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Xue D, Hajat A, Fohner AE. Conceptual frameworks for the integration of genetic and social epidemiology in complex diseases. GLOBAL EPIDEMIOLOGY 2024; 8:100156. [PMID: 39104369 PMCID: PMC11299589 DOI: 10.1016/j.gloepi.2024.100156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/11/2024] [Accepted: 07/06/2024] [Indexed: 08/07/2024] Open
Abstract
Uncovering the root causes of complex diseases requires complex approaches, yet many studies continue to isolate the effects of genetic and social determinants of disease. Epidemiologic efforts that under-utilize genetic epidemiology methods and findings may lead to incomplete understanding of disease. Meanwhile, genetic epidemiology studies are often conducted without consideration of social and environmental context, limiting the public health impact of genomic discoveries. This divide endures despite shared goals and increases in interdisciplinary data due to a lack of shared theoretical frameworks and differing language. Here, we demonstrate that bridging epidemiological divides does not require entirely new ways of thinking. Existing social epidemiology frameworks including Ecosocial theory and Fundamental Cause Theory, can both be extended to incorporate principles from genetic epidemiology. We show that genetic epidemiology can strengthen, rather than detract from, efforts to understand the impact of social determinants of health. In addition to presenting theoretical synergies, we offer practical examples of how genetics can improve the public health impact of epidemiology studies across the field. Ultimately, we aim to provide a guiding framework for trainees and established epidemiologists to think about diseases and complex systems and foster more fruitful collaboration between genetic and traditional epidemiological disciplines.
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Affiliation(s)
- Diane Xue
- Institute for Public Health Genetics, University of Washington School of Public Health, 1959 NE Pacific St, Room H-690, Seattle, WA 98195, USA
| | - Anjum Hajat
- Department of Epidemiology, University of Washington School of Public Health, Hans Rosling Population Health Building, 3980 15th Ave NE, Seattle, WA 98195, USA
| | - Alison E. Fohner
- Institute for Public Health Genetics, University of Washington School of Public Health, 1959 NE Pacific St, Room H-690, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington School of Public Health, Hans Rosling Population Health Building, 3980 15th Ave NE, Seattle, WA 98195, USA
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2
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Herrera-Luis E, Martin-Almeida M, Pino-Yanes M. Asthma-Genomic Advances Toward Risk Prediction. Clin Chest Med 2024; 45:599-610. [PMID: 39069324 PMCID: PMC11284279 DOI: 10.1016/j.ccm.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA.
| | - Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna 38200, Tenerife, Spain
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3
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Stiehler S, Sembill S, Schleicher O, Marx M, Rauh M, Krumbholz M, Karow A, Suttorp M, Woelfle J, Maj C, Metzler M. Imatinib treatment and longitudinal growth in pediatric patients with chronic myeloid leukemia: influence of demographic, pharmacological, and genetic factors in the German CML-PAED cohort. Haematologica 2024; 109:2555-2563. [PMID: 38497150 PMCID: PMC11290534 DOI: 10.3324/haematol.2023.284668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/05/2024] [Indexed: 03/19/2024] Open
Abstract
In children and adolescents, impaired growth due to tyrosine kinase inhibitor therapy remains an insufficiently studied adverse effect. This study examines demographic, pharmacological, and genetic factors associated with impaired longitudinal growth in a uniform pediatric cohort treated with imatinib. We analyzed 94 pediatric patients with chronic myeloid leukemia (CML) diagnosed in the chronic phase and treated with imatinib for >12 months who participated in the Germany-wide CML-PAEDII study between February 2006 and February 2021 (clinicaltrials gov. Identifier: NCT00445822). During imatinib treatment, significant height reduction occurred, with medians of -0.35 standard deviation score (SDS) at 12 months and -0.76 SDS at 24 months. Cumulative height SDS change (Δ height SDS) showed a more pronounced effect in prepubertal patients during the first year but were similar between prepubertal and pubertal subgroups by the second year (-0.55 vs. -0.50). From months 12 to 18 on imatinib, only 18% patients achieved individually longitudinal growth adequate to the growth standard (Δ height SDS ≥0). When patients were divided into two subgroups based on median Δ height SDS (classifier Δ height SDS > or ≤-0.37) after 1 year on imatinib therapy, cohort 1 (Δ height SDS ≤-0.37) showed younger age at diagnosis, a higher proportion of prepubertal children, but also better treatment response and higher imatinib serum levels. Exploring the association of growth parameters with pharmacokinetically relevant single nucleotide polymorphisms, known for affecting imatinib response, showed no correlation. This retrospective study provides new insights into imatinib-related growth impairment. We emphasize the importance of optimizing treatment strategies for pediatric patients to realize their maximum growth potential.
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Affiliation(s)
- Sophie Stiehler
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg
| | - Stephanie Sembill
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen.
| | - Oliver Schleicher
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany; Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg
| | - Michaela Marx
- Pediatric Endocrinology, Department of Pediatrics and Adolescent Medicine, University Children's Hospital, Friedrich-Alexander-Universität, Erlangen-Nürnberg
| | - Manfred Rauh
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg
| | - Manuela Krumbholz
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen
| | - Axel Karow
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen
| | - Meinolf Suttorp
- Pediatric Hemato-Oncology, Medical Faculty, Technical University Dresden, Dresden
| | - Joachim Woelfle
- Pediatric Endocrinology, Department of Pediatrics and Adolescent Medicine, University Children's Hospital, Friedrich-Alexander-Universität, Erlangen-Nürnberg
| | - Carlo Maj
- Centre for Human Genetics, University of Marburg, Marburg
| | - Markus Metzler
- Pediatric Oncology and Hematology, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen
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4
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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; 33:1315-1327. [PMID: 38679805 PMCID: PMC11262744 DOI: 10.1093/hmg/ddae067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [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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5
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Habtewold TD, Wijesiriwardhana P, Biedrzycki RJ, Tekola-Ayele F. Genetic distance and ancestry proportion modify the association between maternal genetic risk score of type 2 diabetes and fetal growth. Hum Genomics 2024; 18:81. [PMID: 39030631 PMCID: PMC11264503 DOI: 10.1186/s40246-024-00645-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/27/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Maternal genetic risk of type 2 diabetes (T2D) has been associated with fetal growth, but the influence of genetic ancestry is not yet fully understood. We aimed to investigate the influence of genetic distance (GD) and genetic ancestry proportion (GAP) on the association of maternal genetic risk score of T2D (GRST2D) with fetal weight and birthweight. METHODS Multi-ancestral pregnant women (n = 1,837) from the NICHD Fetal Growth Studies - Singletons cohort were included in the current analyses. Fetal weight (in grams, g) was estimated from ultrasound measurements of fetal biometry, and birthweight (g) was measured at delivery. GRST2D was calculated using T2D-associated variants identified in the latest trans-ancestral genome-wide association study and was categorized into quartiles. GD and GAP were estimated using genotype data of four reference populations. GD was categorized into closest, middle, and farthest tertiles, and GAP was categorized as highest, medium, and lowest. Linear regression analyses were performed to test the association of GRST2D with fetal weight and birthweight, adjusted for covariates, in each GD and GAP category. RESULTS Among women with the closest GD from African and Amerindigenous ancestries, the fourth and third GRST2D quartile was significantly associated with 5.18 to 7.48 g (weeks 17-20) and 6.83 to 25.44 g (weeks 19-27) larger fetal weight compared to the first quartile, respectively. Among women with middle GD from European ancestry, the fourth GRST2D quartile was significantly associated with 5.73 to 21.21 g (weeks 18-26) larger fetal weight. Furthermore, among women with middle GD from European and African ancestries, the fourth and second GRST2D quartiles were significantly associated with 117.04 g (95% CI = 23.88-210.20, p = 0.014) and 95.05 g (95% CI = 4.73-185.36, p = 0.039) larger birthweight compared to the first quartile, respectively. The absence of significant association among women with the closest GD from East Asian ancestry was complemented by a positive significant association among women with the highest East Asian GAP. CONCLUSIONS The association between maternal GRST2D and fetal growth began in early-second trimester and was influenced by GD and GAP. The results suggest the use of genetic GD and GAP could improve the generalizability of GRS.
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Affiliation(s)
- Tesfa Dejenie Habtewold
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Prabhavi Wijesiriwardhana
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Richard J Biedrzycki
- Glotech, Inc., contractor for Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA.
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6
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Lassen FH, Venkatesh SS, Baya N, Hill B, Zhou W, Bloemendal A, Neale BM, Kessler BM, Whiffin N, Lindgren CM, Palmer DS. Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank. CELL GENOMICS 2024; 4:100602. [PMID: 38944039 PMCID: PMC11293579 DOI: 10.1016/j.xgen.2024.100602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 03/11/2024] [Accepted: 06/07/2024] [Indexed: 07/01/2024]
Abstract
The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.
