Skip to content
2000
image of Transcriptome-wide Association Studies Integrating Four Levels Identify Novel Targets for Idiopathic Pulmonary Fibrosis

Abstract

Introduction

Idiopathic pulmonary fibrosis (IPF) is a kind of interstitial lung disease with a poor prognosis. Even though genome-wide association studies (GWAS) have identified numerous loci linked to IPF risk, the underlying causal genes and biological processes are still mostly unknown.

Methods

The IPF GWAS summary data included 4,125 cases, 20,464 controls from five cohorts. The weight file and related files for transcriptome association studies (TWAS) of plasma protein, multi-tissues, cross-tissue, and single-cell were obtained from Zhang’s study, Mancuso lab, GTExV8 database, and Thompson’s study, respectively. We conducted TWAS employing functional Summary-based Imputation (FUSION) from four levels, which were plasma protein, multiple tissues, cross-tissue, and single cell. Conditional and joint (COJO) analysis and multi-marker analysis of genomic annotation (MAGMA) analysis were used to validate the above results. Summary-data-based Mendelian randomization (SMR) and Bayesian co-localization analysis were utilized to explain the causal association between selected genes and the risk of IPF.

Results

A total of 12, 361, 1187, and 72 genes were calculated from the four dimensions of TWAS. TOLLIP, GCHFR, ZNF318 TALDO1, CD151, and AP4M1 were selected by intersecting the results of the four sets of genes. GCHFR, TALDO1, CD151, and AP4M1 were verified by COJO analysis and MAGMA analysis. SMR and colocalization analyses identified GCHFR as the most significant gene for IPF.

Discussion

We have applied the TWAS approach to identify novel therapeutic targets for IPF in multiple dimensions. Further biological testing will be required in future studies to validate our findings.

Conclusion

In summary, we carried out an extensive TWAS that integrated four dimensions: plasma protein, multiple tissues, cross-tissue, and single cell. GCHFR was identified as the most significant gene for IPF in this study.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673364730250721151447
2025-08-26
2025-11-04

