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Zhukovsky P, Tio ES, Coughlan G, Bennett DA, Wang Y, Hohman TJ, Pizzagalli DA, Mulsant BH, Voineskos AN, Felsky D. Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression. Nat Commun 2024; 15:5207. [PMID: 38890310 PMCID: PMC11189393 DOI: 10.1038/s41467-024-49430-7] [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: 05/08/2023] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10-8), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.
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Grants
- P30 AG072975 NIA NIH HHS
- U01 AG046152 NIA NIH HHS
- U01 AG061356 NIA NIH HHS
- R01 AG017917 NIA NIH HHS
- P30 AG010161 NIA NIH HHS
- R01 AG059716 NIA NIH HHS
- Wellcome Trust
- R01 AG015819 NIA NIH HHS
- Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Skin Research Training Centre)
- D.F. is supported by the generous contributions from the Michael and Sonja Koerner Foundation and the Krembil Family Foundation. D.F. is also supported in part by the Centre for Addiction and Mental Health (CAMH) Discovery Fund and CIHR.
- PZ was funded by the Canadian Institute of Health Research Postdoctoral Fellowship.
- Over the past 3 years, D.A.P has received consulting fees from Albright Stonebridge Group, Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sunovion, and Takeda; he has received honoraria from the Psychonomic Society and American Psychological Association (for editorial work) and from Alkermes; he has received research funding from the Brain and Behavior Research Foundation, the Dana Foundation, Millennium Pharmaceuticals, Wellcome Leap MCPsych, and NIMH; he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics, and Neuroscience Software. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors.
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- A.N.V. currently receives funding from CIHR, the NIH, the National Sciences and Engineering Research Council (NSERC), the CAMH Foundation, and the University of Toronto. E.S.T. was funded by the Ontario Graduate Scholarship.
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Affiliation(s)
- Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Earvin S Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02129, USA
| | - David A Bennett
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Yanling Wang
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Timothy J Hohman
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
| | - Daniel Felsky
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Rotman Research Institute, Baycrest Hospital, Toronto, ON, M6A 2E1, Canada.
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2
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Arron HE, Marsh BD, Kell DB, Khan MA, Jaeger BR, Pretorius E. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: the biology of a neglected disease. Front Immunol 2024; 15:1386607. [PMID: 38887284 PMCID: PMC11180809 DOI: 10.3389/fimmu.2024.1386607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/11/2024] [Indexed: 06/20/2024] Open
Abstract
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic, debilitating disease characterised by a wide range of symptoms that severely impact all aspects of life. Despite its significant prevalence, ME/CFS remains one of the most understudied and misunderstood conditions in modern medicine. ME/CFS lacks standardised diagnostic criteria owing to variations in both inclusion and exclusion criteria across different diagnostic guidelines, and furthermore, there are currently no effective treatments available. Moving beyond the traditional fragmented perspectives that have limited our understanding and management of the disease, our analysis of current information on ME/CFS represents a significant paradigm shift by synthesising the disease's multifactorial origins into a cohesive model. We discuss how ME/CFS emerges from an intricate web of genetic vulnerabilities and environmental triggers, notably viral infections, leading to a complex series of pathological responses including immune dysregulation, chronic inflammation, gut dysbiosis, and metabolic disturbances. This comprehensive model not only advances our understanding of ME/CFS's pathophysiology but also opens new avenues for research and potential therapeutic strategies. By integrating these disparate elements, our work emphasises the necessity of a holistic approach to diagnosing, researching, and treating ME/CFS, urging the scientific community to reconsider the disease's complexity and the multifaceted approach required for its study and management.
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Affiliation(s)
- Hayley E. Arron
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa
| | - Benjamin D. Marsh
- MRCPCH Consultant Paediatric Neurodisability, Exeter, Devon, United Kingdom
| | - Douglas B. Kell
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
- The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - M. Asad Khan
- Directorate of Respiratory Medicine, Manchester University Hospitals, Wythenshawe Hospital, Manchester, United Kingdom
| | - Beate R. Jaeger
- Long COVID department, Clinic St Georg, Bad Aibling, Germany
| | - Etheresia Pretorius
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
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Lazarev D, Chau G, Bloemendal A, Churchhouse C, Neale BM. GUIDE deconstructs genetic architectures using association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592285. [PMID: 38766146 PMCID: PMC11100597 DOI: 10.1101/2024.05.03.592285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Genome-wide association studies have revealed that the genetic architecture of most complex traits is characterized by a large number of distinct effects scattered across the genome. Functional enrichment analyses of these results suggest that the associations for any given complex trait are not purely random. Thus, we set out to leverage the genetic association results from many traits with a view to identifying the set of modules, or latent factors, that mediate these associations. The identification of such modules may aid in disease classification as well as the elucidation of complex disease mechanisms. We propose a method, Genetic Unmixing by Independent Decomposition (GUIDE), to estimate a set of statistically independent latent factors that best express the patterns of association across many traits. The resulting latent factors not only have desirable mathematical properties, such as sparsity and a higher variance explained (for both traits and variants), but are also able to single out and prioritize key biological features or pathophysiological mechanisms underlying a given trait or disease. Moreover, we show that these latent factors can index biological pathways as well as epidemiological and environmental influences that compose the genetic architecture of complex traits.
