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Kong L, Chen Y, Shen Y, Zhang D, Wei C, Lai J, Hu S. Progress and Implications from Genetic Studies of Bipolar Disorder. Neurosci Bull 2024; 40:1160-1172. [PMID: 38206551 PMCID: PMC11306703 DOI: 10.1007/s12264-023-01169-9] [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: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 01/12/2024] Open
Abstract
With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.
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Affiliation(s)
- Lingzhuo Kong
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Yiqing Chen
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Yuting Shen
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Danhua Zhang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Chen Wei
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Jianbo Lai
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brian Medicine, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| | - Shaohua Hu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brian Medicine, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, 310003, China.
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Kolovos A, Hassall MM, Siggs OM, Souzeau E, Craig JE. Polygenic Risk Scores Driving Clinical Change in Glaucoma. Annu Rev Genomics Hum Genet 2024; 25:287-308. [PMID: 38599222 DOI: 10.1146/annurev-genom-121222-105817] [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: 04/12/2024]
Abstract
Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.
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Affiliation(s)
- Antonia Kolovos
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Mark M Hassall
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Owen M Siggs
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia;
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Emmanuelle Souzeau
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Jamie E Craig
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
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53
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Matthews LJ, Zhang Z, Martschenko DO. Schoolhouse risk: Can we mitigate the polygenic Pygmalion effect? Acta Psychol (Amst) 2024; 248:104403. [PMID: 39003994 PMCID: PMC11343671 DOI: 10.1016/j.actpsy.2024.104403] [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: 12/15/2023] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Although limited in predictive accuracy, polygenic scores (PGS) for educational outcomes are currently available to the public via direct-to-consumer genetic testing companies. Further, there is a growing movement to apply PGS in educational settings via 'precision education.' Prior scholarship highlights the potentially negative impacts of such applications, as disappointing results may give rise a "polygenic Pygmalion effect." In this paper two studies were conducted to identify factors that may mitigate or exacerbate negative impacts of PGS. METHODS Two studies were conducted. In each, 1188 students were randomized to one of four conditions: Low-percentile polygenic score for educational attainment (EA-PGS), Low EA-PGS + Mitigating information, Low EA-PGS + Exacerbating information, or Control. Regression analyses were used to examine differences between conditions. RESULTS In Study 1, participants randomized to Control reported significantly higher on the Rosenberg Self-Esteem Scale (RSES), Competence Scale (CS), Academic Efficacy Scale (AES) and Educational Potential Scale (EPS). CS was significantly higher in the Low EA-PGS + Mitigating information condition. CS and AES were significantly lower in the Low EA-PGS + Exacerbating information condition compared to the Low EA-PGS + Mitigating information condition. In Study 2, participants randomized to Control reported significantly higher CS and AES. Pairwise comparisons did not show significant differences in CS and AES. Follow-up pairwise comparisons using Tukey P-value correction did not find significant associations between non-control conditions. CONCLUSION These studies replicated the polygenic Pygmalion effect yet were insufficiently powered to detect significant effects of mitigating contextual information. Regardless of contextual information, disappointing EA-PGS results were significantly associated with lower assessments of self-esteem, competence, academic efficacy, and educational potential.
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Affiliation(s)
- Lucas J Matthews
- Columbia University, Department of Medical Humanities & Ethics, New York, NY, United States; The Hastings Center, New York, NY, United States.
| | - Zhijun Zhang
- New York State Psychiatric Institute, Department of Mental Health and Data Science, New York, NY, United States.
| | - Daphne O Martschenko
- Stanford Center for Biomedical Ethics and Department of Pediatrics, Stanford University; Stanford, CA, United States.
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54
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Takase M, Hirata T, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Narita A, Metoki H, Satoh M, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tamiya G, Hozawa A, Yamamoto M. Associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertens Res 2024; 47:2064-2074. [PMID: 38914703 PMCID: PMC11298407 DOI: 10.1038/s41440-024-01705-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 06/26/2024]
Abstract
No study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34-2.80) and 2.15 (1.60-2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle.
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Affiliation(s)
- Masato Takase
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Kyoto Women's University, Kyoto, Japan
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Narita
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hirohito Metoki
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Michihiro Satoh
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Taku Obara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hisashi Ohseto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Yohei Hamanaka
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Suzuki Memorial Hospital, Satonomori, Iwanumashi, Miyagi, Japan
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
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55
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Federico CA, Trotsyuk AA. Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth. Annu Rev Biomed Data Sci 2024; 7:1-14. [PMID: 38598860 DOI: 10.1146/annurev-biodatasci-102623-104553] [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: 04/12/2024]
Abstract
Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.
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Affiliation(s)
- Carole A Federico
- Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Artem A Trotsyuk
- Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA; ,
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56
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Carey CE, Shafee R, Wedow R, Elliott A, Palmer DS, Compitello J, Kanai M, Abbott L, Schultz P, Karczewski KJ, Bryant SC, Cusick CM, Churchhouse C, Howrigan DP, King D, Davey Smith G, Neale BM, Walters RK, Robinson EB. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. Nat Hum Behav 2024; 8:1599-1615. [PMID: 38965376 PMCID: PMC11343713 DOI: 10.1038/s41562-024-01909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/14/2024] [Indexed: 07/06/2024]
Abstract
Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.
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Affiliation(s)
- Caitlin E Carey
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - Rebecca Shafee
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | - Robbee Wedow
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Sociology, Purdue University, West Lafayette, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- AnalytiXIN, Indianapolis, IN, USA
- Center on Aging and the Life Course, Purdue University, West Lafayette, IN, USA
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Amanda Elliott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Duncan S Palmer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Nuffield Department of Population Health, Medical Sciences Division University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - John Compitello
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Liam Abbott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick Schultz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel C Bryant
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Caroline M Cusick
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claire Churchhouse
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel P Howrigan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel King
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - George Davey Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin M Neale
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Raymond K Walters
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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57
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Liu Y, Meng XH, Wu C, Su KJ, Liu A, Tian Q, Zhao LJ, Qiu C, Luo Z, Gonzalez-Ramirez MI, Shen H, Xiao HM, Deng HW. Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study. PLoS Med 2024; 21:e1004451. [PMID: 39213443 PMCID: PMC11404845 DOI: 10.1371/journal.pmed.1004451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 09/16/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.
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Affiliation(s)
- Yong Liu
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan Province, China
| | - Xiang-He Meng
- Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, Hunan Province, China
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kuan-Jui Su
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Anqi Liu
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Qing Tian
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Lan-Juan Zhao
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Chuan Qiu
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Zhe Luo
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Martha I Gonzalez-Ramirez
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Hui Shen
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Hong-Mei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan Province, China
- Key Laboratory of Biological, Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Hong-Wen Deng
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana, United States of America
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Knoers NVAM, van Eerde AM. The Role of Genetic Testing in Adult CKD. J Am Soc Nephrol 2024; 35:1107-1118. [PMID: 39288914 PMCID: PMC11377809 DOI: 10.1681/asn.0000000000000401] [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] [Indexed: 09/19/2024] Open
Abstract
Mounting evidence indicates that monogenic disorders are the underlying cause in a significant proportion of patients with CKD. In recent years, the diagnostic yield of genetic testing in these patients has increased significantly as a result of revolutionary developments in genetic sequencing techniques and sequencing data analysis. Identification of disease-causing genetic variant(s) in patients with CKD may facilitate prognostication and personalized management, including nephroprotection and decisions around kidney transplantation, and is crucial for genetic counseling and reproductive family planning. A genetic diagnosis in a patient with CKD allows for screening of at-risk family members, which is also important for determining their eligibility as kidney transplant donors. Despite evidence for clinical utility, increased availability, and data supporting the cost-effectiveness of genetic testing in CKD, especially when applied early in the diagnostic process, many nephrologists do not use genetic testing to its full potential because of multiple perceived barriers. Our aim in this article was to empower nephrologists to (further) implement genetic testing as a diagnostic means in their clinical practice, on the basis of the most recent insights and exemplified by patient vignettes. We stress why genetic testing is of significant clinical benefit to many patients with CKD, provide recommendations for which patients to test and which test(s) to order, give guidance about interpretation of genetic testing results, and highlight the necessity for and essential components of pretest and post-test genetic counseling.
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Affiliation(s)
- Nine V A M Knoers
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
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59
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Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, Mosley JD, El Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci 2024; 17:e13893. [PMID: 39078255 PMCID: PMC11287822 DOI: 10.1111/cts.13893] [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: 05/12/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024] Open
Abstract
Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.
