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Baumert P, Mäntyselkä S, Schönfelder M, Heiber M, Jacobs MJ, Swaminathan A, Minderis P, Dirmontas M, Kleigrewe K, Meng C, Gigl M, Ahmetov II, Venckunas T, Degens H, Ratkevicius A, Hulmi JJ, Wackerhage H. Skeletal muscle hypertrophy rewires glucose metabolism: An experimental investigation and systematic review. J Cachexia Sarcopenia Muscle 2024; 15:989-1002. [PMID: 38742477 PMCID: PMC11154753 DOI: 10.1002/jcsm.13468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/06/2024] [Accepted: 03/15/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Proliferating cancer cells shift their metabolism towards glycolysis, even in the presence of oxygen, to especially generate glycolytic intermediates as substrates for anabolic reactions. We hypothesize that a similar metabolic remodelling occurs during skeletal muscle hypertrophy. METHODS We used mass spectrometry in hypertrophying C2C12 myotubes in vitro and plantaris mouse muscle in vivo and assessed metabolomic changes and the incorporation of the [U-13C6]glucose tracer. We performed enzyme inhibition of the key serine synthesis pathway enzyme phosphoglycerate dehydrogenase (Phgdh) for further mechanistic analysis and conducted a systematic review to align any changes in metabolomics during muscle growth with published findings. Finally, the UK Biobank was used to link the findings to population level. RESULTS The metabolomics analysis in myotubes revealed insulin-like growth factor-1 (IGF-1)-induced altered metabolite concentrations in anabolic pathways such as pentose phosphate (ribose-5-phosphate/ribulose-5-phosphate: +40%; P = 0.01) and serine synthesis pathway (serine: -36.8%; P = 0.009). Like the hypertrophy stimulation with IGF-1 in myotubes in vitro, the concentration of the dipeptide l-carnosine was decreased by 26.6% (P = 0.001) during skeletal muscle growth in vivo. However, phosphorylated sugar (glucose-6-phosphate, fructose-6-phosphate or glucose-1-phosphate) decreased by 32.2% (P = 0.004) in the overloaded muscle in vivo while increasing in the IGF-1-stimulated myotubes in vitro. The systematic review revealed that 10 metabolites linked to muscle hypertrophy were directly associated with glycolysis and its interconnected anabolic pathways. We demonstrated that labelled carbon from [U-13C6]glucose is increasingly incorporated by ~13% (P = 0.001) into the non-essential amino acids in hypertrophying myotubes, which is accompanied by an increased depletion of media serine (P = 0.006). The inhibition of Phgdh suppressed muscle protein synthesis in growing myotubes by 58.1% (P < 0.001), highlighting the importance of the serine synthesis pathway for maintaining muscle size. Utilizing data from the UK Biobank (n = 450 243), we then discerned genetic variations linked to the serine synthesis pathway (PHGDH and PSPH) and to its downstream enzyme (SHMT1), revealing their association with appendicular lean mass in humans (P < 5.0e-8). CONCLUSIONS Understanding the mechanisms that regulate skeletal muscle mass will help in developing effective treatments for muscle weakness. Our results provide evidence for the metabolic rewiring of glycolytic intermediates into anabolic pathways during muscle growth, such as in serine synthesis.
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Affiliation(s)
- Philipp Baumert
- School of Medicine and HealthTechnical University of MunichMunichGermany
- Research Institute for Sport and Exercise SciencesLiverpool John Moores UniversityLiverpoolUK
- Research Unit for Orthopaedic Sports Medicine and Injury Prevention (OSMI)UMIT TIROL ‐ Private University for Health Sciences and Health TechnologyInnsbruckAustria
| | - Sakari Mäntyselkä
- Faculty of Sport and Health Sciences, NeuroMuscular Research CenterUniversity of JyväskyläJyväskyläFinland
| | - Martin Schönfelder
- School of Medicine and HealthTechnical University of MunichMunichGermany
| | - Marie Heiber
- School of Medicine and HealthTechnical University of MunichMunichGermany
- Institute of Sport ScienceUniversity of the Bundeswehr MunichNeubibergGermany
| | - Mika Jos Jacobs
- School of Medicine and HealthTechnical University of MunichMunichGermany
| | - Anandini Swaminathan
- Institute of Sport Science and InnovationsLithuanian Sports UniversityKaunasLithuania
| | - Petras Minderis
- Institute of Sport Science and InnovationsLithuanian Sports UniversityKaunasLithuania
| | - Mantas Dirmontas
- Department of Health Promotion and RehabilitationLithuanian Sports UniversityKaunasLithuania
| | - Karin Kleigrewe
- Bavarian Center for Biomolecular Mass SpectrometryTechnical University of MunichMunichGermany
| | - Chen Meng
- Bavarian Center for Biomolecular Mass SpectrometryTechnical University of MunichMunichGermany
| | - Michael Gigl
- Bavarian Center for Biomolecular Mass SpectrometryTechnical University of MunichMunichGermany
| | - Ildus I. Ahmetov
- Research Institute for Sport and Exercise SciencesLiverpool John Moores UniversityLiverpoolUK
| | - Tomas Venckunas
- Institute of Sport Science and InnovationsLithuanian Sports UniversityKaunasLithuania
| | - Hans Degens
- Institute of Sport Science and InnovationsLithuanian Sports UniversityKaunasLithuania
- Department of Life SciencesManchester Metropolitan UniversityManchesterUK
| | - Aivaras Ratkevicius
- Institute of Sport Science and InnovationsLithuanian Sports UniversityKaunasLithuania
- Sports and Exercise Medicine CentreQueen Mary University of LondonLondonUK
| | - Juha J. Hulmi
- Faculty of Sport and Health Sciences, NeuroMuscular Research CenterUniversity of JyväskyläJyväskyläFinland
| | - Henning Wackerhage
- School of Medicine and HealthTechnical University of MunichMunichGermany
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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Kelchtermans J, March ME, Mentch F, Liu Y, Nguyen K, Hakonarson H. GWAS reveals Genetic Susceptibility to Air Pollution-Related Asthma Exacerbations in Children of African Ancestry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24307906. [PMID: 38853886 PMCID: PMC11160834 DOI: 10.1101/2024.05.29.24307906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background The relationship between ambient air pollution (AAP) exposure and asthma exacerbations is well-established. However, mitigation efforts have yielded mixed results, potentially due to genetic variability in the response to AAP. We hypothesize that common single nucleotide polymorphisms (SNPs) are linked to AAP sensitivity and test this through a Genome Wide Association Study (GWAS). Methods We selected a cohort of pediatric asthma patients frequently exposed to AAP. Patients experiencing exacerbations immediately following AAP spikes were deemed sensitive. A GWAS compared sensitive versus non-sensitive patients. Findings were validated using data from the All of Us program. Results Our study included 6,023 pediatric asthma patients. Due to the association between AAP exposure and race, GWAS analysis was feasible only in the African ancestry cohort. Seven risk loci reached genome-wide significance, including four non-intergenic variants. Two variants were validated: rs111970601 associated with sensitivity to CO (odds ratio [OR], 6.58; PL=L1.63L×L10-8; 95% CI, 3.42-12.66) and rs9836522 to PM2.5 sensitivity (OR 0.75; PL=L3,87 ×L10-9; 95% CI, 0.62-0.91). Interpretation While genetic variants have been previously linked to asthma incidence and AAP exposure, this study is the first to link specific SNPs with AAP-related asthma exacerbations. The identified variants implicate genes with a known role in asthma and established links to AAP. Future research should explore how clinical interventions interact with genetic risk to mitigate the effects of AAP, particularly to enhance health equity for vulnerable populations. What is already known on this topic The relationship between ambient air pollution (AAP) exposure and asthma exacerbations is well-established. However, efforts to mitigate the impact of AAP on children with asthma have yielded mixed results, potentially due to genetic variability in response to AAP. What this study adds Using publicly available AAP data, we identify which children with asthma experience exacerbations immediately following spikes in AAP. We then conduct a Genome Wide Association Study (GWAS) comparing these patients with those who have no temporal association between AAP spikes and asthma exacerbations, identifying several Single Nucleotide Polymorphisms (SNPs) significantly associated with AAP sensitivity. How this study might affect research practice or policy While genetic variants have previously been linked to asthma incidence and AAP exposure, this study is the first to link specific SNPs with AAP-related asthma exacerbations. This creates a framework for identifying children especially at risk when exposed to AAP. These children should be targeted with policy interventions to reduce exposure and may require specific treatments to mitigate the effects of ongoing AAP exposure in the interim.
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Aschenbrenner D, Nassiri I, Venkateswaran S, Pandey S, Page M, Drowley L, Armstrong M, Kugathasan S, Fairfax B, Uhlig HH. An isoform quantitative trait locus in SBNO2 links genetic susceptibility to Crohn's disease with defective antimicrobial activity. Nat Commun 2024; 15:4529. [PMID: 38806456 PMCID: PMC11133462 DOI: 10.1038/s41467-024-47218-3] [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: 05/05/2023] [Accepted: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
Despite major advances in linking single genetic variants to single causal genes, the significance of genetic variation on transcript-level regulation of expression, transcript-specific functions, and relevance to human disease has been poorly investigated. Strawberry notch homolog 2 (SBNO2) is a candidate gene in a susceptibility locus with different variants associated with Crohn's disease and bone mineral density. The SBNO2 locus is also differentially methylated in Crohn's disease but the functional mechanisms are unknown. Here we show that the isoforms of SBNO2 are differentially regulated by lipopolysaccharide and IL-10. We identify Crohn's disease associated isoform quantitative trait loci that negatively regulate the expression of the noncanonical isoform 2 corresponding with the methylation signals at the isoform 2 promoter in IBD and CD. The two isoforms of SBNO2 drive differential gene networks with isoform 2 dominantly impacting antimicrobial activity in macrophages. Our data highlight the role of isoform quantitative trait loci to understand disease susceptibility and resolve underlying mechanisms of disease.
