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

ABSTRACT: 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.

SUBMITTER: Hebbring SJ

PROVIDER: S-EPMC3904236 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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

Hebbring Scott J SJ

Immunology 20140201 2

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 bi ...[more]

PMID: 24147732

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Project description:Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.

Dataset Information

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

Publications

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

Similar Datasets

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