Functional_genomics_approaches_to_understand_osteoarthritis

The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the –omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Type: Transcriptome Analysis
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 5 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001005215	The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-01.	Illumina HiSeq 2500	210

Publications	Citations
Accelerating functional gene discovery in osteoarthritis. Butterfield NC, Curry KF, Steinberg J, Dewhurst H, Komla-Ebri D, Mannan NS, Adoum AT, Leitch VD, Logan JG, Waung JA, Ghirardello E, Southam L, Youlten SE, Wilkinson JM, McAninch EA, Vancollie VE, Kussy F, White JK, Lelliott CJ, Adams DJ, Jacques R, Bianco AC, Boyde A, Zeggini E, Croucher PI, Williams GR, Bassett JHD. Nat Commun 12: 2021 467	26
A molecular quantitative trait locus map for osteoarthritis. Steinberg J, Southam L, Roumeliotis TI, Clark MJ, Jayasuriya RL, Swift D, Shah KM, Butterfield NC, Brooks RA, McCaskie AW, Bassett JHD, Williams GR, Choudhary JS, Wilkinson JM, Zeggini E. Nat Commun 12: 2021 1309	39
Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis. Steinberg J, Southam L, Fontalis A, Clark MJ, Jayasuriya RL, Swift D, Shah KM, Brooks RA, McCaskie AW, Wilkinson JM, Zeggini E. Ann Rheum Dis 80: 2021 1070-1074	22
A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis. Katsoula G, Steinberg J, Tuerlings M, Coutinho de Almeida R, Southam L, Swift D, Meulenbelt I, Wilkinson JM, Zeggini E. Hum Mol Genet 31: 2022 2090-2105	10
eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs. Kerimov N, Tambets R, Hayhurst JD, Rahu I, Kolberg P, Raudvere U, Kuzmin I, Chowdhary A, Vija A, Teras HJ, Kanai M, Ulirsch J, Ryten M, Hardy J, Guelfi S, Trabzuni D, Kim-Hellmuth S, Rayner W, Finucane H, Peterson H, Mosaku A, Parkinson H, Alasoo K. PLoS Genet 19: 2023 e1010932	9