  • From This Site

    /content/journals/cmc/10.2174/0109298673364730250721151447
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Barratt S. Creamer A. Hayton C. Chaudhuri N. Idiopathic pulmonary fibrosis (IPF): An overview. J. Clin. Med. 2018 7 8 201 10.3390/jcm7080201 30082599
    [Google Scholar]
  2. Confalonieri P. Volpe M.C. Jacob J. Maiocchi S. Salton F. Ruaro B. Confalonieri M. Braga L. Regeneration or repair? the role of alveolar epithelial cells in the pathogenesis of Idiopathic Pulmonary Fibrosis (IPF). Cells 2022 11 13 2095 10.3390/cells11132095 35805179
    [Google Scholar]
  3. Richeldi L. Collard H.R. Jones M.G. Idiopathic pulmonary fibrosis. Lancet 2017 389 10082 1941 1952 10.1016/S0140‑6736(17)30866‑8 28365056
    [Google Scholar]
  4. Warheit-Niemi H.I. Edwards S.J. SenGupta S. Parent C.A. Zhou X. O’Dwyer D.N. Moore B.B. Fibrotic lung disease inhibits immune responses to staphylococcal pneumonia via impaired neutrophil and macrophage function. JCI Insight 2022 7 4 e152690 10.1172/jci.insight.152690 34990413
    [Google Scholar]
  5. Maher T.M. Bendstrup E. Dron L. Langley J. Smith G. Khalid J.M. Patel H. Kreuter M. Global incidence and prevalence of idiopathic pulmonary fibrosis. Respir. Res. 2021 22 1 197 10.1186/s12931‑021‑01791‑z 34233665
    [Google Scholar]
  6. Allen R.J. Oldham J.M. Jenkins D.A. Leavy O.C. Guillen-Guio B. Melbourne C.A. Ma S.F. Jou J. Kim J.S. Fahy W.A. Oballa E. Hubbard R.B. Navaratnam V. Braybrooke R. Saini G. Roach K.M. Tobin M.D. Hirani N. Whyte M.K.B. Kaminski N. Zhang Y. Martinez F.J. Linderholm A.L. Adegunsoye A. Strek M.E. Maher T.M. Molyneaux P.L. Flores C. Noth I. Gisli Jenkins R. Wain L.V. Longitudinal lung function and gas transfer in individuals with idiopathic pulmonary fibrosis: A genome-wide association study. Lancet Respir. Med. 2023 11 1 65 73 10.1016/S2213‑2600(22)00251‑X 35985358
    [Google Scholar]
  7. Partanen J.J. Häppölä P. Zhou W. Lehisto A.A. Ainola M. Sutinen E. Allen R.J. Stockwell A.D. Leavy O.C. Oldham J.M. Guillen-Guio B. Cox N.J. Hirbo J.B. Schwartz D.A. Fingerlin T.E. Flores C. Noth I. Yaspan B.L. Jenkins R.G. Wain L.V. Ripatti S. Pirinen M. Laitinen T. Kaarteenaho R. Myllärniemi M. Daly M.J. Koskela J.T. Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics. Cell Genomics 2022 2 10 100181 10.1016/j.xgen.2022.100181 36777997
    [Google Scholar]
  8. Cano-Gamez E. Trynka G. From GWAS to function: Using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 2020 11 424 10.3389/fgene.2020.00424 32477401
    [Google Scholar]
  9. Liu H. Association between sleep duration and depression: A Mendelian randomization analysis. J. Affect Disord. 2023 335 152 154 10.1016/j.jad.2023.05.020 37178827
    [Google Scholar]
  10. Liu H. Exploring the mechanism underlying hyperuricemia using comprehensive research on multi-omics. Sci. Rep. 2023 13 7161 10.1038/s41598‑023‑34426‑y
    [Google Scholar]
  11. Wang M. Beckmann N.D. Roussos P. Wang E. Zhou X. Wang Q. Ming C. Neff R. Ma W. Fullard J.F. Hauberg M.E. Bendl J. Peters M.A. Logsdon B. Wang P. Mahajan M. Mangravite L.M. Dammer E.B. Duong D.M. Lah J.J. Seyfried N.T. Levey A.I. Buxbaum J.D. Ehrlich M. Gandy S. Katsel P. Haroutunian V. Schadt E. Zhang B. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci. Data 2018 5 1 180185 10.1038/sdata.2018.185 30204156
