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Singh N, Nagar E, Roy D, Arora N. NLRP3/GSDMD mediated pyroptosis induces lung inflammation susceptibility in diesel exhaust exposed mouse strains. Gene 2024; 918:148459. [PMID: 38608794 DOI: 10.1016/j.gene.2024.148459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Genetic diversity among species influences the disease severity outcomes linked to air pollution. However, the mechanism responsible for this variability remain elusive and needs further investigation. OBJECTIVE To investigate the genetic factors and pathways linked with differential susceptibility in mouse strains associated with diesel exhaust exposure. METHODS C57BL/6 and Balb/c mice were exposed to diesel exhaust (DE) for 5 days/week for 30 min/day for 8 weeks. Body weight of mice was recorded every week and airway hyperresponsiveness towards DE exposure was recorded after 24 h of last exposure. Mice were euthanised to collect BALF, blood, lung tissues for immunobiochemical assays, structural integrity and genetic studies. RESULTS C57BL/6 mice showed significantly decreased body weight in comparison to Balb/c mice (p < 0.05). Both mouse strains showed lung resistance and damage to elastance upon DE exposure compared to respective controls (p < 0.05) with more pronounced effects in C57BL/6 mice. Lung histology showed increase in bronchiolar infiltration and damage to the wall in C57BL/6 mice (p < 0.05). DE exposure upregulated pro-inflammatory and Th2 cytokine levels in C57BL/6 in comparison to Balb/c mice. C57BL/6 mice showed increase in Caspase-1 and ASC expression confirming activation of downstream pathway. This showed significant activation of inflammasome pathway in C57BL/6 mice with ∼2-fold increase in NLRP3 and elevated IL-1β expression. Gasdermin-D levels were increased in C57BL/6 mice demonstrating induction of pyroptosis that corroborated with IL-1β secretion (p < 0.05). Genetic variability among both species was confirmed with sanger's sequencing suggesting presence of SNPs in 3'UTRs of IL-1β gene influencing expression between mouse strains. CONCLUSIONS C57BL/6 mice exhibited increased susceptibility to diesel exhaust in contrast to Balb/c mice via activation of NLRP3-related pyroptosis. Differential susceptibility between strains may be attributed via SNPs in the 3'UTRs of the IL-1β gene.
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Affiliation(s)
- Naresh Singh
- Allergy and Immunology Section, CSIR-Institute of Genomics and Integrative Biology, Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Ekta Nagar
- Allergy and Immunology Section, CSIR-Institute of Genomics and Integrative Biology, Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Deepti Roy
- Allergy and Immunology Section, CSIR-Institute of Genomics and Integrative Biology, Delhi 110007, India
| | - Naveen Arora
- Allergy and Immunology Section, CSIR-Institute of Genomics and Integrative Biology, Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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2
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Kasai F, Fukushima M, Miyagi Y, Nakamura Y. Genetic diversity among the present Japanese population: evidence from genotyping of human cell lines established in Japan. Hum Cell 2024; 37:944-950. [PMID: 38639832 PMCID: PMC11194210 DOI: 10.1007/s13577-024-01055-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Japan is often assumed to have a highly homogeneous ethnic population, because it is an island country. This is evident in human cell lines collected from cell banks; however, these genotypes have not been thoroughly characterized. To examine the population genotypes of human cell lines established in Japan, we conducted SNP genotyping on 57 noncancerous cell lines and 43 lung cancer cell lines. Analysis of biogeographic ancestry revealed that 58 cell lines had non-admixed Japanese genotypes, 21 cell lines had an admixture of Japanese and East Asian genotypes, and the remaining 21 cell lines had East Asian genotypes. The proportion of non-admixed Japanese genotypes was similar between lung cancer and noncancerous cell lines, suggesting that patients in Japan may not exclusively have Japanese genotypes. This could influence the incidence of inherited diseases and should be taken into account in personalized medicine tailored to genetic background. The genetic makeup of the present-day Japanese population cannot be fully explained by the ancestral Jomon and Yayoi lineages. Instead, it is necessary to consider a certain level of genetic admixture between Japanese and neighboring Asian populations. Our study revealed genetic variation among human cell lines derived from Japanese individuals, reflecting the diversity present within the Japanese population.
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Affiliation(s)
- Fumio Kasai
- Cell Engineering Division, BioResource Research Center, RIKEN Cell Bank, Tsukuba, Japan.
| | - Makoto Fukushima
- Cell Engineering Division, BioResource Research Center, RIKEN Cell Bank, Tsukuba, Japan
| | - Yohei Miyagi
- Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Yukio Nakamura
- Cell Engineering Division, BioResource Research Center, RIKEN Cell Bank, Tsukuba, Japan
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3
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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4
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Benjamin KJM, Chen Q, Eagles NJ, Huuki-Myers LA, Collado-Torres L, Stolz JM, Pertea G, Shin JH, Paquola ACM, Hyde TM, Kleinman JE, Jaffe AE, Han S, Weinberger DR. Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry. Nat Neurosci 2024; 27:1064-1074. [PMID: 38769152 PMCID: PMC11156587 DOI: 10.1038/s41593-024-01636-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 03/29/2024] [Indexed: 05/22/2024]
Abstract
Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | | | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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5
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Smith JL, Tcheandjieu C, Dikilitas O, Iyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao PS, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004272. [PMID: 38380516 DOI: 10.1161/circgen.123.004272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. RESULTS Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). CONCLUSIONS The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco (C.T.)
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institute, San Francisco, CA (C.T.)
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Kruthika Iyer
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | - Kazuo Miyazawa
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Austin Hilliard
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taiwan (W.H.-H.S.)
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA (K.-M.C.)
| | - Stavroula Kanoni
- Queen Mary University of London, Cambridge, United Kingdom (S.K.)
| | - Philip S Tsao
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Kaoru Ito
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | - Shoa L Clarke
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | | | - Iftikhar J Kullo
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
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Srivastava AK, Monangi N, Ravichandran V, Solé-Navais P, Jacobsson B, Muglia LJ, Zhang G. Recent Advances in Genomic Studies of Gestational Duration and Preterm Birth. Clin Perinatol 2024; 51:313-329. [PMID: 38705643 PMCID: PMC11189662 DOI: 10.1016/j.clp.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Preterm birth (PTB) is the leading cause of infant mortality and morbidity. For several decades, extensive epidemiologic and genetic studies have highlighted the significant contribution of maternal and offspring genetic factors to PTB. This review discusses the challenges inherent in conventional genomic analyses of PTB and underscores the importance of adopting nonconventional approaches, such as analyzing the mother-child pair as a single analytical unit, to disentangle the intertwined maternal and fetal genetic influences. We elaborate on studies investigating PTB phenotypes through 3 levels of genetic analyses: single-variant, multi-variant, and genome-wide variants.
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Affiliation(s)
- Amit K Srivastava
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - Nagendra Monangi
- Department of Pediatrics, University of Cincinnati College of Medicine, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center and March of Dimes Prematurity Research Center Ohio Collaborative; Division of Neonatology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - Vidhya Ravichandran
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA; Division of Neonatology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - Pol Solé-Navais
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Sciences, University of Gothenburg, Box 100, Gothenburg 405 30, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Sciences, University of Gothenburg, Box 100, Gothenburg 405 30, Sweden; Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Lovisenberggata 8, Oslo 0456, Norway
| | - Louis J Muglia
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center and March of Dimes Prematurity Research Center Ohio Collaborative; The Burroughs Wellcome Fund, 21 Tw Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Ge Zhang
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center and March of Dimes Prematurity Research Center Ohio Collaborative.
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7
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Roman S, Campos-Medina L, Leal-Mercado L. Personalized nutrition: the end of the one-diet-fits-all era. Front Nutr 2024; 11:1370595. [PMID: 38854164 PMCID: PMC11157041 DOI: 10.3389/fnut.2024.1370595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024] Open
Abstract
Personalized Nutrition emerged as a new trend for providing nutritional and food advice based on the individual's genetic composition, a field driven by the advancements in the multi-omic sciences throughout the last century. It intends not only to tailor the recommended daily allowances of nutrients and functional foods that a person may need but also to maintain the principles of sustainability and eco-friendliness. This principle implies the implementation of strategies within the healthcare system to advocate for the ending of the one-diet-fits-all paradigm by considering a personalized diet as an ally to prevent diet-related chronic diseases. In this Perspective, we highlight the potential benefits of such a paradigm within the region of Latin America, particularly Mexico, where the genetic admixture of the population, food biodiversity, and food culture provide unique opportunities to establish personalized nutrigenetic strategies. These strategies could play a crucial role in preventing chronic diseases and addressing the challenges confronted in the region.
