A genetics-led approach defines the drug target landscape of 30 immune-related traits. Fang, H., De Wolf, H., Knezevic, B., Burnham, K., Osgood, J., Sekine, T., Berg, L., Göhlmann, H., W., Sanniti, A., Lledó Lara, A., Kasela, S., Wegner, J., K., O’Callaghan, C., A., Bountra, C., Bowness, P., Milani, L., Sundström, Y., Sundström, M., Knight, J., De Cesco, S., Handunnetthi, L., McCann, F., E., Chen, L., Brennan, P., E., & Peeters, P., J. Nature Genetics, 51:1082-1091, Springer Nature, 6, 2019.
abstract   bibtex   
Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4,5,6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease.
@article{
 title = {A genetics-led approach defines the drug target landscape of 30 immune-related traits},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 pages = {1082-1091},
 volume = {51},
 month = {6},
 publisher = {Springer Nature},
 id = {7e803017-e695-39bc-bf4b-8d0868d63d39},
 created = {2020-01-26T16:10:50.691Z},
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 last_modified = {2020-01-26T16:10:50.691Z},
 read = {false},
 starred = {false},
 authored = {true},
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 citation_key = {fang2019atraits},
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 notes = {This is an author version of the article. The final version is available online from the publisher’s website.},
 private_publication = {false},
 abstract = { Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4,5,6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease. },
 bibtype = {article},
 author = {Fang, H and De Wolf, H and Knezevic, B and Burnham, K and Osgood, J and Sekine, T and Berg, L and Göhlmann, H W and Sanniti, A and Lledó Lara, A and Kasela, S and Wegner, J K and O’Callaghan, C A and Bountra, C and Bowness, P and Milani, L and Sundström, Y and Sundström, M and Knight, J and De Cesco, S and Handunnetthi, L and McCann, F E and Chen, L and Brennan, P E and Peeters, P J},
 journal = {Nature Genetics}
}

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