The challenges, advantages and future of phenome-wide association studies

Over the last decade, significant technological breakthroughs have revolutionized human genomic research in the form of genome‐wide association studies (GWASs). GWASs have identified thousands of statistically significant genetic variants associated with hundreds of human conditions including many with immunological aetiologies (e.g. multiple sclerosis, ankylosing spondylitis and rheumatoid arthritis). Unfortunately, most GWASs fail to identify clinically significant associations. Identifying biologically significant variants by GWAS also presents a challenge. The GWAS is a phenotype‐to‐genotype approach. As a complementary/alternative approach to the GWAS, investigators have begun to exploit extensive electronic medical record systems to conduct a genotype‐to‐phenotype approach when studying human disease – specifically, the phenome‐wide association study (PheWAS). Although the PheWAS approach is in its infancy, this method has already demonstrated its capacity to rediscover important genetic associations related to immunological diseases/conditions. Furthermore, PheWAS has the advantage of identifying genetic variants with pleiotropic properties. This is particularly relevant for HLA variants. For example, PheWAS results have demonstrated that the HLA‐DRB1 variant associated with multiple sclerosis may also be associated with erythematous conditions including rosacea. Likewise, PheWAS has demonstrated that the HLA‐B genotype is not only associated with spondylopathies, uveitis, and variability in platelet count, but may also play an important role in other conditions, such as mastoiditis. This review will discuss and compare general PheWAS methodologies, describe both the challenges and advantages of the PheWAS, and provide insight into the potential directions in which PheWAS may lead.

[1]  International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome , 2001, Nature.

[2]  C. McCarty,et al.  Healthy People 2010 disease prevalence in the Marshfield Clinic Personalized Medicine Research Project cohort: opportunities for public health genomic research. , 2007, Personalized medicine.

[3]  V. Chatterjee,et al.  Congenital hypothyroidism is the most common neonatal metabolic disorder and results in severe neurodevelopmental impairment and infertility if untreated , 2005 .

[4]  J. R. Mitchell,et al.  The clinical response to minocycline in multiple sclerosis is accompanied by beneficial immune changes: a pilot study , 2007, Multiple sclerosis.

[5]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

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

[7]  Gil Alterovitz,et al.  Phenome-Based Analysis as a Means for Discovering Context-Dependent Clinical Reference Ranges , 2012, AMIA.

[8]  Paul Weston,et al.  Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility , 2011, Nature Genetics.

[9]  S. Lewitzky,et al.  OC-035 Elastography for the diagnosis of severity of fibrosis in chronic liver disease: a diagnostic test accuracy meta-analysis , 2010, Gut.

[10]  A. Traboulsee,et al.  Glatiramer acetate in combination with minocycline in patients with relapsing—remitting multiple sclerosis: results of a Canadian, multicenter, double-blind, placebo-controlled trial , 2009, Multiple sclerosis.

[11]  M. Olivier A haplotype map of the human genome , 2003, Nature.

[12]  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.

[13]  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.

[14]  M. Daly,et al.  Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. , 2011, Gastroenterology.

[15]  Christian Gieger,et al.  New gene functions in megakaryopoiesis and platelet formation , 2011, Nature.

[16]  R. Pi,et al.  The prospects of minocycline in multiple sclerosis , 2011, Journal of Neuroimmunology.

[17]  M. Olivier A haplotype map of the human genome. , 2003, Nature.

[18]  Joshua L. Deignan,et al.  ACMG clinical laboratory standards for next-generation sequencing , 2013, Genetics in Medicine.

[19]  Pascal Borry,et al.  Whole-genome sequencing in health care. Recommendations of the European Society of Human Genetics. , 2013, European journal of human genetics : EJHG.

[20]  P. Stenson,et al.  The Human Gene Mutation Database: 2008 update , 2009, Genome Medicine.

[21]  D. Mccormick Sequence the Human Genome , 1986, Bio/Technology.

[22]  HHS proposes adoption of ICD-10 code sets and updated electronic transaction standards. , 2008, Journal of AHIMA.

[23]  Jing Cui,et al.  Common variants at CD40 and other loci confer risk of rheumatoid arthritis , 2008, Nature Genetics.

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

[25]  A. Butte,et al.  Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association , 2010, PloS one.

[26]  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.

[27]  T. Lam,et al.  Interleukin‐6 Receptor Gene Polymorphism Modulates Interleukin‐6 Levels and the Metabolic Syndrome: GBCS‐CVD , 2010, Obesity.

[28]  D. Altshuler,et al.  A map of human genome variation from population-scale sequencing , 2010, Nature.

[29]  M. Marazita,et al.  Genome-wide Association Studies , 2012, Journal of dental research.

[30]  Per Capita,et al.  About the authors , 1995, Machine Vision and Applications.

[31]  Annette Lee,et al.  Differential Genetic Associations for Systemic Lupus Erythematosus Based on Anti–dsDNA Autoantibody Production , 2011, PLoS genetics.

[32]  M. Daly,et al.  Common variants at CD 40 and other loci confer risk of rheumatoid arthritis , 2008 .

[33]  S. Patten,et al.  Minocycline reduces gadolinium‐enhancing magnetic resonance imaging lesions in multiple sclerosis , 2004, Annals of neurology.

[34]  J. Rosenbaum,et al.  An Update on the Genetics of HLA B27-associated Acute Anterior Uveitis , 2011, Ocular immunology and inflammation.

[35]  Quan Ding,et al.  Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[36]  G. Hripcsak,et al.  Discovering medical conditions associated with periodontitis using linked electronic health records. , 2013, Journal of clinical periodontology.

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

[38]  Melissa A. Basford,et al.  Genome- and Phenome-Wide Analyses of Cardiac Conduction Identifies Markers of Arrhythmia Risk , 2013, Circulation.

[39]  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.

[40]  Zhaohui S. Qin,et al.  A second generation human haplotype map of over 3.1 million SNPs , 2007, Nature.

[41]  N Risch,et al.  The Future of Genetic Studies of Complex Human Diseases , 1996, Science.

[42]  Wendy A. Wolf,et al.  The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies , 2011, BMC Medical Genomics.

[43]  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.

[44]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.