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Phenome-wide association study
In genetics and genetic epidemiology, a phenome-wide association study, abbreviated PheWAS, is a study design in which the association between single-nucleotide polymorphisms or other types of DNA variants is tested across a large number of different phenotypes. The aim of PheWAS studies (or PheWASs) is to examine the causal linkage between known sequence differences and any type of trait, including molecular, biochemical, cellular, and especially clinical diagnoses and outcomes. It is a complementary approach to the genome-wide association study, or GWAS, methodology. A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk. In a GWAS, the polarity of analysis is from one or a few phenotypes to many possible DNA variants. The approach has proven useful in rediscovering previously reported genotype-phenotype associations, as well as in identifying new ones.
The PheWAS approach was originally developed due to the widespread availability of both anonymized human clinical electronic health record (EHR) data and matched genotype data, using phenotypes defined by groupings of (ICD) codes called phecodes. Massive genome and phenome data sets for model organisms were being assembled have also proved effective for PheWAS. PheWASs have also been conducted using data from existing epidemiological studies. In 2010, a proof-of-concept PheWAS study was published based on EHR billing codes from a single study site. Though this study was generally underpowered, its results suggested the potential existence of new associations between multiple phenotypes, possibly due to a common underlying cause. This paper also coined the abbreviation "PheWAS". As of 2019, PheWAS in the EHR has been conducted using ICD-9-CM, ICD-10, and ICD-10-CM diagnosis codes.
PheWAS initially started from the growing use of EMR (electronic medical record) for clinical practice and patient care. One of the main components of EMR system is the International Classification of Disease version 9-CM (ICD9) codes, used as a tool for medical billing record. This system includes information of 14,000 diseases binned into different hierarchy codes. These phenotypic information is the basis of the PheWAS study, which associates a genetic variant (or a combination of variants) with a wide range of phenotypes.
Most common PheWAS studies would divide its cohort into two groups: individuals who did not have a specific ICD9 code are treated as "controls" while individuals who has an 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.
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:
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.
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 the Population Architecture using Genomics and Epidemiology (PAGE) study sites. After several quality control and data organization steps, either the standard logistic or linear regression analysis is performed depending on the phenotypic information. Subsequently, all continuous phenotypes are log-transformed before the association between the SNPs and the transformed phenotypes is finally calculated.
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Phenome-wide association study
In genetics and genetic epidemiology, a phenome-wide association study, abbreviated PheWAS, is a study design in which the association between single-nucleotide polymorphisms or other types of DNA variants is tested across a large number of different phenotypes. The aim of PheWAS studies (or PheWASs) is to examine the causal linkage between known sequence differences and any type of trait, including molecular, biochemical, cellular, and especially clinical diagnoses and outcomes. It is a complementary approach to the genome-wide association study, or GWAS, methodology. A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk. In a GWAS, the polarity of analysis is from one or a few phenotypes to many possible DNA variants. The approach has proven useful in rediscovering previously reported genotype-phenotype associations, as well as in identifying new ones.
The PheWAS approach was originally developed due to the widespread availability of both anonymized human clinical electronic health record (EHR) data and matched genotype data, using phenotypes defined by groupings of (ICD) codes called phecodes. Massive genome and phenome data sets for model organisms were being assembled have also proved effective for PheWAS. PheWASs have also been conducted using data from existing epidemiological studies. In 2010, a proof-of-concept PheWAS study was published based on EHR billing codes from a single study site. Though this study was generally underpowered, its results suggested the potential existence of new associations between multiple phenotypes, possibly due to a common underlying cause. This paper also coined the abbreviation "PheWAS". As of 2019, PheWAS in the EHR has been conducted using ICD-9-CM, ICD-10, and ICD-10-CM diagnosis codes.
PheWAS initially started from the growing use of EMR (electronic medical record) for clinical practice and patient care. One of the main components of EMR system is the International Classification of Disease version 9-CM (ICD9) codes, used as a tool for medical billing record. This system includes information of 14,000 diseases binned into different hierarchy codes. These phenotypic information is the basis of the PheWAS study, which associates a genetic variant (or a combination of variants) with a wide range of phenotypes.
Most common PheWAS studies would divide its cohort into two groups: individuals who did not have a specific ICD9 code are treated as "controls" while individuals who has an 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.
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:
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.
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 the Population Architecture using Genomics and Epidemiology (PAGE) study sites. After several quality control and data organization steps, either the standard logistic or linear regression analysis is performed depending on the phenotypic information. Subsequently, all continuous phenotypes are log-transformed before the association between the SNPs and the transformed phenotypes is finally calculated.