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Tong X, Yagan M, Hu R, Nevills S, Doss TD, Stein RW, Balamurugan AN, Gu G. Metabolic Stress Levels Influence the Ability of Myelin Transcription Factors to Regulate β-Cell Identity and Survival. Diabetes 2024; 73:1662-1672. [PMID: 39058602 PMCID: PMC11417441 DOI: 10.2337/db23-0528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/14/2024] [Indexed: 07/28/2024]
Abstract
A hallmark of type 2 diabetes (T2D) is endocrine islet β-cell failure, which can occur via cell dysfunction, loss of identity, and/or death. How each is induced remains largely unknown. We used mouse β-cells deficient for myelin transcription factors (Myt TFs; including Myt1, -2, and -3) to address this question. We previously reported that inactivating all three Myt genes in pancreatic progenitor cells (MytPancΔ) caused β-cell failure and late-onset diabetes in mice. Their lower expression in human β-cells is correlated with β-cell dysfunction, and single nucleotide polymorphisms in MYT2 and MYT3 are associated with a higher risk of T2D. We now show that these Myt TF-deficient postnatal β-cells also dedifferentiate by reactivating several progenitor markers. Intriguingly, mosaic Myt TF inactivation in only a portion of islet β-cells did not result in overt diabetes, but this created a condition where Myt TF-deficient β-cells remained alive while activating several markers of Ppy-expressing islet cells. By transplanting MytPancΔ islets into the anterior eye chambers of immune-compromised mice, we directly show that glycemic and obesity-related conditions influence cell fate, with euglycemia inducing several Ppy+ cell markers and hyperglycemia and insulin resistance inducing additional cell death. These findings suggest that the observed β-cell defects in T2D depend not only on their inherent genetic/epigenetic defects but also on the metabolic load. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Xin Tong
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Mahircan Yagan
- Program in Developmental Biology, Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Ruiying Hu
- Program in Developmental Biology, Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Simone Nevills
- Program in Developmental Biology, Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Teri D. Doss
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Roland W. Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Appakalai N. Balamurugan
- Center for Clinical and Translational Research, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH
| | - Guoqiang Gu
- Program in Developmental Biology, Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
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Wu T, Hu Y, Tang LV. Gene therapy for polygenic or complex diseases. Biomark Res 2024; 12:99. [PMID: 39232780 PMCID: PMC11375922 DOI: 10.1186/s40364-024-00618-5] [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: 05/23/2024] [Accepted: 07/10/2024] [Indexed: 09/06/2024] Open
Abstract
Gene therapy utilizes nucleic acid drugs to treat diseases, encompassing gene supplementation, gene replacement, gene silencing, and gene editing. It represents a distinct therapeutic approach from traditional medications and introduces novel strategies for genetic disorders. Over the past two decades, significant advancements have been made in the field of gene therapy, leading to the approval of various gene therapy drugs. Gene therapy was initially employed for treating genetic diseases and cancers, particularly monogenic conditions classified as orphan diseases due to their low prevalence rates; however, polygenic or complex diseases exhibit higher incidence rates within populations. Extensive research on the etiology of polygenic diseases has unveiled new therapeutic targets that offer fresh opportunities for their treatment. Building upon the progress achieved in gene therapy for monogenic diseases and cancers, extending its application to polygenic or complex diseases would enable targeting a broader range of patient populations. This review aims to discuss the strategies of gene therapy, methods of gene editing (mainly CRISPR-CAS9), and carriers utilized in gene therapy, and highlight the applications of gene therapy in polygenic or complex diseases focused on applications that have either entered clinical stages or are currently undergoing clinical trials.
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Affiliation(s)
- Tingting Wu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapies of the Chinese Ministry of Education, Wuhan, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapies of the Chinese Ministry of Education, Wuhan, China.
| | - Liang V Tang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapies of the Chinese Ministry of Education, Wuhan, China.
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Kibirige D, Katte JC, Hill AV, Sekitoleko I, Lumu W, Knupp J, Squires S, Hattersley AT, Smeeth L, Jones AG, Nyirenda MJ. Ethnic differences in the manifestation of early-onset type 2 diabetes. BMJ Open Diabetes Res Care 2024; 12:e004174. [PMID: 39209773 PMCID: PMC11409382 DOI: 10.1136/bmjdrc-2024-004174] [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: 03/05/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024] Open
Abstract
INTRODUCTION We undertook phenotypic characterization of early-onset and late-onset type 2 diabetes (T2D) in adult black African and white European populations with recently diagnosed T2D to explore ethnic differences in the manifestation of early-onset T2D. RESEARCH DESIGN AND METHODS Using the Uganda Diabetes Phenotype study cohort of 500 adult Ugandans and the UK StartRight study cohort of 714 white Europeans with recently diagnosed islet autoantibody-negative T2D, we compared the phenotypic characteristics of participants with early-onset T2D (diagnosed at <40 years) and late-onset T2D (diagnosed at ≥40 years). RESULTS One hundred and thirty-four adult Ugandans and 113 white Europeans had early-onset T2D. Compared with late-onset T2D, early-onset T2D in white Europeans was significantly associated with a female predominance (52.2% vs 39.1%, p=0.01), increased body mass index (mean (95% CI) 36.7 (35.2-38.1) kg/m2 vs 33.0 (32.4-33.6) kg/m2, p<0.001), waist circumference (112.4 (109.1-115.6) cm vs 108.8 (107.6-110.1) cm, p=0.06), and a higher frequency of obesity (82.3% vs 63.4%, p<0.001). No difference was seen with the post-meal C-peptide levels as a marker of beta-cell function (mean (95% CI) 2130.94 (1905.12-2356.76) pmol/L vs 2039.72 (1956.52-2122.92), p=0.62).In contrast, early-onset T2D in Ugandans was associated with less adiposity (mean (95% CI) waist circumference 93.1 (89.9-96.3) cm vs 97.4 (95.9-98.8) cm, p=0.006) and a greater degree of beta-cell dysfunction (120 min post-glucose load C-peptide mean (95% CI) level 896.08 (780.91-1011.24) pmol/L vs 1310.10 (1179.24-1440.95) pmol/L, p<0.001), without female predominance (53.0% vs 57.9%, p=0.32) and differences in the body mass index (mean (95% CI) 27.3 (26.2-28.4) kg/m2 vs 27.9 (27.3-28.5) kg/m2, p=0.29). CONCLUSIONS These differences in the manifestation of early-onset T2D underscore the need for ethnic-specific and population-specific therapeutic and preventive approaches for the condition.
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Affiliation(s)
- Davis Kibirige
- Non-Communicable Diseases Theme, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda
- Department of Medicine, Uganda Martyrs Hospital Lubaga, Kampala, Uganda
| | - Jean-Claude Katte
- Department of Non-Communicable Diseases Research, National Obesity Centre and Endocrinology and Metabolic Diseases Unit, Yaounde Central Hospital, Yaounde, Cameroon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Anita V Hill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Isaac Sekitoleko
- Non-Communicable Diseases Theme, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda
| | - William Lumu
- Department of Medicine, Mengo Hospital, Kampala, Uganda
| | - Julieanne Knupp
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Steven Squires
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Liam Smeeth
- Department of Non-Communicable Dieseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Moffat J Nyirenda
- Non-Communicable Diseases Theme, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda
- Department of Non-Communicable Dieseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Sagliocchi S, Restolfer F, Cossidente A, Dentice M. The key roles of thyroid hormone in mitochondrial regulation, at interface of human health and disease. J Basic Clin Physiol Pharmacol 2024; 0:jbcpp-2024-0108. [PMID: 39023546 DOI: 10.1515/jbcpp-2024-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 07/06/2024] [Indexed: 07/20/2024]
Abstract
Mitochondria are highly plastic and dynamic organelles long known as the powerhouse of cellular bioenergetics, but also endowed with a critical role in stress responses and homeostasis maintenance, supporting and integrating activities across multifaced cellular processes. As a such, mitochondria dysfunctions are leading causes of a wide range of diseases and pathologies. Thyroid hormones (THs) are endocrine regulators of cellular metabolism, regulating intracellular nutrients fueling of sugars, amino acids and fatty acids. For instance, THs regulate the balance between the anabolism and catabolism of all the macro-molecules, influencing energy homeostasis during different nutritional conditions. Noteworthy, not only most of the TH-dependent metabolic modulations act via the mitochondria, but also THs have been proved to regulate the mitochondrial biosynthesis, dynamics and function. The significance of such an interplay is different in the context of specific tissues and strongly impacts on cellular homeostasis. Thus, a comprehensive understanding of THs-dependent mitochondrial functions and dynamics is required to develop more precise strategies for targeting mitochondrial function. Herein, we describe the mechanisms of TH-dependent metabolic regulation with a focus on mitochondrial action, in different tissue contexts, thus providing new insights for targeted modulation of mitochondrial dynamics.
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Affiliation(s)
- Serena Sagliocchi
- Department of Clinical Medicine and Surgery, 165474 University of Naples "Federico II" , Naples, Italy
| | - Federica Restolfer
- Department of Clinical Medicine and Surgery, 165474 University of Naples "Federico II" , Naples, Italy
| | - Alessandro Cossidente
- Department of Clinical Medicine and Surgery, 165474 University of Naples "Federico II" , Naples, Italy
| | - Monica Dentice
- Department of Clinical Medicine and Surgery, 165474 University of Naples "Federico II" , Naples, Italy
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5
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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] [Grants] [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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6
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Armstrong ND, Patki A, Srinivasasainagendra V, Ge T, Lange LA, Kottyan L, Namjou B, Shah AS, Rasmussen-Torvik LJ, Jarvik GP, Meigs JB, Karlson EW, Limdi NA, Irvin MR, Tiwari HK. Variant level heritability estimates of type 2 diabetes in African Americans. Sci Rep 2024; 14:14009. [PMID: 38890458 PMCID: PMC11189523 DOI: 10.1038/s41598-024-64711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.
