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Li X, Zhou Z, Ma Y, Ding K, Xiao H, Chen D, Liu N. Shared Genetic Architectures between Coronary Artery Disease and Type 2 Diabetes Mellitus in East Asian and European Populations. Biomedicines 2024; 12:1243. [PMID: 38927450 PMCID: PMC11201280 DOI: 10.3390/biomedicines12061243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.
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Affiliation(s)
- Xiaoyi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Zechen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Kexin Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Han Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Na Liu
- Department of Neurology, Peking University Third Hospital, Beijing 100191, China
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Ohta R, Tanigawa Y, Suzuki Y, Kellis M, Morishita S. A polygenic score method boosted by non-additive models. Nat Commun 2024; 15:4433. [PMID: 38811555 DOI: 10.1038/s41467-024-48654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
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Affiliation(s)
- Rikifumi Ohta
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
| | - Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Yuta Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Shinichi Morishita
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
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Rout M, Wander GS, Ralhan S, Singh JR, Aston CE, Blackett PR, Chernausek S, Sanghera DK. Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations. Ther Adv Endocrinol Metab 2023; 14:20420188231220120. [PMID: 38152657 PMCID: PMC10752110 DOI: 10.1177/20420188231220120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 11/11/2023] [Indexed: 12/29/2023] Open
Abstract
Background Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRSAI) and Europeans (PRSEU) using 13,974 AI individuals. Methods Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results Both genetic models (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRSAI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10-21) higher predictability to identify subgroups with higher genetic risk than the PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU. Conclusion Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | | | - Sarju Ralhan
- Hero DMC Heart Institute, Ludhiana, Punjab, India
| | - Jai Rup Singh
- Central University of Punjab, Bathinda, Punjab, India
| | - Christopher E. Aston
- Section of Developmental and Behavioral Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Piers R. Blackett
- Department of Pediatrics, Section of Endocrinology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Steven Chernausek
- Department of Pediatrics, Section of Endocrinology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Dharambir K. Sanghera
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK 73104, USA
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Physiology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
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Malawsky DS, van Walree E, Jacobs BM, Heng TH, Huang QQ, Sabir AH, Rahman S, Sharif SM, Khan A, Mirkov MU, Kuwahara H, Gao X, Alkuraya FS, Posthuma D, Newman WG, Griffiths CJ, Mathur R, van Heel DA, Finer S, O'Connell J, Martin HC. Influence of autozygosity on common disease risk across the phenotypic spectrum. Cell 2023; 186:4514-4527.e14. [PMID: 37757828 PMCID: PMC10580289 DOI: 10.1016/j.cell.2023.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 07/11/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023]
Abstract
Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.
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Affiliation(s)
| | - Eva van Walree
- Department of Clinical Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Benjamin M Jacobs
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK; Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Teng Hiang Heng
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Qin Qin Huang
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ataf H Sabir
- West Midlands Regional Clinical Genetics Unit, Birmingham Women's and Children's NHS FT, Birmingham, UK; Institute of Cancer and Genomics, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Saadia Rahman
- Queen Square Institute of Neurology, University College London, London, UK
| | - Saghira Malik Sharif
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Ahsan Khan
- Waltham Forest Council, Waltham Forest Town Hall, Forest Road, Walthamstow E17 4JF, UK
| | - Maša Umićević Mirkov
- Congenica Limited, BioData Innovation Centre, Wellcome Genome Campus, Hinxton, UK
| | - Hiroyuki Kuwahara
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal 23955, Saudi Arabia
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal 23955, Saudi Arabia
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Danielle Posthuma
- Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - William G Newman
- Division of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Human Sciences, University of Manchester, Manchester M13 9PL, UK; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Christopher J Griffiths
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK; MRC and Asthma UK Centre in Allergic Mechanisms of Asthma, King's College London, London, UK
| | - Rohini Mathur
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sarah Finer
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK; Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Hilary C Martin
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
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5