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Affiliation(s)
- Frederik H Lassen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Samvida S Venkatesh
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Wei Zhou
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Novo Nordisk Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicola Whiffin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
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7
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Forer L, Taliun D, LeFaive J, Smith AV, Boughton A, Coassin S, Lamina C, Kronenberg F, Fuchsberger C, Schönherr S. Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers. Nucleic Acids Res 2024; 52:W70-W77. [PMID: 38709879 PMCID: PMC11223871 DOI: 10.1093/nar/gkae331] [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: 02/21/2024] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.
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Affiliation(s)
- Lukas Forer
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montreal, Québec, Canada
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
| | - Jonathon LeFaive
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Albert V Smith
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew P Boughton
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stefan Coassin
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Claudia Lamina
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
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8
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Harris KD, Greenbaum G. DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data. Nucleic Acids Res 2024; 52:W54-W60. [PMID: 38742634 PMCID: PMC11223807 DOI: 10.1093/nar/gkae373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/17/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
The ability to sequence ancient genomes has revolutionized the way we study evolutionary history by providing access to the most important aspect of evolution-time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and integrating multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool enables browsing sample metadata alongside additional layers of information, such as population structure, climatic data, and unpublished samples. Users can perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on the scientific questions that can be addressed through analysis of aDNA datasets.
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Affiliation(s)
- Keith D Harris
- Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Givat Ram, 9190401 Jerusalem, Israel
| | - Gili Greenbaum
- Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Givat Ram, 9190401 Jerusalem, Israel
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9
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Kizilkaya HS, Sørensen KV, Madsen JS, Lindquist P, Douros JD, Bork-Jensen J, Berghella A, Gerlach PA, Gasbjerg LS, Mokrosiński J, Mowery SA, Knerr PJ, Finan B, Campbell JE, D'Alessio DA, Perez-Tilve D, Faas F, Mathiasen S, Rungby J, Sørensen HT, Vaag A, Nielsen JS, Holm JC, Lauenborg J, Damm P, Pedersen O, Linneberg A, Hartmann B, Holst JJ, Hansen T, Wright SC, Lauschke VM, Grarup N, Hauser AS, Rosenkilde MM. Characterization of genetic variants of GIPR reveals a contribution of β-arrestin to metabolic phenotypes. Nat Metab 2024; 6:1268-1281. [PMID: 38871982 PMCID: PMC11272584 DOI: 10.1038/s42255-024-01061-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/02/2024] [Indexed: 06/15/2024]
Abstract
Incretin-based therapies are highly successful in combatting obesity and type 2 diabetes1. Yet both activation and inhibition of the glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) in combination with glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) activation have resulted in similar clinical outcomes, as demonstrated by the GIPR-GLP-1R co-agonist tirzepatide2 and AMG-133 (ref. 3) combining GIPR antagonism with GLP-1R agonism. This underlines the importance of a better understanding of the GIP system. Here we show the necessity of β-arrestin recruitment for GIPR function, by combining in vitro pharmacological characterization of 47 GIPR variants with burden testing of clinical phenotypes and in vivo studies. Burden testing of variants with distinct ligand-binding capacity, Gs activation (cyclic adenosine monophosphate production) and β-arrestin 2 recruitment and internalization shows that unlike variants solely impaired in Gs signalling, variants impaired in both Gs and β-arrestin 2 recruitment contribute to lower adiposity-related traits. Endosomal Gs-mediated signalling of the variants shows a β-arrestin dependency and genetic ablation of β-arrestin 2 impairs cyclic adenosine monophosphate production and decreases GIP efficacy on glucose control in male mice. This study highlights a crucial impact of β-arrestins in regulating GIPR signalling and overall preservation of biological activity that may facilitate new developments in therapeutic targeting of the GIPR system.
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Affiliation(s)
- Hüsün S Kizilkaya
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kimmie V Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob S Madsen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Peter Lindquist
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan D Douros
- Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA
- Indiana Biosciences Research Institute Indianapolis, Indianapolis, IN, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alessandro Berghella
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Teramo, Italy
| | - Peter A Gerlach
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lærke S Gasbjerg
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Stephanie A Mowery
- Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA
- Indiana Biosciences Research Institute Indianapolis, Indianapolis, IN, USA
| | - Patrick J Knerr
- Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA
- Indiana Biosciences Research Institute Indianapolis, Indianapolis, IN, USA
| | - Brian Finan
- Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Jonathan E Campbell
- Duke Molecular Physiology Institute, Duke University Durham, Durham, NC, USA
| | - David A D'Alessio
- Duke Molecular Physiology Institute, Duke University Durham, Durham, NC, USA
| | - Diego Perez-Tilve
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Felix Faas
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Signe Mathiasen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jørgen Rungby
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark
- Department of Epidemiology, Boston University, Boston, MA, USA
| | - Allan Vaag
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden
| | - Jens S Nielsen
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jens-Christian Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Holbæk Hospital, Holbæk, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeannet Lauenborg
- Department of Obstetrics and Gynecology, Copenhagen University Hospital Herlev, Herlev, Denmark
| | - Peter Damm
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Pregnant Women with Diabetes, Rigshospitalet, Copenhagen, Denmark
- Department of Obstetrics, Rigshospitalet, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Department of Medicine, Gentofte Hospital, Copenhagen, Denmark
| | - Allan Linneberg
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Bolette Hartmann
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens J Holst
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Shane C Wright
- Department of Physiology & Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology & Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Alexander S Hauser
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
| | - Mette M Rosenkilde
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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10
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Stinson SE, Kromann Reim P, Lund MAV, Lausten-Thomsen U, Aas Holm L, Huang Y, Brøns C, Vaag A, Thiele M, Krag A, Fonvig CE, Grarup N, Pedersen O, Christiansen M, Ängquist L, Sørensen TIA, Holm JC, Hansen T. The interplay between birth weight and obesity in determining childhood and adolescent cardiometabolic risk. EBioMedicine 2024; 105:105205. [PMID: 38918147 PMCID: PMC11293585 DOI: 10.1016/j.ebiom.2024.105205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/29/2024] [Accepted: 06/02/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Birth weight (BW) is associated with risk of cardiometabolic disease (CMD) in adulthood, which may depend on the state of obesity, in particular if developed at a young age. We hypothesised that BW and a polygenic score (PGS) for BW were associated with cardiometabolic risk and related plasma protein levels in children and adolescents. We aimed to determine the modifying effect of childhood obesity on these associations. METHODS We used data from The cross-sectional HOLBAEK Study with 4263 participants (median [IQR] age, 11.7 [9.2, 14.3] years; 57.1% girls and 42.9% boys; 48.6% from an obesity clinic and 51.4% from a population-based group). We gathered information on BW and gestational age, anthropometrics, cardiometabolic risk factors, calculated a PGS for BW, and measured plasma proteins using Olink Inflammation and Cardiovascular II panels. We employed multiple linear regression to examine the associations with BW as a continuous variable and performed interaction analyses to assess the effect of childhood obesity on cardiometabolic risk and plasma protein levels. FINDINGS BW and a PGS for BW associated with cardiometabolic risk and plasma protein levels in childhood and adolescence. Childhood obesity modified the associations between BW and measures of insulin resistance, including HOMA-IR (βadj [95% CI per SD] for obesity: -0.12 [-0.15, -0.08]; normal weight: -0.04 [-0.08, 0.00]; Pinteraction = 0.004), c-peptide (obesity: -0.11 [-0.14, -0.08]; normal weight: -0.02 [-0.06, 0.02]; Pinteraction = 5.05E-04), and SBP SDS (obesity: -0.12 [-0.16, -0.08]; normal weight: -0.06 [-0.11, -0.01]; Pinteraction = 0.0479). Childhood obesity also modified the associations between BW and plasma levels of 14 proteins (e.g., IL15RA, MCP1, and XCL1; Pinteraction < 0.05). INTERPRETATION We identified associations between lower BW and adverse metabolic phenotypes, particularly insulin resistance, blood pressure, and altered plasma protein levels, which were more pronounced in children with obesity. Developing effective prevention and treatment strategies for this group is needed to reduce the risk of future CMD. FUNDING Novo Nordisk Foundation (NNF15OC0016544, NNF0064142 to T.H., NNF15OC0016692 to T.H. and A.K., NNF18CC0033668 to S.E.S, NNF18SA0034956 to C.E.F., NNF20SA0067242 to DCA, NNF18CC0034900 to NNF CBMR), The Innovation Fund Denmark (0603-00484B to T.H.), The Danish Cardiovascular Academy (DCA) and the Danish Heart Foundation (HF) (PhD2021007-DCA to P.K.R, 18-R125-A8447-22088 (HF) and 21-R149-A10071-22193 (HF) to M.A.V.L., PhD2023009-HF to L.A.H), EU Horizon (668031, 847989, 825694, 964590 to A.K.), Innovative Health Initiative (101132901 for A.K.), A.P. Møller Foundation (19-L-0366 to T.H.), The Danish National Research Foundation, Steno Diabetes Center Sjælland, and The Region Zealand and Southern Denmark Health Scientific Research Foundation.