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Affiliation(s)
- Nicole D Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Tian Ge
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Amy S Shah
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center &, The University of Cincinnati, Cincinnati, OH, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Elizabeth W Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Nita A Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
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Kibirige D, Sekitoleko I, Lumu W, Thomas N, Hawkins M, Jones AG, Hattersley AT, Smeeth L, Nyirenda MJ. Phenotypic characterization of nonautoimmune diabetes in adult Ugandans with low body mass index. Ther Adv Endocrinol Metab 2024; 15:20420188241252314. [PMID: 38808009 PMCID: PMC11131405 DOI: 10.1177/20420188241252314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/10/2024] [Indexed: 05/30/2024] Open
Abstract
Background Type 2 diabetes is common in relatively lean individuals in sub-Saharan Africa. It is unclear whether phenotypic differences exist between underweight and normal-weight African patients with type 2 diabetes. This study compared specific characteristics between underweight (body mass index <18.5 kg/m2) and normal-weight (body mass index of 18.5-24.9 kg/m2) adult Ugandans with new-onset nonautoimmune diabetes. Methods We collected the demographic, clinical, anthropometric, and metabolic characteristics of 160 participants with nonobese new-onset type 2 diabetes (defined as diabetes diagnosed <3 months, body mass index <25 kg/m2, and absence of islet-cell autoimmunity). These participants were categorized as underweight and normal weight, and their phenotypic characteristics were compared. Results Of the 160 participants with nonobese new-onset type 2 diabetes, 18 participants (11.3%) were underweight. Compared with those with normal weight, underweight participants presented with less co-existing hypertension (5.6% versus 28.2%, p = 0.04) and lower median visceral fat levels [2 (1-3) versus 6 (4-7), p < 0.001], as assessed by bioimpedance analysis. Pathophysiologically, they presented with a lower median 120-min post-glucose load C-peptide level [0.29 (0.13-0.58) versus 0.82 (0.39-1.50) nmol/l, p = 0.04] and a higher prevalence of insulin deficiency (66.7% versus 31.4%, p = 0.003). Conclusion This study demonstrates that nonautoimmune diabetes occurs in underweight individuals in sub-Saharan Africa and is characterized by the absence of visceral adiposity, reduced late-phase insulin secretion, and greater insulin deficiency. These findings necessitate further studies to inform how the prevention, identification, and management of diabetes in such individuals can be individualized.
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Affiliation(s)
- Davis Kibirige
- Non-Communicable Diseases Program, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Plot 51/59 Nakiwogo Road, Entebbe, Uganda
- Department of Medicine, Uganda Martyrs Hospital Lubaga, Kampala +256, Uganda
| | - Isaac Sekitoleko
- Non-Communicable Diseases Program, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda
| | - William Lumu
- Department of Medicine, Mengo Hospital, Kampala, Uganda
| | - Nihal Thomas
- Department of Endocrinology, Diabetes, and Metabolism, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | | | - Angus G. Jones
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Andrew T. Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Liam Smeeth
- Department of Non-Communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Moffat J. Nyirenda
- Non-Communicable Diseases Program, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda
- Department of Non-Communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
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Rasaei N, Daneshzad E, Khadem A, Gholami F, Samadi M, Mirzaei K. Investigation of the interaction between genetic risk score (GRS) and fatty acid quality indices on metabolic syndrome among overweight and obese women. BMC Med Genomics 2024; 17:113. [PMID: 38685080 PMCID: PMC11057091 DOI: 10.1186/s12920-024-01838-2] [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: 02/26/2023] [Accepted: 02/27/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND AND AIM Metabolic syndrome is one of the major public-health challenges, affecting one-quarter of the world population. Fatty acid quality indices are novel determinants of this disease and their interactions with genetic factors may have an impact on metabolic syndrome risk. Therefore, we aimed to investigate the interaction between genetic risk score (GRS) and fatty acid quality indices with metabolic syndrome (MetS) among overweight and obese women. METHODS In the present cross-sectional study, 279 overweight and obese women (18-48 years old) were included. Several anthropometric measurements such as weight, height, body mass index (BMI), waist circumference (WC), and body fat percent (BF%) were measured. Also, systolic and diastolic blood pressure (SBP and DBP) were measured. Biochemical determination was performed for fasting blood glucose (FBS), triglyceride (TG), and high-density lipoprotein (HDL). MetS was determined according to National Cholesterol Education Program (NCEP ATP III) criteria. Dietary intake was evaluated by a validated and reliable 147-item semi-quantitative food frequency questionnaire. Cholesterol-saturated fat index (CSI) and the ratio of omega-6/omega-3 (ω-6/ω-3) essential fatty acids were considered as fat quality indices. The salting-out method was used to extract the total DNA. The unweighted GRS was calculated using the risk alleles of the three single nucleotide polymorphisms. The total average GRS value was 2 and the sum of the risk alleles of the 3 polymorphisms was 6. RESULT The results of our analysis showed that after controlling for age, energy intake, BMI, and physical activity, there was a positive interaction between T2 of GRS and T2 of N6/N3 ratio on WC (β = 7.95, 95%CI = 0.83,15.08, P = 0.029), T3 of GRS and T2 of N6/N3 ratio on DBP (β = 5.93, 95%CI= -0.76,12.63, P = 0.083), and FBS (β = 6.47, 95%CI = 0.59,13.53, P = 0.073), T3 of GRS and T3 of N6/N3 ratio on TG (β = 54.42, 95%CI = 1.76,107.08, P = 0.043), and T3 of GRS and T3 of CSI on BF% (β = 3.55, 95%CI= -0.35,7.45, P = 0.075). Also T2 of GRS in the interaction with T3 of CSI leads to an decrease - 8.35 mg/dl in HDL level after adjustment in (β= -8.35, 95%CI= -17.34,0.62, P = 0.068). CONCLUSION It seems the interaction of GRS and fatty acid quality indices is positively associated with several components of metabolic syndrome such as WC, TG and BF%. Our findings are of importance to public health, considering the high consumption of foods that are high on fatty acids. Conflicting evidence of many previous studies regarding the effect of fat intake and obesity and cardiovascular diseases could be because of the gene-diet interactions and genetic heterogeneity across various ethnic groups. Hence, the synergism effect of genetic and dietay intakes should be considered in future studies.
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Affiliation(s)
- Niloufar Rasaei
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, P. O. Box: 14155-6117, Iran
| | - Elnaz Daneshzad
- Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran
| | - Alireza Khadem
- Department of Nutrition, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Fatemeh Gholami
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, P. O. Box: 14155-6117, Iran
| | - Mahsa Samadi
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, P. O. Box: 14155-6117, Iran
| | - Khadijeh Mirzaei
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, P. O. Box: 14155-6117, Iran.
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Elashi AA, Toor SM, Umlai UKI, Al-Sarraj YA, Taheri S, Suhre K, Abou-Samra AB, Albagha OME. Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus. BMC Med Genomics 2024; 17:115. [PMID: 38685053 PMCID: PMC11059680 DOI: 10.1186/s12920-024-01855-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.
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Affiliation(s)
- Asma A Elashi
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Salman M Toor
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Umm-Kulthum Ismail Umlai
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Yasser A Al-Sarraj
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha, P.O. Box 5825, Qatar
| | - Shahrad Taheri
- Qatar Metabolic Institute, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, P.O. Box 24144, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, 510065, New York, USA
| | | | - Omar M E Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, EH4 2XU, Edinburgh, UK.
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Yu G, Tam HCH, Huang C, Shi M, Lim CKP, Chan JCN, Ma RCW. Lessons and Applications of Omics Research in Diabetes Epidemiology. Curr Diab Rep 2024; 24:27-44. [PMID: 38294727 PMCID: PMC10874344 DOI: 10.1007/s11892-024-01533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE OF REVIEW Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies. RECENT FINDINGS We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
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Affiliation(s)
- Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Henry C H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
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Mahedy L, Anderson EL, Tilling K, Thornton ZA, Elmore AR, Szalma S, Simen A, Culp M, Zicha S, Harel BT, Davey Smith G, Smith EN, Paternoster L. Investigation of genetic determinants of cognitive change in later life. Transl Psychiatry 2024; 14:31. [PMID: 38238328 PMCID: PMC10796929 DOI: 10.1038/s41398-023-02726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 12/14/2023] [Accepted: 12/22/2023] [Indexed: 01/22/2024] Open
Abstract
Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.
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Affiliation(s)
- Liam Mahedy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Zak A Thornton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Andrew R Elmore
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Sándor Szalma
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Arthur Simen
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Meredith Culp
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Stephen Zicha
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Brian T Harel
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Erin N Smith
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK.
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Chillo O, Mzokolo I, Peter E, Malindisa E, Thabit H, Tungu A, Njelekela M, Balandya E. Type 2 Diabetes Mellitus in Tanzania. A Narrative Review of Epidemiology and Disease Trend. Curr Diabetes Rev 2024; 21:e030124225188. [PMID: 38173215 DOI: 10.2174/0115733998267513231208100124] [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: 06/28/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION The prevalence of type 2 diabetes is on a rapid rise in Tanzania, driven by lifestyle modifications, nutritional changes, and increased obesity rates. This article reviews the epidemiology, and disease trends of type 2 diabetes in Tanzania and explores the economic implications and challenges in care, including policy, education, and healthcare systems. METHODOLOGY The study employs a narrative literature review from research articles, local healthcare reports, surveys, and public health records. It evaluates the economic impacts, healthcare capabilities, and patient behaviors in managing type 2 diabetes in Tanzania. RESULTS The economic burden of diabetes in Tanzania is increasing due to direct healthcare costs, lost productivity, and reduced quality of life, placing significant pressure on the already resourcelimited healthcare system. Treatment dropout rates are alarmingly high, and healthcare providers' knowledge of diabetes is insufficient. Insulin and metformin availability are critically low. Cultural norms and dietary habits pose substantial barriers to effective disease management. CONCLUSION The growing prevalence of type 2 diabetes in Tanzania presents a significant public health crisis, necessitating comprehensive strategies for prevention, early detection, and effective disease management. Priorities should include enhancing healthcare infrastructure, increasing public investment, improving healthcare education, and tackling socio-cultural barriers to disease management.
