Pharmacogenomics in the Age of GWAS, Omics Atlases, and PheWAS

The search for causative pharmacogenomic loci is being transformed by integrative omics pipelines, but their outputs have only begun being applied to test design. We assess the direction of the field in light of Biobanks/PheWAS, omics atlases, and AI. We first assess the potential of recent epigenome and spatial genome concepts, datasets, and methods to improve the functionality of PIP-style pipelines. We then discuss new potential methods of genetic test design on the basis of the outputs of such pipelines. We conclude with a vision for a pharmacophenomic atlas, in which omics atlas data, PheWAS associations, and biobank data would be used with AI to design thousands of genetic tests for clinical deployment in an automated parallel process.


The Unfulfilled Promise of Pharmacogenomics
A common sentiment among clinicians is that Pharmacogenomics, while conceptually appealing, has not lived up to its promise and remains limited in clinical application. While Pharmacogenomics scientists may dispute the pessimistic tone of much comment on Pharmacogenomics, preferring instead to emphasize the many important loci the field has discovered, as well as its noteworthy but isolated clinical successes, it is undisputable that there exist many heritable phenotypes of clinical importance for which no genetic tests are available or for which available tests are not widely used. This is so primarily for three reasons, all relating to the current methods of Pharmacogenomic locus discovery and test design and validation: scalability, missing heritability, and tractability. Firstly, there are many pharmacological phenotypes which have not been studied with the cumbersome system-specific and locus-specific tools of traditional Pharmacogenomics, e.g. single system GWAS, mechanistic model system experiments, and prospective clinical trials. Secondly, systems which have been studied frequently exhibit "missing heritability," the divergence between top-down (genetic) and bottom-up (genomic) heritability estimates, and consequently discovered loci cannot sufficiently predict these phenotypes despite their heritability. Finally, even for systems wherein important loci have been discovered, the path of translating loci into tests, and tests into the clinic, is lengthy and expensive, and frequently goes untrod.
Although the discipline of Pharmacogenomics has sometimes operated at a conceptual and professional remove from the rest of genomics, related disciplines have faced similar challenges and developed potent tools to address them, many of which have been underapplied in Pharmacogenomics. The application of integrative multiple omics analysis to Pharmacogenomics under the rubric of the Pharmacoepigenome has made a significant impact on Pharmacogenomics, including new locus and network discoveries for many phenotypes, and new tools for discovering them, as well as nascent predictive models. But in the coming decade, the powerful new capabilities enabled by emerging resources from other areas of biomedical research will do even more to unlock the latent potential of the genome in personalizing medical decisions. These technologies include biobanks which fuse biosamples (and, increasingly, omics data) with longitudinal electronic medical records, Phenome Wide Association Studies (PheWAS), deeper atlases of biomedical omics data, and rapidly advancing capabilities in artificial intelligence.
This perspective is divided into three parts. The first covers the broader context of the use of the epigenome and spatial genome to interpret GWAS, and the ways that this knowledge has been translated to Pharmacogenomics with omics pipelines like the Pharmacoepigenomics Informatics Pipeline (PIP). The second covers a future vision for a next-generation PIP-style pipeline which is designed to benefit from the latest discoveries about the spatial genome and epigenome, and from current and upcoming datasets. And the third and final section describes a future vision and methods for the use of mature PIP-style pipelines to design genetic tests using AI methods on cohort datasets, and eventually to do so in parallel with the outputs of PheWAS, enabling genetic prediction of a broad array of biomedical phenotypes to make itself a routine presence in the clinic. PGx_GWAS_Omics_PheWAS manuscript

The GWAS Interpretation Challenge
Genome wide association studies (GWAS) [Visscher et al 2017] have become a cornerstone technique of biomedical locus discovery over the last twelve years, and all signs point to the continuing escalation of this trend. The parallel measurement of millions of SNPs throughout the genome on a microarray at tractable cost [LaFramboise 2009] has allowed the traditional methods of genotypic association studies to be carried out in parallel across the entire genome. The resulting explosion in locus discovery for many systems has yielded fundamental discoveries in every area of biology and medicine This trend shows no sign of stopping and every sign of accelerating. Over the last five years, the genetic association methods of GWAS have been applied to two additional forms of high throughput biomedical data analysis, which add additional dimensions of parallelization across many phenotypes. In the case of molecular quantitative trait locus (mQTL)  . The amount of GWAS data available, the array of phenotypes tested, the amount of undiscovered insight latent in these experiments, and the interest in interpreting them will continue to grow.
At the same time, however, the results of GWAS have not lived up to some of the original excitement. At the inception of the technique after the sequencing of the human genome, it was believed by many that the GWAS technique would lead to the recovery of the bulk of the genetic heritability of studied phenotypes [Collins et  . Less clarity has emerged on the topic of the omnogenic, rare-variant, epistatic, and epigenetic hypotheses. It is likely that each of these factors contributes to the heritability problem to some extent and that such extents vary for different types of phenotypes, but this landscape remains murky.
Interpreting GWAS results is a very difficult problem. It involves a number of major challenges, principally discerning the reality of associations, attributing causal character to them, finding the causal variants within linkage regions, and discerning their function.