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Kharaghani A, Tio ES, Milic M, Bennett DA, De Jager PL, Schneider JA, Sun L, Felsky D. Association of whole-person eigen-polygenic risk scores with Alzheimer's disease. Hum Mol Genet 2024:ddae067. [PMID: 38679805 DOI: 10.1093/hmg/ddae067] [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: 10/23/2023] [Revised: 03/06/2024] [Accepted: 04/05/2024] [Indexed: 05/01/2024] Open
Abstract
Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.
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Affiliation(s)
- Amin Kharaghani
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada
| | - Earvin S Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
- Institute of Medical Science, Department of Psychiatry, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
| | - Milos Milic
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 West Harrison Street, Chicago, IL 60612, United States
| | - Philip L De Jager
- Centre for Translational and Computational Neuroimmunology, Columbia University Medical Center, 622 West 168th Street, New York, NY 10032, United States
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 West Harrison Street, Chicago, IL 60612, United States
| | - Lei Sun
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada
- Department of Statistical Sciences, University of Toronto, 700 University Avenue, Toronto, ON M5G 1X6, Canada
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada
- Institute of Medical Science, Department of Psychiatry, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
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Rivas MA, Chang C. Efficient storage and regression computation for population-scale genome sequencing studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589062. [PMID: 38659813 PMCID: PMC11042230 DOI: 10.1101/2024.04.11.589062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In the era of big data in human genetics, large-scale biobanks aggregating genetic data from diverse populations have emerged as important for advancing our understanding of human health and disease. However, the computational and storage demands of whole genome sequencing (WGS) studies pose significant challenges, especially for researchers from underfunded institutions or developing countries, creating a disparity in research capabilities. We introduce new approaches that significantly enhance computational efficiency and reduce data storage requirements for WGS studies. By developing algorithms for compressed storage of genetic data, focusing particularly on optimizing the representation of rare variants, and designing regression methods tailored for the scale and complexity of WGS data, we significantly lower computational and storage costs. We integrate our approach into PLINK 2.0. The implementation demonstrates considerable reductions in storage space and computational time without compromising analytical accuracy, as evidenced by the application to the AllofUs project data. We improve runtime of an exome-wide association analysis of 19.4 million variants and a single phenotype from 695.35 minutes (approximately 11.5 hours) on a single machine to 1.57 minutes using 30Gb of memory and 50 threads (8.67 minutes using 4 threads). Similarly, we generalize to multi-phenotype analysis. We anticipate that our approach will enable researchers across the globe to unlock the potential of population biobanks, accelerating the pace of discoveries that can improve our understanding of human health and disease.
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Affiliation(s)
- Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA 94305
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6
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Saliba-Gustafsson P, Justesen JM, Ranta A, Sharma D, Bielczyk-Maczynska E, Li J, Najmi LA, Apodaka M, Aspichueta P, Björck HM, Eriksson P, Franco-Cereceda A, Gloudemans M, Mujica E, den Hoed M, Assimes TL, Quertermous T, Carcamo-Orive I, Park CY, Knowles JW. A functional genomic framework to elucidate novel causal non-alcoholic fatty liver disease genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.03.24302258. [PMID: 38352379 PMCID: PMC10863038 DOI: 10.1101/2024.02.03.24302258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Background & Aims Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for NAFLD have been hampered by the relative paucity of human data from gold-standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using NAFLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined GWAS of NAFLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for NAFLD. Approach & Results We used the UK Biobank to explore the associations of our novel NAFLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study NAFLD genes in vitro using CRISPRi. Our data identify VKORC1, TNKS, LYPLAL1 and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of NAFLD. Conclusions Complementary genetic and genomic approaches are useful for the identification of NAFLD genes. Our data supports VKORC1 as a bona fide NAFLD gene. We have established a functional genomic framework to study at scale putative novel NAFLD genes from human genetic association studies.
