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Review ARTICLE

Front. Pharmacol., 04 December 2018 | https://doi.org/10.3389/fphar.2018.01437

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Yitian Zhou1, Kohei Fujikura2, Souren Mkrtchian1 and Volker M. Lauschke1*
  • 1Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
  • 2Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

Introduction

Inter-individual differences in drug response are clinically important phenomena that result in reduced efficacy or adverse reactions in 25–50% of all patients and genetic factors have been estimated to account for around 20–30% of these (Spear et al., 2001; Sim et al., 2013). Fueled by technological advances in Next-Generation Sequencing (NGS) technologies, the application of comprehensive sequencing approaches is on the rise for various applications, including studies of biodiversity, population genetics and biomedical research (Levy and Myers, 2016). Furthermore, plummeting costs to < 1,000 USD per human genome and increasing worldwide sequencing capacities that we estimate to exceed 100 petabases per year (1015 bases corresponding to the size of around 100,000 human genomes) open tremendous possibilities for NGS to revolutionize precision medicine.

Strikingly, these massive NGS data sets revealed that individuals harbored on average more than 3.7 million single nucleotide variants (SNVs) and more than 350,000 insertions and deletions across different populations, emphasizing the substantial variability of the human genome (The 1000 Genomes Project Consortium, 2012). Particularly genes involved in drug absorption, distribution, metabolism and excretion (ADME) proved to be highly diverse and genetically complex (Fujikura et al., 2015; Bush et al., 2016; Kozyra et al., 2017). Across 208 ADME genes more than 69,000 SNVs have been described, 98.5% of these being rare with minor allele frequencies (MAF) < 1% (Ingelman-Sundberg et al., 2018). The overall pharmacogenetic variability was highly population specific, particularly for isolated populations, such as Ashkenazi Jews (Ahn and Park, 2017; Kozyra et al., 2017; Zhou and Lauschke, 2018). Given this enormous pharmacogenetic variability, one of the key frontiers of contemporary pharmacogenomics is the translation of these comprehensive genomic data into clinically actionable treatment recommendations (Lauschke and Ingelman-Sundberg, 2016a, 2018).

Heterologous expression in cell lines followed by quantitative determination of gene product functionality using appropriate end points is considered as the gold standard strategy to characterize the functional impact of pharmacogenetic variants. Furthermore, epidemiological association studies can provide additional indications about the consequences of genetic variants on drug metabolism related phenotypes in vivo. However, for the functional interpretation of rare variants these approaches suffer from multiple shortcomings:

i) These methods are generally low throughput and are not compatible with the interrogation of tens of thousands of variants.

ii) Experimental characterizations are time consuming, expensive and require specially trained technical staff, which renders them unsuitable for the rapid functional interpretation of the pharmacogenotype of an individual patient at the point of care.

iii) Epidemiological analyses require a sufficient number of patients who carry the allele, which drastically limits their feasibility for rare genetic variant studies.

Thus, in the absence of viable experimental strategies, computational prediction methodologies are routinely used to predict the functional impact of genetic variants. Most of these algorithms focus on predicting the functional consequences of variants that result in amino acid substitutions. However, recently much progress has also been made regarding the interpretation of non-coding variants that affect splice sites, promoters, enhancers or miRNA binding sites (Figure 1).

FIGURE 1
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Figure 1. Overview of features that can be assessed by current computational prediction methods. Different parameters and features are assessed for genetic variants depending on whether they are localized in putatively regulatory sequences, untranslated regions (UTR) of the gene, its coding sequences (CDS) or within introns. ESE/ESS, exonic splicing enhancer/silencer; ISE/ISS, intronic splicing enhancer/silencer; NMD, nonsense-mediated decay; RBP, RNA binding protein.

Prediction algorithms are generally trained on pathogenic variant sets and most tools base their conclusions, at least in part, on the evolutionary conservation of the respective sequence. Importantly however, pharmacogenes are hallmarked by low evolutionary conservation and are generally not associated with human disease. These peculiarities result is specific problems for the interpretation of pharmacogenetic variants. Here, we provide an updated overview of computational approaches for the functional interpretation of genetic variants, specifically focusing on their suitability for pharmacogenetic predictions. We describe the underlying statistical frameworks and discuss their different bases for decision-making. Furthermore, we highlight important progress particularly in the interpretation of non-coding genetic variability. We conclude that computational tools are essential for the functional interpretation of an individual's pharmacogenotype and that their further improvement constitutes one of the most important frontiers for the clinical implementation of NGS-based genotyping.

Interpretation of Variants Resulting in Amino Acid Exchanges

Genetic variants that result in amino acid substitution, henceforth termed missense variants, can impact the functionality of the respective protein by various mechanisms, including alterations in active sites, structural destabilization due to protein misfolding, perturbations in solvent accessibility or modification of post-translational processing. Each individual harbors 10,000–12,000 missense variants, many of which are rare (The 1000 Genomes Project Consortium, 2015). These rare variants have been suggested as important modulators of complex disease risk (Kryukov et al., 2007) and inter-individual differences in drug response (Kozyra et al., 2017). Among all variant classes, missense variants are the most extensively studied and a plethora of computational methods is available for their functional interpretation. Conceptually, these algorithms predict the functional impact of missense variants based on sequence information, primarily evolutionary conservation of the respective residues, and/or structural information of the corresponding gene product. In the following, we highlight recent progress, provide an overview of available tools and discuss their utility for pharmacogenetic predictions. For methodological details we refer the interested reader to excellent recent reviews (Ng and Henikoff, 2006; Peterson et al., 2013; Tang and Thomas, 2016).

Predictions Based on Sequence Information

Evolutionary conservation scores are calculated by analyzing the evolutionary variation dynamics of DNA or amino acid sequences among homologs with the hypothesis that the extent of conservation is a strong predictor of the importance of the respective sequence for structure and function of the corresponding gene product. Thus, positions with a high evolutionary rate are thought to be dispensable, whereas slowly evolving, i.e., conserved sequences indicate a selective pressure against variation in these regions and thus deleterious effects if mutated.

Evolutionary conservation as a metric to distinguish deleterious from neutral variants is considered by most computational prediction algorithms. The majority of approaches that focus on the functional interpretation of missense variants utilize amino acid sequence alignment, whereas others utilize nucleotide sequence alignments or a combination of both methods (Table 1). While alignment of amino acid sequence proved to be effective for the analysis of missense variants, genomic sequence alignments provide additional versatility and allow to extend functional interpretations to variant classes that do not alter the amino acid sequence, such as synonymous and regulatory variants. Notably, commonly used conservation-based functionality predictors do not consider sequence interdependencies. Explicit integration of residue dependency information obtained from multiple sequence alignments was however recently shown to improve predictive performance (Hopf et al., 2017), emphasizing the added value of complementing conservation based functionality predictions with variant interaction data.

TABLE 1
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Table 1. Methods to predict the functional effect of missense variants based on sequence information.

On the basis of multiple sequence alignments, algorithms derive their functionality predictions either based on direct theoretical models, or by various machine-learning approaches. The former methods predict the functional impact of variants based on phenomenological scores derived from theoretical models that are known a priori. In contrast, machine learning methods search for patterns in multi-dimensional training data sets consisting of labeled deleterious and benign variations, which will then be used as the basis to generate predictions on new unlabeled data. Machine learning approaches include support vector machines, random forests, artificial neural networks, naive Bayes approaches, gradient tree boosting and regression models. With increasing wealth of large-scale data sets to learn from, machine learning methods become increasingly popular as versatile tools to generate predictive models in many areas of biomedicine (Camacho et al., 2018).

Commonly used algorithms are generally designed to flag deleterious variants, which are mostly assumed to result in a reduced gene product function, and their performance of gain-of-function variants is substantially worse (Flanagan et al., 2010). Notably, the algorithm B-SIFT, a modified version of the widely used SIFT tool (Ng and Henikoff, 2001), was developed to overcome this limitation (Lee et al., 2009). Conceptually, B-SIFT identifies increased functionality variants based on protein sequence alignments by scoring whether a given mutation results in a change commonly present in protein homologs and the tool successfully identified experimentally validated gain-of-function variants in cancer.

While computational missense variant predictors are generally reported to achieve high predictive accuracies with areas under the receiver operating characteristic curve (AUCROC) that often pivot around 0.9, drastic drops in performance to AUCROC of 0.5–0.75 have been reported on independent, functionally determined human variant datasets (Mahmood et al., 2017). These findings were corroborated by a recent cross-comparison of 23 methods based on three independent pathogenicity datasets in which the authors found that REVEL and VEST3 performed overall best, whereas the most commonly used methods SIFT and PolyPhen-2 performed only medially (Li et al., 2018). Furthermore, no functional consequences could be detected using various in vitro or in vivo tools for 40% of variants predicted to be deleterious by common functionality prediction tools (Miosge et al., 2015). Thus, while current tools have proven powerful in clinical diagnostics to prioritize potentially causative mutations in genetic diseases for further analyses (Boycott et al., 2013), their predictive power is not yet sufficient to predict functional variant effects without substantial subsequent validations.