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Affiliation(s)
- Dominik Aschenbrenner
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- Immunology Disease Area, Novartis Biomedical Research, Basel, CH, Switzerland.
| | - Isar Nassiri
- Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Sumeet Pandey
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
- GSK Immunology Network, GSK Medicines Research Center, Stevenage, UK
| | - Matthew Page
- Translational Bioinformatics, UCB Pharma, Slough, UK
| | | | | | | | - Benjamin Fairfax
- MRC-Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Oncology, University of Oxford & Oxford Cancer Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Holm H Uhlig
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
- Department of Paediatrics, University of Oxford, Oxford, UK.
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Lv X, Shang Y, Ning Y, Yu W, Wang J. Pharmacological targets of SGLT2 inhibitors on IgA nephropathy and membranous nephropathy: a mendelian randomization study. Front Pharmacol 2024; 15:1399881. [PMID: 38846092 PMCID: PMC11155304 DOI: 10.3389/fphar.2024.1399881] [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: 03/12/2024] [Accepted: 04/30/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction Emerging research suggests that sodium-glucose cotransporter 2 (SGLT2) inhibitors may play a pivotal role in the treatment of primary glomerular diseases. This study was aimed to investigate potential pharmacological targets connecting SGLT2 inhibitors with IgA nephropathy (IgAN) and membranous nephropathy (MN). Methods A univariate Mendelian randomization (MR) analysis was conducted using publicly available genome-wide association studies (GWAS) datasets. Co-localization analysis was used to identify potential connections between target genes and IgAN and MN. Then, Comparative Toxicogenomics Database (CTD) was employed to predict diseases associated with these target genes and SGLT2 inhibitors (canagliflozin, dapagliflozin, and empagliflozin). Subsequently, phenotypic scan analyses were applied to explore the causal relationships between the predicted diseases and target genes. Finally, we analyzed the immune signaling pathways involving pharmacological target genes using the Kyoto encyclopedia of genes and genomes (KEGG). Results The results of MR analysis revealed that eight drug targets were causally linked to the occurrence of IgAN, while 14 drug targets were linked to MN. In the case of IgAN, LCN2 and AGER emerged as co-localized genes related to the pharmacological agent of dapagliflozin and the occurrence of IgAN. LCN2 was identified as a risk factor, while AGER was exhibited a protective role. KEGG analysis revealed that LCN2 is involved in the interleukin (IL)-17 immune signaling pathway, while AGER is associated with the neutrophil extracellular traps (NETs) signaling immune pathway. No positive co-localization results of the target genes were observed between two other SGLT2 inhibitors (canagliflozin and empagliflozin) and the occurrence of IgAN, nor between the three SGLT2 inhibitors and the occurrence of MN. Conclusion Our study provided evidence supporting a causal relationship between specific SGLT2 inhibitors and IgAN. Furthermore, we found that dapagliflozin may act on IgAN through the genes LCN2 and AGER.
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Affiliation(s)
- Xin Lv
- Department of Nephrology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Shang
- Department of Nephrology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong Ning
- Department of Nephrology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weimin Yu
- Department of Nephrology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Wang
- Department of Nephrology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
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Linna-Kuosmanen S, Schmauch E, Galani K, Ojanen J, Boix CA, Örd T, Toropainen A, Singha PK, Moreau PR, Harju K, Blazeski A, Segerstolpe Å, Lahtinen V, Hou L, Kang K, Meibalan E, Agudelo LZ, Kokki H, Halonen J, Jalkanen J, Gunn J, MacRae CA, Hollmén M, Hartikainen JEK, Kaikkonen MU, García-Cardeña G, Tavi P, Kiviniemi T, Kellis M. Transcriptomic and spatial dissection of human ex vivo right atrial tissue reveals proinflammatory microvascular changes in ischemic heart disease. Cell Rep Med 2024; 5:101556. [PMID: 38776872 PMCID: PMC11148807 DOI: 10.1016/j.xcrm.2024.101556] [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: 02/02/2023] [Revised: 11/27/2023] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
Cardiovascular disease plays a central role in the electrical and structural remodeling of the right atrium, predisposing to arrhythmias, heart failure, and sudden death. Here, we dissect with single-nuclei RNA sequencing (snRNA-seq) and spatial transcriptomics the gene expression changes in the human ex vivo right atrial tissue and pericardial fluid in ischemic heart disease, myocardial infarction, and ischemic and non-ischemic heart failure using asymptomatic patients with valvular disease who undergo preventive surgery as the control group. We reveal substantial differences in disease-associated gene expression in all cell types, collectively suggesting inflammatory microvascular dysfunction and changes in the right atrial tissue composition as the valvular and vascular diseases progress into heart failure. The data collectively suggest that investigation of human cardiovascular disease should expand to all functionally important parts of the heart, which may help us to identify mechanisms promoting more severe types of the disease.
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Affiliation(s)
- Suvi Linna-Kuosmanen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Eloi Schmauch
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kyriakitsa Galani
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Johannes Ojanen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tiit Örd
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Anu Toropainen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Prosanta K Singha
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pierre R Moreau
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kristiina Harju
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Adriana Blazeski
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Åsa Segerstolpe
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Veikko Lahtinen
- Heart Center, Turku University Hospital, 20521 Turku, Finland; MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Lei Hou
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Kang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Elamaran Meibalan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Leandro Z Agudelo
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hannu Kokki
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland
| | - Jari Halonen
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland; Heart Center, Kuopio University Hospital, 70200 Kuopio, Finland
| | - Juho Jalkanen
- MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Jarmo Gunn
- Heart Center, Turku University Hospital, 20521 Turku, Finland; Department of Medicine, University of Turku, 20500 Turku, Finland
| | - Calum A MacRae
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Cardiovascular Medicine and Network Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Maija Hollmén
- MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Juha E K Hartikainen
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland; Heart Center, Kuopio University Hospital, 70200 Kuopio, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Guillermo García-Cardeña
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Pasi Tavi
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Tuomas Kiviniemi
- Heart Center, Turku University Hospital, 20521 Turku, Finland; Department of Medicine, University of Turku, 20500 Turku, Finland; Cardiovascular Medicine and Network Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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Jee YH, Wang Y, Jung KJ, Lee JY, Kimm H, Duan R, Price AL, Martin AR, Kraft P. Genome-wide association studies in a large Korean cohort identify novel quantitative trait loci for 36 traits and illuminates their genetic architectures. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307550. [PMID: 38798434 PMCID: PMC11118625 DOI: 10.1101/2024.05.17.24307550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Genome-wide association studies (GWAS) have been predominantly conducted in populations of European ancestry, limiting opportunities for biological discovery in diverse populations. We report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 616 novel genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 3,524 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for a diverse set of traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.
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Affiliation(s)
- Yon Ho Jee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Keum Ji Jung
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Ji-Young Lee
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Heejin Kimm
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Transdivisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, USA
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Kraft J, Braun A, Awasthi S, Panagiotaropoulou G, Schipper M, Bell N, Posthuma D, Pardiñas AF, Ripke S, Heilbron K. Identifying drug targets for schizophrenia through gene prioritization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.15.24307423. [PMID: 38798390 PMCID: PMC11118622 DOI: 10.1101/2024.05.15.24307423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. Methods To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool. Results We prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition to DRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2A and GRM3), calcium channels (CACNA1C and CACNB2), and GABAB receptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4B and VRK2). Conclusions We curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.
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Affiliation(s)
- Julia Kraft
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Alice Braun
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Swapnil Awasthi
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Georgia Panagiotaropoulou
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | | | - Nathaniel Bell
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Antonio F. Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | | | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Karl Heilbron
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
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Wood TWP, Henriques WS, Cullen HB, Romero M, Blengini CS, Sarathy S, Sorkin J, Bekele H, Jin C, Kim S, Chemiakine A, Khondker RC, Isola JVV, Stout MB, Gennarino VA, Mogessie B, Jain D, Schindler K, Suh Y, Wiedenheft B, Berchowitz LE. The retrotransposon - derived capsid genes PNMA1 and PNMA4 maintain reproductive capacity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.11.592987. [PMID: 38798495 PMCID: PMC11118267 DOI: 10.1101/2024.05.11.592987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The human genome contains 24 gag -like capsid genes derived from deactivated retrotransposons conserved among eutherians. Although some of their encoded proteins retain the ability to form capsids and even transfer cargo, their fitness benefit has remained elusive. Here we show that the gag -like genes PNMA1 and PNMA4 support reproductive capacity. Six-week-old mice lacking either Pnma1 or Pnma4 are indistinguishable from wild-type littermates, but by six months the mutant mice become prematurely subfertile, with precipitous drops in sex hormone levels, gonadal atrophy, and abdominal obesity; overall they produce markedly fewer offspring than controls. Analysis of donated human ovaries shows that expression of both genes declines normally with aging, while several PNMA1 and PNMA4 variants identified in genome-wide association studies are causally associated with low testosterone, altered puberty onset, or obesity. These findings expand our understanding of factors that maintain human reproductive health and lend insight into the domestication of retrotransposon-derived genes.
