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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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2
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Constantinescu AE, Hughes DA, Bull CJ, Fleming K, Mitchell RE, Zheng J, Kar S, Timpson NJ, Amulic B, Vincent EE. A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis. Hum Genomics 2024; 18:26. [PMID: 38491524 PMCID: PMC10941368 DOI: 10.1186/s40246-024-00585-w] [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: 10/19/2023] [Accepted: 02/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). RESULTS We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. CONCLUSION Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Louisiana State University, Louisiana, USA
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
- Health Data Research UK, London, UK
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases, National Health Commission, Shanghai, People's Republic of China
- Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Early Cancer Insitute, University of Cambridge, Cambridge, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Borko Amulic
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- School of Translational Health Sciences, University of Bristol, Bristol, UK.
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3
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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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4
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Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023; 6:899. [PMID: 37658226 PMCID: PMC10474133 DOI: 10.1038/s42003-023-05279-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.
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Affiliation(s)
- Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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5
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Sun Q, Broadaway KA, Edmiston SN, Fajgenbaum K, Miller-Fleming T, Westerkam LL, Melendez-Gonzalez M, Bui H, Blum FR, Levitt B, Lin L, Hao H, Harris KM, Liu Z, Thomas NE, Cox NJ, Li Y, Mohlke KL, Sayed CJ. Genetic Variants Associated With Hidradenitis Suppurativa. JAMA Dermatol 2023; 159:930-938. [PMID: 37494057 PMCID: PMC10372759 DOI: 10.1001/jamadermatol.2023.2217] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/25/2023] [Indexed: 07/27/2023]
Abstract
Importance Hidradenitis suppurativa (HS) is a common and severely morbid chronic inflammatory skin disease that is reported to be highly heritable. However, the genetic understanding of HS is insufficient, and limited genome-wide association studies (GWASs) have been performed for HS, which have not identified significant risk loci. Objective To identify genetic variants associated with HS and to shed light on the underlying genes and genetic mechanisms. Design, Setting, and Participants This genetic association study recruited 753 patients with HS in the HS Program for Research and Care Excellence (HS ProCARE) at the University of North Carolina Department of Dermatology from August 2018 to July 2021. A GWAS was performed for 720 patients (after quality control) with controls from the Add Health study and then meta-analyzed with 2 large biobanks, UK Biobank (247 cases) and FinnGen (673 cases). Variants at 3 loci were tested for replication in the BioVU biobank (290 cases). Data analysis was performed from September 2021 to December 2022. Main Outcomes and Measures Main outcome measures are loci identified, with association of P < 1 × 10-8 considered significant. Results A total of 753 patients were recruited, with 720 included in the analysis. Mean (SD) age at symptom onset was 20.3 (10.57) years and at enrollment was 35.3 (13.52) years; 360 (50.0%) patients were Black, and 575 (79.7%) were female. In a meta-analysis of the 4 studies, 2 HS-associated loci were identified and replicated, with lead variants rs10512572 (P = 2.3 × 10-11) and rs17090189 (P = 2.1 × 10-8) near the SOX9 and KLF5 genes, respectively. Variants at these loci are located in enhancer regulatory elements detected in skin tissue. Conclusions and Relevance In this genetic association study, common variants associated with HS located near the SOX9 and KLF5 genes were associated with risk of HS. These or other nearby genes may be associated with genetic risk of disease and the development of clinical features, such as cysts, comedones, and inflammatory tunnels, that are unique to HS. New insights into disease pathogenesis related to these genes may help predict disease progression and novel treatment approaches in the future.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | | | - Sharon N. Edmiston
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
- Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina
| | - Kristen Fajgenbaum
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
| | - Tyne Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Linnea Lackstrom Westerkam
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
- University of North Carolina at Chapel Hill School of Medicine
| | | | - Helen Bui
- Department of Internal Medicine, University of North Carolina at Chapel Hill School of Medicine
| | | | - Brandt Levitt
- Carolina Population Center, University of North Carolina at Chapel Hill
| | - Lan Lin
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
| | - Honglin Hao
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina at Chapel Hill
- Sociology Department, University of North Carolina at Chapel Hill
| | - Zhi Liu
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
- Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina
| | - Nancy E. Thomas
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
- Carolina Population Center, University of North Carolina at Chapel Hill
| | - Nancy J. Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill
- Department of Genetics, University of North Carolina at Chapel Hill
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill
| | - Christopher J. Sayed
