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Herrera-Luis E, Martin-Almeida M, Pino-Yanes M. Asthma-Genomic Advances Toward Risk Prediction. Clin Chest Med 2024; 45:599-610. [PMID: 39069324 PMCID: PMC11284279 DOI: 10.1016/j.ccm.2024.03.002] [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] [Indexed: 07/30/2024]
Abstract
Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA.
| | - Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna 38200, Tenerife, Spain
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2
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Voorhies M, Joehnk B, Uehling J, Walcott K, Dubin C, Mead HL, Homer CM, Galgiani JN, Barker BM, Brem RB, Sil A. Inferring the composition of a mixed culture of natural microbial isolates by deep sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606565. [PMID: 39149389 PMCID: PMC11326141 DOI: 10.1101/2024.08.05.606565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Next generation sequencing has unlocked a wealth of genotype information for microbial populations, but phenotyping remains a bottleneck for exploiting this information, particularly for pathogens that are difficult to manipulate. Here, we establish a method for high-throughput phenotyping of mixed cultures, in which the pattern of naturally occurring single-nucleotide polymorphisms in each isolate is used as intrinsic barcodes which can be read out by sequencing. We demonstrate that our method can correctly deconvolute strain proportions in simulated mixed-strain pools. As an experimental test of our method, we perform whole genome sequencing of 66 natural isolates of the thermally dimorphic pathogenic fungus Coccidioides posadasii and infer the strain compositions for large mixed pools of these strains after competition at 37°C and room temperature. We validate the results of these selection experiments by recapitulating the temperature-specific enrichment results in smaller pools. Additionally, we demonstrate that strain fitness estimated by our method can be used as a quantitative trait for genome-wide association studies. We anticipate that our method will be broadly applicable to natural populations of microbes and allow high-throughput phenotyping to match the rate of genomic data acquisition.
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Affiliation(s)
- Mark Voorhies
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
| | - Bastian Joehnk
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
| | - Jessie Uehling
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America
| | - Keith Walcott
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
| | - Claire Dubin
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America
| | - Heather L. Mead
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Christina M. Homer
- Division of Infectious Diseases, University of California San Francisco, San Francisco, California, United States of America
| | - John N. Galgiani
- Valley Fever Center for Excellence, Department of Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Bridget M. Barker
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Rachel B. Brem
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America
| | - Anita Sil
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
- Chan Zuckerberg Biohub – San Francisco, San Francisco, California, United States of America
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3
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Contador I, Buch-Vicente B, del Ser T, Llamas-Velasco S, Villarejo-Galende A, Benito-León J, Bermejo-Pareja F. Charting Alzheimer's Disease and Dementia: Epidemiological Insights, Risk Factors and Prevention Pathways. J Clin Med 2024; 13:4100. [PMID: 39064140 PMCID: PMC11278014 DOI: 10.3390/jcm13144100] [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: 05/23/2024] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Alzheimer's disease (AD), the most common cause of dementia, is a complex and multifactorial condition without cure at present. The latest treatments, based on anti-amyloid monoclonal antibodies, have only a modest effect in reducing the progression of cognitive decline in AD, whereas the possibility of preventing AD has become a crucial area of research. In fact, recent studies have observed a decrease in dementia incidence in developed regions such as the US and Europe. However, these trends have not been mirrored in non-Western countries (Japan or China), and the contributing factors of this reduction remain unclear. The Lancet Commission has delineated a constrained classification of 12 risk factors across different life stages. Nevertheless, the scientific literature has pointed to over 200 factors-including sociodemographic, medical, psychological, and sociocultural conditions-related to the development of dementia/AD. This narrative review aims to synthesize the risk/protective factors of dementia/AD. Essentially, we found that risk/protective factors vary between individuals and populations, complicating the creation of a unified prevention strategy. Moreover, dementia/AD explanatory mechanisms involve a diverse array of genetic and environmental factors that interact from the early stages of life. In the future, studies across different population-based cohorts are essential to validate risk/protective factors of dementia. This evidence would help develop public health policies to decrease the incidence of dementia.
