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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 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] [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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3
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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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4
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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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5
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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 DOI: 10.1016/j.actpsy.2024.104403] [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: 12/15/2023] [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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6
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Yamada S, Umehara T, Sonehara K, Kijima N, Kawabata S, Takano K, Kidani T, Hirayama R, Arita H, Okita Y, Kinoshita M, Kagawa N, Fujinaka T, Fujita T, Wakayama A, Matsuda K, Okada Y, Kishima H. Genome-wide association study on meningioma risk in Japan: a multicenter prospective study. J Neurooncol 2024:10.1007/s11060-024-04727-x. [PMID: 39002029 DOI: 10.1007/s11060-024-04727-x] [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: 04/24/2024] [Accepted: 05/27/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE Although meningiomas are the most common primary intracranial tumors, their genetic etiologies have not been fully elucidated. To date, only two genome-wide association studies (GWASs) have focused on European ancestries, despite ethnic differences in the incidence of meningiomas. The aim of this study was to conduct the first GWAS of Japanese patients with meningiomas to identify the SNPs associated with meningioma susceptibility. METHODS In this multicenter prospective case-control study, we studied 401 Japanese patients with meningioma admitted in five institutions in Japan, and 50,876 control participants of Japanese ancestry enrolled in Biobank Japan. RESULTS The quality control process yielded 536,319 variants and imputation resulted in 8,224,735 variants on the autosomes and 224,820 variants on the X chromosomes. This GWAS eventually revealed no genetic variants with genome-wide significance (P < 5 × 10 - 8) and observed no significant association in the previously reported risk variants rs11012732 and rs2686876 due to low minor allele frequency in the Japanese population. CONCLUSION This is the first GWAS of meningiomas in East Asian populations and is expected to contribute to the development of GWAS research for meningiomas.
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Affiliation(s)
- Shuhei Yamada
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Toru Umehara
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, Hanwa Memorial Hospital, Osaka, Osaka, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Osaka, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Noriyuki Kijima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan.
| | - Shuhei Kawabata
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, Sakai City Medical Center, Sakai, Osaka, Japan
| | - Koji Takano
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Osaka, Japan
| | - Tomoki Kidani
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | - Ryuichi Hirayama
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Osaka, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Hokkaido, Japan
| | - Naoki Kagawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Toshiyuki Fujinaka
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | - Toshiaki Fujita
- Department of Neurosurgery, Hanwa Memorial Hospital, Osaka, Osaka, Japan
| | - Akatsuki Wakayama
- Department of Neurosurgery, Osaka Neurological Institute, Toyonaka, Osaka, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- The Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
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7
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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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8
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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 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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9
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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 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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10
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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 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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11
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Barreiro RAS, de Almeida TF, Gomes CDS, Monfardini F, de Farias AA, Tunes GC, de Souza GM, Duim E, de Sá Correia J, Campos Coelho AV, Caraciolo MP, Oliveira Duarte YA, Zatz M, Amaro E, Oliveira JB, Bitarello BD, Brentani H, Naslavsky MS. Assessing the Risk Stratification of Breast Cancer Polygenic Risk Scores in a Brazilian Cohort. J Mol Diagn 2024:S1525-1578(24)00150-8. [PMID: 38972593 DOI: 10.1016/j.jmoldx.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 05/10/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
Polygenic risk scores (PRSs) for breast cancer have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium and differences in variant frequency (allele frequency differences). Thus, locally sourced population-based phenotypic and genomic data sets are essential to assess the validity of PRSs derived from signals detected across populations. Here, we assess the transferability of a breast cancer PRS composed of 313 risk variants (313-PRS) in a Brazilian trihybrid admixed ancestries (European, African, and Native American) whole-genome sequenced cohort, the Rare Genomes Project. We computed 313-PRS in the Rare Genomes Project (n = 853) using the UK Biobank (UKBB; n = 264,307) as reference. We show that although the Brazilian cohorts have a high European ancestry (EA) component, with allele frequency differences and to a lesser extent linkage disequilibrium patterns similar to those found in EA populations, the 313-PRS distribution is inflated when compared with that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. Interestingly, we find that case controls lead to equivalent predictive power when compared with UKBB-EA samples with area under the receiver operating characteristic curve values of 0.66 to 0.62 compared with 0.63 for UKBB.
