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<jats:title>Abstract</jats:title><jats:p>Human genetics has informed the clinical development of new drugs, and is beginning to influence the selection of new drug targets. Large-scale DNA sequencing studies have created a catalogue of naturally occurring genetic variants predicted to cause loss of function in human genes, which in principle should provide powerful <jats:italic>in vivo</jats:italic> models of human genetic “knockouts” to complement model organism knockout studies and inform drug development. Here, we consider the use of predicted loss-of-function (pLoF) variation catalogued in the Genome Aggregation Database (gnomAD) for the evaluation of genes as potential drug targets. Many drug targets, including the targets of highly successful inhibitors such as aspirin and statins, are under natural selection at least as extreme as known haploinsufficient genes, with pLoF variants almost completely depleted from the population. Thus, metrics of gene essentiality should not be used to eliminate genes from consideration as potential targets. The identification of individual humans harboring “knockouts” (biallelic gene inactivation), followed by individual recall and deep phenotyping, is highly valuable to study gene function. In most genes, pLoF alleles are sufficiently rare that ascertainment will be largely limited to heterozygous individuals in outbred populations. Sampling of diverse bottlenecked populations and consanguineous individuals will aid in identification of total “knockouts”. Careful filtering and curation of pLoF variants in a gene of interest is necessary in order to identify true LoF individuals for follow-up, and the positional distribution or frequency of true LoF variants may reveal important disease biology. Our analysis suggests that the value of pLoF variant data for drug discovery lies in deep curation informed by the nature of the drug and its indication, as well as the biology of the gene, followed by recall-by-genotype studies in targeted populations.</jats:p>

Original publication

DOI

10.1101/530881

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

29/01/2019