Phenome Wide Association Study - PWAS.pptx

The phenome-wide association study (PheWAS) is a high-throughput tool that determines the association between the genotypic variation and phenotype of the organism to get a better understanding of the effect of genotype.

1. Submitted to- Dr. A.R.Nirmal Kumar Assistant Professor Dept. of Crop Physiology Concept of Phenome Wide Association Studies ( PheWAS ) ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY S.V. AGRICULTURAL COLLEGE, TIRUPATI Course No :- PP 605 Course Title :- Plant Phenomics – Next Generatio n Phenomics Platforms Submitted by- P. Tejasree TAD/23-10 PhD 1 st year Dept. of GPBR

2. Introduction The phenome-wide association study ( PheWAS ) is a high-throughput tool that determines the association between the genotypic variation and phenotype of the organism to get a better understanding of the effect of genotype. 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. It is a complementary approach to the  genome-wide association study.

3. GWAS determines genotype-phenotype association by linking a number of genotypic variants like SNPs to a phenotypic trait or disease , whereas PheWAS studies the link of genotypic variation to a number of phenotypic traits . GWAS focuses on the study of a single target phenotype over a number of genotypes (maybe up to 500,000 SNPs) and PheWAS studies of single target genotype to a number of phenotypes (up to 1,000). Phenome-wide association study was recently used in the field of medicines to identify the association of genetic loci with many diseases. However, the application of PheWAS in the field of plant science is not explored yet. GWAS V/S PheWAS

4. A GWAS begins with a phenotype of interest and systematically analyzes variants across the entire genome (i.e., “genome-wide”) for association to the phenotype. GWAS can identify multiple genetic associations to a phenotype in complex or polygenic traits. A PheWAS begins with a genetic variant of interest and systematically analyzes many phenotypes (i.e., “phenome-wide”) for association to the genotype. PheWAS has the ability to identify pleiotropy, or the finding of multiple independent phenotypes associated with a single genetic variant.

5. PheWAS popular in humans not in plants till now why? Limited availability of genotypic data linked to a range of high-throughput, effective phenotypic data. Genotype-to-phenotype approach, starts with a genotype to test for associations over a wide spectrum of phenotypes (phenome). Use of an electronic medical record (EMR) collected from different sources. Marker/SNP information used in PheWAS in humans have known functions that facilitates the characterizing of genetic variants.

6. History : First PheWAS study

7. A PheWAS begins with identification of a genetic variant of interest , such as a single-nucleotide polymorphism (SNP). For a PheWAS using electronic health records (EHRs), phenotypes are then extracted, and transformations are often made to map raw EHR data to defined cases and controls for analysis.  A PheWAS analysis is then performed to test for associations between the SNP and each phenotype, using typical statistical genetics methods. Methodology of PheWAS

8. Information used 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 ) c odes, 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 . 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.

9. 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.

10. Work flow 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 .

11. Results 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.

12. A phenotype-wide association study ( PheWAS ) plot of rs12203592 in  IRF4 . The horizontal red line indicates a Bonferroni correction for the number of phenotypes tested in this PheWAS ( p  = 0.05/1,358 = 3.7 × 10 −5 ); the horizontal blue line indicates  p  = 0.05 . The analysis shows that this single-nucleotide polymorphism is associated with several phenotypes related to sun exposure, such as actinic keratosis, basal cell carcinomas, osteopenia, and solar dermatitis (sunburns); these were new discoveries in this PheWAS

13. 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 the Population Architecture using Genomics and Epidemiology (PAGE) study sites.

14. Drug Response Variability A PheWAS has also successfully highlights discrepancies in drug response among individuals. A quantitative PheWAS study was done to identify variation in  thiopurine  response. The EMR stores quantitative value of  IBD (inflammatory bowel disease)  patient's  TPMT  (thiopurine S-methyltransferase) activity, which then allow researchers to split the patients it into three categories: low TPMTa , normal TPMTa , and very high TPMTa .  It was found that cohorts with very high TPMTa level are associated with  diabetes mellitus  and iron-deficiency  anemia , Performing thiopurine therapy on patient with very high TPMTa level may increase the frequency of anemia episode. This PheWAS finding may further the progress of  personalized treatment  based on patient's measurement. Instead of treating IBD patients with the conventional thiopurine treatment, patient may benefit more from more intensive therapy or other approaches.

15. Limitations Despite the promising potentials, PheWAS has some potential limitations : Statistical limitation :  Bonferroni correction  is potentially not addressing the entirety of the dataset (it may be prohibitively conservative). ICD9 -notation limitation : not every phenotype can be represented in an ICD9 code. Association limitation : upon performing a regression analysis for variant-phenotype association, covariates like age and sex may contribute in the resulting phenotypes. A simple  regression  analysis would fail to take into account these covariates. Every novel  pleiotropy  discovery will need further biological validation to ensure that the data-driven association is not a mere statistical coincident.