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Affiliation(s)
| | | | - Catarina Dos Santos Gomes
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Institute of Psychiatry, University of São Paulo, Medical School, São Paulo, Brazil
| | | | | | | | | | - Etienne Duim
- Big Data and Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | | | | | - Yeda Aparecida Oliveira Duarte
- Medical-Surgical Nursing Department, School of Nursing, University of São Paulo, São Paulo, Brazil; Epidemiology Department, Public Health School, University of São Paulo, São Paulo, Brazil
| | - Mayana Zatz
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil; Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
| | - Edson Amaro
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | | | - Helena Brentani
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Institute of Psychiatry, University of São Paulo, Medical School, São Paulo, Brazil
| | - Michel Satya Naslavsky
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil; Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
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12
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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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13
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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 2024:10.1038/s41562-024-01909-5. [PMID: 38965376 DOI: 10.1038/s41562-024-01909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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14
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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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15
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Biagetti G, Thompson E, O'Brien C, Damrauer S. The Role of Genetics in Managing Peripheral Arterial Disease. Ann Vasc Surg 2024:S0890-5096(24)00298-X. [PMID: 38960093 DOI: 10.1016/j.avsg.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Genome wide association studies (GWAS) have allowed for a rapid increase in our understanding of the underlying genetics and biology of many diseases. By capitalizing on common genetic variation between individuals, GWAS can identify DNA variants associated with diseases of interest. A variety of statistical methods can be applied to GWAS results which allows for risk factor identification, stratification, and to identify potential treatments. Peripheral artery disease (PAD) is a common vascular disease that has been shown to have a strong genetic component. This article provides a review of the modern literature and our current understanding of the role of genetics in PAD. SUMMARY The largest available GWAS on PAD has identified 19 genome wide significant loci, with factor V Leiden and genes responsible for circulating lipoproteins being implicated in the development of PAD. Mendelian randomization studies have identified risk factors and causal associations with smoking, diabetes, and obesity and many other traits; protein-based MR has also identified circulating lipid and clotting factor levels associated with the incidence of PAD. Polygenic risk scores may allow for improved prediction of disease incidence and allow for early identification of at-risk patients but more work needs to be done to validate this approach. CONCLUSIONS Genetic epidemiology has allowed for an increased understanding of PAD in the past decade. Genome-wide association studies have led to improved detection of genetic contributions to PAD, and further genetic analyses have validated risk factors and may provide options for improved screening in at-risk populations. Ongoing biobank studies of CLTI patients and the increasing ancestral diversity in biobank enrollment will allow for even further exploration into the pathogenesis and progression of PAD.