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Ren J, Lin Z, He R, Shen X, Pan W. Using GWAS summary data to impute traits for genotyped individuals. HGG ADVANCES 2023; 4:100197. [PMID: 37181332 PMCID: PMC10173780 DOI: 10.1016/j.xhgg.2023.100197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/07/2023] [Indexed: 05/16/2023] Open
Abstract
Genome-wide association study (GWAS) summary data have become extremely useful in daily routine data analysis, largely facilitating new methods development and new applications. However, a severe limitation with the current use of GWAS summary data is its exclusive restriction to only linear single nucleotide polymorphism (SNP)-trait association analyses. To further expand the use of GWAS summary data, along with a large sample of individual-level genotypes, we propose a nonparametric method for large-scale imputation of the genetic component of the trait for the given genotypes. The imputed individual-level trait values, along with the individual-level genotypes, make it possible to conduct any analysis as with individual-level GWAS data, including nonlinear SNP-trait associations and predictions. We use the UK Biobank data to highlight the usefulness and effectiveness of the proposed method in three applications that currently cannot be done with only GWAS summary data (for SNP-trait associations): marginal SNP-trait association analysis under non-additive genetic models, detection of SNP-SNP interactions, and genetic prediction of a trait using a nonlinear model of SNPs.
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Affiliation(s)
- Jingchen Ren
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ruoyu He
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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Cromer SJ, Lakhani CM, Mercader JM, Majarian TD, Schroeder P, Cole JB, Florez JC, Patel CJ, Manning AK, Burnett-Bowie SAM, Merino J, Udler MS. Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity. Diabetes Care 2023; 46:944-952. [PMID: 36787958 PMCID: PMC10154653 DOI: 10.2337/dc22-1954] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/17/2022] [Indexed: 02/16/2023]
Abstract
OBJECTIVE Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity. RESEARCH DESIGN AND METHODS Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459). RESULTS The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations. CONCLUSIONS Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.
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Affiliation(s)
- Sara J. Cromer
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Chirag M. Lakhani
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Josep M. Mercader
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Philip Schroeder
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Joanne B. Cole
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Division of Endocrinology, Boston Children’s Hospital, Boston, MA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO
| | - Jose C. Florez
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Alisa K. Manning
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Sherri-Ann M. Burnett-Bowie
- Department of Medicine, Harvard Medical School, Boston, MA
- Endocrine Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
| | - Jordi Merino
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Miriam S. Udler
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
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Hecker J, Chun S, Samiei A, Liu C, Laurie C, Kachroo P, Lutz SM, Lee S, Smith AV, Lasky-Su J, Cho MH, Sharma S, Soto Quirós ME, Avila L, Celedón JC, Raby B, Zhou X, Silverman EK, DeMeo DL, Lange C, Weiss ST. FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies. Hum Mol Genet 2023; 32:696-707. [PMID: 36255742 PMCID: PMC9896483 DOI: 10.1093/hmg/ddac258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations. METHODS We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants. RESULTS Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22. CONCLUSION Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Ahmad Samiei
- Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Cuining Liu
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Sharon M Lutz
- Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, MA 02215, USA
| | - Sanghun Lee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin-si, 16890, South Korea
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sunita Sharma
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Lydiana Avila
- Department of Pediatrics, Hospital Nacional de Niños, 10101 San José, Costa Rica
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Benjamin Raby
- Harvard Medical School, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | | | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
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8
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Pang H, Lin J, Luo S, Huang G, Li X, Xie Z, Zhou Z. The missing heritability in type 1 diabetes. Diabetes Obes Metab 2022; 24:1901-1911. [PMID: 35603907 PMCID: PMC9545639 DOI: 10.1111/dom.14777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/04/2022] [Accepted: 05/17/2022] [Indexed: 12/15/2022]
Abstract
Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.
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Affiliation(s)
- Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Jian Lin
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Shuoming Luo
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Gan Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
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