    [Google Scholar]
  12. Akbarian S. Liu C. Knowles J.A. Vaccarino F.M. Farnham P.J. Crawford G.E. Jaffe A.E. Pinto D. Dracheva S. Geschwind D.H. Mill J. Nairn A.C. Abyzov A. Pochareddy S. Prabhakar S. Weissman S. Sullivan P.F. State M.W. Weng Z. Peters M.A. White K.P. Gerstein M.B. Amiri A. Armoskus C. Ashley-Koch A.E. Bae T. Beckel-Mitchener A. Berman B.P. Coetzee G.A. Coppola G. Francoeur N. Fromer M. Gao R. Grennan K. Herstein J. Kavanagh D.H. Ivanov N.A. Jiang Y. Kitchen R.R. Kozlenkov A. Kundakovic M. Li M. Li Z. Liu S. Mangravite L.M. Mattei E. Markenscoff-Papadimitriou E. Navarro F.C.P. North N. Omberg L. Panchision D. Parikshak N. Poschmann J. Price A.J. Purcaro M. Reddy T.E. Roussos P. Schreiner S. Scuderi S. Sebra R. Shibata M. Shieh A.W. Skarica M. Sun W. Swarup V. Thomas A. Tsuji J. van Bakel H. Wang D. Wang Y. Wang K. Werling D.M. Willsey A.J. Witt H. Won H. Wong C.C.Y. Wray G.A. Wu E.Y. Xu X. Yao L. Senthil G. Lehner T. Sklar P. Sestan N. The PsychENCODE project. Nat. Neurosci. 2015 18 12 1707 1712 10.1038/nn.4156 26605881
    [Google Scholar]
  13. Montaner J. Ramiro L. Simats A. Tiedt S. Makris K. Jickling G.C. Debette S. Sanchez J.C. Bustamante A. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat. Rev. Neurol. 2020 16 5 247 264 10.1038/s41582‑020‑0350‑6 32322099
    [Google Scholar]
  14. Nagpal S. Meng X. Epstein M.P. Tsoi L.C. Patrick M. Gibson G. De Jager P.L. Bennett D.A. Wingo A.P. Wingo T.S. Yang J. TIGAR: An improved bayesian tool for transcriptomic data imputation enhances gene mapping of complex traits. Am. J. Hum. Genet. 2019 105 2 258 266 10.1016/j.ajhg.2019.05.018 31230719
    [Google Scholar]
  15. Luningham J.M. Chen J. Tang S. De Jager P.L. Bennett D.A. Buchman A.S. Yang J. Bayesian genome-wide TWAS method to leverage both cis- and trans-eQTL information through summary statistics. Am. J. Hum. Genet. 2020 107 4 714 726 10.1016/j.ajhg.2020.08.022 32961112
    [Google Scholar]
  16. Tang S. Buchman A.S. De Jager P.L. Bennett D.A. Epstein M.P. Yang J. Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia. PLoS Genet. 2021 17 4 e1009482 10.1371/journal.pgen.1009482 33798195
    [Google Scholar]
  17. Gusev A. Ko A. Shi H. Bhatia G. Chung W. Penninx B.W.J.H. Jansen R. de Geus E.J.C. Boomsma D.I. Wright F.A. Sullivan P.F. Nikkola E. Alvarez M. Civelek M. Lusis A.J. Lehtimäki T. Raitoharju E. Kähönen M. Seppälä I. Raitakari O.T. Kuusisto J. Laakso M. Price A.L. Pajukanta P. Pasaniuc B. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 2016 48 3 245 252 10.1038/ng.3506 26854917
    [Google Scholar]
  18. Zhu Z. Zhang F. Hu H. Bakshi A. Robinson M.R. Powell J.E. Montgomery G.W. Goddard M.E. Wray N.R. Visscher P.M. Yang J. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 2016 48 5 481 487 10.1038/ng.3538 27019110
    [Google Scholar]
  19. Li Y.I. van de Geijn B. Raj A. Knowles D.A. Petti A.A. Golan D. Gilad Y. Pritchard J.K. RNA splicing is a primary link between genetic variation and disease. Science 2016 352 6285 600 604 10.1126/science.aad9417 27126046
    [Google Scholar]