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Affiliation(s)
- Sonal Singh
- Merck & Co., IncSouth San FranciscoCaliforniaUSA
| | - Gabriele Stocco
- Department of Medical, Surgical and Health SciencesUniversity of TriesteTriesteItaly
- Institute for Maternal and Child Health IRCCS Burlo GarofoloTriesteItaly
| | - Katherine N. Theken
- Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alyson Dickson
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - QiPing Feng
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, R. Ken Coit College of PharmacyUniversity of ArizonaTucsonArizonaUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jonathan D. Mosley
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Nihal El Rouby
- Division of Pharmacy Practice and Adminstrative Sciences, James L Winkle College of PharmacyUniversity of CincinnatiCincinnatiOhioUSA
- St. Elizabeth HealthcareEdgewoodKentuckyUSA
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60
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Lahey BB, Durham EL, Brislin SJ, Barr PB, Dick DM, Moore TM, Pierce BL, Tong L, Reimann GE, Jeong HJ, Dupont RM, Kaczkurkin AN. Mapping potential pathways from polygenic liability through brain structure to psychological problems across the transition to adolescence. J Child Psychol Psychiatry 2024; 65:1047-1060. [PMID: 38185921 PMCID: PMC11227600 DOI: 10.1111/jcpp.13944] [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] [Accepted: 11/08/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. METHODS Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). RESULTS The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. CONCLUSIONS Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.
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Affiliation(s)
| | | | | | - Peter B. Barr
- SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | | | | | | | - Lin Tong
- University of Chicago, Chicago, IL 60637
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61
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Crone B, Boyle AP. Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration. PLoS Genet 2024; 20:e1011356. [PMID: 39110742 PMCID: PMC11333000 DOI: 10.1371/journal.pgen.1011356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 08/19/2024] [Accepted: 06/27/2024] [Indexed: 08/21/2024] Open
Abstract
Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.
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Affiliation(s)
- Bradley Crone
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alan P. Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
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Jonson C, Levine KS, Lake J, Hertslet L, Jones L, Patel D, Kim J, Bandres‐Ciga S, Terry N, Mata IF, Blauwendraat C, Singleton AB, Nalls MA, Yokoyama JS, Leonard HL. Assessing the lack of diversity in genetics research across neurodegenerative diseases: A systematic review of the GWAS Catalog and literature. Alzheimers Dement 2024; 20:5740-5756. [PMID: 39030740 PMCID: PMC11350004 DOI: 10.1002/alz.13873] [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: 01/19/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 07/22/2024]
Abstract
The under-representation of non-European cohorts in neurodegenerative disease genome-wide association studies (GWAS) hampers precision medicine efforts. Despite the inherent genetic and phenotypic diversity in these diseases, GWAS research consistently exhibits a disproportionate emphasis on participants of European ancestry. This study reviews GWAS up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. We conducted a systematic review of GWAS results and publications up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. Rigorous article inclusion and quality assessment methods were employed. Of 123 neurodegenerative disease (NDD) GWAS reviewed, 82% predominantly featured European ancestry participants. A single European study identified over 90 risk loci, compared to a total of 50 novel loci in identified in all non-European or multi-ancestry studies. Notably, only six of the loci have been replicated. The significant under-representation of non-European ancestries in NDD GWAS hinders comprehensive genetic understanding. Prioritizing genomic diversity in future research is crucial for advancing NDD therapies and understanding. HIGHLIGHTS: Eighty-two percent of neurodegenerative genome-wide association studies (GWAS) focus on Europeans. Only 6 of 50 novel neurodegenerative disease (NDD) genetic loci have been replicated. Lack of diversity significantly hampers understanding of NDDs. Increasing diversity in NDD genetic research is urgently required. New initiatives are aiming to enhance diversity in NDD research.
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Affiliation(s)
- Caroline Jonson
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
- Pharmaceutical Sciences and Pharmacogenomics Graduate ProgramUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Kristin S. Levine
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
| | - Julie Lake
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institutes on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Linnea Hertslet
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
| | - Lietsel Jones
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
| | - Dhairya Patel
- Integrative Neurogenomics UnitLaboratory of NeurogeneticsNational Institute on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Jeff Kim
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institutes on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Sara Bandres‐Ciga
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
| | - Nancy Terry
- Division of Library ServicesOffice of Research ServicesNational Institutes of HealthBethesdaMarylandUSA
| | - Ignacio F. Mata
- Genomic Medicine Institute, Lerner Research Institute, Genomic MedicineCleveland Clinic FoundationClevelandOhioUSA
| | - Cornelis Blauwendraat
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- Integrative Neurogenomics UnitLaboratory of NeurogeneticsNational Institute on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Andrew B. Singleton
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institutes on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Mike A. Nalls
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
- Laboratory of NeurogeneticsNational Institutes on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Jennifer S. Yokoyama
- Pharmaceutical Sciences and Pharmacogenomics Graduate ProgramUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Hampton L. Leonard
- Center for Alzheimer's and Related DementiasNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
- Laboratory of NeurogeneticsNational Institutes on AgingNational Institutes of HealthBethesdaMarylandUSA
- German Center for Neurodegenerative Diseases (DZNE)TübingenGermany
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Dowrick A, Ziebland S, Rai T, Friedemann Smith C, Nicholson BD. A manifesto for improving cancer detection: four key considerations when implementing innovations across the interface of primary and secondary care. Lancet Oncol 2024; 25:e388-e395. [PMID: 38848741 DOI: 10.1016/s1470-2045(24)00102-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 06/09/2024]
Abstract
Improving cancer outcomes through innovative cancer detection initiatives in primary care is an international policy priority. There are unique implementation challenges to the roll-out and scale-up of different innovations, requiring synchronisation between national policy levers and local implementation strategies. We draw on implementation science to highlight key considerations when seeking to sustainably embed cancer detection initiatives within health systems and clinical practice. Points of action include considering the implications of change on the current configuration of responsibility for detecting cancer; investing in understanding how to adapt systems to support innovations; developing strategies to address inequity when planning innovation implementation; and anticipating and making efforts to mitigate the unintended consequences of innovation. We draw on examples of contemporary cancer detection issues to illustrate how to apply these recommendations to practice.
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Affiliation(s)
- Anna Dowrick
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK.
| | - Sue Ziebland
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
| | - Tanvi Rai
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
| | | | - Brian D Nicholson
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
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Ghirardi G, Gil-Hernández CJ, Bernardi F, van Bergen E, Demange P. Interaction of family SES with children's genetic propensity for cognitive and noncognitive skills: No evidence of the Scarr-Rowe hypothesis for educational outcomes. RESEARCH IN SOCIAL STRATIFICATION AND MOBILITY 2024; 92:100960. [PMID: 39220821 PMCID: PMC11364161 DOI: 10.1016/j.rssm.2024.100960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/29/2024] [Accepted: 07/15/2024] [Indexed: 09/04/2024]
Abstract
This study examines the role of genes and environments in predicting educational outcomes. We test the Scarr-Rowe hypothesis, suggesting that enriched environments enable genetic potential to unfold, and the compensatory advantage hypothesis, proposing that low genetic endowments have less impact on education for children from high socioeconomic status (SES) families. We use a pre-registered design with Netherlands Twin Register data (426 ≤ N individuals ≤ 3875). We build polygenic indexes (PGIs) for cognitive and noncognitive skills to predict seven educational outcomes from childhood to adulthood across three designs (between-family, within-family, and trio) accounting for different confounding sources, totalling 42 analyses. Cognitive PGIs, noncognitive PGIs, and parental education positively predict educational outcomes. Providing partial support for the compensatory hypothesis, 39/42 PGI × SES interactions are negative, with 7 reaching statistical significance under Romano-Wolf and 3 under the more conservative Bonferroni multiple testing corrections (p-value < 0.007). In contrast, the Scarr-Rowe hypothesis lacks empirical support, with just 2 non-significant and 1 significant (not surviving Romano-Wolf) positive interactions. Overall, we emphasise the need for future replication studies in larger samples. Our findings demonstrate the value of merging social-stratification and behavioural-genetic theories to better understand the intricate interplay between genetic factors and social contexts.