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Affiliation(s)
- Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pauline Kromann Reim
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten Asp Vonsild Lund
- The Children's Obesity Clinic, Accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ulrik Lausten-Thomsen
- Department of Neonatology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Louise Aas Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; The Children's Obesity Clinic, Accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Allan Vaag
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Cilius Esmann Fonvig
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; The Children's Obesity Clinic, Accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Center for Clinical Metabolic Research, Herlev-Gentofte University Hospital, Denmark
| | - Michael Christiansen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
| | - Lars Ängquist
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Center for Childhood Health, Copenhagen, Denmark
| | - Jens-Christian Holm
- The Children's Obesity Clinic, Accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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11
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Hou K, Xu Z, Ding Y, Mandla R, Shi Z, Boulier K, Harpak A, Pasaniuc B. Calibrated prediction intervals for polygenic scores across diverse contexts. Nat Genet 2024; 56:1386-1396. [PMID: 38886587 DOI: 10.1038/s41588-024-01792-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 05/08/2024] [Indexed: 06/20/2024]
Abstract
Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields. We show that PGS performance varies broadly across contexts and biobanks. Contexts such as age, sex and income can impact PGS accuracy with similar magnitudes as genetic ancestry. Here we introduce an approach (CalPred) that models all contexts jointly to produce prediction intervals that vary across contexts to achieve calibration (include the trait with 90% probability), whereas existing methods are miscalibrated. In analyses of 72 traits across large and diverse biobanks (All of Us and UK Biobank), we find that prediction intervals required adjustment by up to 80% for quantitative traits. For disease traits, PGS-based predictions were miscalibrated across socioeconomic contexts such as annual household income levels, further highlighting the need of accounting for context information in PGS-based prediction across diverse populations.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
| | - Ziqi Xu
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Ravi Mandla
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Zhuozheng Shi
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Kristin Boulier
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arbel Harpak
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA.
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12
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Patel RA, Weiß CL, Zhu H, Mostafavi H, Simons YB, Spence JP, Pritchard JK. Conditional frequency spectra as a tool for studying selection on complex traits in biobanks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.15.599126. [PMID: 38948697 PMCID: PMC11212903 DOI: 10.1101/2024.06.15.599126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. To account for GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insight into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.
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Affiliation(s)
- Roshni A. Patel
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Clemens L. Weiß
- Stanford Cancer Institute Core, Stanford University School of Medicine, Stanford, CA
| | - Huisheng Zhu
- Department of Biology, Stanford University, Stanford, CA
| | - Hakhamanesh Mostafavi
- Center for Human Genetics and Genomics, New York University School of Medicine, New York, NY
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY
| | | | - Jeffrey P. Spence
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Jonathan K. Pritchard
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
- Department of Biology, Stanford University, Stanford, CA
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13
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Herrera-Rivero M, Adli M, Akiyama K, Akula N, Amare AT, Ardau R, Arias B, Aubry JM, Backlund L, Bellivier F, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Birner A, Cearns M, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Colom F, Cruceanu C, Czerski PM, Dalkner N, Degenhardt F, Del Zompo M, DePaulo JR, Etain B, Falkai P, Ferensztajn-Rochowiak E, Forstner AJ, Frank J, Frisén L, Frye MA, Fullerton JM, Gallo C, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hasler R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kato T, Kelsoe J, Kittel-Schneider S, Kuo PH, Kusumi I, König B, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Marie-Claire C, Martinsson L, McCarthy MJ, McElroy SL, Millischer V, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Novák T, Nöthen MM, O'Donovan C, Ozaki N, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Richard-Lepouriel H, Roberts G, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schubert KO, Schulte EC, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Streit F, Tekola-Ayele F, Thalamuthu A, Tortorella A, Turecki G, Veeh J, Vieta E, Viswanath B, Witt SH, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Rietschel M, Schulze TG, Baune BT. Exploring the genetics of lithium response in bipolar disorders. Int J Bipolar Disord 2024; 12:20. [PMID: 38865039 PMCID: PMC11169116 DOI: 10.1186/s40345-024-00341-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/02/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. RESULTS We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. CONCLUSIONS Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.
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Affiliation(s)
- Marisol Herrera-Rivero
- Department of Psychiatry, University of Münster and Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Albert-Schweitzer-Campus 1, Building A9, 48149, Münster, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
- Fliedner Klinik Berlin, Berlin, Germany
| | - Kazufumi Akiyama
- Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Nirmala Akula
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health & Human Services, Baltimore, USA
| | - Azmeraw T Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, Hospital University Agency of Cagliari, Cagliari, Italy
| | - Bárbara Arias
- Unitat de Zoologia i Antropologia Biològica (Dpt. Biologia Evolutiva, Ecologia i Ciències Ambientals), Facultat de Biologia and Institut de Biomedicina (IBUB), University of Barcelona, CIBERSAM, Barcelona, Spain
| | - Jean-Michel Aubry
- Department of Psychiatry, Division of Psychiatric Specialities, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lena Backlund
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine at Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Frank Bellivier
- Département de Psychiatrie et de Médecine Addictologique, INSERM UMR-S 1144, Université Paris Cité, AP-HP, Groupe Hospitalier Saint-Louis-Lariboisière, F. Widal, Paris, France
| | - Antonio Benabarre
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Susanne Bengesser
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | | | - Joanna M Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, USA
| | - Armin Birner
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Micah Cearns
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Pablo Cervantes
- The Neuromodulation Unit, McGill University Health Centre, Montreal, Canada
| | - Hsi-Chung Chen
- Department of Psychiatry & Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, Hospital University Agency of Cagliari, Cagliari, Italy
| | - Sven Cichon
- Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Scott R Clark
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Francesc Colom
- Mental Health Research Group, IMIM-Hospital del Mar, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Cristiana Cruceanu
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Piotr M Czerski
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznań, Poland
| | - Nina Dalkner
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Maria Del Zompo
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique, INSERM UMR-S 1144, Université Paris Cité, AP-HP, Groupe Hospitalier Saint-Louis-Lariboisière, F. Widal, Paris, France
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | | | - Andreas J Forstner
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Louise Frisén
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Mark A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, USA
| | - Janice M Fullerton
- Neuroscience Research, Australia and School of Biomedical Sciences, University of New South Wales, Sydney, Australia
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, San Martín de Porres, Peru
| | - Sébastien Gard
- Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France
| | - Julie S Garnham
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, Canada
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Roland Hasler
- Department of Psychiatry, Division of Psychiatric Specialities, Geneva University Hospitals, Geneva, Switzerland
| | - Joanna Hauser
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznań, Poland
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Stefan Herms
- Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Per Hoffmann
- Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Liping Hou
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health & Human Services, Baltimore, USA
| | - Yi-Hsiang Hsu
- Program for Quantitative Genomics, Harvard School of Public Health and HSL Institute for Aging Research, Harvard Medical School, Boston, USA
| | - Stephane Jamain
- Univ. Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Créteil, France
| | - Esther Jiménez
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, ISCIII, Barcelona, Spain
| | - Jean-Pierre Kahn
- Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de Nancy - Université, Nancy, France
| | - Layla Kassem
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health & Human Services, Baltimore, USA
| | - Tadafumi Kato
- Department of Psychiatry & Behavioral Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - John Kelsoe
- Department of Psychiatry, University of California San Diego, San Diego, USA
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
| | - Po-Hsiu Kuo
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ichiro Kusumi
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Barbara König
- Department of Psychiatry and Psychotherapeutic Medicine, Landesklinikum Neunkirchen, Neunkirchen, Austria
| | - Gonzalo Laje
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health & Human Services, Baltimore, USA
| | - Mikael Landén
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, Gothenburg, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine at Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Marion Leboyer
- Univ. Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, AP-HP, Mondor University Hospital, DMU Impact, Fondation FondaMental, Créteil, France
| | - Susan G Leckband
- Office of Mental Health, VA San Diego Healthcare System, California, USA
| | - Mario Maj
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Caserta, Italy
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, Canada
| | - Cynthia Marie-Claire
- Université Paris Cité, Inserm UMR-S 1144, Optimisation Thérapeutique en Neuropsychopharmacologie, 75006, Paris, France
| | - Lina Martinsson
- Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Michael J McCarthy
- Department of Psychiatry, University of California San Diego, San Diego, USA
- Department of Psychiatry, VA San Diego Healthcare System, San Diego, CA, USA
| | - Susan L McElroy
- Department of Psychiatry, Lindner Center of Hope/University of Cincinnati, Cincinnati, USA
| | - Vincent Millischer
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine at Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Marina Mitjans
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Institut de Biomedicina de La Universitat de Barcelona (IBUB), University of Barcelona, Barcelona, Spain
| | - Francis M Mondimore
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
| | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi, Italy
| | | | - Tomas Novák
- National Institute of Mental Health, Klecany, Czech Republic
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | | | - Norio Ozaki
- Department of Psychiatry & Department of Child and Adolescent Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sergi Papiol
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Dresden, Germany
| | - Claudia Pisanu
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - James B Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Eva Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Hélène Richard-Lepouriel
- Department of Psychiatry, Division of Psychiatric Specialities, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Gloria Roberts
- School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Guy A Rouleau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Janusz K Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznań, Poland
| | - Martin Schalling
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine at Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Peter R Schofield
- Neuroscience Research, Australia and School of Biomedical Sciences, University of New South Wales, Sydney, Australia
| | - Klaus Oliver Schubert
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
- Northern Adelaide Local Health Network, Mental Health Services, Adelaide, Australia
| | - Eva C Schulte
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Medical Faculty University of Bonn, Bonn, Germany
| | - Barbara W Schweizer
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
| | - Giovanni Severino
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Tatyana Shekhtman
- Department of Psychiatry, University of California San Diego, San Diego, USA
| | - Paul D Shilling
- Department of Psychiatry, University of California San Diego, San Diego, USA
| | - Katzutaka Shimoda
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Christian Simhandl
- Medical Faculty, Bipolar Center Wiener Neustadt, Sigmund Freud University, Vienna, Austria
| | - Claire M Slaney
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Pavla Stopkova
- National Institute of Mental Health, Klecany, Czech Republic
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, USA
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | | | - Gustavo Turecki
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Julia Veeh
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, ISCIII, Barcelona, Spain
| | - Biju Viswanath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, 560029, India
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Peter P Zandi
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Dresden, Germany
| | - Francis J McMahon
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health & Human Services, Baltimore, USA
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Thomas G Schulze
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, USA
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster and Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Albert-Schweitzer-Campus 1, Building A9, 48149, Münster, Germany.