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Affiliation(s)
- Gina Biagetti
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Elizabeth Thompson
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ciaran O'Brien
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Scott Damrauer
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael Crescenz VA Medical Center, Philadelphia, PA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
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16
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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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17
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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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18
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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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19
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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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20
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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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21
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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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22
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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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23
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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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24
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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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25
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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 2024:10.1038/s41440-024-01705-8. [PMID: 38914703 DOI: 10.1038/s41440-024-01705-8] [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: 01/08/2024] [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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26
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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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27
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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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28
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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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29
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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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30
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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. [PMID: 39030740 DOI: 10.1002/alz.13873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [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 Dementias, National Institutes of Health, Bethesda, Maryland, USA
- DataTecnica LLC, Washington, District of Columbia, USA
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, California, USA
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
| | - Kristin S Levine
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- DataTecnica LLC, Washington, District of Columbia, USA
| | - Julie Lake
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Linnea Hertslet
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
| | - Lietsel Jones
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- DataTecnica LLC, Washington, District of Columbia, USA
| | - Dhairya Patel
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Jeff Kim
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
| | - Nancy Terry
- Division of Library Services, Office of Research Services, National Institutes of Health, Bethesda, Maryland, USA
| | - Ignacio F Mata
- Genomic Medicine Institute, Lerner Research Institute, Genomic Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Cornelis Blauwendraat
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrew B Singleton
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- DataTecnica LLC, Washington, District of Columbia, USA
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer S Yokoyama
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, California, USA
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, Maryland, USA
- DataTecnica LLC, Washington, District of Columbia, USA
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, Maryland, USA
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
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31
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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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32
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Rout M, Malone-Perez MW, Park G, Lerner M, Kimble Frazer J, Apple B, Vaughn A, Payton M, Stavrakis S, Sidorov E, Fung KA, Sanghera DK. Contribution of circulating Mfge8 to human T2DM and cardiovascular disease. Gene 2024; 927:148712. [PMID: 38901535 DOI: 10.1016/j.gene.2024.148712] [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: 04/23/2024] [Revised: 05/31/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
MFGE8 is a major exosome (EV) protein known to mediate inflammation and atherosclerosis in type 2 diabetes mellitus (T2DM) in animal studies. The pathophysiological role of this protein in obesity, T2DM, and cardiovascular disease is less investigated in humans. Earlier we reported a rare Asian Indian population-specific missense variant (rs371227978; Arg148His) in the MFGE8 gene associated with increased circulating Mfge8 and T2DM. We have further investigated the role of Mfge8 with T2DM risk in additional Asian Indians (n = 4897) and Europeans and other multiethnic cohorts from UK Biobank (UKBB) (n = 455,808) and the US (n = 1150). We also evaluated the exposure of Mfge8-enriched human EVs in zebrafish (ZF) for their impact on cardiometabolic organ system. Most individual carriers of Arg148His variant not only had high circulating Mfge8 but also revealed a positive significant correlation with glucose (r = 0.42; p = 4.9 × 10-04), while the non-carriers showed a negative correlation of Mfge8 with glucose (r = -0.38; p = 0.001) in Asian Indians. The same variant was monomorphic in non-South Asian ethnicities. Even without the variant, serum Mfge8 correlated significantly with blood glucose in other non-South Asian ethnicities (r = 0.47; p = 2.2 × 10-13). Since Mfge8 is an EV marker, we tested the exposure of Mfge8-enriched human EVs to ZF larvae as an exploratory study. The ZF larvae showed rapid effects on insulin-sensitive organs, developing fatty liver disease, heart hypertrophy and exhibiting redundant growth with poor muscular architecture with and without the high-fat diet (HFD). In contrast, the control group fishes developed fatty liver disease and heart hypertrophy only after the HFD feeding. Backed with strong support from animal studies on the role of Mfge8 in obesity, insulin resistance, and atherosclerosis, the current research suggests that circulating Mfge8 may become a potential marker for predicting the risk of T2DM and cardiovascular disease in humans.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Megan W Malone-Perez
- Department of Pediatrics, Section of Hematology and Oncology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Gilseung Park
- Department of Pediatrics, Section of Hematology and Oncology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Megan Lerner
- Department of Surgery, Oklahoma University of Health Sciences Center, Oklahoma City, OK, USA
| | - J Kimble Frazer
- Department of Pediatrics, Section of Hematology and Oncology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Blair Apple
- Department of Neurology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - April Vaughn
- Department of Neurology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Marvin Payton
- Department of Surgery, Oklahoma University of Health Sciences Center, Oklahoma City, OK, USA
| | - Stavros Stavrakis
- Department of Cardiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Evgeny Sidorov
- Department of Neurology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - KarMing A Fung
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Dharambir K Sanghera
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Department of Physiology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
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33
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Jain SS, Elias P, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2487-2496. [PMID: 38593945 DOI: 10.1016/j.jacc.2024.03.401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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34
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Gaggero A, Ajnakina O, Zucchelli E, Hackett RA. The effect of heavy smoking on retirement risk: A mendelian randomisation analysis. Addict Behav 2024; 157:108078. [PMID: 38889551 DOI: 10.1016/j.addbeh.2024.108078] [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: 02/02/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND AND AIMS The extent to which heavy smoking and retirement risk are causally related remains to be determined. To overcome the endogeneity of heavy smoking behaviour, we employed a novel approach by exploiting the genetic predisposition to heavy smoking, as measured with a polygenic risk score (PGS), in a Mendelian Randomisation approach. METHODS 8164 participants (mean age 68.86 years) from the English Longitudinal Study of Ageing had complete data on smoking behaviour, employment and a heavy smoking PGS. Heavy smoking was indexed as smoking at least 20 cigarettes a day. A time-to-event Mendelian Randomization (MR) analysis, using a complementary log-log (cloglog) link function, was employed to model the retirement risk. RESULTS Our results show that being a heavy smoker significantly increases the risk of retirement (β = 1.324, standard error = 0.622, p < 0.05). Results were robust to a battery of checks and a placebo analysis considering the never-smokers. CONCLUSIONS Overall, our findings support a causal pathway from heavy smoking to earlier retirement.