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Affiliation(s)
- Israel Contador
- Department of Basic Psychology, Psychobiology, and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, 37005 Salamanca, Spain
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, 17117 Stockholm, Sweden
| | - Bárbara Buch-Vicente
- Department of Basic Psychology, Psychobiology, and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, 37005 Salamanca, Spain
| | - Teodoro del Ser
- Alzheimer Centre Reina Sofia—CIEN Foundation, Institute of Health Carlos III, 28031 Madrid, Spain;
| | - Sara Llamas-Velasco
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
| | - Alberto Villarejo-Galende
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
| | - Julián Benito-León
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), 28029 Madrid, Spain
- Department of Medicine, Faculty of Medicine, Complutense University, 28040 Madrid, Spain
| | - Félix Bermejo-Pareja
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), 28029 Madrid, Spain
- Department of Medicine, Faculty of Medicine, Complutense University, 28040 Madrid, Spain
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Xu P, Liang S, Hahn A, Zhao V, Lo WT‘J, Haller BC, Sobkowiak B, Chitwood MH, Colijn C, Cohen T, Rhee KY, Messer PW, Wells MT, Clark AG, Kim J. e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.29.601123. [PMID: 39005464 PMCID: PMC11244936 DOI: 10.1101/2024.06.29.601123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.
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Affiliation(s)
- Peiyu Xu
- Department of Molecular Biology & Genetics, Cornell University, Ithaca, NY, USA
| | - Shenni Liang
- Department of Computational Science, Cornell University, Ithaca, NY, USA
| | - Andrew Hahn
- Department of Computational Science, Cornell University, Ithaca, NY, USA
| | - Vivian Zhao
- Department of Computational Science, Cornell University, Ithaca, NY, USA
| | - Wai Tung ‘Jack’ Lo
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Benjamin C. Haller
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, New Haven, CT, USA
| | - Melanie H. Chitwood
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, New Haven, CT, USA
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada
| | - Ted Cohen
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, New Haven, CT, USA
| | - Kyu Y. Rhee
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Philipp W. Messer
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Martin T. Wells
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Andrew G. Clark
- Department of Molecular Biology & Genetics, Cornell University, Ithaca, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Jaehee Kim
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
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5
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Zucker R, Kovalerchik M, Stern A, Kaufman H, Linial M. Revealing the genetic complexity of hypothyroidism: integrating complementary association methods. Front Genet 2024; 15:1409226. [PMID: 38919955 PMCID: PMC11196612 DOI: 10.3389/fgene.2024.1409226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/27/2024] Open
Abstract
Hypothyroidism is a common endocrine disorder whose prevalence increases with age. The disease manifests itself when the thyroid gland fails to produce sufficient thyroid hormones. The disorder includes cases of congenital hypothyroidism (CH), but most cases exhibit hormonal feedback dysregulation and destruction of the thyroid gland by autoantibodies. In this study, we sought to identify causal genes for hypothyroidism in large populations. The study used the UK-Biobank (UKB) database, reporting on 13,687 cases of European ancestry. We used GWAS compilation from Open Targets (OT) and tuned protocols focusing on genes and coding regions, along with complementary association methods of PWAS (proteome-based) and TWAS (transcriptome-based). Comparing summary statistics from numerous GWAS revealed a limited number of variants associated with thyroid development. The proteome-wide association study method identified 77 statistically significant genes, half of which are located within the Chr6-MHC locus and are enriched with autoimmunity-related genes. While coding GWAS and PWAS highlighted the centrality of immune-related genes, OT and transcriptome-wide association study mostly identified genes involved in thyroid developmental programs. We used independent populations from Finland (FinnGen) and the Taiwan cohort to validate the PWAS results. The higher prevalence in females relative to males is substantiated as the polygenic risk score prediction of hypothyroidism relied mostly from the female group genetics. Comparing results from OT, TWAS, and PWAS revealed the complementary facets of hypothyroidism's etiology. This study underscores the significance of synthesizing gene-phenotype association methods for this common, intricate disease. We propose that the integration of established association methods enhances interpretability and clinical utility.
