Johansson, I. | bioRxiv Science

bioRxiv2020

A unifying model to predict variable drug response for personalised medicine

Pharmacogenomics is a key component of personalized medicine. It promises a safer and more effective drug treatment by individualizing the choice of drug and dose based on an individuals genetic profile1,2. The majority of commonly prescribed drugs are metabolized by a small set of Cytochrome P450 (CYP) enzymes3. In clinical practice, genetic biomarkers are being used to categorize patients into predefined *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. Yet, this approach has important limitations as it leaves a large part of variability in drug response unexplained4,5. Here, we present a novel approach and introduce a continuous scale (instead of categorical) assignments to predict metabolic enzyme activity. The proposed strategy uses full gene sequencing data, a neural network model and CYP2D6 mediated tamoxifen metabolism from a prospective study of 561 breast cancer patients. The model explains 79% of the interindividual variability in CYP2D6 activity compared to 54% with the conventional approach. It is capable of assigning accurate enzyme activity to alleles containing previously uncharacterized combinations of variants and were replicated in an independent cohort of tamoxifen treated patients, a cohort of Venlafaxine users as well as in vitro functional assays using HEK cells. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP450 enzyme activity and thereby enables more accurate prediction of individual drug response.

pharmacology and toxicology↗

bioRxiv2019

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis.

BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messagesO_LIThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion. C_LIO_LISystematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization. C_LIO_LIUsing this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health. C_LIO_LIThe method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. C_LIO_LIWe developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries. C_LI

genetics↗