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Affiliation(s)
- Omary Chillo
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Irene Mzokolo
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Elizabeth Peter
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
- Department of Physiology, School of Medicine, Kilimanjaro Christian Medical College, Kilimanjaro, Tanzania
| | - Evangelista Malindisa
- Department of Physiology, School of Medicine, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Hassan Thabit
- Department of Physiology, School of Medicine, State University of Zanzibar, Zanzibar, Tanzania
| | - Alexander Tungu
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Marina Njelekela
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Emmanuel Balandya
- Department of Physiology, College of Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
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Niu S, Ren L. Treatment of obesity by acupuncture combined with medicine based on pathophysiological mechanism: A review. Medicine (Baltimore) 2023; 102:e36071. [PMID: 38050318 PMCID: PMC10695503 DOI: 10.1097/md.0000000000036071] [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: 06/20/2023] [Accepted: 10/20/2023] [Indexed: 12/06/2023] Open
Abstract
Obesity is a complex, multifactorial disease. The incidence of overweight and obesity has doubled worldwide since 1980, and nearly one-third of the world population is now classified as overweight or obese. Obesity rates are increasing in all age groups and for both sexes, regardless of geographic region, race, or socioeconomic status, although they are generally higher in older adults and women. Although the absolute prevalence of overweight and obesity varies widely, this trend is similar across different regions and countries. In some developed countries, the prevalence of obesity has levelled off over the past few years. However, obesity has become a health problem that cannot be ignored in low- and middle-income countries. Although the drug treatment model of modern medicine has a significant therapeutic effect in the treatment of obesity, its adverse effects are also obvious. Acupuncture combined with Chinese medicine treatment of obesity has prominent advantages in terms of clinical efficacy, and its clinical safety is higher, with fewer adverse reactions. The combination of acupuncture and medicine in the treatment of obesity is worth exploring.
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Affiliation(s)
- Shiyu Niu
- Second Affiliated Hospital of Heilongjiang Traditional Chinese Medicine, Harbin, Heilongjiang Province
| | - Lihong Ren
- The Second Hospital of Harbin, Harbin, Heilongjiang Province
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Ansari MA, Chauhan W, Shoaib S, Alyahya SA, Ali M, Ashraf H, Alomary MN, Al-Suhaimi EA. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. Int J Obes (Lond) 2023; 47:1179-1199. [PMID: 37696926 DOI: 10.1038/s41366-023-01369-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/04/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023]
Abstract
Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.
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Affiliation(s)
- Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia.
| | - Waseem Chauhan
- Department of Hematology, Duke University, Durham, NC, 27710, USA
| | - Shoaib Shoaib
- Department of Biochemistry, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Sami A Alyahya
- Wellness and Preventive Medicine Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia
| | - Mubashshir Ali
- USF Health Byrd Alzheimer's Center and Neuroscience Institute, Department of Molecular Medicine, Tampa, FL, USA
| | - Hamid Ashraf
- Rajiv Gandhi Center for Diabetes and Endocrinology, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Mohammad N Alomary
- Advanced Diagnostic and Therapeutic Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia.
| | - Ebtesam A Al-Suhaimi
- King Abdulaziz & his Companions Foundation for Giftedness & Creativity, Riyadh, Saudi Arabia.
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15
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Hoffman KW, Tran KT, Moore TM, Gataviņš MM, Visoki E, DiDomenico GE, Schultz LM, Almasy L, Hayes MR, Daskalakis NP, Barzilay R. Allostatic load in early adolescence: gene / environment contributions and relevance for mental health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.27.23297674. [PMID: 37961462 PMCID: PMC10635214 DOI: 10.1101/2023.10.27.23297674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Allostatic load is the cumulative "wear and tear" on the body due to chronic adversity. We aimed to test poly-environmental (exposomic) and polygenic contributions to allostatic load and their combined contribution to early adolescent mental health. Methods We analyzed data on N = 5,035 diverse youth (mean age 12) from the Adolescent Brain Cognitive Development Study (ABCD). Using dimensionality reduction method, we calculated and overall allostatic load score (AL) using body mass index [BMI], waist circumference, blood pressure, blood glycemia, blood cholesterol, and salivary DHEA. Childhood exposomic risk was quantified using multi-level environmental exposures before age 11. Genetic risk was quantified using polygenic risk scores (PRS) for metabolic system susceptibility (type 2 diabetes [T2D]) and stress-related psychiatric disease (major depressive disorder [MDD]). We used linear mixed effects models to test main, additive, and interactive effects of exposomic and polygenic risk (independent variables) on AL (dependent variable). Mediation models tested the mediating role of AL on the pathway from exposomic and polygenic risk to youth mental health. Models adjusted for demographics and genetic principal components. Results We observed disparities in AL with non-Hispanic White youth having significantly lower AL compared to Hispanic and Non-Hispanic Black youth. In the diverse sample, childhood exposomic burden was associated with AL in adolescence (beta=0.25, 95%CI 0.22-0.29, P<.001). In European ancestry participants (n=2,928), polygenic risk of both T2D and depression was associated with AL (T2D-PRS beta=0.11, 95%CI 0.07-0.14, P<.001; MDD-PRS beta=0.05, 95%CI 0.02-0.09, P=.003). Both polygenic scores showed significant interaction with exposomic risk such that, with greater polygenic risk, the association between exposome and AL was stronger. AL partly mediated the pathway to youth mental health from exposomic risk and from MDD-PRS, and fully mediated the pathway from T2D-PRS. Conclusions AL can be quantified in youth using anthropometric and biological measures and is mapped to exposomic and polygenic risk. Main and interactive environmental and genetic effects support a diathesis-stress model. Findings suggest that both environmental and genetic risk be considered when modeling stress-related health conditions.
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Affiliation(s)
- Kevin W. Hoffman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, US
| | - Kate T. Tran
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Mārtiņš M. Gataviņš
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Elina Visoki
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Grace E. DiDomenico
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Laura M. Schultz
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, US
| | - Laura Almasy
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, US
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
| | - Matthew R. Hayes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
| | - Nikolaos P. Daskalakis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ran Barzilay
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, US
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
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16
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Ngwa NE, Matshazi DM, Davison GM, Kengne AP, Matsha TE. Association between the MTNR1B, HHEX, SLC30A8, and TCF7L2 single nucleotide polymorphisms and cardiometabolic risk profile in a mixed ancestry South African population. Sci Rep 2023; 13:17122. [PMID: 37816730 PMCID: PMC10564755 DOI: 10.1038/s41598-023-43560-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 09/26/2023] [Indexed: 10/12/2023] Open
Abstract
Single nucleotide polymorphisms of the TCF7L2, HHEX, SLC30A8, MTNR1B, SLC2A2 and GLIS3 genes are well established candidate genes for cardiometabolic diseases (CMDs) across different ethnic populations. We investigated their association with CMDs in a mixed ancestry population of South Africa. rs10830963, rs1111875, rs11920090, rs13266634, rs7034200 and rs7903146 SNPs were genotyped by quantitative real time PCR in 1650 participants and Hardy-Weinberg equilibrium (HWE) analyses performed on the SNPs. Diabetes, obesity, hypertension and cardiometabolic traits were compared across genotypes of SNPs in HWE. Linear and logistic regressions adjusting for age, gender and body mass index were used to determine the risk of T2DM, obesity and hypertension. rs7903146 (p = 0.055), rs1111875 (p = 0.465), rs13266634 (p = 0.828), and rs10830963 (p = 0.158) were in HWE. The rs10830963 recessive genotype was able to predict FPG, insulin and HOMA-IR, while the rs1111875 recessive genotype was able to predict total cholesterol, triglyceride, LDL cholesterol and FPG. The rs7903146 recessive genotype was able to predict SBP and LDL cholesterol. The recessive genotypes of MTNRIB and HHEX SNPs were associated with T2DM traits in the study population and could partially explain the high prevalence of T2DM. Further studies are required to confirm these findings and establish candidate genes in the African population.
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Affiliation(s)
- Ndonwi Elvis Ngwa
- South African Medical Research Council/Cape Peninsula University of Technology, Cardio-Metabolic Health Research Unit, Department of Biomedical Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Old Science Building, Cape Town, South Africa.
- Laboratory for Molecular Medicine and Metabolism, Biotechnology Center, University of Yaoundé 1, Yaoundé, Cameroon.
| | - Don Makwakiwe Matshazi
- South African Medical Research Council/Cape Peninsula University of Technology, Cardio-Metabolic Health Research Unit, Department of Biomedical Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Old Science Building, Cape Town, South Africa
| | - Glenda Mary Davison
- South African Medical Research Council/Cape Peninsula University of Technology, Cardio-Metabolic Health Research Unit, Department of Biomedical Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Old Science Building, Cape Town, South Africa
| | - Andre Pascal Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Tandi Edith Matsha
- South African Medical Research Council/Cape Peninsula University of Technology, Cardio-Metabolic Health Research Unit, Department of Biomedical Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Old Science Building, Cape Town, South Africa
- Sefako Makgatho Health Sciences University, Ga-Rankuwa, South Africa
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17
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Doumatey AP, Bentley AR, Akinyemi R, Olanrewaju TO, Adeyemo A, Rotimi C. Genes, environment, and African ancestry in cardiometabolic disorders. Trends Endocrinol Metab 2023; 34:601-621. [PMID: 37598069 PMCID: PMC10548552 DOI: 10.1016/j.tem.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/21/2023]
Abstract
The past two decades have been characterized by a substantial global increase in cardiometabolic diseases, but the prevalence and incidence of these diseases and related traits differ across populations. African ancestry populations are among the most affected yet least included in research. Populations of African descent manifest significant genetic and environmental diversity and this under-representation is a missed opportunity for discovery and could exacerbate existing health disparities and curtail equitable implementation of precision medicine. Here, we discuss cardiometabolic diseases and traits in the context of African descent populations, including both genetic and environmental contributors and emphasizing novel discoveries. We also review new initiatives to include more individuals of African descent in genomics to address current gaps in the field.