  20. Wang K. Yi H. Wang Y. Jin D. Zhang G. Mao Y. Proteome-wide multicenter mendelian randomization analysis to identify novel therapeutic targets for lung cancer. Arch. Bronconeumol. 2024 60 9 553 558 10.1016/j.arbres.2024.05.007 38824092
    [Google Scholar]
  21. Liufu C. Luo L. Pang T. Zheng H. Yang L. Lu L. Chang S. Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders. Mol. Psychiatry 2024 29 10 3141 3150 10.1038/s41380‑024‑02574‑w 38684796
    [Google Scholar]
  22. Baird D.A. Liu J.Z. Zheng J. Sieberts S.K. Perumal T. Elsworth B. Richardson T.G. Chen C.Y. Carrasquillo M.M. Allen M. Reddy J.S. De Jager P.L. Ertekin-Taner N. Mangravite L.M. Logsdon B. Estrada K. Haycock P.C. Hemani G. Runz H. Smith G.D. Gaunt T.R. Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome. PLoS Genet. 2021 17 1 e1009224 10.1371/journal.pgen.1009224 33417599
    [Google Scholar]
  23. Li M. Lyu C. Huang M. Do C. Tycko B. Lupo P.J. MacLeod S.L. Randolph C.E. Liu N. Witte J.S. Hobbs C.A. Mapping methylation quantitative trait loci in cardiac tissues nominates risk loci and biological pathways in congenital heart disease. BMC Genomic Data 2021 22 1 20 10.1186/s12863‑021‑00975‑2 34112112
    [Google Scholar]
  24. Hemani G. Zheng J. Elsworth B. Wade K.H. Haberland V. Baird D. Laurin C. Burgess S. Bowden J. Langdon R. Tan V.Y. Yarmolinsky J. Shihab H.A. Timpson N.J. Evans D.M. Relton C. Martin R.M. Davey Smith G. Gaunt T.R. Haycock P.C. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018 7 e34408 10.7554/eLife.34408 29846171
    [Google Scholar]
  25. Lawlor D.A. Harbord R.M. Sterne J.A.C. Timpson N. Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat. Med. 2008 27 8 1133 1163 10.1002/sim.3034 17886233
    [Google Scholar]
  26. Giambartolomei C. Vukcevic D. Schadt E.E. Franke L. Hingorani A.D. Wallace C. Plagnol V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014 10 5 e1004383 10.1371/journal.pgen.1004383 24830394
    [Google Scholar]
  27. Allen R.J. Stockwell A. Oldham J.M. Guillen-Guio B. Schwartz D.A. Maher T.M. Flores C. Noth I. Yaspan B.L. Jenkins R.G. Wain L.V. Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis. Thorax 2022 77 8 829 833 10.1136/thoraxjnl‑2021‑218577 35688625
    [Google Scholar]
  28. Little J. Higgins J.P.T. Ioannidis J.P.A. Moher D. Gagnon F. von Elm E. Khoury M.J. Cohen B. Davey-Smith G. Grimshaw J. Scheet P. Gwinn M. Williamson R.E. Zou G.Y. Hutchings K. Johnson C.Y. Tait V. Wiens M. Golding J. van Duijn C. McLaughlin J. Paterson A. Wells G. Fortier I. Freedman M. Zecevic M. King R. Infante-Rivard C. Stewart A. Birkett N. Strengthening the reporting of genetic association studies (STREGA): An extension of the STROBE statement. Ann. Intern. Med. 2009 150 3 206 215 10.7326/0003‑4819‑150‑3‑200902030‑00011 19189911
    [Google Scholar]
  29. Zhang J. Dutta D. Köttgen A. Tin A. Schlosser P. Grams M.E. Harvey B. Yu B. Boerwinkle E. Coresh J. Chatterjee N. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat. Genet. 2022 54 5 593 602 10.1038/s41588‑022‑01051‑w 35501419
    [Google Scholar]