- Department of Psychiatry, Melbourne Medical School, University of Melbourne and The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
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14
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome wide association study and genomic risk prediction of age related macular degeneration in Israel. Sci Rep 2024; 14:13034. [PMID: 38844476 PMCID: PMC11156861 DOI: 10.1038/s41598-024-63065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10-5 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10-9). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Affiliation(s)
- Michelle Grunin
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Daria Triffon
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
| | - Gala Beykin
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Regev Schweiger
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Genetics, University of Cambridge, CB21TN, Cambridge, UK
| | - Liran Tiosano
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Samer Khateb
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Shira Hagbi-Levi
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Batya Rinsky
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Refael Munitz
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Eran Halperin
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Anesthesiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel.
| | - Itay Chowers
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel.
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15
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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16
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Henry J, Lin Y, Bouatia-Naji N. Enhancing the Prediction Power of Polygenic Risk Scores in Genetically Diverse Coronary Heart Disease. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004610. [PMID: 38586952 DOI: 10.1161/circgen.124.004610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Joséphine Henry
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
| | - Yilong Lin
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
| | - Nabila Bouatia-Naji
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
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17
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Smith JL, Tcheandjieu C, Dikilitas O, Iyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao PS, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004272. [PMID: 38380516 DOI: 10.1161/circgen.123.004272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. RESULTS Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). CONCLUSIONS The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco (C.T.)
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institute, San Francisco, CA (C.T.)
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Kruthika Iyer
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | - Kazuo Miyazawa
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Austin Hilliard
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taiwan (W.H.-H.S.)
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA (K.-M.C.)
| | - Stavroula Kanoni
- Queen Mary University of London, Cambridge, United Kingdom (S.K.)
| | - Philip S Tsao
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Kaoru Ito
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | - Shoa L Clarke
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | | | - Iftikhar J Kullo
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
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18
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024:dmae012. [PMID: 38805697 DOI: 10.1093/humupd/dmae012] [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: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d'Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l'infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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19
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Hung SC, Chang LW, Hsiao TH, Lin GC, Wang SS, Li JR, Chen IC. Polygenic risk score predicting susceptibility and outcome of benign prostatic hyperplasia in the Han Chinese. Hum Genomics 2024; 18:49. [PMID: 38778357 PMCID: PMC11110300 DOI: 10.1186/s40246-024-00619-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Given the high prevalence of BPH among elderly men, pinpointing those at elevated risk can aid in early intervention and effective management. This study aimed to explore that polygenic risk score (PRS) is effective in predicting benign prostatic hyperplasia (BPH) incidence, prognosis and risk of operation in Han Chinese. METHODS A retrospective cohort study included 12,474 male participants (6,237 with BPH and 6,237 non-BPH controls) from the Taiwan Precision Medicine Initiative (TPMI). Genotyping was performed using the Affymetrix Genome-Wide TWB 2.0 SNP Array. PRS was calculated using PGS001865, comprising 1,712 single nucleotide polymorphisms. Logistic regression models assessed the association between PRS and BPH incidence, adjusting for age and prostate-specific antigen (PSA) levels. The study also examined the relationship between PSA, prostate volume, and response to 5-α-reductase inhibitor (5ARI) treatment, as well as the association between PRS and the risk of TURP. RESULTS Individuals in the highest PRS quartile (Q4) had a significantly higher risk of BPH compared to the lowest quartile (Q1) (OR = 1.51, 95% CI = 1.274-1.783, p < 0.0001), after adjusting for PSA level. The Q4 group exhibited larger prostate volumes and a smaller volume reduction after 5ARI treatment. The Q1 group had a lower cumulative TURP probability at 3, 5, and 10 years compared to the Q4 group. PRS Q4 was an independent risk factor for TURP. CONCLUSIONS In this Han Chinese cohort, higher PRS was associated with an increased susceptibility to BPH, larger prostate volumes, poorer response to 5ARI treatment, and a higher risk of TURP. Larger prospective studies with longer follow-up are warranted to further validate these findings.
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Affiliation(s)
- Sheng-Chun Hung
- Division of Urology, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Li-Wen Chang
- Division of Urology, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Guan-Cheng Lin
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shian-Shiang Wang
- Division of Urology, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou, Taiwan
| | - Jian-Ri Li
- Division of Urology, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Medicine and Nursing, Hungkuang University, Taichung, Taiwan
| | - I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
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20
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Staerk C, Klinkhammer H, Wistuba T, Maj C, Mayr A. Generalizability of polygenic prediction models: how is the R 2 defined on test data? BMC Med Genomics 2024; 17:132. [PMID: 38755654 PMCID: PMC11100126 DOI: 10.1186/s12920-024-01905-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 05/08/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the R 2 is a commonly used measure to evaluate prediction accuracy. While the R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. METHODS Based on large-scale genotype data from the UK Biobank, we compare three definitions of the R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. RESULTS Our analysis shows that the choice of the R 2 definition can lead to considerably different results on test data, making the comparison of R 2 values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of R 2 can provide valuable complementary information. CONCLUSIONS Awareness of the different definitions of the R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.
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Affiliation(s)
- Christian Staerk
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany.
- Institute of Statistics, RWTH Aachen University, Aachen, Germany.
| | - Hannah Klinkhammer
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Tobias Wistuba
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Andreas Mayr
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
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21
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Cho C, Kim B, Kim DS, Hwang MY, Shim I, Song M, Lee YC, Jung SH, Cho SK, Park WY, Myung W, Kim BJ, Do R, Choi HK, Merriman TR, Kim YJ, Won HH. Large-scale cross-ancestry genome-wide meta-analysis of serum urate. Nat Commun 2024; 15:3441. [PMID: 38658550 PMCID: PMC11043400 DOI: 10.1038/s41467-024-47805-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.
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Affiliation(s)
- Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Kweon Cho
- Department of Pharmacology, Ajou University School of Medicine (AUSOM), Suwon, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyon K Choi
- Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tony R Merriman
- Biochemistry Department, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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22
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Troubat L, Fettahoglu D, Henches L, Aschard H, Julienne H. Multi-trait GWAS for diverse ancestries: mapping the knowledge gap. BMC Genomics 2024; 25:375. [PMID: 38627641 PMCID: PMC11022331 DOI: 10.1186/s12864-024-10293-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. METHODS Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)). RESULTS We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria. CONCLUSIONS Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.
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Affiliation(s)
- Lucie Troubat
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Deniz Fettahoglu
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France.
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, F-75015, France.
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23
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Sadler MC, Apostolov A, Cevallos C, Ribeiro DM, Altman RB, Kutalik Z. Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.06.24305415. [PMID: 38633781 PMCID: PMC11023668 DOI: 10.1101/2024.04.06.24305415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Alexander Apostolov
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Caterina Cevallos
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Diogo M. Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
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24
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Shah Y, Kulm S, Nauseef JT, Chen Z, Elemento O, Kensler KH, Sharaf RN. Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort. PLoS Comput Biol 2024; 20:e1011990. [PMID: 38598551 PMCID: PMC11034641 DOI: 10.1371/journal.pcbi.1011990] [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: 04/17/2023] [Revised: 04/22/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.