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Affiliation(s)
- Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Rufus Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training and Centre for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria; Department of Neurology, University College Hospital, Ibadan, Nigeria
| | - Timothy O Olanrewaju
- Division of Nephrology, Department of Medicine, University of Ilorin & University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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18
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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, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Sun YV, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao PS, O’Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano MJ, Madduri RK, Damrauer SM, Liao KP. Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291975. [PMID: 37425708 PMCID: PMC10327290 DOI: 10.1101/2023.06.28.23291975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P < 4.6 × 10 - 11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.
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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
| | - 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
- Surgery, Otolaryngology, UCSD San Diego, La Jolla, California, 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
- Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, 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
| | - 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
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, 30322, 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 S 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
| | - Michael J 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 M 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
- 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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19
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Hassan N, Gregson CL, Tang H, van der Kamp M, Leo P, McInerney‐Leo AM, Zheng J, Brandi ML, Tang JCY, Fraser W, Stone MD, Grundberg E, Brown MA, Duncan EL, Tobias JH. Rare and Common Variants in GALNT3 May Affect Bone Mass Independently of Phosphate Metabolism. J Bone Miner Res 2023; 38:678-691. [PMID: 36824040 PMCID: PMC10729283 DOI: 10.1002/jbmr.4795] [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: 09/19/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 02/25/2023]
Abstract
Anabolic treatment options for osteoporosis remain limited. One approach to discovering novel anabolic drug targets is to identify genetic causes of extreme high bone mass (HBM). We investigated a pedigree with unexplained HBM within the UK HBM study, a national cohort of probands with HBM and their relatives. Whole exome sequencing (WES) in a family with HBM identified a rare heterozygous missense variant (NM_004482.4:c.1657C > T, p.Arg553Trp) in GALNT3, segregating appropriately. Interrogation of data from the UK HBM study and the Anglo-Australasian Osteoporosis Genetics Consortium (AOGC) revealed an unrelated individual with HBM with another rare heterozygous variant (NM_004482.4:c.831 T > A, p.Asp277Glu) within the same gene. In silico protein modeling predicted that p.Arg553Trp would disrupt salt-bridge interactions, causing instability of GALNT3, and that p.Asp277Glu would disrupt manganese binding and consequently GALNT3 catalytic function. Bi-allelic loss-of-function GALNT3 mutations alter FGF23 metabolism, resulting in hyperphosphatemia and causing familial tumoral calcinosis (FTC). However, bone mineral density (BMD) in FTC cases, when reported, has been either normal or low. Common variants in the GALNT3 locus show genome-wide significant associations with lumbar, femoral neck, and total body BMD. However, no significant associations with BMD are observed at loci coding for FGF23, its receptor FGFR1, or coreceptor klotho. Mendelian randomization analysis, using expression quantitative trait loci (eQTL) data from primary human osteoblasts and genome-wide association studies data from UK Biobank, suggested increased expression of GALNT3 reduces total body, lumbar spine, and femoral neck BMD but has no effect on phosphate concentrations. In conclusion, rare heterozygous loss-of-function variants in GALNT3 may cause HBM without altering phosphate concentration. These findings suggest that GALNT3 may affect BMD through pathways other than FGF23 regulation, the identification of which may yield novel anabolic drug targets for osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Neelam Hassan
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Celia L. Gregson
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- MRC Integrated Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Haotian Tang
- MRC Integrated Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | | | - Paul Leo
- Faculty of Health, Translational Genomics Group, Institute of Health and Biomedical InnovationQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Aideen M. McInerney‐Leo
- The Faculty of Medicine, Frazer InstituteThe University of QueenslandWoolloongabbaQueenslandAustralia
| | - Jie Zheng
- MRC Integrated Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR ChinaShanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | | | - Jonathan C. Y. Tang
- Norwich Medical SchoolUniversity of East AngliaNorwichUK
- Clinical Biochemistry, Departments of Laboratory MedicineNorfolk and Norwich University Hospital NHS Foundation TrustNorwichUK
| | - William Fraser
- Norwich Medical SchoolUniversity of East AngliaNorwichUK
- Department of Diabetes, Endocrinology and Clinical BiochemistryNorfolk and Norwich University Hospital NHS Foundation TrustNorwichUK
| | - Michael D. Stone
- University Hospital LlandoughCardiff & Vale University Health BoardCardiffUK
| | - Elin Grundberg
- Genomic Medicine CenterChildren's Mercy Kansas CityKansas CityMissouriUSA
| | | | | | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Jonathan H. Tobias
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- MRC Integrated Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
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20
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Mansour A, Mousa M, Abdelmannan D, Tay G, Hassoun A, Alsafar H. Microvascular and macrovascular complications of type 2 diabetes mellitus: Exome wide association analyses. Front Endocrinol (Lausanne) 2023; 14:1143067. [PMID: 37033211 PMCID: PMC10076756 DOI: 10.3389/fendo.2023.1143067] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is a chronic, metabolic disorder in which concomitant insulin resistance and β-cell impairment lead to hyperglycemia, influenced by genetic and environmental factors. T2DM is associated with long-term complications that have contributed to the burden of morbidity and mortality worldwide. The objective of this manuscript is to conduct an Exome-Wide Association Study (EWAS) on T2DM Emirati individuals to improve our understanding on diabetes-related complications to improve early diagnostic methods and treatment strategies. Methods This cross-sectional study recruited 310 Emirati participants that were stratified according to their medically diagnosed diabetes-related complications: diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, and cardiovascular complications. The Illumina's Infinium Exome-24 array was used and 39,840 SNPs remained for analysis after quality control. Findings The analysis revealed the associations of various genes with each complication category: 1) diabetic retinopathy was associated to SHANK3 gene in locus 22q13.33 (SNP rs9616915; p=5.18 x10-4), ZSCAN5A gene in locus 19q13.43 (SNP rs7252603; p=7.55 x10-4), and DCP1B gene in locus 12p13.33 (SNPs rs715146, rs1044950, rs113147414, rs34730825; p=7.62 x10-4); 2) diabetic neuropathy was associated to ADH4 gene in locus 4q23 (SNP rs4148883; p=1.23 x10-4), SLC11A1 gene in locus 2q35 (SNP rs17235409; p=1.85 x10-4), and MATN4 gene in locus 20q13.12 (SNP rs2072788; p=2.68 x10-4); 3) diabetic nephropathy was associated to PPP1R3A gene in locus 7q31.1 (SNP rs1799999; p=1.91 x10-4), ZNF136 gene in locus 19p13.2 (SNP rs140861589; p=2.80 x10-4), and HSPA12B gene in locus 20p13 (SNP rs6076550; p=2.86 x10-4); and 4) cardiovascular complications was associated to PCNT gene in locus 21q22.3 (SNPs rs7279204, rs6518289, rs2839227, rs2839223; p=2.18 x10-4,3.04 x10-4,4.51 x10-4,5.22 x10-4 respectively), SEPT14 gene in locus 7p11.2 (SNP rs146350220; p=2.77 x10-4), and WDR73 gene in locus 15q25.2 (SNP rs72750868; p=4.47 x10-4). Interpretation We have identified susceptibility loci associated with each category of T2DM-related complications in the Emirati population. Given that only 16% of the markers from the Illumina's Infinium Exome chip passed quality control assessment, this demonstrates that multiple variants were, either, monomorphic in the Arab population or were not genotyped due to the use of a Euro-centric EWAS array that limits the possibility of including targeted ethnic-specific SNPs. Our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci associated to T2DM complications. Further effort must be conducted to improve the representation of diverse populations in genotyping and sequencing studies.
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Affiliation(s)
- Afnan Mansour
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Dima Abdelmannan
- Dubai Health Authority, Dubai Diabetes Center, Dubai, United Arab Emirates
| | - Guan Tay
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Ahmed Hassoun
- Fakeeh University Hospital, Dubai, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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21
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Timasheva Y, Balkhiyarova Z, Avzaletdinova D, Rassoleeva I, Morugova TV, Korytina G, Prokopenko I, Kochetova O. Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes. Int J Mol Sci 2023; 24:ijms24020984. [PMID: 36674502 PMCID: PMC9866792 DOI: 10.3390/ijms24020984] [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: 10/28/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 01/07/2023] Open
Abstract
We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10-5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10-4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10-5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.