Importantly, the quality of prediction models critically relies on accurate training data sets. For instance, models are commonly generated using training sets of pathogenic variants as positive controls and polymorphisms identified to be common in large-scale sequencing projects as negative, i.e., functionally neutral variants. For pharmacogenetic predictions such a strategy is associated with multiple problems: Firstly, training on disease-associated data sets will, in the best case, result in prediction models that accurately predict the pathogenicity of variants. However, only very few ADME genes are directly associated with disease, suggesting that pathogenicity is not the right endpoint to inform about variant effects in the pharmacogenetic arena. Secondly, while evolutionary conservation constitutes a useful metric to predict functional consequences in genes under purifying selection, evolutionary conservation in pharmacogenes is generally much lower (Fujikura, 2016), indicating that conservation cannot reliably inform about functional impacts of variations in pharmacogenes. Finally, the choice of common polymorphisms as neutral training sets is problematic. Genetic variants that occur with high frequencies are not necessarily functionally neutral, particularly in pharmacogenetic loci, as evidenced by a multitude of high-frequency loss of function variants in CYP genes, such as CYP3A5*3 (MAF = 95% in Europeans), CYP2C19*2 (MAF = 34% in South Asians) and CYP2D6*4 (MAF = 16% in Latinos) (Zhou et al., 2017).

The indicated problems incentivized us to develop a prediction framework tailored specifically toward pharmacogenetic functionality assessment (Zhou et al., 2018). Specifically, the model was devised using a two-step procedure: Firstly, functionality classification threshold of 18 commonly used functional prediction algorithms were optimized by leveraging a dataset of 337 experimentally characterized pharmacogenetic variants using 5-fold cross validations. In a second step, we integrated the best performing orthogonal algorithms following a strategy that had been shown to further improve predictive accuracy (Martelotto et al., 2014). The resulting method achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants. Moreover, the returned score can provide quantitative estimates of the effect of the variant in question on gene function, thus facilitating the functional and personalized interpretation of an individual's NGS-based pharmacogenome.

Recent progress in large-scale experimental mutagenesis screens provides a promising approach to further expand the development of powerful training resources for missense variant effect predictors. While such a strategy has already been used to develop a prediction method based on 10 proteins from different species with disparate structures (Gray et al., 2018), we propose that deep mutational scanning data from ADME proteins is likely to substantially refine the resulting model for pharmacogenetic predictions. For such an endeavor, we recommend to use multiple substrates for each protein, as correlations between prediction and experiments improved with more comprehensive interrogation of protein function (Gallion et al., 2017). Combined with ADME-optimized prediction models, we envision that such an approach can further enhance the predictive accuracy of in silico methods and yield sufficiently accurate tools to allow for the clinical implementation of computational pharmacogenetic predictions.

Utilization of Structural Data

While evolutionary conservation scores can provide useful metrics to assess the pathogenicity of missense variants, they have limitations when applied to the less conserved genes, such as most ADME genes, which prompted the search for additional orthogonal in silico methods. To this end, the analysis of predicted or experimental structural data provides an appealing concept, as the correct folding of polypeptide chains into three-dimensional tertiary structures is of paramount importance for their biological functions. Structure-based approaches either directly use known crystal or NMR structures, preferably at high resolution < 2–3 Å (Wlodawer et al., 2008) or, should such data not be available, leverage knowledge of the experimental 3D structures of homologous sequences (Table 2).

TABLE 2
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Table 2. Methods to predict the functional effect of missense variants based primarily on structural features.

The effect of variants is predicted by how the folding free energy difference between the unfolded and folded states (ΔG°) is modified upon point mutations (ΔΔG°) with negative and positive values of ΔΔG° indicating destabilizing and stabilizing mutations, respectively. In recent years a large number of mechanistically diverse approaches have been presented, with machine learning-based strategies being most prevalent. SDM constitutes a statistical potential energy function that can estimate variant effects on protein stability (Topham et al., 1997). This approach pioneered the knowledge-based prediction of mutation effects on protein stability and has also been successfully used in combination with machine learning techniques (Pires et al., 2014a). An updated version of the tool, SDM2 (Pandurangan et al., 2017), with a 5-fold increase in underlying structural information as well as extensions for interaction modeling can be accessed through a free, publically available web server interface. Similarly, the algorithm HOPE (Venselaar et al., 2010) can calculate structural and functional effects of amino acid exchanges based on homology modeling. It should be however noted that most of the current tools are strongly biased toward the detection of destabilizing effects (Pucci et al., 2018).

Approximately 70% of the human proteome can be structurally modeled by homology (Somody et al., 2017). Yet, the number of resolved 3D structures for genes involved in drug ADME remains relatively low, at least in part due to the membrane bound nature of many of these proteins. Furthermore, as many metabolic enzymes, such as cytochrome p450s (CYPs) exhibit marked active-site flexibility, which often results in ligand-induced conformational changes, prediction of variant effects based on direct structural data is difficult for these proteins and substrate-specific effects have to be considered. Thus, while the prediction of amino acid exchanges on substrate metabolism remain difficult, folding stability of variant proteins of interest can be estimated using existing computational tools based on sequence homology modeling (Kulshreshtha et al., 2016).

Evaluation of Truncation Variants

Drug metabolizing enzymes and transporters have been found to harbor a multitude of truncation variants, such as micro-insertions and micro-deletions (indels) causing frameshifts, stop-gain and start-lost variants. Some of these variants are clinically relevant and occur with high frequencies in specific populations, including the stop-gain variant CYP2C19*3 in East Asians and the frameshift variants CYP2D6*3 and CYP2D6*6 in Europeans (Zhou et al., 2017). As most pharmacogenes have only minor endogenous functions, they are under low evolutionary pressure and, consequently, such loss-of-function variants are often not selected against (Lauschke et al., 2017). Moreover, it has been speculated that pharmacogenetic loss-of-function alleles can even be selected for in modern humans, possibly due to reduced bioactivation of dietary toxicants (Fujikura, 2016). Truncation variants are commonly assumed to have deleterious effects and only few studies have been presented that provide approaches to quantitatively assess the functional consequences of such mutations (Cline and Karchin, 2011).

Early bioinformatic tools, such as LOFTEE, prioritize truncation variants based on a set of empirical rules, including whether the variant of interest occurs in the last 5% of transcript or whether the truncating allele is the ancestral state (MacArthur et al., 2012). Other approaches, such as Likelihood-ratio scoring (Zia and Moses, 2011), SIFT Indel (Hu and Ng, 2012) and NutVar (Rausell et al., 2014), primarily utilize the evolutionary conservation of amino acid residues. However, predictive performance of these tools for loss-of-function mutations is limited when trained on only missense mutations. Moreover, these methods are trained on genes that have high-quality annotations, which poses problems for the functional interpretation of truncation variants in genes for which such annotations are not readily available.

To overcome these shortcomings, CADD was developed by integrating many diverse functional genomics annotations into a single score for each variant, which allows to estimate the impact of all classes of genetic variation, including truncating variants (Kircher et al., 2014). Newer approaches, such as DDIG-in (Folkman et al., 2015) and VEST-Indel (Douville et al., 2016) supplement conservation-based features with information about sequence and structural properties at nucleotide and protein levels as well as intrinsic disorder predictions from the region affected by stop gain and frameshift variants. Notably, the recently developed tool ALoFT (Annotation of Loss-of-Function Transcripts) can categorize the pathogenic importance of putative loss-of-function mutations by integrating variant information with redundancy and haplosufficiency data of the corresponding gene (Balasubramanian et al., 2017). However, aforementioned methods are primarily focused on distinguishing benign and disease-causing mutations. Thus, future studies are needed to evaluate whether this emphasis on the pathogenicity of variants might affect the performance of these methods regarding the functionality prediction of truncating variants in genes not associated with disease, such as many ADME genes.

In addition to impacts on functional and structural properties of proteins, truncating variants can affect nonsense-mediated mRNA decay (NMD). NMD is a conserved translation-dependent mechanism that is responsible for recognizing and eliminating aberrant mRNA transcripts to prevent the production of truncated peptides, thereby playing a critical role in preventing the accumulation of misfolded protein and subsequent initiation of the unfolded protein response (UPR) (Kervestin and Jacobson, 2012; Schoenberg and Maquat, 2012). Recently, Hsu et al. presented NMD Classifier, a tool for the systematic classification of NMD events, which was reported to correctly identify 99.3% of the NMD-causing transcript structural changes (Hsu et al., 2017). The incorporation of this information alongside functional estimates is expected to not only increase discriminative power but also to suggest the nature of the functional impact of a given variant. Interestingly, there is evidence that NMD efficiency varies between individuals and that these differences correlate with response to NMD inhibitors in cystic fibrosis patients (Linde et al., 2007; Kerem et al., 2008). While this phenomenon has to the best of our knowledge not been explicitly tested in the context of pharmacogenomics, inter-individual differences in NMD magnitude could, at least in part, explain the large differences in drug response between patients with loss-of-function genotypes (Jukić et al., 2018) and thus have important implications for therapy.

In summary, much progress has been made regarding the functional interpretation of variants causing truncations of the corresponding gene product and current computational tools are able to incorporate a variety of features into their predictions, including evolutionary conservation, sequence and structural information as well as putative effects on NMD. However, it remains to be demonstrated whether these available tools will also be suitable for the prediction of effects of truncation variants in poorly conserved pharmacogenetic loci.

Prediction of Aberrant Splicing Events

Splicing of pre-mRNA is a critical step during mRNA maturation in which introns are excised and exons are ligated. This process necessitates the presence of 5′ and 3′ splicing signals and branch point sequence and is further regulated by exonic and intronic splicing enhancer/silencer (ESE/ESS and ISE/ISS, respectively) (Lee and Rio, 2015; Shi, 2017). Mutations in these regions can disrupt the splicing process and result in aberrantly processed transcripts, which can trigger NMD or result in the production of dysfunctional proteins. The functional importance of genetic variants in splice sites is emphasized by estimates that around 15% of human pathogenic mutations cause dysregulation of splicing (Baralle et al., 2009).