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60
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Zhou Z, Leng H. Deciphering the causal relationship between plasma and cerebrospinal fluid metabolites and glioblastoma multiforme: a Mendelian Randomization study. Aging (Albany NY) 2024; 16:8306-8319. [PMID: 38742944 PMCID: PMC11131984 DOI: 10.18632/aging.205818] [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: 12/26/2023] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is one of the most aggressive and fatal brain cancers. The study of metabolites could be crucial for understanding GBM's biology and reveal new treatment strategies. METHODS The GWAS data for GBM were sourced from the FinnGen database. A total of 1400 plasma metabolites were collected from the GWAS Catalog dataset. The cerebrospinal fluid (CSF) metabolites data were collected from subsets of participants in the WADRC and WRAP studies. We utilized the inverse variance weighting (IVW) method as the primary tool to explore the causal relationship between metabolites in plasma and CSF and glioblastoma, ensuring the exclusion of instances with horizontal pleiotropy. Additionally, four supplementary analytical methods were applied to reinforce our findings. Aberrant results were identified and omitted based on the outcomes of the leave-one-out sensitivity analysis. Conclusively, a reverse Mendelian Randomization analysis was also conducted to further substantiate our results. RESULTS The study identified 69 plasma metabolites associated with GBM. Of these, 40 metabolites demonstrated a significant positive causal relationship with GBM, while 29 exhibited a significant negative causal association. Notably, Trimethylamine N-oxide (TMAO) levels in plasma, not CSF, were found to be a significant exposure factor for GBM (OR = 3.1627, 95% CI = (1.6347, 6.1189), P = 0.0006). The study did not find a reverse causal relationship between GBM and plasma TMAO levels. CONCLUSIONS This research has identified 69 plasma metabolites potentially associated with the incidence of GBM, among which TMAO stands out as a promising candidate for an early detectable biomarker for GBM.
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Affiliation(s)
- Zhiwei Zhou
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
| | - Haibin Leng
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
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61
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Rosean S, Sosa EA, O'Shea D, Raj SM, Seoighe C, Greally JM. Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization. BMC Bioinformatics 2024; 25:179. [PMID: 38714913 PMCID: PMC11075237 DOI: 10.1186/s12859-024-05794-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.
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Affiliation(s)
- Samuel Rosean
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Eric A Sosa
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Dónal O'Shea
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Srilakshmi M Raj
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Cathal Seoighe
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - John M Greally
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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Siraj L, Castro RI, Dewey H, Kales S, Nguyen TTL, Kanai M, Berenzy D, Mouri K, Wang QS, McCaw ZR, Gosai SJ, Aguet F, Cui R, Vockley CM, Lareau CA, Okada Y, Gusev A, Jones TR, Lander ES, Sabeti PC, Finucane HK, Reilly SK, Ulirsch JC, Tewhey R. Functional dissection of complex and molecular trait variants at single nucleotide resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592437. [PMID: 38766054 PMCID: PMC11100724 DOI: 10.1101/2024.05.05.592437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
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Affiliation(s)
- Layla Siraj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biophysics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Qingbo S. Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | | | - Sager J. Gosai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - François Aguet
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Caleb A. Lareau
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thouis R. Jones
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric S. Lander
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jacob C. Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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Seo J, Gaddis NC, Patchen BK, Xu J, Barr RG, O'Connor G, Manichaikul AW, Gharib SA, Dupuis J, North KE, Cassano PA, Hancock DB. Exploiting meta-analysis of genome-wide interaction with serum 25-hydroxyvitamin D to identify novel genetic loci associated with pulmonary function. Am J Clin Nutr 2024; 119:1227-1237. [PMID: 38484975 PMCID: PMC11130669 DOI: 10.1016/j.ajcnut.2024.03.007] [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: 08/11/2023] [Revised: 02/12/2024] [Accepted: 03/07/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Higher 25-hydroxyvitamin D (25(OH)D) concentrations in serum has a positive association with pulmonary function. Investigating genome-wide interactions with 25(OH)D may reveal new biological insights into pulmonary function. OBJECTIVES We aimed to identify novel genetic variants associated with pulmonary function by accounting for 25(OH)D interactions. METHODS We included 211,264 participants from the observational United Kingdom Biobank study with pulmonary function tests (PFTs), genome-wide genotypes, and 25(OH)D concentrations from 4 ancestral backgrounds-European, African, East Asian, and South Asian. Among PFTs, we focused on forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) because both were previously associated with 25(OH)D. We performed genome-wide association study (GWAS) analyses that accounted for variant×25(OH)D interaction using the joint 2 degree-of-freedom (2df) method, stratified by participants' smoking history and ancestry, and meta-analyzed results. We evaluated interaction effects to determine how variant-PFT associations were modified by 25(OH)D concentrations and conducted pathway enrichment analysis to examine the biological relevance of our findings. RESULTS Our GWAS meta-analyses, accounting for interaction with 25(OH)D, revealed 30 genetic variants significantly associated with FEV1 or FVC (P2df <5.00×10-8) that were not previously reported for PFT-related traits. These novel variant signals were enriched in lung function-relevant pathways, including the p38 MAPK pathway. Among variants with genome-wide-significant 2df results, smoking-stratified meta-analyses identified 5 variants with 25(OH)D interactions that influenced FEV1 in both smoking groups (never smokers P1df interaction<2.65×10-4; ever smokers P1df interaction<1.71×10-5); rs3130553, rs2894186, rs79277477, and rs3130929 associations were only evident in never smokers, and the rs4678408 association was only found in ever smokers. CONCLUSION Genetic variant associations with lung function can be modified by 25(OH)D, and smoking history can further modify variant×25(OH)D interactions. These results expand the known genetic architecture of pulmonary function and add evidence that gene-environment interactions, including with 25(OH)D and smoking, influence lung function.
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Affiliation(s)
- Jungkyun Seo
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States
| | - Nathan C Gaddis
- RTI International, Research Triangle Park, NC, United States
| | - Bonnie K Patchen
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States
| | - Jiayi Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - R Graham Barr
- Divisions of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York, NY, United States
| | - George O'Connor
- Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Sina A Gharib
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States; Division of Pulmonary, Critical Care and Sleep Medicine, Computational Medicine Core, Center for Lung Biology, University of Washington, Seattle, WA, United States
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States
| | - Patricia A Cassano
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States; Division of Epidemiology, Department of Population Health Sciences, Weill Cornell Medicine, NY, United States
| | - Dana B Hancock
- RTI International, Research Triangle Park, NC, United States.
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Minikel EV, Painter JL, Dong CC, Nelson MR. Refining the impact of genetic evidence on clinical success. Nature 2024; 629:624-629. [PMID: 38632401 PMCID: PMC11096124 DOI: 10.1038/s41586-024-07316-0] [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: 07/05/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024]
Abstract
The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
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Affiliation(s)
| | - Jeffery L Painter
- JiveCast, Raleigh, NC, USA
- GlaxoSmithKline, Research Triangle Park, NC, USA
| | | | - Matthew R Nelson
- Deerfield Management Company LP, New York, NY, USA.
- Genscience LLC, New York, NY, USA.
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Kerns S, Owen KA, Schwalbe D, Grammer AC, Lipsky PE. Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci. Hum Genet 2024; 143:703-719. [PMID: 38609570 DOI: 10.1007/s00439-024-02672-3] [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: 10/05/2023] [Accepted: 03/24/2024] [Indexed: 04/14/2024]
Abstract
Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.
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Affiliation(s)
- Sophia Kerns
- AMPEL BioSolutions, LLC, Charlottesville, VA, 22902, USA.
- The RILITE Research Institute, Charlottesville, VA, 22902, USA.
| | - Katherine A Owen
- AMPEL BioSolutions, LLC, Charlottesville, VA, 22902, USA
- The RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Dana Schwalbe
- AMPEL BioSolutions, LLC, Charlottesville, VA, 22902, USA
- The RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Amrie C Grammer
- AMPEL BioSolutions, LLC, Charlottesville, VA, 22902, USA
- The RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Peter E Lipsky
- AMPEL BioSolutions, LLC, Charlottesville, VA, 22902, USA
- The RILITE Research Institute, Charlottesville, VA, 22902, USA
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Henkel C, Erikstrup C, Ostrowski SR, Pedersen OB, Troelsen A. Genetics may affect the risk of undergoing surgery for rhizarthrosis. J Orthop Res 2024; 42:1001-1008. [PMID: 38263870 DOI: 10.1002/jor.25753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/25/2023] [Accepted: 11/28/2023] [Indexed: 01/25/2024]
Abstract
Osteoarthritis is a prevalent and severe disease. Involvement of the trapeziometacarpal joint is common and can lead to both pain and disability. Genetics are known to affect the risk of osteoarthritis, but it remains unclear how genetics affect disease trajectories. In this study, we investigated whether the genetic associations of trapeziometacarpal osteoarthritis (rhizarthrosis) vary with the need for surgical treatment. The study was conducted as a case-control genome-wide association study using individuals from the Copenhagen Hospital Biobank pain and degenerative musculoskeletal disease study and the Danish Blood Donor Study (N = 208,342). We identified patients diagnosed with rhizarthrosis and grouped them by treatment status, resulting in two case groups: surgical (N = 1083) and nonsurgical (N = 1888). The case groups were tested against osteoarthritis-free controls in two genome-wide association studies. We then compared variants suggestive of association (p < 10-6) in either of these analyses directly between the treatment groups (surgical vs. nonsurgical rhizarthrosis). We identified 10 variants suggestive of association with either surgical (seven variants) or nonsurgical (three variants) rhizarthrosis. None of the variants reached nominal significance in the opposite treatment group (p ≥ 0.14), and all 10 variants were significantly different between the treatment groups at a false discovery rate of 5%. These results suggest possible differences in the genetic associations of rhizarthrosis depending on surgical treatment. Clinical significance: Uncovering genetic differences between clinically distinct patient groups can reveal biological determinants of disease trajectories.
