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A genetics-led approach defines the drug target landscape of 30 immune-related traits.

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 challenging4-6. 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.

SUBMITTER: Fang H

PROVIDER: S-EPMC7124888 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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A genetics-led approach defines the drug target landscape of 30 immune-related traits.

Fang Hai H De Wolf Hans H Knezevic Bogdan B Burnham Katie L KL Osgood Julie J Sanniti Anna A Lledó Lara Alicia A Kasela Silva S De Cesco Stephane S Wegner Jörg K JK Handunnetthi Lahiru L McCann Fiona E FE Chen Liye L Sekine Takuya T Brennan Paul E PE Marsden Brian D BD Damerell David D O'Callaghan Chris A CA Bountra Chas C Bowness Paul P Sundström Yvonne Y Milani Lili L Berg Louise L Göhlmann Hinrich W HW Peeters Pieter J PJ Fairfax Benjamin P BP Sundström Michael M Knight Julian C JC

Nature genetics 20190628 7

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 challenging4-6. Here, we show that integration of functional genomic and immune-related annotations, to ...[more]

PMID: 31253980

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Project description:To understand how landscape characteristics affect gene flow in species with diverging ecological traits, it is important to analyze taxonomically related sympatric species in the same landscape using identical methods. Here, we present such a comparative landscape genetic study involving three closely related Hesperid butterflies of the genus Thymelicus that represent a gradient of diverging ecological traits. We analyzed landscape effects on their gene flow by deriving inter-population connectivity estimates based on different species distribution models (SDMs), which were calculated from multiple landscape parameters. We then used SDM output maps to calculate circuit-theoretic connectivity estimates and statistically compared these estimates to actual genetic differentiation in each species. We based our inferences on two different analytical methods and two metrics of genetic differentiation. Results indicate that land use patterns influence population connectivity in the least mobile specialist T. acteon. In contrast, populations of the highly mobile generalist T. lineola were panmictic, lacking any landscape related effect on genetic differentiation. In the species with ecological traits in between those of the congeners, T. sylvestris, climate has a strong impact on inter-population connectivity. However, the relative importance of different landscape factors for connectivity varies when using different metrics of genetic differentiation in this species. Our results show that closely related species representing a gradient of ecological traits also show genetic structures and landscape genetic relationships that gradually change from a geographical macro- to micro-scale. Thus, the type and magnitude of landscape effects on gene flow can differ strongly even among closely related species inhabiting the same landscape, and depend on their relative degree of specialization. In addition, the use of different genetic differentiation metrics makes it possible to detect recent changes in the relative importance of landscape factors affecting gene flow, which likely change as a result of contemporary habitat alterations.

Dataset Information

A genetics-led approach defines the drug target landscape of 30 immune-related traits.

Publications

A genetics-led approach defines the drug target landscape of 30 immune-related traits.

Similar Datasets

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