Variants located in canonical splice sites are considered having the largest effect on splicing events. Therefore, a multitude of computational algorithms were developed to handle the prediction of 5′ and 3′ splice site, such as NNSplice (Reese et al., 1997), MaxEntScan (Yeo and Burge, 2004), GeneSplicer (Pertea et al., 2001), and SplicePort (Dogan et al., 2007; Table 3). Moreover, variants outside splice sites can have substantial effects on splicing (Soukarieh et al., 2016) and a variety of computational methods have been developed to predict the effect of such regulatory sequences. Examples are sequence the conservation-based algorithm Skippy (Woolfe et al., 2010) and the machine learning tools MutPred Splice (Mort et al., 2014), scSNVEL (Jian et al., 2014b), SPANR (Xiong et al., 2015), and CryptSplice (Lee et al., 2017). Further tools are available for the identification of branch point sequences (Corvelo et al., 2010; Zhang et al., 2017). Lastly, the secondary structure of pre-mRNAs can interfere with splice-site recognition, modulate spliceosome binding or can facilitate splicing efficiency by bringing splice donors and acceptors into close proximity (Warf and Berglund, 2010). Consequently, genetic variants that alter pre-mRNA structure were found to promote alternative splicing (Wan et al., 2014), incentivizing the incorporation of structural information provided by tools, such as TurboFold (Harmanci et al., 2011) or CentroidFold (Sato et al., 2009), into variant effect predictions. For a more detailed description of structural RNA analyses we refer the interested reader to excellent recent reviews (Jian et al., 2014a; Lorenz et al., 2016; Ohno et al., 2018).

TABLE 3
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Table 3. Tools for the prediction of variant effects on splicing, transcript levels or translation.

In ADME genes, dysregulation of splicing has long been recognized as a cause for inter-individual variability drug metabolism (Hanioka et al., 1990) and toxicity (Raida et al., 2001) and the liver was found to be is among the tissues with highest levels of alternative splicing activity (Yeo et al., 2004). As splicing is highly tissue specific, these data indicate that algorithms for the prediction of variant splice effects in pharmacogenetics should ideally be trained on positive control sets for which aberrant splicing is confirmed in the tissue of interest, i.e., primarily liver. To this end, the GTEx project (GTEx Consortium, 2017) provides a rich resource that has already been successfully utilized for the identification of tissue-specific splice events in pharmacogenes (Chhibber et al., 2017).

In summary, the toolkit of available computational algorithms for the prediction of variant effects on splicing has rapidly grown and by now allows not only to evaluate direct impact on splice sites, but also to assess mutations in regulatory splice enhancers and silencers, as well as branch points. For the application of these methods for pharmacogenomics there is a need to benchmark available tools on splice variants in ADME genes. Moreover, we anticipate that the utilization of tissue-specific expression data will further refine splice site predictions.

Functional Impact of Variants in Untranslated Regions

miRNAs play important roles in the regulation of mRNA stability and translation. miRNA-mRNA interaction occurs through conserved miRNA binding sites in the 3′-UTRs and at least 10% of all SNPs are located in 3′-UTRs and might affect complementary miRNA-mRNA pairing (Xiao et al., 2009). Furthermore, miRNAs have been shown to be important modulators of ADME gene expression profiles (Rieger et al., 2013). Therefore, functional interpretation of genetic variations within miRNA target sites constitutes an important factor for the prediction of the fate of corresponding transcript. Thus, to evaluate the potential relevance of genetic polymorphisms in UTRs various databases, such as the polymiRTS Database 3.0 (Bhattacharya et al., 2014) or MirSNP (Liu et al., 2012), provide useful resources that contains a collection of experimentally confirmed SNPs and indels not only in miRNA target sites but also in miRNA seed regions responsible for mRNA binding. Furthermore, a variety of other SNP effect prediction servers are publically available (Fehlmann et al., 2017).

In case no experimental data is available, various computational tools can be used to predict possible disruption of the miRNA-mRNA pairing for a given variant (Table 3). MicroSNiPer (Barenboim et al., 2010) and ImiRP (Ryan et al., 2016) identify and predict such disruptions by comparing the mutant 3′-UTR sequences with major variant databases. Similarly, mrSNP can predict the effect of any variant identified in NGS-based projects on miRNA-target transcript interaction (Deveci et al., 2014). However, it is important to note that miRNA target predictions seem to have a high false-positive rate (Pinzón et al., 2017), suggesting that these problems might be lingering for studies utilizing miRNA-target databases without stringent experimental validations. Besides predicting the effect of genetic variants in putative miRNA target sites, multiple online tools are available for inverse approaches, analyzing variants in miRNAs or pre-miRNAs for possible deleterious effects. For more comprehensive collection of miRNA related variant interpretation tools the reader is referred to the recent reviews and online resources (Akhtar et al., 2016; Moszynska et al., 2017).

In addition, recent approaches expanded the methodological portfolio beyond miRNA binding site prediction to include effects of UTR variants on binding of RNA-binding proteins (RBPs), translational efficacy and ribosomal loading. Effects of indels on RBP binding can be evaluated using PinPor, which has been demonstrated to have some success in distinguishing disease-causing and neutral indels (Zhang et al., 2014). Furthermore, Sample et al. presented the preprint of a deep learning approach based on experimental polysome profiling to predict the impact of UTR sequence on translation (Sample et al., 2018). These developments nicely indicate the diversification of parameters that can incorporated into variant effect predictions, thus further refining biological interpretation of NGS data sets.

Analysis of Regulatory Variants

Non-coding regions account for more than 99% of the human genome and, consequently, their consideration substantially expands the analysis space of computational predictions. Variants in non-coding regions can affect regulatory elements, such as promoters, enhancers, silencers, and insulators, which, in turn, may alter their affinity to transcription factor or remodel the local chromatin structure (Zhang and Lupski, 2015; Deplancke et al., 2016). Accurate prediction of the functional consequences of such variants constitutes one of the major challenges in human genetics.

To interpret noncoding variants, a variety of different strategies have been presented. The first approaches, such as SiPhy (Garber et al., 2009), PhyloP (Pollard et al., 2010), PhastCons (Siepel et al., 2005), GERP++ (Davydov et al., 2010), or SCONE (Asthana et al., 2007), were based on evolutionary constraint using sequence alignments. However, the observation that no enhanced constraints were identified in regulatory elements at the level of DNA sequence despite conserved transcription factor binding led to the realization that conservation of regulatory regions can only be a weak indicator of the functional effects of SNVs in regulatory regions (Schmidt et al., 2010; Arbiza et al., 2013). Consequently, conservation metrics were complemented with additional functional genomics features, such as the sequence and genic context, transcription factor binding profiles (Johnson et al., 2007), histone modification data (Zhang et al., 2010) and DNase I hypersensitive sites (Boyle et al., 2008) in an attempt to improve prediction quality. Based on these rich data sets, a variety of ensemble classifiers were developed using various machine learning approaches that aim to distinguish neutral from pathogenic variants, including GWAVA (Ritchie et al., 2014), CADD (Kircher et al., 2014), FATHMM (Shihab et al., 2013, 2015; Rogers et al., 2018), DANN (Quang et al., 2015), DIVAN (Chen et al., 2016), and Genomiser (Smedley et al., 2016) (Table 4).

TABLE 4
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Table 4. Algorithms for the functional interpretation of regulatory variants.

In contrast, other methods, such as gkm-SVM (Lee et al., 2015) and DeepSEA (Zhou and Troyanskaya, 2015) have been developed to predict regulatory elements based on primary sequence alone. Trained on publically available cell type-specific chromatin data provided by ENCODE (The ENCODE Project Consortium, 2012) and the Roadmap Epigenomics Project (Roadmap Epigenomics Consortium et al., 2015) as well as transcription factor binding patterns accessible via JASPAR (Khan et al., 2018), these algorithms predict to what extent a genetic variant will cause changes to the local chromatin profiles and how these effects translate into functional consequences. The resulting data demonstrate that inferring consequences from functional genomics data is highly cell type and context specific and relies on biologically appropriate training sets. These convincing findings incentivize the generation of functional genomics data from carefully phenotyped human tissues involved in drug ADME to derive tissue-specific regulatory lexica and we envision that training machine learning approaches on these data sets will substantially increase the power of regulatory pharmacogenetic prediction classifiers.

As with coding variants, the use of potentially biased training sets and multi-dimensional circularity between training and test data constitutes an inherent problem for current variant prediction tools (Grimm et al., 2015). For instance, a variety of algorithms consider common variants from the 1000 Genomes project as functionally neutral control sets for model training. However, while these variants are likely to be depleted of pathogenic variants in haploinsufficient genes, many common variants entail functional consequences in their respective gene product, particularly if the gene is rapidly evolving, such as many CYP genes. Similar problems arise when the model is trained using phenotype associated GWAS polymorphisms as functional variant sets, as only 5.5% of GWAS index SNPs are estimated to be causal whereas the remainder is only in linkage disequilibrium with the true functional variant in the locus (Farh et al., 2015).

To overcome these problems, unsupervised approaches have been developed that do not rely on the labeling of training data, thereby reducing the dependence on preexisting variant classifications and existing models of mutation. These unsupervised models, such as GenoCanyon (Lu et al., 2015) and Eigen (Ionita-Laza et al., 2016), represent powerful tools for the genome-wide interpretation of variants. However, as they are calibrated on genome-wide data, it remains to be determined whether gene class-specific peculiarities, such as low evolutionary conservation in ADME genes, might affect the predictive accuracy of these approaches for pharmacogenetic applications.