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Affiliation(s)
- Sonia Roman
- Department of Genomic Medicine in Hepatology, Civil Hospital of Guadalajara, Fray Antonio Alcalde, Guadalajara, Jalisco, Mexico
- Health Sciences Center, University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - Liliana Campos-Medina
- Department of Genomic Medicine in Hepatology, Civil Hospital of Guadalajara, Fray Antonio Alcalde, Guadalajara, Jalisco, Mexico
- Health Sciences Center, University of Guadalajara, Guadalajara, Jalisco, Mexico
- Doctoral Program in Molecular Biology in Medicine, Health Sciences Center, University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - Leonardo Leal-Mercado
- Department of Genomic Medicine in Hepatology, Civil Hospital of Guadalajara, Fray Antonio Alcalde, Guadalajara, Jalisco, Mexico
- Health Sciences Center, University of Guadalajara, Guadalajara, Jalisco, Mexico
- Doctoral Program in Molecular Biology in Medicine, Health Sciences Center, University of Guadalajara, Guadalajara, Jalisco, Mexico
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Wang Y, He Y, Shi Y, Qian DC, Gray KJ, Winn R, Martin AR. Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds. Am J Hum Genet 2024; 111:809-824. [PMID: 38642557 PMCID: PMC11080611 DOI: 10.1016/j.ajhg.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/22/2024] Open
Abstract
Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.
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Affiliation(s)
- Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
| | - Yixuan He
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Yue Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Reproductive Medicine Center, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - David C Qian
- Department of Thoracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kathryn J Gray
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, USA
| | - Robert Winn
- Virginia Commonwealth University Massey Cancer Center, Richmond, VA, USA
| | - Alicia R Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
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9
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Zammit M, Agius R, Fava S, Vassallo J, Pace NP. Association between a polygenic lipodystrophy genetic risk score and diabetes risk in the high prevalence Maltese population. Acta Diabetol 2024; 61:555-564. [PMID: 38280973 DOI: 10.1007/s00592-023-02230-9] [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: 09/11/2023] [Accepted: 12/23/2023] [Indexed: 01/29/2024]
Abstract
BACKGROUND Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic complications and is typically associated with obesity. A group of common variants at eleven loci are associated with insulin resistance and risk of both type 2 diabetes and coronary artery disease. These variants describe a polygenic correlate of lipodystrophy, with a high metabolic disease risk despite a low BMI. OBJECTIVES In this cross-sectional study, we sought to investigate the association of a polygenic risk score composed of eleven lipodystrophy variants with anthropometric, glycaemic and metabolic traits in an island population characterised by a high prevalence of both obesity and type 2 diabetes. METHODS 814 unrelated adults (n = 477 controls and n = 337 T2DM cases) of Maltese-Caucasian ethnicity were genotyped and associations with phenotypes explored. RESULTS A higher polygenic lipodystrophy risk score was correlated with lower adiposity indices (lower waist circumference and body mass index measurements) and higher HOMA-IR, atherogenic dyslipidaemia and visceral fat dysfunction as assessed by the visceral adiposity index in the DM group. In crude and covariate-adjusted models, individuals in the top quartile of polygenic risk had a higher T2DM risk relative to individuals in the first quartile of the risk score distribution. CONCLUSION This study consolidates the association between polygenic lipodystrophy risk alleles, metabolic syndrome parameters and T2DM risk particularly in normal-weight individuals. Our findings demonstrate that polygenic lipodystrophy risk alleles drive insulin resistance and diabetes risk independent of an increased BMI.
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Affiliation(s)
- Maria Zammit
- Department of Pathology, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Rachel Agius
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Stephen Fava
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Josanne Vassallo
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Nikolai Paul Pace
- Department of Anatomy, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta.
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Room 325, Msida, MSD2080, Malta.
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10
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Gerussi A, Cappadona C, Bernasconi DP, Cristoferi L, Valsecchi MG, Carbone M, Invernizzi P, Asselta R. Improving predictive accuracy in primary biliary cholangitis: A new genetic risk score. Liver Int 2024. [PMID: 38619000 DOI: 10.1111/liv.15916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND AND AIMS Genetic variants influence primary biliary cholangitis (PBC) risk. We established and tested an accurate polygenic risk score (PRS) using these variants. METHODS Data from two Italian cohorts (OldIT 444 cases, 901 controls; NewIT 255 cases, 579 controls) were analysed. The latest international genome-wide meta-analysis provided effect size estimates. The PRS, together with human leukocyte antigen (HLA) status and sex, was included in an integrated risk model. RESULTS Starting from 46 non-HLA genes, 22 variants were selected. PBC patients in the OldIT cohort showed a higher risk score than controls: -.014 (interquartile range, IQR, -.023, .005) versus -.022 (IQR -.030, -.013) (p < 2.2 × 10-16). For genetic-based prediction, the area under the curve (AUC) was .72; adding sex increased the AUC to .82. Validation in the NewIT cohort confirmed the model's accuracy (.71 without sex, .81 with sex). Individuals in the top group, representing the highest 25%, had a PBC risk approximately 14 times higher than that of the reference group (lowest 25%; p < 10-6). CONCLUSION The combination of sex and a novel PRS accurately discriminated between PBC cases and controls. The model identified a subset of individuals at increased risk of PBC who might benefit from tailored monitoring.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Claudio Cappadona
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
| | - Davide Paolo Bernasconi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maria Grazia Valsecchi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Biostatistics and Clinical Epidemiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
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11
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Timmins IR, Dudbridge F. Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer. PLoS Genet 2024; 20:e1011212. [PMID: 38630784 PMCID: PMC11023298 DOI: 10.1371/journal.pgen.1011212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/07/2024] [Indexed: 04/19/2024] Open
Abstract
Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.
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Affiliation(s)
- Iain R. Timmins
- Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
- Statistical Innovation, AstraZeneca, Cambridge, United Kingdom
| | | | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
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12
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Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med 2024; 22:258. [PMID: 38461317 PMCID: PMC10924384 DOI: 10.1186/s12967-024-05053-6] [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: 01/27/2023] [Accepted: 03/01/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.
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Affiliation(s)
- Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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13
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Drury NE. Myocardial protection in paediatric cardiac surgery: building an evidence-based strategy. Ann R Coll Surg Engl 2024; 106:277-282. [PMID: 37249560 PMCID: PMC10904256 DOI: 10.1308/rcsann.2023.0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2023] [Indexed: 05/31/2023] Open
Abstract
Cardioplegia is fundamental to the surgical repair of congenital heart defects by protecting the heart against ischaemia/reperfusion injury, characterised by low cardiac output and troponin release in the early postoperative period. The immature myocardium exhibits structural, physiological and metabolic differences from the adult heart, with a greater sensitivity to calcium overload-mediated injury during reperfusion. Del Nido cardioplegia was designed specifically to protect the immature heart, is widely used in North America and may provide better myocardial protection in children; however, it has not been commercially available in the UK, where most centres use St Thomas' blood cardioplegia. There are no phase 3 clinical trials in children to support one solution over another and this lack of evidence, combined with variations in practice, suggests the presence of clinical equipoise. The best cardioplegia solution for use in children, and the impact of age and other clinical factors remain unknown. In this Hunterian lecture, I propose an evidence-based strategy to improve myocardial protection during cardiac surgery in children through: (1) conducting multicentre clinical trials of established techniques; (2) improving our knowledge of ischaemia/reperfusion injury in the setting of cardioplegic arrest; (3) applying this to drive innovation, moving beyond current cardioplegia solutions; (4) empowering personalised medicine, through combining clinical and genomic data, including ethnic diversity; and (5) understanding the impact of cardioplegic arrest on the late outcomes that matter to patients and their families.
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14
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Akalu YT, Bogunovic D. Inborn errors of immunity: an expanding universe of disease and genetic architecture. Nat Rev Genet 2024; 25:184-195. [PMID: 37863939 DOI: 10.1038/s41576-023-00656-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 10/22/2023]
Abstract
Inborn errors of immunity (IEIs) are generally considered to be rare monogenic disorders of the immune system that cause immunodeficiency, autoinflammation, autoimmunity, allergy and/or cancer. Here, we discuss evidence that IEIs need not be rare disorders or exclusively affect the immune system. Namely, an increasing number of patients with IEIs present with severe dysregulations of the central nervous, digestive, renal or pulmonary systems. Current challenges in the diagnosis of IEIs that result from the segregated practice of specialized medicine could thus be mitigated, in part, by immunogenetic approaches. Starting with a brief historical overview of IEIs, we then discuss the technological advances that are facilitating the immunogenetic study of IEIs, progress in understanding disease penetrance in IEIs, the expanding universe of IEIs affecting distal organ systems and the future of genetic, biochemical and medical discoveries in this field.