  30. Ardlie K.G. Deluca D.S. Segrè A.V. Sullivan T.J. Young T.R. Gelfand E.T. Trowbridge C.A. Maller J.B. Tukiainen T. Lek M. Ward L.D. Kheradpour P. Iriarte B. Meng Y. Palmer C.D. Esko T. Winckler W. Hirschhorn J.N. Kellis M. MacArthur D.G. Getz G. Shabalin A.A. Li G. Zhou Y-H. Nobel A.B. Rusyn I. Wright F.A. Lappalainen T. Ferreira P.G. Ongen H. Rivas M.A. Battle A. Mostafavi S. Monlong J. Sammeth M. Mele M. Reverter F. Goldmann J.M. Koller D. Guigó R. McCarthy M.I. Dermitzakis E.T. Gamazon E.R. Im H.K. Konkashbaev A. Nicolae D.L. Cox N.J. Flutre T. Wen X. Stephens M. Pritchard J.K. Tu Z. Zhang B. Huang T. Long Q. Lin L. Yang J. Zhu J. Liu J. Brown A. Mestichelli B. Tidwell D. Lo E. Salvatore M. Shad S. Thomas J.A. Lonsdale J.T. Moser M.T. Gillard B.M. Karasik E. Ramsey K. Choi C. Foster B.A. Syron J. Fleming J. Magazine H. Hasz R. Walters G.D. Bridge J.P. Miklos M. Sullivan S. Barker L.K. Traino H.M. Mosavel M. Siminoff L.A. Valley D.R. Rohrer D.C. Jewell S.D. Branton P.A. Sobin L.H. Barcus M. Qi L. McLean J. Hariharan P. Um K.S. Wu S. Tabor D. Shive C. Smith A.M. Buia S.A. Undale A.H. Robinson K.L. Roche N. Valentino K.M. Britton A. Burges R. Bradbury D. Hambright K.W. Seleski J. Korzeniewski G.E. Erickson K. Marcus Y. Tejada J. Taherian M. Lu C. Basile M. Mash D.C. Volpi S. Struewing J.P. Temple G.F. Boyer J. Colantuoni D. Little R. Koester S. Carithers L.J. Moore H.M. Guan P. Compton C. Sawyer S.J. Demchok J.P. Vaught J.B. Rabiner C.A. Lockhart N.C. Ardlie K.G. Getz G. Wright F.A. Kellis M. Volpi S. Dermitzakis E.T. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 2015 348 6235 648 660 10.1126/science.1262110 25954001
    [Google Scholar]
  31. Lonsdale J. Thomas J. Salvatore M. Phillips R. Lo E. Shad S. Hasz R. Walters G. Garcia F. Young N. Foster B. Moser M. Karasik E. Gillard B. Ramsey K. Sullivan S. Bridge J. Magazine H. Syron J. Fleming J. Siminoff L. Traino H. Mosavel M. Barker L. Jewell S. Rohrer D. Maxim D. Filkins D. Harbach P. Cortadillo E. Berghuis B. Turner L. Hudson E. Feenstra K. Sobin L. Robb J. Branton P. Korzeniewski G. Shive C. Tabor D. Qi L. Groch K. Nampally S. Buia S. Zimmerman A. Smith A. Burges R. Robinson K. Valentino K. Bradbury D. Cosentino M. Diaz-Mayoral N. Kennedy M. Engel T. Williams P. Erickson K. Ardlie K. Winckler W. Getz G. DeLuca D. MacArthur D. Kellis M. Thomson A. Young T. Gelfand E. Donovan M. Meng Y. Grant G. Mash D. Marcus Y. Basile M. Liu J. Zhu J. Tu Z. Cox N.J. Nicolae D.L. Gamazon E.R. Im H.K. Konkashbaev A. Pritchard J. Stevens M. Flutre T. Wen X. Dermitzakis E.T. Lappalainen T. Guigo R. Monlong J. Sammeth M. Koller D. Battle A. Mostafavi S. McCarthy M. Rivas M. Maller J. Rusyn I. Nobel A. Wright F. Shabalin A. Feolo M. Sharopova N. Sturcke A. Paschal J. Anderson J.M. Wilder E.L. Derr L.K. Green E.D. Struewing J.P. Temple G. Volpi S. Boyer J.T. Thomson E.J. Guyer M.S. Ng C. Abdallah A. Colantuoni D. Insel T.R. Koester S.E. Little A.R. Bender P.K. Lehner T. Yao Y. Compton C.C. Vaught J.B. Sawyer S. Lockhart N.C. Demchok J. Moore H.F. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013 45 6 580 585 10.1038/ng.2653 23715323
    [Google Scholar]
  32. Feng H. Mancuso N. Gusev A. Majumdar A. Major M. Pasaniuc B. Kraft P. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. PLoS Genet. 2021 17 4 e1008973 10.1371/journal.pgen.1008973 33831007
    [Google Scholar]
  33. Thompson M. Gordon M.G. Lu A. Tandon A. Halperin E. Gusev A. Ye C.J. Balliu B. Zaitlen N. Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nat. Commun. 2022 13 1 5704 10.1038/s41467‑022‑33212‑0 36171194