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Affiliation(s)
- Yanina Timasheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
- Department of Medical Genetics and Fundamental Medicine, Bashkir State Medical University, 450008 Ufa, Russia
- Correspondence:
| | - Zhanna Balkhiyarova
- Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Diana Avzaletdinova
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Irina Rassoleeva
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Tatiana V. Morugova
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Gulnaz Korytina
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK
| | - Olga Kochetova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
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22
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Goedecke JH, Mendham AE. Pathophysiology of type 2 diabetes in sub-Saharan Africans. Diabetologia 2022; 65:1967-1980. [PMID: 36166072 PMCID: PMC9630207 DOI: 10.1007/s00125-022-05795-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/12/2022] [Indexed: 01/11/2023]
Abstract
Sub-Saharan Africa (SSA) is the region with the highest projected rates of increase in type 2 diabetes (129% by 2045), which will exacerbate the already high prevalence of type 2 diabetes complications and comorbidities in SSA. In addition, SSA is grappling with poverty-related health problems and infectious diseases and is also undergoing the most rapid rates of urbanisation globally. These socioenvironmental and lifestyle factors may interact with genetic factors to alter the pathophysiological sequence leading to type 2 diabetes in sub-Saharan African populations. Indeed, current evidence from SSA and the diaspora suggests that the pathophysiology of type 2 diabetes in Black Africans is different from that in their European counterparts. Studies from the diaspora suggest that insulin clearance is the primary defect underlying the development of type 2 diabetes. We propose that, among Black Africans from SSA, hyperinsulinaemia due to a combination of both increased insulin secretion and reduced hepatic insulin clearance is the primary defect, which promotes obesity and insulin resistance, exacerbating the hyperinsulinaemia and eventually leading to beta cell failure and type 2 diabetes. Nonetheless, the current understanding of the pathogenesis of type 2 diabetes and the clinical guidelines for preventing and managing the disease are largely based on studies including participants of predominately White European ancestry. In this review, we summarise the existing knowledge base and data from the only non-pharmacological intervention that explores the pathophysiology of type 2 diabetes in SSA. We also highlight factors that may influence the pathogenesis of type 2 diabetes in SSA, such as social determinants, infectious diseases and genetic and epigenetic influences.
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Affiliation(s)
- Julia H Goedecke
- Biomedical Research and Innovation Platform and Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa.
- South African Medical Research Council/WITS Developmental Pathways for Health Research Unit (DPHRU), Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Health through Physical Activity, Lifestyle and Sport Research Centre (HPALS), FIMS International Collaborating Centre of Sports Medicine, Division of Physiological Sciences, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
| | - Amy E Mendham
- South African Medical Research Council/WITS Developmental Pathways for Health Research Unit (DPHRU), Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Health through Physical Activity, Lifestyle and Sport Research Centre (HPALS), FIMS International Collaborating Centre of Sports Medicine, Division of Physiological Sciences, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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23
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Pipal KV, Mamtani M, Patel AA, Jaiswal SG, Jaisinghani MT, Kulkarni H. Susceptibility Loci for Type 2 Diabetes in the Ethnically Endogamous Indian Sindhi Population: A Pooled Blood Genome-Wide Association Study. Genes (Basel) 2022; 13:1298. [PMID: 35893037 PMCID: PMC9331904 DOI: 10.3390/genes13081298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022] Open
Abstract
Type 2 diabetes (T2D) is a complex metabolic derangement that has a strong genetic basis. There is substantial population-specificity in the association of genetic variants with T2D. The Indian urban Sindhi population is at a high risk of T2D. The genetic basis of T2D in this population is unknown. We interrogated 28 pooled whole blood genomes of 1402 participants from the Diabetes In Sindhi Families In Nagpur (DISFIN) study using Illumina's Global Screening Array. From a total of 608,550 biallelic variants, 140 were significantly associated with T2D after adjusting for comorbidities, batch effects, pooling error, kinship status and pooling variation in a random effects multivariable logistic regression framework. Of the 102 well-characterized genes that these variants mapped onto, 70 genes have been previously reported to be associated with T2D to varying degrees with known functional relevance. Excluding open reading frames, intergenic non-coding elements and pseudogenes, our study identified 22 novel candidate genes in the Sindhi population studied. Our study thus points to the potential, interesting candidate genes associated with T2D in an ethnically endogamous population. These candidate genes need to be fully investigated in future studies.
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Affiliation(s)
- Kanchan V. Pipal
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Manju Mamtani
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
- M&H Research, LLC, San Antonio, TX 78249, USA
| | - Ashwini A. Patel
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Sujeet G. Jaiswal
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Manisha T. Jaisinghani
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Hemant Kulkarni
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
- M&H Research, LLC, San Antonio, TX 78249, USA
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24
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Privé F. Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics. Bioinformatics 2022; 38:3477-3480. [PMID: 35604078 PMCID: PMC9237724 DOI: 10.1093/bioinformatics/btac348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Measuring genetic diversity is an important problem because increasing genetic diversity is a key to making new genetic discoveries, while also being a major source of confounding to be aware of in genetics studies. RESULTS Using the UK Biobank data, a prospective cohort study with deep genetic and phenotypic data collected on almost 500 000 individuals from across the UK, we carefully define 21 distinct ancestry groups from all four corners of the world. These ancestry groups can serve as a global reference of worldwide populations, with a handful of applications. Here, we develop a method that uses allele frequencies and principal components derived from these ancestry groups to effectively measure ancestry proportions from allele frequencies of any genetic dataset. AVAILABILITY AND IMPLEMENTATION This method is implemented in function snp_ancestry_summary of R package bigsnpr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus 8210, Denmark
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25
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Schultz LM, Merikangas AK, Ruparel K, Jacquemont S, Glahn DC, Gur RE, Barzilay R, Almasy L. Stability of polygenic scores across discovery genome-wide association studies. HGG ADVANCES 2022; 3:100091. [PMID: 35199043 PMCID: PMC8841810 DOI: 10.1016/j.xhgg.2022.100091] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 01/18/2022] [Indexed: 01/19/2023] Open
Abstract
Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.
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Affiliation(s)
- Laura M. Schultz
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Alison K. Merikangas
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sébastien Jacquemont
- UHC Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
- Department of Pediatrics, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - David C. Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Raquel E. Gur
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ran Barzilay
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Motala AA, Mbanya JC, Ramaiya K, Pirie FJ, Ekoru K. Type 2 diabetes mellitus in sub-Saharan Africa: challenges and opportunities. Nat Rev Endocrinol 2022; 18:219-229. [PMID: 34983969 DOI: 10.1038/s41574-021-00613-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/26/2022]
Abstract
Type 2 diabetes mellitus (T2DM), which was once thought to be rare in sub-Saharan Africa (SSA), is now well established in this region. The SSA region is undergoing a rapid but variable epidemiological transition fuelled by the pace of urbanization, with disease burden profiles shifting from communicable diseases to non-communicable diseases (NCDs). Information on the epidemiology of T2DM has increased, but wide variations in study methods, diagnostic biomarkers and criteria hamper analytical comparison, and data from high-quality studies are limited. The prevalence of T2DM is still low in some rural populations but moderate or high rates are reported in many countries/regions, with evidence for an increase in some. In addition, the proportion of undiagnosed T2DM is still high. The prevalence of T2DM is highest in African people living in urban areas, and the gradient between African people living in urban areas and people in the African diaspora is rapidly fading. However, data from longitudinal studies are lacking and there is limited information on chronic complications and the genetics of T2DM. The large unmet needs for T2DM care call for greater investment of resources into health systems to manage NCDs in SSA. Proposed health-system paradigms are being developed in some countries/regions. However, national NCD programmes need to be adequately funded and coordinated to stem the tide of T2DM and its complications.
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Affiliation(s)
- Ayesha A Motala
- Inkosi Albert Luthuli Central Hospital, Durban, South Africa.
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa.
| | - Jean Claude Mbanya
- Department of Internal Medicine and Specialities, Faculty of Medicine and Biomedical Sciences University of Yaounde 1, Yaounde, Cameroon
| | | | - Fraser J Pirie
- Inkosi Albert Luthuli Central Hospital, Durban, South Africa
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Kenneth Ekoru
- Centre for Research on Genomics and Global Health, National Human Genome Research Institute, National Institute of Health, Bethesda, MD, USA
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Chikowore T, Ekoru K, Vujkovi M, Gill D, Pirie F, Young E, Sandhu MS, McCarthy M, Rotimi C, Adeyemo A, Motala A, Fatumo S. Polygenic Prediction of Type 2 Diabetes in Africa. Diabetes Care 2022; 45:717-723. [PMID: 35015074 PMCID: PMC8918234 DOI: 10.2337/dc21-0365] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 11/23/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans. RESEARCH DESIGN AND METHODS Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects). RESULTS The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American-derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19-4.03; P = 2.79 × 10-17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile. CONCLUSIONS African American-derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.
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Affiliation(s)
- Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Institute of Health, Bethesda, MD
| | - Marijana Vujkovi
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, U.K
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | | | | | - Mark McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Institute of Health, Bethesda, MD
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Institute of Health, Bethesda, MD
| | - Ayesha Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Segun Fatumo
- London School of Hygiene and Tropical Medicine, London, U.K
- The African Computational Genomics Research Group, MRC/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine (Uganda Research Unit), Entebbe, Uganda
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28
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Barroso I. The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits. Diabetologia 2021; 64:2653-2664. [PMID: 34595549 PMCID: PMC8563561 DOI: 10.1007/s00125-021-05575-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 07/07/2021] [Indexed: 12/11/2022]
Abstract
Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.
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Affiliation(s)
- Inês Barroso
- Exeter Centre of Excellence for Diabetes research (EXCEED), University of Exeter Medical School, Exeter, UK.
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Bakhashab S, Batarfi AA, Filimban N, Bajouh OS, Dallol A, Alqahtani MH. Polycystic ovary syndrome is linked with the fat mass obesity (FTO) gene variants rs17817449 and rs1421085 in western Saudi Arabia. Bioinformation 2021; 17:904-910. [PMID: 35655906 PMCID: PMC9148595 DOI: 10.6026/97320630017904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 11/23/2022] Open
Abstract
Polycystic ovary syndrome (PCOS) is characterised by infertility, obesity, insulin resistance and clinical and/or biochemical signs of hyperandrogenism. Obesity is known to be correlated with PCOS causing ovulatory dysfunction and hormone imbalances. Moreover, fat mass and the obesity gene (FTO) were linked with obesity and PCOS. Therefore, it is of interest to determine the genotype and allele frequency for three FTO variants - rs17817449 (G/T), rs1421085 (C/T) and rs8050136 (A/C) -in western Saudi population. 95 PCOS patients and 94 controls were recruited for this study. The genetic variants were assayed using real-time polymerase chain reaction using TaqMan genotyping assays. The chi-squared test was applied to investigate the difference between single nucleotide polymorphisms on PCOS and control subjects, and binary logistic regression was used to determine the association of FTO variants with PCOS symptoms. Variants rs17817449 and rs1421085 were significantly linked with PCOS susceptibility in the study population. Rs17817449 and rs8050136 were significantly associated with hair loss in the PCOS group. Furthermore, rs1421085 and rs8050136 were associated with a high body mass index (BMI>30 kg/m2). Risk alleles in our population associated with hair loss and elevated BMI in women with PCOS were homozygous C for rs8050136. This data will help in defining the genetic predisposition of PCOS among women in western Saudi Arabia.