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Affiliation(s)
- Alessio Gaggero
- Department of Quantitative Methods for Economics and Business, Universidad de Granada (UGR), Spain.
| | - Olesya Ajnakina
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK.
| | - Eugenio Zucchelli
- Department of Economic Analysis: Economic Theory and Economic History, Universidad Autónoma de Madrid (UAM), Spain; Division of Health Research, Faculty of Health & Medicine, Lancaster University, Lancaster, UK; Institute of Labor Economics (IZA), Bonn, Germany.
| | - Ruth A Hackett
- Health Psychology Section, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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Patel RA, Weiß CL, Zhu H, Mostafavi H, Simons YB, Spence JP, Pritchard JK. Conditional frequency spectra as a tool for studying selection on complex traits in biobanks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.15.599126. [PMID: 38948697 PMCID: PMC11212903 DOI: 10.1101/2024.06.15.599126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. To account for GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insight into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.
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Affiliation(s)
- Roshni A. Patel
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Clemens L. Weiß
- Stanford Cancer Institute Core, Stanford University School of Medicine, Stanford, CA
| | - Huisheng Zhu
- Department of Biology, Stanford University, Stanford, CA
| | - Hakhamanesh Mostafavi
- Center for Human Genetics and Genomics, New York University School of Medicine, New York, NY
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY
| | | | - Jeffrey P. Spence
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Jonathan K. Pritchard
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
- Department of Biology, Stanford University, Stanford, CA
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36
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Thorpe HHA, Fontanillas P, Meredith JJ, Jennings MV, Cupertino RB, Pakala S, Elson SL, Khokhar JY, Davis LK, Johnson EC, Palmer AA, Sanchez-Roige S. Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308946. [PMID: 38947071 PMCID: PMC11213095 DOI: 10.1101/2024.06.14.24308946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Cannabis is one of the most widely used drugs globally. Decriminalization of cannabis is further increasing cannabis consumption. We performed genome-wide association studies (GWASs) of lifetime (N=131,895) and frequency (N=73,374) of cannabis use. Lifetime cannabis use GWAS identified two loci, one near CADM2 (rs11922956, p=2.40E-11) and another near GRM3 (rs12673181, p=6.90E-09). Frequency of use GWAS identified one locus near CADM2 (rs4856591, p=8.10E-09; r2 =0.76 with rs11922956). Both traits were heritable and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use associated cannabis use phenotypes in AllofUs participants. Phenome-wide association study of lifetime cannabis use PGS in a hospital cohort replicated associations with substance use and mood disorders, and uncovered associations with celiac and infectious diseases. This work demonstrates the value of GWASs of CUD transition risk factors.
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Affiliation(s)
- Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Renata B Cupertino
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Shreya Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | | | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
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Zhang J, Weissenkampen JD, Kember RL, Grove J, Børglum AD, Robinson EB, Brodkin ES, Almasy L, Bucan M, Sebro R. Phenotypic and ancestry-related assortative mating in autism. Mol Autism 2024; 15:27. [PMID: 38877467 PMCID: PMC11177537 DOI: 10.1186/s13229-024-00605-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 05/30/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. METHODS To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). RESULTS We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5). LIMITATIONS We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. CONCLUSIONS This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.
