Pharmacophenomics in precision medicine

The phenotype-genotype relationship is studied traditionally using the strategies of either forward genetics （ top-down）, which starts with one phenotype （disease） and look at its link with one or many genotypes, or reverse genetics （bottom-up） , which starts with one genotype and see whether its relationship with the phenotype can be different omits methods have been used to establish the relationship between confirmed. In the post-genome era, various diseases and genome/proteome characterizations. These omic studies have unraveled the etiology and patho- physiology of many diseases in a big-data fashion and also revealed an unequable relation between the increasingly detailed variations in genomics, proteomics, metabolomics, and the complex clinical phenotypes of disease and drug therapy. The recent Genome-wide Association Studies （GWAS） have provided a powerful systematic method to in- vestigate the impact of common genomic variations on human disease. In a GWAS, SNPs from across the entire ge- nome are studied agnostically, without premeditation regarding their possible functionEll. While some studies star- ted to use the clustering of common clinical domain phenotypes, however, most of current GWAS are still using the hypothesis-driven strategy for identifying genetic mechanisms and remains largely focused on clinical categories of targeted disease that usually do not provide adequate etiological information. In these studies, a single pre-defined disease or clinical phenotype （trait） is studied and the accrual of a large number of single gene variants - pheno- type associations by GWAS could only fortuitously identify one gene affecting one disease or responsible for more than one phenotypic characteristics or single loci associated with multiple diseases （pleiotropy）. Most common dis- eases are not fully explained by genetic variations. Epigeneticists have proposed that studying the epigenetic （or epigenomic） landscape may provide explanation for this gapE21. Similarly, while the extensive pharmacogenomic studieshave documented pervasive effects of genetic polymorphisms on drug responses （efficacy and toxicity） and highlighted the necessity for the genomically-guided personalized drug therapy, translation of the knowledge of phar- macogenomics into the clinical practice of personalized medicine has become a major challenge due to the intrinsic complexity of the targeted disease and the outcomes of drug treatment, which often involve dynamic alternations in tens or hundreds of genes and environmental factors. Clearly, as indicated by the recent US National Research Council＇ s report ＂Toward Precision Medicine＂, E31 newer approaches are needed for the redefinition of diseases u- sing the underlying molecular causes and other factors in addition to traditional signs and symptoms. In this lectureI willintroduce a new method named ＂Phenome-Wide Association Study （PheWAS）＂ as an alternative approach that complements GWAS and utilizes phenomics and big-data technologies to analyze all genetic/proteomic variants and all available phenotypic information from electronic medical records （EMRs）, electronic health records （EHRs）, or observational cohort containing all types of diagnoses of clinical phenotypes such as data from the Clinical Data Warehouse （CDW） in the estimation of association between genome-phenome and detection of pleiotropy. Phenom- its is a recently developed new trans-discipline that provides a suite of new technologies and platforms for the transi- tion from focused phenotype-genotype study to a systematic phenome-genome approach, which can be used to rede- fine the clinical phenotypes of diseases. In the terms of phenomics, disease is now defined as a clinical phe- nome, which is the sum total of a patient＇s clinical characteristics orphenomic traits that signify the expression of the whole genome, proteome, and metabolome under specific environmental influence （e. g. , microbiome）. With the PheWAS, associations between a specific genetic variant and a wide range of physiological and/or clinical out- comes and phenotypes can be explored either by using algorithms to parse the data collected in EHRs andEMRs, or by analyzing CDW data. Since 2010, PheWAS has been used to investigate whether single nucleotide polymor- phisms （SNPs） associated with one phenotype are also associated with other phenotypes. Robust test of the EMR-based PheWASallows unbiased interrogation across all domains of disease （heart diseases, hypertension, stroke, brain diseases, diabetes, cancers, etc. ） or multiple phenotypes in EMR/EHR-based cohorts, andhas been shown to be able to replicate what is known about individual genotype-phenotype associations with various SNPsand to uncover novel associations with a wide range of phenotypesin EMR/EHR-based cohorts. With the fast advance and development of big-data technology and phenomics, we believe that the application of the EMR/EHR data- based PheWAS in medicine opens important avenues to enhance systematically-integrated analysis of the genomic basis of human disease and responses to drug therapy and to reform our understanding and clinical treatment of these diseaseswith a newconcept ofwholism. With well-defined clinical disease phenome, a new transdiscipline termed ＂pharmacophenomics＂ has been emergingE21. As a counterpart of pharmacogenomics, pharmacoproteomics, and pharmacometabolomics, pharmacophenomicsoffers a suite of new technologies and platforms for the transition from focused phenotype-genotype study to a systematic phenome-genome approach and refine drug research with systematically-defined drug response and therapeutic targets. Therefore, pharmacophenomicswill provide a new par- adigm for the study of drug response including effects and toxicitiesat the level of systems biology and will identify the corresponding therapeutic targets suitable for precision medicine.