Conclusions

Technical progress in NGS technology has resulted in its routine application in medical genetics and clinical diagnostics. In contrast, clinical implementation of NGS-based pharmacogenomics is largely lagging behind (Lauschke and Ingelman-Sundberg, 2016b; Ji et al., 2018). Most importantly, in order to utilize the major advantage of NGS-based genotyping, which is the discovery of the entire panorama of the individual's genetic portfolio, tools have to be in place, which allow to translate these variability data into functional consequences and clinical recommendations. Whereas, the identification of rare putatively deleterious mutations in congenital diseases is aided by clear phenotypic alterations of the affected patient and the possibility to perform comparative genomic analyses of unaffected family members, pharmacogenomic phenotypes are generally more difficult to detect as they only present in a given context, such as exposure to specific medications. In the absence of drug response associations or experimental characterizations that support the functional interpretation of rare variants, there is thus an urgent need for reliable computational prediction tools to fill this space.

Importantly, recent developments in computational variant effect prediction methods promise to narrow the gap to meet the exacting demands on genomics applications in the clinics. Machine learning constitutes an important tool kit to fully harness the power of large data sets provided by NGS. However, these approaches rely on accurate labeling of input variants, i.e., training data need to be correctly classified into deleterious and functionally neutral variants. Thus, we advocate for approaches that leverage smaller data sets of variants for which comprehensive experimental or functional genomic data is available instead of training algorithms on large but functionally poorly annotated data, such as treating all common polymorphisms identified in the 1000 Genomes Project as functionally neutral. In addition, we endorse previous appeals for the sharing of codes and data sets, which will enable comparative benchmarking of newly developed tools and algorithms and will accelerate research progress within the area of computational pharmacogenomics and beyond (Kalinin et al., 2018).

The functional consequences of missense variants have been most extensively studied. Respective methods base their predictions on evolutionary conservation and structural information of the polypeptide encoded by the respective gene. Importantly, while evolutionary conservation is a suitable measure to inform about the deleteriousness of a variant, i.e., its effect on organismal fitness, it is not suitable for the prediction of variant effects in genes under low selective pressure, such as most pharmacogenes. Recognition of these conceptual problems resulted in the development of computational predictors trained specifically on ADME missense variants (Zhou et al., 2018). We envision that these approaches will become more powerful with increasing functionally annotated pharmacogenetic variant data.

Furthermore, multiple strategies have been developed to analyze the functional impact of variants in non-coding regions of the genome, which are increasingly recognized as a substantial contributor to inter-individual variability. An increasing number of algorithms is by now available that base their predictions on a multitude of different parameters, including effects on miRNA binding or translational efficiency, modulation of splicing and impacts on transcriptional events by disruption of transcription factor binding sites or polymerase loading (Figure 1). While these developments provide a methodological arsenal to comprehensively characterize all different classes of genetic variants, these methods are generally trained on pathogenic variant sets and have not been benchmarked on independent data sets. Thus, their predictive power for pharmacogenetic assessments remains to be evaluated.

The prediction of drug metabolism phenotypes based on the genotype of the individual has made tremendous progress over the last decades (Figure 2). Conventional approaches use data from few candidate variants for which substantial in vitro or in vivo characterization data was available to predict drug response. While this strategy has been successful in incorporating common pharmacogenetic variability into clinical decision-making, they fail to address functional effects of the vast extent of rare genetic variants. To also include rare variants, pilot programs were initiated in which WES was used to comprehensively interrogate the genetic landscape of pharmacogenomic loci (Bielinski et al., 2014). However, analyses were restricted to pharmacogenetic missense variants and the effects of SNVs with unknown functional relevance were interpreted using computational models trained on pathogenic data sets with negative impacts on the accuracy of phenotype predictions, as discussed above. Thus, while these strategies constitute an important step toward the further personalization of genotype-guided treatment decisions their predictive accuracy is rather low.

FIGURE 2
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Figure 2. The past, present and future of pharmacogenetic phenotype predictions. (A) Conventionally, pharmacogenetic predictions were based on the interrogation of few common candidate SNPs, whose functional effects were predicted based on extensive literature evidence, resulting in high predictive accuracy but only few considered variations. (B) With increasing prevalence of whole exome sequencing (WES), a multitude of pharmacogenetic variants with unknown functional relevance are identified. These variants can be interpreted using computational methods. However, current algorithms are generally trained to detect the pathogenicity rather than the functionality of queried variants, resulting in overall relatively low predictive accuracy. Furthermore, only effects of missense and nonsense variants are evaluated. (C) In the near future, whole genome sequencing (WGS) will become the predominant genotyping methodology, revealing not only coding variants but also variants in regulatory regions and introns. To facilitate interpretation of this data, we envision that pharmacogenetic predictors will be directly trained on functionally annotated ADME data sets. Emerging technologies, such as deep mutational scanning for the systematic interrogation of missense variants or mutagenesis screens in microphysiological systems (MPS) for the characterization of variants in regulatory regions, provide powerful tools to generate these data, boosting the predictive performance of data hungry machine learning tools. These advances allow to go beyond the interpretation of missense and nonsense variants and to include also non-coding and regulatory variations into pharmacogenetic assessments.

We expect that technological, methodological and analytical progress will contribute to a further refinement of NGS-guided drug treatment in the near future. Firstly, technological advances will result in an increasing dissemination of WGS, which facilitates the incorporation of the entire profile of an individual's genetic variability, including regulatory variants, into pharmacogenetic predictions. Secondly, we envision that novel high-throughput methodologies for functional characterizations, such as deep mutational scanning, will provide powerful approaches to generate large functionally annotated pharmacogenetic variant data sets. In addition, recent advances in the development of microphysiological systems (MPS) that allow to model key target tissues associated with drug metabolism or safety provide (Ewart et al., 2018) provide promising tools to generate tissue-specific and human-relevant data sets for studies of gene-drug interactions (Ingelman-Sundberg and Lauschke, 2018). Using this integrated wealth of functional pharmacogenetic data to train machine learning models aspires to provide high-accuracy predictions based on the entire genetic variability landscape of the respective patient.

Importantly, leveraging this information as guidance for clinical decision-making promises to increase treatment efficacy and reduce the risks of adverse events in carriers of pharmacogenetic variants whose effects have not been experimentally evaluated. Current market analysis estimates suggests that implementation of artificial intelligence into the clinical decision support toolbox might increase average life expectancy in the Western World by 0.2–1.3 years and reduce total health care expenditures by 5–9%, corresponding to 2 trillion to 10 trillion USD globally per year (Bughin et al., 2017). However, in order to realize these exciting prospects, there is a need for prospective, randomized controlled trials that evaluate patient outcomes and cost-effectiveness of such preemptive advice across genes, drugs and health care systems.

In summary, computational prediction methods are essential for the implementation of NGS into clinical decision-making. While much progress has been made and a plethora of conceptually diverse tools is already available, there is a need to develop specialized methods that are optimized for the prediction of variant functionality rather than pathogenicity and are calibrated specifically on pharmacogenetic data. We envision that technological, methodological and analytical advances will soon allow to comprehensively predict variant effects with sufficient accuracy to justify the design of trials in which the clinical value of NGS-guided treatment decisions can be tested in a prospective setting.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Funding

The work in the authors' laboratory is supported by the Swedish Research Council [grant agreement numbers: 2016-01153 and 2016-01154], by the Strategic Research Programme in Diabetes at Karolinska Institutet, by the European Union's Horizon 2020 research and innovation program U-PGx [grant agreement No. 668353], and by the Lennart Philipson and Harald och Greta Jeansson Foundations.

Conflict of Interest Statement

VL is co-founder and owner of HepaPredict AB.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., et al. (2010). A method and server for predicting damaging missense mutations. Nat Methods 7, 248–249. doi: 10.1038/nmeth0410-248

PubMed Abstract | CrossRef Full Text | Google Scholar

Ahn, E., and Park, T. (2017). Analysis of population-specific pharmacogenomic variants using next-generation sequencing data. Sci. Rep. 7:8416. doi: 10.1038/s41598-017-08468-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Akhtar, M. M., Micolucci, L., Islam, M. S., Olivieri, F., and Procopio, A. D. (2016). Bioinformatic tools for microRNA dissection. Nucleic Acids Res. 44, 24–44. doi: 10.1093/nar/gkv1221

PubMed Abstract | CrossRef Full Text | Google Scholar

Ancien, F., Pucci, F., Godfroid, M., and Rooman, M. (2018). Prediction and interpretation of deleterious coding variants in terms of protein structural stability. Sci. Rep. 8:4480. doi: 10.1038/s41598-018-22531-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Arbiza, L., Gronau, I., Aksoy, B. A., Hubisz, M. J., Gulko, B., Keinan, A., et al. (2013). Genome-wide inference of natural selection on human transcription factor binding sites. Nat. Genet. 45, 723–729. doi: 10.1038/ng.2658

PubMed Abstract | CrossRef Full Text | Google Scholar

Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., and Pupko, T. (2016). ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350. doi: 10.1093/nar/gkw408

PubMed Abstract | CrossRef Full Text | Google Scholar

Asthana, S., Roytberg, M., Stamatoyannopoulos, J., and Sunyaev, S. (2007). Analysis of sequence conservation at nucleotide resolution. PLoS Comput. Biol. 3:e254. doi: 10.1371/journal.pcbi.0030254