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Affiliation(s)
- Cecilie Henkel
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital Køge, Køge, Denmark
| | - Anders Troelsen
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Cao X, Zhang S, Sha Q. A novel method for multiple phenotype association studies based on genotype and phenotype network. PLoS Genet 2024; 20:e1011245. [PMID: 38728360 PMCID: PMC11111089 DOI: 10.1371/journal.pgen.1011245] [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: 08/18/2023] [Revised: 05/22/2024] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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Moss CE, Johnston SA, Kimble JV, Clements M, Codd V, Hamby S, Goodall AH, Deshmukh S, Sudbery I, Coca D, Wilson HL, Kiss-Toth E. Aging-related defects in macrophage function are driven by MYC and USF1 transcriptional programs. Cell Rep 2024; 43:114073. [PMID: 38578825 DOI: 10.1016/j.celrep.2024.114073] [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: 10/27/2023] [Revised: 02/15/2024] [Accepted: 03/21/2024] [Indexed: 04/07/2024] Open
Abstract
Macrophages are central innate immune cells whose function declines with age. The molecular mechanisms underlying age-related changes remain poorly understood, particularly in human macrophages. We report a substantial reduction in phagocytosis, migration, and chemotaxis in human monocyte-derived macrophages (MDMs) from older (>50 years old) compared with younger (18-30 years old) donors, alongside downregulation of transcription factors MYC and USF1. In MDMs from young donors, knockdown of MYC or USF1 decreases phagocytosis and chemotaxis and alters the expression of associated genes, alongside adhesion and extracellular matrix remodeling. A concordant dysregulation of MYC and USF1 target genes is also seen in MDMs from older donors. Furthermore, older age and loss of either MYC or USF1 in MDMs leads to an increased cell size, altered morphology, and reduced actin content. Together, these results define MYC and USF1 as key drivers of MDM age-related functional decline and identify downstream targets to improve macrophage function in aging.
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Affiliation(s)
- Charlotte E Moss
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Healthy Lifespan Institute, University of Sheffield, Sheffield, UK
| | - Simon A Johnston
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Joshua V Kimble
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Healthy Lifespan Institute, University of Sheffield, Sheffield, UK
| | - Martha Clements
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; National Institute for Healthcare Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Stephen Hamby
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; National Institute for Healthcare Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Alison H Goodall
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; National Institute for Healthcare Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Sumeet Deshmukh
- School of Biosciences, University of Sheffield, Sheffield, UK
| | - Ian Sudbery
- School of Biosciences, University of Sheffield, Sheffield, UK
| | - Daniel Coca
- Healthy Lifespan Institute, University of Sheffield, Sheffield, UK; Department of Autonomic Control and Systems Engineering, University of Sheffield, Sheffield, UK
| | - Heather L Wilson
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Healthy Lifespan Institute, University of Sheffield, Sheffield, UK.
| | - Endre Kiss-Toth
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Healthy Lifespan Institute, University of Sheffield, Sheffield, UK; Biological Research Centre, Szeged, Hungary.
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69
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Bao C, Tan T, Wang S, Gao C, Lu C, Yang S, Diao Y, Jiang L, Jing D, Chen L, Lv H, Fang H. A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation. CELL REPORTS METHODS 2024; 4:100757. [PMID: 38631345 PMCID: PMC11046034 DOI: 10.1016/j.crmeth.2024.100757] [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: 03/27/2023] [Revised: 01/08/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chenxu Gao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, W12 0HS London, UK
| | - Siyue Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, Hunan 410079, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, BS1 3NY Bristol, UK
| | - Duohui Jing
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, OX3 7LD Oxford, UK.
| | - Haitao Lv
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Chinese Medicine, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Chinese Medicine Phenome Research Center, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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70
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Peruchet-Noray L, Sedlmeier AM, Dimou N, Baurecht H, Fervers B, Fontvieille E, Konzok J, Tsilidis KK, Christakoudi S, Jansana A, Cordova R, Bohmann P, Stein MJ, Weber A, Bézieau S, Brenner H, Chan AT, Cheng I, Figueiredo JC, Garcia-Etxebarria K, Moreno V, Newton CC, Schmit SL, Song M, Ulrich CM, Ferrari P, Viallon V, Carreras-Torres R, Gunter MJ, Freisling H. Tissue-specific genetic variation suggests distinct molecular pathways between body shape phenotypes and colorectal cancer. SCIENCE ADVANCES 2024; 10:eadj1987. [PMID: 38640244 PMCID: PMC11029802 DOI: 10.1126/sciadv.adj1987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/12/2024] [Indexed: 04/21/2024]
Abstract
It remains unknown whether adiposity subtypes are differentially associated with colorectal cancer (CRC). To move beyond single-trait anthropometric indicators, we derived four multi-trait body shape phenotypes reflecting adiposity subtypes from principal components analysis on body mass index, height, weight, waist-to-hip ratio, and waist and hip circumference. A generally obese (PC1) and a tall, centrally obese (PC3) body shape were both positively associated with CRC risk in observational analyses in 329,828 UK Biobank participants (3728 cases). In genome-wide association studies in 460,198 UK Biobank participants, we identified 3414 genetic variants across four body shapes and Mendelian randomization analyses confirmed positive associations of PC1 and PC3 with CRC risk (52,775 cases/45,940 controls from GECCO/CORECT/CCFR). Brain tissue-specific genetic instruments, mapped to PC1 through enrichment analysis, were responsible for the relationship between PC1 and CRC, while the relationship between PC3 and CRC was predominantly driven by adipose tissue-specific genetic instruments. This study suggests distinct putative causal pathways between adiposity subtypes and CRC.
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Affiliation(s)
- Laia Peruchet-Noray
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Anja M. Sedlmeier
- Center for Translational Oncology, University Hospital Regensburg, Regensburg, Germany
- Bavarian Cancer Research Center (BZKF), Regensburg, Germany
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Niki Dimou
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Béatrice Fervers
- Département Prévention Cancer Environnement, Centre Léon Bérard, Lyon, France
| | - Emma Fontvieille
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
| | - Julian Konzok
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Kostas K. Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Sofia Christakoudi
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK
- Department of Inflammation Biology, School of Immunology & Microbial Sciences, King’s College London, London, UK
| | - Anna Jansana
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
| | - Reynalda Cordova
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
- Department of Nutritional Sciences, University of Vienna, Vienna, Austria
| | - Patricia Bohmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Michael J. Stein
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Andrea Weber
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andrew T. Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Jane C. Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Koldo Garcia-Etxebarria
- Biodonostia, Gastrointestinal Genetics Group, San Sebastián, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Barcelona, Spain
| | - Victor Moreno
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | | | - Stephanie L. Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Epidemiology and Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Cornelia M. Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Pietro Ferrari
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
| | - Robert Carreras-Torres
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain
| | - Marc J. Gunter
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK
| | - Heinz Freisling
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 69366 Lyon CEDEX 07, France
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71
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Wang C, Chen C, Lei B, Qin S, Zhang Y, Li K, Zhang S, Liu Y. Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs. Genet Sel Evol 2024; 56:28. [PMID: 38594607 PMCID: PMC11003151 DOI: 10.1186/s12711-024-00897-4] [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: 08/31/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking. RESULTS Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model. CONCLUSIONS Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.
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Affiliation(s)
- Chao Wang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Choulin Chen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Bowen Lei
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Shenghua Qin
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
| | - Yuanyuan Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- School of Life Sciences, Henan University, Kaifeng, 475004, People's Republic of China
- Shenzhen Research Institute of Henan University, Shenzhen, 518000, People's Republic of China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China
| | - Song Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China.
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China.
| | - Yuwen Liu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China.
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, People's Republic of China.
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan, 528226, People's Republic of China.