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Affiliation(s)
- Peter Saliba-Gustafsson
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- CardioMetabolic Unit at the Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Johanne M. Justesen
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark
| | - Amanda Ranta
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Disha Sharma
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Ewa Bielczyk-Maczynska
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
- The Hormel Institute, University of Minnesota, MN, USA
| | - Jiehan Li
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Laeya A. Najmi
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Maider Apodaka
- University of the Basque Country (UPV/EHU), Faculty of Medicine and Nursing, Department of Physiology, Leioa, Spain
| | - Patricia Aspichueta
- University of the Basque Country (UPV/EHU), Faculty of Medicine and Nursing, Department of Physiology, Leioa, Spain
- National Institute for the Study of Liver and Gastrointestinal Diseases (CIBERehd, Instituto de Salud Carlos III)
| | - Hanna M. Björck
- Division of Cardiovascular Medicine, Centre for Molecular Medicine, Department of Medicine, Solna, Karolinska Inistitutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Per Eriksson
- Division of Cardiovascular Medicine, Centre for Molecular Medicine, Department of Medicine, Solna, Karolinska Inistitutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | | | - Mike Gloudemans
- Department of Pathology, Stanford University School of Medicine, CA, USA
| | - Endrina Mujica
- Department of Immunology, Genetics and Pathology, Uppsala University, Sweden
| | - Marcel den Hoed
- Department of Immunology, Genetics and Pathology, Uppsala University, Sweden
| | - Themistocles L. Assimes
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto CA, USA
| | - Thomas Quertermous
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark
| | - Ivan Carcamo-Orive
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Chong Y. Park
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Joshua W. Knowles
- Department of Medicine, Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
- Stanford Prevention Research Center, Stanford, CA, USA
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Przybylowicz PK, Sokolowska KE, Rola H, Wojdacz TK. DNA Methylation Changes in Blood Cells of Fibromyalgia and Chronic Fatigue Syndrome Patients. J Pain Res 2023; 16:4025-4036. [PMID: 38054109 PMCID: PMC10695140 DOI: 10.2147/jpr.s439412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 11/13/2023] [Indexed: 12/07/2023] Open
Abstract
Purpose Fibromyalgia (FM) and Chronic Fatigue Syndrome (CFS) affect 0.4% and 1% of society, respectively, and the prevalence of these pain syndromes is increasing. To date, no strong association between these syndromes and the genetic background of affected individuals has been shown. Therefore, it is plausible that epigenetic changes might play a role in the development of these syndromes. Patients and Methods Three previous studies have attempted to elaborate the involvement of genome-wide methylation changes in blood cells in the development of fibromyalgia and chronic fatigue syndrome. These studies included 22 patients with fibromyalgia and 127 patients with CFS, and the results of the studies were largely discrepant. Contradicting results of those studies may be attributed to differences in the omics data analysis approaches used in each study. We reanalyzed the data collected in these studies using an updated and coherent data-analysis framework. Results Overall, the methylation changes that we observed overlapped with previous results only to some extent. However, the gene set enrichment analyses based on genes annotated to methylation changes identified in each of the analyzed datasets were surprisingly coherent and uniformly associated with the physiological processes that, when affected, may result in symptoms characteristic of fibromyalgia and chronic fatigue syndrome. Conclusion Methylomes of the blood cells of patients with FM and CFS in three independent studies have shown methylation changes that appear to be implicated in the pathogenesis of these syndromes.
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Affiliation(s)
| | | | - Hubert Rola
- Independent Clinical Epigenetics Laboratory, Pomeranian Medical University, Szczecin, Poland
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Tanigawa Y, Kellis M. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet 2023; 110:1888-1902. [PMID: 37890495 PMCID: PMC10645553 DOI: 10.1016/j.ajhg.2023.09.013] [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: 01/23/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.
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Affiliation(s)
- Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Zhang Z, Jung J, Kim A, Suboc N, Gazal S, Mancuso N. A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics. Am J Hum Genet 2023; 110:1863-1874. [PMID: 37879338 PMCID: PMC10645558 DOI: 10.1016/j.ajhg.2023.09.015] [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: 03/27/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/27/2023] Open
Abstract
Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.
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Affiliation(s)
- Zixuan Zhang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Noah Suboc
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, 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, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, 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, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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10
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Jiang X, Zhang MJ, Zhang Y, Durvasula A, Inouye M, Holmes C, Price AL, McVean G. Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. Nat Genet 2023; 55:1854-1865. [PMID: 37814053 PMCID: PMC10632146 DOI: 10.1038/s41588-023-01522-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/31/2023] [Indexed: 10/11/2023]
Abstract
The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.
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Affiliation(s)
- Xilin Jiang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- 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.
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yidong Zhang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Michael Inouye
- 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
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Research Excellence, Department of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- The Alan Turing Institute, London, UK
| | - Chris Holmes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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11
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Bielczyk-Maczynska E, Sharma D, Blencowe M, Saliba Gustafsson P, Gloudemans MJ, Yang X, Carcamo-Orive I, Wabitsch M, Svensson KJ, Park CY, Quertermous T, Knowles JW, Li J. A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes. Am J Physiol Cell Physiol 2023; 325:C648-C660. [PMID: 37486064 PMCID: PMC10635647 DOI: 10.1152/ajpcell.00148.2023] [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: 04/18/2023] [Revised: 07/07/2023] [Accepted: 07/19/2023] [Indexed: 07/25/2023]
Abstract
CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of PPARG and CEBPB (which encode key proadipogenic transcription factors) resulted in the inhibition of adipogenesis. Gene set enrichment analysis generated hypotheses regarding the molecular function of novel genes. MAFF knockdown led to downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes and to decreased CXCL-16 and IL-6 secretion. TIPARP knockdown resulted in increased expression of adipogenesis markers. In summary, this powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte, and adipocyte function associated with metabolic disease.NEW & NOTEWORTHY Genomics efforts led to the identification of many genomic loci that are associated with metabolic traits, many of which are tied to adipose tissue function. However, determination of the causal genes, and their mechanism of action in metabolism, is a time-consuming process. Here, we use an approach to determine the transcriptional outcome of candidate gene knockdown for multiple genes at the same time in a human cell model of adipogenesis.
