bioRxivBioinformaticsPreprint
Pharmacogenetic variants associated with off-target adverse drug reactions are mostly predicted to be benign
Abstract
Tools that predict the functional importance of genetic variation almost always rely on sequence conservation across deep evolutionary divergences as a primary discriminator. However, sequence conservation information is misleading when predicting the functional importance of pharmacogenetic variants related to off-target adverse drug reactions. Sequence conservation is largely maintained by evolutionary purifying selection, which has not been relevant for most drugs until very recently, especially for off-target effects. Here, we use a simple classification criteria to identify variants with off-target pharmacogenetic effects from the PharmGKB database. We show that off-target pharmacogenetic variation is predicted mostly to be benign by all state-of-the-art prediction tools we tested. Hence, off-target pharmacogenetic variants are overwhelmingly invisible to all predictive methodologies currently employed. Very different analytical approaches will be needed to address this important problem.\n\nAuthor SummaryWhen a personal genome sequence is obtained for a given person, the sequence is compared to the human reference sequence to identify where it differs from the genome of that person. One application of this information is that it may identify how a specific person may react to particular drugs. However, when computationally predicting the functional importance of a genetic variant, the tools used rely heavily on sequence conservation information to make their prediction. From an evolutionary point of view, the use of drugs to treat diseases is a very recent activity - and one that has not had time to cause certain variants to either be selected for or removed from the population. This produces a blind-spot for tools that predict variant functional effects, especially for drugs with off-target interactions that may produce unanticipated effects.
- organism: human
- assay: GWAS summary statistics
- research focus: variant effect prediction
- research focus: benchmarking
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 0
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