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72
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Kentistou KA, Lim BEM, Kaisinger LR, Steinthorsdottir V, Sharp LN, Patel KA, Tragante V, Hawkes G, Gardner EJ, Olafsdottir T, Wood AR, Zhao Y, Thorleifsson G, Day FR, Ozanne SE, Hattersley AT, O'Rahilly S, Stefansson K, Ong KK, Beaumont RN, Perry JRB, Freathy RM. Rare variant associations with birth weight identify genes involved in adipose tissue regulation, placental function and insulin-like growth factor signalling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.03.24305248. [PMID: 38633783 PMCID: PMC11023655 DOI: 10.1101/2024.04.03.24305248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. Genome-wide association studies of birth weight have highlighted associated variants in more than 200 regions of the genome, but the causal genes are mostly unknown. Rare genetic variants with robust evidence of association are more likely to point to causal genes, but to date, only a few rare variants are known to influence birth weight. We aimed to identify genes that harbour rare variants that impact birth weight when carried by either the fetus or the mother, by analysing whole exome sequence data in UK Biobank participants. We annotated rare (minor allele frequency <0.1%) protein-truncating or high impact missense variants on whole exome sequence data in up to 234,675 participants with data on their own birth weight (fetal variants), and up to 181,883 mothers who reported the birth weight of their first child (maternal variants). Variants within each gene were collapsed to perform gene burden tests and for each associated gene, we compared the observed fetal and maternal effects. We identified 8 genes with evidence of rare fetal variant effects on birth weight, of which 2 also showed maternal effects. One additional gene showed evidence of maternal effects only. We observed 10/11 directionally concordant associations in an independent sample of up to 45,622 individuals (sign test P=0.01). Of the genes identified, IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (all fetal-acting) have known roles in adipose tissue regulation and rare variants in the latter two also showed associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG in both adipocyte differentiation and placental angiogenesis. NOS3, NRK, and ADAMTS8 (fetal and maternal-acting) have been implicated in both placental function and hypertension. Analysis of rare coding variants has identified regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, as well as further evidence for the role of insulin-like growth factors.
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Affiliation(s)
- Katherine A Kentistou
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Brandon E M Lim
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Luke N Sharp
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Kashyap A Patel
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | | | - Gareth Hawkes
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Felix R Day
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Susan E Ozanne
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., 102 Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Ken K Ong
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
- Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - John R B Perry
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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73
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Holmgren A, Akkouh I, O'Connell KS, Osete JR, Bjørnstad PM, Djurovic S, Hughes T. Bipolar patients display stoichiometric imbalance of gene expression in post-mortem brain samples. Mol Psychiatry 2024; 29:1128-1138. [PMID: 38351171 PMCID: PMC11176081 DOI: 10.1038/s41380-023-02398-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/19/2024]
Abstract
Bipolar disorder is a severe neuro-psychiatric condition where genome-wide association and sequencing studies have pointed to dysregulated gene expression as likely to be causal. We observed strong correlation in expression between GWAS-associated genes and hypothesised that healthy function depends on balance in the relative expression levels of the associated genes and that patients display stoichiometric imbalance. We developed a method for quantifying stoichiometric imbalance and used this to predict each sample's diagnosis probability in four cortical brain RNAseq datasets. The percentage of phenotypic variance on the liability-scale explained by these probabilities ranged from 10.0 to 17.4% (AUC: 69.4-76.4%) which is a multiple of the classification performance achieved using absolute expression levels or GWAS-based polygenic risk scores. Most patients display stoichiometric imbalance in three to ten genes, suggesting that dysregulation of only a small fraction of associated genes can trigger the disorder, with the identity of these genes varying between individuals.
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Affiliation(s)
- Asbjørn Holmgren
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Ibrahim Akkouh
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin Sean O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jordi Requena Osete
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Timothy Hughes
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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Wu X, Zhang H. Omics Approaches Unveiling the Biology of Human Atherosclerotic Plaques. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:482-498. [PMID: 38280419 PMCID: PMC10988765 DOI: 10.1016/j.ajpath.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 12/16/2023] [Accepted: 12/20/2023] [Indexed: 01/29/2024]
Abstract
Atherosclerosis is a chronic inflammatory disease of the arterial wall, characterized by the buildup of plaques with the accumulation and transformation of lipids, immune cells, vascular smooth muscle cells, and necrotic cell debris. Plaques with collagen-poor thin fibrous caps infiltrated by macrophages and lymphocytes are considered unstable because they are at the greatest risk of rupture and clinical events. However, the current histologic definition of plaque types may not fully capture the complex molecular nature of atherosclerotic plaque biology and the underlying mechanisms contributing to plaque progression, rupture, and erosion. The advances in omics technologies have changed the understanding of atherosclerosis plaque biology, offering new possibilities to improve risk prediction and discover novel therapeutic targets. Genomic studies have shed light on the genetic predisposition to atherosclerosis, and integrative genomic analyses expedite the translation of genomic discoveries. Transcriptomic, proteomic, metabolomic, and lipidomic studies have refined the understanding of the molecular signature of atherosclerotic plaques, aiding in data-driven hypothesis generation for mechanistic studies and offering new prospects for biomarker discovery. Furthermore, advancements in single-cell technologies and emerging spatial analysis techniques have unveiled the heterogeneity and plasticity of plaque cells. This review discusses key omics-based discoveries that have advanced the understanding of human atherosclerotic plaque biology, focusing on insights derived from omics profiling of human atherosclerotic vascular specimens.
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Affiliation(s)
- Xun Wu
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Hanrui Zhang
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York.
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Nataf S, Guillen M, Pays L. The Immunometabolic Gene N-Acetylglucosamine Kinase Is Uniquely Involved in the Heritability of Multiple Sclerosis Severity. Int J Mol Sci 2024; 25:3803. [PMID: 38612613 PMCID: PMC11011344 DOI: 10.3390/ijms25073803] [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: 02/23/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
The clinical severity of multiple sclerosis (MS), an autoimmune disorder of the central nervous system, is thought to be determined by environmental and genetic factors that have not yet been identified. In a recent genome-wide association study (GWAS), a single nucleotide polymorphism (SNP), rs10191329, has been associated with MS severity in two large independent cohorts of patients. Different approaches were followed by the authors to prioritize the genes that are transcriptionally regulated by such an SNP. It was concluded that the identified SNP regulates a group of proximal genes involved in brain resilience and cognitive abilities rather than immunity. Here, by conducting an alternative strategy for gene prioritization, we reached the opposite conclusion. According to our re-analysis, the main target of rs10191329 is N-Acetylglucosamine Kinase (NAGK), a metabolic gene recently shown to exert major immune functions via the regulation of the nucleotide-binding oligomerization domain-containing protein 2 (NOD2) pathway. To gain more insights into the immunometabolic functions of NAGK, we analyzed the currently known list of NAGK protein partners. We observed that NAGK integrates a dense network of human proteins that are involved in glucose metabolism and are highly expressed by classical monocytes. Our findings hold potentially major implications for the understanding of MS pathophysiology.
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Affiliation(s)
- Serge Nataf
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
- Lyon-Est School of Medicine, University Claude Bernard Lyon 1, 43 Bd du 11 Novembre 1918, F-69100 Villeurbanne, France
| | - Marine Guillen
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
| | - Laurent Pays
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
- Lyon-Est School of Medicine, University Claude Bernard Lyon 1, 43 Bd du 11 Novembre 1918, F-69100 Villeurbanne, France
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76
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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77
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Tang AS, Rankin KP, Cerono G, Miramontes S, Mills H, Roger J, Zeng B, Nelson C, Soman K, Woldemariam S, Li Y, Lee A, Bove R, Glymour M, Aghaeepour N, Oskotsky TT, Miller Z, Allen IE, Sanders SJ, Baranzini S, Sirota M. Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights. NATURE AGING 2024; 4:379-395. [PMID: 38383858 PMCID: PMC10950787 DOI: 10.1038/s43587-024-00573-8] [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] [Received: 03/21/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024]
Abstract
Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA, USA.
| | - Katherine P Rankin
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Gabriel Cerono
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Hunter Mills
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Billy Zeng
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Charlotte Nelson
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Karthik Soman
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Yaqiao Li
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Albert Lee
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Riley Bove
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Maria Glymour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Isabel E Allen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J Sanders
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Sergio Baranzini
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, CA, USA.
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78
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Holen B, Kutrolli G, Shadrin AA, Icick R, Hindley G, Rødevand L, O'Connell KS, Frei O, Parker N, Tesfaye M, Deak JD, Jahołkowski P, Dale AM, Djurovic S, Andreassen OA, Smeland OB. Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability. Drug Alcohol Depend 2024; 256:111058. [PMID: 38244365 DOI: 10.1016/j.drugalcdep.2023.111058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/09/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. METHODS We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. RESULTS We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. CONCLUSION We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.