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Affiliation(s)
- Sherin Bakhashab
- Biochemistry Department, King Abdulaziz University, P.O. Box 80218, Jeddah 21589, Saudi Arabia
- Centre of Innovation in Personalized Medicine, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
| | - Asma A Batarfi
- Biochemistry Department, King Abdulaziz University, P.O. Box 80218, Jeddah 21589, Saudi Arabia
| | - Najlaa Filimban
- Centre of Innovation in Personalized Medicine, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
- King Faisal Specialist Hospital and Research Center, Clinical Genomics, Department of Genetics, P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Osama S Bajouh
- Centre of Innovation in Personalized Medicine, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
- Department of Obstetrics and Gynaecology, Faculty of Medicine, King Abdulaziz University, P.O. Box 80205, Jeddah 21589, Saudi Arabia
| | - Ashraf Dallol
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
| | - Mohammed H Alqahtani
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
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Tinajero MG, Malik VS. An Update on the Epidemiology of Type 2 Diabetes: A Global Perspective. Endocrinol Metab Clin North Am 2021; 50:337-355. [PMID: 34399949 DOI: 10.1016/j.ecl.2021.05.013] [Citation(s) in RCA: 157] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Type 2 diabetes (T2D) is a public health burden associated with immense health care and societal costs, early death, and morbidity. Largely because of epidemiologic changes, including nutrition transitions, urbanization, and sedentary lifestyles, T2D is increasing in every region of the world, particularly in low-income and middle-income countries. This article highlights global trends in T2D and discusses the role of genes, early-life exposures, and lifestyle risk factors in the cause of T2D, with an emphasis on populations in current hotspots of the epidemic. It also considers potential impacts of the coronavirus disease 2019 pandemic and T2D prevention policies and action.
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Affiliation(s)
- Maria G Tinajero
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, 5th Floor, Toronto, ON M5S 1A8, Canada
| | - Vasanti S Malik
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, 5th Floor, Toronto, ON M5S 1A8, Canada; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Liu C, Sun YV. Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics. Endocrinol Metab Clin North Am 2021; 50:559-574. [PMID: 34399961 DOI: 10.1016/j.ecl.2021.05.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.
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Affiliation(s)
- Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA; Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033, USA.
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Evidence for opposing selective forces operating on human-specific duplicated TCAF genes in Neanderthals and humans. Nat Commun 2021; 12:5118. [PMID: 34433829 PMCID: PMC8387397 DOI: 10.1038/s41467-021-25435-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 08/04/2021] [Indexed: 11/30/2022] Open
Abstract
TRP channel-associated factor 1/2 (TCAF1/TCAF2) proteins antagonistically regulate the cold-sensor protein TRPM8 in multiple human tissues. Understanding their significance has been complicated given the locus spans a gap-ridden region with complex segmental duplications in GRCh38. Using long-read sequencing, we sequence-resolve the locus, annotate full-length TCAF models in primate genomes, and show substantial human-specific TCAF copy number variation. We identify two human super haplogroups, H4 and H5, and establish that TCAF duplications originated ~1.7 million years ago but diversified only in Homo sapiens by recurrent structural mutations. Conversely, in all archaic-hominin samples the fixation for a specific H4 haplotype without duplication is likely due to positive selection. Here, our results of TCAF copy number expansion, selection signals in hominins, and differential TCAF2 expression between haplogroups and high TCAF2 and TRPM8 expression in liver and prostate in modern-day humans imply TCAF diversification among hominins potentially in response to cold or dietary adaptations. Duplications of gene segments can allow novel physiological adaptations to evolve. A detailed analysis of the TCAF gene family in primates and archaic humans suggest rapid duplication and diversification in this gene family is associated with cold or dietary adaptations.
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Kim DS, Gloyn AL, Knowles JW. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. J Am Coll Cardiol 2021; 78:496-512. [PMID: 34325839 PMCID: PMC8328195 DOI: 10.1016/j.jacc.2021.03.346] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/14/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.
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Affiliation(s)
- Daniel Seung Kim
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Anna L Gloyn
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA; Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Joshua W Knowles
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA; Stanford Diabetes Research Center, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA.
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Ruiz-Narváez EA. Genetic architecture of type 2 diabetes and its shared genetic component with low birth weight in African Americans. Curr Opin Clin Nutr Metab Care 2021; 24:326-332. [PMID: 33883416 DOI: 10.1097/mco.0000000000000757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Recent large-scale multiancestry efforts has contributed to our knowledge of the hereditary basis of type 2 diabetes (T2D). The present review will summarize findings of the genetic basis of T2D in African Americans, a population group with a disproportionate burden of this disease. RECENT FINDINGS To date, >400 risk genetic variants have been found to be associated with the risk of T2D across populations of different ancestries. Although these findings are based on primarily European-ancestry populations, most of the identified loci show similar associations in African Americans. Ancestry-specific analyses including genome-wide associations studies (GWAS) in African Americans, Africans; as well as admixture mapping scans in African Americans have identified additional risk variants and genomic loci associate with the risk of T2D. These efforts have also uncovered new genetic links between low birth weight and T2D. In particular, admixture mapping approaches have identified a shared genetic ancestry component of both phenotypic traits in African Americans. SUMMARY Recent findings have helped us to better understand the genetic basis of T2D in African Americans. Of particular interest are new genetic discoveries linking low birth weight and T2D, two conditions with a much higher prevalence in African Americans compared to U.S. whites. Continuing work, including large-scale sequencing efforts would add to our knowledge of the genetic architecture of T2D in African Americans, as well as genetic links with other conditions.
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Affiliation(s)
- Edward A Ruiz-Narváez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
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Chilunga FP, Henneman P, Venema A, Meeks KAC, Gonzalez JR, Ruiz-Arenas C, Requena-Méndez A, Beune E, Spranger J, Smeeth L, Bahendeka S, Owusu-Dabo E, Klipstein-Grobusch K, Adeyemo A, Mannens MMAM, Agyemang C. DNA methylation as the link between migration and the major noncommunicable diseases: the RODAM study. Epigenomics 2021; 13:653-666. [PMID: 33890479 PMCID: PMC8173498 DOI: 10.2217/epi-2020-0329] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/29/2021] [Indexed: 01/19/2023] Open
Abstract
Aim: We assessed epigenome-wide DNA methylation (DNAm) differences between migrant and non-migrant Ghanaians. Materials & methods: We used the Illumina Infinium® HumanMethylation450 BeadChip to profile DNAm of 712 Ghanaians in whole blood. We used linear models to detect differentially methylated positions (DMPs) associated with migration. We performed multiple post hoc analyses to validate our findings. Results: We identified 13 DMPs associated with migration (delta-beta values: 0.2-4.5%). Seven DMPs in CPLX2, EIF4E3, MEF2D, TLX3, ST8SIA1, ANG and CHRM3 were independent of extrinsic genomic influences in public databases. Two DMPs in NLRC5 were associated with duration of stay in Europe among migrants. All DMPs were biologically linked to migration-related factors. Conclusion: Our findings provide the first insights into DNAm differences between migrants and non-migrants.
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Affiliation(s)
- Felix P Chilunga
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Peter Henneman
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Andrea Venema
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Karlijn AC Meeks
- Center for Research on Genomics & Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
| | - Carlos Ruiz-Arenas
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
| | - Ana Requena-Méndez
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
- Department of Global Public Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Erik Beune
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Joachim Spranger
- Department of Endocrinology, Diabetes & Metabolism, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Liam Smeeth
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, 1E 7HT, UK
| | - Silver Bahendeka
- Department of Medicine, MKPGMS-Uganda Martyrs University, 8H33+5M Kampala, Uganda
| | - Ellis Owusu-Dabo
- School of Public Health, Kwame Nkrumah University of Science & Technology, MCFH+R9 Kumasi, Ghana
| | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of The Witwatersrand, 2193 Johannesburg, South Africa
| | - Adebowale Adeyemo
- Center for Research on Genomics & Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA
| | - Marcel MAM Mannens
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Charles Agyemang
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
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African genetic diversity and adaptation inform a precision medicine agenda. Nat Rev Genet 2021; 22:284-306. [PMID: 33432191 DOI: 10.1038/s41576-020-00306-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 01/29/2023]
Abstract
The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.
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Zhang Z, Xu L, Xu X. The role of transcription factor 7-like 2 in metabolic disorders. Obes Rev 2021; 22:e13166. [PMID: 33615650 DOI: 10.1111/obr.13166] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 10/08/2020] [Accepted: 10/08/2020] [Indexed: 12/13/2022]
Abstract
Transcription factor 7-like 2 (TCF7L2), a member of the T cell factor/lymphoid enhancer factor family, generally forms a complex with β-catenin to regulate the downstream target genes as an effector of the canonical Wnt signalling pathway. TCF7L2 plays a vital role in various biological processes and functions in many organs and tissues, including the liver, islet and adipose tissues. Further, TCF7L2 down-regulates hepatic gluconeogenesis and promotes lipid accumulation. In islets, TCF7L2 not only affects the insulin secretion of the β-cells but also has an impact on other cells. In addition, TCF7L2 influences adipogenesis in adipose tissues. Thus, an out-of-control TCF7L2 expression can result in metabolic disorders. The TCF7L2 gene is composed of 17 exons, generating 13 different transcripts, and has many single-nucleotide polymorphisms (SNPs). The discovery that these SNPs have an impact on the risk of type 2 diabetes (T2D) has attracted thorough investigations in the study of TCF7L2. Apart from T2D, TCF7L2 SNPs are also associated with type 1, posttransplant and other types of diabetes. Furthermore, TCF7L2 variants affect the progression of other disorders, such as obesity, cancers, metabolic syndrome and heart diseases. Finally, the interaction between TCF7L2 variants and diet also needs to be investigated.