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Affiliation(s)
- Jing Zhang
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Grove
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Edward S Brodkin
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
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38
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Tian J, Zhang M, Zhang F, Gao K, Lu Z, Cai Y, Chen C, Ning C, Li Y, Qian S, Bai H, Liu Y, Zhang H, Chen S, Li X, Wei Y, Li B, Zhu Y, Yang J, Jin M, Miao X, Chen K. Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies. Genome Med 2024; 16:81. [PMID: 38872215 PMCID: PMC11170922 DOI: 10.1186/s13073-024-01355-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Early detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population. METHODS To develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS148); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS183); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRSGenomewide). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants. RESULTS Three trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose-response effect of PRS183 on incident colorectal neoplasms. Incorporating PRS183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32). CONCLUSIONS Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.
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Affiliation(s)
- Jianbo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
| | - Ming Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Fuwei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Kai Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Can Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Caibo Ning
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Yanmin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Sangni Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hao Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yizhuo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Heng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Shuoni Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Xiangpan Li
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Yongchang Wei
- Department of Gastrointestinal Oncology, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Jinhua Yang
- Jiashan Institute of Cancer Prevention and Treatment, Jiashan, China
| | - Mingjuan Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
| | - Kun Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Jermy B, Läll K, Wolford BN, Wang Y, Zguro K, Cheng Y, Kanai M, Kanoni S, Yang Z, Hartonen T, Monti R, Wanner J, Youssef O, Lippert C, van Heel D, Okada Y, McCartney DL, Hayward C, Marioni RE, Furini S, Renieri A, Martin AR, Neale BM, Hveem K, Mägi R, Palotie A, Heyne H, Mars N, Ganna A, Ripatti S. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nat Commun 2024; 15:5007. [PMID: 38866767 PMCID: PMC11169548 DOI: 10.1038/s41467-024-48938-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
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Affiliation(s)
- Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - 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
| | - Ying Wang
- 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
| | - Kristina Zguro
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Masahiro Kanai
- 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
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Remo Monti
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Julian Wanner
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Omar Youssef
- Helsinki Biobank, Hospital District of Helsinki and Uusimaa (HUS), Helsinki, Finland
- Pathology Department, University of Helsinki, Helsinki, Finland
| | - Christoph Lippert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Alessandra Renieri
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Medical Genetics, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Alicia R Martin
- 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
| | - Benjamin M Neale
- 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
| | - 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
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, 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
| | - Henrike Heyne
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, 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
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
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40
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Rapoport N, Pavelchek C, Michelson AP, Shew MA. Artificial Intelligence in Otology and Neurotology. Otolaryngol Clin North Am 2024:S0030-6665(24)00067-7. [PMID: 38871535 DOI: 10.1016/j.otc.2024.04.009] [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: 06/15/2024]
Abstract
Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.
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Affiliation(s)
- Nicholas Rapoport
- Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA
| | - Cole Pavelchek
- Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA
| | - Andrew P Michelson
- Department of Pulmonary Critical Care, Washington University School of Medicine, 660 South Euclid Avenue, PO Box 8052-43-14, St Louis, MO 63110, USA; Institute for Informatics, Washington University School of Medicine, St Louis, MO, USA
| | - Matthew A Shew
- Otology & Neurotology, Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA.