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Affiliation(s)
- Børge Holen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
| | - Gleda Kutrolli
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Romain Icick
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; INSERM UMR-S1144, Université Paris Cité, F-75006, France
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Nadine Parker
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Markos Tesfaye
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Joseph D Deak
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Piotr Jahołkowski
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
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79
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Hukerikar N, Hingorani AD, Asselbergs FW, Finan C, Schmidt AF. Prioritising genetic findings for drug target identification and validation. Atherosclerosis 2024; 390:117462. [PMID: 38325120 DOI: 10.1016/j.atherosclerosis.2024.117462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
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Affiliation(s)
- Nikita Hukerikar
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
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80
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Ma Y, Zhou Y, Jiang D, Dai W, Li J, Deng C, Chen C, Zheng G, Zhang Y, Qiu F, Sun H, Xing S, Han H, Qu J, Wu N, Yao Y, Su J. Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19. Cell Prolif 2024; 57:e13558. [PMID: 37807299 PMCID: PMC10905359 DOI: 10.1111/cpr.13558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023] Open
Abstract
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Affiliation(s)
- Yunlong Ma
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yijun Zhou
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Dingping Jiang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Wei Dai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jingjing Li
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Cheng Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Gongwei Zheng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Yaru Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Fei Qiu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haojun Sun
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haijun Han
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Nan Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Key Laboratory of Big Data for Spinal Deformities, Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
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81
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Igoe A, Merjanah S, Harley ITW, Clark DH, Sun C, Kaufman KM, Harley JB, Kaelber DC, Scofield RH. Association between systemic lupus erythematosus and myasthenia gravis: A population-based National Study. Clin Immunol 2024; 260:109810. [PMID: 37949200 DOI: 10.1016/j.clim.2023.109810] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) and myasthenia gravis (MG) are autoimmune diseases. Previous case reports and case series suggest an association may exist between these diseases, as well as an increased risk of SLE after thymectomy for MG. We undertook this study to determine whether SLE and MG were associated in large cohorts. METHODS We searched the IBM Watson Health Explorys platform and the Department of Veterans Affairs Million Veteran Program (MVP) database for diagnoses of SLE and MG. In addition, we examined subjects enrolled in the Lupus Family Registry and Repository (LFRR) as well as controls for a diagnosis of MG. RESULTS Among 59,780,210 individuals captured in Explorys, there were 25,750 with MG and 65,370 with SLE. 370 subjects had both. Those with MG were >10 times more likely to have SLE than those without MG. Those with both diseases were more likely to be women, African American, and at a younger age than MG subjects without SLE. In addition, the MG patients who underwent thymectomy had an increased risk of SLE compared to MG patients who had not undergone thymectomy (OR 3.11, 95% CI: 2.12 to 4.55). Autoimmune diseases such as pernicious anemia and miscellaneous comorbidities such as chronic kidney disease were significantly more common in MG patients who developed SLE. In the MVP, SLE and MG were also significantly associated. Association of SLE and MG in a large SLE cohort with rigorous SLE classification confirmed the association of SLE with MG at a similar level. CONCLUSION While the number of patients with both MG and SLE is small, SLE and MG are strongly associated together in very large databases and a large SLE cohort.
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Affiliation(s)
- Ann Igoe
- OhioHealth Hospital, Rheumatology Department, Mansfield, OH 44903, USA
| | - Sali Merjanah
- Boston University Medical Center, Section of Rheumatology, Department of Medicine, Boston, MA 02118, USA
| | - Isaac T W Harley
- Division of Rheumatology, Departments of Medicine and Immunology/Microbiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Medicine Service, Rheumatology Section, Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO 80045, USA
| | - Dennis H Clark
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - Celi Sun
- Research Service, US Department of Veterans Affairs Medical Center, Oklahoma City, OK 73104, USA
| | - Kenneth M Kaufman
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA
| | - John B Harley
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA; Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, USA
| | - David C Kaelber
- Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine and The Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, OH 44109, USA
| | - R Hal Scofield
- Research Service, US Department of Veterans Affairs Medical Center, Oklahoma City, OK 73104, USA; Department of Medicine, University of Oklahoma Health Sciences Center, Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, and Medical/Research Service, and Medicine Service, US Department of Veterans Affairs Medical Center, Oklahoma City, OK 73104, USA.
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82
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Yu E, Larivière R, Thomas RA, Liu L, Senkevich K, Rahayel S, Trempe JF, Fon EA, Gan-Or Z. Machine learning nominates the inositol pathway and novel genes in Parkinson's disease. Brain 2024; 147:887-899. [PMID: 37804111 PMCID: PMC10907089 DOI: 10.1093/brain/awad345] [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: 07/04/2023] [Revised: 09/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.
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Affiliation(s)
- Eric Yu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Roxanne Larivière
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Rhalena A Thomas
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Lang Liu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Shady Rahayel
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec H4J 1C5, Canada
- Department of Medicine, University of Montreal, Montreal, Quebec H3C 3J7, Canada
| | - Jean-François Trempe
- Department of Pharmacology and Therapeutics and Centre de Recherche en Biologie Structurale, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Edward A Fon
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
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83
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Lappalainen T, Li YI, Ramachandran S, Gusev A. Genetic and molecular architecture of complex traits. Cell 2024; 187:1059-1075. [PMID: 38428388 DOI: 10.1016/j.cell.2024.01.023] [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: 10/03/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 03/03/2024]
Abstract
Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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Affiliation(s)
- Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sohini Ramachandran
- Ecology, Evolution and Organismal Biology, Center for Computational Molecular Biology, and the Data Science Institute, Brown University, Providence, RI 029129, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
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84
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Coombes BJ, Ovsyannikova IG, Schaid DJ, Warner ND, Poland GA, Kennedy RB. Polygenic Prediction of Cellular Immune Responses to Mumps Vaccine. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.23.24303277. [PMID: 38464113 PMCID: PMC10925362 DOI: 10.1101/2024.02.23.24303277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
In this report, we provide a follow-up analysis of a previously published genome-wide association study of host genetic variants associated with inter-individual variations in cellular immune responses to mumps vaccine. Here we report the results of a polygenic score (PGS) analysis showing how common variants can predict mumps vaccine response. We found higher PGS for IFNγ, IL-2, and TNFα were predictive of higher post-vaccine IFNγ (p-value = 2e-6), IL-2 (p = 2e-7), and TNFα (p = 0.004) levels, respectively. Control of immune responses after vaccination is complex and polygenic in nature. Our results suggest that the PGS-based approach enables better capture of the combined genetic effects that contribute to mumps vaccine-induced immunity, potentially offering a more comprehensive understanding than traditional single-variant GWAS. This approach will likely have broad utility in studying genetic control of immune responses to other vaccines and to infectious diseases.
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85
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Ji Q, Jiang X, Wang M, Xin Z, Zhang W, Qu J, Liu GH. Multimodal Omics Approaches to Aging and Age-Related Diseases. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:56-71. [PMID: 38605908 PMCID: PMC11003952 DOI: 10.1007/s43657-023-00125-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 04/13/2024]
Abstract
Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.
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Affiliation(s)
- Qianzhao Ji
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Xiaoyu Jiang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Minxian Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zijuan Xin
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
- Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053 China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
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86
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Schnitzler GR, Kang H, Fang S, Angom RS, Lee-Kim VS, Ma XR, Zhou R, Zeng T, Guo K, Taylor MS, Vellarikkal SK, Barry AE, Sias-Garcia O, Bloemendal A, Munson G, Guckelberger P, Nguyen TH, Bergman DT, Hinshaw S, Cheng N, Cleary B, Aragam K, Lander ES, Finucane HK, Mukhopadhyay D, Gupta RM, Engreitz JM. Convergence of coronary artery disease genes onto endothelial cell programs. Nature 2024; 626:799-807. [PMID: 38326615 PMCID: PMC10921916 DOI: 10.1038/s41586-024-07022-x] [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: 10/17/2022] [Accepted: 01/03/2024] [Indexed: 02/09/2024]
Abstract
Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.
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Affiliation(s)
- Gavin R Schnitzler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Helen Kang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Shi Fang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ramcharan S Angom
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Vivian S Lee-Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - X Rosa Ma
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Ronghao Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Tony Zeng
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Martin S Taylor
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Shamsudheen K Vellarikkal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aurelie E Barry
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Oscar Sias-Garcia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Glen Munson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Tung H Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Drew T Bergman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Stephen Hinshaw
- Department of Chemical and Systems Biology, ChEM-H, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nathan Cheng
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Faculty of Computing and Data Sciences, Departments of Biology and Biomedical Engineering, Biological Design Center, and Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Krishna Aragam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Debabrata Mukhopadhyay
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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87
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Raghavan A, Pirruccello JP, Ellinor PT, Lindsay ME. Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease. Arterioscler Thromb Vasc Biol 2024; 44:334-351. [PMID: 38095107 PMCID: PMC10843699 DOI: 10.1161/atvbaha.123.318771] [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: 06/26/2023] [Accepted: 11/21/2023] [Indexed: 01/04/2024]
Abstract
Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
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Affiliation(s)
- Avanthi Raghavan
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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88
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Wu J, Mao X, Liu X, Mao J, Yang X, Zhou X, Tianzhu L, Ji Y, Li Z, Xu H. Integrative single-cell analysis: dissecting CD8 + memory cell roles in LUAD and COVID-19 via eQTLs and Mendelian Randomization. Hereditas 2024; 161:7. [PMID: 38297377 PMCID: PMC10829297 DOI: 10.1186/s41065-023-00307-7] [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: 11/06/2023] [Accepted: 12/14/2023] [Indexed: 02/02/2024] Open
Abstract
Lung adenocarcinoma exhibits high incidence and mortality rates, presenting a significant health concern. Concurrently, the COVID-19 pandemic has emerged as a grave global public health challenge. Existing literature suggests that T cells, pivotal components of cellular immunity, are integral to both antiviral and antitumor responses. Yet, the nuanced alterations and consequent functions of T cells across diverse disease states have not been comprehensively elucidated. We gathered transcriptomic data of peripheral blood mononuclear cells from lung adenocarcinoma patients, COVID-19 patients, and healthy controls. We followed a standardized analytical approach for quality assurance, batch effect adjustments, and preliminary data processing. We discerned distinct T cell subsets and conducted differential gene expression analysis. Potential key genes and pathways were inferred from GO and Pathway enrichment analyses. Additionally, we implemented Mendelian randomization to probe the potential links between pivotal genes and lung adenocarcinoma susceptibility. Our findings underscored a notable reduction in mature CD8 + central memory T cells in both lung adenocarcinoma and COVID-19 cohorts relative to the control group. Notably, the downregulation of specific genes, such as TRGV9, could impede the immunological efficacy of CD8 + T cells. Comprehensive multi-omics assessment highlighted genetic aberrations in genes, including TRGV9, correlating with heightened lung adenocarcinoma risk. Through rigorous single-cell transcriptomic analyses, this investigation meticulously delineated variations in T cell subsets across different pathological states and extrapolated key regulatory genes via an integrated multi-omics approach, establishing a robust groundwork for future functional inquiries. This study furnishes valuable perspectives into the etiology of multifaceted diseases and augments the progression of precision medicine.
