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Pilot_experiment_on_functional_genomics_in_osteoarthritis_RNA

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). In this pilot we will assess the feasibility of the approach in the relevant tissue. We will obtain diseased and non-diseased tissue (cartilage and endochondral bone) following TJR, coupled with a blood sample, from 12 patients. We will characterise the 12 pairs of diseased and non-diseased tissue samples in terms of transcription (RNASeq) The pilot will help assess the feasibility of isolating sufficient levels of starting material for the different approaches, and will instigate the development of analytical approaches to synthesising the resulting data.

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
EGAD00001001331 Illumina HiSeq 2000 24
Publications Citations
Integrative epigenomics, transcriptomics and proteomics of patient chondrocytes reveal genes and pathways involved in osteoarthritis.
Sci Rep 7: 2017 8935
64
Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis.
Nat Genet 50: 2018 549-558
150
Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data.
Nat Genet 51: 2019 230-236
237
Accelerating functional gene discovery in osteoarthritis.
Nat Commun 12: 2021 467
26
A molecular quantitative trait locus map for osteoarthritis.
Nat Commun 12: 2021 1309
36
Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis.
Ann Rheum Dis 80: 2021 1070-1074
22
A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis.
Hum Mol Genet 31: 2022 2090-2105
10
Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions.
J Transl Med 22: 2024 281
2