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41
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Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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Schaffer LS, Breunig S, Lawrence JM, Foote IF, Grotzinger AD. Characterizing Genetic Pathways Unique to Autism Spectrum Disorder at Multiple Levels of Biological Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.07.24308616. [PMID: 38883730 PMCID: PMC11178016 DOI: 10.1101/2024.06.07.24308616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical patterns of social functioning and repetitive/restricted behaviors. ASD commonly co-occurs with ADHD and, despite their clinical distinctiveness, the two share considerable genetic overlap. Given their shared genetic liability, it is unclear which genetic pathways confer unique risk for ASD independent of ADHD. We applied Genomic Structural Equation Modeling (SEM) to GWAS summary statistics for ASD and ADHD, decomposing the genetic signal for ASD into that which is unique to ASD (uASD) and that which is shared with ADHD. We computed genetic correlations between uASD and 75 external traits to estimate genetic overlap between uASD and other clinically relevant phenotypes. We went on to apply Stratified Genomic SEM to identify classes of genes enriched for uASD. Finally, we implemented Transcriptome-Wide SEM (T-SEM) to explore patterns of gene-expression associated with uASD. We observed positive genetic correlations between uASD and several external traits, most notably those relating to cognitive/educational outcomes and internalizing psychiatric traits. Stratified Genomic SEM showed that heritability for uASD was significantly enriched in genes involved in evolutionarily conserved processes, as well as for a histone mark in the germinal matrix. T-SEM revealed 83 unique genes with expression associated with uASD, many of which were novel. These findings delineate the unique biological underpinnings of ASD which exist independent of ADHD and demonstrate the utility of Genomic SEM and its extensions for disambiguating shared and unique risk pathways for genetically overlapping traits.
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Affiliation(s)
- Lukas S Schaffer
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO
| | - Sophie Breunig
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO
| | - Jeremy M Lawrence
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO
| | - Isabelle F Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO
| | - Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO
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43
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Lewis ACF, Chisholm RL, Connolly JJ, Esplin ED, Glessner J, Gordon A, Green RC, Hakonarson H, Harr M, Holm IA, Jarvik GP, Karlson E, Kenny EE, Kottyan L, Lennon N, Linder JE, Luo Y, Martin LJ, Perez E, Puckelwartz MJ, Rasmussen-Torvik LJ, Sabatello M, Sharp RR, Smoller JW, Sterling R, Terek S, Wei WQ, Fullerton SM. Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network. Am J Hum Genet 2024; 111:999-1005. [PMID: 38688278 PMCID: PMC11179244 DOI: 10.1016/j.ajhg.2024.04.005] [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: 02/27/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.
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Affiliation(s)
- Anna C F Lewis
- Edmond and Lily Safra Center for Ethics, Harvard University, Cambridge, MA, USA; Department of Genetics, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Joe Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam Gordon
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Robert C Green
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret Harr
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine and Department of Genome Science, University of Washington Medical Center, Seattle, WA, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine, New York City, NY, USA; Center for Clinical Translational Genomics, Icahn School of Medicine, New York City, NY, USA; Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, New York City, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York City, NY, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Emma Perez
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Megan J Puckelwartz
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Laura J Rasmussen-Torvik
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York City, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center, New York City, NY, USA
| | | | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Rene Sterling
- Division of Genomics and Society, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shannon Terek
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome wide association study and genomic risk prediction of age related macular degeneration in Israel. Sci Rep 2024; 14:13034. [PMID: 38844476 PMCID: PMC11156861 DOI: 10.1038/s41598-024-63065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10-5 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10-9). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Affiliation(s)
- Michelle Grunin
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Daria Triffon
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
| | - Gala Beykin
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Regev Schweiger
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Genetics, University of Cambridge, CB21TN, Cambridge, UK
| | - Liran Tiosano
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Samer Khateb
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Shira Hagbi-Levi
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Batya Rinsky
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Refael Munitz
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Eran Halperin
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Anesthesiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel.
| | - Itay Chowers
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel.