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Affiliation(s)
- Yemsratch T Akalu
- Center for Inborn Errors of Immunity, Precision Immunology Institute, Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dusan Bogunovic
- Center for Inborn Errors of Immunity, Precision Immunology Institute, Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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15
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Tindula G, Issac B, Mukherjee SK, Ekramullah SM, Arman DM, Islam J, Suchanda HS, Sun L, Rockowitz S, Christiani DC, Warf BC, Mazumdar M. Genome-wide analysis of spina bifida risk variants in a case-control study from Bangladesh. Birth Defects Res 2024; 116:e2331. [PMID: 38526198 PMCID: PMC10963057 DOI: 10.1002/bdr2.2331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Human studies of genetic risk factors for neural tube defects, severe birth defects associated with long-term health consequences in surviving children, have predominantly been restricted to a subset of candidate genes in specific biological pathways including folate metabolism. METHODS In this study, we investigated the association of genetic variants spanning the genome with risk of spina bifida (i.e., myelomeningocele and meningocele) in a subset of families enrolled from December 2016 through December 2022 in a case-control study in Bangladesh, a population often underrepresented in genetic studies. Saliva DNA samples were analyzed using the Illumina Global Screening Array. We performed genetic association analyses to compare allele frequencies between 112 case and 121 control children, 272 mothers, and 128 trios. RESULTS In the transmission disequilibrium test analyses with trios only, we identified three novel exonic spina bifida risk loci, including rs140199800 (SULT1C2, p = 1.9 × 10-7), rs45580033 (ASB2, p = 4.2 × 10-10), and rs75426652 (LHPP, p = 7.2 × 10-14), after adjusting for multiple hypothesis testing. Association analyses comparing cases and controls, as well as models that included their mothers, did not identify genome-wide significant variants. CONCLUSIONS This study identified three novel single nucleotide polymorphisms involved in biological pathways not previously associated with neural tube defects. The study warrants replication in larger groups to validate findings and to inform targeted prevention strategies.
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Affiliation(s)
- Gwen Tindula
- Department of Neurology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Department of Neurology, Harvard Medical School, Boston, MA, 02115, United States
| | - Biju Issac
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
| | - Sudipta Kumer Mukherjee
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Sheikh Muhammad Ekramullah
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - DM Arman
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Joynul Islam
- Department of Clinical Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Hafiza Sultana Suchanda
- Pediatric Neurosurgery Research Committee, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Liang Sun
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
| | - Shira Rockowitz
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, 02115, United States
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, United States
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, United States
| | - Benjamin C. Warf
- Department of Neurosurgery, Boston Children's Hospital, Boston, MA, 02115, United States
| | - Maitreyi Mazumdar
- Department of Neurology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Department of Neurology, Harvard Medical School, Boston, MA, 02115, United States
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, United States
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16
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Kolobkov D, Mishra Sharma S, Medvedev A, Lebedev M, Kosaretskiy E, Vakhitov R. Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project. Front Big Data 2024; 7:1266031. [PMID: 38487517 PMCID: PMC10937521 DOI: 10.3389/fdata.2024.1266031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/31/2024] [Indexed: 03/17/2024] Open
Abstract
Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.
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Affiliation(s)
- Dmitry Kolobkov
- GENXT, Hinxton, United Kingdom
- Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Moscow, Russia
| | - Satyarth Mishra Sharma
- GENXT, Hinxton, United Kingdom
- Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Aleksandr Medvedev
- GENXT, Hinxton, United Kingdom
- Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, Moscow, Russia
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17
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Arruda AL, Morris AP, Zeggini E. Advancing equity in human genomics through tissue-specific multi-ancestry molecular data. CELL GENOMICS 2024; 4:100485. [PMID: 38272034 PMCID: PMC10879035 DOI: 10.1016/j.xgen.2023.100485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/03/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
There is a pressing need to generate molecular data from diverse tissues across global populations. These currently missing data are necessary to resolve genome-wide association study loci, identify effector genes, and move the translational genomics needle beyond European-ancestry individuals and the minority of diseases for which blood is the relevant tissue.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Munich, 85764 Neuherberg, Germany; Munich School for Data Science, Helmholtz Munich, 85764 Neuherberg, Germany; Technical University of Munich, School of Medicine, Graduate School of Experimental Medicine, 81675 Munich, Germany
| | - Andrew P Morris
- Institute of Translational Genomics, Helmholtz Munich, 85764 Neuherberg, Germany; Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester M13 9PT, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Munich, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University Munich and Klinikum Rechts der Isar, 81675 Munich, Germany.
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18
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Sun Q, Rowland BT, Chen J, Mikhaylova AV, Avery C, Peters U, Lundin J, Matise T, Buyske S, Tao R, Mathias RA, Reiner AP, Auer PL, Cox NJ, Kooperberg C, Thornton TA, Raffield LM, Li Y. Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI. Nat Commun 2024; 15:1016. [PMID: 38310129 PMCID: PMC10838303 DOI: 10.1038/s41467-024-45135-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bryce T Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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19
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Wu B, Yee SW, Xiao S, Xu F, Sridhar SB, Yang M, Hochstadt S, Cabral W, Lanfear DE, Hedderson MM, Giacomini KM, Williams LK. Genome-Wide Association Study Identifies Pharmacogenomic Variants Associated With Metformin Glycemic Response in African American Patients With Type 2 Diabetes. Diabetes Care 2024; 47:208-215. [PMID: 37639712 PMCID: PMC10834390 DOI: 10.2337/dc22-2494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 08/03/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Metformin is the most common treatment for type 2 diabetes (T2D). However, there have been no pharmacogenomic studies for T2D in which a population of color was used in the discovery analysis. This study sought to identify genomic variants associated with metformin response in African American patients with diabetes. RESEARCH DESIGN AND METHODS Patients in the discovery set were adult, African American participants from the Diabetes Multi-omic Investigation of Drug Response (DIAMOND), a cohort study of patients with T2D from a health system serving southeast Michigan. DIAMOND participants had genome-wide genotype data and longitudinal electronic records of laboratory results and medication fills. The genome-wide discovery analysis identified polymorphisms correlated to changes in glycated hemoglobin (HbA1c) levels among individuals on metformin monotherapy. Lead associations were assessed for replication in an independent cohort of African American participants from Kaiser Permanente Northern California (KPNC) and in European American participants from DIAMOND. RESULTS The discovery set consisted of 447 African American participants, whereas the replication sets included 353 African American KPNC participants and 466 European American DIAMOND participants. The primary analysis identified a variant, rs143276236, in the gene ARFGEF3, which met the threshold for genome-wide significance, replicated in KPNC African Americans, and was still significant in the meta-analysis (P = 1.17 × 10-9). None of the significant discovery variants replicated in European Americans DIAMOND participants. CONCLUSIONS We identified a novel and biologically plausible genetic variant associated with a change in HbA1c levels among African American patients on metformin monotherapy. These results highlight the importance of diversity in pharmacogenomic studies.
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Affiliation(s)
- Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, School of Pharmacy, University of California San Francisco, San Francisco, CA
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Fei Xu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - Sneha B. Sridhar
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - Mao Yang
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Samantha Hochstadt
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Whitney Cabral
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - David E. Lanfear
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | | | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, School of Pharmacy, University of California San Francisco, San Francisco, CA
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
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20
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Saberi F, Youssef O, Kokkola A, Khodadoostan M, Puolakkainen P, Salehi R, Knuutila S. The frequency of NRAS mutation in stool samples of Iranian colorectal cancers compared to Finnish patients. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2024; 29:4. [PMID: 38524743 PMCID: PMC10956560 DOI: 10.4103/jrms.jrms_208_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/25/2023] [Accepted: 10/18/2023] [Indexed: 03/26/2024]
Abstract
Background Stools from colorectal cancer patients are noninvasive samples that could be used to compare the frequency of hotspot mutations between two different ethnic cohorts. Materials and Methods We collected stool samples from the Iranian cohort (52 patients and 49 controls) and the Finnish cohort (40 patients and 14 controls). Following stool DNA extraction, we used the AmpliSeq Colon and Lung Cancer panel to prepare DNA libraries before sequencing. Results The Iranian cohort exhibited 35 hotspot mutations in the BRAF, ERBB4, FBXW7, FGFR1, FGFR3, KRAS, MAP2K, MET, NRAS, PIK3C, SMAD4, and TP53 genes. In the Finnish cohort, 13 hotspot mutations were found in the AKT1, APC, KIT, KRAS, SMO, STK11, and TP53 genes. Mutations in NRAS and FGFR3 were observed only in the Iranian cohort, while APC mutations were exclusive for the Finnish cohort. Conclusion Genes involved in MAPK and PI3K-MAPK pathways showed a higher frequency of mutations in Iranian patients which may have therapeutic implications.
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Affiliation(s)
- Farideh Saberi
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland, Europe
- Department of Clinical and Chemical Pathology, National Cancer Institute, Cairo University, Cairo, Egypt
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Finland, Europe
| | - Arto Kokkola
- The HUCH Gastrointestinal Clinic, University Central Hospital of Helsinki, Helsinki, Finland, Europe
| | - Mahsa Khodadoostan
- Department of Gastroenterology and Hepatology, Alzahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Pauli Puolakkainen
- The HUCH Gastrointestinal Clinic, University Central Hospital of Helsinki, Helsinki, Finland, Europe
| | - Rasoul Salehi
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Noncommunicable Diseases, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sakari Knuutila
- Department of Pathology, University of Helsinki, Helsinki, Finland, Europe
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21
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Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res 2024; 52:D1143-D1154. [PMID: 38183205 PMCID: PMC10767851 DOI: 10.1093/nar/gkad989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 01/07/2024] Open
Abstract
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.