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Affiliation(s)
- Zhensheng Zhang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China.,Zhejiang University School of Medicine, Hangzhou, China
| | - Li Xu
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang University Cancer Center, Hangzhou, China.,NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China.,Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang University Cancer Center, Hangzhou, China
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Polfus LM, Darst BF, Highland H, Sheng X, Ng MCY, Below JE, Petty L, Bien S, Sim X, Wang W, Fontanillas P, Patel Y, Preuss M, Schurmann C, Du Z, Lu Y, Rhie SK, Mercader JM, Tusie-Luna T, González-Villalpando C, Orozco L, Spracklen CN, Cade BE, Jensen RA, Sun M, Joo YY, An P, Yanek LR, Bielak LF, Tajuddin S, Nicolas A, Chen G, Raffield L, Guo X, Chen WM, Nadkarni GN, Graff M, Tao R, Pankow JS, Daviglus M, Qi Q, Boerwinkle EA, Liu S, Phillips LS, Peters U, Carlson C, Wikens LR, Marchand LL, North KE, Buyske S, Kooperberg C, Loos RJF, Stram DO, Haiman CA. Genetic discovery and risk characterization in type 2 diabetes across diverse populations. HGG ADVANCES 2021; 2. [PMID: 34604815 PMCID: PMC8486151 DOI: 10.1016/j.xhgg.2021.100029] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10−8), which replicated in independent studies of African ancestry (p = 6.26 × 10−23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10−9), which also replicated in independent studies (p = 3.45 × 10−4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10−20). Comparing individuals in the top GRS risk category (40%–60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.
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Affiliation(s)
- Linda M Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.,These authors contributed equally
| | - Burcu F Darst
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.,These authors contributed equally
| | - Heather Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xin Sheng
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Maggie C Y Ng
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer E Below
- The Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren Petty
- The Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | | | - Yesha Patel
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Michael Preuss
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Claudia Schurmann
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Zhaohui Du
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Yingchang Lu
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Meng Sun
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK, USA
| | - Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ping An
- Division of Statistical Genomics, School of Medicine, Washington University, St. Louis, MO, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Salman Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Institute, National Institutes of Health, Bethesda, MD, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Girish N Nadkarni
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL, USA
| | - Qibin Qi
- Center for Population Cohorts, Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric A Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Simin Liu
- School of Public Health, Brown University, Providence, RI, USA
| | - Lawrence S Phillips
- Atlanta VA Medical Center, Decatur, GA, USA.,Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Ulrike Peters
- Division of Public Health Sciences, University of Washington, Department of Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lynne R Wikens
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Loic Le Marchand
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Daniel O Stram
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
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39
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Ekoru K, Adeyemo AA, Chen G, Doumatey AP, Zhou J, Bentley AR, Shriner D, Rotimi CN. Genetic risk scores for cardiometabolic traits in sub-Saharan African populations. Int J Epidemiol 2021; 50:1283-1296. [PMID: 33729508 DOI: 10.1093/ije/dyab046] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 02/25/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. GRS for cardiometabolic traits were evaluated in sub-Saharan Africans in comparison with African Americans and European Americans. METHODS We evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n = 5200), African Americans (AA; n = 9139) and European Americans (EUR; n = 9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and the increase in discriminatory ability over traditional risk factors [age, sex and body mass index (BMI)], with adjustment for ancestry-derived principal components. RESULTS Across all traits, GRS showed up to a 5-fold and 20-fold greater predictive utility in EUR relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percentage increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EUR, 26.6% to 65.8% among AA and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EUR relative to AA and up to 44-fold greater in EUR relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EUR. CONCLUSIONS This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiple ethnic populations in genomic studies to ensure equitable clinical translation of GRS.
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Affiliation(s)
- Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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40
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Meeks KAC, Bentley AR, Adeyemo AA, Rotimi CN. Evolutionary forces in diabetes and hypertension pathogenesis in Africans. Hum Mol Genet 2021; 30:R110-R118. [PMID: 33734377 DOI: 10.1093/hmg/ddaa238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/16/2020] [Accepted: 10/22/2020] [Indexed: 11/12/2022] Open
Abstract
Rates of type 2 diabetes (T2D) and hypertension are increasing rapidly in urbanizing sub-Saharan Africa (SSA). While lifestyle factors drive the increases in T2D and hypertension prevalence, evidence across populations shows that genetic variation, which is driven by evolutionary forces including a natural selection that shaped the human genome, also plays a role. Here we report the evidence for the effect of selection in African genomes on mechanisms underlying T2D and hypertension, including energy metabolism, adipose tissue biology, insulin action and salt retention. Selection effects found for variants in genes PPARA and TCF7L2 may have enabled Africans to respond to nutritional challenges by altering carbohydrate and lipid metabolism. Likewise, African-ancestry-specific characteristics of adipose tissue biology (low visceral adipose tissue [VAT], high intermuscular adipose tissue and a strong association between VAT and adiponectin) may have been selected for in response to nutritional and infectious disease challenges in the African environment. Evidence for selection effects on insulin action, including insulin resistance and secretion, has been found for several genes including MPHOSPH9, TMEM127, ZRANB3 and MC3R. These effects may have been historically adaptive in critical conditions, such as famine and inflammation. A strong correlation between hypertension susceptibility variants and latitude supports the hypothesis of selection for salt retention mechanisms in warm, humid climates. Nevertheless, adaptive genomics studies in African populations are scarce. More work is needed, particularly genomics studies covering the wide diversity of African populations in SSA and Africans in diaspora, as well as further functional assessment of established risk loci.
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Affiliation(s)
- Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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41
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Chikowore T, Kamiza AB, Oduaran OH, Machipisa T, Fatumo S. Non-communicable diseases pandemic and precision medicine: Is Africa ready? EBioMedicine 2021; 65:103260. [PMID: 33639396 PMCID: PMC7921515 DOI: 10.1016/j.ebiom.2021.103260] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 01/12/2021] [Accepted: 02/10/2021] [Indexed: 12/14/2022] Open
Abstract
Non-communicable diseases (NCDs) kill more than 41 million people every year, accounting for 71% of all deaths globally. The prevalence of NCDs is estimated to be higher than that of infectious diseases in Africa by 2030. Precision medicine may help with early identification of cases, resulting in timely prevention and improvement in the efficacy of treatments. However, Africa has been lagging behind in genetic research, a key component of the precision medicine initiative. A number of genomic research initiatives which could lead to translational genomics are emerging on the African continent which includes the Non-communicable Diseases Genetic Heritage Study (NCDGHS) and the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network. These offer a promise that precision medicine can be applied in African countries. This review evaluates the advances of genetic studies for cancer, hypertension, type 2 diabetes and body mass index (BMI) in Africa.
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Affiliation(s)
- Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Abram Bunya Kamiza
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ovokeraye H Oduaran
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Tafadzwa Machipisa
- Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA), Department of Medicine, University of Cape Town, Cape Town, South Africa; Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON L8L 2 × 2, Canada
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Uganda; London School of Hygiene and Tropical Medicine London UK; H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
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42
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Sailer S, Keller MA, Werner ER, Watschinger K. The Emerging Physiological Role of AGMO 10 Years after Its Gene Identification. Life (Basel) 2021; 11:life11020088. [PMID: 33530536 PMCID: PMC7911779 DOI: 10.3390/life11020088] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/21/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
The gene encoding alkylglycerol monooxygenase (AGMO) was assigned 10 years ago. So far, AGMO is the only known enzyme capable of catalysing the breakdown of alkylglycerols and lyso-alkylglycerophospholipids. With the knowledge of the genetic information, it was possible to relate a potential contribution for mutations in the AGMO locus to human diseases by genome-wide association studies. A possible role for AGMO was implicated by genetic analyses in a variety of human pathologies such as type 2 diabetes, neurodevelopmental disorders, cancer, and immune defence. Deficient catabolism of stored lipids carrying an alkyl bond by an absence of AGMO was shown to impact on the overall lipid composition also outside the ether lipid pool. This review focuses on the current evidence of AGMO in human diseases and summarises experimental evidence for its role in immunity, energy homeostasis, and development in humans and several model organisms. With the progress in lipidomics platform and genetic identification of enzymes involved in ether lipid metabolism such as AGMO, it is now possible to study the consequence of gene ablation on the global lipid pool and further on certain signalling cascades in a variety of model organisms in more detail.
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Affiliation(s)
- Sabrina Sailer
- Biocenter, Institute of Biological Chemistry, Medical University of Innsbruck, 6020 Innsbruck, Austria; (S.S.); (E.R.W.)
| | - Markus A. Keller
- Institute of Human Genetics, Medical University of Innsbruck, 6020 Innsbruck, Austria;
| | - Ernst R. Werner
- Biocenter, Institute of Biological Chemistry, Medical University of Innsbruck, 6020 Innsbruck, Austria; (S.S.); (E.R.W.)
| | - Katrin Watschinger
- Biocenter, Institute of Biological Chemistry, Medical University of Innsbruck, 6020 Innsbruck, Austria; (S.S.); (E.R.W.)
- Correspondence: ; Tel.: +43-512-9003-70344
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43
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Esoh KK, Apinjoh TO, Nyanjom SG, Wonkam A, Chimusa ER, Amenga-Etego L, Amambua-Ngwa A, Achidi EA. Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection. Sci Rep 2021; 11:1039. [PMID: 33441574 PMCID: PMC7807043 DOI: 10.1038/s41598-020-79124-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023] Open
Abstract
Inferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.