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Affiliation(s)
- Yajas Shah
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York City, New York, United States of America
| | - Scott Kulm
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York City, New York, United States of America
| | - Jones T. Nauseef
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Medicine—Hematology and Medical Oncology, Weill Cornell Medicine, New York City, New York, United States of America
| | - Zhengming Chen
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, United States of America
| | - Olivier Elemento
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York City, New York, United States of America
| | - Kevin H. Kensler
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, United States of America
| | - Ravi N. Sharaf
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, United States of America
- Department of Medicine–Gastroenterology and Hepatology, Weill Cornell Medicine, New York City, New York, United States of America
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25
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Geneviève N, Mojgan Y, Nahid Y, Vincenzo F, Simon G, Daniel S, Maja K, Nathalie A, Despoina M. Genetic susceptibility and late bone outcomes in childhood acute lymphoblastic leukemia survivors. J Bone Miner Res 2024; 39:130-138. [PMID: 38477791 DOI: 10.1093/jbmr/zjad013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 03/14/2024]
Abstract
Childhood acute lymphoblastic leukemia (cALL) survivors are at increased risk for bone comorbidities, but accurate screening tools for such comorbidities are limited. Polygenic scores (PGS) could stratify cALL survivors for risk of long-term adverse bone outcomes. We evaluated 214 (51% female) cALL survivors from the Prévenir les Effets TArdifs de la LEucémie study (median age 21 yr). Bone mineral density (BMD) measurements were obtained using dual X-ray absorptiometry at the lumbar spine (LS-BMD), femoral neck (FN-BMD), and total body (TB-BMD), and vertebral fractures (VF) were documented using the vertebral deformity criterion. We computed a PGS for adult heel quantitative ultrasound speed of sound (gSOS), known to be associated with the risk of osteoporotic fracture, using imputed genotype data of the participants, and tested it for association with BMD Z-scores and VF risk, adjusting for clinical risk factors, and in sex and prognostic risk-stratified analyses. We found that a gSOS below the mean was associated with lower BMD in all three sites in univariate and multivariate models. In univariate analyses, 1 SD increase in gSOS conferred a 0.16 SD increase in LS-BMD (95% CI 0.005-0.31), whereas a gSOS above the mean was associated with a 0.31 SD higher LS-BMD (95% CI 0.008-0.61), a 0.36 SD higher TB-BMD (95% CI 0.06-0.67), and a 0.43 SD higher FN-BMD (95% CI 0.13-0.72). Models combining gSOS with clinical risk factors explained up to 16% of the variance of BMD phenotypes and obtained an area under the receiver operating characteristic curve for VF of 0.77 in subgroup analyses. Cranial radiation, high cumulative glucocorticoid doses, high risk group, and male sex were significant risk factors for lower BMD Z-scores. In conclusion, a PGS, in combination with clinical risk factors, could be used as a tool to risk stratify cALL survivors for treatment-related bone morbidity.
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Affiliation(s)
- Nadeau Geneviève
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Yazdanpanah Mojgan
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Yazdanpanah Nahid
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Forgetta Vincenzo
- Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Saguenay, QC G7H 2B1, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada
| | - Girard Simon
- Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Saguenay, QC G7H 2B1, Canada
- Centre Intersectoriel en Santé Durable (CISD), Université du Québec à Chicoutimi, Saguenay, QC G7H 2B1, Canada
- Centre de Recherche CERVO, Université Laval, Québec, QC G1E 1T2, Canada
| | - Sinnett Daniel
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Krajinovic Maja
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
- Department of Pharmacology, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Alos Nathalie
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Manousaki Despoina
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, QC H3T 1C5, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
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26
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Warren TL, Tubbs JD, Lesh TA, Corona MB, Pakzad SS, Albuquerque MD, Singh P, Zarubin V, Morse SJ, Sham PC, Carter CS, Nord AS. Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02457-0. [PMID: 38491343 DOI: 10.1038/s41380-024-02457-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/18/2024]
Abstract
A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 205 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.
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Affiliation(s)
- Tracy L Warren
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Justin D Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Mylena B Corona
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Sarvenaz S Pakzad
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Marina D Albuquerque
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Praveena Singh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Vanessa Zarubin
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Sarah J Morse
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
| | - Cameron S Carter
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA.
| | - Alex S Nord
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Center for Neuroscience, University of California, Davis, CA, USA.
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Kolobkov D, Mishra Sharma S, Medvedev A, Lebedev M, Kosaretskiy E, Vakhitov R. Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project. Front Big Data 2024; 7:1266031. [PMID: 38487517 PMCID: PMC10937521 DOI: 10.3389/fdata.2024.1266031] [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: 07/26/2023] [Accepted: 01/31/2024] [Indexed: 03/17/2024] Open
Abstract
Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.
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Affiliation(s)
- Dmitry Kolobkov
- GENXT, Hinxton, United Kingdom
- Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Moscow, Russia
| | - Satyarth Mishra Sharma
- GENXT, Hinxton, United Kingdom
- Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Aleksandr Medvedev
- GENXT, Hinxton, United Kingdom
- Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, Moscow, Russia
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28
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Chang T, Fu M, Valiente-Banuet L, Wadhwa S, Pasaniuc B, Vossel K. Improving genetic risk modeling of dementia from real-world data in underrepresented populations. RESEARCH SQUARE 2024:rs.3.rs-3911508. [PMID: 38410460 PMCID: PMC10896371 DOI: 10.21203/rs.3.rs-3911508/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
BACKGROUND Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
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Affiliation(s)
- Timothy Chang
- David Geffen School of Medicine, University of California, Los Angeles
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Qafoud F, Elshrif M, Kunji K, Althani A, Salam A, Al Suwaidi J, Asaad N, Darbar D, Saad M. Genetic Susceptibility to Arrhythmia Phenotypes in a Middle Eastern Cohort of 14,259 Whole-Genome Sequenced Individuals. J Clin Med 2024; 13:1102. [PMID: 38398418 PMCID: PMC10888535 DOI: 10.3390/jcm13041102] [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: 01/16/2024] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Background: The current study explores the genetic underpinnings of cardiac arrhythmia phenotypes within Middle Eastern populations, which are under-represented in genomic medicine research. Methods: Whole-genome sequencing data from 14,259 individuals from the Qatar Biobank were used and contained 47.8% of Arab ancestry, 18.4% of South Asian ancestry, and 4.6% of African ancestry. The frequency of rare functional variants within a set of 410 candidate genes for cardiac arrhythmias was assessed. Polygenic risk score (PRS) performance for atrial fibrillation (AF) prediction was evaluated. Results: This study identified 1196 rare functional variants, including 162 previously linked to arrhythmia phenotypes, with varying frequencies across Arab, South Asian, and African ancestries. Of these, 137 variants met the pathogenic or likely pathogenic (P/LP) criteria according to ACMG guidelines. Of these, 91 were in ACMG actionable genes and were present in 1030 individuals (~7%). Ten P/LP variants showed significant associations with atrial fibrillation p < 2.4 × 10-10. Five out of ten existing PRSs were significantly associated with AF (e.g., PGS000727, p = 0.03, OR = 1.43 [1.03, 1.97]). Conclusions: Our study is the largest to study the genetic predisposition to arrhythmia phenotypes in the Middle East using whole-genome sequence data. It underscores the importance of including diverse populations in genomic investigations to elucidate the genetic landscape of cardiac arrhythmias and mitigate health disparities in genomic medicine.
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Affiliation(s)
- Fatima Qafoud
- College of Health Sciences, Qatar University, Doha P.O. Box 2713, Qatar; (F.Q.); (A.A.)
| | - Mohamed Elshrif
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
| | - Asma Althani
- College of Health Sciences, Qatar University, Doha P.O. Box 2713, Qatar; (F.Q.); (A.A.)
| | - Amar Salam
- Department of Cardiology, Al-Khor Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar;
| | - Jassim Al Suwaidi
- Heart Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar; (J.A.S.); (N.A.)
| | - Nidal Asaad
- Heart Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar; (J.A.S.); (N.A.)
| | - Dawood Darbar
- Division of Cardiology, Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA;
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
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Fu M, Valiente-Banuet L, Wadhwa SS, Pasaniuc B, Vossel K, Chang TS. Improving genetic risk modeling of dementia from real-world data in underrepresented populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302355. [PMID: 38370649 PMCID: PMC10871463 DOI: 10.1101/2024.02.05.24302355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
BACKGROUND Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOE and the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
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Affiliation(s)
- Mingzhou Fu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90024, United States
| | - Leopoldo Valiente-Banuet
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Satpal S. Wadhwa
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | | | | | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Keith Vossel
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Timothy S. Chang
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
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31
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Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, Hägg S, Marioni RE, Widschwendter M, Fortney K, Fedichev PO, Zhavoronkov A, Barzilai N, Lasky-Su J, Kiel DP, Kennedy BK, Cummings S, Slagboom PE, Verdin E, Maier AB, Sebastiano V, Snyder MP, Gladyshev VN, Horvath S, Ferrucci L. Validation of biomarkers of aging. Nat Med 2024; 30:360-372. [PMID: 38355974 PMCID: PMC11090477 DOI: 10.1038/s41591-023-02784-9] [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: 09/07/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024]
Abstract
The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.