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Affiliation(s)
- Gaia Ghirardi
- Department of Political and Social Sciences, European University Institute (EUI), Florence, Italy
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Carlos J. Gil-Hernández
- European Commission, Centre for Advanced Studies, Joint Research Centre, Sevilla, Spain
- Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy
| | - Fabrizio Bernardi
- Department of Sociology II, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit (VU), Amsterdam, the Netherlands
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Mallabar-Rimmer B, Merriel SWD, Webster AP, Jackson L, Wood AR, Barclay M, Tyrrell J, Ruth KS, Thirlwell C, Oram R, Weedon MN, Bailey SER, Green HD. Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study. Eur J Hum Genet 2024:10.1038/s41431-024-01654-3. [PMID: 39090236 DOI: 10.1038/s41431-024-01654-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 08/04/2024] Open
Abstract
Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.
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Affiliation(s)
| | - Samuel W D Merriel
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, UK
| | - Amy P Webster
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Leigh Jackson
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Matthew Barclay
- Department of Behavioural Science & Health, Institute of Epidemiology & Health Care, University College London, London, UK
| | - Jessica Tyrrell
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Katherine S Ruth
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | | | - Richard Oram
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Sarah E R Bailey
- Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - Harry D Green
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
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66
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Burt CH. Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation. SOCIOLOGICAL METHODOLOGY 2024; 54:300-350. [PMID: 39091537 PMCID: PMC11293310 DOI: 10.1177/00811750241236482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.
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Nagpal S, Gibson G. Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.29.24311065. [PMID: 39132477 PMCID: PMC11312673 DOI: 10.1101/2024.07.29.24311065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.
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Affiliation(s)
- Sini Nagpal
- Center for Integrative Genomics and School of Biological Sciences, Georgia Institute of Technology Atlanta, GA 30302
| | - Greg Gibson
- Center for Integrative Genomics and School of Biological Sciences, Georgia Institute of Technology Atlanta, GA 30302
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68
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Jones AC, Patki A, Srinivasasainagendra V, Tiwari HK, Armstrong ND, Chaudhary NS, Limdi NA, Hidalgo BA, Davis B, Cimino JJ, Khan A, Kiryluk K, Lange LA, Lange EM, Arnett DK, Young BA, Diamantidis CJ, Franceschini N, Wassertheil-Smoller S, Rich SS, Rotter JI, Mychaleckyj JC, Kramer HJ, Chen YDI, Psaty BM, Brody JA, de Boer IH, Bansal N, Bis JC, Irvin MR. Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations. J Am Soc Nephrol 2024:00001751-990000000-00377. [PMID: 39073889 DOI: 10.1681/asn.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Key Points
The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits.However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations.Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.
Background
CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.
Methods
This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.
Results
The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.
Conclusions
In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.
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Affiliation(s)
- Alana C Jones
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nicole D Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ninad S Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brittney Davis
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - James J Cimino
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, South Carolina
| | - Bessie A Young
- Division of Nephrology, University of Washington, Seattle, Washington
| | | | - Nora Franceschini
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Stephen S Rich
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Josyf C Mychaleckyj
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Holly J Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, Illinois
| | - Yii-Der I Chen
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Bruce M Psaty
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian H de Boer
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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Heyne HO, Pajuste FD, Wanner J, Daniel Onwuchekwa JI, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. Nat Commun 2024; 15:6277. [PMID: 39054313 PMCID: PMC11272783 DOI: 10.1038/s41467-024-50295-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 700k Finns and Estonians. We found that a high genetic generalized epilepsy PRS (PRSGGE) increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.73 per PRSGGE standard deviation [SD]) across lifetime and within 10 years after an unspecified seizure event. The effect of PRSGGE was significantly larger on idiopathic generalized epilepsies, in females and for earlier epilepsy onset. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). Here, we outline the potential of epilepsy specific PRSs to serve as biomarkers after a first seizure event.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
- Hasso Plattner Institute, Mount Sinai School of Medicine, New York, NY, US.
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for 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
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for 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
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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70
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Misra A, Truong B, Urbut SM, Sui Y, Fahed AC, Smoller JW, Patel AP, Natarajan P. Instability of high polygenic risk classification and mitigation by integrative scoring. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.24.24310897. [PMID: 39211853 PMCID: PMC11361234 DOI: 10.1101/2024.07.24.24310897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and third-party laboratories are increasingly deploying PRS reports to patients. Although new PRS show improving strengths of association with traits, it is unknown how the classification of high polygenic risk changes across individual PRS for the same trait. Here, we determined classification of high genetic risk from all cataloged PRS for three complex traits. While each PRS for each trait demonstrated generally consistent population-level strengths of associations, classification of individuals in the top 10% of each PRS distribution varied widely. Using the PRSMix framework, which incorporates information across several PRS to improve prediction, we generated sequential add-one-in (AOI) PRSMix_AOI scores based on order of publication. PRSMix_AOI n led to improved PRS performance and more consistent high-risk classification compared with the PRS n . The PRSMix_AOI approach provides more stable and reliable classification of high-risk as new PRS continue to be generated toward PRS standardization.
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71
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Usoltsev D, Kolosov N, Rotar O, Loboda A, Boyarinova M, Moguchaya E, Kolesova E, Erina A, Tolkunova K, Rezapova V, Molotkov I, Melnik O, Freylikhman O, Paskar N, Alieva A, Baranova E, Bazhenova E, Beliaeva O, Vasilyeva E, Kibkalo S, Skitchenko R, Babenko A, Sergushichev A, Dushina A, Lopina E, Basyrova I, Libis R, Duplyakov D, Cherepanova N, Donner K, Laiho P, Kostareva A, Konradi A, Shlyakhto E, Palotie A, Daly MJ, Artomov M. Complex trait susceptibilities and population diversity in a sample of 4,145 Russians. Nat Commun 2024; 15:6212. [PMID: 39043636 PMCID: PMC11266540 DOI: 10.1038/s41467-024-50304-1] [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: 03/27/2023] [Accepted: 07/02/2024] [Indexed: 07/25/2024] Open
Abstract
The population of Russia consists of more than 150 local ethnicities. The ethnic diversity and geographic origins, which extend from eastern Europe to Asia, make the population uniquely positioned to investigate the shared properties of inherited disease risks between European and Asian ancestries. We present the analysis of genetic and phenotypic data from a cohort of 4,145 individuals collected in three metro areas in western Russia. We show the presence of multiple admixed genetic ancestry clusters spanning from primarily European to Asian and high identity-by-descent sharing with the Finnish population. As a result, there was notable enrichment of Finnish-specific variants in Russia. We illustrate the utility of Russian-descent cohorts for discovery of novel population-specific genetic associations, as well as replication of previously identified associations that were thought to be population-specific in other cohorts. Finally, we provide access to a database of allele frequencies and GWAS results for 464 phenotypes.
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Affiliation(s)
- Dmitrii Usoltsev
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
- Broad Institute, Cambridge, MA, USA
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Nikita Kolosov
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
- Broad Institute, Cambridge, MA, USA
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Oxana Rotar
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Alexander Loboda
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
- Broad Institute, Cambridge, MA, USA
| | | | | | | | - Anastasia Erina
- Almazov National Medical Research Centre, St Petersburg, Russia
| | | | - Valeriia Rezapova
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
- Broad Institute, Cambridge, MA, USA
| | - Ivan Molotkov
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Olesya Melnik
- Almazov National Medical Research Centre, St Petersburg, Russia
| | | | - Nadezhda Paskar
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Asiiat Alieva
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Elena Baranova
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Elena Bazhenova
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Olga Beliaeva
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Elena Vasilyeva
- Almazov National Medical Research Centre, St Petersburg, Russia
| | - Sofia Kibkalo
- Almazov National Medical Research Centre, St Petersburg, Russia
| | | | - Alina Babenko
- Almazov National Medical Research Centre, St Petersburg, Russia
| | | | | | | | | | - Roman Libis
- Orenburg State Medical University, Orenburg, Russia
| | - Dmitrii Duplyakov
- Samara State Medical University, Samara, Russia
- Samara Regional Cardiology Dispensary, Samara, Russia
| | | | - Kati Donner
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Paivi Laiho
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Anna Kostareva
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
| | - Alexandra Konradi
- Almazov National Medical Research Centre, St Petersburg, Russia
- ITMO University, St Petersburg, Russia
| | | | - Aarno Palotie
- Broad Institute, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Broad Institute, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mykyta Artomov
- Almazov National Medical Research Centre, St Petersburg, Russia.
- ITMO University, St Petersburg, Russia.
- Broad Institute, Cambridge, MA, USA.
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA.
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA.
