title: OPENING THE DOOR TO THE LARGE SCALE USE OF CLINICAL LAB MEASURES FOR ASSOCIATION TESTING: EXPLORING DIFFERENT METHODS FOR DEFINING PHENOTYPES.
abstract: The past decade has seen exponential growth in the numbers of sequenced and genotyped individuals and a corresponding increase in our ability of collect and catalogue phenotypic data for use in the clinic. We now face the challenge of integrating these diverse data in new ways new that can provide useful diagnostics and precise medical interventions for individual patients. One of the first steps in this process is to accurately map the phenotypic consequences of the genetic variation in human populations. The most common approach for this is the genome wide association study (GWAS). While this technique is relatively simple to implement for a given phenotype, the choice of how to define a phenotype is critical. It is becoming increasingly common for each individual in a GWAS cohort to have a large profile of quantitative measures. The standard approach is to test for associations with one measure at a time; however, there are many justifiable ways to define a set of phenotypes, and the genetic associations that are revealed will vary based on these definitions. Some phenotypes may only show a significant genetic association signal when considered together, such as through principle components analysis (PCA). Combining correlated measures may increase the power to detect association by reducing the noise present in individual variables and reduce the multiple hypothesis testing burden. Here we show that PCA and k-means clustering are two complimentary methods for identifying novel genotype-phenotype relationships within a set of quantitative human traits derived from the Geisinger Health System electronic health record (EHR). Using a diverse set of approaches for defining phenotype may yield more insights into the genetic architecture of complex traits and the findings presented here highlight a clear need for further investigation into other methods for defining the most relevant phenotypes in a set of variables. As the data of EHR continue to grow, addressing these issues will become increasingly important in our efforts to use genomic data effectively in medicine.
authors.affiliation: Biomedical & Translational Informatics, Geisinger Health System, 100 N. Academy Ave. Danville, PA 17821, USA, cbauer@geisinger.com.
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Bauer
authors.forename: Daniel
authors.initials: D
authors.lastname: Lavage
authors.forename: John
authors.initials: J
authors.lastname: Snyder
authors.forename: Joseph
authors.initials: J
authors.lastname: Leader
authors.forename: J Matthew
authors.initials: JM
authors.lastname: Mahoney
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Pendergrass
mesh_terms: D002984:Clinical Laboratory Information Systems / Q000706:statistics & numerical data; D016000:Cluster Analysis; D015331:Cohort Studies; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D057286:Electronic Health Records / Q000706:statistics & numerical data; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis