1
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Olafsdottir TA, Thorleifsson G, Lopez de Lapuente Portilla A, Jonsson S, Stefansdottir L, Niroula A, Jonasdottir A, Eggertsson HP, Halldorsson GH, Thorlacius GE, Arnthorsson AO, Bjornsdottir US, Asselbergs FW, Bentlage AEH, Eyjolfsson GI, Gudmundsdottir S, Gunnarsdottir K, Halldorsson BV, Holm H, Ludviksson BR, Melsted P, Norddahl GL, Olafsson I, Saevarsdottir S, Sigurdardottir O, Sigurdsson A, Temming R, Önundarson PT, Thorsteinsdottir U, Vidarsson G, Sulem P, Gudbjartsson DF, Jonsdottir I, Nilsson B, Stefansson K. Sequence variants influencing the regulation of serum IgG subclass levels. Nat Commun 2024; 15:8054. [PMID: 39277589 PMCID: PMC11401918 DOI: 10.1038/s41467-024-52470-8] [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: 12/01/2023] [Accepted: 09/10/2024] [Indexed: 09/17/2024] Open
Abstract
Immunoglobulin G (IgG) is the main isotype of antibody in human blood. IgG consists of four subclasses (IgG1 to IgG4), encoded by separate constant region genes within the Ig heavy chain locus (IGH). Here, we report a genome-wide association study on blood IgG subclass levels. Across 4334 adults and 4571 individuals under 18 years, we discover ten new and identify four known variants at five loci influencing IgG subclass levels. These variants also affect the risk of asthma, autoimmune diseases, and blood traits. Seven variants map to the IGH locus, three to the Fcγ receptor (FCGR) locus, and two to the human leukocyte antigen (HLA) region, affecting the levels of all IgG subclasses. The most significant associations are observed between the G1m (f), G2m(n) and G3m(b*) allotypes, and IgG1, IgG2 and IgG3, respectively. Additionally, we describe selective associations with IgG4 at 16p11.2 (ITGAX) and 17q21.1 (IKZF3, ZPBP2, GSDMB, ORMDL3). Interestingly, the latter coincides with a highly pleiotropic signal where the allele associated with lower IgG4 levels protects against childhood asthma but predisposes to inflammatory bowel disease. Our results provide insight into the regulation of antibody-mediated immunity that can potentially be useful in the development of antibody based therapeutics.
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Affiliation(s)
- Thorunn A Olafsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
| | | | - Aitzkoa Lopez de Lapuente Portilla
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Stefan Jonsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Alvotech, Sæmundargötu 15-19, Reykjavík, Iceland
| | | | - Abhishek Niroula
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Broad Institute, Cambridge, MA, USA
| | | | | | - Gisli H Halldorsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Unnur S Bjornsdottir
- Department of Respiratory Medicine and Sleep, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Folkert W Asselbergs
- Institute of Health Informatics, 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, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Arthur E H Bentlage
- Immunoglobulin Research laboratory, Sanquin Research, Amsterdam, The Netherlands
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands
| | | | | | | | - Bjarni V Halldorsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Bjorn R Ludviksson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology, Landsspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | - Pall Melsted
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landsspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Olof Sigurdardottir
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Clinical Biochemistry, Akureyri Hospital, Akureyri, Iceland
| | | | - Robin Temming
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands
- The Laboratory in Mjodd, Reykjavik, Iceland
| | - Pall T Önundarson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Clinical Biochemistry, Akureyri Hospital, Akureyri, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Gestur Vidarsson
- Immunoglobulin Research laboratory, Sanquin Research, Amsterdam, The Netherlands
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands
| | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Björn Nilsson
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden.
- Lund Stem Cell Center, Lund University, Lund, Sweden.
- Broad Institute, Cambridge, MA, USA.
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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2
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Tervi A, Ramste M, Abner E, Cheng P, Lane JM, Maher M, Valliere J, Lammi V, Strausz S, Riikonen J, Nguyen T, Martyn GE, Sheth MU, Xia F, Docampo ML, Gu W, Esko T, Saxena R, Pirinen M, Palotie A, Ripatti S, Sinnott-Armstrong N, Daly M, Engreitz JM, Rabinovitch M, Heckman CA, Quertermous T, Jones SE, Ollila HM. Genetic and functional analysis of Raynaud's syndrome implicates loci in vasculature and immunity. CELL GENOMICS 2024; 4:100630. [PMID: 39142284 DOI: 10.1016/j.xgen.2024.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/25/2024] [Accepted: 07/14/2024] [Indexed: 08/16/2024]
Abstract
Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.
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Affiliation(s)
- Anniina Tervi
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Markus Ramste
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Erik Abner
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Paul Cheng
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jacqueline M Lane
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew Maher
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jesse Valliere
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vilma Lammi
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Satu Strausz
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juha Riikonen
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Trieu Nguyen
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gabriella E Martyn
- 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
| | - Maya U Sheth
- 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
| | - Fan Xia
- 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
| | - Mauro Lago Docampo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Children's Health Betty Irene Moore Children's Heart Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wenduo Gu
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland; Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nasa Sinnott-Armstrong
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutch, Seattle, WA, USA
| | - Mark Daly
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jesse M Engreitz
- 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; The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Marlene Rabinovitch
- Stanford Children's Health Betty Irene Moore Children's Heart Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Samuel E Jones
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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3
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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 PMCID: PMC11365900 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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4
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Kamp M, Pain O, Lewis CM, Ramsay M. Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations. Genome Med 2024; 16:106. [PMID: 39187845 PMCID: PMC11346299 DOI: 10.1186/s13073-024-01377-6] [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: 12/19/2023] [Accepted: 08/12/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Cardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations. METHODS We used summary statistics from a genome-wide association meta-analysis (n = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability. RESULTS Our African-specific PGS displayed significant but variable within- and cross- trait prediction (max.R2 = 6.8%, p = 1.86 × 10-173). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p = 2.18 × 10-21) and low-density lipoprotein (logOR = - 0.141, SE = 0.022, p = 1.30 × 10-20); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10-4); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p = 2.22 × 10-5), hip circumference (logOR = 0.122, SE = 0.029, p = 2.28 × 10-5), waist circumference (logOR = 0.013, SE = 0.098, p = 8.13 × 10-4) and weight (logOR = 0.103, SE = 0.029, p = 4.89 × 10-5). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R2 increase = 2.6%, p = 4.45 × 10-12; hypertension: R2 increase = 2.6%, p = 2.37 × 10-13; obesity: R2 increase = 5.5%, 1.33 × 10-34). CONCLUSIONS In African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.
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Affiliation(s)
- Michelle Kamp
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, The University of the Witwatersrand, Johannesburg, South Africa.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, Psychology & Neuroscience, London, UK.
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, Psychology & Neuroscience, London, UK
- Department of Medical & Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Michèle Ramsay
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, The University of the Witwatersrand, Johannesburg, South Africa.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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5
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Alves I, Giemza J, Blum MGB, Bernhardsson C, Chatel S, Karakachoff M, Saint Pierre A, Herzig AF, Olaso R, Monteil M, Gallien V, Cabot E, Svensson E, Bacq D, Baron E, Berthelier C, Besse C, Blanché H, Bocher O, Boland A, Bonnaud S, Charpentier E, Dandine-Roulland C, Férec C, Fruchet C, Lecointe S, Le Floch E, Ludwig TE, Marenne G, Meyer V, Quellery E, Racimo F, Rouault K, Sandron F, Schott JJ, Velo-Suarez L, Violleau J, Willerslev E, Coativy Y, Jézéquel M, Le Bris D, Nicolas C, Pailler Y, Goldberg M, Zins M, Le Marec H, Jakobsson M, Darlu P, Génin E, Deleuze JF, Redon R, Dina C. Human genetic structure in Northwest France provides new insights into West European historical demography. Nat Commun 2024; 15:6710. [PMID: 39112481 PMCID: PMC11306750 DOI: 10.1038/s41467-024-51087-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
The demographical history of France remains largely understudied despite its central role toward understanding modern population structure across Western Europe. Here, by exploring publicly available Europe-wide genotype datasets together with the genomes of 3234 present-day and six newly sequenced medieval individuals from Northern France, we found extensive fine-scale population structure across Brittany and the downstream Loire basin and increased population differentiation between the northern and southern sides of the river Loire, associated with higher proportions of steppe vs. Neolithic-related ancestry. We also found increased allele sharing between individuals from Western Brittany and those associated with the Bell Beaker complex. Our results emphasise the need for investigating local populations to better understand the distribution of rare (putatively deleterious) variants across space and the importance of common genetic legacy in understanding the sharing of disease-related alleles between Brittany and people from western Britain and Ireland.
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Affiliation(s)
- Isabel Alves
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
- Université de Strasbourg, CNRS, GMGM, Strasbourg, France
| | - Joanna Giemza
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Michael G B Blum
- TIMC-IMAG, UMR 5525 CNRS, Univ. Grenoble Alpes, Grenoble, France
| | - Carolina Bernhardsson
- Department of Organismal Biology, Human Evolution, Uppsala University, Uppsala, Sweden
| | - Stéphanie Chatel
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Matilde Karakachoff
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
- Nantes Université, CHU Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERMCIC 1413, Nantes, France
| | | | | | - Robert Olaso
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Martial Monteil
- Nantes Université, CNRS, Ministère de la Culture, CReAAH, LARA, Nantes, France
| | - Véronique Gallien
- INRAP - Institut national de recherches archéologiques préventives, Paris, France
- CEPAM UMR7264 - Culture et Environnements, Préhistoire, Antiquité, Moyen-Age, Nice, France
| | - Elodie Cabot
- INRAP - Institut national de recherches archéologiques préventives, Paris, France
- Anthropologie Bio-Culturelle, Droit, Ethique et Santé, Faculté de Médecine Site Nord, Marseille, France
| | - Emma Svensson
- Department of Organismal Biology, Human Evolution, Uppsala University, Uppsala, Sweden
| | - Delphine Bacq
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Estelle Baron
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Charlotte Berthelier
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Céline Besse
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | | | - Ozvan Bocher
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Stéphanie Bonnaud
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Eric Charpentier
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Claire Dandine-Roulland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Claude Férec
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- CHRU Brest, Brest, France
| | - Christine Fruchet
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Simon Lecointe
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Edith Le Floch
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Thomas E Ludwig
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- CHRU Brest, Brest, France
| | | | - Vincent Meyer
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
| | - Elisabeth Quellery
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Fernando Racimo
- Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Karen Rouault
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- CHRU Brest, Brest, France
| | - Florian Sandron
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
| | - Jean-Jacques Schott
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | | | - Jade Violleau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Eske Willerslev
- Lundbeck GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yves Coativy
- Centre de Recherche Bretonne et Celtique, UR 4451, Université de Bretagne Occidentale, Brest, France
| | - Mael Jézéquel
- Centre de Recherche Bretonne et Celtique, UR 4451, Université de Bretagne Occidentale, Brest, France
| | - Daniel Le Bris
- Centre de Recherche Bretonne et Celtique, UR 4451, Université de Bretagne Occidentale, Brest, France
| | - Clément Nicolas
- CNRS UMR 8215 Trajectoires, Université Paris 1 Panthéon-Sorbonne, Centre Malher, 9 rue Malher, Paris, France
| | - Yvan Pailler
- CPJ ArMeRIE UBO, UMR 6554 LETG, CNRS, Université de Brest, Université de Nantes, Université de Rennes 2, Institut Universitaire Européen de la Mer, Plouzané, France
| | - Marcel Goldberg
- Université Paris Cité, "Population-based Cohorts Unit", INSERM, Paris Saclay University, UVSQ, Paris, France
| | - Marie Zins
- Université Paris Cité, "Population-based Cohorts Unit", INSERM, Paris Saclay University, UVSQ, Paris, France
| | - Hervé Le Marec
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
| | - Mattias Jakobsson
- Department of Organismal Biology, Human Evolution, Uppsala University, Uppsala, Sweden
| | - Pierre Darlu
- UMR 7206 Eco-anthropologie, Musée de l'Homme, MNHN, CNRS, Université de Paris Cité, Paris, France
| | - Emmanuelle Génin
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- CHRU Brest, Brest, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
- Labex GenMed, Evry, France
- Fondation Jean Dausset, CEPH, Paris, France
| | - Richard Redon
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France.