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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Sequeira JJ, Panda M, Dixit S, Kumawat R, Mustak MS, Sharma AN, Chaubey G, Shrivastava P. Forensic Characterization, Genomic Variability and Ancestry Analysis of Six Populations from Odisha Using mtDNA SNPs and Autosomal STRs. Biochem Genet 2024:10.1007/s10528-024-10887-2. [PMID: 39039324 DOI: 10.1007/s10528-024-10887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 07/14/2024] [Indexed: 07/24/2024]
Abstract
Located on India's eastern coast, Odisha is known for its diverse tribes and castes. In the early days of genome sequencing technology, researchers primarily studied the Austroasiatic communities inhabiting this region to reconstruct the ancient origins and dispersal of this broad linguistic group. However, current research has shifted towards identifying population and individual-specific genome variation for forensic applications. This study aims to analyze the forensic efficiency and ancestry of six populations from Odisha. We assessed the SF mtDNA-SNP60™ PCR Amplification Kit by comparing it with PowerPlex® Fusion 6C System, a widely used autosomal STR (aSTR) kit, in an Indian cohort. Although the mtDNA SNP kit showed low discriminating power for individuals of a diverse population, it could identify deep lineage divergence. Also, we utilized mitochondrial and autosomal variation information to analyze the ancestry of six endogamous ethnic groups in Odisha. We observe two extremities-populations with higher West Asian affinity and those with East Asian affinity. This observation is in congruence with the existing information of their tribal and non-tribal affiliation. When compared with neighbouring populations from Central and Eastern India, multivariate analysis showed that the Brahmins clustered separately or with the Gopala, Kaibarta appeared as an intermediate, Pana and Kandha clustered with the Gonds, and Savara with the Munda tribes. Our findings indicate significant deep lineage stratification in the ethnic populations of Odisha and a gene flow from West and East Asia. The artefacts of unique deep lineage in such a diverse population will help in improving forensic identification. In addition, we conclude that the SF mtDNA-SNP60 PCR Amplification Kit may be used only as a supplementary tool for forensic analysis.
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Affiliation(s)
- Jaison Jeevan Sequeira
- Department of Applied Zoology, Mangalore University, Mangalagangothri, Mangalore, 574199, India
| | - Muktikanta Panda
- Department of Anthropology, Model Degree College, Malkangiri, Odisha, 764045, India
- Department of Anthropology, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
| | - Shivani Dixit
- DNA Division, Central Forensic Science Laboratory, Chandigarh, 160036, India
| | - Ramkishan Kumawat
- DNA Division, State Forensic Science Laboratory, Jaipur, Rajasthan, India
| | - Mohammed S Mustak
- Department of Applied Zoology, Mangalore University, Mangalagangothri, Mangalore, 574199, India
| | - Awdhesh Narayan Sharma
- Department of Anthropology, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
| | - Gyaneshwer Chaubey
- DNA Division, Central Forensic Science Laboratory, Chandigarh, 160036, India
- Department of Zoology, Banaras Hindu University (BHU), Varanasi, India
| | - Pankaj Shrivastava
- Department of Anthropology, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India.
- Regional Forensic Science Laboratory, Government of MP, Gwalior, Madhya Pradesh, India.
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73
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Al Ageeli E. Exploring the Genetic Roles of Diet and Other Modifiable Risk Factors in the Risk of Angina: A Causal Investigation Using Mendelian Randomization in UK Biobank and FinnGen Cohorts. Life (Basel) 2024; 14:905. [PMID: 39063658 PMCID: PMC11278461 DOI: 10.3390/life14070905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 07/17/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Angina pectoris, a debilitating manifestation of coronary artery disease, has been associated with various modifiable risk factors. However, the causal underpinnings of these associations remain unclear. This study leveraged Mendelian randomization (MR) to investigate the causal roles of dietary patterns, smoking behaviors, body mass index (BMI), and physical activity in the development of angina. METHODS Two-sample MR analyses were performed using summary-level data from large-scale genome-wide association studies (GWASs) and biobank resources, including the UK Biobank (UKB) and FinnGen cohorts. Genetic variants associated with various types of exposure such as fruit and salad intake, smoking initiation and intensity, BMI, and physical activity were used as instrumental variables, and their causal effects on angina risk were assessed. RESULTS In the UKB cohort (336,683 individuals, 10,618 cases), genetically proxied fruit (OR = 0.95, 95% CI: 0.93-0.97) and cheese intake (OR = 0.98, 95% CI: 0.97-0.99) were associated with decreased angina risk, while smoking initiation (OR = 1.01, 95% CI: 1.002-1.012), maternal smoking (OR = 1.06, 95% CI: 1.03-1.09), and BMI (OR = 1.01, 95% CI: 1.01-1.02) were associated with increased risk. In the FinnGen cohort (206,008 individuals, 18,168 cases), fruit (OR = 0.30, 95% CI: 0.17-0.53) and salad intake (OR = 0.31, 95% CI: 0.12-0.55) were found to be protective, while smoking initiation (OR = 1.20, 95% CI: 1.04-1.37) and intensity (OR = 1.15, 95% CI: 1.04-1.26) and BMI (OR = 1.31, 95% CI: 1.18-1.47) increased angina risk. CONCLUSIONS This study provides robust evidence for the causal roles of various modifiable risk factors associated with angina development, highlighting the potential benefits of dietary interventions that promote increased fruit and vegetable consumption, smoking cessation, and weight management to mitigate angina risk. Further investigation is needed to generalize these findings to populations with diverse genetic backgrounds, lifestyles, and environmental exposures.
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Affiliation(s)
- Essam Al Ageeli
- Department of Basic Medical Sciences (Medical Genetics), Faculty of Medicine, Jazan University, Jazan 45142, Saudi Arabia
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74
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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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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science 2024; 385:eadj1182. [PMID: 39024449 DOI: 10.1126/science.adj1182] [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: 06/30/2023] [Accepted: 05/10/2024] [Indexed: 07/20/2024]
Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Otolaryngology, UCSD San Diego, La Jolla, CA 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN 37232, USA
| | - Sudha K Iyengar
- Departments of Population and Quantitative Health Sciences, Genetics and Genome Sciences, and Ophthalmology and Visual Sciences and the Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Noah Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Philip Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - J Michael Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Scott Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA 02115, USA
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Akiyama M, Tamiya G, Fujiwara K, Shiga Y, Yokoyama Y, Hashimoto K, Sato M, Sato K, Narita A, Hashimoto S, Ueda E, Furuta Y, Hata J, Miyake M, Ikeda HO, Suda K, Numa S, Mori Y, Morino K, Murakami Y, Shimokawa S, Nakamura S, Yawata N, Fujisawa K, Yamana S, Mori K, Ikeda Y, Miyata K, Mori K, Ogino K, Koyanagi Y, Kamatani Y, Ninomiya T, Sonoda KH, Nakazawa T. Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals. Ophthalmology 2024:S0161-6420(24)00362-2. [PMID: 39023470 DOI: 10.1016/j.ophtha.2024.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 07/20/2024] Open
Abstract
PURPOSE To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals. DESIGN Cross-sectional analysis. PARTICIPANTS Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study). METHODS We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements. MAIN OUTCOME MEASURE Proportion of patients with POAG after stratification according to the GRS. RESULTS We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10-4) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10-6). CONCLUSIONS The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kohta Fujiwara
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yukihiro Shiga
- Department of Neuroscience, Université de Montréal, Montréal, Canada; Neuroscience Division, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Canada; Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yu Yokoyama
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazuki Hashimoto
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masataka Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kota Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Sawako Hashimoto
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Emi Ueda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Miyake
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hanako O Ikeda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Suda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shogo Numa
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuki Mori
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kazuya Morino
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yusuke Murakami
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sakurako Shimokawa
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shun Nakamura
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Nobuyo Yawata
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kimihiko Fujisawa
- Department of Ophthalmology, Japan Community Healthcare Organization Kyushu Hospital, Fukuoka, Japan
| | - Satoshi Yamana
- Department of Ophthalmology, National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Kenichiro Mori
- Department of Ophthalmology, Aso Iizuka Hospital, Iizuka, Japan
| | - Yasuhiro Ikeda
- Department of Ophthalmology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | | | - Keisuke Mori
- Department of Ophthalmology, International University of Health and Welfare, Nasu-shiobara, Tochigi, Japan
| | - Ken Ogino
- Department of Ophthalmology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Yoshito Koyanagi
- Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koh-Hei Sonoda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan; Department of Retinal Disease Control, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan.