    [Google Scholar]
  34. Liao C. Laporte A.D. Spiegelman D. Akçimen F. Joober R. Dion P.A. Rouleau G.A. Transcriptome-wide association study of attention deficit hyperactivity disorder identifies associated genes and phenotypes. Nat. Commun. 2019 10 1 4450 10.1038/s41467‑019‑12450‑9 31575856
    [Google Scholar]
  35. Gui J. Yang X. Tan C. Wang L. Meng L. Han Z. Liu J. Jiang L. A cross-tissue transcriptome-wide association study reveals novel susceptibility genes for migraine. J. Headache Pain 2024 25 1 94 10.1186/s10194‑024‑01802‑6 38840241
    [Google Scholar]
  36. de Leeuw C.A. Mooij J.M. Heskes T. Posthuma D. MAGMA: Generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 2015 11 4 e1004219 10.1371/journal.pcbi.1004219 25885710
    [Google Scholar]
  37. de Leeuw C.A. Neale B.M. Heskes T. Posthuma D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 2016 17 6 353 364 10.1038/nrg.2016.29 27070863
    [Google Scholar]
  38. de Leeuw C.A. Stringer S. Dekkers I.A. Heskes T. Posthuma D. Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure. Nat. Commun. 2018 9 1 3768 10.1038/s41467‑018‑06022‑6 30218068
    [Google Scholar]
  39. Chen J. Ruan X. Sun Y. Lu S. Hu S. Yuan S. Li X. Multi-omic insight into the molecular networks of mitochondrial dysfunction in the pathogenesis of inflammatory bowel disease. EBioMedicine 2024 99 104934 10.1016/j.ebiom.2023.104934 38103512
    [Google Scholar]
  40. Lona-Durazo F. Omachi K. Fermin D. Association of genetically predicted skipping of COL4A4 Exon 27 with Hematuria and Albuminuria. J. Am. Soc. Nephrol. 2025 36 1 48 59 10.1681/ASN.0000000000000480 39190490
    [Google Scholar]
  41. Dutta D Guo X Winter TD. Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma. Am. J. Hum. Genet. 2024 111 9 1864 1876 10.1016/j.ajhg.2024.07.012 39137781
    [Google Scholar]
  42. Zhu Q. Nambiar R. Schultz E. Gao X. Liang S. Flamand Y. Stevenson K. Cole P.D. Gennarini L. Harris M.H. Kahn J.M. Ladas E.J. Athale U.H. Hoa Tran T. Michon B. Welch J.J.G. Sallan S.E. Silverman L.B. Kelly K.M. Yao S. Genome-wide study identifies novel genes associated with bone toxicities in children with acute lymphoblastic leukaemia. Br. J. Haematol. 2024 205 5 1889 1898 10.1111/bjh.19696 39143423
    [Google Scholar]
  43. Wu C Liu H Zuo Q Identifying novel risk genes in intracranial aneurysm by integrating human proteomes and genetics. Brain 2024 147 8 2817 2825 10.1093/brain/awae111 39084678
    [Google Scholar]
  44. Tan M.C.B. Isom C.A. Liu Y. Trégouët D.A. Lindstrom S. Wang L. Smith E. Gordon W. Van Hylckama Vlieg A. De Andrade M. Brody J. Pattee J. Haessler J. Brumpton B. Chasman D. Suchon P. Chen M-H. Turman C. Germain M. Wiggins K. MacDonald J. Braekkan S. Armasu S. Pankratz N. Jackson R. Nielsen J. Giulianini F. Puurunen M. Ibrahim M. Heckbert S. Bammler T. Frazer K. McCauley B. Taylor K. Pankow J. Reiner A. Gabrielsen M. Deleuze J-F. O’Donnell C. Kim J. McKnight B. Kraft P. Hansen J-B. Rosendaal F. Heit J. Psaty B. Tang W. Kooperberg C. Hveem K. Ridker P. Morange P-E. Johnson A. Kabrhel C. Trégouët D-A. Smith N. Wu L. Zhou D. Gamazon E.R. Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism. EBioMedicine 2024 106 105233 10.1016/j.ebiom.2024.105233 39002386
