Abstract
Genome-wide association studies (GWASs) have identified hundreds of genetic loci for osteoporosis (OP) and rheumatoid arthritis (RA), individually, however, a large proportion of the total trait heritability remains unexplained. Previous studies demonstrated that these two diseases may share some common genetic determination and risk factors, but they were generally focused on individual trait and failed to identify the common variants that play key functional roles in the etiology of these two diseases. Here, we performed a conditional false discovery rate (cFDR) analysis to identify novel pleiotropic variants shared between them by integrating two independent GWASs with summary statistics for total body bone mineral density (TB-BMD, a major risk factor for osteoporosis) (n = 66,628) and RA (n = 58,284). A fine-mapping approach was also applied to identify the most probable causal variants with biological effects on both TB-BMD and RA. As a result, we found 47 independent pleiotropic SNPs shared between TB-BMD and RA, and 40 of them were validated in heel ultrasound estimated BMD (eBMD), femoral neck BMD (FN-BMD) or lumbar spine (LS-BMD). We detected one SNP (rs13299616) was novel and not identified by previous BMD or RA-related studies. Combined with fine-mapping and GWAS-eQTL colocalization analyses, our results suggested that locus 1p13.2 (including PTPN22, MAGI3, PHTF1, and RSBN1) was an important region to regulate TB-BMD and RA simultaneously. These findings provide new insights into the shared biological mechanisms and functional genetic determinants between OP and RA, and novel potential targets for treatment development.
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Data Availability
The GWAS datasets for TB-BMD, FN-BMD, LS-BMD, and eBMD used in this study was obtained from Genetic Factors for Osteoporosis (GEFOS) Consortium (http://www.gefos.org/?q=content/gefos-lifecourse-tb-bmd-gwas-results). The RA GWAS dataset used in this study was obtained from public datasets (http://plaza.umin.ac.jp/~yokada/datasource/software.htm).
Code Availability
Software: ANNOVAR: http://annovar.openbioinformatics.org/en/latest/user-guide/download/PAINTOR_V3.0: https://github.com/gkichaev/PAINTOR_V3.0, SSIMP: https://github.com/aaronmcdaid/ssimp_software, MR-base: https://www.mrbase.org/, R code for cFDR: See supplemental material.
References
Cummings SR, Bates D, Black DM (2002) Clinical use of bone densitometry: scientific review. JAMA 288(15):1889–1897
Tenne M, McGuigan F, Besjakov J, Gerdhem P, Akesson K (2013) Degenerative changes at the lumbar spine–implications for bone mineral density measurement in elderly women. Osteoporos Int 24(4):1419–1428. https://doi.org/10.1007/s00198-012-2048-0
Franck H, Munz M (2000) Total body and regional bone mineral densitometry (BMD) and soft tissue measurements: correlations of BMD parameter to lumbar spine and hip. Calcif Tissue Int 67(2):111–115. https://doi.org/10.1007/s00223001124
MacGregor AJ, Snieder H, Rigby AS, Koskenvuo M, Kaprio J, Aho K et al (2000) Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum 43(1):30–37. https://doi.org/10.1002/1529-0131(200001)43:1%3c30::Aid-anr5%3e3.0.Co;2-b
Ioannidis JP, Ng MY, Sham PC, Zintzaras E, Lewis CM, Deng HW et al (2007) Meta-analysis of genome-wide scans provides evidence for sex- and site-specific regulation of bone mass. J Bone Miner Res 22(2):173–183. https://doi.org/10.1359/jbmr.060806
Joffe I, Epstein S (1991) Osteoporosis associated with rheumatoid arthritis: pathogenesis and management. Semin Arthritis Rheum 20(4):256–272. https://doi.org/10.1016/0049-0172(91)90021-q
Cagnetta V, Patella V (2012) The role of the immune system in the physiopathology of osteoporosis. Clinical cases in mineral and bone metabolism : the official journal of the Italian Society of Osteoporosis, Mineral Metabolism, and Skeletal Diseases 9(2):85–88
Corvaisier M, Delneste Y, Jeanvoine H, Preisser L, Blanchard S, Garo E et al (2012) IL-26 is overexpressed in rheumatoid arthritis and induces proinflammatory cytokine production and Th17 cell generation. PLoS Biol 10(9):e1001395. https://doi.org/10.1371/journal.pbio.1001395
Medina-Gomez C, Kemp JP, Trajanoska K, Luan J, Chesi A, Ahluwalia TS et al (2018) Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. Am J Hum Genet 102(1):88–102. https://doi.org/10.1016/j.ajhg.2017.12.005
Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K et al (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506(7488):376–381. https://doi.org/10.1038/nature12873