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Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jamie N Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Brian H Chen
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jessica Lasky-Su
- Department of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Musculoskeletal Research Center, Hinda and Arthur Marcus Institute for Aging Research and Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn NS, Arias J, Belbin G, Below JE, Berndt SI, Chung WK, Cimino JJ, Clayton EW, Connolly JJ, Crosslin DR, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth RR, Ge T, Glessner JT, Gordon AS, Patterson C, Hakonarson H, Harden M, Harr M, Hirschhorn JN, Hoggart C, Hsu L, Irvin MR, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos RJF, Luo Y, Malolepsza E, Manolio TA, Martin LJ, McCarthy L, McNally EM, Meigs JB, Mersha TB, Mosley JD, Musick A, Namjou B, Pai N, Pesce LL, Peters U, Peterson JF, Prows CA, Puckelwartz MJ, Rehm HL, Roden DM, Rosenthal EA, Rowley R, Sawicki KT, Schaid DJ, Smit RAJ, Smith JL, Smoller JW, Thomas M, Tiwari H, Toledo DM, Vaitinadin NS, Veenstra D, Walunas TL, Wang Z, Wei WQ, Weng C, Wiesner GL, Yin X, Kenny EE. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30:480-487. [PMID: 38374346 PMCID: PMC10878968 DOI: 10.1038/s41591-024-02796-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.
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Affiliation(s)
| | - Leah C Kottyan
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Josh Arias
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gillian Belbin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sonja I Berndt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - James J Cimino
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - David R Crosslin
- Tulane University, New Orleans, LA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - QiPing Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Tian Ge
- Mass General Brigham, Boston, MA, USA
| | | | | | | | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Clive Hoggart
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Hsu
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | | | | | | | - Amit Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Katie Larkin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nita Limdi
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Luo
- Northwestern University, Evanston, IL, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Li McCarthy
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Nihal Pai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Cynthia A Prows
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dan M Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Zhe Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Aw AJ, McRae J, Rahmani E, Song YS. Highly parameterized polygenic scores tend to overfit to population stratification via random effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577589. [PMID: 38352303 PMCID: PMC10862757 DOI: 10.1101/2024.01.27.577589] [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: 02/22/2024]
Abstract
Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.
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Affiliation(s)
- Alan J. Aw
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Artificial Intelligence Laboratory, Illumina Inc
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina Inc
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles
| | - Yun S. Song
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
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Venkateswaran V, Boulier K, Ding Y, Johnson R, Bhattacharya A, Pasaniuc B. Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles. Transl Psychiatry 2024; 14:38. [PMID: 38238290 PMCID: PMC10796315 DOI: 10.1038/s41398-024-02743-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we evaluated the predictive performance, risk stratification, and potential systemic health effects of tobacco use disorder (TUD) predisposing germline variants using a European- ancestry-derived polygenic score (PGS) in 24,202 participants from the multi-ancestry, hospital-based UCLA ATLAS biobank. Among genetically inferred ancestry groups (GIAs), TUD-PGS was significantly associated with TUD in European American (EA) (OR: 1.20, CI: [1.16, 1.24]), Hispanic/Latin American (HL) (OR:1.19, CI: [1.11, 1.28]), and East Asian American (EAA) (OR: 1.18, CI: [1.06, 1.31]) GIAs but not in African American (AA) GIA (OR: 1.04, CI: [0.93, 1.17]). Similarly, TUD-PGS offered strong risk stratification across PGS quantiles in EA and HL GIAs and inconsistently in EAA and AA GIAs. In a cross-ancestry phenome-wide association meta-analysis, TUD-PGS was associated with cardiometabolic, respiratory, and psychiatric phecodes (17 phecodes at P < 2.7E-05). In individuals with no history of smoking, the top TUD-PGS associations with obesity and alcohol-related disorders (P = 3.54E-07, 1.61E-06) persist. Mendelian Randomization (MR) analysis provides evidence of a causal association between adiposity measures and tobacco use. Inconsistent predictive performance of the TUD-PGS across GIAs motivates the inclusion of multiple ancestry populations at all levels of genetic research of tobacco use for equitable clinical translation of TUD-PGS. Phenome associations suggest that TUD-predisposed individuals may require comprehensive tobacco use prevention and management approaches to address underlying addictive tendencies.
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Affiliation(s)
- Vidhya Venkateswaran
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Oral Biology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Office of the Director and National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Kristin Boulier
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yi Ding
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Ruth Johnson
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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Janivara R, Hazra U, Pfennig A, Harlemon M, Kim MS, Eaaswarkhanth M, Chen WC, Ogunbiyi A, Kachambwa P, Petersen LN, Jalloh M, Mensah JE, Adjei AA, Adusei B, Joffe M, Gueye SM, Aisuodionoe-Shadrach OI, Fernandez PW, Rohan TE, Andrews C, Rebbeck TR, Adebiyi AO, Agalliu I, Lachance J. Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.12.575396. [PMID: 38293167 PMCID: PMC10827056 DOI: 10.1101/2024.01.12.575396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male pattern baldness comes from individuals of European descent. Here, we examined a novel dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using a custom array. We first tested how genetic predictions of baldness generalize from Europe to Africa, finding that polygenic scores from European GWAS yielded AUC statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness in African populations performed notably worse than in European populations. Subsequently, we conducted the first African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for present age, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p-value < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosomes does not appear to have a large impact on baldness in African men. Finally, we examined the evolutionary causes of continental differences in genetic architecture. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, we did not find evidence that European-ascertained baldness hits were enriched for signatures of ancient introgression. Most loci that are associated with androgenetic alopecia are evolving neutrally. However, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how evolutionary history contributes to the limited portability of genetic predictions across ancestries.
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Affiliation(s)
- Rohini Janivara
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Ujani Hazra
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Aaron Pfennig
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Maxine Harlemon
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- Department of Biology, Morgan State University, Baltimore, Maryland, USA
| | - Michelle S Kim
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- Department of Human Genetics University of Michigan, Ann Arbor, Michigan, USA
| | | | - Wenlong C Chen
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Cancer Registry, National Institute for Communicable Diseases a Division of the National Health Laboratory Service, Johannesburg, South Africa
| | | | - Paidamoyo Kachambwa
- Centre for Proteomic and Genomic Research, Cape Town, South Africa
- Mediclinic Precise Southern Africa, Cape Town, South Africa
| | - Lindsay N Petersen
- Centre for Proteomic and Genomic Research, Cape Town, South Africa
- Mediclinic Precise Southern Africa, Cape Town, South Africa
| | - Mohamed Jalloh
- Université Cheikh Anta Diop de Dakar, Dakar, Senegal
- Université Iba Der Thiam de Thiès, Thiès, Senegal
| | - James E Mensah
- Korle-Bu Teaching Hospital and University of Ghana Medical School, Accra, Ghana
| | - Andrew A Adjei
- Department of Pathology, University of Ghana Medical School, Accra, Ghana
| | | | - Maureen Joffe
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Oseremen I Aisuodionoe-Shadrach
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Centre, Abuja, Nigeria
| | - Pedro W Fernandez
- Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Thomas E Rohan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | | | - Timothy R Rebbeck
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Ilir Agalliu
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
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37
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Hughes O, Bentley AR, Breeze CE, Aguet F, Xu X, Nadkarni G, Sun Q, Lin BM, Gilliland T, Meyer MC, Du J, Raffield LM, Kramer H, Morton RW, Gouveia MH, Atkinson EG, Valladares-Salgado A, Wacher-Rodarte N, Dueker ND, Guo X, Hai Y, Adeyemo A, Best LG, Cai J, Chen G, Chong M, Doumatey A, Eales J, Goodarzi MO, Ipp E, Irvin MR, Jiang M, Jones AC, Kooperberg C, Krieger JE, Lange EM, Lanktree MB, Lash JP, Lotufo PA, Loos RJF, Ha My VT, Peralta-Romero J, Qi L, Raffel LJ, Rich SS, Rodriquez EJ, Tarazona-Santos E, Taylor KD, Umans JG, Wen J, Young BA, Yu Z, Zhang Y, Ida Chen YD, Rundek T, Rotter JI, Cruz M, Fornage M, Lima-Costa MF, Pereira AC, Paré G, Natarajan P, Cole SA, Carson AP, Lange LA, Li Y, Perez-Stable EJ, Do R, Charchar FJ, Tomaszewski M, Mychaleckyj JC, Rotimi C, Morris AP, Franceschini N. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas. CELL GENOMICS 2024; 4:100468. [PMID: 38190104 PMCID: PMC10794846 DOI: 10.1016/j.xgen.2023.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/31/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024]
Abstract
Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.
