ICD9 code associated with them are considered “cases”. Starting from the given genetic variant, a PheWAS would systematically perform genetic variant (typically a SNP) analysis to identify how a particular genotype would be associated to a phenotype. From the variant data, PheWAS calculates their genotype distribution and the chi-squared distribution, followed by Fisher's exact test to calculate the P-value, identifying how relevant a genotype would be to a certain phenotype of interest from the EMR. Often times, Bonferroni correction is then applied to take into consideration the multiple comparisons done while calculating the P-value. Proof of Concept The first study of PheWAS was done on 6000 European-American population with 5 SNPs of interest picked for validation: rs1333049, rs2200733, rs3135388, rs6457620, and rs1333049. Quality control was done by examining marker and sample genotyping efficiency, allele frequency calculations, and Hardy-Weinberg equilibrium tests. This initial PheWAS aim to examine the impact of genetic variants across various phenotypes. Since the ICD9 was not specifically designed for research purposes, this PheWAS devised a new way to simplify the code for genetic studies. Specifically, three modifications were made to the ICD9: First, they combine three-digit codes from diseases that arise from the same or similar origin. For example, tuberculosis has three subtypes and all three are merged to one case group of 010. Secondly, the addition of a fourth digit identifier for phenotypes that are clinically distinct, but are categorized to be the same. An instance would be Type I and Type II diabetes, two clinically distinct phenotypes that fall under ICD9 code of ‘250’. An additional fourth digit will be added to differentiate the two phenotypes. Lastly, codes that are deemed to be useless for genotypic-phenotypic analysis are ignored. Cases such as foreign object contamination or non-specific symptoms / non-specific laboratory result would fall under this category. As one example of its successes, this PheWAS show evidence of strong association between rs3135388 and multiple sclerosis (MS), which was a previously studied association. Twenty-two other diseases also demonstrated significant associations with P < 0.05. Applications Pleiotropy Study One of the main advantages of the PheWAS study is its potential to identify genomic variants with pleiotropic properties. Understanding cross-phenotype (CP) associations, where one genetic variation can affect two or more independent phenotypes, is the key to understanding the pleiotropic effect. The pleiotropic effect study was done by first obtaining the summary of genotype and phenotype data from