| | - Christian Dina
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France.
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6
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Gretzinger J, Schmitt F, Mötsch A, Carlhoff S, Lamnidis TC, Huang Y, Ringbauer H, Knipper C, Francken M, Mandt F, Hansen L, Freund C, Posth C, Rathmann H, Harvati K, Wieland G, Granehäll L, Maixner F, Zink A, Schier W, Krausse D, Krause J, Schiffels S. Evidence for dynastic succession among early Celtic elites in Central Europe. Nat Hum Behav 2024; 8:1467-1480. [PMID: 38831077 PMCID: PMC11343710 DOI: 10.1038/s41562-024-01888-7] [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: 08/10/2023] [Accepted: 04/15/2024] [Indexed: 06/05/2024]
Abstract
The early Iron Age (800 to 450 BCE) in France, Germany and Switzerland, known as the 'West-Hallstattkreis', stands out as featuring the earliest evidence for supra-regional organization north of the Alps. Often referred to as 'early Celtic', suggesting tentative connections to later cultural phenomena, its societal and population structure remain enigmatic. Here we present genomic and isotope data from 31 individuals from this context in southern Germany, dating between 616 and 200 BCE. We identify multiple biologically related groups spanning three elite burials as far as 100 km apart, supported by trans-regional individual mobility inferred from isotope data. These include a close biological relationship between two of the richest burial mounds of the Hallstatt culture. Bayesian modelling points to an avuncular relationship between the two individuals, which may suggest a practice of matrilineal dynastic succession in early Celtic elites. We show that their ancestry is shared on a broad geographic scale from Iberia throughout Central-Eastern Europe, undergoing a decline after the late Iron Age (450 BCE to ~50 CE).
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Affiliation(s)
- Joscha Gretzinger
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Felicitas Schmitt
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany
| | - Angela Mötsch
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Selina Carlhoff
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | | | - Yilei Huang
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Harald Ringbauer
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Corina Knipper
- Curt Engelhorn Zentrum Archäometrie gGmbH, Mannheim, Germany
| | - Michael Francken
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany
| | - Franziska Mandt
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany
| | - Leif Hansen
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany
| | - Cäcilia Freund
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Cosimo Posth
- Institute for Archaeological Sciences, Department of Geosciences, Eberhard Karls University of Tübingen, Tübingen, Germany
- Senckenberg Centre for Human Evolution and Palaeoenvironment, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Hannes Rathmann
- Institute for Archaeological Sciences, Department of Geosciences, Eberhard Karls University of Tübingen, Tübingen, Germany
- Senckenberg Centre for Human Evolution and Palaeoenvironment, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Katerina Harvati
- Institute for Archaeological Sciences, Department of Geosciences, Eberhard Karls University of Tübingen, Tübingen, Germany
- Senckenberg Centre for Human Evolution and Palaeoenvironment, Eberhard Karls University of Tübingen, Tübingen, Germany
- DFG Center for Advanced Studies in the Humanities 'Words, Bones, Genes, Tools: Tracking Linguistic, Cultural and Biological Trajectories of the Human Past', Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Günther Wieland
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany
| | - Lena Granehäll
- Institute for Mummy Studies, EURAC Research, Bolzano, Italy
| | - Frank Maixner
- Institute for Mummy Studies, EURAC Research, Bolzano, Italy
| | - Albert Zink
- Institute for Mummy Studies, EURAC Research, Bolzano, Italy
| | - Wolfram Schier
- Institut für Prähistorische Archäologie, Freie Universität Berlin, Berlin, Germany
| | - Dirk Krausse
- Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart, Esslingen, Germany.
| | - Johannes Krause
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
| | - Stephan Schiffels
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
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7
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Lin J, Cao DY. Associations Between Temporomandibular Disorders and Brain Imaging-Derived Phenotypes. Int Dent J 2024; 74:784-793. [PMID: 38365503 PMCID: PMC11287171 DOI: 10.1016/j.identj.2024.01.008] [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: 10/08/2023] [Revised: 12/29/2023] [Accepted: 01/14/2024] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVE Temporomandibular disorders (TMD) affect the temporomandibular joint and associated structures. Despite its prevalence and impact on quality of life, the underlying mechanisms of TMD remain unclear. Magnetic resonance imaging studies suggest brain abnormalities in patients with TMD. However, these lines of evidence are essentially observational and cannot infer a causal relationship. This study employs Mendelian randomisation (MR) to probe causal relationships between TMD and brain changes. METHODS Genome-wide association study (GWAS) summary statistics for TMD were collected, along with brain imaging-derived phenotypes (IDPs). Instrumental variables were selected from the GWAS summary statistics and used in bidirectional 2-sample MR analyses. The inverse-variance weighted analysis was chosen as the primary method. In addition, false discovery rate (FDR) correction of P value was used. RESULTS Eleven IDPs related to brain imaging alterations showed significant causal associations with TMD (P-FDR < .05), validated through sensitivity analysis. In forward MR, the mean thickness of left caudal middle frontal gyrus (OR, 0.76; 95% CI, 0.67-0.87; P-FDR = 1.15 × 10-2) and the volume of right superior frontal gyrus (OR, 1.24; 95% CI, 1.10-1.39; P-FDR = 2.26 × 10-2) exerted significant causal effects on TMD. In the reverse MR analysis, TMD exerted a significant causal effect on 9 IDPs, including the mean thickness of the left medial orbitofrontal cortex (β = -0.10; 95% CI, -0.13 to -0.08; P-FDR = 2.06 × 10-11), the volume of the left magnocellular nucleus (β = -0.15; 95% CI, -0.22 to -0.09; P-FDR = 3.26 × 10-4), the mean intensity of the right inferior-lateral ventricle (β = -0.09; 95% CI, -0.14 to -0.04; P-FDR = 2.23 × 10-2), the volume of grey matter in the anterior division of the left superior temporal gyrus (β = 0.09; 95% CI, 0.04-0.14; P-FDR = 1.69 × 10-2), and so forth. CONCLUSIONS This study provides genetic evidence supporting the bidirectional causal associations between TMD and brain IDPs, shedding light on potential neurobiological mechanisms underlying TMD development and its relationship with brain structure.
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Affiliation(s)
- Jun Lin
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, Testing Center of Stomatology, Xi'an Jiaotong University College of Stomatology, Xi'an, Shaanxi, China
| | - Dong-Yuan Cao
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, Testing Center of Stomatology, Xi'an Jiaotong University College of Stomatology, Xi'an, Shaanxi, China.
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8
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Mendes M, Chen DZ, Engchuan W, Leal TP, Thiruvahindrapuram B, Trost B, Howe JL, Pellecchia G, Nalpathamkalam T, Alexandrova R, Salazar NB, McKee EA, Alfaro NR, Lai MC, Bandres-Ciga S, Roshandel D, Bradley CA, Anagnostou E, Sun L, Scherer SW. Chromosome X-Wide Common Variant Association Study (XWAS) in Autism Spectrum Disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.18.24310640. [PMID: 39108515 PMCID: PMC11302709 DOI: 10.1101/2024.07.18.24310640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/12/2024]
Abstract
Autism Spectrum Disorder (ASD) displays a notable male bias in prevalence. Research into rare (<0.1) genetic variants on the X chromosome has implicated over 20 genes in ASD pathogenesis, such as MECP2, DDX3X, and DMD. The "female protective effect" in ASD suggests that females may require a higher genetic burden to manifest similar symptoms as males, yet the mechanisms remain unclear. Despite technological advances in genomics, the complexity of the biological nature of sex chromosomes leave them underrepresented in genome-wide studies. Here, we conducted an X chromosome-wide association study (XWAS) using whole-genome sequencing data from 6,873 individuals with ASD (82% males) across Autism Speaks MSSNG, Simons Simplex Cohort SSC, and Simons Foundation Powering Autism Research SPARK, alongside 8,981 population controls (43% males). We analyzed 418,652 X-chromosome variants, identifying 59 associated with ASD (p-values 7.9×10-6 to 1.51×10-5), surpassing Bonferroni-corrected thresholds. Key findings include significant regions on chrXp22.2 (lead SNP=rs12687599, p=3.57×10-7) harboring ASB9/ASB11, and another encompassing DDX53/PTCHD1-AS long non-coding RNA (lead SNP=rs5926125, p=9.47×10-6). When mapping genes within 10kb of the 59 most significantly associated SNPs, 91 genes were found, 17 of which yielded association with ASD (GRPR, AP1S2, DDX53, HDAC8, PCDH19, PTCHD1, PCDH11X, PTCHD1-AS, DMD, SYAP1, CNKSR2, GLRA2, OFD1, CDKL5, GPRASP2, NXF5, SH3KBP1). FGF13 emerged as a novel X-linked ASD candidate gene, highlighted by sex-specific differences in minor allele frequencies. These results reveal significant new insights into X chromosome biology in ASD, confirming and nominating genes and pathways for further investigation.