- Department of Dermatology, University of North Carolina at Chapel Hill School of Medicine
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6
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Kachuri L, Mak ACY, Hu D, Eng C, Huntsman S, Elhawary JR, Gupta N, Gabriel S, Xiao S, Keys KL, Oni-Orisan A, Rodríguez-Santana JR, LeNoir MA, Borrell LN, Zaitlen NA, Williams LK, Gignoux CR, Burchard EG, Ziv E. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat Genet 2023; 55:952-963. [PMID: 37231098 PMCID: PMC10260401 DOI: 10.1038/s41588-023-01377-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/21/2023] [Indexed: 05/27/2023]
Abstract
We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Akinyemi Oni-Orisan
- Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Esteban González Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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7
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Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP, Dong MX, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas MJ, Marinescu VD, Wang C, Wallerman O, Xue J, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins LM, Lawler A, Keough KC, Zheng Z, Zeng J, Wray NR, Li Y, Johnson J, Chen J, Paten B, Reilly SK, Hughes GM, Weng Z, Pollard KS, Pfenning AR, Forsberg-Nilsson K, Karlsson EK, Lindblad-Toh K. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 2023; 380:eabn2937. [PMID: 37104612 PMCID: PMC10259825 DOI: 10.1126/science.abn2937] [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: 11/23/2021] [Accepted: 02/09/2023] [Indexed: 04/29/2023]
Abstract
Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
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Affiliation(s)
- Patrick F. Sullivan
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - BaDoi N. Phan
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Xue Li
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Diane P. Genereux
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Michael X. Dong
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Matteo Bianchi
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Gregory Andrews
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Sharadha Sakthikumar
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Jessika Nordin
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ananya Roy
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Matthew J. Christmas
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Voichita D. Marinescu
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Chao Wang
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ola Wallerman
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - James Xue
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Center for System Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Quan Sun
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jin Szatkiewicz
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Laura M. Huckins
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alyssa Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kathleen C. Keough
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Yun Li
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jessica Johnson
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | | | - Benedict Paten
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95064, USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Graham M. Hughes
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Katherine S. Pollard
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Karin Forsberg-Nilsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | - Elinor K. Karlsson
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
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8
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Zhong W, Liu W, Chen J, Sun Q, Hu M, Li Y. Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants. Front Cell Dev Biol 2022; 10:957292. [PMID: 36060805 PMCID: PMC9437546 DOI: 10.3389/fcell.2022.957292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/21/2022] [Indexed: 01/11/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.
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Affiliation(s)
- Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co, Inc, Rahway, NJ, United States
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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9
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Yin X, Kim K, Suetsugu H, Bang SY, Wen L, Koido M, Ha E, Liu L, Sakamoto Y, Jo S, Leng RX, Otomo N, Kwon YC, Sheng Y, Sugano N, Hwang MY, Li W, Mukai M, Yoon K, Cai M, Ishigaki K, Chung WT, Huang H, Takahashi D, Lee SS, Wang M, Karino K, Shim SC, Zheng X, Miyamura T, Kang YM, Ye D, Nakamura J, Suh CH, Tang Y, Motomura G, Park YB, Ding H, Kuroda T, Choe JY, Li C, Niiro H, Park Y, Shen C, Miyamoto T, Ahn GY, Fei W, Takeuchi T, Shin JM, Li K, Kawaguchi Y, Lee YK, Wang YF, Amano K, Park DJ, Yang W, Tada Y, Lau YL, Yamaji K, Zhu Z, Shimizu M, Atsumi T, Suzuki A, Sumida T, Okada Y, Matsuda K, Matsuo K, Kochi Y, Yamamoto K, Ohmura K, Kim TH, Yang S, Yamamoto T, Kim BJ, Shen N, Ikegawa S, Lee HS, Zhang X, Terao C, Cui Y, Bae SC. Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study. Ann Rheum Dis 2022; 81:1273-1280. [PMID: 35609976 PMCID: PMC9380500 DOI: 10.1136/annrheumdis-2022-222345] [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] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 05/11/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Genome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis. METHODS We built gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches. RESULTS TWAS identified 171 genes for SLE (p<1.0×10-5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association between CD83 and SLE (p<7.7×10-8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10-9) around CD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect on ACAP1, and that presence of the SLE risk allele decreased ACAP1 expression. CONCLUSIONS Cell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.