The Epigenome
During the same period of time, array-and sequencing-based assays for a large number of epigenome features, and experiments and atlases conducted with such assays, have revealed that the multifaceted, tissue-specific epigenome has a relationship with gene expression, cell fate, organismal function and dysfunction, and disease, which is both intricate and powerful.
In contrast to the genome, which is relatively well defined, the epigenome comprises a large and growing number of modalities measurable with various assays, most of which take the form of "tracks" comprising numeric levels of observed signal at various positions in the genome, and often a genome-wide vector of signal. , as well as more focused efforts from many quarters. IHEC data now includes a set of core epigenome marks for over a hundred tissues throughout the human body. As a result of this, despite the inherent sparsity of any real dataset, the epigenome is increasingly regarded like the reference genome: as a resource to be consulted for systems and loci of interest, rather than an unknown quantity to be queried experimentally in specific contexts.
The epigenome atlases have identified a set of "core" epigenome elements which determine a set of chromatin states corresponding to the various categories of regulatory states (for genes) and regulatory elements (for noncoding regions of the genome). The most influential method for calling chromatin states is ChromHMM [Ernst et al 2012], which uses a hidden Markov model on "core" epigenome tracks to call fifteen chromatin states including seven types of promoters/enhancers and eight types of activity/repression states. Chromatin states which emerge in particular genomic locations in particular tissues have become a widely used and powerful guide to the functions of the host loci and the tissues in which they operate.
In addition to this, the validation of enhancer and promoter elements with reporter assays based on the transcription of their target genes under the influence of element excision with CRISPR genome editing [Lopes et al 2016, Gasperini et al 2017, Klein et al 2018], what is referred to as "CRISPR validation" of an element, has been increasingly used. Under the influence of these methods, researchers are increasingly able to assess the function, activity, and targets of regulatory elements in a tissue-specific manner by consulting these resources, even in the absence of any purpose-specific experiments.
In addition to this, a powerful set of tools have emerged that use machine learning and explicit algorithms to make predictions about the effects of sequence changes and other perturbations on epigenome outcomes. These have included both explicit algorithms and machine learning methods for predicting TF binding, machine learning methods for predicting variant effects on epigenome tracks and chromatin states, and machine learning models for predicting enhancer targets.

The Spatial Genome
The period since the sequencing of the genome has also seen two epochal discoveries advancing in parallel: firstly that the spatial organization of the genome inside the 3D space of the nucleus varies from cell type to cell type Prior to the emergence of genome sequencing based biochemical assays, the existence of a role for genomic spatial organization was already known from imaging studies and the biochemical methods of chromosome conformation capture (  And the developments in sequencing-based methods for gauging chromatin spatial information were even more profound. The sequential development of parallel 3C-based methods including 4C (one genomic location against the genome) and 5C (a collection of probes against each other) reached a disjunction with the development of Hi-C: high throughput chromatin conformation capture. It involves cross-linking fixed cells and digesting the genome with a restriction enzyme PGx_GWAS_Omics_PheWAS manuscript (or, for Micro-C, mainly used in small genomes, an unselective endonuclease like MNase), followed by religation, sonication, and paired end sequencing. The result is a paired end sequencing library wherein the genomic locations of the paired reads do not correspond to elements close to each other in sequence space, but in physical space. Such reads can be compiled into a chromatin contact map, potentially of the entire squared genome.
This method, which allowed parallel 3C-style measurement of the entire genome against the entire genome, producing maps of chromatin contacts in squared genome space, electrified the field of chromatin structure and became the standard in genome structure research. Progressive protocol optimization has allowed the methods to produce higher quality data with lower effort, and to address a wider collection of cell lines and tissues. Experiments in a large number of cell lines and conditions have highlighted the commonalities in genome organization and the ways that organization differs over time. Hi-C data is increasingly being used for fundamental genomics tasks like assembling reference genomes But even more than this, Hi-C data has been produced a revolution in our understanding of spatial genome organization, the largest component of which has been the discovery of topologically associating domains ( : the contact frequency in any given portion of the squared genome will more closely correlate with a more sequence-distant portion which is in the same TAD pair than a sequence-proximal portion spanning TAD boundaries.
While the portion of a genome which composes a TAD is relatively invariant, TADs differ from one cell type and biological condition to another in their degree of transcriptional activity. This differentiation between the "A" and "B" compartments is connected with the sign of the dominant eigenvector of a genome-wide Hi-C matrix , which uses single cell sequencing of cryosectioned nuclei followed by statistical modeling of the cooccurrence of pairs of sequence regions, cannot produce high resolution intra-TAD information, but generates Hi-C style contact maps with less sequencing, and also can be used to produce calibrated physical distances.
This proliferation of data is increasingly being used to form three dimensional models of chromosome territories and even the entire nucleus. Early work in this area met with difficulty due to low resolution data, the complexity of the task and the overconstrained nature of the data, and the challenges caused by structural heterogeneity [Nagano et al 2013]. This may be clearly seen by the preeminence and then rejection of the "superaxis" model of chromosome territory structure, in which the sequence of a chromosome is approximately recapitulated by the spatial ordering of TADs along a "superaxis," with the string of tads threading back and forth between the A and B compartments of the territory, possibly by means of a coil. Multiple spatial modeling investigations based on different modeling methods and different Hi-C datasets independently discovered the superaxis , and do not produce superaxis behavior in the models. They show better accord with 3D FISH data than prior models.