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77
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Kanjira SC, Adams MJ, Jiang Y, Tian C, Lewis CM, Kuchenbaecker K, McIntosh AM. Polygenic prediction of major depressive disorder and related traits in African ancestries UK Biobank participants. Mol Psychiatry 2024:10.1038/s41380-024-02662-x. [PMID: 39014000 DOI: 10.1038/s41380-024-02662-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 06/27/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024]
Abstract
Genome-Wide Association Studies (GWAS) over-represent European ancestries, neglecting all other ancestry groups and low-income nations. Consequently, polygenic risk scores (PRS) more accurately predict complex traits in Europeans than African Ancestries groups. Very few studies have looked at the transferability of European-derived PRS for behavioural and mental health phenotypes to Africans. We assessed the comparative accuracy of depression PRS trained on European and African Ancestries GWAS studies to predict major depressive disorder (MDD) and related traits in African ancestry participants from the UK Biobank. UK Biobank participants were selected based on Principal component analysis clustering with an African genetic similarity reference population, MDD was assessed with the Composite International Diagnostic Interview (CIDI). PRS were computed using PRSice2 software using either European or African Ancestries GWAS summary statistics. PRS trained on European ancestry samples (246,363 cases) predicted case control status in Africans of the UK Biobank with similar accuracies (R2 = 2%, β = 0.32, empirical p-value = 0.002) to PRS trained on far much smaller samples of African Ancestries participants from 23andMe, Inc. (5045 cases, R² = 1.8%, β = 0.28, empirical p-value = 0.008). This suggests that prediction of MDD status from Africans to Africans had greater efficiency relative to discovery sample size than prediction of MDD from Europeans to Africans. Prediction of MDD status in African UK Biobank participants using GWAS findings of likely causal risk factors from European ancestries was non-significant. GWAS of MDD in European ancestries are inefficient for improving polygenic prediction in African samples; urgent MDD studies in Africa are needed.
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Affiliation(s)
- S C Kanjira
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - M J Adams
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Y Jiang
- 23andMe Inc, Sunnyvale, CA, USA
| | - C Tian
- 23andMe Inc, Sunnyvale, CA, USA
| | - C M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - K Kuchenbaecker
- UCL Genetics Institute, University College London, London, UK
| | - A M McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK.
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78
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Tubbs JD, Chen Y, Duan R, Huang H, Ge T. Real-time dynamic polygenic prediction for streaming data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.12.24310357. [PMID: 39040195 PMCID: PMC11261927 DOI: 10.1101/2024.07.12.24310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies (GWASs), which are often updated at lengthy intervals. As genetic data and health outcomes are continuously being generated at an ever-increasing pace, the current PRS training and deployment paradigm is suboptimal in maximizing the prediction accuracy of PRSs for incoming patients in healthcare settings. Here, we introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and calibration of PRS as each new sample is collected, without the need to perform intermediate GWASs. Through extensive simulation studies, we evaluate the performance of rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from the Mass General Brigham Biobank and UK Biobank, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts in 7 Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically predicting and stratifying disease risk across diverse genetic ancestries.
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Affiliation(s)
- Justin D. Tubbs
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Yu Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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79
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Pereira JL, de Souza CA, Neyra JEM, Leite JMRS, Cerqueira A, Mingroni-Netto RC, Soler JMP, Rogero MM, Sarti FM, Fisberg RM. Genetic Ancestry and Self-Reported "Skin Color/Race" in the Urban Admixed Population of São Paulo City, Brazil. Genes (Basel) 2024; 15:917. [PMID: 39062696 PMCID: PMC11276533 DOI: 10.3390/genes15070917] [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: 06/17/2024] [Revised: 07/08/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Epidemiological studies frequently classify groups based on phenotypes like self-reported skin color/race, which inaccurately represent genetic ancestry and may lead to misclassification, particularly among individuals of multiracial backgrounds. This study aimed to characterize both global and local genome-wide genetic ancestries and to assess their relationship with self-reported skin color/race in an admixed population of Sao Paulo city. We analyzed 226,346 single-nucleotide polymorphisms from 841 individuals participating in the population-based ISA-Nutrition study. Our findings confirmed the admixed nature of the population, demonstrating substantial European, significant Sub-Saharan African, and minor Native American ancestries, irrespective of skin color. A correlation was observed between global genetic ancestry and self-reported color-race, which was more evident in the extreme proportions of African and European ancestries. Individuals with higher African ancestry tended to identify as Black, those with higher European ancestry tended to identify as White, and individuals with higher Native American ancestry were more likely to self-identify as Mixed, a group with diverse ancestral compositions. However, at the individual level, this correlation was notably weak, and no deviations were observed for specific regions throughout the individual's genome. Our findings emphasize the significance of accurately defining and thoroughly analyzing race and ancestry, especially within admixed populations.
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Affiliation(s)
- Jaqueline L. Pereira
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil; (J.L.P.); (J.M.R.S.L.); (M.M.R.)
| | - Camila A. de Souza
- Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil; (C.A.d.S.); (J.E.M.N.); (J.M.P.S.)
| | - Jennyfer E. M. Neyra
- Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil; (C.A.d.S.); (J.E.M.N.); (J.M.P.S.)
| | - Jean M. R. S. Leite
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil; (J.L.P.); (J.M.R.S.L.); (M.M.R.)
| | - Andressa Cerqueira
- Department of Statistics, Federal University of Sao Carlos, São Carlos 13565-905, Brazil;
| | - Regina C. Mingroni-Netto
- Human Genome and Stem Cell Research Center, Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo 05508-090, Brazil;
| | - Julia M. P. Soler
- Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil; (C.A.d.S.); (J.E.M.N.); (J.M.P.S.)
| | - Marcelo M. Rogero
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil; (J.L.P.); (J.M.R.S.L.); (M.M.R.)
| | - Flavia M. Sarti
- School of Arts, Sciences and Humanities, University of Sao Paulo, São Paulo 03828-000, Brazil;
| | - Regina M. Fisberg
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil; (J.L.P.); (J.M.R.S.L.); (M.M.R.)
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80
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Santiago-Lamelas L, Dos Santos-Sobrín R, Carracedo Á, Castro-Santos P, Díaz-Peña R. Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases. Best Pract Res Clin Rheumatol 2024:101973. [PMID: 38997822 DOI: 10.1016/j.berh.2024.101973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024]
Abstract
Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.
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Affiliation(s)
- Lucía Santiago-Lamelas
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Raquel Dos Santos-Sobrín
- Reumatología, Hospital Clínico Universitario, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Ángel Carracedo
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Grupo de Medicina Xenómica, CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Patricia Castro-Santos
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
| | - Roberto Díaz-Peña
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
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81
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Sawchuk EA, Sirak KA, Manthi FK, Ndiema EK, Ogola CA, Prendergast ME, Reich D, Aluvaala E, Ayodo G, Badji L, Bird N, Black W, Fregel R, Gachihi N, Gibbon VE, Gidna A, Goldstein ST, Hamad R, Hassan HY, Hayes VM, Hellenthal G, Kebede S, Kurewa A, Kusimba C, Kyazike E, Lane PJ, MacEachern S, Massilani D, Mbua E, Morris AG, Mutinda C, M'Mbogori FN, Reynolds AW, Tishkoff S, Vilar M, Yimer G. Charting a landmark-driven path forward for population genetics and ancient DNA research in Africa. Am J Hum Genet 2024; 111:1243-1251. [PMID: 38996465 PMCID: PMC11267517 DOI: 10.1016/j.ajhg.2024.05.019] [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: 03/26/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 07/14/2024] Open
Abstract
Population history-focused DNA and ancient DNA (aDNA) research in Africa has dramatically increased in the past decade, enabling increasingly fine-scale investigations into the continent's past. However, while international interest in human genomics research in Africa grows, major structural barriers limit the ability of African scholars to lead and engage in such research and impede local communities from partnering with researchers and benefitting from research outcomes. Because conversations about research on African people and their past are often held outside Africa and exclude African voices, an important step for African DNA and aDNA research is moving these conversations to the continent. In May 2023 we held the DNAirobi workshop in Nairobi, Kenya and here we synthesize what emerged most prominently in our discussions. We propose an ideal vision for population history-focused DNA and aDNA research in Africa in ten years' time and acknowledge that to realize this future, we need to chart a path connecting a series of "landmarks" that represent points of consensus in our discussions. These include effective communication across multiple audiences, reframed relationships and capacity building, and action toward structural changes that support science and beyond. We concluded there is no single path to creating an equitable and self-sustaining research ecosystem, but rather many possible routes linking these landmarks. Here we share our diverse perspectives as geneticists, anthropologists, archaeologists, museum curators, and educators to articulate challenges and opportunities for African DNA and aDNA research and share an initial map toward a more inclusive and equitable future.