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Affiliation(s)
- Ewa Bielczyk-Maczynska
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Disha Sharma
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States
| | - Peter Saliba Gustafsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine at BioClinicum, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Michael J Gloudemans
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States
- Biomedical Informatics Training Program, Stanford, California, United States
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States
| | - Ivan Carcamo-Orive
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Martin Wabitsch
- Department of Pediatrics and Adolescent Medicine, Center for Rare Endocrine Diseases, Division of Pediatric Endocrinology and Diabetes, Ulm University Medical Centre, Ulm, Germany
| | - Katrin J Svensson
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States
| | - Chong Y Park
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Joshua W Knowles
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, United States
| | - Jiehan Li
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
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12
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 06/06/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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13
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 11/23/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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14
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Zhang Z, Jung J, Kim A, Suboc N, Gazal S, Mancuso N. A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.27.23287801. [PMID: 37034739 PMCID: PMC10081403 DOI: 10.1101/2023.03.27.23287801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.
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Affiliation(s)
- Zixuan Zhang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Artem Kim
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Noah Suboc
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
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15
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Malato J, Graça L, Sepúlveda N. Impact of Misdiagnosis in Case-Control Studies of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Diagnostics (Basel) 2023; 13:diagnostics13030531. [PMID: 36766636 PMCID: PMC9914258 DOI: 10.3390/diagnostics13030531] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/20/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023] Open
Abstract
Misdiagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can occur when different case definitions are used by clinicians (relative misdiagnosis) or when failing the genuine diagnosis of another disease (misdiagnosis in a strict sense). This problem translates to a recurrent difficulty in reproducing research findings. To tackle this problem, we simulated data from case-control studies under misdiagnosis in a strict sense. We then estimated the power to detect a genuine association between a potential causal factor and ME/CFS. A minimum power of 80% was obtained for studies with more than 500 individuals per study group. When the simulation study was extended to the situation where the potential causal factor could not be determined perfectly (e.g., seropositive/seronegative in serological association studies), the minimum power of 80% could only be achieved in studies with more than 1000 individuals per group. In conclusion, current ME/CFS studies have suboptimal power under the assumption of misdiagnosis. This power can be improved by increasing the overall sample size using multi-centric studies, reporting the excluded illnesses and their exclusion criteria, or focusing on a homogeneous cohort of ME/CFS patients with a specific pathological mechanism where the chance of misdiagnosis is reduced.
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Affiliation(s)
- João Malato
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- CEAUL—Centro de Estatística e Aplicações da Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Luís Graça
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
| | - Nuno Sepúlveda
- CEAUL—Centro de Estatística e Aplicações da Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warszawa, Poland
- Correspondence:
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16
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Bielczyk-Maczynska E, Zhao M, Zushin PJH, Schnurr TM, Kim HJ, Li J, Nallagatla P, Sangwung P, Park CY, Cornn C, Stahl A, Svensson KJ, Knowles JW. G protein-coupled receptor 151 regulates glucose metabolism and hepatic gluconeogenesis. Nat Commun 2022; 13:7408. [PMID: 36456565 PMCID: PMC9715671 DOI: 10.1038/s41467-022-35069-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
Human genetics has been instrumental in identification of genetic variants linked to type 2 diabetes. Recently a rare, putative loss-of-function mutation in the orphan G-protein coupled receptor 151 (GPR151) was found to be associated with lower odds ratio for type 2 diabetes, but the mechanism behind this association has remained elusive. Here we show that Gpr151 is a fasting- and glucagon-responsive hepatic gene which regulates hepatic gluconeogenesis. Gpr151 ablation in mice leads to suppression of hepatic gluconeogenesis genes and reduced hepatic glucose production in response to pyruvate. Importantly, the restoration of hepatic Gpr151 levels in the Gpr151 knockout mice reverses the reduced hepatic glucose production. In this work, we establish a previously unknown role of Gpr151 in the liver that provides an explanation to the lowered type 2 diabetes risk in individuals with nonsynonymous mutations in GPR151.
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Affiliation(s)
- Ewa Bielczyk-Maczynska
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Meng Zhao
- grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Peter-James H. Zushin
- grid.47840.3f0000 0001 2181 7878Department of Nutritional Sciences and Toxicology, University of California at Berkeley, Berkeley, CA USA
| | - Theresia M. Schnurr
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Hyun-Jung Kim
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Jiehan Li
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Pratima Nallagatla
- grid.168010.e0000000419368956Genetics Bioinformatics Service Center, Stanford University School of Medicine, Stanford, CA USA
| | - Panjamaporn Sangwung
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Chong Y. Park
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Cameron Cornn
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Andreas Stahl
- grid.47840.3f0000 0001 2181 7878Department of Nutritional Sciences and Toxicology, University of California at Berkeley, Berkeley, CA USA
| | - Katrin J. Svensson
- grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Joshua W. Knowles
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA USA
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17
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Zhukovsky P, Wainberg M, Milic M, Tripathy SJ, Mulsant BH, Felsky D, Voineskos AN. Multiscale neural signatures of major depressive, anxiety, and stress-related disorders. Proc Natl Acad Sci U S A 2022; 119:e2204433119. [PMID: 35648832 PMCID: PMC9191681 DOI: 10.1073/pnas.2204433119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The extent of shared and distinct neural mechanisms underlying major depressive disorder (MDD), anxiety, and stress-related disorders is still unclear. We compared the neural signatures of these disorders in 5,405 UK Biobank patients and 21,727 healthy controls. We found the greatest case–control differences in resting-state functional connectivity and cortical thickness in MDD, followed by anxiety and stress-related disorders. Neural signatures for MDD and anxiety disorders were highly concordant, whereas stress-related disorders showed a distinct pattern. Controlling for cross-disorder genetic risk somewhat decreased the similarity between functional neural signatures of stress-related disorders and both MDD and anxiety disorders. Among cases and healthy controls, reduced within-network and increased between-network frontoparietal and default mode connectivity were associated with poorer cognitive performance (processing speed, attention, associative learning, and fluid intelligence). These results provide evidence for distinct neural circuit function impairments in MDD and anxiety disorders compared to stress disorders, yet cognitive impairment appears unrelated to diagnosis and varies with circuit function.