PubMed Abstract | CrossRef Full Text | Google Scholar

Balasubramanian, S., Fu, Y., Pawashe, M., McGillivray, P., Jin, M., Liu, J., et al. (2017). Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes. Nat. Commun. 8:382. doi: 10.1038/s41467-017-00443-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Baralle, D., Lucassen, A., and Buratti, E. (2009). Missed threads. The impact of pre-mRNA splicing defects on clinical practice. EMBO Rep. 10, 810–816. doi: 10.1038/embor.2009.170

PubMed Abstract | CrossRef Full Text | Google Scholar

Barenboim, M., Zoltick, B. J., Guo, Y., and Weinberger, D. R. (2010). MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum. Mutat. 31, 1223–1232. doi: 10.1002/humu.21349

PubMed Abstract | CrossRef Full Text | Google Scholar

Baugh, E. H., Simmons-Edler, R., Müller, C. L., Alford, R. F., Volfovsky, N., Lash, A. E., et al. (2016). Robust classification of protein variation using structural modelling and large-scale data integration. Nucleic Acids Res. 44, 2501–2513. doi: 10.1093/nar/gkw120

PubMed Abstract | CrossRef Full Text | Google Scholar

Bendl, J., Stourac, J., Salanda, O., Pavelka, A., Wieben, E. D., Zendulka, J., et al. (2014). PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput. Biol. 10:e1003440. doi: 10.1371/journal.pcbi.1003440

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhattacharya, A., Ziebarth, J. D., and Cui, Y. (2014). PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res. 42, D86–D91. doi: 10.1093/nar/gkt1028

PubMed Abstract | CrossRef Full Text | Google Scholar

Bielinski, S. J., Olson, J. E., Pathak, J., Weinshilboum, R. M., Wang, L., Lyke, K. J., et al. (2014). Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol. Mayo Clin. Proc. 89, 25–33. doi: 10.1016/j.mayocp.2013.10.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Boycott, K. M., Vanstone, M. R., Bulman, D. E., and MacKenzie, A. E. (2013). Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14, 681–691. doi: 10.1038/nrg3555

PubMed Abstract | CrossRef Full Text | Google Scholar

Boyle, A. P., Davis, S., Shulha, H. P., Meltzer, P., Margulies, E. H., Weng, Z., et al. (2008). High-Resolution Mapping and Characterization of Open Chromatin across the Genome. Cell 132, 311–322. doi: 10.1016/j.cell.2007.12.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Bughin, J., Hazan, E., Ramaswamy, S., Chui, S., Allas, T., Dahlström, P., et al. (2017). Artificial Intelligence the Next Digital Frontier? McKinsey and Company Global Institute.

Bush, W. S., Crosslin, D. R., Owusu-Obeng, A., Wallace, J., Almoguera, B., Basford, M. A., et al. (2016). Genetic variation among 82 pharmacogenes: the PGRNseq data from the eMERGE network. Clin. Pharmacol. Therapeut. 100, 160–169. doi: 10.1002/cpt.350

PubMed Abstract | CrossRef Full Text | Google Scholar

Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L., and Casadio, R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 30, 1237–1244. doi: 10.1002/humu.21047

PubMed Abstract | CrossRef Full Text | Google Scholar

Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., and Collins, J. J. (2018). Next-generation machine learning for biological networks. Cell 173, 1581–1592. doi: 10.1016/j.cell.2018.05.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Capriotti, E., Calabrese, R., and Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734. doi: 10.1093/bioinformatics/btl423

PubMed Abstract | CrossRef Full Text | Google Scholar

Capriotti, E., Fariselli, P., and Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33, W306–W310. doi: 10.1093/nar/gki375

CrossRef Full Text | Google Scholar

Carter, H., Douville, C., Stenson, P. D., Cooper, D. N., and Karchin, R. (2013). Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14(Suppl. 3), S3. doi: 10.1186/1471-2164-14-S3-S3

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, L., Jin, P., and Qin, Z. S. (2016). DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles. Genome Biol. 17:252. doi: 10.1186/s13059-016-1112-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Chhibber, A., French, C. E., Yee, S. W., Gamazon, E. R., Theusch, E., Qin, X., et al. (2017). Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines. Pharmacogenomics J. 17, 137–145. doi: 10.1038/tpj.2015.93

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., and Chan, A. P. (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS ONE 7:e46688. doi: 10.1371/journal.pone.0046688

PubMed Abstract | CrossRef Full Text | Google Scholar

Chun, S., and Fay, J. C. (2009). Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561. doi: 10.1101/gr.092619.109

PubMed Abstract | CrossRef Full Text | Google Scholar

Cline, M. S., and Karchin, R. (2011). Using bioinformatics to predict the functional impact of SNVs. Bioinformatics 27, 441–448. doi: 10.1093/bioinformatics/btq695

PubMed Abstract | CrossRef Full Text | Google Scholar

Corvelo, A., Hallegger, M., Smith, C. W., and Eyras, E. (2010). Genome-wide association between branch point properties and alternative splicing. PLoS Comput. Biol. 6:e1001016. doi: 10.1371/journal.pcbi.1001016

PubMed Abstract | CrossRef Full Text | Google Scholar

Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., and Batzoglou, S. (2010). Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6:e1001025–e1001013. doi: 10.1371/journal.pcbi.1001025

PubMed Abstract | CrossRef Full Text | Google Scholar

Deplancke, B., Alpern, D., and Gardeux, V. (2016). The genetics of transcription factor DNA binding variation. Cell 166, 538–554. doi: 10.1016/j.cell.2016.07.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Deveci, M., Catalyürek, U. V., and Toland, A. E. (2014). mrSNP: software to detect SNP effects on microRNA binding. BMC Bioinformatics 15:73. doi: 10.1186/1471-2105-15-73

PubMed Abstract | CrossRef Full Text | Google Scholar

Dogan, R. I., Getoor, L., Wilbur, W. J., and Mount, S. M. (2007). SplicePort–an interactive splice-site analysis tool. Nucleic Acids Res. 35, W285–W291. doi: 10.1093/nar/gkm407

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, C., Wei, P., Jian, X., Gibbs, R., Boerwinkle, E., Wang, K., et al. (2015). Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137. doi: 10.1093/hmg/ddu733

PubMed Abstract | CrossRef Full Text | Google Scholar

Douville, C., Masica, D. L., Stenson, P. D., Cooper, D. N., Gygax, D. M., Kim, R., et al. (2016). Assessing the pathogenicity of insertion and deletion variants with the variant effect scoring tool (VEST-Indel). Hum. Mutat. 37, 28–35. doi: 10.1002/humu.22911

PubMed Abstract | CrossRef Full Text | Google Scholar

Ewart, L., Dehne, E.-M., Fabre, K., Gibbs, S., Hickman, J., Hornberg, E., et al. (2018). Application of microphysiological systems to enhance safety assessment in drug discovery. Annu. Rev. Pharmacol. Toxicol. 58, 65–82. doi: 10.1146/annurev-pharmtox-010617-052722

PubMed Abstract | CrossRef Full Text | Google Scholar

Farh, K. K., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W. J., Beik, S., et al. (2015). Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343. doi: 10.1038/nature13835

PubMed Abstract | CrossRef Full Text | Google Scholar

Fehlmann, T., Sahay, S., Keller, A., and Backes, C. (2017). A review of databases predicting the effects of SNPs in miRNA genes or miRNA-binding sites. Brief. Bioinformatics. doi: 10.1093/bib/bbx155. [Epub ahead of print].

CrossRef Full Text | Google Scholar

Flanagan, S. E., Patch, A.-M., and Ellard, S. (2010). Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet. Test. Mol. Biomarkers 14, 533–537. doi: 10.1089/gtmb.2010.0036

PubMed Abstract | CrossRef Full Text | Google Scholar

Folkman, L., Yang, Y., Li, Z., Stantic, B., Sattar, A., Mort, M., et al. (2015). DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels. Bioinformatics 31, 1599–1606. doi: 10.1093/bioinformatics/btu862

PubMed Abstract | CrossRef Full Text | Google Scholar

Fujikura, K. (2016). Premature termination codons in modern human genomes. Sci. Rep. 6:22468. doi: 10.1038/srep22468

PubMed Abstract | CrossRef Full Text | Google Scholar

Fujikura, K., Ingelman-Sundberg, M., and Lauschke, V. M. (2015). Genetic variation in the human cytochrome P450 supergene family. Pharmacogenet. Genomics 25, 584–594. doi: 10.1097/FPC.0000000000000172

PubMed Abstract | CrossRef Full Text | Google Scholar

Gallion, J., Koire, A., Katsonis, P., Schoenegge, A.-M., Bouvier, M., and Lichtarge, O. (2017). Predicting phenotype from genotype: improving accuracy through more robust experimental and computational modeling. Hum. Mutat. 38, 569–580. doi: 10.1002/humu.23193

PubMed Abstract | CrossRef Full Text | Google Scholar

Garber, M., Guttman, M., Clamp, M., Zody, M. C., Friedman, N., and Xie, X. (2009). Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics 25, i54–i62. doi: 10.1093/bioinformatics/btp190

PubMed Abstract | CrossRef Full Text | Google Scholar

Getov, I., Petukh, M., and Alexov, E. (2016). SAAFEC: predicting the effect of single point mutations on protein folding free energy using a knowledge-modified MM/PBSA approach. Int. J. Mol. Sci. 17, 512–514. doi: 10.3390/ijms17040512