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Affiliation(s)
- Roei Zucker
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael Kovalerchik
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amos Stern
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hadasa Kaufman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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6
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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7
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Hatton AA, Cheng FF, Lin T, Shen RJ, Chen J, Zheng Z, Qu J, Lyu F, Harris SE, Cox SR, Jin ZB, Martin NG, Fan D, Montgomery GW, Yang J, Wray NR, Marioni RE, Visscher PM, McRae AF. Genetic control of DNA methylation is largely shared across European and East Asian populations. Nat Commun 2024; 15:2713. [PMID: 38548728 PMCID: PMC10978881 DOI: 10.1038/s41467-024-47005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/15/2024] [Indexed: 04/01/2024] Open
Abstract
DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.
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Affiliation(s)
- Alesha A Hatton
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Fei-Fei Cheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ren-Juan Shen
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jie Chen
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jia Qu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fan Lyu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, 4006, Australia
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, 100191, Beijing, China
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
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Takahashi S, Kojima T, Wasano K, Homma K. Functional Studies of Deafness-Associated Pendrin and Prestin Variants. Int J Mol Sci 2024; 25:2759. [PMID: 38474007 PMCID: PMC10931795 DOI: 10.3390/ijms25052759] [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: 01/30/2024] [Revised: 02/21/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
Pendrin and prestin are evolutionary-conserved membrane proteins that are essential for normal hearing. Dysfunction of these proteins results in hearing loss in humans, and numerous deafness-associated pendrin and prestin variants have been identified in patients. However, the pathogenic impacts of many of these variants are ambiguous. Here, we report results from our ongoing efforts to experimentally characterize pendrin and prestin variants using in vitro functional assays. With previously established fluorometric anion transport assays, we determined that many of the pendrin variants identified on transmembrane (TM) 10, which contains the essential anion binding site, and on the neighboring TM9 within the core domain resulted in impaired anion transport activity. We also determined the range of functional impairment in three deafness-associated prestin variants by measuring nonlinear capacitance (NLC), a proxy for motor function. Using the results from our functional analyses, we also evaluated the performance of AlphaMissense (AM), a computational tool for predicting the pathogenicity of missense variants. AM prediction scores correlated well with our experimental results; however, some variants were misclassified, underscoring the necessity of experimentally assessing the effects of variants. Together, our experimental efforts provide invaluable information regarding the pathogenicity of deafness-associated pendrin and prestin variants.
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Affiliation(s)
- Satoe Takahashi
- Department of Otolaryngology—Head and Neck Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Takashi Kojima
- Department of Otolaryngology—Head and Neck Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Department of Otolaryngology, Head and Neck Surgery, National Hospital Organization Tochigi Medical Center, Tochigi 320-0057, Japan
| | - Koichiro Wasano
- Department of Otolaryngology—Head and Neck Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Department of Otolaryngology, Head and Neck Surgery, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Kazuaki Homma
- Department of Otolaryngology—Head and Neck Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- The Hugh Knowles Center for Clinical and Basic Science in Hearing and Its Disorders, Northwestern University, Evanston, IL 60208, USA
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9
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Tandon R, Nasrallah H, Akbarian S, Carpenter WT, DeLisi LE, Gaebel W, Green MF, Gur RE, Heckers S, Kane JM, Malaspina D, Meyer-Lindenberg A, Murray R, Owen M, Smoller JW, Yassin W, Keshavan M. The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. Schizophr Res 2024; 264:1-28. [PMID: 38086109 DOI: 10.1016/j.schres.2023.11.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/23/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.