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Affiliation(s)
- Marla Mendes
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Desmond Zeya Chen
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, ON, M5G 1X6, Canada
| | - Worrawat Engchuan
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Thiago Peixoto Leal
- Lerner Research Institute, Genomic Medicine, Cleveland Clinic, Cleveland, OH, 44106, USA
| | - Bhooma Thiruvahindrapuram
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Brett Trost
- Molecular Medicine Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Jennifer L. Howe
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Giovanna Pellecchia
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Thomas Nalpathamkalam
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Roumiana Alexandrova
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Nelson Bautista Salazar
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Ethan Alexander McKee
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Natalia Rivera Alfaro
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5G 2C1, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, M5G 1E8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Sara Bandres-Ciga
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Delnaz Roshandel
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Clarrisa A. Bradley
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, M4G 1R8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, ON, M5G 1X6, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 3E3, Canada
| | - Stephen W. Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
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9
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [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: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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10
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Zhang J, Weissenkampen JD, Kember RL, Grove J, Børglum AD, Robinson EB, Brodkin ES, Almasy L, Bucan M, Sebro R. Phenotypic and ancestry-related assortative mating in autism. Mol Autism 2024; 15:27. [PMID: 38877467 PMCID: PMC11177537 DOI: 10.1186/s13229-024-00605-5] [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: 12/08/2023] [Accepted: 05/30/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. METHODS To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). RESULTS We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5). LIMITATIONS We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. CONCLUSIONS This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.
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Affiliation(s)
- Jing Zhang
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Grove
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Edward S Brodkin
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
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11
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Zhao L, Tang Y, Tu Y, Cao J. Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy. J Headache Pain 2024; 25:93. [PMID: 38840235 DOI: 10.1186/s10194-024-01801-7] [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: 05/06/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR). METHODS We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis. RESULTS The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect. DISCUSSION This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.
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Affiliation(s)
- Lei Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Yilan Tang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Yiheng Tu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Jin Cao
- School of Life Sciences, Beijing University of Chinese Medicine, 11 North third Ring Road East, Beijing, China.
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12
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Rossen J, Shi H, Strober BJ, Zhang MJ, Kanai M, McCaw ZR, Liang L, Weissbrod O, Price AL. MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.13.24307291. [PMID: 38798542 PMCID: PMC11118590 DOI: 10.1101/2024.05.13.24307291] [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
Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.
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13
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Beck JJ, Slunecka JL, Johnson BN, Van Asselt AJ, Finnicum CT, Ageton C, Krie A, Nickles H, Cowan K, Maxwell J, Boomsma DI, de Geus E, Ehli EA, Hottenga JJ. Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation. Cancers (Basel) 2024; 16:1578. [PMID: 38672660 PMCID: PMC11048743 DOI: 10.3390/cancers16081578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/30/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.
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Affiliation(s)
- Jeffrey J. Beck
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | - John L. Slunecka
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | - Brandon N. Johnson
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | - Austin J. Van Asselt
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | - Casey T. Finnicum
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | | | - Amy Krie
- Avera Cancer Institute, Sioux Falls, SD 57105, USA
| | | | - Kenneth Cowan
- Fred and Pamela Buffet Cancer Center and Eppley Institute for Research in Cancer at University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Jessica Maxwell
- Fred and Pamela Buffet Cancer Center and Eppley Institute for Research in Cancer at University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Dorret I. Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands (J.-J.H.)
| | - Eco de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands (J.-J.H.)
| | - Erik A. Ehli
- Avera Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD 57105, USA (E.A.E.)
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands (J.-J.H.)
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14
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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15
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Grinde KE, Browning BL, Reiner AP, Thornton TA, Browning SR. Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587682. [PMID: 38617337 PMCID: PMC11014513 DOI: 10.1101/2024.04.02.587682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/24/2024]
Abstract
Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.
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Affiliation(s)
- Kelsey E. Grinde
- Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, 55105, USA
| | - Brian L. Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, 98195, USA
| | - Alexander P. Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Timothy A. Thornton
- Regeneron Genetics Center, Tarrytown, New York, 10591, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
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16
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Liu X, Yuan J, Wang X, Tang M, Meng X, Zhang L, Wang S, Zhang H. Association between rheumatoid arthritis and autoimmune thyroid disease: evidence from complementary genetic methods. Endocrine 2024; 84:171-178. [PMID: 37884826 DOI: 10.1007/s12020-023-03571-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVES To assess the causal association of Rheumatoid Arthritis (RA) with Autoimmune thyroid disease (AITD). METHOD Complementary genetic approaches, including genetic correlation, Mendelian randomization (MR) and colocalization analysis, were conducted to assess the potential causal association between RA and AITD using summary statistics from large-scale genome-wide association studies (GWASs). Various sensitivity analyses had been conducted to assess the robustness and the consistency of the findings. RESULTS The linkage disequilibrium score regression revealed a shared genetic structure between RA and AITD, with the significant genetic correlation between RA and autoimmune hyperthyroidism and autoimmune hypothyroidism estimated to be 0.3945 (P = 2.83 × 10-6) and 0.2771 (P = 1.04 × 10-6) respectively. The results of MR analysis showed that RA had a positive causal relationship with autoimmune hypothyroidism and autoimmune hyperthyroidism. The odds ratio (OR) were 1.29 (95% CI, 1.17-1.42; P = 1.08 × 10-7) and 1.47 (95% CI, 1.25-1.72; P = 1.85 × 10-6), respectively. In reverse MR analysis, autoimmune hypothyroidism had a positive causal relationship with RA, OR was 1.51 (95% CI, 1.37-1.66; P = 1.10 × 10-16); autoimmune hyperthyroidism had no causal relationship with RA relationship (P = 0.22). Similar results were found using different MR methods. In addition, colocalization analysis suggested that shared causal variants existed between RA and AITD. CONCLUSIONS Our study suggested a potentially causal effect of genetically predicted RA on autoimmune hyperthyroidism and a bidirectional causal relationship between RA and autoimmune hypothyroidism was also observed with complementary genetic approaches, which supports the importance and necessity of thyroid function screening and monitoring in RA patient management in clinical practice.
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Affiliation(s)
- Xue Liu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Jie Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China
| | - Xinhui Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Mulin Tang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Xue Meng
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Li Zhang
- Department of Vascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
| | - Haiqing Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, 250021, China.
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, 250021, China.
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17
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Shin D, Kim CN, Ross J, Hennick KM, Wu SR, Paranjape N, Leonard R, Wang JC, Keefe MG, Pavlovic BJ, Donohue KC, Moreau C, Wigdor EM, Larson HH, Allen DE, Cadwell CR, Bhaduri A, Popova G, Bearden CE, Pollen AA, Jacquemont S, Sanders SJ, Haussler D, Wiita AP, Frost NA, Sohal VS, Nowakowski TJ. Thalamocortical organoids enable in vitro modeling of 22q11.2 microdeletion associated with neuropsychiatric disorders. Cell Stem Cell 2024; 31:421-432.e8. [PMID: 38382530 PMCID: PMC10939828 DOI: 10.1016/j.stem.2024.01.010] [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: 08/19/2022] [Revised: 12/14/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024]
Abstract
Thalamic dysfunction has been implicated in multiple psychiatric disorders. We sought to study the mechanisms by which abnormalities emerge in the context of the 22q11.2 microdeletion, which confers significant genetic risk for psychiatric disorders. We investigated early stages of human thalamus development using human pluripotent stem cell-derived organoids and show that the 22q11.2 microdeletion underlies widespread transcriptional dysregulation associated with psychiatric disorders in thalamic neurons and glia, including elevated expression of FOXP2. Using an organoid co-culture model, we demonstrate that the 22q11.2 microdeletion mediates an overgrowth of thalamic axons in a FOXP2-dependent manner. Finally, we identify ROBO2 as a candidate molecular mediator of the effects of FOXP2 overexpression on thalamic axon overgrowth. Together, our study suggests that early steps in thalamic development are dysregulated in a model of genetic risk for schizophrenia and contribute to neural phenotypes in 22q11.2 deletion syndrome.
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Affiliation(s)
- David Shin
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chang N Kim
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jayden Ross
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kelsey M Hennick
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sih-Rong Wu
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Neha Paranjape
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94107, USA
| | - Rachel Leonard
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jerrick C Wang
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Matthew G Keefe
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Bryan J Pavlovic
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kevin C Donohue
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Clara Moreau
- Sainte Justine Research Center, University of Montréal, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada; Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Emilie M Wigdor
- Institute of Developmental and Regenerative Medicine, University of Oxford, Headington, Oxford OX3 7TY, UK
| | - H Hanh Larson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Denise E Allen
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Cathryn R Cadwell
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Galina Popova
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Carrie E Bearden
- Integrative Center for Neurogenetics, Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA
| | - Alex A Pollen
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute of Developmental and Regenerative Medicine, University of Oxford, Headington, Oxford OX3 7TY, UK
| | - David Haussler
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA; Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Arun P Wiita
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94107, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158
| | - Nicholas A Frost
- Department of Neurology, University of Utah, Salt Lake City, UT 84108, USA
| | - Vikaas S Sohal
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Tomasz J Nowakowski
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94158, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA.
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18
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Green RE, Sudre CH, Warren‐Gash C, Butt J, Waterboer T, Hughes AD, Schott JM, Richards M, Chaturvedi N, Williams DM. Common infections and neuroimaging markers of dementia in three UK cohort studies. Alzheimers Dement 2024; 20:2128-2142. [PMID: 38248636 PMCID: PMC10984486 DOI: 10.1002/alz.13613] [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: 07/20/2023] [Revised: 10/13/2023] [Accepted: 11/25/2023] [Indexed: 01/23/2024]
Abstract
INTRODUCTION We aimed to investigate associations between common infections and neuroimaging markers of dementia risk (brain volume, hippocampal volume, white matter lesions) across three population-based studies. METHODS We tested associations between serology measures (pathogen serostatus, cumulative burden, continuous antibody responses) and outcomes using linear regression, including adjustments for total intracranial volume and scanner/clinic information (basic model), age, sex, ethnicity, education, socioeconomic position, alcohol, body mass index, and smoking (fully adjusted model). Interactions between serology measures and apolipoprotein E (APOE) genotype were tested. Findings were meta-analyzed across cohorts (Nmain = 2632; NAPOE-interaction = 1810). RESULTS Seropositivity to John Cunningham virus associated with smaller brain volumes in basic models (β = -3.89 mL [-5.81, -1.97], Padjusted < 0.05); these were largely attenuated in fully adjusted models (β = -1.59 mL [-3.55, 0.36], P = 0.11). No other relationships were robust to multiple testing corrections and sensitivity analyses, but several suggestive associations were observed. DISCUSSION We did not find clear evidence for relationships between common infections and markers of dementia risk. Some suggestive findings warrant testing for replication.
