Pharmacogenetics at scale: An analysis of the UK Biobank

McInnes, Gregory; Lavertu, Adam; Sangkuhl, Katrin; Klein, Teri E.; Whirl‐Carrillo, Michelle; Altman, Russ B.

doi:10.1101/2020.05.30.125583

Cited by 16 publications

(29 citation statements)

References 39 publications

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“…We investigated drug-gene relationships for nine important pharmacogenes in the UK Biobank for 222,114 participants using primary care data from the National Health System, provided by the UK Biobank 10 . The pharmacogenetic alleles used in this study were derived from a previously reported procedure, described here in brief 8 . The genetic data used in this study was imputed from the Axiom Biobank Array for each participant.…”

Section: Pharmacogenetic Allele Callingmentioning

confidence: 99%

“…Biobanks offer an opportunity to retrospectively assess known drug-gene relationships in a clinical setting as well as offer the opportunity to discover new drug-gene associations. Biobanks and electronic health records have been used to perform targeted association studies between genomics and response to individual drugs 7 as well as characterize frequency of pharmacogenetic alleles in populations 8,9 , but studies of drug response across a large number of drugs have not yet been performed.…”

Section: Introductionmentioning

confidence: 99%

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Drug Response Pharmacogenetics for 200,000 UK Biobank Participants

McInnes

Altman²

2020

Preprint

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Pharmacogenetics studies how genetic variation leads to variability in drug response. Guidelines for selecting the right drug and right dose to patients based on their genetics are clinically effective, but are still widely unused. For some drugs, the normal clinical decision making process may lead to the optimal dose of a drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may observe and adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and drug maintenance dose as well as side effect incidence. We find support for several known drug-gene associations as well as novel pharmacogenetic interactions.

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Section: Pharmacogenetic Allele Callingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drug Response Pharmacogenetics for 200,000 UK Biobank Participants

McInnes

Altman²

2020

Preprint

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“…These variants can affect the metabolism, transport and action of drugs throughout the body and may influence efficacy or lead to adverse events. Studies have shown as many as 99.8% of individuals carry at least one genetic variant that could lead to adverse outcomes for at least one drug [6][7][8] . In the past, clinical practice overlooked the influence of genetics on drug response, and-except for several extreme cases 9 -used a standardized dose of any particular drug for most patients, with some trial-and-adjustment to determine the ideal drug and dosage.…”

Section: Pgx and Iem In Current Clinical Practicementioning

confidence: 99%

Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes

McInnes¹,

Ag²,

Ml³

et al. 2020

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Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

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“…The current reliance on manual curation for scoring of CYP2D6 haplotypes limits the ultimate utility of CYP2D6 phenotype prediction for pharmacogenetic guidance of treatment decisions. It is estimated that individuals carrying CYP2D6 haplotypes with an unknown, uncertain, or uncurated function (herein referred to collectively as uncurated) range from 2 to 9%, with a study of the UK Biobank finding that 3.4% of individuals carry haplotypes that cannot be mapped to a predefined function[ 22 , 23 ]. Individuals carrying a haplotype with uncurated function cannot be assigned to a distinct metabolizer group using the AS and are instead labeled as “Indeterminate”.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning enables prediction of CYP2D6 haplotype function

et al. 2020

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Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

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Pharmacogenetics at scale: An analysis of the UK Biobank

Cited by 16 publications

References 39 publications

Drug Response Pharmacogenetics for 200,000 UK Biobank Participants

Drug Response Pharmacogenetics for 200,000 UK Biobank Participants

Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes

Transfer learning enables prediction of CYP2D6 haplotype function

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