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Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI 49008, United States of America.
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati College of Medicine Cincinnati, OH 45267, United States of America
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America
| | - Lynn E DeLisi
- Department of Psychiatry, Cambridge Health Alliance and Harvard Medical School, Cambridge, MA 02139, United States of America
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, LVR-Klinikum Dusseldorf, Heinrich-Heine University, Dusseldorf, Germany
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute of Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90024, United States of America; Greater Los Angeles Veterans' Administration Healthcare System, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Stephan Heckers
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Glen Oaks, NY 11004, United States of America
| | - Dolores Malaspina
- Department of Psychiatry, Neuroscience, Genetics, and Genomics, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannhein/Heidelberg University, Mannheim, Germany
| | - Robin Murray
- Institute of Psychiatry, Psychology, and Neuroscience, Kings College, London, UK
| | - Michael Owen
- Centre for Neuropsychiatric Genetics and Genomics, and Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Psychiatric and Neurodevelopmental Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States of America
| | - Walid Yassin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
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10
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Takahashi S, Kojima T, Wasano K, Homma K. Functional studies of deafness-associated pendrin and prestin variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576877. [PMID: 38328051 PMCID: PMC10849616 DOI: 10.1101/2024.01.23.576877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Pendrin and prestin are evolutionary conserved membrane proteins that are essential for normal hearing. Pendrin is an anion transporter required for normal development and maintenance of ion homeostasis in the inner ear, while prestin is a voltage-dependent motor responsible for cochlear amplification essential for high sensitivity and frequency selectivity of mammalian hearing. Dysfunction of these proteins result in hearing loss in humans, and numerous deafness-associated pendrin and prestin variants have been identified in patients. However, the pathogenic impacts of many of these variants are ambiguous. Here we report results from our ongoing efforts in experimentally characterizing pendrin and prestin variants using in vitro functional assays, providing invaluable information regarding their pathogenicity.
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11
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So HC, Xue X, Ma Z, Sham PC. SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates. Int J Mol Sci 2024; 25:1347. [PMID: 38279346 PMCID: PMC10816209 DOI: 10.3390/ijms25021347] [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: 10/13/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.
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Affiliation(s)
- Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen 518057, China
- Margaret K. L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Xiao Xue
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Zhijie Ma
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Pak-Chung Sham
- Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China;
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12
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Abudahab S, Slattum PW, Price ET, McClay JL. Epigenetic regulation of drug metabolism in aging: utilizing epigenetics to optimize geriatric pharmacotherapy. Pharmacogenomics 2024; 25:41-54. [PMID: 38126340 PMCID: PMC10794944 DOI: 10.2217/pgs-2023-0199] [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: 10/19/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
We explore the relationship between epigenetic aging and drug metabolism. We review current evidence for changes in drug metabolism in normal aging, followed by a description of how epigenetic modifications associated with age can regulate the expression and functionality of genes. In particular, we focus on the role of epigenome-wide studies of human and mouse liver in understanding these age-related processes with respect to xenobiotic processing. We highlight genes encoding drug metabolizing enzymes and transporters revealed to be affected by epigenetic aging in these studies. We conclude that substantial evidence exists for epigenetic aging impacting drug metabolism and transport genes, but more work is needed. We further highlight the promise of pharmacoepigenetics applied to enhancing drug safety in older adults.