PubMed Abstract | CrossRef Full Text | Google Scholar

Giresi, P. G., Kim, J., McDaniell, R. M., Iyer, V. R., and Lieb, J. D. (2007). FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 17, 877–885. doi: 10.1101/gr.5533506

PubMed Abstract | CrossRef Full Text | Google Scholar

González-Pérez, A., and López-Bigas, N. (2011). Improving the assessment of the outcome of Nonsynonymous SNVs with a consensus deleteriousness score, condel. Am. J. Hum. Genet. 88, 440–449. doi: 10.1016/j.ajhg.2011.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., and Fowler, D. M. (2018). Quantitative missense variant effect prediction using large-scale mutagenesis data. Cell Syst. 6, 116–124.e3. doi: 10.1016/j.cels.2017.11.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Grimm, D. G., Azencott, C.-A., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., et al. (2015). The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 36, 513–523. doi: 10.1002/humu.22768

PubMed Abstract | CrossRef Full Text | Google Scholar

Gronau, I., Arbiza, L., Mohammed, J., and Siepel, A. (2011). Inference of natural selection from interspersed genomic elements based on polymorphism and divergence. Mol. Biol. Evol. 30, 1159–1171. doi: 10.1093/molbev/mst019

PubMed Abstract | CrossRef Full Text | Google Scholar

GTEx Consortium (2017). Genetic effects on gene expression across human tissues. Nature 550, 204-213. doi: 10.1038/nature24277

CrossRef Full Text

Gulko, B., Hubisz, M. J., Gronau, I., and Siepel, A. (2015). A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276–283. doi: 10.1038/ng.3196

PubMed Abstract | CrossRef Full Text | Google Scholar

Hanioka, N., Kimura, S., Meyer, U. A., and Gonzalez, F. J. (1990). The Human Cyp2d locus associated with a common genetic-defect in drug oxidation-a G1934-]a base change in intron-3 of a mutant Cyp2d6 allele results in an Aberrant-3' Splice Recognition site. Am. J. Hum. Genet. 47, 994–1001.

PubMed Abstract | Google Scholar

Harmanci, A. O., Sharma, G., and Mathews, D. H. (2011). TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences. BMC Bioinformatics 12:108. doi: 10.1186/1471-2105-12-108

PubMed Abstract | CrossRef Full Text | Google Scholar

Hecht, M., Bromberg, Y., and Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC Genomics 16(Suppl 8):S1. doi: 10.1186/1471-2164-16-S8-S1

PubMed Abstract | CrossRef Full Text | Google Scholar

Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P., Springer, M., Sander, C., et al. (2017). Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 35, 128–135. doi: 10.1038/nbt.3769

PubMed Abstract | CrossRef Full Text | Google Scholar

Hsu, M.-K., Lin, H.-Y., and Chen, F.-C. (2017). NMD Classifier: A reliable and systematic classification tool for nonsense-mediated decay events. PLoS ONE 12:e0174798. doi: 10.1371/journal.pone.0174798

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, J., and Ng, P. C. (2012). Predicting the effects of frameshifting indels. Genome Biol. 13, R9. doi: 10.1186/gb-2012-13-2-r9

PubMed Abstract | CrossRef Full Text | Google Scholar

Ingelman-Sundberg, M., and Lauschke, V. M. (2018). Human liver spheroids in chemically defined conditions for studies of gene–drug, drug–drug and disease–drug interactions. Pharmacogenomics 19, 1133–1138. doi: 10.2217/pgs-2018-0096

PubMed Abstract | CrossRef Full Text | Google Scholar

Ingelman-Sundberg, M., Mkrtchian, S., Zhou, Y., and Lauschke, V. M. (2018). Integrating rare genetic variants into pharmacogenetic drug response predictions. Hum. Genomics 12:26. doi: 10.1186/s40246-018-0157-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., et al. (2016). REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885. doi: 10.1016/j.ajhg.2016.08.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Ionita-Laza, I., McCallum, K., Xu, B., and Buxbaum, J. D. (2016). A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Rev. Clin. Oncol. 48, 214–220. doi: 10.1038/ng.3477

PubMed Abstract | CrossRef Full Text | Google Scholar

Ji, Y., Si, Y., McMillin, G. A., and Lyon, E. (2018). Clinical pharmacogenomics testing in the era of next generation sequencing: challenges and opportunities for precision medicine. Expert Rev. Mol. Diagn. 18, 411–421. doi: 10.1080/14737159.2018.1461561

PubMed Abstract | CrossRef Full Text | Google Scholar

Jian, X., Boerwinkle, E., and Liu, X. (2014a). In silico tools for splicing defect prediction: a survey from the viewpoint of end users. Genetics in Medicine 16, 497–503. doi: 10.1038/gim.2013.176

PubMed Abstract | CrossRef Full Text | Google Scholar

Jian, X., Boerwinkle, E., and Liu, X. (2014b). In silico prediction of splice-altering single nucleotide, variants in the human genome. Nucleic Acids Res. 42, 13534–13544. doi: 10.1093/nar/gku1206

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnson, D. S., Mortazavi, A., Myers, R. M., and Wold, B. (2007). Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502. doi: 10.1126/science.1141319

PubMed Abstract | CrossRef Full Text | Google Scholar

Jukić, M. M., Haslemo, T., Molden, E., and Ingelman-Sundberg, M. (2018). Impact of CYP2C19 genotype on escitalopram exposure and therapeutic failure: a retrospective study based on 2,087 patients. Am. J. Psychiatry 175, 463–470. doi: 10.1176/appi.ajp.2017.17050550

PubMed Abstract | CrossRef Full Text | Google Scholar

Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., et al. (2018). Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19, 629–650. doi: 10.2217/pgs-2018-0008

PubMed Abstract | CrossRef Full Text | Google Scholar

Katsonis, P., and Lichtarge, O. (2014). A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness. Genome Res. 24, 2050–2058. doi: 10.1101/gr.176214.114

PubMed Abstract | CrossRef Full Text | Google Scholar

Kerem, E., Hirawat, S., Armoni, S., Yaakov, Y., Shoseyov, D., Cohen, M., et al. (2008). Effectiveness of PTC124 treatment of cystic fibrosis caused by nonsense mutations: a prospective phase II trial. Lancet 372, 719–727. doi: 10.1016/S0140-6736(08)61168-X

PubMed Abstract | CrossRef Full Text | Google Scholar

Kervestin, S., and Jacobson, A. (2012). NMD: a multifaceted response to premature translational termination. Nat. Rev. Mol. Cell Biol. 13, 700–712. doi: 10.1038/nrm.3454

PubMed Abstract | CrossRef Full Text | Google Scholar

Khan, A., Fornes, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J. A. R., et al. (2018). JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266. doi: 10.1093/nar/gkx1188

CrossRef Full Text | Google Scholar

Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., and Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Rev. Clin. Oncol. 46, 310–315. doi: 10.1038/ng.2892

PubMed Abstract | CrossRef Full Text | Google Scholar

Kozyra, M., Ingelman-Sundberg, M., and Lauschke, V. M. (2017). Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response. Genet. Med. 19, 20–29. doi: 10.1038/gim.2016.33

PubMed Abstract | CrossRef Full Text | Google Scholar

Kryukov, G. V., Pennacchio, L. A., and Sunyaev, S. R. (2007). Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am. J. Hum. Genet. 80, 727–739. doi: 10.1086/513473

PubMed Abstract | CrossRef Full Text | Google Scholar

Kulshreshtha, S., Chaudhary, V., Goswami, G. K., and Mathur, N. (2016). Computational approaches for predicting mutant protein stability. J. Comput. Aided Mol. Des. 30, 401–412. doi: 10.1007/s10822-016-9914-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Lauschke, V. M., and Ingelman-Sundberg, M. (2016a). Precision medicine and rare genetic variants. Trends Pharmacol. Sci. 37, 85–86. doi: 10.1016/j.tips.2015.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Lauschke, V. M., and Ingelman-Sundberg, M. (2016b). Requirements for comprehensive pharmacogenetic genotyping platforms. Pharmacogenomics 17, 917–924. doi: 10.2217/pgs-2016-0023

PubMed Abstract | CrossRef Full Text | Google Scholar

Lauschke, V. M., and Ingelman-Sundberg, M. (2018). How to consider rare genetic variants in personalized drug therapy. Clin. Pharmacol. Therapeut. 19, 20. doi: 10.1002/cpt.976

CrossRef Full Text | Google Scholar

Lauschke, V. M., Milani, L., and Ingelman-Sundberg, M. (2017). Pharmacogenomic biomarkers for improved drug therapy-recent progress and future developments. AAPS J. 20, 4. doi: 10.1208/s12248-017-0161-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, D., Gorkin, D. U., Baker, M., Strober, B. J., Asoni, A. L., McCallion, A. S., et al. (2015). A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961. doi: 10.1038/ng.3331

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, M., Roos, P., Sharma, N., Atalar, M., Evans, T. A., Pellicore, M. J., et al. (2017). Systematic computational identification of variants that activate exonic and intronic cryptic splice sites. Am. J. Hum. Genet. 100, 751–765. doi: 10.1016/j.ajhg.2017.04.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, W., Zhang, Y., Mukhyala, K., Lazarus, R. A., and Zhang, Z. (2009). Bi-directional SIFT predicts a subset of activating mutations. PLoS ONE 4:e8311. doi: 10.1371/journal.pone.0008311

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, Y., and Rio, D. C. (2015). Mechanisms and Regulation of Alternative Pre-mRNA Splicing. Annu. Rev. Biochem. 84, 291–323. doi: 10.1146/annurev-biochem-060614-034316