Integrative Epigenome Models of Variant Function Applied to Association Hits in Pharmacogenomics
The These pipelines work by annotating SNPs multimodally with information from multiple types of omics information. In the case of RegulomeDB, this included DNase sensitive regions, validated promoter and enhancer regions, transcription factor binding sites, and predicted regulatory elements. HaploReg expanded on this suite by adding tools to gauge the relevance of SNPs in the area of a lead SNP with linkage, and looking at PWM perturbation by SNPs, deeper epigenome data from the Roadmap, and eQTL data. Both have been widely used.
All such models rely on an overall paradigm of genomic regulation sometimes described as the "pharmacoepigenome," [Higgins et al 2015] in which, by virtue of the facts that 1) associations arise from causative regulatory variants influencing the underlying genomic machinery of a phenotype, 2) regulatory variants and their targets can be identified and parsed by looking for the hallmarks of regulation in epigenome datasets, and 3) the machinery underlying genetic variation in a phenotype is often joint with the machinery underlying the phenotype itself and related phenotypes, it is concluded that powerful insights into the mechanisms of a wide variety of phenotype, not necessarily constrained to pharmacogenomics, can be obtained by looking for tissue-specific regulatory variants and their targets in the regions surrounding association hits for a collection of related phenotypes.
Such models have been used to interpret GWAS, looking in significance regions for regulatory variants, by a large number of different methods mostly comprising individual ad-hoc analyses. They differ significantly but largely adhere (fully or partially) to a number of common themes: 1) Analyzing multiple GWAS on the same (or related) phenotypes together in the same analysis.
2) Analyzing many variants, not just lead SNPs. For primary GWAS this is often done with the significance region or with a p-value fold change cutoff from the lead SNP. In secondary analysis, it is often done by linkage. Some analyses have used sequence distance cutoffs, but this is not biologically informed and is inadvisable.
3) Looking for regulatory variants with tissue-specific data that is relevant to the phenotype under investigation. PGx_GWAS_Omics_PheWAS manuscript 4) Attempting to identify the target genes of candidate regulatory variants discovered in the analysis.
5) Filtering, ranking, and organizing the result genes together with pathways and ontologies, looking either for genes of preexisting known relationships to the phenotype, or genes that cohere with each other, e.g. by shared pathway or ontology membership, or coregulation by a known transcription factor.
Such analyses have become more common, with epigenome-based causal variant methods appearing in even primary GWAS analyses.

The Paucity of Pharmacoepigenomics in Clinical Practice
By contrast, the discipline of pharmacogenomics has historically made little use of any of the advanced variant discovery and interpretation methods discussed above. To date, the most widely used biomarkers for clinical pharmacogenomic testing are a set of pharmacokinetic (PK) gene variants located in CYP genes, encoding the main drug metabolizing enzymes, while only a small family of pharmacodynamic (PD) genes have been utilized, and regulatory variants, even less . By and large, however, these tests are designed manually using variants in a small pool of candidate genes.
Although pharmacogenomic phenotypes have been investigated with GWAS since its inception [Giacomini et al 2017], most available pharmacogenomics tests continue to be based on highly penetrant coding variants revealed by gene-specific work, with GWAS findings, PD genes, and regulatory variants persistently underutilized. Deployment of GWAS in pharmacogenomics variant discovery has lagged deployment in other disciplines, epigenomic interpretation of GWAS results has been underutilized, and the translation of GWAS results into clinical tests has been slower still in most areas.
Indeed, much pharmacogenomics variant discovery still proceeds along traditional lines involving the search for coding variants to be designated as star (*) alleles, with a particular emphasis on PK genes for absorption, distribution, metabolism, and excretion (ADME). Such genes have formed the focus for test development for response, dosing, and adverse drug events (ADEs) and adverse drug reactions (ADRs). PGx_GWAS_Omics_PheWAS manuscript The PIP is an integrative multiple omics variant discovery pipeline specifically designed for the reanalysis of GWAS for pharmacogenomics. The PIP's function is conceptualized with the five box model of regulatory variant discovery (Figure 1), reflecting a conceptual scheme of five general properties a variant may exhibit which provide evidence, individually and collectively, that it may be a causal regulatory variant in a phenotype. They are permissive candidacy, regulatory function, variant dependence, target genes, and network analysis.
By permissive candidacy, we mean that the variant has in some way come to the attention of a genome-wide screen: that it is located in a population-specific linkage disequilibrium with a variant associated with a phenotype of interest, or that it regulates a gene whose mechanistic importance to the phenotype of interest has already been established.
By regulatory function, we mean that the variant is resident in a portion of the genome which is a regulatory element, a promoter or enhancer, in one or more of the particular tissues which are relevant to the drug-disease system.
By variant dependence, we mean that the function of this regulatory element must be dependent on the status of that variant, through the alteration of sequence features which help to determine the epigenome. Such sequence features may be a specific binding site for a transcription factor, but may also be a more general propensity score for an epigenome feature, as determined by an appropriate bioinformatics algorithm such as a learning machine.
By target genes, we mean the variant must have identifiable target genes with which it is spatially and/or functionally associated, putatively whose expression it regulates.
And by pathway analysis, we mean that we may say of a collection of putative genes and variants for a phenotype or phenotype cluster, that taken as a totality they are associated with each other and with ontologic and/or network categories which are connected to the phenotype under investigation.
These five concepts may be evaluated in different ways under different circumstances: with different datasets, with different algorithms, manually and under automation. Nevertheless they are conceptually durable, and all the pre-PIP workflows, each extant version of the PIP, and our future plans for a PIP-successor pipeline, fall within this overall orienting framework.