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Affiliation(s)
- Rebecca E. Green
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
| | - Carole H. Sudre
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringCentre for Medical Image Computing (CMIC)University College London (UCL)LondonUK
| | - Charlotte Warren‐Gash
- Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
| | - Julia Butt
- Division of Infections and Cancer EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Tim Waterboer
- Division of Infections and Cancer EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Alun D. Hughes
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
| | | | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
| | - Dylan M. Williams
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
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19
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Reay WR, Kiltschewskij DJ, Di Biase MA, Gerring ZF, Kundu K, Surendran P, Greco LA, Clarke ED, Collins CE, Mondul AM, Albanes D, Cairns MJ. Genetic influences on circulating retinol and its relationship to human health. Nat Commun 2024; 15:1490. [PMID: 38374065 PMCID: PMC10876955 DOI: 10.1038/s41467-024-45779-x] [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: 08/23/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024] Open
Abstract
Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia.
| | - Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zachary F Gerring
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kousik Kundu
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Laura A Greco
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Erin D Clarke
- School of Health Sciences, The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Clare E Collins
- School of Health Sciences, The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
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20
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Minnai F, Biscarini F, Esposito M, Dragani TA, Bujanda L, Rahmouni S, Alarcón-Riquelme ME, Bernardo D, Carnero-Montoro E, Buti M, Zeberg H, Asselta R, Romero-Gómez M, Fernandez-Cadenas I, Fallerini C, Zguro K, Croci S, Baldassarri M, Bruttini M, Furini S, Renieri A, Colombo F. A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death. Sci Rep 2024; 14:3000. [PMID: 38321133 PMCID: PMC10847137 DOI: 10.1038/s41598-024-53310-x] [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: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024] Open
Abstract
The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.
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Affiliation(s)
- Francesca Minnai
- Institute of Biomedical Technologies, National Research Council, Via F.lli Cervi, 93, 20054, Segrate, MI, Italy
- Department of Medical Biotechnology and Translational Medicine (BioMeTra), Università degli Studi di Milano, Milan, Italy
| | - Filippo Biscarini
- Institute of Agricultural Biology and Biotechnology, National Research Council, Milan, Italy
| | - Martina Esposito
- Institute of Biomedical Technologies, National Research Council, Via F.lli Cervi, 93, 20054, Segrate, MI, Italy
| | | | - Luis Bujanda
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Biodonostia Health Research Institute, Universidad del País Vasco (UPV/EHU), San Sebastián, Spain
| | | | - Marta E Alarcón-Riquelme
- GENYO, University of Granada, Andalusian Regional Government, Granada, Spain
- Institute for Environmental Medicine, Karolinska Institute, Solna, Sweden
| | - David Bernardo
- Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Valladolid, Spain
| | - Elena Carnero-Montoro
- GENYO, University of Granada, Andalusian Regional Government, Granada, Spain
- University of Granada, Granada, Spain
| | - Maria Buti
- Vall D'Hebron Institut de Recerca, Barcelona, Spain
| | - Hugo Zeberg
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy
- IRCCS Humanitas Research Hospital, Rozzano, MI, Italy
| | - Manuel Romero-Gómez
- Digestive Diseases Unit and CiberehdVirgen del Rocío University HospitalInstitute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain
| | - Israel Fernandez-Cadenas
- Stroke Pharmacogenomics and Genetics Group, Sant Pau Hospital Research Institute, Barcelona, Spain
| | - Chiara Fallerini
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
| | - Kristina Zguro
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
| | - Susanna Croci
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
| | - Margherita Baldassarri
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
| | - Mirella Bruttini
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100, Siena, Italy
| | - Simone Furini
- Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi", Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Alessandra Renieri
- Medical Genetics, University of Siena, 53100, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, 53100, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100, Siena, Italy
| | - Francesca Colombo
- Institute of Biomedical Technologies, National Research Council, Via F.lli Cervi, 93, 20054, Segrate, MI, Italy.
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21
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Yao S, Harder A, Darki F, Chang YW, Li A, Nikouei K, Volpe G, Lundström JN, Zeng J, Wray N, Lu Y, Sullivan PF, Leffler JH. Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301478. [PMID: 38410450 PMCID: PMC10896415 DOI: 10.1101/2024.01.18.24301478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.
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Affiliation(s)
- Shuyang Yao
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fahimeh Darki
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Ang Li
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Kasra Nikouei
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Johan N Lundström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Naomi Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Jens Hjerling Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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22
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Cohen NM, Lifshitz A, Jaschek R, Rinott E, Balicer R, Shlush LI, Barbash GI, Tanay A. Longitudinal machine learning uncouples healthy aging factors from chronic disease risks. NATURE AGING 2024; 4:129-144. [PMID: 38062254 DOI: 10.1038/s43587-023-00536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/02/2023] [Indexed: 01/21/2024]
Abstract
To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.
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Affiliation(s)
- Netta Mendelson Cohen
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rami Jaschek
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ehud Rinott
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ran Balicer
- Clalit Research Institute, Ramat Gan, Israel
| | - Liran I Shlush
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gabriel I Barbash
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Amos Tanay
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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23
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Smolen C, Jensen M, Dyer L, Pizzo L, Tyryshkina A, Banerjee D, Rohan L, Huber E, El Khattabi L, Prontera P, Caberg JH, Van Dijck A, Schwartz C, Faivre L, Callier P, Mosca-Boidron AL, Lefebvre M, Pope K, Snell P, Lockhart PJ, Castiglia L, Galesi O, Avola E, Mattina T, Fichera M, Luana Mandarà GM, Bruccheri MG, Pichon O, Le Caignec C, Stoeva R, Cuinat S, Mercier S, Bénéteau C, Blesson S, Nordsletten A, Martin-Coignard D, Sistermans E, Kooy RF, Amor DJ, Romano C, Isidor B, Juusola J, Girirajan S. Assortative mating and parental genetic relatedness contribute to the pathogenicity of variably expressive variants. Am J Hum Genet 2023; 110:2015-2028. [PMID: 37979581 PMCID: PMC10716518 DOI: 10.1016/j.ajhg.2023.10.015] [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: 05/17/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/20/2023] Open
Abstract
We examined more than 97,000 families from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents contributing to neurodevelopmental disease risk in children. We identified within- and cross-disorder correlations between six phenotypes in parents and children, such as obsessive-compulsive disorder (R = 0.32-0.38, p < 10-126). We also found that measures of sub-clinical autism features in parents are associated with several autism severity measures in children, including biparental mean Social Responsiveness Scale scores and proband Repetitive Behaviors Scale scores (regression coefficient = 0.14, p = 3.38 × 10-4). We further describe patterns of phenotypic similarity between spouses, where spouses show correlations for six neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R = 0.24-0.68, p < 0.001) and a cross-disorder correlation between anxiety and bipolar disorder (R = 0.09-0.22, p < 10-92). Using a simulated population, we also found that assortative mating can lead to increases in disease liability over generations and the appearance of "genetic anticipation" in families carrying rare variants. We identified several families in a neurodevelopmental disease cohort where the proband inherited multiple rare variants in disease-associated genes from each of their affected parents. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse relationship with variant pathogenicity and propose that parental relatedness modulates disease risk by increasing genome-wide homozygosity in children (R = 0.05-0.26, p < 0.05). Our results highlight the utility of assessing parent phenotypes and genotypes toward predicting features in children who carry rare variably expressive variants and implicate assortative mating as a risk factor for increased disease severity in these families.
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Affiliation(s)
- Corrine Smolen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Matthew Jensen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | | | - Lucilla Pizzo
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Tyryshkina
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
| | - Deepro Banerjee
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Laura Rohan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Emily Huber
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Laila El Khattabi
- Assistance Publique-Hôpitaux de Paris, Department of Medical Genetics, Armand Trousseau and Pitié-Salpêtrière Hospitals, Paris, France
| | - Paolo Prontera
- Medical Genetics Unit, Hospital "Santa Maria della Misericordia", Perugia, Italy
| | - Jean-Hubert Caberg
- Centre Hospitalier Universitaire de Liège. Domaine Universitaire du Sart Tilman, Liège, Belgium
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | | | - Laurence Faivre
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d'Enfants, CHU Dijon, Dijon, France; GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Patrick Callier
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d'Enfants, CHU Dijon, Dijon, France; GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | | | - Mathilde Lefebvre
- GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Kate Pope
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Penny Snell
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Paul J Lockhart
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Bruce Lefroy Center, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Lucia Castiglia
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Ornella Galesi
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Emanuela Avola
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Teresa Mattina
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Marco Fichera
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy; Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | | | - Maria Grazia Bruccheri
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Olivier Pichon
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | - Cedric Le Caignec
- CHU Toulouse, Department of Medical Genetics, Toulouse, France; ToNIC, Toulouse Neuro Imaging, Center, Inserm, UPS, Université de Toulouse, Toulouse, France
| | - Radka Stoeva
- Service de Cytogenetique, CHU de Le Mans, Le Mans, France
| | | | - Sandra Mercier
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | | | - Sophie Blesson
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | | | | | - Erik Sistermans
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, the Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - David J Amor
- Bruce Lefroy Center, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Corrado Romano
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy; Medical Genetics, ASP Ragusa, Ragusa, Italy
| | | | | | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA; Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, USA; Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
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Tallman S, Sungo MDD, Saranga S, Beleza S. Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations. Nat Commun 2023; 14:7967. [PMID: 38042927 PMCID: PMC10693643 DOI: 10.1038/s41467-023-43717-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
As the continent of origin for our species, Africa harbours the highest levels of diversity anywhere on Earth. However, many regions of Africa remain under-sampled genetically. Here we present 350 whole genomes from Angola and Mozambique belonging to ten Bantu ethnolinguistic groups, enabling the construction of a reference variation catalogue including 2.9 million novel SNPs. We investigate the emergence of Bantu speaker population structure, admixture involving migrations across sub-Saharan Africa and model the demographic histories of Angolan and Mozambican Bantu speakers. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion. Moreover, we generate reference panels that better represents the diversity of African populations involved in the trans-Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians. We anticipate that our collection of genomes will form the foundation for future African genomic healthcare initiatives.
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Affiliation(s)
- Sam Tallman
- University of Leicester, Department of Genetics & Genome Biology, University Road, Leicester, LE1 7RH, UK
- Genomics England, 1 Canada Square, London, E14 5AB, UK
| | | | - Sílvio Saranga
- Universidade Pedagógica, Avenida Eduardo Mondlane, CP 2107, Maputo, Mozambique
| | - Sandra Beleza
- University of Leicester, Department of Genetics & Genome Biology, University Road, Leicester, LE1 7RH, UK.
