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An optimized prediction framework to assess the functional impact of pharmacogenetic variants.

ABSTRACT: Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1-50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC6462826 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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An optimized prediction framework to assess the functional impact of pharmacogenetic variants.

Zhou Yitian Y Mkrtchian Souren S Kumondai Masaki M Hiratsuka Masahiro M Lauschke Volker M VM

The pharmacogenomics journal 20180912 2

Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved ...[more]

PMID: 30206299

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Project description:BackgroundPharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools-these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect.MethodsHere we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations.ResultsAs previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as 'benign'. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as 'benign'. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms.ConclusionIn this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

Project description:BackgroundHealth and medical research funding agencies are increasingly interested in measuring the impact of funded research. We present a research impact case study for the first four years of an Australian National Health and Medical Research Council funded Centre of Research Excellence in Cardiovascular Outcomes Improvement (2016-2020). The primary aim of this paper was to explore the application of a research impact matrix to assess the impact of cardiovascular outcomes improvement research.MethodsWe applied a research impact matrix developed from a systematic review of existing methodological frameworks used to measure research impact. This impact matrix was used as a bespoke tool to identify and understand various research impacts over different time frames. Data sources included a review of existing internal documentation from the research centre and publicly available information sources, informal iterative discussions with 10 centre investigators, and confirmation of information from centre grant and scholarship recipients.ResultsBy July 2019, the impact on the short-term research domain category included over 41 direct publications, which were cited over 87 times (median journal impact factor of 2.84). There were over 61 conference presentations, seven PhD candidacies, five new academic collaborations, and six new database linkages conducted. The impact on the mid-term research domain category involved contributions towards the development of a national cardiac registry, cardiovascular guidelines, application for a Medicare Benefits Schedule reimbursement item number, introduction of patient-reported outcome measures into several databases, and the establishment of nine new industry collaborations. Evidence of long-term impacts were described as the development and use of contemporary management for aortic stenosis, a cardiovascular risk prediction model and prevention targets in several data registries, and the establishment of cost-effectiveness for stenting compared to surgery.ConclusionsWe considered the research impact matrix a feasible tool to identify evidence of academic and policy impact in the short- to midterm; however, we experienced challenges in capturing long-term impacts. Cost containment and broader economic impacts represented another difficult area of impact to measure.

Project description:Structural variants which cause changes in copy numbers constitute an important component of genomic variability. They account for 0.7% of genomic differences in two individual genomes, of which copy number variants (CNVs) are the largest component. A recent population-based CNV study revealed the need of better characterization of CNVs, especially the small ones (<500 bp).We propose a three step computational framework (Identification of germline Changes in Copy Number or IgC2N) to discover and genotype germline CNVs. First, we detect candidate CNV loci by combining information across multiple samples without imposing restrictions to the number of coverage markers or to the variant size. Secondly, we fine tune the detection of rare variants and infer the putative copy number classes for each locus. Last, for each variant we combine the relative distance between consecutive copy number classes with genetic information in a novel attempt to estimate the reference model bias. This computational approach is applied to genome-wide data from 1250 HapMap individuals. Novel variants were discovered and characterized in terms of size, minor allele frequency, type of polymorphism (gains, losses or both), and mechanism of formation. Using data generated for a subset of individuals by a 42 million marker platform, we validated the majority of the variants with the highest validation rate (66.7%) was for variants of size larger than 1 kb. Finally, we queried transcriptomic data from 129 individuals determined by RNA-sequencing as further validation and to assess the functional role of the new variants. We investigated the possible enrichment for variant's regulatory effect and found that smaller variants (<1 Kb) are more likely to regulate gene transcript than larger variants (p-value = 2.04e-08). Our results support the validity of the computational framework to detect novel variants relevant to disease susceptibility studies and provide evidence of the importance of genetic variants in regulatory network studies.

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An optimized prediction framework to assess the functional impact of pharmacogenetic variants.

Publications

An optimized prediction framework to assess the functional impact of pharmacogenetic variants.

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