Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study approach, which has been used to identify single nucleotide polymorphisms associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype–phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

[1]  William A Fera The next IT challenge. , 2010, Journal of AHIMA.

[2]  L. Liang,et al.  Mapping complex disease traits with global gene expression , 2009, Nature Reviews Genetics.

[3]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[4]  Tyrone D. Cannon,et al.  Phenomics: the systematic study of phenotypes on a genome-wide scale , 2009, Neuroscience.

[5]  Marylyn D. Ritchie,et al.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations , 2010, Bioinform..

[6]  Marylyn D. Ritchie,et al.  Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols , 2014, Open forum infectious diseases.

[7]  Annalise B. Paaby,et al.  The many faces of pleiotropy. , 2013, Trends in Genetics.

[8]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[9]  Wei Zheng,et al.  Addressing Population‐Specific Multiple Testing Burdens in Genetic Association Studies , 2015, Annals of human genetics.

[10]  Melissa A. Basford,et al.  Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. , 2011, American journal of human genetics.

[11]  Patrice Degoulet,et al.  Phenome-Wide Association Studies on a Quantitative Trait: Application to TPMT Enzyme Activity and Thiopurine Therapy in Pharmacogenomics , 2013, PLoS Comput. Biol..

[12]  Christian Darabos,et al.  The multiscale backbone of the human phenotype network based on biological pathways , 2014, BioData Mining.

[13]  Marylyn D. Ritchie,et al.  Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network , 2013, PLoS genetics.

[14]  Steven J. Schrodi,et al.  A PheWAS approach in studying HLA-DRB1*1501 , 2013, Genes and Immunity.

[15]  A. Rzhetsky,et al.  Probing genetic overlap among complex human phenotypes , 2007, Proceedings of the National Academy of Sciences.

[16]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[17]  Robert A Hegele,et al.  Phenomics: Expanding the Role of Clinical Evaluation in Genomic Studies , 2010, Journal of investigative medicine : the official publication of the American Federation for Clinical Research.

[18]  J. Haines,et al.  eMERGEing progress in genomics—the first seven years , 2014, Front. Genet..

[19]  Yi Zeng,et al.  PhenX RISING: real world implementation and sharing of PhenX measures , 2014, BMC Medical Genomics.

[20]  Jean Golding,et al.  The search for genenotype/phenotype associations and the phenome scan. , 2005, Paediatric and perinatal epidemiology.

[21]  Melissa A. Basford,et al.  The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future , 2013, Genetics in Medicine.

[22]  Eric Farber-Eger,et al.  Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies , 2014, BioData Mining.

[23]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[24]  Christopher G. Chute,et al.  A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects , 2013, Human Genetics.

[25]  Russell A Wilke,et al.  Clinical phenome scanning. , 2007, Personalized medicine.

[26]  M. Huynen,et al.  Phenome connections. , 2008, Trends in genetics : TIG.

[27]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[28]  T. Silhavy,et al.  Microbial genetics: The art and design of genetic screens: Escherichia coli , 2003, Nature Reviews Genetics.

[29]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[30]  Peter Szolovits,et al.  Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls. , 2013, Arthritis and rheumatism.

[31]  C Kooperberg,et al.  The use of phenome‐wide association studies (PheWAS) for exploration of novel genotype‐phenotype relationships and pleiotropy discovery , 2011, Genetic epidemiology.

[32]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[33]  D. Roden,et al.  Development of a Large‐Scale De‐Identified DNA Biobank to Enable Personalized Medicine , 2008, Clinical pharmacology and therapeutics.

[34]  Margaret R. Karagas,et al.  A Dietary-Wide Association Study (DWAS) of Environmental Metal Exposure in US Children and Adults , 2014, PloS one.

[35]  Christian Darabos,et al.  Using the Bipartite Human Phenotype Network to Reveal Pleiotropy and Epistasis Beyond the Gene , 2014, Pacific Symposium on Biocomputing.

[36]  Jonathan L. Haines,et al.  Correcting Away the Hidden Heritability , 2011, Annals of human genetics.

[37]  Dana C Crawford,et al.  Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank. , 2013, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[38]  Zhan Ye,et al.  Phenome-wide association studies (PheWASs) for functional variants , 2014, European Journal of Human Genetics.

[39]  Dana C Crawford,et al.  Detecting and Characterizing Pleiotropy: New Methods for Uncovering the Connection Between the Complexity of Genomic Architecture and Multiple phenotypes. , 2014, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[40]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[41]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[42]  Julian C. Knight,et al.  Genomic modulators of the immune response , 2013, Trends in genetics : TIG.

[43]  Wei Lu,et al.  CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis , 2013, PLoS Comput. Biol..

[44]  Marylyn D. Ritchie,et al.  Learning Phenotype Mapping for Integrating Large Genetic Data , 2011, BioNLP@ACL.

[45]  E. Xing,et al.  Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network , 2009, PLoS genetics.

[46]  Suzette J. Bielinski,et al.  eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants , 2016, BMC Medical Genomics.

[47]  Kasper Lage,et al.  Pervasive Sharing of Genetic Effects in Autoimmune Disease , 2011, PLoS genetics.

[48]  S. Purcell,et al.  Pleiotropy in complex traits: challenges and strategies , 2013, Nature Reviews Genetics.

[49]  M. Stephens A Unified Framework for Association Analysis with Multiple Related Phenotypes , 2013, PloS one.

[50]  Atul J. Butte,et al.  An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus , 2010, PloS one.

[51]  Eric Boerwinkle,et al.  Pleiotropic genes for metabolic syndrome and inflammation. , 2014, Molecular genetics and metabolism.

[52]  Marylyn D. Ritchie,et al.  Detection of Pleiotropy through a Phenome-Wide Association Study (PheWAS) of Epidemiologic Data as Part of the Environmental Architecture for Genes Linked to Environment (EAGLE) Study , 2014, PLoS genetics.