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25
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Fournier R, Tsangalidou Z, Reich D, Palamara PF. Haplotype-based inference of recent effective population size in modern and ancient DNA samples. Nat Commun 2023; 14:7945. [PMID: 38040695 PMCID: PMC10692198 DOI: 10.1038/s41467-023-43522-6] [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: 07/03/2022] [Accepted: 11/10/2023] [Indexed: 12/03/2023] Open
Abstract
Individuals sharing recent ancestors are likely to co-inherit large identical-by-descent (IBD) genomic regions. The distribution of these IBD segments in a population may be used to reconstruct past demographic events such as effective population size variation, but accurate IBD detection is difficult in ancient DNA data and in underrepresented populations with limited reference data. In this work, we introduce an accurate method for inferring effective population size variation during the past ~2000 years in both modern and ancient DNA data, called HapNe. HapNe infers recent population size fluctuations using either IBD sharing (HapNe-IBD) or linkage disequilibrium (HapNe-LD), which does not require phasing and can be computed in low coverage data, including data sets with heterogeneous sampling times. HapNe shows improved accuracy in a range of simulated demographic scenarios compared to currently available methods for IBD-based and LD-based inference of recent effective population size, while requiring fewer computational resources. We apply HapNe to several modern populations from the 1,000 Genomes Project, the UK Biobank, the Allen Ancient DNA Resource, and recently published samples from Iron Age Britain, detecting multiple instances of recent effective population size variation across these groups.
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Affiliation(s)
| | | | - David Reich
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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26
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SHELTON BA, SAWINSKI D, PETER I, MACLENNAN PA, PELLETIER NF, NADKARNI G, JULIAN B, SAAG M, FATIMA H, CRANE H, LEE W, MOORE RD, CHRISTOPOULOS K, JACOBSON JM, ERON JJ, KUMAR V, LOCKE JE. African American/Black race, apolipoprotein L1 , and serum creatinine among persons with HIV. AIDS 2023; 37:2349-2357. [PMID: 37650767 PMCID: PMC10843645 DOI: 10.1097/qad.0000000000003708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
OBJECTIVE Accurate estimation of kidney function is critical among persons with HIV (PWH) to avoid under-dosing of antiretroviral therapies and ensure timely referral for kidney transplantation. Existing estimation equations for kidney function include race, the appropriateness of which has been debated. Given advancements in understanding of race and the necessity of accuracy in kidney function estimation, this study aimed to examine whether race, or genetic factors, improved prediction of serum creatinine among PWH. DESIGN This cross-sectional study utilized data from the Center for AIDS Research Network of Integrated Clinical Systems cohort (2008-2018). The outcome was baseline serum creatinine. METHODS Ordinary least squares regression was used to examine whether inclusion of race or genetic factors [ apolipoprotein-L1 ( APOL1 ) variants and genetic African ancestry] improved serum creatinine prediction. A reduction in root mean squared error (RMSE) greater than 2% was a clinically relevant improvement in predictive ability. RESULTS There were 4183 PWH included. Among PWH whose serum creatinine was less than 1.7 mg/dl, race was significantly associated with serum creatinine ( β = 0.06, SE = 0.01, P < 0.001) but did not improve predictive ability. African ancestry and APOL1 variants similarly failed to improve predictive ability. Whereas, when serum creatinine was at least 1.7 mg/dl, inclusion of race reduced the RMSE by 2.1%, indicating improvement in predictive ability. APOL1 variants further improved predictive ability by reducing the RMSE by 2.9%. CONCLUSION These data suggest that, among PWH, inclusion of race or genetic factors may only be warranted at higher serum creatinine levels. Work eliminating existing healthcare disparities while preserving the utility of estimating equations is needed.
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Affiliation(s)
- Brittany A. SHELTON
- University of Alabama at Birmingham School of Medicine
- The University of Tennessee, Knoxville Department of Public Health
| | | | | | | | | | | | - Bruce JULIAN
- University of Alabama at Birmingham School of Medicine
| | - Michael SAAG
- University of Alabama at Birmingham School of Medicine
| | - Huma FATIMA
- University of Alabama at Birmingham School of Medicine
| | | | | | | | | | | | - Joseph J. ERON
- University of North Carolina at Chapel Hill School of Medicine
| | - Vineeta KUMAR
- University of Alabama at Birmingham School of Medicine
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27
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Goovaerts S, Hoskens H, Eller RJ, Herrick N, Musolf AM, Justice CM, Yuan M, Naqvi S, Lee MK, Vandermeulen D, Szabo-Rogers HL, Romitti PA, Boyadjiev SA, Marazita ML, Shaffer JR, Shriver MD, Wysocka J, Walsh S, Weinberg SM, Claes P. Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape. Nat Commun 2023; 14:7436. [PMID: 37973980 PMCID: PMC10654897 DOI: 10.1038/s41467-023-43237-8] [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: 12/07/2022] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.
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Affiliation(s)
- Seppe Goovaerts
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
| | - Hanne Hoskens
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Ryan J Eller
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Noah Herrick
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Anthony M Musolf
- Statistical Genetics Section, Computational and Statistical Genomics Branch, NHGRI, NIH, MD, Baltimore, USA
| | - Cristina M Justice
- Genometrics Section, Computational and Statistical Genomics Branch, Division of Intramural Research, NHGRI, NIH, Baltimore, MD, USA
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meng Yuan
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Myoung Keun Lee
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dirk Vandermeulen
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Heather L Szabo-Rogers
- Department of Anatomy, Physiology and Pharmacology, University of Saskatchewan, Saskatchewan, Canada
| | - Paul A Romitti
- Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA, USA
| | - Simeon A Boyadjiev
- Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - John R Shaffer
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.
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28
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Ottensmann L, Tabassum R, Ruotsalainen SE, Gerl MJ, Klose C, Widén E, Simons K, Ripatti S, Pirinen M. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun 2023; 14:6934. [PMID: 37907536 PMCID: PMC10618167 DOI: 10.1038/s41467-023-42532-8] [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: 01/11/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023] Open
Abstract
The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.
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Affiliation(s)
- Linda Ottensmann
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Rubina Tabassum
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | | | - Elisabeth Widén
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Samuli Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
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29
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Luo P, Ying J, Li J, Yang Z, Sun X, Ye D, Liu C, Wang J, Mao Y. Air Pollution and Allergic Rhinitis: Findings from a Prospective Cohort Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15835-15845. [PMID: 37831419 DOI: 10.1021/acs.est.3c04527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
To investigate the association of long-term exposure to ambient air pollution with the risk of allergic rhinitis (AR), we performed a longitudinal analysis of 379,488 participants (47.4% women) free of AR at baseline in the UK Biobank. The annual average concentrations of PM2.5, PMcoarse, PM10, NO2, and NOx were estimated by land use regression models. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). A weighted polygenic risk score was constructed. During a median follow-up period of 12.5 years, 3095 AR cases were identified. We observed significant associations between the risk of AR and PM2.5 (HR: 1.51, 95% CI: 1.27-1.79, per 5 μg/m3), PMcoarse (HR: 1.28, 95% CI: 1.06-1.55, per 5 μg/m3), PM10 (HR: 1.45, 95% CI: 1.20-1.74, per 10 μg/m3), NO2 (HR: 1.14, 95% CI: 1.09-1.19, per 10 μg/m3), and NOx (HR: 1.10, 95% CI: 1.05-1.15, per 20 μg/m3). Moreover, participants with high air pollution combined with high genetic risk showed the highest risk of AR, although no multiplicative or additive interaction was observed. In conclusion, long-term exposure to air pollutants was associated with an elevated risk of AR, particularly in high-genetic-risk populations, emphasizing the urgent need to improve air quality.
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Affiliation(s)
- Peiyang Luo
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jiacheng Ying
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jiayu Li
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Zongming Yang
- Department of Public Health, and Department of Endocrinology of the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou 310058, China
| | - Xiaohui Sun
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Ding Ye
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Cuiqing Liu
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jianbing Wang
- Department of Public Health, and Department of Endocrinology of the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou 310058, China
| | - Yingying Mao
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
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30
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Haas CB, Jordahl KM, Nance RM, Whitney BM, Wang L, Delaney JAC, Ruderman S, Jia T, Mathews WC, Saag MS, Lee SA, Napravnik S, Jacobson JM, Chander G, McCall EM, Moore RD, Mayer KH, Mukherjee S, Lee WJ, Crane PK, Crane H, Peter I, Lindström S. Assessing the associations between known genetic variants and substance use in people with HIV in the United States. PLoS One 2023; 18:e0292068. [PMID: 37796845 PMCID: PMC10553320 DOI: 10.1371/journal.pone.0292068] [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: 05/11/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND The prevalence of substance use in people with HIV (PWH) in the United States is higher than in the general population and is an important driver of HIV-related outcomes. We sought to assess if previously identified genetic associations that contribute to substance use are also observed in a population of PWH. METHODS We performed genome-wide association studies (GWAS) of alcohol, smoking, and cannabis use phenotypes in a multi-ancestry population of 7,542 PWH from the Center for AIDS Research Network of Integrated Clinical Systems (CNICS). We conducted multi-ancestry GWAS for individuals of African (n = 3,748), Admixed American (n = 1,334), and European (n = 2,460) ancestry. Phenotype data were self-reported and collected using patient reported outcomes (PROs) and three questions from AUDIT-C, an alcohol screening tool. We analyzed nine phenotypes: 1) frequency of alcohol consumption, 2) typical number of drinks on a day when drinking alcohol, 3) frequency of five or more alcoholic drinks in a 30-day period, 4) smoking initiation, 5) smoking cessation, 6) cigarettes per day, 7) cannabis use initiation, 8) cannabis use cessation, 9) frequency of cannabis use during the previous 30 days. For each phenotype we considered a) variants previously identified as associated with a substance use trait and b) novel associations. RESULTS We observed evidence for effects of previously reported single nucleotide polymorphisms (SNPs) related to alcohol (rs1229984, p = 0.001), tobacco (rs11783093, p = 2.22E-4), and cannabis use (rs2875907, p = 0.005). We also report two novel loci (19p13.2, p = 1.3E-8; and 20p11.21, p = 2.1E-8) associated with cannabis use cessation. CONCLUSIONS Our analyses contribute to understanding the genetic bases of substance use in a population with relatively higher rates of use compared to the general population.