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Affiliation(s)
- Sara Abudahab
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Patricia W Slattum
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
- Virginia Center on Aging, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Elvin T Price
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Joseph L McClay
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
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13
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Hannan AJ. Expanding horizons of tandem repeats in biology and medicine: Why 'genomic dark matter' matters. Emerg Top Life Sci 2023; 7:ETLS20230075. [PMID: 38088823 PMCID: PMC10754335 DOI: 10.1042/etls20230075] [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: 10/25/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023]
Abstract
Approximately half of the human genome includes repetitive sequences, and these DNA sequences (as well as their transcribed repetitive RNA and translated amino-acid repeat sequences) are known as the repeatome. Within this repeatome there are a couple of million tandem repeats, dispersed throughout the genome. These tandem repeats have been estimated to constitute ∼8% of the entire human genome. These tandem repeats can be located throughout exons, introns and intergenic regions, thus potentially affecting the structure and function of tandemly repetitive DNA, RNA and protein sequences. Over more than three decades, more than 60 monogenic human disorders have been found to be caused by tandem-repeat mutations. These monogenic tandem-repeat disorders include Huntington's disease, a variety of ataxias, amyotrophic lateral sclerosis and frontotemporal dementia, as well as many other neurodegenerative diseases. Furthermore, tandem-repeat disorders can include fragile X syndrome, related fragile X disorders, as well as other neurological and psychiatric disorders. However, these monogenic tandem-repeat disorders, which were discovered via their dominant or recessive modes of inheritance, may represent the 'tip of the iceberg' with respect to tandem-repeat contributions to human disorders. A previous proposal that tandem repeats may contribute to the 'missing heritability' of various common polygenic human disorders has recently been supported by a variety of new evidence. This includes genome-wide studies that associate tandem-repeat mutations with autism, schizophrenia, Parkinson's disease and various types of cancers. In this article, I will discuss how tandem-repeat mutations and polymorphisms could contribute to a wide range of common disorders, along with some of the many major challenges of tandem-repeat biology and medicine. Finally, I will discuss the potential of tandem repeats to be therapeutically targeted, so as to prevent and treat an expanding range of human disorders.
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Affiliation(s)
- Anthony J Hannan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria 3010, Australia
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14
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Benegas G, Batra SS, Song YS. DNA language models are powerful predictors of genome-wide variant effects. Proc Natl Acad Sci U S A 2023; 120:e2311219120. [PMID: 37883436 PMCID: PMC10622914 DOI: 10.1073/pnas.2311219120] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/08/2023] [Indexed: 10/28/2023] Open
Abstract
The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN), a model designed to learn genome-wide variant effects through unsupervised pretraining on genomic DNA sequences. Our model also successfully learns gene structure and DNA motifs without any supervision. To demonstrate its utility, we train GPN on unaligned reference genomes of Arabidopsis thaliana and seven related species within the Brassicales order and evaluate its ability to predict the functional impact of genetic variants in A. thaliana by utilizing allele frequencies from the 1001 Genomes Project and a comprehensive database of GWAS. Notably, GPN outperforms predictors based on popular conservation scores such as phyloP and phastCons. Our predictions for A. thaliana can be visualized as sequence logos in the UCSC Genome Browser (https://genome.ucsc.edu/s/gbenegas/gpn-arabidopsis). We provide code (https://github.com/songlab-cal/gpn) to train GPN for any given species using its DNA sequence alone, enabling unsupervised prediction of variant effects across the entire genome.
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Affiliation(s)
- Gonzalo Benegas
- Graduate Group in Computational Biology, University of California, Berkeley, CA94720
| | | | - Yun S. Song
- Computer Science Division, University of California, Berkeley, CA94720
- Department of Statistics, University of California, Berkeley, CA94720
- Center for Computational Biology, University of California, Berkeley, CA94720
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15
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Malfait J, Wan J, Spicuglia S. Epromoters are new players in the regulatory landscape with potential pleiotropic roles. Bioessays 2023; 45:e2300012. [PMID: 37246247 DOI: 10.1002/bies.202300012] [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: 01/17/2023] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Precise spatiotemporal control of gene expression during normal development and cell differentiation is achieved by the combined action of proximal (promoters) and distal (enhancers) cis-regulatory elements. Recent studies have reported that a subset of promoters, termed Epromoters, works also as enhancers to regulate distal genes. This new paradigm opened novel questions regarding the complexity of our genome and raises the possibility that genetic variation within Epromoters has pleiotropic effects on various physiological and pathological traits by differentially impacting multiple proximal and distal genes. Here, we discuss the different observations pointing to an important role of Epromoters in the regulatory landscape and summarize the evidence supporting a pleiotropic impact of these elements in disease. We further hypothesize that Epromoter might represent a major contributor to phenotypic variation and disease.