Pharmacogenomics in the Age of GWAS, Omics Atlases, and PheWAS
The Pharmacoepigenomics Informatics Pipeline and associated tools and methods have added value in the identification of causative enhancer variants for phenotypes of interest, and the identification of their target genes for future mechanistic work. The variants, genes, and networks discovered for warfarin can be used directly in traditional test design, and other phenotypes can be investigated with these tools.
However, this featureset is not the last word. Advances in a number of areas, including the underlying biology of enhancer function, artificial intelligence, omics atlases, biomedical ontologies, and genotyped medical records from biobanks and clinical trials have opened the doorway to much more powerful PIP-style pipelines, which are more sensitive to interactions which cannot be detected by the current PIP, and more thorough in pruning out interactions which are not as promising. More, they have opened the door to a future wherein such pipelines are used pervasively in parallel against thousands of biomedical phenotypes, with results that can be used in routine medical practice.
The second portion of this perspective describes an evolved PIP featureset which is based on current cutting edge methods in all the subdomains of biomedical inference from which the PIP draws, and some notions of how to score variants and genes and pathways in an evolved PIP with machine learning. It describes an orienting framework for how to use the output of PIP-style pipelines in biomedical genetic test design. And finally, it concludes with a vision for the use of such a pipeline with biobank datasets and biomedical ontologies to create a genome-wide, phenome-wide pharmacophenomic atlas of predictive models for thousands of phenotypes, which could be then be implemented directly in a clinical decision support system.
The availability of genetic prediction on a routine basis for medically important phenotypes, shortening the translation cycle on genetic discovery, can improve patient care.

An Evolved PIP Featureset
The underlying scientific domains of epigenome regulation, spatial genomics, and population genetics on which the PIP is based have not stood still since the PIP featureset was laid down in 2016. They have continued to advance. And while a future evolved PIP featureset will still be based on the overall Five Box Model of regulatory variant discovery, every portion will be affected by these significant discoveries. In addition to this, the universe of data from which a future PIPstyle pipeline can draw has expanded vastly.
This will begin with data input: unlike the current PIP, which treats all variant inputs and all tissues uniformly, an evolved PIP would separately take tissue inputs for a collection of related phenotypes, and separate inputs of the relevant variants and populations for each phenotype, as well as information about the degree of relatedness of phenotypes to each other. In addition, the directionality of the variant effect is important: an evolved PIP would track which of the alleles of the SNP had which effect on the phenotype, and in the context of the variant dependence portion of the ERV workflow, the directionality of effects on TF binding and enhancer function will be gauged as well. The concordance of this information within the scores for each SNP, and between the common regulatory SNPs for a gene, will function as important information in the context of scoring. PGx_GWAS_Omics_PheWAS manuscript Such a PIP would have the opportunity to draw on a wealth of data sources on which the current PIP does not draw. In addition to the resources of the current PIP, new and not-previouslyavailable datasets which would add significant value would include:  Primary GWAS and genotyped cohorts for relevant phenotypes

Permissive Candidacy
Currently, all PCV identification in the PIP proceeds in a "forward" mode, proceeding from the phenotype toward a locus by association. This is currently done with GWAS lead SNPs by population-specific linkage analysis.
This forward mode of PCV discovery can be expanded. For one, linkage analysis with 1000 Genomes [1000 Genomes Project Consortium 2015] populations can be enhanced with the use of more relevant population groups as the number of analyzable genomes continues to expand, and the granularity of population genetics mapping continues to refine. For another, while linkage analysis has been the best approach for secondary analysis of GWAS for which only topline results are available, primary analysis of GWAS and CGAS can offer a more granular approach, when such data are available. Rather than using linkage, the collection of associated variants can be used together, in sum.
Moreover, however, a second mode of PCV identification, the "backward" mode, can take on a more prominent role. In the current PIP, we identify body SNPs of known genes of mechanistic importance in a phenotype as PCVs. In fact, however, what is desired is not body SNPs, but those SNPs which alter the sequence or expression of these genes, i.e. both coding SNPs and regulatory SNPs for this gene, regardless of their position in the genome. Thus, the evolved featureset can take gene inputs, just like the current one, and look for not only coding variants, but also tissuespecific regulatory variants for these genes, no matter where they may be in the genome, using the same target gene tools as the ERV workflow to find PCVs.
Moreover, although the current PIP treats all PCVs equally, PCV status need not be a binary. The strength of a PCV relationship may be modulated by a number of factors. For example, currently all affiliated phenotypes are weighted equally; in the Warfarin analysis, this included not just response and adverse events, but also disease risk and background phenotypes. In an evolved featureset, we would wish to give more weight to variants for more directly related phenotypes.
But for another, the strength and directionality of identification matters. For example, a variant exhibiting a lower p-value in a source GWAS, or stronger linkage to a lead SNP, is probably a stronger candidate. So, too, is a "backward mode" variant with a stronger target gene relationship, or a relationship with a more validated target gene. In addition, a variant identified through both "forward" and "backward" methods may be considered a particularly strong candidate.