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Affiliation(s)
- Xianyong Yin
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, People's Republic of China
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - Kwangwoo Kim
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Korea
| | - Hiroyuki Suetsugu
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Leilei Wen
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Eunji Ha
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Korea
| | - Lu Liu
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Yuma Sakamoto
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Koga Hospital 21, Kurume, Japan
| | - Sungsin Jo
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Rui-Xue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People's Republic of China
| | - Nao Otomo
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Young-Chang Kwon
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Yujun Sheng
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Nobuhiko Sugano
- Department of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Weiran Li
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masaya Mukai
- Department of Rheumatology & Clinical Immunology, Sapporo City General Hospital, Hokkaido, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Minglong Cai
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Won Tae Chung
- Department of Internal Medicine, Dong-A University Hospital, Busan, South Korea
| | - He Huang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Daisuke Takahashi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Shin-Seok Lee
- Division of Rheumatology, Department of Internal Medicine, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Mengwei Wang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Kohei Karino
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Seung-Cheol Shim
- Division of Rheumatology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, South Korea
| | - Xiaodong Zheng
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Tomoya Miyamura
- Department of Internal Medicine and Rheumatology, National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Young Mo Kang
- Division of Rheumatology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, South Korea
| | - Dongqing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People's Republic of China
| | - Junichi Nakamura
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Chang-Hee Suh
- Department of Rheumatology, Ajou University School of Medicine, Suwon, South Korea
| | - Yuanjia Tang
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
| | - Goro Motomura
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yong-Beom Park
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Huihua Ding
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
| | - Takeshi Kuroda
- Niigata University Health Administration Center, Niigata, Japan
| | - Jung-Yoon Choe
- Department of Rheumatology, Catholic University of Daegu School of Medicine, Daegu, South Korea
| | - Chengxu Li
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Hiroaki Niiro
- Department of Medical Education, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Youngho Park
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Changbing Shen
- Department of Dermatology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory for Translational Medicine of Dermatology, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong, People's Republic of China
| | - Takeshi Miyamoto
- Department of Orthopaedic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Ga-Young Ahn
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Wenmin Fei
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Jung-Min Shin
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Keke Li
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Yasushi Kawaguchi
- Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan
| | - Yeon-Kyung Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Koichi Amano
- Department of Rheumatology & Clinical Immunology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Dae Jin Park
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Yoshifumi Tada
- Department of Rheumatology, Faculty of Medicine, Saga University, Saga, Japan
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Ken Yamaji
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | - Zhengwei Zhu
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masato Shimizu
- Hokkaido Medical Center for Rheumatic Diseases, Sapporo, Japan
| | - Takashi Atsumi
- Department of Orthopaedic Surgery, Showa University School of Medicine, Tokyo, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Takayuki Sumida
- Department of Internal Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuta Kochi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tae-Hwan Kim
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Sen Yang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Takuaki Yamamoto
- Department of Orthopaedic Surgery, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Nan Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, People's Republic of China
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Xuejun Zhang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
- Department of Dermatology, Institute of Dermatology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yong Cui
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
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