Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG)

Shefali S. Verma, Alex T. Frase, Anurag Verma, Sarah A. Pendergrass, Shaun A. Mahony, David W. Haas, Marylyn Deriggi Ritchie

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genome-wide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient’s response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.

Original languageEnglish (US)
Pages (from-to)57-68
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - Jan 1 2016
Event21st Pacific Symposium on Biocomputing, PSB 2016 - Big Island, United States
Duration: Jan 4 2016Jan 8 2016

Fingerprint

Genes
Clinical Trials
Phenotype
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Glucose
Blood Glucose
Fasting
Blood
Genome
Drug therapy
Drug Therapy
Lipoproteins
Genome-Wide Association Study
HDL Lipoproteins
Linear regression
Regression analysis
Chromatin
Linear Models

All Science Journal Classification (ASJC) codes

  • Medicine(all)

Cite this

Verma, S. S., Frase, A. T., Verma, A., Pendergrass, S. A., Mahony, S. A., Haas, D. W., & Ritchie, M. D. (2016). Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG). Pacific Symposium on Biocomputing, 57-68.
Verma, Shefali S. ; Frase, Alex T. ; Verma, Anurag ; Pendergrass, Sarah A. ; Mahony, Shaun A. ; Haas, David W. ; Ritchie, Marylyn Deriggi. / Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG). In: Pacific Symposium on Biocomputing. 2016 ; pp. 57-68.
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abstract = "Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genome-wide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient’s response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.",
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Verma, SS, Frase, AT, Verma, A, Pendergrass, SA, Mahony, SA, Haas, DW & Ritchie, MD 2016, 'Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG)', Pacific Symposium on Biocomputing, pp. 57-68.

Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG). / Verma, Shefali S.; Frase, Alex T.; Verma, Anurag; Pendergrass, Sarah A.; Mahony, Shaun A.; Haas, David W.; Ritchie, Marylyn Deriggi.

In: Pacific Symposium on Biocomputing, 01.01.2016, p. 57-68.

Research output: Contribution to journalConference article

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T1 - Phenome-wide interaction study (PheWIS) in aids clinical trials group data (ACTG)

AU - Verma, Shefali S.

AU - Frase, Alex T.

AU - Verma, Anurag

AU - Pendergrass, Sarah A.

AU - Mahony, Shaun A.

AU - Haas, David W.

AU - Ritchie, Marylyn Deriggi

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N2 - Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genome-wide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient’s response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.

AB - Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genome-wide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient’s response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.

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