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Affiliation(s)
- Max Schubach
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thorben Maass
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
| | - Lusiné Nazaretyan
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Röner
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Kircher
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
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22
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Ribeiro HP, Baraldi BM, Rodrigues-Soares F, Planello AC. Psychiatric Level 1A evidence pharmacogenomics in a Brazilian admixed cohort and global populations. Pharmacogenomics 2024; 25:69-78. [PMID: 38288577 DOI: 10.2217/pgs-2023-0211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024] Open
Abstract
Purpose: To compare minor allele frequencies (MAFs) of psychiatric drug response variants in a Brazilian admixed cohort with global populations and other Brazilian groups. Methods: PharmGKB MAFs were gathered from publicly available genetic datasets for Brazil and worldwide. Results: Among 146 variants in CYP2D6 and CYP2C19, 41 were present in Brazil, mostly rare (MAF <1%). 11 variants showed significant MAF differences with large effect sizes compared with global populations. CYP2C19*3 (rs4986893), CYP2C19*17 (rs12248560), CYP2D6*17 (rs28371706-A) and CYP2D6*29 (rs61736512) exhibited higher frequencies in Brazil, with the latter three also differing from other Brazilian groups. Conclusion: This study highlights significant pharmacogenomic diversity in Brazil and globally, underscoring the need for more research in personalized psychiatric drug therapy.
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Affiliation(s)
- Helena Pereira Ribeiro
- Department of Morphology & Basic Pathology - Medical School, Faculdade de Medicina de Jundiaí, Jundiaí, 13202-550, Brazil
| | - Beatriz Meza Baraldi
- Department of Morphology & Basic Pathology - Medical School, Faculdade de Medicina de Jundiaí, Jundiaí, 13202-550, Brazil
| | - Fernanda Rodrigues-Soares
- Department of Pathology, Genetics, & Evolution, Institute of Biological & Natural Sciences, Universidade Federal do Triângulo Mineiro, Uberaba, 38035-180, Brazil
| | - Aline Cristiane Planello
- Department of Morphology & Basic Pathology - Medical School, Faculdade de Medicina de Jundiaí, Jundiaí, 13202-550, Brazil
- Department of Bioscience, Faculdade de Odontologia de Piracicaba/Universidade de Campinas, 13414-903, Brazil
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23
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Fu Z, Ma Y, Yang C, Liu Q, Liang J, Weng Z, Li W, Zhou S, Chen X, Xu J, Xu C, Huang T, Zhou Y, Gu A. Association of air pollution exposure and increased coronary artery disease risk: the modifying effect of genetic susceptibility. Environ Health 2023; 22:85. [PMID: 38062446 PMCID: PMC10704645 DOI: 10.1186/s12940-023-01038-y] [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] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Both genetic factors and air pollution are risk factors for coronary artery disease (CAD), but their combined effects on CAD are uncertain. The study aimed to comprehensively investigate their separate, combined and interaction effects on the onset of CAD. METHODS We utilized data from the UK Biobank with a recruitment of 487,507 participants who were free of CAD at baseline from 2006 to 2010. We explored the separate, combined effect or interaction association among genetic factors, air pollution and CAD with the polygenic risk score (PRS) and Cox proportional hazard models. RESULTS The hazard ratios (HRs) [95% confidence interval (CI)] of CAD for 10-µg/m3 increases in PM2.5, NO2 and NOx concentrations were 1.25 (1.09, 1.44), 1.03 (1.01, 1.05) and 1.01 (1.00, 1.02), respectively. Participants with high PRS and air pollution exposure had a higher risk of CAD than those with the low genetic risk and low air pollution exposure, and the HRs (95% CI) of CAD in the PM2.5, PM10, NO2 and NOx high joint exposure groups were 1.56 (1.48, 1.64), 1.55(1.48, 1.63), 1.57 (1.49, 1.65), and 1.57 (1.49, 1.65), respectively. Air pollution and genetic factors exerted significant additive effects on the development of CAD (relative excess risk due to the interaction [RERI]: 0.12 (0.05, 0.19) for PM2.5, 0.17 (0.10, 0.24) for PM10, 0.14 (0.07, 0.21) for NO2, and 0.17 (0.10, 0.24) for NOx; attributable proportion due to the interaction [AP]: 0.09 (0.04, 0.14) for PM2.5, 0.12 (0.07, 0.18) for PM10, 0.11 (0.06, 0.16) for NO2, and 0.13 (0.08, 0.18) for NOx). CONCLUSION Exposure to air pollution was significantly related to an increased CAD risk, which could be further strengthened by CAD gene susceptibility. Additionally, there were positive additive interactions between genetic factors and air pollution on the onset of CAD. This can provide a more comprehensive, precise and individualized scientific basis for the risk assessment, prevention and control of CAD.
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Affiliation(s)
- Zuqiang Fu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
- School of Public Health, Southeast University, 101 Longmian Avenue, Nanjing, 211166, China
| | - Yuanyuan Ma
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Changjie Yang
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Qian Liu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jingjia Liang
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhenkun Weng
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Wenxiang Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Shijie Zhou
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Xiu Chen
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jin Xu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China
- Department of Maternal, Child, and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Cheng Xu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China.
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China.
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China.
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
| | - Yong Zhou
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, No. 320 Yueyang Road, Shanghai, 200031, China.
| | - Aihua Gu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China.
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China.
- Department of Toxicology, Center for Global Health, Nanjing Medical University, Nanjing, China.
- School of Public Health, Southeast University, 101 Longmian Avenue, Nanjing, 211166, China.
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24
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Paetznick C, Okoro O. The Intersection between Pharmacogenomics and Health Equity: A Case Example. PHARMACY 2023; 11:186. [PMID: 38133461 PMCID: PMC10747429 DOI: 10.3390/pharmacy11060186] [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: 09/01/2023] [Revised: 10/25/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Pharmacogenomics (PGx) and the study of precision medicine has substantial power to either uplift health equity efforts or further widen the gap of our already existing health disparities. In either occurrence, the medication experience plays an integral role within this intersection on an individual and population level. Examples of this intertwined web are highlighted through a case discussion. With these perspectives in mind, several recommendations for the research and clinical communities are highlighted to promote equitable healthcare with PGx integrated.
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Affiliation(s)
| | - Olihe Okoro
- Department of Pharmacy Practice and Pharmaceutical Sciences, College of Pharmacy, University of Minnesota, Duluth, MN 55812, USA
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25
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Tallman S, Sungo MDD, Saranga S, Beleza S. Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations. Nat Commun 2023; 14:7967. [PMID: 38042927 PMCID: PMC10693643 DOI: 10.1038/s41467-023-43717-x] [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: 03/10/2022] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
As the continent of origin for our species, Africa harbours the highest levels of diversity anywhere on Earth. However, many regions of Africa remain under-sampled genetically. Here we present 350 whole genomes from Angola and Mozambique belonging to ten Bantu ethnolinguistic groups, enabling the construction of a reference variation catalogue including 2.9 million novel SNPs. We investigate the emergence of Bantu speaker population structure, admixture involving migrations across sub-Saharan Africa and model the demographic histories of Angolan and Mozambican Bantu speakers. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion. Moreover, we generate reference panels that better represents the diversity of African populations involved in the trans-Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians. We anticipate that our collection of genomes will form the foundation for future African genomic healthcare initiatives.
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Affiliation(s)
- Sam Tallman
- University of Leicester, Department of Genetics & Genome Biology, University Road, Leicester, LE1 7RH, UK
- Genomics England, 1 Canada Square, London, E14 5AB, UK
| | | | - Sílvio Saranga
- Universidade Pedagógica, Avenida Eduardo Mondlane, CP 2107, Maputo, Mozambique
| | - Sandra Beleza
- University of Leicester, Department of Genetics & Genome Biology, University Road, Leicester, LE1 7RH, UK.
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26
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Shapira G, Volkov H, Fabian I, Mohr DW, Bettinotti M, Shomron N, Avery RK, Arav-Boger R. Genomic Markers Associated with Cytomegalovirus DNAemia in Kidney Transplant Recipients. Viruses 2023; 15:2227. [PMID: 38005904 PMCID: PMC10674338 DOI: 10.3390/v15112227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/29/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023] Open
Abstract
Human cytomegalovirus (CMV) is a major pathogen after solid organ transplantation, leading to high morbidity and mortality. Transplantation from a CMV-seropositive donor to a CMV-seronegative recipient (D+/R-) is associated with high risk of CMV disease. However, that risk is not uniform, suggesting a role for host factors in immune control of CMV. To identify host genetic factors that control CMV DNAemia post transplantation, we performed a whole-exome association study in two cohorts of D+/R- kidney transplant recipients. Quantitative CMV DNA was measured for at least one year following transplantation. Several CMV-protective single-nucleotide polymorphisms (SNPs) were identified in the first cohort (72 patients) but were not reproducible in the second cohort (126 patients). A meta-analysis of both cohorts revealed several SNPs that were significantly associated with protection from CMV DNAemia. The copy number variation of several genes was significantly different between recipients with and without CMV DNAemia. Amongst patients with CMV DNAemia in the second cohort, several variants of interest (p < 5 × 10-5), the most common of which was NLRC5, were associated with peak viral load. We provide new predictive genetic markers for protection of CMV DNAemia. These markers should be validated in larger cohorts.