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Affiliation(s)
- Cameron B. Haas
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
| | - Kristina M. Jordahl
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
| | - Robin M. Nance
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Bridget M. Whitney
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
| | | | - Stephanie Ruderman
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
| | - Tongqiu Jia
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
| | - Wm. Christopher Mathews
- Department of Medicine, University of California at San Diego, San Diego, CA, United States of America
| | - Michael S. Saag
- Department of Medicine at the School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Sulggi A. Lee
- Department of Medicine, University of California at San Francisco, San Francisco, CA, United States of America
| | - Sonia Napravnik
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States of America
| | - Jeffrey M. Jacobson
- Center for AIDS Research, Case Western Reserve University/University Hospitals Case Medical Center, Cleveland, OH, United States of America
| | - Geetanjali Chander
- Department of Medicine, University of Washington, Seattle, WA, United States of America
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Elizabeth M. McCall
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Richard D. Moore
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Kenneth H. Mayer
- Harvard Medical School, Beth Israel Deaconess Medical Center, Fenway Health, Boston, MA, United States of America
| | - Shubhabrata Mukherjee
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Won Jun Lee
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Heidi Crane
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
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31
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Mahmood K, Thomas M, Qu C, Hsu L, Buchanan DD, Peters U. Elucidating the Risk of Colorectal Cancer for Variants in Hereditary Colorectal Cancer Genes. Gastroenterology 2023; 165:1070-1076.e3. [PMID: 37453563 PMCID: PMC10866455 DOI: 10.1053/j.gastro.2023.06.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/07/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Center for Cancer Research, Victorian Comprehensive Cancer Center, Parkville, Victoria, Australia; Melbourne Bioinformatics, The University of Melbourne, Parkville, Victoria, Australia
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Biostatistics, University of Washington, Seattle, Washington.
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Center for Cancer Research, Victorian Comprehensive Cancer Center, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Epidemiology, University of Washington, Seattle, Washington.
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32
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White KM, Hess JL, Glatt SJ, Maisto SA, Zvolensky MJ, Ditre JW. Polygenic risk for alcohol consumption and multisite chronic pain: Associations with ad lib drinking behavior. Exp Clin Psychopharmacol 2023; 31:933-941. [PMID: 36480390 PMCID: PMC10247901 DOI: 10.1037/pha0000630] [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] [Indexed: 12/13/2022]
Abstract
Interrelations between alcohol use disorder and chronic pain have received increasing empirical attention, and several lines of evidence support the possibility of shared genetic liability. However, research on the genetic contributions to the component processes of these complex and potentially overlapping phenotypes remains scarce. The goal of the present study was to test polygenic risk scores (PRSs) for alcohol consumption and multisite chronic pain as predictors of ad lib drinking behavior during an experimental taste test. PRSs were calculated for 209 pain-free, moderate-to-heavy drinkers (57.9% male; 63.6% White). Among White participants, the alcohol and chronic pain PRSs showed nominally significant (ps < .05) positive associations with the volume of alcohol consumed and peak blood alcohol concentration (BAC), respectively. However, associations did not survive correction for multiple comparisons. When stratifying results by experimental condition (between-subjects design: no-pain vs. pain), the alcohol PRS was significantly and negatively associated with the volume of alcohol poured, consumed, and peak BAC among Black participants randomized to the no-pain condition (all false discovery rate [FDR]p < .05). Conversely, the chronic pain PRS was significantly and positively associated with study outcomes among White participants in both the no-pain (alcohol consumed; FDRp = .037) and pain conditions (peak BAC; FDRp = .017). These findings lend partial support to the assertion that alcohol consumption in the laboratory is reflective of drinking behavior in naturalistic settings. This was also the first study to use a pain-related PRS to predict alcohol outcomes, which may be indicative of shared etiology between base and target traits. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Kyle M. White
- Department of Psychology, Syracuse University, Syracuse, NY 13244, United States
| | - Jonathan L. Hess
- Department of Psychiatry and Behavioral Sciences, The State University of New York (SUNY) Upstate Medical University, Syracuse, NY 13210, United States
| | - Stephen J. Glatt
- Department of Psychiatry and Behavioral Sciences, The State University of New York (SUNY) Upstate Medical University, Syracuse, NY 13210, United States
| | - Stephen A. Maisto
- Department of Psychology, Syracuse University, Syracuse, NY 13244, United States
| | - Michael J. Zvolensky
- Department of Psychology, University of Houston, Houston, TX 77004, United States
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- HEALTH Institute, University of Houston, Houston, TX 77204, United States
| | - Joseph W. Ditre
- Department of Psychology, Syracuse University, Syracuse, NY 13244, United States
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33
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Sohail M, Palma-Martínez MJ, Chong AY, Quinto-Cortés CD, Barberena-Jonas C, Medina-Muñoz SG, Ragsdale A, Delgado-Sánchez G, Cruz-Hervert LP, Ferreyra-Reyes L, Ferreira-Guerrero E, Mongua-Rodríguez N, Canizales-Quintero S, Jimenez-Kaufmann A, Moreno-Macías H, Aguilar-Salinas CA, Auckland K, Cortés A, Acuña-Alonzo V, Gignoux CR, Wojcik GL, Ioannidis AG, Fernández-Valverde SL, Hill AVS, Tusié-Luna MT, Mentzer AJ, Novembre J, García-García L, Moreno-Estrada A. Mexican Biobank advances population and medical genomics of diverse ancestries. Nature 2023; 622:775-783. [PMID: 37821706 PMCID: PMC10600006 DOI: 10.1038/s41586-023-06560-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/22/2023] [Indexed: 10/13/2023]
Abstract
Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.
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Affiliation(s)
- Mashaal Sohail
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Mexico.
| | - María J Palma-Martínez
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Amanda Y Chong
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Consuelo D Quinto-Cortés
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Carmina Barberena-Jonas
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Santiago G Medina-Muñoz
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Aaron Ragsdale
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Luis Pablo Cruz-Hervert
- Instituto Nacional de Salud Pública (INSP), Cuernavaca, Mexico
- División de Estudios de Posgrado e Investigación, Facultad de Odontología, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | | | | | | | | | - Andrés Jimenez-Kaufmann
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Hortensia Moreno-Macías
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas UNAM/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Universidad Autónoma Metropolitana, Mexico City, Mexico
| | - Carlos A Aguilar-Salinas
- Division de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Kathryn Auckland
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Adrián Cortés
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Selene L Fernández-Valverde
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
- School of Biotechnology and Biomolecular Sciences and the RNA Institute, The University of New South Wales, Sydney, New South Wales, Australia
| | - Adrian V S Hill
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- The Jenner Institute, University of Oxford, Oxford, UK
| | - María Teresa Tusié-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas UNAM/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Alexander J Mentzer
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | | | - Andrés Moreno-Estrada
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico.
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Bu C, Zheng X, Mai J, Nie Z, Zeng J, Qian Q, Xu T, Sun Y, Bao Y, Xiao J. CCLHunter: An efficient toolkit for cancer cell line authentication. Comput Struct Biotechnol J 2023; 21:4675-4682. [PMID: 37841327 PMCID: PMC10568302 DOI: 10.1016/j.csbj.2023.09.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
Cancer cell lines are essential in cancer research, yet accurate authentication of these cell lines can be challenging, particularly for consanguineous cell lines with close genetic similarities. We introduce a new Cancer Cell Line Hunter (CCLHunter) method to tackle this challenge. This approach utilizes the information of single nucleotide polymorphisms, expression profiles, and kindred topology to authenticate 1389 human cancer cell lines accurately. CCLHunter can precisely and efficiently authenticate cell lines from consanguineous lineages and those derived from other tissues of the same individual. Our evaluation results indicate that CCLHunter has a complete accuracy rate of 93.27%, with an accuracy of 89.28% even for consanguineous cell lines, outperforming existing methods. Additionally, we provide convenient access to CCLHunter through standalone software and a web server at https://ngdc.cncb.ac.cn/cclhunter.
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Affiliation(s)
- Congfan Bu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Xinchang Zheng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Zhi Nie
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Qiheng Qian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Tianyi Xu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Yanling Sun
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, 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
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Bhowmik N, Seaborn T, Ringwall KA, Dahlen CR, Swanson KC, Hulsman Hanna LL. Genetic Distinctness and Diversity of American Aberdeen Cattle Compared to Common Beef Breeds in the United States. Genes (Basel) 2023; 14:1842. [PMID: 37895190 PMCID: PMC10606367 DOI: 10.3390/genes14101842] [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: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023] Open
Abstract
American Aberdeen (AD) cattle in the USA descend from an Aberdeen Angus herd originally brought to the Trangie Agricultural Research Centre, New South Wales, AUS. Although put under specific selection pressure for yearling growth rate, AD remain genomically uncharacterized. The objective was to characterize the genetic diversity and structure of purebred and crossbred AD cattle relative to seven common USA beef breeds using available whole-genome SNP data. A total of 1140 animals consisting of 404 purebred (n = 8 types) and 736 admixed individuals (n = 10 types) was used. Genetic diversity metrics, an analysis of molecular variance, and a discriminant analysis of principal components were employed. When linkage disequilibrium was not accounted for, markers influenced basic diversity parameter estimates, especially for AD cattle. Even so, intrapopulation and interpopulation estimates separate AD cattle from other purebred types (e.g., Latter's pairwise FST ranged from 0.1129 to 0.2209), where AD cattle were less heterozygous and had lower allelic richness than other purebred types. The admixed AD-influenced cattle were intermediate to other admixed types for similar parameters. The diversity metrics separation and differences support strong artificial selection pressures during and after AD breed development, shaping the evolution of the breed and making them genomically distinct from similar breeds.
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Affiliation(s)
- Nayan Bhowmik
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Travis Seaborn
- School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Kris A. Ringwall
- Dickinson Research Extension Center, North Dakota State University, Dickinson, ND 58601, USA
| | - Carl R. Dahlen
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Kendall C. Swanson
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
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36
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González-Peñas J, de Hoyos L, Díaz-Caneja CM, Andreu-Bernabeu Á, Stella C, Gurriarán X, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Vilella E, Muntané G, Molto MD, Gonzalez-Piqueras JC, Parellada M, Arango C, Costas J. Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy. Sci Rep 2023; 13:15500. [PMID: 37726359 PMCID: PMC10509162 DOI: 10.1038/s41598-023-42578-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.
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Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain.
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Carol Stella
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Lourdes Fañanás
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences - Psychiatry, Universidad de Oviedo, ISPA, INEUROPA, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Psychiatry, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
| | - Lourdes Martorell
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Elisabet Vilella
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Gerard Muntané
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - María Dolores Molto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Genetics, University of Valencia, Campus of Burjassot, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Jose Carlos Gonzalez-Piqueras
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
- Fundación Investigación Hospital Clínico de Valencia, INCLIVA, 46010, Valencia, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
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Asgari Y, Sugier PE, Baghfalaki T, Lucotte E, Karimi M, Sedki M, Ngo A, Liquet B, Truong T. GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genom Bioinform 2023; 5:lqad065. [PMID: 37416786 PMCID: PMC10320750 DOI: 10.1093/nargab/lqad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/16/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.