    [Google Scholar]
  45. Guo S. Yang J. Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia. Alzheimers Res. Ther. 2024 16 1 120 10.1186/s13195‑024‑01488‑7 38824563
    [Google Scholar]
  46. Milstien S. Jaffe H. Kowlessur D. Bonner T.I. Purification and cloning of the GTP cyclohydrolase I feedback regulatory protein, GFRP. J. Biol. Chem. 1996 271 33 19743 19751 10.1074/jbc.271.33.19743 8702680
    [Google Scholar]
  47. Li L. Rezvan A. Salerno J.C. Husain A. Kwon K. Jo H. Harrison D.G. Chen W. GTP cyclohydrolase I phosphorylation and interaction with GTP cyclohydrolase feedback regulatory protein provide novel regulation of endothelial tetrahydrobiopterin and nitric oxide. Circ. Res. 2010 106 2 328 336 10.1161/CIRCRESAHA.109.210658 19926872
    [Google Scholar]
  48. McHugh P.C. Joyce P.R. Deng X. Kennedy M.A. A polymorphism of the GTP-cyclohydrolase I feedback regulator gene alters transcriptional activity and may affect response to SSRI antidepressants. Pharmacogenomics J. 2011 11 3 207 213 10.1038/tpj.2010.23 20351752
    [Google Scholar]
  49. Hussein D. Starr A. Heikal L. McNeill E. Channon K.M. Brown P.R. Sutton B.J. McDonnell J.M. Nandi M. Validating the GTP -cyclohydrolase 1-feedback regulatory complex as a therapeutic target using biophysical and in vivo approaches. Br. J. Pharmacol. 2015 172 16 4146 4157 10.1111/bph.13202 26014146
    [Google Scholar]
  50. Nikpay M. Multiomics screening identified CpG sites and genes that mediate the impact of exposure to environmental chemicals on cardiometabolic traits. Epigenomes 2024 8 3 29 10.3390/epigenomes8030029 39189255
    [Google Scholar]
  51. Du J. The protein partners of GTP cyclohydrolase I in rat organs. PLoS One 2012 7 3 e33991 10.1371/journal.pone.0033991 22479495
    [Google Scholar]
  52. Li L. A novel high-throughput screening assay for discovery of molecules that increase cellular tetrahydrobiopterin. J. Biomol. Screen. 2011 16 8 836 844 10.1177/1087057111411088 21693765
    [Google Scholar]
  53. Noth I. Zhang Y. Ma S.F. Flores C. Barber M. Huang Y. Broderick S.M. Wade M.S. Hysi P. Scuirba J. Richards T.J. Juan-Guardela B.M. Vij R. Han M.K. Martinez F.J. Kossen K. Seiwert S.D. Christie J.D. Nicolae D. Kaminski N. Garcia J.G.N. Genetic variants associated with idiopathic pulmonary fibrosis susceptibility and mortality: A genome-wide association study. Lancet Respir. Med. 2013 1 4 309 317 10.1016/S2213‑2600(13)70045‑6 24429156
    [Google Scholar]
  54. Li X. Kim S.E. Chen T.Y. Wang J. Yang X. Tabib T. Tan J. Guo B. Fung S. Zhao J. Sembrat J. Rojas M. Shiva S. Lafyatis R. St Croix C. Alder J.K. Di Y.P. Kass D.J. Zhang Y. Toll interacting protein protects bronchial epithelial cells from bleomycin-induced apoptosis. FASEB J. 2020 34 8 9884 9898 10.1096/fj.201902636RR 32596871
    [Google Scholar]
  55. Bonella F. Campo I. Zorzetto M. Boerner E. Ohshimo S. Theegarten D. Taube C. Costabel U. Potential clinical utility of MUC5B und TOLLIP single nucleotide polymorphisms (SNPs) in the management of patients with IPF. Orphanet J. Rare Dis. 2021 16 1 111 10.1186/s13023‑021‑01750‑3 33639995
    [Google Scholar]