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Affiliation(s)
- Odessica Hughes
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles E Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department Health and Human Services, Bethesda, MD, USA; UCL Cancer Institute, University College London, London, UK
| | - Francois Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Girish Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bridget M Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thomas Gilliland
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mariah C Meyer
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jiawen Du
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Holly Kramer
- Division of Nephrology and Hypertension, Loyola University Chicago, Maywood, IL, USA
| | - Robert W Morton
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Nicole D Dueker
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lyle G Best
- Missouri Breaks Industries Research Inc., Eagle Butte, SD, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael Chong
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Ayo Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - James Eales
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Eli Ipp
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marguerite Ryan Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Minzhi Jiang
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alana C Jones
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jose E Krieger
- Laboratório de Genética e Cardiologia Molecular do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Matthew B Lanktree
- Division of Nephrology, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - James P Lash
- Division of Nephrology, Department of Medicine, University of Illinois, Chicago, IL, USA
| | - Paulo A Lotufo
- Center for Clinical and Epidemiological Research, Hospital Universitário, Universidade de São Paulo (USP), São Paulo, Brazil
| | - Ruth J F Loos
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vy Thi Ha My
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jesús Peralta-Romero
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Lihong Qi
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, USA
| | - Leslie J Raffel
- Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, Irvine, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Erik J Rodriquez
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eduardo Tarazona-Santos
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville MD and Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bessie A Young
- University of Washington School of Medicine, Seattle, WA, USA; Office of Healthcare Equity, UW Justice, Equity, Diversity, and Inclusion Center for Transformational Research (UW JEDI-CTR), University of Washington, Seattle, WA, USA; Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA; Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Zhi Yu
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Ying Zhang
- Center for American Indian Health Research, Department of Biostatistics and Epidemiology, Hudson College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma, OK, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Tanja Rundek
- Department of Neurology, Epidemiology and Public Health, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, Houston, TX, USA
| | | | - Alexandre C Pereira
- Laboratório de Genética e Cardiologia Molecular do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil; Aging Division, Brigham Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Shelley A Cole
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eliseo J Perez-Stable
- National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fadi J Charchar
- School of Science, Psychology and Sport, Federation University, Ballarat, VIC, Australia; Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; Department of Physiology, University of Melbourne, Melbourne, VIC, Australia
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK; Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Josyf C Mychaleckyj
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Xue D, Blue EE, Conomos MP, Fohner AE. The power of representation: Statistical analysis of diversity in US Alzheimer's disease genetics data. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12462. [PMID: 38500778 PMCID: PMC10945594 DOI: 10.1002/trc2.12462] [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] [Received: 10/13/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 03/20/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a complex disease influenced by genetics and environment. More than 75 susceptibility loci have been linked to late-onset AD, but most of these loci were discovered in genome-wide association studies (GWAS) exclusive to non-Hispanic White individuals. There are wide disparities in AD risk across racially stratified groups, and while these disparities are not due to genetic differences, underrepresentation in genetic research can further exacerbate and contribute to their persistence. We investigated the racial/ethnic representation of participants in United States (US)-based AD genetics and the statistical implications of current representation. METHODS We compared racial/ethnic data of participants from array and sequencing studies in US AD genetics databases, including National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) and NIAGADS Data Sharing Service (dssNIAGADS), to AD and related dementia (ADRD) prevalence and mortality. We then simulated the statistical power of these datasets to identify risk variants from non-White populations. RESULTS There is insufficient statistical power (probability <80%) to detect single nucleotide polymorphisms (SNPs) with low to moderate effect sizes (odds ratio [OR]<1.5) using array data from Black and Hispanic participants; studies of Asian participants are not powered to detect variants OR <= 2. Using available and projected sequencing data from Black and Hispanic participants, risk variants with OR = 1.2 are detectable at high allele frequencies. Sample sizes remain insufficiently powered to detect these variants in Asian populations. DISCUSSION AD genetics datasets are largely representative of US ADRD burden. However, there is a wide discrepancy between proportional representation and statistically meaningful representation. Most variation identified in GWAS of non-Hispanic White individuals have low to moderate effects. Comparable risk variants in non-White populations are not detectable given current sample sizes, which could lead to disparities in future studies and drug development. We urge AD genetics researchers and institutions to continue investing in recruiting diverse participants and use community-based participatory research practices.
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Affiliation(s)
- Diane Xue
- Institute for Public Health GeneticsUniversity of Washington School of Public HealthSeattleWashingtonUSA
| | - Elizabeth E. Blue
- Institute for Public Health GeneticsUniversity of Washington School of Public HealthSeattleWashingtonUSA
- Division of Medical GeneticsDepartment of MedicineUniversity of WashingtonSeattleWashingtonUSA
- Brotman Baty InstituteSeattleWashingtonUSA
| | - Matthew P. Conomos
- Department of BiostatisticsUniversity of Washington School of Public HealthSeattleWashingtonUSA
| | - Alison E. Fohner
- Institute for Public Health GeneticsUniversity of Washington School of Public HealthSeattleWashingtonUSA
- Department of EpidemiologyUniversity of Washington School of Public HealthSeattleWashingtonUSA
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Lo YC, Chan TF, Jeon S, Maskarinec G, Taparra K, Nakatsuka N, Yu M, Chen CY, Lin YF, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. The accuracy of polygenic score models for anthropometric traits and Type II Diabetes in the Native Hawaiian Population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.25.23300499. [PMID: 38234828 PMCID: PMC10793530 DOI: 10.1101/2023.12.25.23300499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations, and their accuracies have not been evaluated for Native Hawaiians. Using body mass index, height, and type-2 diabetes as examples of highly polygenic traits, we evaluated the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5,300 individuals. We evaluated both publicly available PGS models or genome-wide PGS models trained in this study using the largest available GWAS. We found evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also found that using the Native Hawaiian samples as an optimization cohort during training did not consistently improve PGS performance. Moreover, even the best performing PGS models among Native Hawaiians would have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.
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Affiliation(s)
- Ying-Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gertraud Maskarinec
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Kekoa Taparra
- Standard Health Care, Department of Radiation Oncology, Palo Alto, CA, USA
| | | | - Mingrui Yu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Chia-Yen Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Biogen, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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40
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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Herrera-Rivero M, Adli M, Akiyama K, Akula N, Amare AT, Ardau R, Arias B, Aubry JM, Backlund L, Bellivier F, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Birner A, Cearns M, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Colom F, Cruceanu C, Czerski PM, Dalkner N, Degenhardt F, Del Zompo M, DePaulo JR, Etain B, Falkai P, Ferensztajn-Rochowiak E, Forstner AJ, Frank J, Frisén L, Frye MA, Fullerton JM, Gallo C, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hasler R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kato T, Kelsoe J, Kittel-Schneider S, Kuo PH, Kusumi I, König B, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Marie-Claire C, Martinsson L, McCarthy MJ, McElroy SL, Millischer V, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Novák T, Nöthen MM, O'Donovan C, Ozaki N, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Richard-Lepouriel H, Roberts G, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schubert KO, Schulte EC, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Streit F, Tekola-Ayele F, Thalamuthu A, Tortorella A, Turecki G, Veeh J, Vieta E, Viswanath B, Witt SH, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Rietschel M, Schulze TG, Baune BT. Exploring the genetics of lithium response in bipolar disorders. RESEARCH SQUARE 2023:rs.3.rs-3677630. [PMID: 38077040 PMCID: PMC10705597 DOI: 10.21203/rs.3.rs-3677630/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Background Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N=2,064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.
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Affiliation(s)
| | | | | | - Nirmala Akula
- United States Department of Health and Human Services
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Josef Frank
- Central Institute of Mental Health, University of Heidelberg
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Liping Hou
- United States Department of Health and Human Services
| | | | | | | | | | - Layla Kassem
- United States Department of Health and Human Services
| | | | | | | | | | | | | | - Gonzalo Laje
- United States Department of Health and Human Services
| | | | | | | | | | - Mario Maj
- University of Campania 'Luigi Vanvitelli'
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Andrea Pfennig
- University Hospital Carl Gustav Carus, Technische Universität Dresden
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Fabian Streit
- Central Institute of Mental Health, University of Heidelberg
| | | | | | | | | | | | - Eduard Vieta
- Hospital Clinic, University of Barcelona, IDIBAPS
| | | | | | | | | | - Michael Bauer
- University Hospital Carl Gustav Carus, Technische Universität Dresden
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Smeeth D, May AK, Karam EG, Rieder MJ, Elzagallaai AA, van Uum S, Pluess M. Risk and resilience in Syrian refugee children: A multisystem analysis. Dev Psychopathol 2023; 35:2275-2287. [PMID: 37933522 DOI: 10.1017/s0954579423000433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Refugee children are often exposed to substantial trauma, placing them at increased risk for mental illness. However, this risk can be mitigated by a capacity for resilience, conferred from multiple ecological systems (e.g., family, community), including at an individual biological level. We examined the ability of hair cortisol concentrations and polygenic scores for mental health to predict risk and resilience in a sample of Syrian refugee children (n = 1359). Children were categorized as either at-risk or resilient depending on clinical thresholds for posttraumatic stress disorder, depression, and externalizing behavior problems. Logistic regression was used to examine main and interacting effects while controlling for covariates. Elevated hair cortisol concentrations were significantly associated with reduced resilience (odds ratio (OR)=0.58, 95%CI [0.40, 0.83]) while controlling for levels of war exposure. Polygenic scores for depression, self-harm, and neuroticism were not found to have any significant main effects. However, a significant interaction emerged between hair cortisol and polygenic scores for depression (OR=0.04, 95%CI [0.003 0.47]), suggesting that children predisposed to depression were more at risk for mental health problems when hair cortisol concentrations were high. Our results suggest that biomarkers (separately and in combination) might support early identification of refugee children at risk for mental health problems.