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Affiliation(s)
- Peter Zhukovsky
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Michael Wainberg
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Milos Milic
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Shreejoy J. Tripathy
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- cDepartment of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Benoit H. Mulsant
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- dInstitute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Daniel Felsky
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- dInstitute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- eDalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Aristotle N. Voineskos
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- 2To whom correspondence may be addressed.
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18
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Ballard JL, O'Connor LJ. Shared components of heritability across genetically correlated traits. Am J Hum Genet 2022; 109:989-1006. [PMID: 35477001 PMCID: PMC9247834 DOI: 10.1016/j.ajhg.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/01/2022] [Indexed: 11/01/2022] Open
Abstract
Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed pleiotropic decomposition regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of five to six traits genetically correlated with coronary artery disease (CAD), asthma, and type II diabetes (T2D), producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension, and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes (compared with the original summary statistics) by 94% and 70% for asthma and T2D out of sample, respectively, and by a predicted 300% for CAD.
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Affiliation(s)
- Jenna Lee Ballard
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Luke Jen O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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19
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Gurtan A, Dominy J, Khalid S, Vong L, Caplan S, Currie T, Richards S, Lamarche L, Denning D, Shpektor D, Gurinovich A, Rasheed A, Hameed S, Saeed S, Saleem I, Jalal A, Abbas S, Sultana R, Rasheed SZ, Memon FUR, Shah N, Ishaq M, Khera AV, Danesh J, Frossard P, Saleheen D. Analyzing human knockouts to validate GPR151 as a therapeutic target for reduction of body mass index. PLoS Genet 2022; 18:e1010093. [PMID: 35381001 PMCID: PMC9022822 DOI: 10.1371/journal.pgen.1010093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/21/2022] [Accepted: 02/13/2022] [Indexed: 11/30/2022] Open
Abstract
Novel drug targets for sustained reduction in body mass index (BMI) are needed to curb the epidemic of obesity, which affects 650 million individuals worldwide and is a causal driver of cardiovascular and metabolic disease and mortality. Previous studies reported that the Arg95Ter nonsense variant of GPR151, an orphan G protein-coupled receptor, is associated with reduced BMI and reduced risk of Type 2 Diabetes (T2D). Here, we further investigate GPR151 with the Pakistan Genome Resource (PGR), which is one of the largest exome biobanks of human homozygous loss-of-function carriers (knockouts) in the world. Among PGR participants, we identify eleven GPR151 putative loss-of-function (plof) variants, three of which are present at homozygosity (Arg95Ter, Tyr99Ter, and Phe175LeufsTer7), with a cumulative allele frequency of 2.2%. We confirm these alleles in vitro as loss-of-function. We test if GPR151 plof is associated with BMI, T2D, or other metabolic traits and find that GPR151 deficiency in complete human knockouts is not associated with clinically significant differences in these traits. Relative to Gpr151+/+ mice, Gpr151-/- animals exhibit no difference in body weight on normal chow and higher body weight on a high-fat diet. Together, our findings indicate that GPR151 antagonism is not a compelling therapeutic approach to treatment of obesity.
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Affiliation(s)
- Allan Gurtan
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - John Dominy
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Shareef Khalid
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, United States of America
- Department of Cardiology, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Linh Vong
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Shari Caplan
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Treeve Currie
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Sean Richards
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Lindsey Lamarche
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Daniel Denning
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Diana Shpektor
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Anastasia Gurinovich
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
- Tufts Medical Center, Boston, Massachusetts, United States of America
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan
- TopMed Hospital, Karachi, Sindh, Pakistan
| | | | - Subhan Saeed
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan
| | - Imran Saleem
- Punjab Institute of Cardiology, Lahore, Pakistan
| | - Anjum Jalal
- Faisalabad Institute of Cardiology, Faisalabad, Pakistan
| | - Shahid Abbas
- Faisalabad Institute of Cardiology, Faisalabad, Pakistan
| | | | | | | | - Nabi Shah
- Department of Pharmacy, COMSATS University Islamabad, Islamabad, Pakistan
| | | | - Amit V. Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge University & Health Data Research UK, Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, United States of America
- Department of Cardiology, Columbia University Irving Medical Center, New York, New York, United States of America
- * E-mail:
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20
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Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, Hastie T, Rivas MA. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 2022; 18:e1010105. [PMID: 35324888 PMCID: PMC8946745 DOI: 10.1371/journal.pgen.1010105] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/15/2022] [Indexed: 01/05/2023] Open
Abstract
We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).