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Affiliation(s)
- Kevin K Esoh
- Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000, Nairobi, City Square, Kenya
| | - Tobias O Apinjoh
- Department of Biochemistry and Molecular Biology, University of Buea, P.O. Box 63, Buea, South West Region, Cameroon.
| | - Steven G Nyanjom
- Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000, Nairobi, City Square, Kenya
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Lucas Amenga-Etego
- West African Centre for Cell Biology of Infectious Pathogens, University of Ghana, Legon, Accra, Ghana
| | | | - Eric A Achidi
- Department of Biochemistry and Molecular Biology, University of Buea, P.O. Box 63, Buea, South West Region, Cameroon
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44
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Lin X, Li H. Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front Endocrinol (Lausanne) 2021; 12:706978. [PMID: 34552557 PMCID: PMC8450866 DOI: 10.3389/fendo.2021.706978] [Citation(s) in RCA: 416] [Impact Index Per Article: 138.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 08/10/2021] [Indexed: 12/20/2022] Open
Abstract
Obesity is a complex multifactorial disease that accumulated excess body fat leads to negative effects on health. Obesity continues to accelerate resulting in an unprecedented epidemic that shows no significant signs of slowing down any time soon. Raised body mass index (BMI) is a risk factor for noncommunicable diseases such as diabetes, cardiovascular diseases, and musculoskeletal disorders, resulting in dramatic decrease of life quality and expectancy. The main cause of obesity is long-term energy imbalance between consumed calories and expended calories. Here, we explore the biological mechanisms of obesity with the aim of providing actionable treatment strategies to achieve a healthy body weight from nature to nurture. This review summarizes the global trends in obesity with a special focus on the pathogenesis of obesity from genetic factors to epigenetic factors, from social environmental factors to microenvironment factors. Against this background, we discuss several possible intervention strategies to minimize BMI.
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45
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Yaghootkar H, Whitcher B, Bell JD, Thomas EL. Ethnic differences in adiposity and diabetes risk - insights from genetic studies. J Intern Med 2020; 288:271-283. [PMID: 32367627 DOI: 10.1111/joim.13082] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Type 2 diabetes is more common in non-Europeans and starts at a younger age and at lower BMI cut-offs. This review discusses the insights from genetic studies about pathophysiological mechanisms which determine risk of disease with a focus on the role of adiposity and body fat distribution in ethnic disparity in risk of type 2 diabetes. During the past decade, genome-wide association studies (GWAS) have identified more than 400 genetic variants associated with the risk of type 2 diabetes. The Eurocentric nature of these genetic studies has made them less effective in identifying mechanisms that make non-Europeans more susceptible to higher risk of disease. One possible mechanism suggested by epidemiological studies is the role of ethnic difference in body fat distribution. Using genetic variants associated with an ability to store extra fat in a safe place, which is subcutaneous adipose tissue, we discuss how different ethnic groups could be genetically less susceptible to type 2 diabetes by developing a more favourable fat distribution.
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Affiliation(s)
- H Yaghootkar
- From the, Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK.,School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK.,Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - B Whitcher
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
| | - J D Bell
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
| | - E L Thomas
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
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46
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Abstract
Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.
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Affiliation(s)
- Joanne B Cole
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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47
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Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population. Nutrients 2020; 12:nu12071906. [PMID: 32605047 PMCID: PMC7400498 DOI: 10.3390/nu12071906] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/04/2020] [Accepted: 06/24/2020] [Indexed: 02/07/2023] Open
Abstract
Obesity is a multifactorial condition arising from the interaction between genetic and lifestyle factors. We aimed to assess the impact of lifestyle and genetic factors on obesity-related traits in 302 healthy Ghanaian adults. Dietary intake and physical activity were assessed using a 3 day repeated 24 h dietary recall and global physical activity questionnaire, respectively. Twelve single nucleotide polymorphisms (SNPs) were used to construct 4-SNP, 8-SNP and 12-SNP genetic risk scores (GRSs). The 4-SNP GRS showed significant interactions with dietary fat intakes on waist circumference (WC) (Total fat, Pinteraction = 0.01; saturated fatty acids (SFA), Pinteraction = 0.02; polyunsaturated fatty acids (PUFA), Pinteraction = 0.01 and monounsaturated fatty acids (MUFA), Pinteraction = 0.01). Among individuals with higher intakes of total fat (>47 g/d), SFA (>14 g/d), PUFA (>16 g/d) and MUFA (>16 g/d), individuals with ≥3 risk alleles had a significantly higher WC compared to those with <3 risk alleles. This is the first study of its kind in this population, suggesting that a higher consumption of dietary fatty acid may have the potential to increase the genetic susceptibility of becoming centrally obese. These results support the general dietary recommendations to decrease the intakes of total fat and SFA, to reduce the risk of obesity, particularly in individuals with a higher genetic predisposition to central obesity.
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48
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Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J, Tcheandjieu C, Huffman JE, Assimes TL, Lorenz K, Zhu X, Hilliard AT, Judy RL, Huang J, Lee KM, Klarin D, Pyarajan S, Danesh J, Melander O, Rasheed A, Mallick NH, Hameed S, Qureshi IH, Afzal MN, Malik U, Jalal A, Abbas S, Sheng X, Gao L, Kaestner KH, Susztak K, Sun YV, DuVall SL, Cho K, Lee JS, Gaziano JM, Phillips LS, Meigs JB, Reaven PD, Wilson PW, Edwards TL, Rader DJ, Damrauer SM, O'Donnell CJ, Tsao PS, Chang KM, Voight BF, Saleheen D. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 2020; 52:680-691. [PMID: 32541925 PMCID: PMC7343592 DOI: 10.1038/s41588-020-0637-y] [Citation(s) in RCA: 400] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 04/29/2020] [Indexed: 12/19/2022]
Abstract
We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ethnic meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program, DIAMANTE, Biobank Japan, and other studies. We report 568 associations, including 286 autosomal, 7 X chromosomal, and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D-associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD), and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in MVP and observed statistical SNP-T2D interactions at 13 variants, including coronary heart disease, CKD, PAD, and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.
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Affiliation(s)
- Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jacob M Keaton
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julie A Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,College of Nursing and Health Sciences, University of Massachusetts, Lowell, MA, USA
| | - Donald R Miller
- Edith Nourse Rogers Memorial VA Hospital, Bedford, MA, USA.,Center for Population Health, University of Massachusetts, Lowell, MA, USA
| | - Jin Zhou
- Phoenix VA Health Care System, Phoenix, AZ, USA.,Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Catherine Tcheandjieu
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.,Department of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Themistocles L Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kimberly Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.,Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Xiang Zhu
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Austin T Hilliard
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Renae L Judy
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.,Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jie Huang
- VA Boston Healthcare System, Boston, MA, USA.,Department of Global Health, Peking University School of Public Health, Beijing, China
| | - Kyung M Lee
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Derek Klarin
- VA Boston Healthcare System, 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.,Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, USA
| | - Saiju Pyarajan
- VA Boston Healthcare System, Boston, MA, USA.,Department of Medicine, Brigham Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Olle Melander
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan
| | | | - Shahid Hameed
- Punjab Institute of Cardiology, Lahore, Punjab, Pakistan
| | - Irshad H Qureshi
- Department of Medicine, King Edward Medical University, Lahore, Punjab, Pakistan.,Mayo Hospital, Lahore, Punjab, Pakistan
| | - Muhammad Naeem Afzal
- Department of Medicine, King Edward Medical University, Lahore, Punjab, Pakistan.,Mayo Hospital, Lahore, Punjab, Pakistan
| | - Uzma Malik
- Department of Medicine, King Edward Medical University, Lahore, Punjab, Pakistan.,Mayo Hospital, Lahore, Punjab, Pakistan
| | - Anjum Jalal
- Department of Cardiology, Faisalabad Institute of Cardiology, Faisalabad, Punjab, Pakistan
| | - Shahid Abbas
- Department of Cardiology, Faisalabad Institute of Cardiology, Faisalabad, Punjab, Pakistan
| | - Xin Sheng
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Long Gao
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yan V Sun
- Atlanta VA Medical Center, Decatur, GA, USA.,Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kelly Cho
- VA Boston Healthcare System, Boston, MA, USA.,Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - Jennifer S Lee
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - J Michael Gaziano
- VA Boston Healthcare System, Boston, MA, USA.,Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - Lawrence S Phillips
- Atlanta VA Medical Center, Decatur, GA, USA.,Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ, USA.,College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - Peter W Wilson
- Atlanta VA Medical Center, Decatur, GA, USA.,Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd L Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Nashville VA Medical Center, Nashville, TN, USA
| | - Daniel J Rader
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.,Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Boston, MA, USA.,Department of Medicine, Brigham Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | | | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,South Texas Veterans Health Care System, San Antonio, TX, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA. .,Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. .,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan. .,Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA. .,Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA.
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49
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Soller M, Abu-Toamih Atamni HJ, Binenbaum I, Chatziioannou A, Iraqi FA. Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population. ACTA ACUST UNITED AC 2020; 9:e66. [PMID: 31756057 DOI: 10.1002/cpmo.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).
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Affiliation(s)
- Morris Soller
- Department of Genetics, Silverman Institute for Life Sciences, Hebrew University, Jerusalem, Israel
| | - Hanifa J Abu-Toamih Atamni
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel
| | - Ilona Binenbaum
- Department of Biology, University of Patras, Patras, Greece.,Institute of Biology, Medicinal Chemistry & Biotechnology, NHRF, Athens, Greece
| | | | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel
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50
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Bentley AR, Callier SL, Rotimi CN. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom Med 2020; 5:5. [PMID: 32140257 PMCID: PMC7042246 DOI: 10.1038/s41525-019-0111-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/16/2019] [Indexed: 12/24/2022] Open
Abstract
The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.
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Affiliation(s)
- Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Shawneequa L. Callier
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
- Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health Sciences, Washington, DC USA
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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