Richards JB, Zheng HF, Spector TD (2012) Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat Rev Genet 13(8):576–588. https://doi.org/10.1038/nrg3228
Andreassen OA, Djurovic S, Thompson WK, Schork AJ, Kendler KS, O’Donovan MC et al (2013) Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am J Hum Genet 92(2):197–209. https://doi.org/10.1016/j.ajhg.2013.01.001
Hu Y, Tan LJ, Chen XD, Greenbaum J, Deng HW (2018) Identification of novel variants associated with osteoporosis, type 2 diabetes and potentially pleiotropic loci using pleiotropic cFDR method. Bone 117:6–14. https://doi.org/10.1016/j.bone.2018.08.020
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J et al (2015) An integrated map of structural variation in 2,504 human genomes. Nature 526(7571):75–81. https://doi.org/10.1038/nature15394
Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. https://doi.org/10.1093/nar/gkn923
Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38(16):e164. https://doi.org/10.1093/nar/gkq603
Rueger S, McDaid A, Kutalik Z (2018) Evaluation and application of summary statistic imputation to discover new height-associated loci. PLoS Genet 14(5):e1007371. https://doi.org/10.1371/journal.pgen.1007371
Kichaev G, Roytman M, Johnson R, Eskin E, Lindstrom S, Kraft P et al (2017) Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 33(2):248–255. https://doi.org/10.1093/bioinformatics/btw615
Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J et al (2019) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773. https://doi.org/10.1093/nar/gky955
Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337(6099):1190–1195. https://doi.org/10.1126/science.1222794
Wallace C (2020) Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet 16(4):e1008720. https://doi.org/10.1371/journal.pgen.1008720
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife. https://doi.org/10.7554/eLife.34408
Shiina T, Hosomichi K, Inoko H, Kulski JK (2009) The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet 54(1):15–39. https://doi.org/10.1038/jhg.2008.5
Pickrell JK, Berisa T, Liu JZ, Segurel L, Tung JY, Hinds DA (2016) Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet 48(7):709–717. https://doi.org/10.1038/ng.3570
Gregersen PK, Amos CI, Lee AT, Lu Y, Remmers EF, Kastner DL et al (2009) REL, encoding a member of the NF-kappaB family of transcription factors, is a newly defined risk locus for rheumatoid arthritis. Nat Genet 41(7):820–823. https://doi.org/10.1038/ng.395
Raychaudhuri S, Remmers EF, Lee AT, Hackett R, Guiducci C, Burtt NP et al (2008) Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nat Genet 40(10):1216–1223. https://doi.org/10.1038/ng.233
Steer S, Abkevich V, Gutin A, Cordell HJ, Gendall KL, Merriman ME et al (2007) Genomic DNA pooling for whole-genome association scans in complex disease: empirical demonstration of efficacy in rheumatoid arthritis. Genes Immun 8(1):57–68. https://doi.org/10.1038/sj.gene.6364359
Chang M, Rowland CM, Garcia VE, Schrodi SJ, Catanese JJ, van der Helm-van Mil AH et al (2008) A large-scale rheumatoid arthritis genetic study identifies association at chromosome 9q332. PLoS Genet 4(6):e1000107. https://doi.org/10.1371/journal.pgen.1000107
Zhernakova A, Stahl EA, Trynka G, Raychaudhuri S, Festen EA, Franke L et al (2011) Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci. PLoS Genet 7(2):e1002004. https://doi.org/10.1371/journal.pgen.1002004
Lenert A, Fardo DW (2017) Detecting novel micro RNAs in rheumatoid arthritis with gene-based association testing. Clin Exp Rheumatol 35(4):586–592
Arnold M, Raffler J, Pfeufer A, Suhre K, Kastenmuller G (2015) SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31(8):1334–1336. https://doi.org/10.1093/bioinformatics/btu779
Pei YF, Liu L, Liu TL, Yang XL, Zhang H, Wei XT et al (2019) Joint Association Analysis Identified 18 New Loci for Bone Mineral Density. J Bone Miner Res 34(6):1086–1094. https://doi.org/10.1002/jbmr.3681
Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I et al (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res 46(D1):D794–D801. https://doi.org/10.1093/nar/gkx1081
Vignal C, Bansal AT, Balding DJ, Binks MH, Dickson MC, Montgomery DS et al (2009) Genetic association of the major histocompatibility complex with rheumatoid arthritis implicates two non-DRB1 loci. Arthritis Rheum 60(1):53–62. https://doi.org/10.1002/art.24138