  56. Isshiki T. Koyama K. Homma S. Sakamoto S. Yamasaki A. Shimizu H. Miyoshi S. Nakamura Y. Kishi K. Association of rs3750920 polymorphism in TOLLIP with clinical characteristics of fibrosing interstitial lung diseases in Japanese. Sci. Rep. 2021 11 1 16250 10.1038/s41598‑021‑95869‑9 34376770
    [Google Scholar]
  57. Mota P.C. Soares M.L. Vasconcelos C.D. Ferreira A.C. Lima B.A. Manduchi E. Moore J.H. Melo N. Novais-Bastos H. Pereira J.M. Guimarães S. Moura C.S. Marques J.A. Morais A. Predictive value of common genetic variants in idiopathic pulmonary fibrosis survival. J. Mol. Med. 2022 100 9 1341 1353 10.1007/s00109‑022‑02242‑y 35986225
    [Google Scholar]
  58. Novikova G. Kapoor M. Tcw J. Abud E.M. Efthymiou A.G. Chen S.X. Cheng H. Fullard J.F. Bendl J. Liu Y. Roussos P. Björkegren J.L.M. Liu Y. Poon W.W. Hao K. Marcora E. Goate A.M. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 2021 12 1 1610 10.1038/s41467‑021‑21823‑y 33712570
    [Google Scholar]
  59. Tsujino K. Takeda Y. Arai T. Shintani Y. Inagaki R. Saiga H. Iwasaki T. Tetsumoto S. Jin Y. Ihara S. Minami T. Suzuki M. Nagatomo I. Inoue K. Kida H. Kijima T. Ito M. Kitaichi M. Inoue Y. Tachibana I. Takeda K. Okumura M. Hemler M.E. Kumanogoh A. Tetraspanin CD151 protects against pulmonary fibrosis by maintaining epithelial integrity. Am. J. Respir. Crit. Care Med. 2012 186 2 170 180 10.1164/rccm.201201‑0117OC 22592804
    [Google Scholar]
  60. Tripathi L.P. Itoh M.N. Takeda Y. Tsujino K. Kondo Y. Kumanogoh A. Mizuguchi K. Integrative analysis reveals common and unique roles of tetraspanins in fibrosis and emphysema. Front. Genet. 2020 11 585998 10.3389/fgene.2020.585998 33424923
    [Google Scholar]
  61. Liu H. Tang T. Pan-cancer genetic analysis of disulfidptosis-related gene set. Cancer Genet. 2023 278-279 91 103 10.1016/j.cancergen.2023.10.001 37879141
    [Google Scholar]
  62. Agarwal K. Liu H. Potential cancer biomarkers: Mitotic Intra-S DNA damage checkpoint genes. BioRxiv 2024 10.1101/2024.09.19.613851
    [Google Scholar]
  63. Liu H. Pan-cancer profiles of the cuproptosis gene set. Am. J. Cancer Res. 2022 12 8 4074 4081 36119826
    [Google Scholar]
  64. Liu H. Tang T. Pan-cancer genetic analysis of cuproptosis and copper metabolism-related gene set. Front. Oncol. 2022 12 952290 10.3389/fonc.2022.952290 36276096
    [Google Scholar]
  65. Arumilli S. Liu H. Protein kinases in phagocytosis: Promising genetic biomarkers for cancer. BioRxiv 2024 1 5 10.1101/2024.10.09.617495
    [Google Scholar]
  66. Shi J Zhang L A multi-omic study integrating plasma protein, multiple tissues, and single-cell identifies RNASET2 as a key gene for lung cancer. Discov. Oncol. 2025 16 1 152 10.1007/s12672‑025‑01899‑4
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673364730250721151447
Loading
Transcriptome-wide Association Studies Integrating Four Levels Identify Novel Targets for Idiopathic Pulmonary Fibrosis
Bentham Science Publishers ; https://doi.org/10.2174/0109298673364730250721151447
/content/journals/cmc/10.2174/0109298673364730250721151447
/content/journals/cmc/10.2174/0109298673364730250721151447
Loading

Data & Media loading...

Supplements

file format download Download

  • Article Type:     Research Article
Keywords: FUSION ; IPF ; TWAS ; SMR ; MAGMA ; GCHFR

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test