Regulatory Function
In the current PIP, variants are evaluated for regulatory function in a rigid way: they must have one a promoter or enhancer chromatin state in a relevant tissue, and each of a collection of relevant histone marks in a relevant tissue, and be located in accessible chromatin in a relevant tissue. However, this status is awarded in a simple binary manner, and with no requirement that these tissues be concordant with each other or with other tissue-specific information.
In an evolved featureset, in addition to using larger datasets with higher quality and more relevant data (e.g. on tissues of more granularity), the criterion could enforce concordance between the PGx_GWAS_Omics_PheWAS manuscript tissues for each omics modality. It would also serve to look for membership in databases of validated enhancers, and particularly potent categories of FIREs and super enhancers. It would place more emphasis on the features whose relevance has been emphasized in recent work, particularly chromatin accessibility, Hi-C contacts, TF binding, and chromatin state, over individual histone marks and DNA methylation, which have been downplayed.
In addition, of course, this status need not be binary (as in the current PIP), but could scale with the strength of the appearance of regulatory status, the level of inter-tissue concordance, and the degree of relevance of the tissues to the phenotypes for which the PCV attained PCV status.

Variant Dependence
The current PIP uses only one modality for variant dependence analysis, and this modality is both limited, and poorly evaluated. This is PWM analysis, using showed that concordant changes in TFBS affinity for enhancers located at ChIP-seq-validated binding sites were a good mark of regulatory variant function.
However, the current methods in the PIP are weakened by an obsolete database of PWMs, as well as a crude and unmaintained algorithm for evaluating conformity. In addition, there is a format conversion from probability to frequency matrices which introduces some imprecision. In addition to this, although the current PIP tests for conformity and tests for TF binding, it does not value concordance between these measures.
An evolved PIP featureset would use a comprehensive versioned and updated PWM library in its native format, along with compatible and maintained code for PWM conformity. MotifDB There are now many machine learning applications that score features, such as DHS for prioritization of regulatory function and protein annotation of chromatin loops, to predict functional enhancer-promoter interactions and drug-target inference. Variants that modify TFBS for TFs with binding activity at those loci, and with predicted allele bias, would have very robust evidence of allele dependence in the regulatory function of the host loci. In addition, concordant directionality between TF binding and enhancer function predictions will be an important form of evidence.

Target Genes
The target gene module in the current PIP, which evaluates target genes only by sequence proximity and QTL status (neglecting QTL targets), is inadequate. Both the "variants" and valproate analyses showed the value of finding target genes with Hi-C data, and recent work on target gene analysis with molecular QTLs and Hi-C data, along with machine learning, has been extremely fruitful.
An evolved PIP would locate target genes of regulatory variants with four methods:
2) A comprehensive genome-wide and tissue-specific collection of molecular QTLs, comprising expression QTLs, DNA methylation QTLs, DNase accessibility QTLs, histone acetylation QTLs, and other modalities of molecular QTLs. Such a library should address major eQTL mapping experiments from GTEx and the national biobanks. A QTL relationship between a SNP and the expression of a gene or the epigenomic status of the gene body is indicative of an enhancer relationship.
3) Hi-C based methods for proximal target identification, such as TargetFinder

4) H-GREEN [Allyn-Feuer et al 2018] or similar methods for distal target identification.
The number, type, and strength of target identifications with these methods will be an important gauge of the strength of a regulatory interaction.
In addition, it may be advisable to consider using the squared genome wide collection of tissue specific enhancer target interactions with matrix densification machine learning methods to densify sparse measures of target genes. Of the methods above, both mQTL mapping and distal Hi-C mapping suffer from significant data sparsity, and densification methods may add value. PGx_GWAS_Omics_PheWAS manuscript

Pathway Mapping
The pathway mapping functionality in the current PIP relies on the human all-tissue grow and connect functions of Ingenuity Pathway Analysis [Kramer et al 2013], consulted manually on the basis of the output genes. It should be possible to improve on this functionality in several ways: Firstly, it is important that an evolved PIP's pathway mapping function run locally, run in an automated manner, and use versioned data. IPA licenses are available which offer API access to IPA commands, along with local storage of the database and/or versioned online access to historical quarterly updates. If the evolved PIP is to use IPA or another commercial or open-source pathway mapping tool, such features are essential.
More fundamentally, however, IPA, in evaluating genes only without attention to their origin or their PIP-determined relationships, is in some sense operating in a reductive manner. It can detect whether the collection of genes identified in a PIP experiment have known relationships with the phenotype under investigation or with each other, but it cannot assess the internal relationships among the set of genes and variants as identified by the PIP. In addition to the external, literature and experiment based relationships, it should strike us as important to pathway relationships if, for example:  The same TFBS is altered and/or the same TF is present at multiple loci for a phenotype  The same gene is regulated by multiple variants for a phenotype  The same variant for a phenotype regulated multiple related genes  The same genes and variants are concordantly identified for a cluster of related phenotypes While a full exposition of the types of relationships we should seek and evaluate would be essentially a reinvention of the entire field of pathway mapping, and thus outside the scope of this discussion, it will suffice to note that different pathway mapping options may exist, that new functionality recently added to IPA around regulatory variants may be helpful, and that particular needed functionality can be added.

