Validating therapeutic targets through human genetics

By Robert M. Plenge, E. Scolnick, David Altshuler. 2013

688 citations recorded by the source corpus.

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Citations in the corpus

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016, background): “2633265 networks is critical in understanding the functionality and control decisions enacted by the network in tasks such as cell differentiation, cell death, or directing metabolic flux [6]–[9].”
Evaluating drug targets through human loss-of-function genetic variation (2019, unknown): “evidence that the compound engages its target, too often the target itself is not causally related to human disease 1 .”
Developing medicines that mimic the natural successes of the human genome: lessons from NPC1L1, HMGCR, PCSK9, APOC3, and CETP. (2015, background): “Why were these results not anticipated? Two reasons stand out (5).”
Genetics of rheumatoid arthritis contributes to biology and drug discovery (2013, unknown): “A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA) 1 .”
The druggable genome and support for target identification and validation in drug development (2016, background): “For example, immune system–related genetic variants associate with the risk of developing type 1 diabetes, but useful therapies arising from this knowledge may be difficult to realize, because by the time the disease is diagnosed, immune-mediated damage to the pancreatic b cells may be too advanced ”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, background): “It is anticipated that this will lead to moresuccessful drug development [6] , and potentially to more efficient and personalised treatments and to better diagnoses.”
Evaluating potential drug targets through human loss-of-function genetic variation (2019, background): “Constraint differs from evolutionary conservation in that constraint (1) informs on selection in humans, not other species; (2) primarily reflects selection against variants in a heterozygous state; and (3) can more finely discriminate strong versus weak selection signals(39,40).”
The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 (2018, background): “The Catalog’s data have many applications, for example, to identify causal variants (17), understand disease mechanisms, analyse expression quantitative trait loci (eQTL) (18,19) and establish targets for novel therapies (8,20).”