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Affiliation(s)
- Elizabeth A Sawchuk
- Cleveland Museum of Natural History, Cleveland, OH, USA; Department of Anthropology, University of Alberta, Edmonton, AB, Canada; Department of Anthropology, Stony Brook University, Stony Brook, NY, USA.
| | - Kendra A Sirak
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | | | | | | | | | - David Reich
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Eva Aluvaala
- Kenya Medical Research Institute, Nairobi, Kenya
| | - George Ayodo
- Jaramogi Oginga Odinga University of Science and Technology, Bondo, Kenya
| | - Lamine Badji
- Cultural Engineering Research Unit (URICA) of IFAN-University Cheikh Anta Diop, Dakar, Senegal
| | - Nancy Bird
- UCL Genetics Institute and Research Department of Genetics, Evolution, and Environment, University College London, London, UK
| | - Wendy Black
- Archaeology Unit, Department of Research & Exhibitions, Iziko Museums of South Africa, Cape Town, South Africa; Human Evolution Research Institute, University of Cape Town, Cape Town, South Africa
| | - Rosa Fregel
- Evolution, Paleogenomics and Population Genetics Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain
| | | | - Victoria E Gibbon
- Division of Clinical Anatomy and Biological Anthropology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Agness Gidna
- Department of Cultural Heritage, Ngorongoro Conservation Area Authority, Arusha, Tanzania
| | - Steven T Goldstein
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Reem Hamad
- Diversity and Diseases Group, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
| | - Hisham Y Hassan
- Bahrain Defence Force Hospital, Royal Medical Services, Riffa, Kingdom of Bahrain
| | - Vanessa M Hayes
- School of Medical Sciences, University of Sydney, Sydney, NSW, Australia; School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - Garrett Hellenthal
- UCL Genetics Institute and Research Department of Genetics, Evolution, and Environment, University College London, London, UK
| | - Solomon Kebede
- Authority for Research and Conservation of Cultural Heritage Ethiopia, Addis Ababa, Ethiopia
| | - Abdikadir Kurewa
- National Museums of Kenya, Nairobi, Kenya; Department of Anthropology, University of Florida, Gainesville, FL, USA
| | | | - Elizabeth Kyazike
- Department of History, Archaeology and Heritage Studies, Faculty of Arts and Humanities, Kyambogo University, Kampala, Uganda
| | - Paul J Lane
- Department of Archaeology, University of Cambridge, Cambridge, UK; School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott MacEachern
- Department of Archaeology and Anthropology, Duke Kunshan University, Kunshan, China
| | - Diyendo Massilani
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Emma Mbua
- National Museums of Kenya, Nairobi, Kenya
| | - Alan G Morris
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | | | | | - Austin W Reynolds
- Department of Microbiology, Immunology, and Genetics, School of Biomedical Sciences, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Sarah Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Global Genomics & Health Equity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Miguel Vilar
- Department of Anthropology, University of Maryland, College Park, MD, USA
| | - Getnet Yimer
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Global Genomics & Health Equity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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83
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Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024; 4:100591. [PMID: 38925123 PMCID: PMC11293590 DOI: 10.1016/j.xgen.2024.100591] [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: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
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Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
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84
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Li L, Sun M, Wang J, Wan S. Multi-omics based artificial intelligence for cancer research. Adv Cancer Res 2024; 163:303-356. [PMID: 39271266 DOI: 10.1016/bs.acr.2024.06.005] [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: 09/15/2024]
Abstract
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.
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Affiliation(s)
- Lusheng Li
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States
| | - Mengtao Sun
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States
| | - Jieqiong Wang
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, United States
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States.
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85
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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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86
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Xia R, Jian X, Rodrigue AL, Bressler J, Boerwinkle E, Cui B, Daviglus ML, DeCarli C, Gallo LC, Glahn DC, Knowles EEM, Moon JY, Mosley TH, Satizabal CL, Sofer T, Tarraf W, Testai F, Blangero J, Seshadri S, González HM, Fornage M. Admixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos. Alzheimers Dement 2024. [PMID: 38946675 DOI: 10.1002/alz.14082] [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: 04/11/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024]
Abstract
INTRODUCTION We conducted admixture mapping and fine-mapping analyses to identify ancestry-of-origin loci influencing cognitive abilities. METHODS We estimated the association of local ancestry intervals across the genome with five neurocognitive measures in 7140 diverse Hispanic and Latino adults (mean age 55 years). We prioritized genetic variants in associated loci and tested them for replication in four independent cohorts. RESULTS We identified nine local ancestry-associated regions for the five neurocognitive measures. There was strong biological support for the observed associations to cognitive function at all loci and there was statistical evidence of independent replication at 4q12, 9p22.1, and 13q12.13. DISCUSSION Our study identified multiple novel loci harboring genes implicated in cognitive functioning and dementia, and uncovered ancestry-relevant genetic variants. It adds to our understanding of the genetic architecture of cognitive function in Hispanic and Latino adults and demonstrates the power of admixture mapping to discover unique haplotypes influencing cognitive function, complementing genome-wide association studies. HIGHLIGHTS We identified nine ancestry-of-origin chromosomal regions associated with five neurocognitive traits. In each associated region, we identified single nucleotide polymorphisms (SNPs) that explained, at least in part, the admixture signal and were tested for replication in independent samples of Black, non-Hispanic White, and Hispanic/Latino adults with the same or similar neurocognitive tests. Statistical evidence of independent replication of the prioritized SNPs was observed for three of the nine associations, at chr4q12, chr9p22.1, and chr13q12.13. At all loci, there was strong biological support for the observed associations to cognitive function and dementia, prioritizing genes such as KIT, implicated in autophagic clearance of neurotoxic proteins and on mast cell and microglial-mediated inflammation; SLC24A2, implicated in synaptic plasticity associated with learning and memory; and MTMR6, implicated in phosphoinositide lipids metabolism.
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Affiliation(s)
- Rui Xia
- Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xueqiu Jian
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Amanda L Rodrigue
- Department of Psychiatry, Harvard Medical School, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Biqi Cui
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, Illinois, USA
| | - Charles DeCarli
- Department of Neurology, University of California Davis, Sacramento, California, USA
| | - Linda C Gallo
- Department of Psychology, San Diego State University, San Diego, California, USA
| | - David C Glahn
- Department of Psychiatry, Harvard Medical School, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Emma E M Knowles
- Department of Psychiatry, Harvard Medical School, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jee-Young Moon
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Thomas H Mosley
- The MIND Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Department of Population Health Sciences, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare Sciences, Wayne State University, Detroit, Michigan, USA
| | - Fernando Testai
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, Illinois, USA
| | - John Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Hector M González
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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87
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Roberts MC, Holt KE, Del Fiol G, Baccarelli AA, Allen CG. Precision public health in the era of genomics and big data. Nat Med 2024; 30:1865-1873. [PMID: 38992127 DOI: 10.1038/s41591-024-03098-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/29/2024] [Indexed: 07/13/2024]
Abstract
Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level-thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for 'precision' to enhance traditional public health approaches.