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Affiliation(s)
- Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Junyang Qian
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Guhan Venkataraman
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Johanne Marie Justesen
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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21
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Grant AJ, Gill D, Kirk PDW, Burgess S. Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity. PLoS Genet 2022; 18:e1009975. [PMID: 35085229 PMCID: PMC8794082 DOI: 10.1371/journal.pgen.1009975] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 12/01/2021] [Indexed: 11/25/2022] Open
Abstract
Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.
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Affiliation(s)
- Andrew J. Grant
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Hospital, Imperial College London, London, United Kingdom
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, United Kingdom
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, United Kingdom
| | - Paul D. W. Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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22
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Schilder BM, Navarro E, Raj T. Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms. Neurobiol Dis 2021; 163:105580. [PMID: 34871738 PMCID: PMC10101343 DOI: 10.1016/j.nbd.2021.105580] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/17/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom; UK Dementia Research Institute at Imperial College London, London, United Kingdom.
| | - Elisa Navarro
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Sección Departamental de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
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23
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Mirzaeicheshmeh E, Zerrweck C, Centeno-Cruz F, Baca-Peynado P, Martinez-Hernandez A, García-Ortiz H, Contreras-Cubas C, Salas-Martínez MG, Saldaña-Alvarez Y, Mendoza-Caamal EC, Barajas-Olmos F, Orozco L. Alterations of DNA methylation during adipogenesis differentiation of mesenchymal stem cells isolated from adipose tissue of patients with obesity is associated with type 2 diabetes. Adipocyte 2021; 10:493-504. [PMID: 34699309 PMCID: PMC8555535 DOI: 10.1080/21623945.2021.1978157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 11/22/2022] Open
Abstract
Adipogenesis regulation is crucial for mature adipocyte function. In obesity, a major driver of type 2 diabetes (T2D), this process is disrupted and remains poorly characterized. Here we identified altered DNA methylation profiles in diabetic obese patients, during three adipocytes differentiation stages. We isolated mesenchymal cells from visceral adipose tissue of obese patients with and without T2D to analyse DNA methylation profiles at 0, 3, and 18 days of ex vivo differentiation and documented their impact on gene expression. Methylation and gene expression were analysed with EPIC and Clarion S arrays, respectively. Patients with T2D had epigenetic alterations in all the analysed stages, and these were mainly observed in genes important in adipogenesis, insulin resistance, cell death programming, and immune effector processes. Importantly, at 3 days, we found six-fold more methylated CpG alterations than in the other stages. This is the first study to document epigenetic markers that persist through all three adipogenesis stages and their impact on gene expression, which could be a cellular metabolic memory involved in T2D. Our data provided evidence that, throughout the adipogenesis process, alterations occur in methylation that might impact mature adipocyte function, cause tissue malfunction, and potentially, lead to the development of T2D.
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Affiliation(s)
- Elaheh Mirzaeicheshmeh
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Carlos Zerrweck
- Clínica de Obesidad del Hospital General Tláhuac, SSA, Mexico City, Mexico
- Facultad De Medicina, Alta Especialidad En Cirugía Bariatrica, Unam, Mexico City, Mexico
| | - Federico Centeno-Cruz
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Paulina Baca-Peynado
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Angélica Martinez-Hernandez
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Humberto García-Ortiz
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Cecilia Contreras-Cubas
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | | | - Yolanda Saldaña-Alvarez
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | | | - Francisco Barajas-Olmos
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
| | - Lorena Orozco
- Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional De Medicina Genómica, Ss, Mexico City, Mexico
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24
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Murphy AE, Schilder BM, Skene NG. MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics. Bioinformatics 2021; 37:4593-4596. [PMID: 34601555 PMCID: PMC8652100 DOI: 10.1093/bioinformatics/btab665] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/12/2021] [Accepted: 08/29/2021] [Indexed: 11/15/2022] Open
Abstract
Motivation Genome‐wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object. Availability and implementation MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alan E Murphy
- UK Dementia Research Institute at Imperial College London
| | | | - Nathan G Skene
- UK Dementia Research Institute at Imperial College London
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25
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Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, Asai S, Takahashi Y, Suzuki T, Shinozaki N, Yamaguchi H, Minami S, Murayama S, Yoshimori K, Nagayama S, Obata D, Higashiyama M, Masumoto A, Koretsune Y, Ito K, Terao C, Yamauchi T, Komuro I, Kadowaki T, Tamiya G, Yamamoto M, Nakamura Y, Kubo M, Murakami Y, Yamamoto K, Kamatani Y, Palotie A, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 2021; 53:1415-1424. [PMID: 34594039 DOI: 10.1038/s41588-021-00931-x] [Citation(s) in RCA: 506] [Impact Index Per Article: 168.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 08/04/2021] [Indexed: 02/08/2023]
Abstract
Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.