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, S. E., and Myers, R. M. (2016). Advancements in next-generation sequencing. Annu. Rev. Genomics Hum. Genet. 17, 95–115. doi: 10.1146/annurev-genom-083115-022413

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, J., Zhao, T., Zhang, Y., Zhang, K., Shi, L., Chen, Y., et al. (2018). Performance evaluation of pathogenicity-computation methods for missense variants. Nucleic Acids Res. 46, 7793–7804. doi: 10.1093/nar/gky678

PubMed Abstract | CrossRef Full Text | Google Scholar

Linde, L., Boelz, S., Nissim-Rafinia, M., Oren, Y. S., Wilschanski, M., Yaacov, Y., et al. (2007). Nonsense-mediated mRNA decay affects nonsense transcript levels and governs response of cystic fibrosis patients to gentamicin. J. Clin. Invest. 117, 683–692. doi: 10.1172/JCI28523

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, C., Zhang, F., Li, T., Lu, M., Wang, L., Yue, W., et al. (2012). MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics 13:661. doi: 10.1186/1471-2164-13-661

PubMed Abstract | CrossRef Full Text | Google Scholar

Lorenz, R., Wolfinger, M. T., Tanzer, A., and Hofacker, I. L. (2016). Predicting RNA secondary structures from sequence and probing data. Methods 103, 86–98. doi: 10.1016/j.ymeth.2016.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, Q., Hu, Y., Sun, J., Cheng, Y., Cheung, K.-H., and Zhao, H. (2015). A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci. Rep. 5:10576. doi: 10.1038/srep10576

PubMed Abstract | CrossRef Full Text | Google Scholar

MacArthur, D. G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., et al. (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828. doi: 10.1126/science.1215040

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahmood, K., Jung, C.-H., Philip, G., Georgeson, P., Chung, J., Pope, B. J., et al. (2017). Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics. Hum. Genomics 11:10. doi: 10.1186/s40246-017-0104-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Martelotto, L. G., Ng, C. K., De Filippo, M. R., Zhang, Y., Piscuoglio, S., Lim, R. S., et al. (2014). Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biol. 15:484. doi: 10.1186/s13059-014-0484-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Miosge, L. A., Field, M. A., Sontani, Y., Cho, V., Johnson, S., Palkova, A., et al. (2015). Comparison of predicted and actual consequences of missense mutations. Proc. Natl. Acad. Sci. U.S.A. 112, E5189–E5198. doi: 10.1073/pnas.1511585112

PubMed Abstract | CrossRef Full Text | Google Scholar

Mort, M., Sterne-Weiler, T., Li, B., Ball, E. V., Cooper, D. N., Radivojac, P., et al. (2014). MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing. Genome Biol. 15:R19. doi: 10.1186/gb-2014-15-1-r19

PubMed Abstract | CrossRef Full Text | Google Scholar

Moszynska, A., Gebert, M., Collawn, J. F., and Bartoszewski, R. (2017). SNPs in microRNA target sites and their potential role in human disease. Open Biol. 7:170019. doi: 10.1098/rsob.170019

PubMed Abstract | CrossRef Full Text | Google Scholar

Ng, P. C., and Henikoff, S. (2001). Predicting deleterious amino acid substitutions. Genome Res. 11, 863–874. doi: 10.1101/gr.176601

PubMed Abstract | CrossRef Full Text | Google Scholar

Ng, P. C., and Henikoff, S. (2006). Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7, 61–80. doi: 10.1146/annurev.genom.7.080505.115630

PubMed Abstract | CrossRef Full Text | Google Scholar

Ohno, K., Takeda, J.-I., and Masuda, A. (2018). Rules and tools to predict the splicing effects of exonic and intronic mutations. Wiley Interdiscip. Rev. 9:e1451. doi: 10.1002/wrna.1451

PubMed Abstract | CrossRef Full Text | Google Scholar

Pan, Y., Liu, D., and Deng, L. (2017). Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties. PLoS ONE 12:e0179314. doi: 10.1371/journal.pone.0179314

PubMed Abstract | CrossRef Full Text | Google Scholar

Pandurangan, A. P., Ochoa-Montaño, B., Ascher, D. B., and Blundell, T. L. (2017). SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. 45, W229–W235. doi: 10.1093/nar/gkx439

PubMed Abstract | CrossRef Full Text | Google Scholar

Pertea, M., Lin, X., and Salzberg, S. L. (2001). GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res. 29, 1185–1190. doi: 10.1093/nar/29.5.1185

PubMed Abstract | CrossRef Full Text | Google Scholar

Peterson, T. A., Doughty, E., and Kann, M. G. (2013). Towards precision medicine: advances in computational approaches for the analysis of human variants. J. Mol. Biol. 425, 4047–4063. doi: 10.1016/j.jmb.2013.08.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Pinzón, N., Li, B., Martinez, L., Sergeeva, A., Presumey, J., Apparailly, F., et al. (2017). microRNA target prediction programs predict many false positives. Genome Res. 27, 234–245. doi: 10.1101/gr.205146.116

PubMed Abstract | CrossRef Full Text | Google Scholar

Pires, D. E., Ascher, D. B., and Blundell, T. L. (2014a). DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res. 42, W314–W319. doi: 10.1093/nar/gku411

PubMed Abstract | CrossRef Full Text | Google Scholar

Pires, D. E., Ascher, D. B., and Blundell, T. L. (2014b). mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30, 335–342. doi: 10.1093/bioinformatics/btt691

PubMed Abstract | CrossRef Full Text | Google Scholar

Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., and Siepel, A. (2010). Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121. doi: 10.1101/gr.097857.109

PubMed Abstract | CrossRef Full Text | Google Scholar

Pucci, F., Bernaerts, K., Kwasigroch, J. M., and Rooman, M. (2018). Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics 13:3031. doi: 10.1093/bioinformatics/bty348

CrossRef Full Text | Google Scholar

Quan, L., Lv, Q., and Zhang, Y. (2016). STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics 32, 2936–2946. doi: 10.1093/bioinformatics/btw361

PubMed Abstract | CrossRef Full Text | Google Scholar

Quang, D., Chen, Y., and Xie, X. (2015). DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763. doi: 10.1093/bioinformatics/btu703

PubMed Abstract | CrossRef Full Text | Google Scholar

Raida, M., Schwabe, W., Häusler, P., Van Kuilenburg, A. B., Van Gennip, A. H., Behnke, D., et al. (2001). Prevalence of a common point mutation in the Dihydropyrimidine dehydrogenase (DPD) gene within the 5'-splice donor site of intron 14 in patients with severe 5-fluorouracil (5-FU)-related toxicity compared with controls. Clin. Cancer Res. 7, 2832–2839.

PubMed Abstract | Google Scholar

Raimondi, D., Tanyalcin, I., Ferté, J., Gazzo, A., Orlando, G., Lenaerts, T., et al. (2017). DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Res. 45, W201–W206. doi: 10.1093/nar/gkx390

PubMed Abstract | CrossRef Full Text | Google Scholar

Rausell, A., Mohammadi, P., McLaren, P. J., Bartha, I., Xenarios, I., Fellay, J., et al. (2014). Analysis of stop-gain and frameshift variants in human innate immunity genes. PLoS Comput. Biol. 10:e1003757. doi: 10.1371/journal.pcbi.1003757

PubMed Abstract | CrossRef Full Text | Google Scholar

Reese, M. G., Eeckman, F. H., Kulp, D., and Haussler, D. (1997). Improved splice site detection in Genie. J. Comput. Biol. 4, 311–323. doi: 10.1089/cmb.1997.4.311

PubMed Abstract | CrossRef Full Text | Google Scholar

Reva, B., Antipin, Y., and Sander, C. (2011). Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39:e118. doi: 10.1093/nar/gkr407

PubMed Abstract | CrossRef Full Text | Google Scholar

Rieger, J. K., Klein, K., Winter, S., and Zanger, U. M. (2013). Expression variability of absorption, distribution, metabolism, excretion-related micrornas in human liver: influence of nongenetic factors and association with gene expression. Drug Metab. Dispos. 41, 1752–1762. doi: 10.1124/dmd.113.052126

PubMed Abstract | CrossRef Full Text | Google Scholar

Ritchie, G. R., Dunham, I., Zeggini, E., and Flicek, P. (2014). Functional annotation of noncoding sequence variants. Nat. Methods 11, 294–296. doi: 10.1038/nmeth.2832

PubMed Abstract | CrossRef Full Text | Google Scholar

Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., A. Heravi-Moussavi. (2015). P, Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330. doi: 10.1038/nature14248

CrossRef Full Text | Google Scholar

Rogers, M. F., Shihab, H. A., Mort, M., Cooper, D. N., Gaunt, T. R., and Campbell, C. (2018). FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34, 511–513. doi: 10.1093/bioinformatics/btx536

PubMed Abstract | CrossRef Full Text | Google Scholar

Ryan, B. C., Werner, T. S., Howard, P. L., and Chow, R. L. (2016). ImiRP: a computational approach to microRNA target site mutation. BMC Bioinformatics 17:190. doi: 10.1186/s12859-016-1057-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Sample, P. J., Wang, B., Reid, D. W., Presnyak, V., McFadyen, I., Morris, D. R., et al. (2018). Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. bioRxiv. doi: 10.1101/310375

CrossRef Full Text | Google Scholar

Sato, K., Hamada, M., Asai, K., and Mituyama, T. (2009). CENTROIDFOLD: a web server for RNA secondary structure prediction. Nucleic Acids Res. 37, W277–W280. doi: 10.1093/nar/gkp367