Scoring
The scoring algorithm of the current PIP is minimal in nature, comprising an explicit scoring algorithm resulting a binary determination of status for each PCV for each of the components of the five box model, and a simple intersection at the end of scoring to determine a set of intermediate candidates. This method was adopted not because it is optimal but because it is simple.
In the evolved PIP, each component will generate a vector of numeric and qualitative scores of various types, which we wish to integrate into a holistic picture, not necessarily of which variants "pass" a final binary, but of which variants from the set of PCVs are most likely to be predictive, which among them are likely to be mechanistically related, and which genes are likely to play in the mechanism.
Among possible scoring systems, systems in which "points" are awarded for various categoric and numeric elements and summed up to determine a "score" are appealing for their conceptual PGx_GWAS_Omics_PheWAS manuscript simplicity, but unlikely to be suited for this complex task, because of the interrelated and cooperative nature of the types of evidence which make up a regulatory variant determination. For example, a variant with no evidence of variant dependence is probably null, regardless of the potency of an enhancer element in which it is located or the target genes of that enhancer. Despite our experiments with "points" based scoring systems, they are unlikely to be fruitful.
The universe of possible explicit scoring algorithms, incorporating "points" and thresholds and sliding scales, is vast. And complex explicit algorithms have achieved great success in many applications in biology, bioinformatics, and medicine. It is possible to envision an ensemble of explicit methods capturing a great deal of complexity and yielding "good" scoring. Nevertheless, it will be useful to consider another possibility: the use of machine learning methods to address this question.

Machine Learning for PIP scoring: Supervised Classification, Overfitting, and Data Availability
Most successful deep learning applications rely on large labeled data. While many biological and clinical datasets until recently were limited by amount of available labeled samples compared with the big data analytics applications such as natural image processing and NLP, as the number of samples increases and the number of relevant high-quality labeled datasets expanded, the wealth of pertinent pharmacogenomics data that can be used for analytics is now a big data challenge on par with contemporary applications in other domains. Thus, variant-based learning machines have proliferated and are now widely used in genomics, as described above.
Multimodal, multi-task, and transfer learning are often used to alleviate data limitations to some extent. Transfer learning approaches include training a deep network on a large existing dataset, and then using this pre-initialized model to learn from a smaller dataset, which typically leads to Data quality is another important concern in deep learning applications. Although deep learning models can be trained directly on raw data, low quality datasets may require additional pre-processing and cleaning. In addition, there is the challenge of preventing overfitting, which requires a very careful design of the model evaluation scheme, including usage of cross-validation techniques, normalization by subjects, and suitable validation metrics.
In the context of the PIP, the consequences of this are clear. There does not currently exist a set of adequately characterized positive controls for pharmacogenomics regulatory variants, of a type, scale, or breadth, to allow the construction of a monolithic supervised machine learning algorithm for scoring PIP variants. Indeed it is not entirely clear from where, with any plausible level of effort, such a training set would come. This complicates matters considerably.
However, subcomponents of the overall model exist wherein training sets for machine learning are available, including the portions described above where machine learning algorithms are used to generate scores in individual modules of the PIP featureset. These include the use of hidden Markov models to generate chromatin states, the use of neural networks to predict variant effects on DNase accessibility and chromatin state, motif discovery by clustering, matrix densification for PGx_GWAS_Omics_PheWAS manuscript regulatory variant target discovery, etc. Artificial intelligence algorithms also have been demonstrated for variant imputation.
It may be that databases of validated tissue specific enhancer variants may function as a training set along with omics atlas data, so that the entire ERV workflow (regulatory function, variant dependence, and target genes) may be carried out with a supervised-training machine learning algorithm to predict the presence or absence of a "validated-appearing" enhancer variant.
In addition to this, the relationship between metrics of PCV status, like target gene status, association p-values, etc, and status as a plausible causative variant for the phenotype, among variants which are called as potent regulatory variants, may be addressable with Bayesian statistical approaches constructed synthetically from statistical models in population genetics. Similar approaches may be applied to network membership in the pathway mapping portion of an evolved workflow.
Thus, although it appears intractable to attempt to replace PIP scoring with a monolithic machine learning algorithm using any current tools, it will be possible to gain more insight from more data by the replacement of progressively larger portions of the intermediate scoring (the more defined portions) with machine learning algorithms, even as the overall scoring system remains an ensemble constructed explicitly.