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Affiliation(s)
- Guy Shapira
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; (G.S.)
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Hadas Volkov
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; (G.S.)
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Itai Fabian
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; (G.S.)
| | - David W. Mohr
- Johns Hopkins Genetic Resources Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Maria Bettinotti
- Immunogenetics Laboratory, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA;
| | - Noam Shomron
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; (G.S.)
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Robin K. Avery
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA;
| | - Ravit Arav-Boger
- Department of Pediatrics, Division of Infectious Diseases, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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27
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DeGorter MK, Goddard PC, Karakoc E, Kundu S, Yan SM, Nachun D, Abell N, Aguirre M, Carstensen T, Chen Z, Durrant M, Dwaracherla VR, Feng K, Gloudemans MJ, Hunter N, Moorthy MPS, Pomilla C, Rodrigues KB, Smith CJ, Smith KS, Ungar RA, Balliu B, Fellay J, Flicek P, McLaren PJ, Henn B, McCoy RC, Sugden L, Kundaje A, Sandhu MS, Gurdasani D, Montgomery SB. Transcriptomics and chromatin accessibility in multiple African population samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.564839. [PMID: 37986808 PMCID: PMC10659267 DOI: 10.1101/2023.11.04.564839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.
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Affiliation(s)
| | - Page C Goddard
- Department of Genetics, Stanford University, Stanford, CA
| | - Emre Karakoc
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Soumya Kundu
- Department of Computer Science, Stanford University, Stanford CA
| | | | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA
| | - Nathan Abell
- Department of Genetics, Stanford University, Stanford, CA
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | - Tommy Carstensen
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Ziwei Chen
- Department of Computer Science, Stanford University, Stanford CA
| | | | | | - Karen Feng
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | | | - Naiomi Hunter
- Department of Genetics, Stanford University, Stanford, CA
| | | | - Cristina Pomilla
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | | | | | - Kevin S Smith
- Department of Pathology, Stanford University, Stanford, CA
| | - Rachel A Ungar
- Department of Genetics, Stanford University, Stanford, CA
| | - Brunilda Balliu
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA and Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA
| | - Jacques Fellay
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland and Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Paul Flicek
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Paul J McLaren
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Canada and Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
| | - Brenna Henn
- Department of Anthropology, University of California Davis, Davis CA and Genome Center, University of California Davis, Davis CA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore
| | - Lauren Sugden
- Department of Mathematics and Computer Science, Dusquesne University, Pittsburgh, PA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA
- Department of Computer Science, Stanford University, Stanford CA
| | | | - Deepti Gurdasani
- William Harvey Research Institute, Queen Mary University of London, London, UK; Kirby Institute, University of New South Wales, Australia; School of Medicine, University of Western Australia, Australia
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28
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Tanigawa Y, Kellis M. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet 2023; 110:1888-1902. [PMID: 37890495 PMCID: PMC10645553 DOI: 10.1016/j.ajhg.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.
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Affiliation(s)
- Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Benjamin KJM, Chen Q, Eagles NJ, Huuki-Myers LA, Collado-Torres L, Stolz JM, Pertea G, Shin JH, Paquola ACM, Hyde TM, Kleinman JE, Jaffe AE, Han S, Weinberger DR. Genetic and environmental contributions to ancestry differences in gene expression in the human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534458. [PMID: 37034760 PMCID: PMC10081196 DOI: 10.1101/2023.03.28.534458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | | | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Astarita G, Kelly RS, Lasky-Su J. Metabolomics and lipidomics strategies in modern drug discovery and development. Drug Discov Today 2023; 28:103751. [PMID: 37640150 PMCID: PMC10543515 DOI: 10.1016/j.drudis.2023.103751] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.
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Affiliation(s)
- Giuseppe Astarita
- Georgetown University, Washington, DC, USA; Arkuda Therapeutics, Watertown, MA, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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31
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Galimova E, Rätsep R, Traks T, Chernov A, Gaysina D, Kingo K, Kõks S. Polymorphisms in corticotrophin-releasing hormone-proopiomalanocortin (CRH-POMC) system genes: Neuroimmune contributions to psoriasis disease. J Eur Acad Dermatol Venereol 2023; 37:2028-2040. [PMID: 37319102 DOI: 10.1111/jdv.19257] [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: 01/07/2023] [Accepted: 05/03/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Skin is a target organ and source of the corticotropin-releasing hormone-proopiomelanocortin (CRH-POMC) system, operating as a coordinator and executor of responses to stress. Environmental stress exacerbates and triggers inflammatory skin diseases through modifying the cellular components of the immune system supporting the importance of CRH-POMC system in the pathogenesis of psoriasis. The aim of this study was to analyse the association of CRH-POMC polymorphisms with psoriasis and evaluate transcript expression of lesional psoriatic and normal skin in RNA-seq data. METHODS Samples of 104 patients with psoriasis and 174 healthy controls were genotyped for 42 single nucleotide polymorphisms (SNPs) of CRH-POMC using Applied Biosystems SNPlex™ method. The transcript quantification was performed using Salmon software v1.3.0. RESULTS This study demonstrated the associations between melanocortin 1 receptor (MC1R) polymorphisms rs2228479, rs3212369, dopachrome tautomerase (DCT) polymorphisms rs7987802, rs2031526, rs9524501 and psoriasis in the Tatar population. Very strong association was evident for the SNP rs7987802 in the DCT gene (pc = 5.95е-006) in psoriasis patients. Additionally, the haplotype analysis provided AT DCT (rs7992630 and rs7987802) and AGA MC1R (rs3212358, 2228479 and 885479) haplotypes significantly associated (pc ˂ 0.05) with psoriasis in the Tatar population, supporting the involvement of DCT and MC1R to the psoriasis susceptibility. Moreover, MC1R-203 and DCT-201 expression levels were decreased in psoriasis lesional skin compared with healthy control skin. CONCLUSIONS This study is the first to identify genetic variants of the MC1R and DCT genes significantly associated with psoriasis in Tatar population. Our results support potential roles of CRH-POMC system genes and DCT in the pathogenesis of psoriasis.
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Affiliation(s)
- Elvira Galimova
- Department of Physiology, University of Tartu, Tartu, Estonia
| | - Ranno Rätsep
- Department of Physiology, University of Tartu, Tartu, Estonia
| | - Tanel Traks
- Department of Dermatology and Venereology, University of Tartu, Tartu, Estonia
| | - Alexandr Chernov
- Department of Life Sciences, Ben-Gurion University, Beer Sheva, Israel
| | - Darya Gaysina
- School of Psychology, University of Sussex, Brighton, UK
| | - Külli Kingo
- Department of Dermatology and Venereology, University of Tartu, Tartu, Estonia
| | - Sulev Kõks
- Perron Institute for Neurological and Translational Science, University of Western Australia, Perth, Western Australia, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, Western Australia, Australia
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32
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Yang X, Kar S, Antoniou AC, Pharoah PDP. Polygenic scores in cancer. Nat Rev Cancer 2023; 23:619-630. [PMID: 37479830 DOI: 10.1038/s41568-023-00599-x] [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] [Accepted: 06/12/2023] [Indexed: 07/23/2023]
Abstract
Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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33
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Montano MA. Emerging Life Sciences Series: Q & A with the Editor: Genetics and Metabolism. Adv Biol (Weinh) 2023; 7:e2300160. [PMID: 37697709 DOI: 10.1002/adbi.202300160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
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34
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Gao Y, Sharma T, Cui Y. Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective. Annu Rev Biomed Data Sci 2023; 6:153-171. [PMID: 37104653 PMCID: PMC10529864 DOI: 10.1146/annurev-biodatasci-020722-020704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Teena Sharma
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Yan Cui
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
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35
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Latapiat V, Saez M, Pedroso I, Martin AJM. Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective. Front Genet 2023; 14:1209416. [PMID: 37636264 PMCID: PMC10449456 DOI: 10.3389/fgene.2023.1209416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.
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Affiliation(s)
- Verónica Latapiat
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
| | - Mauricio Saez
- Centro de Oncología de Precisión, Facultad de Medicina y Ciencias de la Salud, Universidad Mayor, Santiago, Chile
- Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco, Chile
| | - Inti Pedroso
- Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
| | - Alberto J. M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
- Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile
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36
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Trajanoska K, Bhérer C, Taliun D, Zhou S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature 2023; 620:737-745. [PMID: 37612393 DOI: 10.1038/s41586-023-06388-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/29/2023] [Indexed: 08/25/2023]
Abstract
The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.
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Affiliation(s)
- Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Claude Bhérer
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada.
