelopmental disorders are transversally presented from an epidemiological, genetic and functional point of view. To provide a complete scenario, we report the analysis of original data on the epidemiological link between platelets and depression symptoms suggesting moderating and interactive effects of sex on this association. Epidemiological and genetic studies discussed suggest that blood platelets might also be relevant biomarkers of MDD prediction and occurrence in the context of MDD comorbidities. Finally, this review has the ambition to formulate some directives and perspectives for future research on this topic.
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Babb de Villiers C, Kroese M, Moorthie S. Understanding polygenic models, their development and the potential application of polygenic scores in healthcare. J Med Genet 2020; 57:725-732. [PMID: 32376789 PMCID: PMC7591711 DOI: 10.1136/jmedgenet-2019-106763] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 03/09/2020] [Accepted: 03/28/2020] [Indexed: 02/06/2023]
Abstract
The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.
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Affiliation(s)
| | - Mark Kroese
- PHG Foundation, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Sowmiya Moorthie
- PHG Foundation, University of Cambridge, Cambridge, Cambridgeshire, UK
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Interaction between the genetic risk score and dietary protein intake on cardiometabolic traits in Southeast Asian. GENES AND NUTRITION 2020; 15:19. [PMID: 33045981 PMCID: PMC7552350 DOI: 10.1186/s12263-020-00678-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 09/30/2020] [Indexed: 12/18/2022]
Abstract
Background Cardiometabolic diseases are complex traits which are influenced by several single nucleotide polymorphisms (SNPs). Thus, analysing the combined effects of multiple gene variants might provide a better understanding of disease risk than using a single gene variant approach. Furthermore, studies have found that the effect of SNPs on cardiometabolic traits can be influenced by lifestyle factors, highlighting the importance of analysing gene-lifestyle interactions. Aims In the present study, we investigated the association of 15 gene variants with cardiometabolic traits and examined whether these associations were modified by lifestyle factors such as dietary intake and physical activity. Methods The study included 110 Minangkabau women [aged 25–60 years and body mass index (BMI) 25.13 ± 4.2 kg/m2] from Padang, Indonesia. All participants underwent a physical examination followed by anthropometric, biochemical and dietary assessments and genetic tests. A genetic risk score (GRS) was developed based on 15 cardiometabolic disease-related SNPs. The effect of GRS on cardiometabolic traits was analysed using general linear models. GRS-lifestyle interactions on continuous outcomes were tested by including the interaction term (e.g. lifestyle factor*GRS) in the regression model. Models were adjusted for age, BMI and location (rural or urban), wherever appropriate. Results There was a significant association between GRS and BMI, where individuals carrying 6 or more risk alleles had higher BMI compared to those carrying 5 or less risk alleles (P = 0.018). Furthermore, there were significant interactions of GRS with protein intake on waist circumference (WC) and triglyceride concentrations (Pinteraction = 0.002 and 0.003, respectively). Among women who had a lower protein intake (13.51 ± 1.18% of the total daily energy intake), carriers of six or more risk alleles had significantly lower WC and triglyceride concentrations compared with carriers of five or less risk alleles (P = 0.0118 and 0.002, respectively). Conclusions Our study confirmed the association of GRS with higher BMI and further showed a significant effect of the GRS on WC and triglyceride levels through the influence of a low-protein diet. These findings suggest that following a lower protein diet, particularly in genetically predisposed individuals, might be an effective approach for addressing cardiometabolic diseases among Southeast Asian women.
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48
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Domingue BW, Trejo S, Armstrong-Carter E, Tucker-Drob EM. Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges. SOCIOLOGICAL SCIENCE 2020; 7:465-486. [PMID: 36091972 PMCID: PMC9455807 DOI: 10.15195/v7.a19] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Interest in the study of gene-environment interaction has recently grown due to the sudden availability of molecular genetic data-in particular, polygenic scores-in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene-environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene-environment interaction studies.
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Choi SW, Mak TSH, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc 2020; 15:2759-2772. [PMID: 32709988 PMCID: PMC7612115 DOI: 10.1038/s41596-020-0353-1] [Citation(s) in RCA: 757] [Impact Index Per Article: 189.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 05/05/2020] [Indexed: 02/08/2023]
Abstract
A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.
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Affiliation(s)
- Shing Wan Choi
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, London, UK, SE5 8AF,Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, 1 Gustave L. Levy Pl, New York City, NY 10029, USA
| | | | - Paul F. O’Reilly
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, London, UK, SE5 8AF,Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, 1 Gustave L. Levy Pl, New York City, NY 10029, USA
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Fryett JJ, Morris AP, Cordell HJ. Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies. Genet Epidemiol 2020; 44:425-441. [PMID: 32190932 PMCID: PMC8641384 DOI: 10.1002/gepi.22290] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/05/2020] [Accepted: 03/06/2020] [Indexed: 01/14/2023]
Abstract
In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods-LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests-by performing cross-validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.
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Affiliation(s)
- James J. Fryett
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew P. Morris
- Division of Musculoskeletal and Dermatological SciencesUniversity of ManchesterManchesterUK
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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