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Affiliation(s)
- Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. .,Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Center for Data Sciences, Harvard Medical School, Boston, MA, USA. .,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Masahiro Kanai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Juha Karjalainen
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Mitja Kurki
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Sendai, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Takahiro Konuma
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Masato Akiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Wataru Obara
- Department of Urology, Iwate Medical University, Iwate, Japan
| | - Ken Yamaji
- Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kazuhisa Takahashi
- Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Satoshi Asai
- Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan.,Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan
| | - Yasuo Takahashi
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan
| | | | | | | | - Shiro Minami
- Department of Bioregulation, Nippon Medical School, Kawasaki, Japan
| | - Shigeo Murayama
- Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan
| | - Kozo Yoshimori
- Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan
| | - Satoshi Nagayama
- The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Daisuke Obata
- Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Otsu, Japan
| | - Masahiko Higashiyama
- Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan
| | | | | | | | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Toranomon Hospital, Tokyo, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Sendai, Japan.,Graduate School of Medicine, Tohoku University, Sendai, Japan.,Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Sendai, Japan.,Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yusuke Nakamura
- Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.,Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Aarno Palotie
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Mark J Daly
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. .,Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. .,Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA. .,Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
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26
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Dibble JJ, McGrath SJ, Ponting CP. Genetic risk factors of ME/CFS: a critical review. Hum Mol Genet 2021; 29:R117-R124. [PMID: 32744306 PMCID: PMC7530519 DOI: 10.1093/hmg/ddaa169] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 07/28/2020] [Accepted: 07/28/2020] [Indexed: 02/06/2023] Open
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex multisystem illness that lacks effective therapy and a biomedical understanding of its causes. Despite a prevalence of ∼0.2-0.4% and its high public health burden, and evidence that it has a heritable component, ME/CFS has not yet benefited from the advances in technology and analytical tools that have improved our understanding of many other complex diseases. Here we critically review existing evidence that genetic factors alter ME/CFS risk before concluding that most ME/CFS candidate gene associations are not replicated by the larger CFS cohort within the UK Biobank. Multiple genome-wide association studies of this cohort also have not yielded consistently significant associations. Ahead of upcoming larger genome-wide association studies, we discuss how these could generate new lines of enquiry into the DNA variants, genes and cell types that are causally involved in ME/CFS disease.
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Affiliation(s)
- Joshua J Dibble
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
| | | | - Chris P Ponting
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
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27
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Julienne H, Laville V, McCaw ZR, He Z, Guillemot V, Lasry C, Ziyatdinov A, Nerin C, Vaysse A, Lechat P, Ménager H, Le Goff W, Dube MP, Kraft P, Ionita-Laza I, Vilhjálmsson BJ, Aschard H. Multitrait GWAS to connect disease variants and biological mechanisms. PLoS Genet 2021; 17:e1009713. [PMID: 34460823 PMCID: PMC8437297 DOI: 10.1371/journal.pgen.1009713] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 09/13/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
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Affiliation(s)
- Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Zachary R. McCaw
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Vincent Guillemot
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Carla Lasry
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Andrey Ziyatdinov
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Cyril Nerin
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Amaury Vaysse
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Pierre Lechat
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Hervé Ménager
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Wilfried Le Goff
- Sorbonne Université, INSERM, Institute of Cardiometabolism and Nutrition (ICAN), UMR_S 1166, Paris, France
| | - Marie-Pierre Dube
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, Canada
- Université de Montréal, Faculty of Medicine, Department of medicine, Université de Montréal, Montreal, Canada
| | - Peter Kraft
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, New York, United States of America
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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28
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Foley CN, Mason AM, Kirk PDW, Burgess S. MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates. Bioinformatics 2021; 37:531-541. [PMID: 32915962 PMCID: PMC8088327 DOI: 10.1093/bioinformatics/btaa778] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 08/06/2020] [Accepted: 09/01/2020] [Indexed: 01/22/2023] Open
Abstract
Motivation Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters. Results Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and implementation MR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher N Foley
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK
| | - Amy M Mason
- Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge CB1 8RN, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK.,Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK.,Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge CB1 8RN, UK
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29
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Bouchard C. Genetics of Obesity: What We Have Learned Over Decades of Research. Obesity (Silver Spring) 2021; 29:802-820. [PMID: 33899337 DOI: 10.1002/oby.23116] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/14/2022]
Abstract
There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.