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37
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McLaren PJ, Porreca I, Iaconis G, Mok HP, Mukhopadhyay S, Karakoc E, Cristinelli S, Pomilla C, Bartha I, Thorball CW, Tough RH, Angelino P, Kiar CS, Carstensen T, Fatumo S, Porter T, Jarvis I, Skarnes WC, Bassett A, DeGorter MK, Sathya Moorthy MP, Tuff JF, Kim EY, Walter M, Simons LM, Bashirova A, Buchbinder S, Carrington M, Cossarizza A, De Luca A, Goedert JJ, Goldstein DB, Haas DW, Herbeck JT, Johnson EO, Kaleebu P, Kilembe W, Kirk GD, Kootstra NA, Kral AH, Lambotte O, Luo M, Mallal S, Martinez-Picado J, Meyer L, Miro JM, Moodley P, Motala AA, Mullins JI, Nam K, Obel N, Pirie F, Plummer FA, Poli G, Price MA, Rauch A, Theodorou I, Trkola A, Walker BD, Winkler CA, Zagury JF, Montgomery SB, Ciuffi A, Hultquist JF, Wolinsky SM, Dougan G, Lever AML, Gurdasani D, Groom H, Sandhu MS, Fellay J. Africa-specific human genetic variation near CHD1L associates with HIV-1 load. Nature 2023; 620:1025-1030. [PMID: 37532928 PMCID: PMC10848312 DOI: 10.1038/s41586-023-06370-4] [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: 11/28/2018] [Accepted: 06/26/2023] [Indexed: 08/04/2023]
Abstract
HIV-1 remains a global health crisis1, highlighting the need to identify new targets for therapies. Here, given the disproportionate HIV-1 burden and marked human genome diversity in Africa2, we assessed the genetic determinants of control of set-point viral load in 3,879 people of African ancestries living with HIV-1 participating in the international collaboration for the genomics of HIV3. We identify a previously undescribed association signal on chromosome 1 where the peak variant associates with an approximately 0.3 log10-transformed copies per ml lower set-point viral load per minor allele copy and is specific to populations of African descent. The top associated variant is intergenic and lies between a long intergenic non-coding RNA (LINC00624) and the coding gene CHD1L, which encodes a helicase that is involved in DNA repair4. Infection assays in iPS cell-derived macrophages and other immortalized cell lines showed increased HIV-1 replication in CHD1L-knockdown and CHD1L-knockout cells. We provide evidence from population genetic studies that Africa-specific genetic variation near CHD1L associates with HIV replication in vivo. Although experimental studies suggest that CHD1L is able to limit HIV infection in some cell types in vitro, further investigation is required to understand the mechanisms underlying our observations, including any potential indirect effects of CHD1L on HIV spread in vivo that our cell-based assays cannot recapitulate.
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Affiliation(s)
- Paul J McLaren
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada.
| | | | - Gennaro Iaconis
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Hoi Ping Mok
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Subhankar Mukhopadhyay
- Peter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King's College London, London, UK
| | | | - Sara Cristinelli
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - István Bartha
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christian W Thorball
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Riley H Tough
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Paolo Angelino
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Cher S Kiar
- Peter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Tommy Carstensen
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
- Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Isobel Jarvis
- Department of Medicine, University of Cambridge, Cambridge, UK
| | | | | | - Marianne K DeGorter
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mohana Prasad Sathya Moorthy
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey F Tuff
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Eun-Young Kim
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Miriam Walter
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lacy M Simons
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Arman Bashirova
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Susan Buchbinder
- Bridge HIV, San Francisco Department of Public Health, San Francisco, CA, USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Andrea De Luca
- University Division of Infectious Diseases, Siena University Hospital, Siena, Italy
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - James J Goedert
- Epidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University, New York, NY, USA
| | - David W Haas
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Joshua T Herbeck
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center and Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Pontiano Kaleebu
- Medical Research Council/Uganda Virus Research Institute & London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Gregory D Kirk
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Neeltje A Kootstra
- Department of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Alex H Kral
- Community Health Research Division, RTI International, Berkeley, CA, USA
| | - Olivier Lambotte
- Université Paris Saclay, Inserm UMR1184, CEA, Le Kremlin-Bicêtre, France
- APHP, Department of Clinical Immunology, Bicêtre Hospital, Le Kremlin-Bicêtre, France
| | - Ma Luo
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
- Vaccine and Therapeutics Laboratory, Medical and Scientific Affairs, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Simon Mallal
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Institute for Immunology & Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Javier Martinez-Picado
- University of Vic-Central University of Catalonia, Vic, Spain
- IrsiCaixa AIDS Research Institute, Badalona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Laurence Meyer
- INSERM U1018, Université Paris-Saclay, Le Kremlin Bicêtre, France
- AP-HP, Hôpital de Bicêtre, Département d'Épidémiologie, Le Kremlin Bicêtre, France
| | - José M Miro
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
- Infectious Diseases Service, Hospital Clinic-Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Pravi Moodley
- National Health Laboratory Service, South Africa and University of KwaZulu-Natal, Durban, South Africa
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - James I Mullins
- Department of Microbiology, University of Washington, Seattle, WA, USA
| | - Kireem Nam
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Niels Obel
- Department of Infectious Diseases, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Francis A Plummer
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Guido Poli
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Matthew A Price
- International AIDS Vaccine Initiative, New York, NY, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Andri Rauch
- Department of Infectious Diseases, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ioannis Theodorou
- Laboratoire d'Immunologie, Hôpital Robert Debré Paris, Paris, France
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Bruce D Walker
- Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Cheryl A Winkler
- Basic Research Laboratory, Molecular Genetic Epidemiology Section, Frederick National Laboratory for Cancer Research and Cancer Innovative Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Jean-François Zagury
- Laboratoire Génomique, Bioinformatique et Chimie Moléculaire, EA7528, Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Angela Ciuffi
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Judd F Hultquist
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Steven M Wolinsky
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gordon Dougan
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Andrew M L Lever
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Medicine, National University of Singapore, Singapore, Singapore
| | - Deepti Gurdasani
- Queen Mary University of London, London, UK
- Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Harriet Groom
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Manjinder S Sandhu
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
- Omnigen Biodata, Cambridge, UK.
| | - Jacques Fellay
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
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Ciochetti NP, Lugli-Moraes B, da Silva BS, Rovaris DL. Genome-wide association studies: utility and limitations for research in physiology. J Physiol 2023; 601:2771-2799. [PMID: 37208942 PMCID: PMC10527550 DOI: 10.1113/jp284241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023] Open
Abstract
Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.
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Affiliation(s)
- Nicolas Pereira Ciochetti
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Beatriz Lugli-Moraes
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Bruna Santos da Silva
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
| | - Diego Luiz Rovaris
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
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Xu W, Mesa-Eguiagaray I, Kirkpatrick T, Devlin J, Brogan S, Turner P, Macdonald C, Thornton M, Zhang X, He Y, Li X, Timofeeva M, Farrington S, Din F, Dunlop M, Theodoratou E. Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms. J Pers Med 2023; 13:1065. [PMID: 37511678 PMCID: PMC10381199 DOI: 10.3390/jpm13071065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.
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Affiliation(s)
- Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Ines Mesa-Eguiagaray
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Theresa Kirkpatrick
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Jennifer Devlin
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Stephanie Brogan
- Clinical Research Team, Oncology Department, Forth Valley Royal Hospital, Stirling Road, Larbert FK5 4WR, UK
| | - Patricia Turner
- Clinical Research Team, Oncology Department, Forth Valley Royal Hospital, Stirling Road, Larbert FK5 4WR, UK
| | - Chloe Macdonald
- University Hospital Wishaw & University Hospital Monklands, NHS Lanarkshire, Airdrie ML6 0JS, UK
| | | | - Xiaomeng Zhang
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Yazhou He
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Xue Li
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Maria Timofeeva
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Danish Institute for Advanced Study, Research Unit of Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, 5230 Odense M, Denmark
| | - Susan Farrington
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Farhat Din
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Malcolm Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
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Bebo A, Jarmul JA, Pletcher MJ, Hasbani NR, Couper D, Nambi V, Ballantyne CM, Fornage M, Morrison AC, Avery CL, de Vries PS. Coronary heart disease and ischemic stroke polygenic risk scores and atherosclerotic cardiovascular disease in a diverse, population-based cohort study. PLoS One 2023; 18:e0285259. [PMID: 37327218 PMCID: PMC10275447 DOI: 10.1371/journal.pone.0285259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 04/18/2023] [Indexed: 06/18/2023] Open
Abstract
The predictive ability of coronary heart disease (CHD) and ischemic stroke (IS) polygenic risk scores (PRS) have been evaluated individually, but whether they predict the combined outcome of atherosclerotic cardiovascular disease (ASCVD) remains insufficiently researched. It is also unclear whether associations of the CHD and IS PRS with ASCVD are independent of subclinical atherosclerosis measures. 7,286 White and 2,016 Black participants from the population-based Atherosclerosis Risk in Communities study who were free of cardiovascular disease and type 2 diabetes at baseline were included. We computed previously validated CHD and IS PRS consisting of 1,745,179 and 3,225,583 genetic variants, respectively. Cox proportional hazards models were used to test the association between each PRS and ASCVD, adjusting for traditional risk factors, ankle-brachial index, carotid intima media thickness, and carotid plaque. The hazard ratios (HR) for the CHD and IS PRS were significant with HR of 1.50 (95% CI: 1.36-1.66) and 1.31 (95% CI: 1.18-1.45) respectively for the risk of incident ASCVD per standard deviation increase in CHD and IS PRS among White participants after adjusting for traditional risk factors. The HR for the CHD PRS was not significant with an HR of 0.95 (95% CI: 0.79-1.13) for the risk of incident ASCVD in Black participants. The HR for the IS PRS was significant with an HR of 1.26 (95%CI: 1.05-1.51) for the risk of incident ASCVD in Black participants. The association of the CHD and IS PRS with ASCVD was not attenuated in White participants after adjustment for ankle-brachial index, carotid intima media thickness, and carotid plaque. The CHD and IS PRS do not cross-predict well, and predict better the outcome for which they were created than the composite ASCVD outcome. Thus, the use of the composite outcome of ASCVD may not be ideal for genetic risk prediction.