Buchwald ZS, Aurora R (2013) Osteoclasts and CD8 T cells form a negative feedback loop that contributes to homeostasis of both the skeletal and immune systems. Clin Dev Immunol 2013:429373. https://doi.org/10.1155/2013/429373
Lyu L, Yao J, Wang M, Zheng Y, Xu P, Wang S et al (2020) Overexpressed Pseudogene HLA-DPB2 Promotes Tumor Immune Infiltrates by Regulating HLA-DPB1 and Indicates a Better Prognosis in Breast Cancer. Front Oncol 10:1245. https://doi.org/10.3389/fonc.2020.01245
Morris JA, Kemp JP, Youlten SE, Laurent L, Logan JG, Chai RC et al (2018) An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51(2):258–266. https://doi.org/10.1038/s41588-018-0302-x
He M, Xu M, Zhang B, Liang J, Chen P, Lee J-Y et al (2015) Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci. Hum Mol Genet 24(6):1791–1800. https://doi.org/10.1093/hmg/ddu583
Bult CJ, Blake JA, Smith CL, Kadin JA, Richardson JE, Mouse Genome Database G (2019) Mouse Genome Database (MGD) 2019. Nucleic Acids Res 47(D1):D801–D806. https://doi.org/10.1093/nar/gky1056
Beauregard M, Gagnon E, Guay-Belanger S, Morissette J, Brown JP, Michou L (2014) Identification of rare genetic variants in novel loci associated with Paget’s disease of bone. Hum Genet 133(6):755–768. https://doi.org/10.1007/s00439-013-1409-x
Wang Q, Yang C, Gelernter J, Zhao H (2015) Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Hum Genet 134(11–12):1195–1209. https://doi.org/10.1007/s00439-015-1596-8
Mustelin T, Bottini N, Stanford SM (2019) The Contribution of PTPN22 to Rheumatic Disease. Arthritis Rheumatol 71(4):486–495. https://doi.org/10.1002/art.40790
Zheng J, Petersen F, Yu X (2014) The role of PTPN22 in autoimmunity: learning from mice. Autoimmun Rev 13(3):266–271. https://doi.org/10.1016/j.autrev.2013.10.011
Zhu H, Xia W, Mo XB, Lin X, Qiu YH, Yi NJ et al (2016) Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis. PLoS ONE 11(11):e0167212. https://doi.org/10.1371/journal.pone.0167212
Ando K, Kanazawa S, Tetsuka T, Ohta S, Jiang X, Tada T et al (2003) Induction of Notch signaling by tumor necrosis factor in rheumatoid synovial fibroblasts. Oncogene 22(49):7796–7803. https://doi.org/10.1038/sj.onc.1206965
Zuo C, Huang Y, Bajis R, Sahih M, Li YP, Dai K et al (2012) Osteoblastogenesis regulation signals in bone remodeling. Osteoporos Int 23(6):1653–1663. https://doi.org/10.1007/s00198-012-1909-x
Ma Q, Zhang Y, Meng R, Xie KM, Xiong Y, Lin S et al (2015) MAGI3 Suppresses Glioma Cell Proliferation via Upregulation of PTEN Expression. Biomed Environ Sci 28(7):502–509. https://doi.org/10.3967/bes2015.072
Zheng D, Cui C, Yu M, Li X, Wang L, Chen X et al (2018) Coenzyme Q10 promotes osteoblast proliferation and differentiation and protects against ovariectomy-induced osteoporosis. Mol Med Rep 17(1):400–407. https://doi.org/10.3892/mmr.2017.7907
Lu Y, Quan C, Chen H, Bo X, Zhang C (2017) 3DSNP: a database for linking human noncoding SNPs to their three-dimensional interacting genes. Nucleic Acids Res 45(D1):D643–D649. https://doi.org/10.1093/nar/gkw1022
Wei W, Zeve D, Suh JM, Wang X, Du Y, Zerwekh JE et al (2011) Biphasic and Dosage-Dependent Regulation of Osteoclastogenesis by -Catenin. Mol Cell Biol 31(23):4706–4719. https://doi.org/10.1128/mcb.05980-11
Levin A, Minis A, Lalazar G, Rodriguez J, Steller H (2018) PSMD5 Inactivation Promotes 26S Proteasome Assembly during Colorectal Tumor Progression. Cancer Res 78(13):3458–3468. https://doi.org/10.1158/0008-5472.CAN-17-2296
Lee K, Kim MY, Ahn H, Kim HS, Shin HI, Jeong D (2017) Blocking of the Ubiquitin-Proteasome System Prevents Inflammation-Induced Bone Loss by Accelerating M-CSF Receptor c-Fms Degradation in Osteoclast Differentiation. Int J Mol Sci. https://doi.org/10.3390/ijms18102054
Zhou R, Lin X, Li DY, Wang XF, Greenbaum J, Chen YC et al (2017) Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach. PLoS ONE 12(8):e0183842. https://doi.org/10.1371/journal.pone.0183842
Funding
This work was partially supported by grants from National Institutes of Health [R01AR059781, P20GM109036, R01MH107354, R01MH104680, R01GM109068, R01AR069055, U19AG055373, R01DK115679].
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Liu, YQ., Liu, Y., Zhang, Q. et al. Identification of Novel Pleiotropic SNPs Associated with Osteoporosis and Rheumatoid Arthritis. Calcif Tissue Int 109, 17–31 (2021). https://doi.org/10.1007/s00223-021-00817-4
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DOI: https://doi.org/10.1007/s00223-021-00817-4