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Affiliation(s)
- Jintao Wu
- Nanchang University Jiangxi Medical College, Nanchang, Jiangxi, China
| | - Xiaocheng Mao
- Departments of Blood Transfusion, Institute of Transfusion, Jiangxi Key Laboratory of Transfusion, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Key Laboratory of Jiangxi Province for Transfusion Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiaohua Liu
- Departments of Blood Transfusion, Institute of Transfusion, Jiangxi Key Laboratory of Transfusion, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Key Laboratory of Jiangxi Province for Transfusion Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Junying Mao
- The First People's Hospital of Wenling, Affiliated Wenling Hospital, Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xianxin Yang
- The Fifth Affiliated Hospital of Jinan University, Heyuan, Guangdong, China
| | - Xiangwu Zhou
- The Fifth Affiliated Hospital of Shantou University, Shantou, Guangdong, China
| | - Lu Tianzhu
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China, 330006
- NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital of Nanchang University), Nanchang, Jiangxi, 330006, People's Republic of China
| | - Yulong Ji
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Jiangxi Province, China
| | - Zhao Li
- Department of Thoracic Surgery, Jiangxi Cancer Hospital, Nanchang, Jiangxi Province, China
| | - Huijuan Xu
- Department of Clinical Laboratory, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
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89
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Alamad B, Elliott K, Knight JC. Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity. CAMBRIDGE PRISMS. PRECISION MEDICINE 2024; 2:e3. [PMID: 38549844 PMCID: PMC10953767 DOI: 10.1017/pcm.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/15/2023] [Accepted: 12/18/2023] [Indexed: 04/26/2024]
Abstract
The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.
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Affiliation(s)
- Bana Alamad
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kate Elliott
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Julian C. Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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90
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Wang L, Lu Y, Li D, Zhou Y, Yu L, Mesa Eguiagaray I, Campbell H, Li X, Theodoratou E. The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Brief Bioinform 2024; 25:bbad527. [PMID: 38279645 PMCID: PMC10818097 DOI: 10.1093/bib/bbad527] [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: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
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Affiliation(s)
- Lijuan Wang
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Lu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Doudou Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajing Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ines Mesa Eguiagaray
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Cancer, Edinburgh, UK
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91
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Mahedy L, Anderson EL, Tilling K, Thornton ZA, Elmore AR, Szalma S, Simen A, Culp M, Zicha S, Harel BT, Davey Smith G, Smith EN, Paternoster L. Investigation of genetic determinants of cognitive change in later life. Transl Psychiatry 2024; 14:31. [PMID: 38238328 PMCID: PMC10796929 DOI: 10.1038/s41398-023-02726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 12/14/2023] [Accepted: 12/22/2023] [Indexed: 01/22/2024] Open
Abstract
Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.
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Affiliation(s)
- Liam Mahedy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Zak A Thornton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Andrew R Elmore
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Sándor Szalma
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Arthur Simen
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Meredith Culp
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Stephen Zicha
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Brian T Harel
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Erin N Smith
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK.
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92
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Yang T, Mills LJ, Hubbard AK, Cao R, Raduski A, Machiela MJ, Spector LG. Genetic analyses identify evidence for a causal relationship between Ewing sarcoma and hernias. HGG ADVANCES 2024; 5:100254. [PMID: 37919896 PMCID: PMC10692953 DOI: 10.1016/j.xhgg.2023.100254] [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: 08/01/2023] [Revised: 10/25/2023] [Accepted: 10/25/2023] [Indexed: 11/04/2023] Open
Abstract
Knowledge of Ewing sarcoma (EWS) risk factors is exceedingly limited; however, multiple small, independent studies have suggested a possible connection between hernia and EWS. By leveraging hernia summary statistics from the UK Biobank and a recently published genome-wide association study of EWS (733 EWS cases and 1,346 controls), we conducted a genetic investigation of the relationship of 5 hernia types (diaphragmatic, inguinal, umbilical, femoral, and ventral) and EWS. We discovered a positive causal relationship between inguinal hernia and EWS (OR 1.27, 95% confidence interval [CI] 1.01-1.59, and p = 0.041) through Mendelian randomization analysis. Further analyses suggested shared pathways through three genes: HMGA2, LOX, and FBXW7. Diaphragmatic hernia showed a stronger causal relationship with EWS among all of the hernia types (OR 2.26, 95% CI 1.30-3.95, p = 0.004), but no statistically significant local correlation pattern was observed. No evidence of a causal or genetic relationship was observed between EWS and the other three hernia types, including umbilical hernia, despite a previous report indicating an OR as high as 3.3. The finding of our genetic analysis provided additional support to the hypothesis that EWS and hernias may share a common origin.
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Affiliation(s)
- Tianzhong Yang
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lauren J Mills
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Aubrey K Hubbard
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA
| | - Rui Cao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Andrew Raduski
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA
| | - Logan G Spector
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA.
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93
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Choquet H, Jiang C, Yin J, Kim Y, Hoffmann TJ, Jorgenson E, Asgari MM. Multi-ancestry genome-wide meta-analysis identifies novel basal cell carcinoma loci and shared genetic effects with squamous cell carcinoma. Commun Biol 2024; 7:33. [PMID: 38182794 PMCID: PMC10770328 DOI: 10.1038/s42003-023-05753-7] [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: 02/02/2023] [Accepted: 12/28/2023] [Indexed: 01/07/2024] Open
Abstract
Basal cell carcinoma (BCC) is one of the most common malignancies worldwide, yet its genetic determinants are incompletely defined. We perform a European ancestry genome-wide association (GWA) meta-analysis and a Hispanic/Latino ancestry GWA meta-analysis and meta-analyze both in a multi-ancestry GWAS meta-analysis of BCC, totaling 50,531 BCC cases and 762,234 controls from four cohorts (GERA, Mass-General Brigham Biobank, UK Biobank, and 23andMe research cohort). Here we identify 122 BCC-associated loci, of which 36 were novel, and subsequently fine-mapped these associations. We also identify an association of the well-known pigment gene SLC45A2 as well as associations at RCC2 and CLPTM1L with BCC in Hispanic/Latinos. We examine these BCC loci for association with cutaneous squamous cell carcinoma (cSCC) in 16,407 SCC cases and 762,486 controls of European ancestry, and 33 SNPs show evidence of association. Our study findings provide important insights into the genetic basis of BCC and cSCC susceptibility.
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Affiliation(s)
- Hélène Choquet
- Kaiser Permanente Northern California (KPNC), Division of Research, Oakland, CA, USA.
| | - Chen Jiang
- Kaiser Permanente Northern California (KPNC), Division of Research, Oakland, CA, USA
| | - Jie Yin
- Kaiser Permanente Northern California (KPNC), Division of Research, Oakland, CA, USA
| | - Yuhree Kim
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Thomas J Hoffmann
- Institute for Human Genetics, University of California, San Francisco (UCSF), San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | | | - Maryam M Asgari
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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94
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Duffy Á, Petrazzini BO, Stein D, Park JK, Forrest IS, Gibson K, Vy HM, Chen R, Márquez-Luna C, Mort M, Verbanck M, Schlessinger A, Itan Y, Cooper DN, Rocheleau G, Jordan DM, Do R. Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications. Nat Genet 2024; 56:51-59. [PMID: 38172303 DOI: 10.1038/s41588-023-01609-2] [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: 12/11/2022] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.
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Affiliation(s)
- Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ben Omega Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David Stein
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Kyle Gibson
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ha My Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Robert Chen
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Carla Márquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | | | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
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95
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Homilius M, Zhu W, Eddy SS, Thompson PC, Zheng H, Warren CN, Evans CG, Kim DD, Xuan LL, Nsubuga C, Strecker Z, Pettit CJ, Cho J, Howie MN, Thaler AS, Wilson E, Wollison B, Smith C, Nascimben JB, Nascimben DN, Lunati GM, Folks HC, Cupelo M, Sridaran S, Rheinstein C, McClennen T, Goto S, Truslow JG, Vandenwijngaert S, MacRae CA, Deo RC. Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases. Nat Genet 2024; 56:37-50. [PMID: 38049662 PMCID: PMC10786715 DOI: 10.1038/s41588-023-01600-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/27/2023] [Indexed: 12/06/2023]
Abstract
Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.