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmidt, D., Wilson, M. D., Ballester, B., Schwalie, P. C., Brown, G. D., Marshall, A., et al. (2010). Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328, 1036–1040. doi: 10.1126/science.1186176

PubMed Abstract | CrossRef Full Text

Schoenberg, D. R., and Maquat, L. E. (2012). Regulation of cytoplasmic mRNA decay. Nat. Rev. Genet. 13, 246–259. doi: 10.1038/nrg3160

PubMed Abstract | CrossRef Full Text | Google Scholar

Schwarz, J. M., Cooper, D. N., Schuelke, M., and Seelow, D. (2014). MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361–362. doi: 10.1038/nmeth.2890

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, Y. (2017). Mechanistic insights into precursor messenger RNA splicing by the spliceosome. Nat. Rev. Mol. Cell Biol. 18, 655–670. doi: 10.1038/nrm.2017.86

PubMed Abstract | CrossRef Full Text | Google Scholar

Shihab, H. A., Gough, J., Cooper, D. N., Stenson, P. D., Barker, G. L., Edwards, K. J., et al. (2013). Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. 34, 57–65. doi: 10.1002/humu.22225

PubMed Abstract | CrossRef Full Text | Google Scholar

Shihab, H. A., Rogers, M. F., Gough, J., Mort, M., Cooper, D. N., Day, I. N., et al. (2015). An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31, 1536–1543. doi: 10.1093/bioinformatics/btv009

PubMed Abstract | CrossRef Full Text | Google Scholar

Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., et al. (2005). Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050. doi: 10.1101/gr.3715005

PubMed Abstract | CrossRef Full Text | Google Scholar

Sim, S. C., Kacevska, M., and Ingelman-Sundberg, M. (2013). Pharmacogenomics of drug-metabolizing enzymes: a recent update on clinical implications and endogenous effects. Pharmacogenomics J. 13, 1–11. doi: 10.1038/tpj.2012.45

PubMed Abstract | CrossRef Full Text | Google Scholar

Smedley, D., Schubach, M., Jacobsen, J. O. B., Köhler, S., Zemojtel, T., Spielmann, M., et al. (2016). A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am. J. Hum. Genet. 99, 595–606. doi: 10.1016/j.ajhg.2016.07.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Somody, J. C., MacKinnon, S. S., and Windemuth, A. (2017). Structural coverage of the proteome for pharmaceutical applications. Drug Discov. Today 22, 1792–1799. doi: 10.1016/j.drudis.2017.08.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Soukarieh, O., Gaildrat, P., Hamieh, M., Drouet, A., Baert-Desurmont, S., Frébourg, T., et al. (2016). Exonic splicing mutations are more prevalent than currently estimated and can be predicted by using in silico tools. PLoS Genet. 12:e1005756. doi: 10.1371/journal.pgen.1005756

CrossRef Full Text | Google Scholar

Spear, B. B., Heath-Chiozzi, M., and Huff, J. (2001). Clinical application of pharmacogenetics. Trends Mol. Med. 7, 201–204. doi: 10.1016/S1471-4914(01)01986-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Stone, E. A., and Sidow, A. (2005). Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 15, 978–986. doi: 10.1101/gr.3804205

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, H., and Thomas, P. D. (2016). Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics 203, 635–647. doi: 10.1534/genetics.116.190033

PubMed Abstract | CrossRef Full Text | Google Scholar

The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65. doi: 10.1038/nature11632

CrossRef Full Text

The 1000 Genomes Project Consortium (2015). A global reference for human genetic variation. Nature 526, 68-74. doi: 10.1038/nature15393

CrossRef Full Text

The ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74. doi: 10.1038/nature11247

CrossRef Full Text

Thomas, P. D., Campbell, M. J., Kejariwal, A., Mi, H., Karlak, B., Daverman, R., et al. (2003). PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141. doi: 10.1101/gr.772403

PubMed Abstract | CrossRef Full Text | Google Scholar

Topham, C. M., Srinivasan, N., and Blundell, T. L. (1997). Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. Protein Eng. 10, 7–21. doi: 10.1093/protein/10.1.7

PubMed Abstract | CrossRef Full Text | Google Scholar

Venselaar, H., Te Beek, T. A., Kuipers, R. K., Hekkelman, M. L., and Vriend, G. (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics 11:548. doi: 10.1186/1471-2105-11-548

PubMed Abstract | CrossRef Full Text | Google Scholar

Wan, Y., Qu, K., Zhang, Q. C., Flynn, R. A., Manor, O., Ouyang, Z., et al. (2014). Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709. doi: 10.1038/nature12946

PubMed Abstract | CrossRef Full Text | Google Scholar

Warf, M. B., and Berglund, J. A. (2010). Role of RNA structure in regulating pre-mRNA splicing. Trends Biochem. Sci. 35, 169–178. doi: 10.1016/j.tibs.2009.10.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Witvliet, D. K., Strokach, A., Giraldo-Forero, A. F., Teyra, J., Colak, R., and Kim, P. M. (2016). ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 32, 1589–1591. doi: 10.1093/bioinformatics/btw031

PubMed Abstract | CrossRef Full Text | Google Scholar

Wlodawer, A., Minor, W., Dauter, Z., and Jaskolski, M. (2008). Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J. 275, 1–21. doi: 10.1111/j.1742-4658.2007.06178.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Woolfe, A., Mullikin, J. C., and Elnitski, L. (2010). Genomic features defining exonic variants that modulate splicing. Genome Biol. 11:R20. doi: 10.1186/gb-2010-11-2-r20

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, Y., Wigneshweraraj, S. R., Weinzierl, R., Wang, Y.-P., and Buck, M. (2009). Construction and functional analyses of a comprehensive sigma54 site-directed mutant library using alanine-cysteine mutagenesis. Nucleic Acids Res. 37, 4482–4497. doi: 10.1093/nar/gkp419

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D. R., Yuen, R. K., et al. (2015). RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347: 1254806. doi: 10.1126/science.1254806

CrossRef Full Text

Yates, C. M., Filippis, I., Kelley, L. A., and Sternberg, M. J. (2014). SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features. J. Mol. Biol. 426, 2692–2701. doi: 10.1016/j.jmb.2014.04.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeo, G., and Burge, C. B. (2004). Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394. doi: 10.1089/1066527041410418

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeo, G., Holste, D., Kreiman, G., and Burge, C. B. (2004). Variation in alternative splicing across human tissues. Genome Biol. 5, R74–R15. doi: 10.1186/gb-2004-5-10-r74

PubMed Abstract | CrossRef Full Text | Google Scholar

Yue, P., Melamud, E., and Moult, J. (2006). SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7:166. doi: 10.1186/1471-2105-7-166

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, F., and Lupski, J. R. (2015). Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110. doi: 10.1093/hmg/ddv259

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Q., Fan, X., Wang, Y., Sun, M.-A., Shao, J., and Guo, D. (2017). BPP: a sequence-based algorithm for branch point prediction. Bioinformatics 33, 3166–3172. doi: 10.1093/bioinformatics/btx401

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, X., Lin, H., Zhao, H., Hao, Y., Mort, M., Cooper, D. N., et al. (2014). Impact of human pathogenic micro-insertions and micro-deletions on post-transcriptional regulation. Hum. Mol. Genet. 23, 3024–3034. doi: 10.1093/hmg/ddu019

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., Lv, J., Liu, H., Zhu, J., Su, J., Wu, Q., et al. (2010). HHMD: the human histone modification database. Nucleic Acids Res. 38, D149–D154. doi: 10.1093/nar/gkp968

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, J., and Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934. doi: 10.1038/nmeth.3547

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, Y., Ingelman-Sundberg, M., and Lauschke, V. M. (2017). Worldwide distribution of cytochrome P450 Alleles: a meta-analysis of population-scale sequencing projects. Clin. Pharmacol. Therapeut. 102, 688–700. doi: 10.1002/cpt.690

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, Y., and Lauschke, V. M. (2018). Comprehensive overview of the pharmacogenetic diversity in Ashkenazi Jews. J. Med. Genet. 55, 617–627. doi: 10.1136/jmedgenet-2018-105429

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, Y., Mkrtchian, S., Kumondai, M., Hiratsuka, M., and Lauschke, V. M. (2018). An optimized prediction framework to assess the functional impact of pharmacogenetic variants. Pharmacogenomics J. 28:1. doi: 10.1038/s41397-018-0044-2

CrossRef Full Text | Google Scholar

Zia, A., and Moses, A. M. (2011). Ranking insertion, deletion and nonsense mutations based on their effect on genetic information. BMC Bioinformatics 12:299. doi: 10.1186/1471-2105-12-299

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: precision medicine, personalized medicine, variant effect prediction, ADME, NGS, rare variant analysis, noncoding variation, pharmacogenomics

Citation: Zhou Y, Fujikura K, Mkrtchian S and Lauschke VM (2018) Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front. Pharmacol. 9:1437. doi: 10.3389/fphar.2018.01437

Received: 03 August 2018; Accepted: 20 November 2018;
Published: 04 December 2018.

Edited by:

Ulrich M. Zanger, Dr. Margarete Fischer-Bosch Institut für Klinische Pharmakologie (IKP), Germany

Reviewed by:

Theodora Katsila, University of Patras, Greece
Greg Slodkowicz, MRC Laboratory of Molecular Biology (MRC), United Kingdom

Copyright © 2018 Zhou, Fujikura, Mkrtchian and Lauschke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Volker M. Lauschke, volker.lauschke@ki.se