Using the Output of PIP-Style Pipelines to Develop Genetic Tests
The output of the PIP is a set of variants, genes, and pathways with a putative causal role in a phenotype of interest. Although such results may be useful for purposes of mechanistic research in the biology of a phenotype, or the search for druggable targets and repurposing opportunities. However, in the context of pharmacogenomics the object of most PIP analyses will be to develop genetic tests (and more broadly, clinical predictive models) for eventual clinical deployment. While an authoritative discussion of this subject is beyond the scope of this thesis, it will serve to discuss this topic briefly in light of the above.
Typically, first generation pharmacogenomics tests were designed by a simple linear combination of variant effect sizes, or with a linear regression model. Later first-generation tests such as the Genesight [Health Quality Ontario 2017] psychotropic panel were constructed manually as an explicit combinatorial decision tree algorithm on the basis of effect sizes, paired variant effects, and manual expertise and adjustment. Then, these explicitly constructed tests could be validated against genotyped cohorts with response data before being tested in randomized controlled trials.
The PIP and PIP-style pipelines can be used to design tests in this mode. But doing so would deprive the designer of much of the benefit of the PIP and of modern data sources. With significant pleiotropy and epistasis of pharmacogenomic loci, the reduction of features to a tractable number, and the availability of genotyped cohorts and machine learning methods, it is anticipated that the ideal method for designing a pharmacogenomics test on the basis of PIP results would incorporate these advances. PGx_GWAS_Omics_PheWAS manuscript In this case, a genotyped cohort with genomic information on all the output loci of a PIP experiment, along with clinical variables deemed important in test design, and outcome information, would be gathered, preferably retrospectively from a GWAS or biobank. Then, a machine learning predictor would be trained to predict the outcome variable with this cohort, with cross validation. Since this is a relatively low dimensional space with supervisory information, and because the intention would be to clinically deploy the finished predictor, a relatively simple machine learning method like an SVM or Random Forest would probably suffice. Then, features could be reduced using an iterative marginal information analysis approach to arrive at a set of informative loci and clinical features to be used. A predictive model trained on these features would function as a finished genetic test and could be validated and deployed.
This approach is described schematically in Figure 3. PGx_GWAS_Omics_PheWAS manuscript

Figure 3: Using PIP-Style Pipelines to Design Genetic Tests
Schematic of an overall method for using PIP-style pipelines to design genetic-clinical tests. After loci are generated by a PIP experiment, they are used along with tractable and predictive clinical features in a cohort dataset to generate a predictive machine learning model, which is tuned with ablation analysis to find features with high marginal information. The minimal set of such features is used to generate a final predictive model which functions as a test, and can be validated in prospective and retrospective clinical trials. PGx_GWAS_Omics_PheWAS manuscript Although historic regulatory approaches have demanded prospective clinical trials before clinical deployment of genetic tests and genetic prediction modules for clinical decision support, scientifically there is little conceptual distinction between a retrospective trial on a cohort which was not used in the construction of the test, and a prospective trial. As the scientific consensus around these issues congeals and makes its way into the culture of regulatory thinking, it is likely that the greater speed and lower expense of this approach, along with the clinical utility of making genetic testing more widespread, will carry the day in favor of this kind of development.
But in any event, even if prospective clinical trials were required in order to validate such predictive models, the fact that they were initiated on the basis of PIP-discovered variants with mechanistic validity should advantage such classifiers in the regulatory environment, compared with agnostic machine learning approaches based on whole genomes.
Much has been made of regulatory barriers and cultural hesitance to use genetic information in some medical specialties as explanatory elements for the slow progress of pharmacogenomics testing deployment in many clinical specialties. Historically, however, the single biggest factor preventing pharmacogenomics testing from reaching the clinic in any given case has not been regulatory or cultural barriers but the clinical utility of the underlying prediction. In instances in oncology and neuropsychiatry where such tests have added value, they have typically met with at least enough regulatory permission and cultural tolerance to be applied. It may be anticipated that if, in the fullness of time, tests for new phenotypes do add such utility, neither governments nor conservative clinicians will stand in their way indefinitely. And as the number of domains where such tests add value grows, they may become the object of much enthusiasm.

Using PIP-style Pipelines to Construct a Phenome-wide Pharmacoepigenomic Atlas
A PIP-style pipeline analysis, intended to discover variants with a causative role in a particular phenotype, takes place using a large amount of omics data which comes from the same database for each experiment, and is selected on the basis of preexisting knowledge about the phenotype under investigation. In the current version of the PIP, this principally comprises the MVF and MTF, containing information about the key mechanistic genes and associated variants, and the relevant tissues. In the case of the evolved featureset discussed in this perspective, other key information will come to the fore, including the most relevant Hi-C datasets, the degree of relatedness of clustered phenotypes, and the tissues for each phenotype. But regardless, a five box model pipeline requires a certain set of specific information about a phenotype system in order to run. The maturity of these trends will culminate in a world wherein, instead of performing a targeted PIP-style analysis on a particular phenotype of interest and using it to design a test, the converged datasets and methods described here will be used to perform parallel analyses and parallel test design for thousands of phenotypes. The predictors for these phenotypes could, for genotyped patients, then be contributed directly to an EHR system and used for clinical decision support. Furthermore, the genotypes, clinical records, and outcomes of patients within an EHR system could be used to refine the predictors for such a system on an ongoing basis. PGx_GWAS_Omics_PheWAS manuscript