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Affiliation(s)
- Max Homilius
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Wandi Zhu
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Samuel S Eddy
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Patrick C Thompson
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Huahua Zheng
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Caleb N Warren
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Chiara G Evans
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David D Kim
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Lucius L Xuan
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Cissy Nsubuga
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Zachary Strecker
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Christopher J Pettit
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jungwoo Cho
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mikayla N Howie
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra S Thaler
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Evan Wilson
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Bruce Wollison
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Courtney Smith
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Julia B Nascimben
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Diana N Nascimben
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Gabriella M Lunati
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Hassan C Folks
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Cupelo
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Suriya Sridaran
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Carolyn Rheinstein
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Taylor McClennen
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shinichi Goto
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - James G Truslow
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sara Vandenwijngaert
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Calum A MacRae
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Rahul C Deo
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Atman Health Inc, Needham, MA, USA.
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96
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Tian Z, Jiang Z, Hu S, Shi L. Immune factors have a complex causal regulation on pulmonary fibrosis: Insights from a two-sample Mendelian randomization analysis. Medicine (Baltimore) 2023; 102:e36781. [PMID: 38206731 PMCID: PMC10754615 DOI: 10.1097/md.0000000000036781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
Pulmonary fibrosis is a chronic, progressive lung disease characterized by excessive scarring of lung tissue, and its pathophysiological mechanisms have not been fully elucidated. Immune cells play a key role in many diseases, and this study aims to explore the causal link between immune cell characteristics and pulmonary fibrosis using Mendelian randomization. Utilizing the public GWAS database Open GWAS, this study collected whole-genome association study datasets of peripheral blood immune phenotypes and summary data of GWAS related to pulmonary fibrosis. Through Mendelian randomization (MR) analysis, we identified single nucleotide polymorphisms (SNPs) significantly associated with immune traits as instrumental variables. After pleiotropy and heterogeneity tests, causal effects were assessed using methods such as inverse-variance weighted (IVW), weighted median, and MR-Egger. Comprehensive MR analysis indicated a significant causal relationship between various immune cell types, including regulatory T cells (Tregs), natural killer (NK) cells, and specific monocyte subgroups, with the risk of pulmonary fibrosis. Specifically, phenotypes such as Activated & resting Treg %CD4+, CCR2-positive monocytes, and CD16-CD56 positive NK cells were associated with a reduced risk of pulmonary fibrosis. In contrast, CD8 + T cell subgroups were associated with an increased risk. This study provides evidence of a causal relationship between immune cell characteristics and pulmonary fibrosis, highlighting the protective role of regulatory T cells and specific NK cell subgroups, as well as the potential harm of CD8 + T cell subgroups. These findings offer new insights into the immunoregulatory mechanisms of pulmonary fibrosis and the development of novel therapeutic strategies.
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Affiliation(s)
- Zhiyu Tian
- Changchun University of Traditional Chinese Medicine, Changchun, Jilin Province, China
| | - Zhanliang Jiang
- Changchun University of Traditional Chinese Medicine, Changchun, Jilin Province, China
| | - Shaodan Hu
- The Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin Province, China
| | - Li Shi
- The Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin Province, China
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97
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Sigala RE, Lagou V, Shmeliov A, Atito S, Kouchaki S, Awais M, Prokopenko I, Mahdi A, Demirkan A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes (Basel) 2023; 15:34. [PMID: 38254924 PMCID: PMC10815885 DOI: 10.3390/genes15010034] [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: 11/16/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
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Affiliation(s)
- Rafaella E. Sigala
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Vasiliki Lagou
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Aleksey Shmeliov
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Sara Atito
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Samaneh Kouchaki
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Muhammad Awais
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
| | - Adam Mahdi
- Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, Oxfordshire, UK;
| | - Ayse Demirkan
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
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98
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Chen YH, van Zon S, Adams A, Schmidt-Arras D, Laurence ADJ, Uhlig HH. The Human GP130 Cytokine Receptor and Its Expression-an Atlas and Functional Taxonomy of Genetic Variants. J Clin Immunol 2023; 44:30. [PMID: 38133879 PMCID: PMC10746620 DOI: 10.1007/s10875-023-01603-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/30/2023] [Indexed: 12/23/2023]
Abstract
Genetic variants in IL6ST encoding the shared cytokine receptor for the IL-6 cytokine family GP130 have been associated with a diverse number of clinical phenotypes and disorders. We provide a molecular classification for 59 reported rare IL6ST pathogenic or likely pathogenic variants and additional polymorphisms. Based on loss- or gain-of-function, cytokine selectivity, mono- and biallelic associations, and variable cellular mosaicism, we grade six classes of IL6ST variants and explore the potential for additional variants. We classify variants according to the American College of Medical Genetics and Genomics criteria. Loss-of-function variants with (i) biallelic complete loss of GP130 function that presents with extended Stüve-Wiedemann Syndrome; (ii) autosomal recessive hyper-IgE syndrome (HIES) caused by biallelic; and (iii) autosomal dominant HIES caused by monoallelic IL6ST variants both causing selective IL-6 and IL-11 cytokine loss-of-function defects; (iv) a biallelic cytokine-specific variant that exclusively impairs IL-11 signaling, associated with craniosynostosis and tooth abnormalities; (v) somatic monoallelic mosaic constitutively active gain-of-function variants in hepatocytes that present with inflammatory hepatocellular adenoma; and (vi) mosaic constitutively active gain-of-function variants in hematopoietic and non-hematopoietic cells that are associated with an immune dysregulation syndrome. In addition to Mendelian IL6ST coding variants, there are common non-coding cis-acting variants that modify gene expression, which are associated with an increased risk of complex immune-mediated disorders and trans-acting variants that affect GP130 protein function. Our taxonomy highlights IL6ST as a gene with particularly strong functional and phenotypic diversity due to the combinatorial biology of the IL-6 cytokine family and predicts additional genotype-phenotype associations.
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Affiliation(s)
- Yin-Huai Chen
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Sarah van Zon
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Alex Adams
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Dirk Schmidt-Arras
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | | | - Holm H Uhlig
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- Biomedical Research Centre, University of Oxford, Oxford, UK.
- Department of Paediatrics, University of Oxford, Oxford, UK.
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99
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Chang Y, Zhou Y, Zhou J, Li W, Cao J, Jing Y, Zhang S, Shen Y, Lin Q, Fan X, Yang H, Dong X, Zhang S, Yi X, Shuai L, Shi L, Liu Z, Yang J, Ma X, Hao J, Chen K, Li MJ, Wang F, Huang D. Unraveling the causal genes and transcriptomic determinants of human telomere length. Nat Commun 2023; 14:8517. [PMID: 38129441 PMCID: PMC10739845 DOI: 10.1038/s41467-023-44355-z] [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: 01/18/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
Telomere length (TL) shortening is a pivotal indicator of biological aging and is associated with many human diseases. The genetic determinates of human TL have been widely investigated, however, most existing studies were conducted based on adult tissues which are heavily influenced by lifetime exposure. Based on the analyses of terminal restriction fragment (TRF) length of telomere, individual genotypes, and gene expressions on 166 healthy placental tissues, we systematically interrogate TL-modulated genes and their potential functions. We discover that the TL in the placenta is comparatively longer than in other adult tissues, but exhibiting an intra-tissue homogeneity. Trans-ancestral TL genome-wide association studies (GWASs) on 644,553 individuals identify 20 newly discovered genetic associations and provide increased polygenic determination of human TL. Next, we integrate the powerful TL GWAS with placental expression quantitative trait locus (eQTL) mapping to prioritize 23 likely causal genes, among which 4 are functionally validated, including MMUT, RRM1, KIAA1429, and YWHAZ. Finally, modeling transcriptomic signatures and TRF-based TL improve the prediction performance of human TL. This study deepens our understanding of causal genes and transcriptomic determinants of human TL, promoting the mechanistic research on fine-grained TL regulation.
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Affiliation(s)
- Ying Chang
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Junrui Zhou
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Clinical Laboratory, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Wen Li
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Jiasong Cao
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Yaqing Jing
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Shan Zhang
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yongmei Shen
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Qimei Lin
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hongxi Yang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaobao Dong
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Shijie Zhang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xianfu Yi
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Ling Shuai
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Central Hospital of Gynecology Obstetrics/Tianjin Key Laboratory of Human Development and Reproductive Regulation, Nankai University, Tianjin, China
| | - Lei Shi
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zhe Liu
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jie Yang
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xin Ma
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Jihui Hao
- Department of Pancreatic Cancer, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
| | - Feng Wang
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Tianjin Medical University School of Stomatology, Tianjin Medical University, Tianjin, China.
- Department of Geriatrics, Tianjin Medical University General Hospital; Tianjin Geriatrics Institute, Tianjin, China.
| | - Dandan Huang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Wuxi School of Medicine, Jiangnan University, Wuxi, China.
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100
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Lv JH, Hou AJ, Zhang SH, Dong JJ, Kuang HX, Yang L, Jiang H. WGCNA combined with machine learning to find potential biomarkers of liver cancer. Medicine (Baltimore) 2023; 102:e36536. [PMID: 38115320 PMCID: PMC10727608 DOI: 10.1097/md.0000000000036536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023] Open
Abstract
The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.
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Affiliation(s)
- Jia-Hao Lv
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - A-Jiao Hou
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - Shi-Hao Zhang
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - Jiao-Jiao Dong
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - Hai-Xue Kuang
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - Liu Yang
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
| | - Hai Jiang
- Key Laboratory of Basic and Application Research of Beiyao, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin, China
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