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Affiliation(s)
- Yazdan Asgari
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Pierre-Emmanuel Sugier
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
| | | | - Elise Lucotte
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mojgan Karimi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mohammed Sedki
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Psychiatrie du développement et trajectoires, 94807 Villejuif, France
| | - Amélie Ngo
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Benoit Liquet
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
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38
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Walters RG, Millwood IY, Lin K, Schmidt Valle D, McDonnell P, Hacker A, Avery D, Edris A, Fry H, Cai N, Kretzschmar WW, Ansari MA, Lyons PA, Collins R, Donnelly P, Hill M, Peto R, Shen H, Jin X, Nie C, Xu X, Guo Y, Yu C, Lv J, Clarke RJ, Li L, Chen Z. Genotyping and population characteristics of the China Kadoorie Biobank. CELL GENOMICS 2023; 3:100361. [PMID: 37601966 PMCID: PMC10435379 DOI: 10.1016/j.xgen.2023.100361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 02/09/2023] [Accepted: 06/24/2023] [Indexed: 08/22/2023]
Abstract
The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants using custom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.
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Affiliation(s)
- Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Dan Schmidt Valle
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Pandora McDonnell
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Alex Hacker
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Daniel Avery
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Ahmed Edris
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Hannah Fry
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Na Cai
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | | | - M. Azim Ansari
- Nuffield Department of Medicine, Oxford University, Oxford OX1 3SY, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Paul A. Lyons
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Peter Donnelly
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Michael Hill
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Richard Peto
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Hongbing Shen
- Department of Epidemiology, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211116, China
| | - Xin Jin
- BGI-Shenzhen, Shenzhen 518083, China
| | - Chao Nie
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Robert J. Clarke
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - China Kadoorie Biobank Collaborative Group
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Nuffield Department of Medicine, Oxford University, Oxford OX1 3SY, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Epidemiology, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211116, China
- BGI-Shenzhen, Shenzhen 518083, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
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39
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Benonisdottir S, Kong A. Studying the genetics of participation using footprints left on the ascertained genotypes. Nat Genet 2023; 55:1413-1420. [PMID: 37443256 PMCID: PMC10412458 DOI: 10.1038/s41588-023-01439-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/31/2023] [Indexed: 07/15/2023]
Abstract
The trait of participating in a genetic study probably has a genetic component. Identifying this component is difficult as we cannot compare genetic information of participants with nonparticipants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than nonparticipants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. In nonoverlapping samples, a polygenic score constructed from that analysis is significantly associated with educational attainment, body mass index and being invited to a dietary study. The estimated correlation between the genetic components underlying participation in UK Biobank and educational attainment is estimated to be 36.6%-substantial but far from total. Taking participation behaviour into account would improve the analyses of the study data, including those of health traits.
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Affiliation(s)
- Stefania Benonisdottir
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK.
| | - Augustine Kong
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK.
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK.
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40
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Yu K, Chen XF, Guo J, Wang S, Huang XT, Guo Y, Dong SS, Yang TL. Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a Mendelian randomization study. BMC Med 2023; 21:271. [PMID: 37491271 PMCID: PMC10369749 DOI: 10.1186/s12916-023-02982-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/17/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Stroke is a major cause of mortality and long-term disability worldwide. Whether the associations between brain imaging-derived phenotypes (IDPs) and stroke are causal is uncertain. METHODS We performed two-sample bidirectional Mendelian randomization (MR) analyses to explore the causal associations between IDPs and stroke. Summary data of 587 brain IDPs (up to 33,224 individuals) from the UK Biobank and five stroke types (sample size range from 301,663 to 446,696, case number range from 5,386 to 40,585) from the MEGASTROKE consortium were used. RESULTS Forward MR indicated 14 IDPs belong to projection fibers or association fibers were associated with stroke. For example, higher genetically determined mean diffusivity (MD) in the right external capsule was causally associated with an increased risk of small vessel stroke (IVW OR = 2.76, 95% CI 2.07 to 3.68, P = 5.87 × 10-12). Reverse MR indicated that genetically determined higher risk of any ischemic stroke was associated with increased isotropic or free water volume fraction (ISOVF) in body of corpus callosum (IVW β = 0.23, 95% CI 0.14 to 0.33, P = 3.22 × 10-7). This IDP is a commissural fiber and it is not included in the IDPs identified by forward MR. CONCLUSIONS We identified 14 IDPs with statistically significant evidence of causal effects on stroke or stroke subtypes. We also identified potential causal effects of stroke on one IDP of commissural fiber. These findings might guide further work toward identifying preventative strategies at the brain imaging levels.
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Affiliation(s)
- Ke Yu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Sen Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Xiao-Ting Huang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
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41
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Beck JJ, Ahmed T, Finnicum CT, Zwinderman K, Ehli EA, Boomsma DI, Hottenga JJ. Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods. Genes (Basel) 2023; 14:1497. [PMID: 37510400 PMCID: PMC10379078 DOI: 10.3390/genes14071497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.
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Affiliation(s)
- Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Talitha Ahmed
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Casey T Finnicum
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Koos Zwinderman
- Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Dorret I Boomsma
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
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42
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Als TD, Kurki MI, Grove J, Voloudakis G, Therrien K, Tasanko E, Nielsen TT, Naamanka J, Veerapen K, Levey DF, Bendl J, Bybjerg-Grauholm J, Zeng B, Demontis D, Rosengren A, Athanasiadis G, Bækved-Hansen M, Qvist P, Bragi Walters G, Thorgeirsson T, Stefánsson H, Musliner KL, Rajagopal VM, Farajzadeh L, Thirstrup J, Vilhjálmsson BJ, McGrath JJ, Mattheisen M, Meier S, Agerbo E, Stefánsson K, Nordentoft M, Werge T, Hougaard DM, Mortensen PB, Stein MB, Gelernter J, Hovatta I, Roussos P, Daly MJ, Mors O, Palotie A, Børglum AD. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat Med 2023; 29:1832-1844. [PMID: 37464041 PMCID: PMC10839245 DOI: 10.1038/s41591-023-02352-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 04/17/2023] [Indexed: 07/20/2023]
Abstract
Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.
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Affiliation(s)
- Thomas D Als
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
| | - Mitja I Kurki
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jakob Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elisa Tasanko
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Trine Tollerup Nielsen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Joonas Naamanka
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Kumar Veerapen
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ditte Demontis
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders Rosengren
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Georgios Athanasiadis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain
| | - Marie Bækved-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Per Qvist
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | | | | | | | - Katherine L Musliner
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- The Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Veera M Rajagopal
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Leila Farajzadeh
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Janne Thirstrup
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Manuel Mattheisen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Sandra Meier
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | | | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Institute of Clinical Sciences and GLOBE Institute, LF Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Preben B Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Departments of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Iiris Hovatta
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
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43
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Liu N, Zhang L, Tian T, Cheng J, Zhang B, Qiu S, Geng Z, Cui G, Zhang Q, Liao W, Yu Y, Zhang H, Gao B, Xu X, Han T, Yao Z, Qin W, Liu F, Liang M, Xu Q, Fu J, Xu J, Zhu W, Zhang P, Li W, Shi D, Wang C, Lui S, Yan Z, Chen F, Li J, Zhang J, Wang D, Shen W, Miao Y, Xian J, Gao JH, Zhang X, Li MJ, Xu K, Zuo XN, Wang M, Ye Z, Yu C. Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes. Nat Genet 2023:10.1038/s41588-023-01425-8. [PMID: 37337106 DOI: 10.1038/s41588-023-01425-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/11/2023] [Indexed: 06/21/2023]
Abstract
The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.
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Affiliation(s)
- Nana Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Shijun Qiu
- Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Zuojun Geng
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Molecular Imaging Research Center of Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hui Zhang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Bo Gao
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jiance Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Yanwei Miao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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Smolen C, Jensen M, Dyer L, Pizzo L, Tyryshkina A, Banerjee D, Rohan L, Huber E, El Khattabi L, Prontera P, Caberg JH, Van Dijck A, Schwartz C, Faivre L, Callier P, Mosca-Boidron AL, Lefebvre M, Pope K, Snell P, Lockhart PJ, Castiglia L, Galesi O, Avola E, Mattina T, Fichera M, Mandarà GML, Bruccheri MG, Pichon O, Le Caignec C, Stoeva R, Cuinat S, Mercier S, Bénéteau C, Blesson S, Nordsletten A, Martin-Coignard D, Sistermans E, Kooy RF, Amor DJ, Romano C, Isidor B, Juusola J, Girirajan S. Assortative mating and parental genetic relatedness drive the pathogenicity of variably expressive variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.18.23290169. [PMID: 37292616 PMCID: PMC10246151 DOI: 10.1101/2023.05.18.23290169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We examined more than 38,000 spouse pairs from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents associated with neurodevelopmental disease risk in children. We identified correlations between six phenotypes in parents and children, including correlations of clinical diagnoses such as obsessive-compulsive disorder (R=0.31-0.49, p<0.001), and two measures of sub-clinical autism features in parents affecting several autism severity measures in children, such as bi-parental mean Social Responsiveness Scale (SRS) scores affecting proband SRS scores (regression coefficient=0.11, p=0.003). We further describe patterns of phenotypic and genetic similarity between spouses, where spouses show both within- and cross-disorder correlations for seven neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R=0.25-0.72, p<0.001) and a cross-disorder correlation between schizophrenia and personality disorder (R=0.20-0.57, p<0.001). Further, these spouses with similar phenotypes were significantly correlated for rare variant burden (R=0.07-0.57, p<0.0001). We propose that assortative mating on these features may drive the increases in genetic risk over generations and the appearance of "genetic anticipation" associated with many variably expressive variants. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse correlations with burden and pathogenicity of rare variants and propose that parental relatedness drives disease risk by increasing genome-wide homozygosity in children (R=0.09-0.30, p<0.001). Our results highlight the utility of assessing parent phenotypes and genotypes in predicting features in children carrying variably expressive variants and counseling families carrying these variants.