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Affiliation(s)
- Demelza Smeeth
- Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Andrew K May
- Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Elie G Karam
- Department of Psychiatry and Clinical Psychology, St Georges Hospital University Medical Center, Beirut, Lebanon
| | - Michael J Rieder
- Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Abdelbaset A Elzagallaai
- Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Stan van Uum
- Division of Endocrinology and Metabolism, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Michael Pluess
- Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- Department of Psychological Sciences, School of Psychology, University of Surrey, Guildford, UK
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Chen JJ, Chen IC, Wei CY, Lin SY, Chen YM. Utilize polygenic risk score to enhance fracture risk estimation and improve the performance of FRAX in patients with osteoporosis. Arch Osteoporos 2023; 18:147. [PMID: 38036866 DOI: 10.1007/s11657-023-01357-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
This study examined the use of polygenic risk scores (PGS) in combination with the Fracture Risk Assessment Tool (FRAX) to enhance fragility fractures risk estimation in osteoporosis patients. Analyzing data from over 57,000 participants, PGS improved fracture risk estimation, especially for individuals with intermediate to low risks, allowing personalized preventive strategies. INTRODUCTION Osteoporosis and fragility fractures are multifactorial, with contributions from both clinical and genetic determinants. However, whether using polygenic risk scores (PGS) may enhance the risk estimation of osteoporotic fracture in addition to Fracture Risk Assessment Tool (FRAX) remains unknown. This study investigated the collective association of PGS and FRAX with fragility fracture. METHODS We conducted a cohort study from the Taiwan Precision Medicine Initiative (TPMI) at Taichung Veterans General Hospital, Taiwan. Genotyping was performed to compute PGS associated with bone mineral density (BMD). Phenome-wide association studies were executed to pinpoint phenotypes correlated with the PGS. Logistic regression analysis was conducted to ascertain factors associated with osteoporotic fractures. RESULTS Among all 57,257 TPMI participants, 3744 (904 men and 2840 women, with a mean age of 66.7) individuals had BMD testing, with 540 (14.42%) presenting with fractures. The 3744 individuals who underwent BMD testing were categorized into four quartiles (Q1-Q4) based on PGS; 540 (14.42%) presented with fractures. Individuals with PGS-Q1 exhibited lower BMD, a higher prevalence of major fractures, and elevated FRAX-major and FRAX-hip than those with PGS-Q4. PGS was associated with major fractures after adjusting age, sex, and FRAX scores. Notably, the risk of major fractures (PGS-Q1 vs. Q4) was significantly higher in the subgroups of FRAX-major scores < 10% and 10-20%, but not in participants with a FRAX-major score ≧ 20%. CONCLUSIONS Our study highlights the potential of PGS to augment fracture risk estimation in conjunction with FRAX, particularly in individuals with middle to low risks. Incorporating genetic testing could empower physicians to tailor personalized preventive strategies for osteoporosis.
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Affiliation(s)
- Jian-Jiun Chen
- Department of Orthopedics, Taipei Veterans General Hospital, Taipei, Taiwan
| | - I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chia-Yi Wei
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shih-Yi Lin
- Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung-Hsing University, Taichung, Taiwan.
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung-Hsing University, Taichung, Taiwan.
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan.
- Institute of Biomedical Science and Rong-Hsing Research Center for Translational Medicine, Chung-Hsing University, Taichung, Taiwan.
- Precision Medicine Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Vaskimo LM, Gomon G, Naamane N, Cordell HJ, Pratt A, Knevel R. The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective. Genes (Basel) 2023; 14:2167. [PMID: 38136989 PMCID: PMC10743278 DOI: 10.3390/genes14122167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
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Affiliation(s)
- Lotta M. Vaskimo
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Georgy Gomon
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Najib Naamane
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather J. Cordell
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Arthur Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Department of Rheumatology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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Gharahkhani P, He W, Han X, Ong JS, Rentería ME, Wiggs JL, Khawaja AP, Trzaskowski M, Mackey DA, Craig JE, Hewitt AW, MacGregor S, Wu Y. WITHDRAWN: Genome-wide risk prediction of primary open-angle glaucoma across multiple ancestries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.08.23298255. [PMID: 37986775 PMCID: PMC10659472 DOI: 10.1101/2023.11.08.23298255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
This manuscript has been withdrawn by medRxiv following a formal request by the QIMR Berghofer Medical Research Institute Research Integrity Office owing to lack of author consent.
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Veller C, Przeworski M, Coop G. Causal interpretations of family GWAS in the presence of heterogeneous effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566950. [PMID: 38014124 PMCID: PMC10680648 DOI: 10.1101/2023.11.13.566950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect (or average treatment effect; ATE) of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance. In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated.
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Affiliation(s)
- Carl Veller
- Department of Ecology and Evolution, University of Chicago
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University
- Department of Systems Biology, Columbia University
| | - Graham Coop
- Center for Population Biology and Department of Evolution and Ecology, University of California, Davis
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Rumker L, Sakaue S, Reshef Y, Kang JB, Yazar S, Alquicira-Hernandez J, Valencia C, Lagattuta KA, Mah-Som A, Nathan A, Powell JE, Loh PR, Raychaudhuri S. Identifying genetic variants that influence the abundance of cell states in single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566919. [PMID: 38014313 PMCID: PMC10680752 DOI: 10.1101/2023.11.13.566919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96×10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.
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Affiliation(s)
- Laurie Rumker
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joyce B. Kang
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seyhan Yazar
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Jose Alquicira-Hernandez
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annelise Mah-Som
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph E. Powell
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Po-Ru Loh
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Warren TL, Tubbs JD, Lesh TA, Corona MB, Pakzad S, Albuquerque M, Singh P, Zarubin V, Morse S, Sham PC, Carter CS, Nord AS. Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290465. [PMID: 37292649 PMCID: PMC10246134 DOI: 10.1101/2023.05.24.23290465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 206 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.
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Affiliation(s)
| | - Justin D. Tubbs
- Department of Psychiatry, The University of Hong Kong
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Department of Psychiatry, Harvard Medical School
| | | | | | | | | | | | | | | | - Pak Chung Sham
- Department of Psychiatry, The University of Hong Kong
- Centre for PanorOmic Sciences, The University of Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong
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Ying S, Heung T, Thiruvahindrapuram B, Engchuan W, Yin Y, Blagojevic C, Zhang Z, Hegele RA, Yuen RKC, Bassett AS. Polygenic risk for triglyceride levels in the presence of a high impact rare variant. BMC Med Genomics 2023; 16:281. [PMID: 37940981 PMCID: PMC10634078 DOI: 10.1186/s12920-023-01717-2] [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: 04/23/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Elevated triglyceride (TG) levels are a heritable and modifiable risk factor for cardiovascular disease and have well-established associations with common genetic variation captured in a polygenic risk score (PRS). In young adulthood, the 22q11.2 microdeletion conveys a 2-fold increased risk for mild-moderate hypertriglyceridemia. This study aimed to assess the role of the TG-PRS in individuals with this elevated baseline risk for mild-moderate hypertriglyceridemia. METHODS We studied a deeply phenotyped cohort of adults (n = 157, median age 34 years) with a 22q11.2 microdeletion and available genome sequencing, lipid level, and other clinical data. The association between a previously developed TG-PRS and TG levels was assessed using a multivariable regression model adjusting for effects of sex, BMI, and other covariates. We also constructed receiver operating characteristic (ROC) curves using logistic regression models to assess the ability of TG-PRS and significant clinical variables to predict mild-moderate hypertriglyceridemia status. RESULTS The TG-PRS was a significant predictor of TG-levels (p = 1.52E-04), along with male sex and BMI, in a multivariable model (pmodel = 7.26E-05). The effect of TG-PRS appeared to be slightly stronger in individuals with obesity (BMI ≥ 30) (beta = 0.4617) than without (beta = 0.1778), in a model unadjusted for other covariates (p-interaction = 0.045). Among ROC curves constructed, the inclusion of TG-PRS, sex, and BMI as predictor variables produced the greatest area under the curve (0.749) for classifying those with mild-moderate hypertriglyceridemia, achieving an optimal sensitivity and specificity of 0.746 and 0.707, respectively. CONCLUSIONS These results demonstrate that in addition to significant effects of sex and BMI, genome-wide common variation captured in a PRS also contributes to the variable expression of the 22q11.2 microdeletion with respect to elevated TG levels.
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Affiliation(s)
- Shengjie Ying
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tracy Heung
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
- The Dalglish Family 22Q Clinic, University Health Network, Toronto, ON, Canada
| | | | - Worrawat Engchuan
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Yue Yin
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Christina Blagojevic
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zhaolei Zhang
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Robert A Hegele
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Ryan K C Yuen
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Anne S Bassett
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- The Dalglish Family 22Q Clinic, University Health Network, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Toronto General Hospital Research Institute and Campbell Family Mental Health Research Institute, Toronto, ON, Canada.
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Busby GB, Kulm S, Bolli A, Kintzle J, Domenico PD, Bottà G. Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments. Nat Commun 2023; 14:7105. [PMID: 37925478 PMCID: PMC10625612 DOI: 10.1038/s41467-023-42897-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 10/25/2023] [Indexed: 11/06/2023] Open
Abstract
Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.
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Affiliation(s)
| | - Scott Kulm
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
| | | | - Jen Kintzle
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
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