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Affiliation(s)
- Megan C Roberts
- Division of Pharmaceutical Outcomes and Policy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA.
| | - Kathryn E Holt
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Guilherme Del Fiol
- Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrea A Baccarelli
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Caitlin G Allen
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
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88
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Im C, Neupane A, Baedke JL, Lenny B, Delaney A, Dixon SB, Chow EJ, Mostoufi-Moab S, Yang T, Richard MA, Gramatges MM, Lupo PJ, Sharafeldin N, Bhatia S, Armstrong GT, Hudson MM, Ness KK, Robison LL, Yasui Y, Wilson CL, Sapkota Y. Trans-Ancestral Genetic Risk Factors for Treatment-Related Type 2 Diabetes Mellitus in Survivors of Childhood Cancer. J Clin Oncol 2024; 42:2306-2316. [PMID: 38652878 PMCID: PMC11209771 DOI: 10.1200/jco.23.02281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Type 2 diabetes mellitus (T2D) is a prevalent long-term complication of treatment in survivors of childhood cancer, with marked racial/ethnic differences in burden. In this study, we investigated trans-ancestral genetic risks for treatment-related T2D. PATIENTS AND METHODS Leveraging whole-genome sequencing data from the St Jude Lifetime Cohort (N = 3,676, 304 clinically ascertained cases), we conducted ancestry-specific genome-wide association studies among survivors of African and European genetic ancestry (AFR and EUR, respectively) followed by trans-ancestry meta-analysis. Trans-/within-ancestry replication including data from the Childhood Cancer Survivor Study (N = 5,965) was required for prioritization. Three external general population T2D polygenic risk scores (PRSs) were assessed, including multiancestry PRSs. Treatment risk effect modification was evaluated for prioritized loci. RESULTS Four novel T2D risk loci showing trans-/within-ancestry replication evidence were identified, with three loci achieving genome-wide significance (P < 5 × 10-8). Among these, common variants at 5p15.2 (LINC02112), 2p25.3 (MYT1L), and 19p12 (ZNF492) showed evidence of modifying alkylating agent-related T2D risk in both ancestral groups, but showed disproportionately greater risk in AFR survivors (AFR odds ratios [ORs], 3.95-17.81; EUR ORs, 2.37-3.32). In survivor-specific RNA-sequencing data (N = 207), the 19p12 locus variant was associated with greater ZNF492 expression dysregulation after exposures to alkylators. Elevated T2D risks across ancestry groups were only observed with increasing values for multiancestry T2D PRSs and were especially increased among survivors treated with alkylators (top v bottom quintiles: ORAFR, 20.18; P = .023; OREUR, 13.44; P = 1.3 × 10-9). CONCLUSION Our findings suggest therapy-related genetic risks contribute to the increased T2D burden among non-Hispanic Black childhood cancer survivors. Additional study of how therapy-related genetic susceptibility contributes to this disparity is needed.
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Affiliation(s)
- Cindy Im
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Achal Neupane
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Jessica L. Baedke
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Brian Lenny
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Angela Delaney
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Stephanie B. Dixon
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Eric J. Chow
- Public Health Sciences and Clinical Research Divisions, Fred Hutchinson Research Center, Seattle, WA, 98109, USA
| | - Sogol Mostoufi-Moab
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19146, USA
| | - Tianzhong Yang
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Melissa A. Richard
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - M. Monica Gramatges
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Philip J. Lupo
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Noha Sharafeldin
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Melissa M. Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Kirsten K. Ness
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Carmen L. Wilson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yadav Sapkota
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
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89
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Kim J, Chu MK. Genome-Wide Architecture of East Asian Patients With Migraine: A Genome-Wide Association Study Based on Familial History. J Clin Neurol 2024; 20:351-352. [PMID: 38951969 PMCID: PMC11220348 DOI: 10.3988/jcn.2024.0241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 05/24/2024] [Indexed: 07/03/2024] Open
Affiliation(s)
- Joonho Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Min Kyung Chu
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
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90
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Kaiser B, Uberoi D, Raven-Adams MC, Cheung K, Bruns A, Chandrasekharan S, Otlowski M, Prince AER, Tiller J, Ahmed A, Bombard Y, Dupras C, Moreno PG, Ryan R, Valderrama-Aguirre A, Joly Y. A proposal for an inclusive working definition of genetic discrimination to promote a more coherent debate. Nat Genet 2024; 56:1339-1345. [PMID: 38914718 DOI: 10.1038/s41588-024-01786-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 05/03/2024] [Indexed: 06/26/2024]
Abstract
Genetic discrimination is an evolving phenomenon that impacts fundamental human rights such as dignity, justice and equity. Although, in the past, various definitions to better conceptualize genetic discrimination have been proposed, these have been unable to capture several key facets of the phenomenon. In this Perspective, we explore definitions of genetic discrimination across disciplines, consider criticisms of such definitions and show how other forms of discrimination and stigmatization can compound genetic discrimination in a way that affects individuals, groups and systems. We propose a nuanced and inclusive definition of genetic discrimination, which reflects its multifaceted impact that should remain relevant in the face of an evolving social context and advancing science. We argue that our definition should be adopted as a guiding academic framework to facilitate scientific and policy discussions about genetic discrimination and support the development of laws and industry policies seeking to address the phenomenon.
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Affiliation(s)
- Beatrice Kaiser
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | - Diya Uberoi
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | | | - Katherine Cheung
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | - Andreas Bruns
- The German Human Genome-Phenome Archive, University Hospital, Heidelberg, Germany
| | | | - Margaret Otlowski
- Centre for Health, Law and Emerging Technologies, University of Oxford, Oxford, UK
| | | | - Jane Tiller
- Monash University, Parkville, Victoria, Australia
| | | | - Yvonne Bombard
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Genomics Health Services Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | | | | | | | | | - Yann Joly
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada.
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91
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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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92
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Katz AE, Gupte T, Ganesh SK. From Atherosclerosis to Spontaneous Coronary Artery Dissection: Defining a Clinical and Genetic Risk Spectrum for Myocardial Infarction. Curr Atheroscler Rep 2024; 26:331-340. [PMID: 38761354 DOI: 10.1007/s11883-024-01208-4] [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] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE OF REVIEW Spontaneous coronary artery dissection (SCAD) has been increasingly recognized as a significant cause of acute myocardial infarction (AMI) in young and middle-aged women and arises through mechanisms independent of atherosclerosis. SCAD has a multifactorial etiology that includes environmental, individual, and genetic factors distinct from those typically associated with coronary artery disease. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD and highlight those factors which differentiate SCAD from atherosclerotic coronary artery disease. RECENT FINDINGS Recent studies have revealed several associated variants with varying effect sizes for SCAD, giving rise to a complex genetic architecture. Associated genes highlight an important role for arterial cells and their extracellular matrix in the pathogenesis of SCAD, as well as notable genetic overlap between SCAD and other systemic arteriopathies such as fibromuscular dysplasia and vascular connective tissue diseases. Further investigation of individual variants (including in the associated gene PHACTR1) along with polygenic score analysis have demonstrated an inverse genetic relationship between SCAD and atherosclerosis as distinct causes of AMI. SCAD represents an increasingly recognized cause of AMI with opposing clinical and genetic risk factors from that of AMI due to atherosclerosis, and it is often associated with complex underlying genetic conditions. Genetic study of SCAD on a larger scale and with more diverse cohorts will not only further our evolving understanding of a newly defined genetic spectrum for AMI, but it will also inform the clinical utility of integrating genetic testing in AMI prevention and management moving forward.
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Affiliation(s)
- Alexander E Katz
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Trisha Gupte
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
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93
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Blechter B, Wang X, Shi J, Shiraishi K, Choi J, Matsuo K, Chen TY, Dai J, Hung RJ, Chen K, Shu XO, Kim YT, Choudhury PP, Williams J, Landi MT, Lin D, Zheng W, Yin Z, Song B, Chang IS, Hong YC, ChaVerjee N, Gorlova OY, Amos CI, Shen H, Hsiung CA, Chanock SJ, Rothman N, Kohno T, Lan Q, Zhang H. Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.26.24309127. [PMID: 38978671 PMCID: PMC11230324 DOI: 10.1101/2024.06.26.24309127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Lung adenocarcinoma (LUAD) among never-smokers is a public health burden especially prevalent in East Asian (EAS) women. Polygenic risk scores (PRSs), which quanefy geneec suscepebility, are promising for straefying risk, yet have mainly been developed in European (EUR) populaeons. We developed and validated single-and mule-ancestry PRSs for LUAD in EAS never-smokers, using the largest available genome-wide associaeon study (GWAS) dataset. Methods We used GWAS summary staesecs from both EAS (8,002 cases; 20,782 controls) and EUR (2,058 cases; 5,575 controls) populaeons, as well as independent EAS individual level data. We evaluated several PRSs approaches: a single-ancestry PRS using 25 variants that reached genome-wide significance (PRS-25), a genome-wide Bayesian based approach (LDpred2), and a mule-ancestry approach that models geneec correlaeons across ancestries (CT-SLEB). PRS performance was evaluated based on the associaeon with LUAD and AUC values. We then esemated the lifeeme absolute risk of LUAD (age 30-80) and projected the AUC at different sample sizes using EAS-derived effect-size distribueon and heritability esemates. Findings The CT-SLEB PRS showed a strong associaeon with LUAD risk (odds raeo=1.71, 95% confidence interval (CI): 1.61, 1.82) with an AUC of 0.640 (95% CI: 0.629, 0.653). Individuals in the 95 th percenele of the PRS had an esemated 6.69% lifeeme absolute risk of LUAD. Comparison of LUAD risk between individuals in the highest and lowest 20% PRS quaneles revealed a 3.92-fold increase. Projeceon analyses indicated that achieving an AUC of 0.70, which approaches the maximized prediceon poteneal of the PRS given the esemated geneec variance, would require a future study encompassing 55,000 EAS LUAD cases with a 1:10 case-control raeo. Interpretations Our study underscores the poteneal of mule-ancestry PRS approaches to enhance LUAD risk straeficaeon in never-smokers, parecularly in EAS populaeons, and highlights the necessary scale of future research to uncover the geneec underpinnings of LUAD.