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Affiliation(s)
- Corrine Smolen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Matthew Jensen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | | | - Lucilla Pizzo
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Tyryshkina
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Neuroscience Graduate program, Pennsylvania State University, University Park, PA 16802
| | - Deepro Banerjee
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Laura Rohan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Emily Huber
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Laila El Khattabi
- Assistance Publique–Hôpitaux de Paris, Department of Medical Genetics, Armand Trousseau and Pitié-Salpêtrière Hospitals, Paris, France
| | - Paolo Prontera
- Medical Genetics Unit, Hospital “Santa Maria della Misericordia”, Perugia, Italy
| | - Jean-Hubert Caberg
- Centre Hospitalier Universitaire de Liège. Domaine Universitaire du Sart Tilman, Liège, Belgium
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | | | - Laurence Faivre
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d’Enfants, CHU Dijon, Dijon, France
- GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Patrick Callier
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d’Enfants, CHU Dijon, Dijon, France
- GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | | | - Mathilde Lefebvre
- GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Kate Pope
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Penny Snell
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Paul J. Lockhart
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- Bruce Lefroy Center, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Lucia Castiglia
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Ornella Galesi
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Emanuela Avola
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Teresa Mattina
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Marco Fichera
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | | | - Maria Grazia Bruccheri
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Olivier Pichon
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | - Cedric Le Caignec
- CHU Toulouse, Department of Medical Genetics, Toulouse, France
- ToNIC, Toulouse Neuro Imaging, Center, Inserm, UPS, Université de Toulouse, Toulouse, France
| | - Radka Stoeva
- Service de Cytogenetique, CHU de Le Mans, Le Mans, France
| | | | - Sandra Mercier
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | | | - Sophie Blesson
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | | | | | - Erik Sistermans
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, The Netherlands
| | - R. Frank Kooy
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - David J. Amor
- Bruce Lefroy Center, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Corrado Romano
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
- Medical Genetics, ASP Ragusa, Ragusa, Italy
| | | | | | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
- Neuroscience Graduate program, Pennsylvania State University, University Park, PA 16802
- Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA
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45
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Dai J, Xu Y, Wang T, Zeng P. Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: From genetic correlation to causality. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110700. [PMID: 36566903 DOI: 10.1016/j.pnpbp.2022.110700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Patients with Alzheimer's disease (AD) are markedly increasing as population aging and no disease-modifying therapies are currently available for AD. Previous studies suggested a broad link between socioeconomic status and a variety of disorders, including mental illness and cognitive abilities. However, the association between socioeconomic deprivation and AD has been unknown. We here employed Townsend deprivation index (TDI) to explore such relation and found a positive genetic correlation (r̂g=0.211, P = 8.00 × 10-4) between the two traits with summary statistics data (N = 455,258 for TDI and N = 455,815 for AD). Then, we performed pleiotropy analysis at both variant and gene levels using a powerful method called PLACO and detected 87 distinct pleiotropic genes. Functional analysis demonstrated these genes were significantly enriched in pancreas, liver, heart, blood, brain, and muscle tissues. Using Mendelian randomization methods, we further found that one genetically predicted standard deviation elevation in TDI could lead to approximately 18.5% (95% confidence intervals 1.6- 38.2%, P = 0.031) increase of AD risk, and that the identified causal association was robust against used MR approaches, horizontal pleiotropy, and instrumental selection. Overall, this study provides deep insight into common genetic components underlying TDI and AD, and further reveals causal connection between them. It is also helpful to develop a more suitable plan for ameliorating inequities, hardship, and disadvantage, with the hope of improving health outcomes among economically disadvantaged people.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yue Xu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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46
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Shi Y, Niu Y, Zhang P, Luo H, Liu S, Zhang S, Wang J, Li Y, Liu X, Song T, Xu T, He S. Characterization of genome-wide STR variation in 6487 human genomes. Nat Commun 2023; 14:2092. [PMID: 37045857 PMCID: PMC10097659 DOI: 10.1038/s41467-023-37690-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/27/2023] [Indexed: 04/14/2023] Open
Abstract
Short tandem repeats (STRs) are abundant and highly mutagenic in the human genome. Many STR loci have been associated with a range of human genetic disorders. However, most population-scale studies on STR variation in humans have focused on European ancestry cohorts or are limited by sequencing depth. Here, we depicted a comprehensive map of 366,013 polymorphic STRs (pSTRs) constructed from 6487 deeply sequenced genomes, comprising 3983 Chinese samples (~31.5x, NyuWa) and 2504 samples from the 1000 Genomes Project (~33.3x, 1KGP). We found that STR mutations were affected by motif length, chromosome context and epigenetic features. We identified 3273 and 1117 pSTRs whose repeat numbers were associated with gene expression and 3'UTR alternative polyadenylation, respectively. We also implemented population analysis, investigated population differentiated signatures, and genotyped 60 known disease-causing STRs. Overall, this study further extends the scale of STR variation in humans and propels our understanding of the semantics of STRs.
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Affiliation(s)
- Yirong Shi
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yiwei Niu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuai Liu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Sijia Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jiajia Wang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yanyan Li
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinyue Liu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.
| | - Shunmin He
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
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47
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Hamid I, Korunes KL, Schrider DR, Goldberg A. Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes. Mol Biol Evol 2023; 40:msad074. [PMID: 36947126 PMCID: PMC10116606 DOI: 10.1093/molbev/msad074] [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: 09/04/2022] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 03/23/2023] Open
Abstract
Gene flow between previously differentiated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry "outliers" compared with the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared with multiple or long windows obtained using two other ancestry-based methods.
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Affiliation(s)
- Iman Hamid
- Department of Evolutionary Anthropology, Duke University, Durham, NC
| | | | - Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC
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48
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Lin BD, Vermeulen JM, Bolhuis K, Chang X, Schirmbeck F, van Eijk KR, Guloksuz S, Blankers M, van den Brink W, de Haan L, Luykx JJ. Associations between genetic liabilities to smoking behavior and schizophrenia symptoms in patients with a psychotic disorder, their siblings and healthy controls. Psychiatry Res 2023; 323:115143. [PMID: 36948018 DOI: 10.1016/j.psychres.2023.115143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/21/2023] [Accepted: 03/01/2023] [Indexed: 03/24/2023]
Abstract
It is unknown how smoking behavior polygenic scores (PRS) relate to psychosis and psychotic symptoms. To elucidate this, genotype and phenotype data were collected from patients with schizophrenia, their unaffected siblings, and healthy controls in a six-year follow-up prospective cohort study. Associations between smoking behaviors, PRS and schizophrenia symptoms were explored using linear mixed-effect models. The mean number of cigarettes smoked per day were 18 for patients, 13 for siblings and 12 for controls. In the overall sample, PRSs-smoking initiation (i.e., ever smoking as a binary phenotype, PRS-SI) were positively associated with positive symptoms, negative symptoms, and depressive symptoms, whereas PRSs-AI (age at regular smoking initiation) were negatively associated with all symptom dimensions, with similar effect sizes. When considering groups separately, PRS were only associated with psychotic symptoms in siblings and controls. In conclusion, unaffected siblings show smoking behaviors at an intermediate level between patients and healthy controls. Additionally, PRS-SI and PRS-AI are associated with all symptom dimensions only in unaffected siblings and healthy controls, possibly owing to the dominant role of other (genetic) risk factors in patients. Future studies may examine mechanisms via which genetic risk for smoking affects mental health symptoms.
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Affiliation(s)
- Bochao Danae Lin
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jentien M Vermeulen
- Department of Psychiatry, Amsterdam UMC location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - K Bolhuis
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Xiao Chang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Frederike Schirmbeck
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; Arkin Mental Health Care, Amsterdam, The Netherlands
| | - Kristel R van Eijk
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | | | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Matthijs Blankers
- Arkin Mental Health Care, Amsterdam, The Netherlands; Trimbos institute - The Netherlands institute of Mental Health and Addiction, Utrecht, The Netherlands
| | - W van den Brink
- Department of Psychiatry, Amsterdam UMC location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lieuwe de Haan
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands; Arkin Mental Health Care, Amsterdam, The Netherlands
| | - Jurjen J Luykx
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Brain Center Rudolf Magnus, Utrecht, The Netherlands; GGNet Mental Health, Warnsveld, The Netherlands.
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49
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Estimating human mobility in Holocene Western Eurasia with large-scale ancient genomic data. Proc Natl Acad Sci U S A 2023; 120:e2218375120. [PMID: 36821583 PMCID: PMC9992830 DOI: 10.1073/pnas.2218375120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
The recent increase in openly available ancient human DNA samples allows for large-scale meta-analysis applications. Trans-generational past human mobility is one of the key aspects that ancient genomics can contribute to since changes in genetic ancestry-unlike cultural changes seen in the archaeological record-necessarily reflect movements of people. Here, we present an algorithm for spatiotemporal mapping of genetic profiles, which allow for direct estimates of past human mobility from large ancient genomic datasets. The key idea of the method is to derive a spatial probability surface of genetic similarity for each individual in its respective past. This is achieved by first creating an interpolated ancestry field through space and time based on multivariate statistics and Gaussian process regression and then using this field to map the ancient individuals into space according to their genetic profile. We apply this algorithm to a dataset of 3138 aDNA samples with genome-wide data from Western Eurasia in the last 10,000 y. Finally, we condense this sample-wise record with a simple summary statistic into a diachronic measure of mobility for subregions in Western, Central, and Southern Europe. For regions and periods with sufficient data coverage, our similarity surfaces and mobility estimates show general concordance with previous results and provide a meta-perspective of genetic changes and human mobility.
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50
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Zhao L, Zhao W, Cao J, Tu Y. Causal relationships between migraine and microstructural white matter: a Mendelian randomization study. J Headache Pain 2023; 24:10. [PMID: 36793015 PMCID: PMC9933315 DOI: 10.1186/s10194-023-01550-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Migraine is a disabling neurological disorder with the pathophysiology yet to be understood. The microstructural alteration in brain white matter (WM) has been suggested to be related to migraine in recent studies, but these evidence are observational essentially and cannot infer a causal relationship. The present study aims to reveal the causal relationship between migraine and microstructural WM using genetic data and Mendelian randomization (MR). METHODS We collected the Genome-wide association study (GWAS) summary statistics of migraine (48,975 cases / 550,381 controls) and 360 WM imaging-derived phenotypes (IDPs) (31,356 samples) that were used to measure microstructural WM. Based on instrumental variables (IVs) selected from the GWAS summary statistics, we conducted bidirectional two-sample MR analyses to infer bidirectional causal associations between migraine and microstructural WM. In forward MR analysis, we inferred the causal effect of microstructural WM on migraine by reporting the odds ratio (OR) that quantified the risk change of migraine for per 1 standard deviation (SD) increase of IDPs. In reverse MR analysis, we inferred the causal effect of migraine on microstructural WM by reporting the β value that represented SDs of changes in IDPs were caused by migraine. RESULTS Three WM IDPs showed significant causal associations (p < 3.29 × 10- 4, Bonferroni correction) with migraine and were proved to be reliable via sensitivity analysis. The mode of anisotropy (MO) of left inferior fronto-occipital fasciculus (OR = 1.76, p = 6.46 × 10- 5) and orientation dispersion index (OD) of right posterior thalamic radiation (OR = 0.78, p = 1.86 × 10- 4) exerted significant causal effects on migraine. Migraine exerted a significant causal effect on the OD of left superior cerebellar peduncle (β = - 0.09, p = 2.78 × 10- 4). CONCLUSIONS Our findings provided genetic evidence for the causal relationships between migraine and microstructural WM, bringing new insights into brain structure for the development and experience of migraine.
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Affiliation(s)
- Lei Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenhui Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jin Cao
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
| | - Yiheng Tu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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