Figure 4: Conceptual Process for Constructing a Pharmacophenomic Atlas
Schematic representation of a conceptual process for parallelizing a PIP-style pipeline across thousands of phenotypes to create a genome-wide, phenome-wide pharmacophenome atlas.
With either automation of parallelized manual work, MVFs and MTFs are created for thousands of phenotypes on the basis of PheWAS, biobanks, and/or the GWAS catalog, and thousands of PIP experiments run. Then, automated test generation as described in Figure 3 is undertaken in parallel for all the phenotypes based on separate cohorts from a biobank and/or PheWAS. Finally, this set of thousands of predictive models for a comprehensive collection of pharmacological phenotypes may be used in every CDSS and research context wherein it may add value. PGx_GWAS_Omics_PheWAS manuscript The concluding section of this perspective will describe a future vision for the means by which this process could take place, which is also pictured schematically in Figure 4.
The first step in such an analysis would be the construction of a phenotype set for investigation. Each PheWAS analysis already constructs such a set, typically manually.
The construction of tissue files on an automated basis requires a degree of natural language processing. Ontologic mapping of phenotypes to relevant diseases, along with crawling of structured literature databases like the EBI GWAS catalog [Macarthur et al 2017], and natural language processing, could be used to create variant input files on an automated basis, by locating the relevant phenotypes in natural language and extracting the locus and population information.
In addition to this, the output of a PheWAS on the same set of phenotypes could be used to construct the variant files.
Similarly, ontologies mapping drugs and diseases to their sites of action (e.g. SNOMED With this done, it would be possible to run a PIP-style pipeline in parallel on many drug-disease systems to create a genome wide, phenome wide atlas of significant pharmacogenomic loci and gene networks. In particular, such experiments could be conducted in parallel on the basis of association data from EHR records for large populations [Denny et al 2013]. This atlas could include all the drug disease systems, disease risks, and other pharmacological phenotypes for which the underlying genetic associations and tissue specific omics are available.
Theoretically, if such automated methods as are described above proved impractical, and if sufficient resources were available, the manual curation of a library of tissue and variant files could be undertaken for a large library of phenotypes. The creation of input files for the current version of the PIP has already been reduced to a matter of days for two investigators, and with a suitable graphical interface for curation, could be reduced further, perhaps to the point where manual curation by a small dedicated staff with access to a variety of medical specialists became a tractable alternative. Certainly this has been the predominant approach in PheWAS design.
With a separate linked biobank not used in the original analysis, such a "PIP-WAS" atlas could be used to design predictive models for every phenotype under investigation. This would require the phenotypic extraction, key clinical variables, and clinical variable extraction for each phenotype from the second biobank to be automated as well. In addition, it would require the test generation and marginal information ablation analysis to be automated, a requirement which would require a lot of hyperparameter tuning. Nevertheless we are confident it is possible with current methods.
This comprehensive pharmacogenomics atlas would represent the industrialization of pharmacogenomics. It would enable, among other things, the scalable parallel design of tests for many diseases and drugs on the basis of genotype-linked EHR data, and the development of PGx_GWAS_Omics_PheWAS manuscript microarrays or sequencing panels containing the genetic information for many tests, or all, in one package. Such information could then be available in the EHR for a variety of purposes, and could even be present on, e.g., a military "dog tag" in the form of a QR code or other linked identifier for use in emergency and traumatic care settings.
Nor, indeed, would the construction of such an atlas need to be a discrete versioned event. If genotyped EHR data were added to the system, predictive models could refresh on an ongoing basis with new information, and new phenotypes could be added to the catalogue as their relevance was established.

Pharmacogenomics in 2030
What would medicine look like, in a world wherein such an atlas had been constructed, and wherein it had been incorporated into the EHR systems in use in the clinic, and in which patient genotypes were routinely available? Every time a patient required general health guidance, clinicians would have access to predictive information suggesting the diseases and syndromes which might present the greatest risk. For every prescription decision, the pharmacogenomics of the various drugs available for such an indication could be displayed, along with the particular adverse events most concerning for each one, for the individual patient. And for public health concerns with rare indications, the application of an optimized classifier to the covered population of a health system could prospectively identify a cohort who would benefit from prophylactic guidance or treatment.
Only twelve years ago, before the advent of GWAS, genetic tests were designed manually for each phenotype on the basis of painstaking, locus-specific mechanistic work, almost exclusively using coding variants, and then loci were assayed individually before being reported, often with a delay of weeks. While the clinical side of this picture has seen only muted change in the time since, rapid and fundamental advances on the research side, including omics atlases, PIP-style omics pipelines, spatial interaction data, biobanked EMRs, and PheWAS, have made it possible to see forward to a future, twelve years from now, when the parallel design of genetic tests for thousands of phenotypes, incorporating tissue-specific regulatory variants, machine learning, and large cohort datasets, has made the display of genetic predictive information on relevant phenotypes a routine and automatic part of life in the health care clinic.