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94
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Blanc J, Berg JJ. Testing for differences in polygenic scores in the presence of confounding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.12.532301. [PMID: 36993707 PMCID: PMC10055004 DOI: 10.1101/2023.03.12.532301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals' phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification.
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Affiliation(s)
- Jennifer Blanc
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Jeremy J. Berg
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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95
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Martinez KL, Klein A, Martin JR, Sampson CU, Giles JB, Beck ML, Bhakta K, Quatraro G, Farol J, Karnes JH. Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program. J Am Med Inform Assoc 2024:ocae161. [PMID: 38917427 DOI: 10.1093/jamia/ocae161] [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: 03/20/2024] [Revised: 05/02/2024] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
OBJECTIVES ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature. MATERIALS AND METHODS Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program. RESULTS Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23). DISCUSSION Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals. CONCLUSION Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.
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Affiliation(s)
- Kiana L Martinez
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Andrew Klein
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Jennifer R Martin
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
- Department of the University of Arizona Health Sciences Library, The University of Arizona, Tucson, AZ 85721, United States
| | - Chinwuwanuju U Sampson
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Jason B Giles
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Madison L Beck
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Krupa Bhakta
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Gino Quatraro
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
| | - Juvie Farol
- Department of Clinical and Translational Science, The University of Arizona College of Medicine, Tucson, AZ 85721, United States
| | - Jason H Karnes
- Department of Pharmacy Practice and Science, The University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ 85721, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, United States
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Chen Y, Xue H, Zhou J, Shu X, He Z, Ai S, Feng H, Zhang J, Liang YY, Lv Y, Zhou Y. Childhood maltreatment, genetic risk, and subsequent risk of arrhythmias: a prospective cohort study. Eur J Psychotraumatol 2024; 15:2366055. [PMID: 38912597 PMCID: PMC11198125 DOI: 10.1080/20008066.2024.2366055] [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: 03/28/2024] [Accepted: 06/03/2024] [Indexed: 06/25/2024] Open
Abstract
Background: Emerging evidence has linked childhood maltreatment with cardiovascular disease risk; however, the association between childhood maltreatment and cardiac arrhythmias remains unclear. Moreover, any genetic predispositions to atrial fibrillation (AF), a common cardiac arrhythmia associated with an elevated risk of stroke, heart failure, and mortality, that modify such associations have been undocumented.Purpose: To examine the associations between childhood maltreatment and incident arrhythmias, and whether a genetic predisposition to arrhythmias modifies these associations.Methods: This prospective analysis included 151,741 participants from the UK Biobank (mean age 55.8 years, 43.4% male). Childhood maltreatment, including five types, was measured using the Childhood Trauma Screener (CTS). Incident arrhythmias (AF, ventricular arrhythmias [VA], and bradyarrhythmia [BA]) were documented through linked hospital admission and death registry. Weighted AF genetic risk score was calculated. Cox proportional hazard models were conducted to test for associations between childhood maltreatment and incident arrhythmias.Results: During a median follow-up of 12.21 years (interquartile range, 11.49-12.90 years), 6,588 AF, 2,093 BA, and 742 VA events occurred. Compared with the absence of childhood maltreatment, having 3-5 types of childhood maltreatment was associated with an increased risk of incident AF (HR, 1.23; 95%CI 1.09-1.37), VA (HR, 1.39; 95%CI 1.03-1.89), and BA (HR, 1.32; 95%CI 1.09-1.61) after adjusting demographic, socioeconomic and lifestyle factors. The associations between cumulative type of childhood maltreatment and the risk of AF (Poverall < .001; Pnonlinear = .674) and BA (Poverall = .007; Pnonlinear = .377) demonstrated a linear pattern. There was a gradient association between childhood maltreatment and AF risks across the intermediate and high genetic risk groups (both Ptrend < .05) but not within the low genetic risk group (Ptrend = .378), irrespective of non-significant interaction effect (Pinteraction = .204).Conclusion: Childhood maltreatment was associated with higher risks of incident arrhythmias, especially AF and BA. Genetic risk of AF did not modify these associations.
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Affiliation(s)
- Yilin Chen
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Huachen Xue
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Jiajin Zhou
- The Affiliated Hospital of Kunming University of Science and Technology, The First People’s Hospital of Yunnan Province, Kunming, People’s Republic of China
| | - Xinyue Shu
- School of Medicine, Jinan University, Guangzhou, People’s Republic of China
| | - Zhixuan He
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Sizhi Ai
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
- Department of Cardiology, Heart Center, The First Affiliated Hospital of Xinxiang Medical University, Weihui, People’s Republic of China
| | - Hongliang Feng
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Jihui Zhang
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yannis Yan Liang
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
- Institute of Psycho-neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yunhui Lv
- The Affiliated Hospital of Kunming University of Science and Technology, The First People’s Hospital of Yunnan Province, Kunming, People’s Republic of China
| | - Yujing Zhou
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China
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97
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Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, Yamamoto M. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population. J Atheroscler Thromb 2024:64906. [PMID: 38910120 DOI: 10.5551/jat.64906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
AIM This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes. METHODS This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC). RESULT Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732]). CONCLUSION Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.
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Affiliation(s)
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University
- Kyoto Women's University
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology
| | - Akira Narita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Taku Obara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | | | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
- Suzuki Memorial Hospital
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo
| | - Yoko Izumi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kengo Kinoshita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- RIKEN Center for Advanced Intelligence Project
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
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98
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Himmerich H, Keeler JL, Davies HL, Tessema SA, Treasure J. The evolving profile of eating disorders and their treatment in a changing and globalised world. Lancet 2024; 403:2671-2675. [PMID: 38705161 DOI: 10.1016/s0140-6736(24)00874-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024]
Affiliation(s)
- Hubertus Himmerich
- Centre for Research in Eating and Weight Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London, UK.
| | - Johanna Louise Keeler
- Centre for Research in Eating and Weight Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, UK
| | - Helena L Davies
- Center for Eating and Feeding Disorders Research, Mental Health Center Ballerup, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark; Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | | | - Janet Treasure
- Centre for Research in Eating and Weight Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London, UK
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99
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Suleman M, Parker MJ, Qureshi N. Ethical implications of disparities in translation genomic medicine: from research to practice. JOURNAL OF MEDICAL ETHICS 2024; 50:435-436. [PMID: 38906545 DOI: 10.1136/jme-2024-110151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/23/2024]
Affiliation(s)
- Mehrunisha Suleman
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Nadeem Qureshi
- School of Medicine, University of Nottingham, Nottingham, UK
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100
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Harnett NG, Merrill LC, Fani N. Racial and ethnic socioenvironmental inequity and neuroimaging in psychiatry: a brief review of the past and recommendations for the future. Neuropsychopharmacology 2024:10.1038/s41386-024-01901-7. [PMID: 38902354 DOI: 10.1038/s41386-024-01901-7] [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: 03/15/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging is a major tool that holds immense translational potential for understanding psychiatric disorder phenomenology and treatment. However, although epidemiological and social research highlights the many ways inequity and representativeness influences mental health, there is a lack of consideration of how such issues may impact neuroimaging features in psychiatric research. More specifically, the potential extent to which racialized inequities may affect underlying neurobiology and impact the generalizability of neural models of disorders is unclear. The present review synthesizes research focused on understanding the potential consequences of racial/ethnic inequities relevant to neuroimaging in psychiatry. We first discuss historical and contemporary drivers of inequities that persist today. We then discuss the neurobiological consequences of these inequities as revealed through current research, and note emergent research demonstrating the impact such inequities have on our ability to use neuroimaging to understand psychiatric disease. We end with a set of recommendations and practices to move the field towards more equitable approaches that will advance our abilities to develop truly generalizable neurobiological models of psychiatric disorders.
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Affiliation(s)
- Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Livia C Merrill
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
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