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Affiliation(s)
- Juliette Malfait
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
| | - Jing Wan
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
| | - Salvatore Spicuglia
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
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16
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Khadzhieva MB, Kolobkov DS, Kashatnikova DA, Gracheva AS, Redkin IV, Kuzovlev AN, Salnikova LE. Rare Variants in Primary Immunodeficiency Genes and Their Functional Partners in Severe COVID-19. Biomolecules 2023; 13:1380. [PMID: 37759780 PMCID: PMC10526997 DOI: 10.3390/biom13091380] [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: 07/25/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein-protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19.
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Affiliation(s)
- Maryam B. Khadzhieva
- The Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 107031 Moscow, Russia; (M.B.K.); (A.S.G.); (A.N.K.)
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (D.A.K.)
- The Laboratory of Molecular Immunology, National Research Center of Pediatric Hematology, Oncology and Immunology, 117997 Moscow, Russia
| | - Dmitry S. Kolobkov
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (D.A.K.)
| | - Darya A. Kashatnikova
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (D.A.K.)
| | - Alesya S. Gracheva
- The Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 107031 Moscow, Russia; (M.B.K.); (A.S.G.); (A.N.K.)
- The Department of Population Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Ivan V. Redkin
- Competence Center for the Development of AI Technology, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 107031 Moscow, Russia;
| | - Artem N. Kuzovlev
- The Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 107031 Moscow, Russia; (M.B.K.); (A.S.G.); (A.N.K.)
| | - Lyubov E. Salnikova
- The Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 107031 Moscow, Russia; (M.B.K.); (A.S.G.); (A.N.K.)
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (D.A.K.)
- The Laboratory of Molecular Immunology, National Research Center of Pediatric Hematology, Oncology and Immunology, 117997 Moscow, Russia
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17
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Brandes N, Goldman G, Wang CH, Ye CJ, Ntranos V. Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet 2023; 55:1512-1522. [PMID: 37563329 PMCID: PMC10484790 DOI: 10.1038/s41588-023-01465-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 07/05/2023] [Indexed: 08/12/2023]
Abstract
Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.
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Affiliation(s)
- Nadav Brandes
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Grant Goldman
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Charlotte H Wang
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Vasilis Ntranos
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA.
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18
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Sigurdsson AI, Louloudis I, Banasik K, Westergaard D, Winther O, Lund O, Ostrowski S, Erikstrup C, Pedersen O, Nyegaard M, Brunak S, Vilhjálmsson B, Rasmussen S. Deep integrative models for large-scale human genomics. Nucleic Acids Res 2023; 51:e67. [PMID: 37224538 PMCID: PMC10325897 DOI: 10.1093/nar/gkad373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/18/2023] [Accepted: 04/28/2023] [Indexed: 05/26/2023] Open
Abstract
Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.
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Affiliation(s)
- Arnór I Sigurdsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ioannis Louloudis
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Ole Winther
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital), Copenhagen 2100, Denmark
| | - Ole Lund
- Danish National Genome Center, Ørestads Boulevard 5, 2300 Copenhagen S, Denmark
- DTU Health Tech, Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Rigshospitalet, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, 8000 Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, 8000 Aarhus C, Denmark
| | - Ole Birger Vesterager Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Clinical Immunology, Zealand University Hospital, 4600 Køge, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, DK- 9260 Gistrup, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research (NCRR), Aarhus University, 8000 Aarhus C, Denmark
- Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), 8210 Aarhus V, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, 8000 Aarhus C, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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19
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Zucker R, Kovalerchik M, Linial M. Gene-based association study reveals a distinct female genetic signal in primary hypertension. Hum Genet 2023:10.1007/s00439-023-02567-9. [PMID: 37133573 DOI: 10.1007/s00439-023-02567-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023]
Abstract
Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.