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45
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Gokhman D, Harris KD, Carmi S, Greenbaum G. Predicting the direction of phenotypic difference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581566. [PMID: 38895291 PMCID: PMC11185551 DOI: 10.1101/2024.02.22.581566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Predicting phenotypes from genomic data is a key goal in genetics, but for most complex phenotypes, predictions are hampered by incomplete genotype-to-phenotype mapping. Here, we describe a more attainable approach than quantitative predictions, which is aimed at qualitatively predicting phenotypic differences. Despite incomplete genotype-to-phenotype mapping, we show that it is relatively easy to determine which of two individuals has a greater phenotypic value. This question is central in many scenarios, e.g., comparing disease risk between individuals, the yield of crop strains, or the anatomy of extinct vs extant species. To evaluate prediction accuracy, i.e., the probability that the individual with the greater predicted phenotype indeed has a greater phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We evaluated prediction accuracy using human data from tens of thousands of individuals from either the same family or the same population, as well as data from different species. We found that, in many cases, even when only a small fraction of the loci affecting a phenotype is known, the individual with the greater phenotypic value can be identified with over 90% accuracy. Our approach also circumvents some of the limitations in transferring genetic association results across populations. Overall, we introduce an approach that enables accurate predictions of key information on phenotypes - the direction of phenotypic difference - and suggest that more phenotypic information can be extracted from genomic data than previously appreciated.
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Affiliation(s)
- David Gokhman
- Department of Molecular Genetics, The Weizmann Institute of Science, Rehovot 76100, Israel
| | - Keith D Harris
- Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Gili Greenbaum
- Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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46
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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47
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Miller AP, Bogdan R, Agrawal A, Hatoum AS. Generalized genetic liability to substance use disorders. J Clin Invest 2024; 134:e172881. [PMID: 38828723 PMCID: PMC11142744 DOI: 10.1172/jci172881] [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: 06/05/2024] Open
Abstract
Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.
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Affiliation(s)
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Alexander S. Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
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48
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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:S1470-2045(24)00102-5. [PMID: 38848741 DOI: 10.1016/s1470-2045(24)00102-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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49
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Topriceanu CC, Chaturvedi N, Mathur R, Garfield V. Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations. Eur J Hum Genet 2024; 32:697-707. [PMID: 38182743 PMCID: PMC11153583 DOI: 10.1038/s41431-023-01517-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 10/29/2023] [Accepted: 11/28/2023] [Indexed: 01/07/2024] Open
Abstract
Polygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Nish Chaturvedi
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Rohini Mathur
- Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Victoria Garfield
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
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50
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Miller AP, Gizer IR. Dual-systems models of the genetic architecture of impulsive personality traits: neurogenetic evidence of distinct but related factors. Psychol Med 2024; 54:1533-1543. [PMID: 38016992 PMCID: PMC11132950 DOI: 10.1017/s0033291723003367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
BACKGROUND Dual-systems models, positing an interaction between two distinct and competing systems (i.e. top-down self-control, and bottom-up reward- or emotion-based drive), provide a parsimonious framework for investigating the interplay between cortical and subcortical brain regions relevant to impulsive personality traits (IPTs) and their associations with psychopathology. Despite recent developments in multivariate analysis of genome-wide association studies (GWAS), molecular genetic investigations of these models have not been conducted. METHODS Using IPT GWAS, we conducted confirmatory genomic structural equation models (GenomicSEM) to empirically evaluate dual-systems models of the genetic architecture of IPTs. Genetic correlations between dual-systems factors and relevant cortical and subcortical neuroimaging phenotypes (regional/structural volume, cortical surface area, cortical thickness) were estimated and compared. RESULTS GenomicSEM dual-systems models underscored important sources of shared and unique genetic variance between top-down and bottom-up constructs. Specifically, a dual-systems genomic model consisting of sensation seeking and lack of self-control factors demonstrated distinct but related sources of genetic influences (rg = 0.60). Genetic correlation analyses provided evidence of differential associations between dual-systems factors and cortical neuroimaging phenotypes (e.g. lack of self-control negatively associated with cortical thickness, sensation seeking positively associated with cortical surface area). No significant associations were observed with subcortical phenotypes. CONCLUSIONS Dual-systems models of the genetic architecture of IPTs tested were consistent with study hypotheses, but associations with relevant neuroimaging phenotypes were mixed (e.g. no associations with subcortical volumes). Findings demonstrate the utility of dual-systems models for studying IPT genetic influences, but also highlight potential limitations as a framework for interpreting IPTs as endophenotypes for psychopathology.
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Affiliation(s)
- Alex P. Miller
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Ian R. Gizer
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
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