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Affiliation(s)
- Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
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30
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Cannabis, schizophrenia genetic risk, and psychotic experiences: a cross-sectional study of 109,308 participants from the UK Biobank. Transl Psychiatry 2021; 11:211. [PMID: 33837184 PMCID: PMC8035271 DOI: 10.1038/s41398-021-01330-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/25/2021] [Accepted: 03/19/2021] [Indexed: 02/06/2023] Open
Abstract
Cannabis is known to produce acute, transient psychotic-like experiences. However, it is unclear whether cannabis disproportionately increases the risk of specific types of psychotic experiences and whether genetic predisposition influences the relationship between cannabis use and psychotic experiences. In this cross-sectional study of 109,308 UK Biobank participants, we examined how schizophrenia polygenic risk modulates the association between self-reported cannabis use and four types of self-reported psychotic experiences (auditory hallucinations, visual hallucinations, persecutory delusions, and delusions of reference). Cohort-wide, we found a strong, dose-dependent relationship between cannabis use and all four types of psychotic experiences, especially persecutory delusions. Cannabis users' psychotic experiences tended to be earlier-onset and cause greater distress than non-users', but were not more likely to lead to help-seeking. Participants with high schizophrenia polygenic risk scores showed stronger associations between cannabis use and auditory hallucinations, visual hallucinations, and delusions of reference, as well as psychotic experiences overall. For instance, cannabis ever-use was associated with 67% greater adjusted odds of delusions of reference among individuals in the top fifth of polygenic risk, but only 7% greater adjusted odds among the bottom fifth. Our results suggest that cannabis use is a predictive risk factor for psychotic experiences, including early-onset and distressing experiences. Individuals genetically predisposed to schizophrenia may be especially vulnerable to psychotic experiences as a result of using cannabis, supporting a long-postulated hypothesis. This study exemplifies the utility of population-scale biobanks for elucidating gene-by-environment interactions relating substance use to neuropsychiatric outcomes and points to the translational potential of using polygenic risk scores to inform personalized harm reduction interventions.
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31
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Aguirre M, Tanigawa Y, Venkataraman GR, Tibshirani R, Hastie T, Rivas MA. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet 2021; 29:1071-1081. [PMID: 33558700 DOI: 10.1038/s41431-021-00813-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 12/26/2020] [Accepted: 01/14/2021] [Indexed: 02/06/2023] Open
Abstract
Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Guhan Ram Venkataraman
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Rob Tibshirani
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
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32
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Abstract
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
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33
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Burren OS, Reales G, Wong L, Bowes J, Lee JC, Barton A, Lyons PA, Smith KGC, Thomson W, Kirk PDW, Wallace C. Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases. Genome Med 2020; 12:106. [PMID: 33239102 PMCID: PMC7687775 DOI: 10.1186/s13073-020-00797-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/02/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. METHODS Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension "basis" that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. RESULTS We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. CONCLUSIONS Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.
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Affiliation(s)
- Oliver S Burren
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Limy Wong
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - John Bowes
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - James C Lee
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Anne Barton
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Kenneth G C Smith
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Wendy Thomson
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Paul D W Kirk
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
- Cancer Research UK Cambridge Centre, Ovarian Cancer Programme, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
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34
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Qian J, Tanigawa Y, Du W, Aguirre M, Chang C, Tibshirani R, Rivas MA, Hastie T. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet 2020; 16:e1009141. [PMID: 33095761 PMCID: PMC7641476 DOI: 10.1371/journal.pgen.1009141] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 11/04/2020] [Accepted: 09/04/2020] [Indexed: 11/18/2022] Open
Abstract
The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.
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Affiliation(s)
- Junyang Qian
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Wenfei Du
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Chris Chang
- Grail, Inc., Menlo Park, CA, United States of America
| | - Robert Tibshirani
- Department of Statistics, Stanford University, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Trevor Hastie
- Department of Statistics, Stanford University, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
- * E-mail:
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35
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Flynn E, Tanigawa Y, Rodriguez F, Altman RB, Sinnott-Armstrong N, Rivas MA. Sex-specific genetic effects across biomarkers. Eur J Hum Genet 2020; 29:154-163. [PMID: 32873964 DOI: 10.1038/s41431-020-00712-w] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/28/2020] [Accepted: 08/04/2020] [Indexed: 11/09/2022] Open
Abstract
Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.
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Affiliation(s)
- Emily Flynn
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
| | - Yosuke Tanigawa
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Department of Medicine, Division of Cardiovascular Medicine and the Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.,Department of Medicine, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Manuel A Rivas
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
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Pividori M, Rajagopal PS, Barbeira A, Liang Y, Melia O, Bastarache L, Park Y, Consortium GTE, Wen X, Im HK. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. SCIENCE ADVANCES 2020; 6:eaba2083. [PMID: 32917697 PMCID: PMC11206444 DOI: 10.1126/sciadv.aba2083] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 07/29/2020] [Indexed: 05/02/2023]
Abstract
Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.
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Affiliation(s)
- Milton Pividori
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Padma S Rajagopal
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Alvaro Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Yanyu Liang
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Owen Melia
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University, Nashville, TN, USA
- Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - YoSon Park
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
| | - Hae K Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
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Tanigawa Y, Wainberg M, Karjalainen J, Kiiskinen T, Venkataraman G, Lemmelä S, Turunen JA, Graham RR, Havulinna AS, Perola M, Palotie A, Daly MJ, Rivas MA. Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma. PLoS Genet 2020; 16:e1008682. [PMID: 32369491 PMCID: PMC7199928 DOI: 10.1371/journal.pgen.1008682] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 02/18/2020] [Indexed: 12/17/2022] Open
Abstract
Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.
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Affiliation(s)
- Yosuke Tanigawa
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Michael Wainberg
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Juha Karjalainen
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Guhan Venkataraman
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Susanna Lemmelä
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Joni A. Turunen
- Department of Ophthalmology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland
| | - Robert R. Graham
- Maze Therapeutics, South San Francisco, California, United States of America
| | - Aki S. Havulinna
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aarno Palotie
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | | | - Mark J. Daly
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Manuel A. Rivas
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
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