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Affiliation(s)
- Allison Bebo
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Jamie A. Jarmul
- Gillings School of Public Health, Department of Health Policy and Management, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
- School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
| | - Mark J. Pletcher
- Departments of Epidemiology and Biostatistics and Medicine, University of California, San Francisco, San Francisco, CA, United States of America
| | - Natalie R. Hasbani
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - David Couper
- Gillings School of Public Health, Department of Biostatistics, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
- Collaborative Studies Coordinating Center, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
| | - Vijay Nambi
- Baylor College of Medicine, Houston, TX, United States of America
- Michael E DeBakey Veterans Affairs Medical Center, Houston, TX, United States of America
| | | | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
- McGovern Medical School Institute of Molecular Medicine Research Center for Human Genetics, Houston, TX, United States of America
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Christy L. Avery
- Gillings School of Public Health, Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
- Carolina Population Center, University of North Carolina–Chapel Hill, Chapel Hill, NC, United States of America
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
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Qiao J, Wu Y, Zhang S, Xu Y, Zhang J, Zeng P, Wang T. Evaluating significance of European-associated index SNPs in the East Asian population for 31 complex phenotypes. BMC Genomics 2023; 24:324. [PMID: 37312035 DOI: 10.1186/s12864-023-09425-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. RESULTS By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. CONCLUSIONS Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.
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Affiliation(s)
- Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yue Xu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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42
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Smith JL, Tcheandjieu C, Dikilitas O, lyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao P, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290896. [PMID: 37609230 PMCID: PMC10441485 DOI: 10.1101/2023.06.02.23290896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.
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Affiliation(s)
- Johanna L. Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kruthika lyer
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kazuo Miyazawa
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Austin Hilliard
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Julie Lynch
- Salt Lake City VA Met CTR., Salt Lake City, UT, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Ctr. Philadelphia, PA, USA
| | | | - Phil Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Kaoru Ito
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Shoa L. Clarke
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Daniel J. Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Liu J, Wang L, Cui X, Shen Q, Wu D, Yang M, Dong Y, Liu Y, Chen H, Yang Z, Liu Y, Zhu M, Ma H, Jin G, Qian Y. Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study. Nutrients 2023; 15:2144. [PMID: 37432247 DOI: 10.3390/nu15092144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 07/12/2023] Open
Abstract
The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.
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Affiliation(s)
- Jia Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Xuan Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qian Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Dun Wu
- College of Arts and Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Man Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yunqiu Dong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yongchao Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Hai Chen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Zhijie Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yaqi Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yun Qian
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
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Tariq Z, Qadeer MI, Anjum I, Hano C, Anjum S. Thalassemia and Nanotheragnostics: Advanced Approaches for Diagnosis and Treatment. BIOSENSORS 2023; 13:bios13040450. [PMID: 37185525 PMCID: PMC10136341 DOI: 10.3390/bios13040450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023]
Abstract
Thalassemia is a monogenic autosomal recessive disorder caused by mutations, which lead to abnormal or reduced production of hemoglobin. Ineffective erythropoiesis, hemolysis, hepcidin suppression, and iron overload are common manifestations that vary according to genotypes and dictate, which diagnosis and therapeutic modalities, including transfusion therapy, iron chelation therapy, HbF induction, gene therapy, and editing, are performed. These conventional therapeutic methods have proven to be effective, yet have several disadvantages, specifically iron toxicity, associated with them; therefore, there are demands for advanced therapeutic methods. Nanotechnology-based applications, such as the use of nanoparticles and nanomedicines for theragnostic purposes have emerged that are simple, convenient, and cost-effective methods. The therapeutic potential of various nanoparticles has been explored by developing artificial hemoglobin, nano-based iron chelating agents, and nanocarriers for globin gene editing by CRISPR/Cas9. Au, Ag, carbon, graphene, silicon, porous nanoparticles, dendrimers, hydrogels, quantum dots, etc., have been used in electrochemical biosensors development for diagnosis of thalassemia, quantification of hemoglobin in these patients, and analysis of conventional iron chelating agents. This review summarizes the potential of nanotechnology in the development of various theragnostic approaches to determine thalassemia-causing gene mutations using various nano-based biosensors along with the employment of efficacious nano-based therapeutic procedures, in contrast to conventional therapies.
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Affiliation(s)
- Zahra Tariq
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
| | | | - Iram Anjum
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
| | - Christophe Hano
- Department of Chemical Biology, Eure & Loir Campus, University of Orleans, 28000 Chartres, France
| | - Sumaira Anjum
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
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45
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Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 2023; 55:549-558. [PMID: 36941441 PMCID: PMC11120833 DOI: 10.1038/s41588-023-01338-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Rachel Mester
- Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - David V Conti
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcu F Darst
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle Kim
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Kari E North
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ulrike Peters
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Zhou
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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46
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Dron JS. The clinical utility of polygenic risk scores for combined hyperlipidemia. Curr Opin Lipidol 2023; 34:44-51. [PMID: 36602940 DOI: 10.1097/mol.0000000000000865] [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] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Combined hyperlipidemia is the most common lipid disorder and is strongly polygenic. Given its prevalence and associated risk for atherosclerotic cardiovascular disease, this review describes the potential for utilizing polygenic risk scores for risk prediction and management of combined hyperlipidemia. RECENT FINDINGS Different diagnostic criteria have led to inconsistent prevalence estimates and missed diagnoses. Given that individuals with combined hyperlipidemia have risk estimates for incident coronary artery disease similar to individuals with familial hypercholesterolemia, early identification and therapeutic management of those affected is crucial. With diagnostic criteria including traits such apolipoprotein B, low-density lipoprotein cholesterol, and triglyceride, polygenic risk scores for these traits strongly associate with combined hyperlipidemia and could be used in combination for clinical risk prediction models and developing specific treatment plans for patients. SUMMARY Polygenic risk scores are effective tools in risk prediction of combined hyperlipidemia, can provide insight into disease pathophysiology, and may be useful in managing and guiding treatment plans for patients. However, efforts to ensure equitable polygenic risk score performance across different genetic ancestry groups is necessary before clinical implementation in order to prevent the exacerbation of racial disparities in the clinic.
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Affiliation(s)
- Jacqueline S Dron
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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47
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Carlberg C. Nutrigenomics in the context of evolution. Redox Biol 2023; 62:102656. [PMID: 36933390 PMCID: PMC10036735 DOI: 10.1016/j.redox.2023.102656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/13/2023] Open
Abstract
Nutrigenomics describes the interaction between nutrients and our genome. Since the origin of our species most of these nutrient-gene communication pathways have not changed. However, our genome experienced over the past 50,000 years a number of evolutionary pressures, which are based on the migration to new environments concerning geography and climate, the transition from hunter-gatherers to farmers including the zoonotic transfer of many pathogenic microbes and the rather recent change of societies to a preferentially sedentary lifestyle and the dominance of Western diet. Human populations responded to these challenges not only by specific anthropometric adaptations, such as skin color and body stature, but also through diversity in dietary intake and different resistance to complex diseases like the metabolic syndrome, cancer and immune disorders. The genetic basis of this adaptation process has been investigated by whole genome genotyping and sequencing including that of DNA extracted from ancient bones. In addition to genomic changes, also the programming of epigenomes in pre- and postnatal phases of life has an important contribution to the response to environmental changes. Thus, insight into the variation of our (epi)genome in the context of our individual's risk for developing complex diseases, helps to understand the evolutionary basis how and why we become ill. This review will discuss the relation of diet, modern environment and our (epi)genome including aspects of redox biology. This has numerous implications for the interpretation of the risks for disease and their prevention.
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Affiliation(s)
- Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, ul. Juliana Tuwima 10, PL-10748, Olsztyn, Poland; School of Medicine, Institute of Biomedicine, University of Eastern Finland, FI-70211, Kuopio, Finland.