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Affiliation(s)
- Roei Zucker
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michael Kovalerchik
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
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20
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Sato H, Osonoi K, Sharlin CS, Shoda T. Genetic and Molecular Contributors in Eosinophilic Esophagitis. Curr Allergy Asthma Rep 2023; 23:255-266. [PMID: 37084008 PMCID: PMC11136533 DOI: 10.1007/s11882-023-01075-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2023] [Indexed: 04/22/2023]
Abstract
PURPOSE OF REVIEW Eosinophilic esophagitis (EoE) is an allergic inflammatory esophageal disorder with a complex underlying genetic and molecular etiology. The interest of the scientific community in EoE has grown considerably over the past three decades, and the understanding of the genetic and molecular mechanisms involved in this disease has greatly increased. RECENT FINDINGS In this article, we aim to provide both historic aspects and updates on the recent genetic and molecular advances in the understanding of EoE. Although EoE is a relatively newly described disorder, much progress has been made toward identifying the genetic and molecular factors contributing to the disease pathogenesis by a variety of approaches with next-generation sequencing technologies, including genome-wide association study, whole exome sequencing, and bulk and single-cell RNA sequencing. This review highlights the multifaceted impacts of various findings that have shaped the current molecular and genetic landscape of EoE, providing insights that facilitate further understanding of the disease process.
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Affiliation(s)
- Hiroki Sato
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kasumi Osonoi
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Colby S Sharlin
- Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Tetsuo Shoda
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7028, 45229, Cincinnati, OH, USA.
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21
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Andrews SJ, Renton AE, Fulton-Howard B, Podlesny-Drabiniok A, Marcora E, Goate AM. The complex genetic architecture of Alzheimer's disease: novel insights and future directions. EBioMedicine 2023; 90:104511. [PMID: 36907103 PMCID: PMC10024184 DOI: 10.1016/j.ebiom.2023.104511] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disorder and the most common form of dementia. AD is highly heritable, with heritability estimates of ∼70% from twin studies. Progressively larger genome-wide association studies (GWAS) have continued to expand our knowledge of AD/dementia genetic architecture. Until recently these efforts had identified 39 disease susceptibility loci in European ancestry populations. RECENT DEVELOPMENTS Two new AD/dementia GWAS have dramatically expanded the sample sizes and the number of disease susceptibility loci. The first increased total sample size to 1,126,563-with an effective sample size of 332,376-by predominantly including new biobank and population-based dementia datasets. The second, expands on an earlier GWAS from the International Genomics of Alzheimer's Project (IGAP) by increasing the number of clinically-defined AD cases/controls in addition to incorporating biobank dementia datasets, resulting in a total sample size to 788,989 and an effective sample size of 382,472. Collectively both GWAS identified 90 independent variants across 75 AD/dementia susceptibility loci, including 42 novel loci. Pathway analyses indicate the susceptibility loci are enriched for genes involved in amyloid plaque and neurofibrillary tangle formation, cholesterol metabolism, endocytosis/phagocytosis, and the innate immune system. Gene prioritization efforts for the novel loci identified 62 candidate causal genes. Many of the candidate genes from known and newly discovered loci play key roles in macrophages and highlight phagocytic clearance of cholesterol-rich brain tissue debris by microglia (efferocytosis) as a core pathogenetic hub and putative therapeutic target for AD. WHERE NEXT?: While GWAS in European ancestry populations have substantially enhanced our understanding of AD genetic architecture, heritability estimates from population based GWAS cohorts are markedly smaller than those from twin studies. While this missing heritability is likely due to a combination of factors, it highlights that our understanding of AD genetic architecture and genetic risk mechanisms remains incomplete. These knowledge gaps result from several underexplored areas in AD research. First, rare variants remain understudied due to methodological issues in identifying them and the cost of generating sufficiently powered whole exome/genome sequencing datasets. Second, sample sizes of non-European ancestry populations in AD GWAS remain small. Third, GWAS of AD neuroimaging and cerebrospinal fluid endophenotypes remains limited due to low compliance and high costs associated with measuring amyloid-β and tau levels and other disease-relevant biomarkers. Studies generating sequencing data, including diverse populations, and incorporating blood-based AD biomarkers are set to substantially improve our knowledge of AD genetic architecture.