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48
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Juárez-Luis J, Canseco-Ocaña M, Cid-Soto MA, Castro-Martínez XH, Martínez-Hernández A, Orozco L, Hernández-Zavala A, Córdova EJ. Single nucleotide variants in microRNA biosynthesis genes in Mexican individuals. Front Genet 2023; 14:1022912. [PMID: 36968598 PMCID: PMC10037310 DOI: 10.3389/fgene.2023.1022912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/02/2023] [Indexed: 03/08/2023] Open
Abstract
Background: MicroRNAs (miRNAs) are important regulators in a variety of biological processes, and their dysregulation is associated with multiple human diseases. Single nucleotide variants (SNVs) in genes involved in the processing of microRNAs may alter miRNA regulation and could present high allele heterogeneity in populations from different ethnic groups. Thus, the aim of this study was to genotype 15 SNVs in eight genes involved in the miRNA processing pathway in Mexican individuals and compare their frequencies across 21 populations from five continental groups.Methods: Genomic DNA was obtained from 399 healthy Mexican individuals. SNVs in AGO2 (rs2293939 and rs4961280), DGCR8 (rs720012), DICER (rs3742330 and rs13078), DROSHA (rs10719 and rs6877842), GEMIN3 (rs197388 and rs197414), GEMIN4 (rs7813, rs2740349, and rs4968104), TNRC6B (rs9611280), and XP05 (rs11077 and rs34324334) were genotyped using TaqMan probes. The minor allele frequency of each SNV was compared to those reported in the 1,000 Genomes database using chi-squared. Sankey plot was created in the SankeyMATIC package to visualize the frequency range of each variant in the different countries analyzed.Results: In Mexican individuals, all 15 SNVs were found in Hardy-Weinberg equilibrium, with frequencies ranging from 0.04 to 0.45. The SNVs rs4961280, rs2740349, rs34324334, and rs720012 in Mexican individuals had the highest minor allele frequencies worldwide, whereas the minor allele frequencies of rs197388, rs10719, rs197414, and rs1107 were among the lowest in Mexican individuals. The variants had high allele heterogeneity among the sub-continental populations, ranging from monomorphic, as was the case for rs9611280 and rs34324334 in African groups, to >0.50, which was the case for variants rs11077 and rs10719 in most of the populations. Importantly, the variants rs197388, rs720012, and rs197414 had FST values > 0.18, indicating a directional selective process. Finally, the SNVs rs13078 and rs10719 significantly correlated with both latitude and longitude.Conclusion: These data indicate the presence of high allelic heterogeneity in the worldwide distribution of the frequency of SNVs located in components of the miRNA processing pathway, which could modify the genetic susceptibility associated with human diseases in populations with different ancestry.
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Affiliation(s)
- Jesús Juárez-Luis
- Section of Research and Postgraduate, Superior School of Medicine, National Institute Polytechnique, Mexico City, Mexico
| | - Moisés Canseco-Ocaña
- Oncogenomics Consortium Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Miguel Angel Cid-Soto
- Oncogenomics Consortium Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Xochitl H. Castro-Martínez
- Genomics of Psychiatric and Neurogenerative diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Angélica Martínez-Hernández
- Immunogenomics and Metabolic diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Lorena Orozco
- Immunogenomics and Metabolic diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Araceli Hernández-Zavala
- Section of Research and Postgraduate, Superior School of Medicine, National Institute Polytechnique, Mexico City, Mexico
| | - Emilio J. Córdova
- Oncogenomics Consortium Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
- *Correspondence: Emilio J. Córdova,
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49
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Liu J, Cerutti J, Lussier AA, Zhu Y, Smith BJ, Smith ADAC, Dunn EC. Socioeconomic changes predict genome-wide DNA methylation in childhood. Hum Mol Genet 2023; 32:709-719. [PMID: 35899434 PMCID: PMC10365844 DOI: 10.1093/hmg/ddac171] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/01/2022] [Accepted: 07/22/2022] [Indexed: 01/25/2023] Open
Abstract
Childhood socioeconomic position (SEP) is a major determinant of health and well-being across the entire life course. To effectively prevent and reduce health risks related to SEP, it is critical to better understand when and under what circumstances socioeconomic adversity shapes biological processes. DNA methylation (DNAm) is one such mechanism for how early life adversity 'gets under the skin'. In this study, we evaluated the dynamic relationship between SEP and DNAm across childhood using data from 946 mother-child pairs in the Avon Longitudinal Study of Parents and Children. We assessed six SEP indicators spanning financial, occupational and residential domains during very early childhood (ages 0-2), early childhood (ages 3-5) and middle childhood (ages 6-7). Epigenome-wide DNAm was measured at 412 956 cytosine-guanines (CpGs) from peripheral blood at age 7. Using an innovative two-stage structured life-course modeling approach, we tested three life-course hypotheses for how SEP shapes DNAm profiles-accumulation, sensitive period and mobility. We showed that changes in the socioeconomic environment were associated with the greatest differences in DNAm, and that middle childhood may be a potential sensitive period when socioeconomic instability is especially important in shaping DNAm. Top SEP-related DNAm CpGs were overrepresented in genes involved in pathways important for neural development, immune function and metabolic processes. Our findings highlight the importance of socioeconomic stability during childhood and if replicated, may emphasize the need for public programs to help children and families experiencing socioeconomic instability and other forms of socioeconomic adversity.
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Affiliation(s)
- Jiaxuan Liu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Janine Cerutti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Alexandre A Lussier
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yiwen Zhu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Brooke J Smith
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Andrew D A C Smith
- Mathematics and Statistics Research Group, University of the West of England, Bristol BS8 1QU, UK
| | - Erin C Dunn
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Center on the Developing Child, Harvard University, Cambridge, MA 02138, USA
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50
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Cui Q, Liu Z, Li J, Liu F, Niu X, Shen C, Hu D, Huang K, Chen S, Zhao Y, Lu F, Liu X, Cao J, Wang L, Ma H, Yu L, Wu X, Li Y, Zhang H, Mo X, Zhao L, Hu Z, Shen H, Huang J, Lu X, Gu D. Impact of cardiovascular health and genetic risk on coronary artery disease in Chinese adults. Heart 2023; 109:756-762. [PMID: 36539268 DOI: 10.1136/heartjnl-2022-321657] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVE To examine whether adherence to ideal cardiovascular health (CVH) can mitigate the genetic risk of coronary artery disease (CAD) in non-European populations. METHODS Fine and Grey's models were used to calculate HRs and their corresponding 95% CIs, as well as the lifetime risk of CVH metrics across Polygenic Risk Score (PRS) categories. RESULTS We included 39 755 individuals aged 30-75 years in Chinese prospective cohorts. 1275 CAD cases were recorded over a mean follow-up of 12.9 years. Compared with unfavourable CVH profile (zero to three ideal CVH metrics), favourable CVH profile (six to seven ideal CVH metrics) demonstrated similar relative effects across PRS categories, with the HRs of 0.40 (95% CI 0.24 to 0.67), 0.41 (95% CI 0.32 to 0.52) and 0.36 (95% CI 0.26 to 0.52) in low (bottom quintile of PRS), intermediate (two to four quintiles of PRS) and high (top quintile of PRS) PRS categories, respectively. For the absolute risk reduction (ARR), individuals with high PRS achieved the greatest benefit from favourable CVH, mitigating the risk to the average level of population (from 21.1% to 8.7%), and the gradient was strengthened in individuals at the top 5% of PRS. Moreover, compared with individuals at low PRS, those at high PRS obtained longer CAD-free years (2.6 vs 1.1) from favourable CVH at the index age of 35 years. CONCLUSION Favourable CVH profile reduced the CAD relative risk by similar magnitude across PRS categories, while the ARR from favourable CVH was most pronounced in high PRS category. Attaining favourable CVH should be encouraged for all individuals, especially in individuals with high genetic susceptibility.
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Affiliation(s)
- Qingmei Cui
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhongying Liu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fangchao Liu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoge Niu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Nephrology, Henan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Chong Shen
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.,Department of Prevention Medicine, Shenzhen University College of Medicine, Shenzhen, Guangdong, China
| | - Keyong Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shufeng Chen
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yingxin Zhao
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Fanghong Lu
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Xiaoqing Liu
- Division of Epidemiology, Guangdong Provincial People's Hospital Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
| | - Jie Cao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Laiyuan Wang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ling Yu
- Department of Cardiology, Fujian Provincial People's Hospital, Fuzhou, Fujian, China
| | - Xianping Wu
- Department of Chronic and Non-communicable Disease Control and Prevention, Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | - Ying Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huan Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University Medical College, Suzhou, Jiangsu, China
| | - Xingbo Mo
- Center for Genetic Epidemiology and Genomics, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University Medical College, Suzhou, Jiangsu, China
| | - Liancheng Zhao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
| | - Jianfeng Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China .,Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Dongfeng Gu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China .,Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China
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