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Affiliation(s)
- Shea J Andrews
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA.
| | - Alan E Renton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fulton-Howard
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anna Podlesny-Drabiniok
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edoardo Marcora
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison M Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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22
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Enamel Phenotypes: Genetic and Environmental Determinants. Genes (Basel) 2023; 14:genes14030545. [PMID: 36980818 PMCID: PMC10048525 DOI: 10.3390/genes14030545] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
Dental enamel is a specialized tissue that has adapted over millions of years of evolution to enhance the survival of a variety of species. In humans, enamel evolved to form the exterior protective layer for the crown of the exposed tooth crown. Its unique composition, structure, physical properties and attachment to the underlying dentin tissue allow it to be a resilient, although not self-repairing, tissue. The process of enamel formation, known as amelogenesis, involves epithelial-derived cells called ameloblasts that secrete a unique extracellular matrix that influences the structure of the mineralizing enamel crystallites. There are over 115 known genetic conditions affecting amelogenesis that are associated with enamel phenotypes characterized by either a reduction of enamel amount and or mineralization. Amelogenesis involves many processes that are sensitive to perturbation and can be altered by numerous environmental stressors. Genetics, epigenetics, and environment factors can influence enamel formation and play a role in resistance/risk for developmental defects and the complex disease, dental caries. Understanding why and how enamel is affected and the enamel phenotypes seen clinically support diagnostics, prognosis prediction, and the selection of treatment approaches that are appropriate for the specific tissue defects (e.g., deficient amount, decreased mineral, reduced insulation and hypersensitivity). The current level of knowledge regarding the heritable enamel defects is sufficient to develop a new classification system and consensus nosology that effectively communicate the mode of inheritance, molecular defect/pathway, and the functional aberration and resulting enamel phenotype.
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23
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Ogbunugafor CB, Edge MD. Gattaca as a lens on contemporary genetics: marking 25 years into the film's "not-too-distant" future. Genetics 2022; 222:iyac142. [PMID: 36218390 PMCID: PMC9713434 DOI: 10.1093/genetics/iyac142] [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: 07/14/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the "completion" of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. The current moment-25 years after the film's release-offers an opportunity to reflect on where society currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research-genetic prediction, embryo selection, forensic genetics, and others-that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society's expectations and fears with respect to the ways that genetic science has manifested in the real world. In accompanying supplemental material, we offer some thought questions to guide group discussions inside and outside of the classroom.
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Affiliation(s)
- C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Vermont Complex Systems Center, Burlington, VT 05401, USA
| | - Michael D Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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24
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Pérez-Granado J, Piñero J, Furlong LI. Benchmarking post-GWAS analysis tools in major depression: Challenges and implications. Front Genet 2022; 13:1006903. [PMID: 36276939 PMCID: PMC9579284 DOI: 10.3389/fgene.2022.1006903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/20/2022] [Indexed: 12/05/2022] Open
Abstract
Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.
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Affiliation(s)
- Judith Pérez-Granado
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
- *Correspondence: Laura I. Furlong,
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25
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Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5:293-320. [PMID: 35576555 PMCID: PMC9828290 DOI: 10.1146/annurev-biodatasci-111721-074830] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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