Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: Selective serotonin reuptake inhibitor response pharmacogenomics

Ryan Abo, Scott Hebbring, Yuan Ji, Hongjie Zhu, Zhao Bang Zeng, Anthony Batzler, Gregory D. Jenkins, Joanna Biernacka, Karen Snyder, Maureen Drews, Oliver Fiehn, Brooke Fridley, Daniel Schaid, Naoyuki Kamatani, Yusuke Nakamura, Michiaki Kubo, Taisei Mushiroda, Rima Kaddurah-Daouk, David A. Mrazek, Richard M. Weinshilboum

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Abstract

OBJECTIVE: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. METHODS: We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. RESULTS: Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. CONCLUSION: These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.

Original languageEnglish
Pages (from-to)247-253
Number of pages7
JournalPharmacogenetics and genomics
Volume22
Issue number4
DOIs
Publication statusPublished - 01-04-2012

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Pharmacogenetics
Serotonin Uptake Inhibitors
Single Nucleotide Polymorphism
Genome
Citalopram
Metabolomics
Genes
Glycine Dehydrogenase
Glycine
Genotype
HapMap Project

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Cite this

Abo, Ryan ; Hebbring, Scott ; Ji, Yuan ; Zhu, Hongjie ; Zeng, Zhao Bang ; Batzler, Anthony ; Jenkins, Gregory D. ; Biernacka, Joanna ; Snyder, Karen ; Drews, Maureen ; Fiehn, Oliver ; Fridley, Brooke ; Schaid, Daniel ; Kamatani, Naoyuki ; Nakamura, Yusuke ; Kubo, Michiaki ; Mushiroda, Taisei ; Kaddurah-Daouk, Rima ; Mrazek, David A. ; Weinshilboum, Richard M. / Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation : Selective serotonin reuptake inhibitor response pharmacogenomics. In: Pharmacogenetics and genomics. 2012 ; Vol. 22, No. 4. pp. 247-253.
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abstract = "OBJECTIVE: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. METHODS: We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. RESULTS: Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4{\%}) than HapMap 2 (93.2{\%}) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. CONCLUSION: These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.",
author = "Ryan Abo and Scott Hebbring and Yuan Ji and Hongjie Zhu and Zeng, {Zhao Bang} and Anthony Batzler and Jenkins, {Gregory D.} and Joanna Biernacka and Karen Snyder and Maureen Drews and Oliver Fiehn and Brooke Fridley and Daniel Schaid and Naoyuki Kamatani and Yusuke Nakamura and Michiaki Kubo and Taisei Mushiroda and Rima Kaddurah-Daouk and Mrazek, {David A.} and Weinshilboum, {Richard M.}",
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Abo, R, Hebbring, S, Ji, Y, Zhu, H, Zeng, ZB, Batzler, A, Jenkins, GD, Biernacka, J, Snyder, K, Drews, M, Fiehn, O, Fridley, B, Schaid, D, Kamatani, N, Nakamura, Y, Kubo, M, Mushiroda, T, Kaddurah-Daouk, R, Mrazek, DA & Weinshilboum, RM 2012, 'Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: Selective serotonin reuptake inhibitor response pharmacogenomics', Pharmacogenetics and genomics, vol. 22, no. 4, pp. 247-253. https://doi.org/10.1097/FPC.0b013e32835001c9

Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation : Selective serotonin reuptake inhibitor response pharmacogenomics. / Abo, Ryan; Hebbring, Scott; Ji, Yuan; Zhu, Hongjie; Zeng, Zhao Bang; Batzler, Anthony; Jenkins, Gregory D.; Biernacka, Joanna; Snyder, Karen; Drews, Maureen; Fiehn, Oliver; Fridley, Brooke; Schaid, Daniel; Kamatani, Naoyuki; Nakamura, Yusuke; Kubo, Michiaki; Mushiroda, Taisei; Kaddurah-Daouk, Rima; Mrazek, David A.; Weinshilboum, Richard M.

In: Pharmacogenetics and genomics, Vol. 22, No. 4, 01.04.2012, p. 247-253.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation

T2 - Selective serotonin reuptake inhibitor response pharmacogenomics

AU - Abo, Ryan

AU - Hebbring, Scott

AU - Ji, Yuan

AU - Zhu, Hongjie

AU - Zeng, Zhao Bang

AU - Batzler, Anthony

AU - Jenkins, Gregory D.

AU - Biernacka, Joanna

AU - Snyder, Karen

AU - Drews, Maureen

AU - Fiehn, Oliver

AU - Fridley, Brooke

AU - Schaid, Daniel

AU - Kamatani, Naoyuki

AU - Nakamura, Yusuke

AU - Kubo, Michiaki

AU - Mushiroda, Taisei

AU - Kaddurah-Daouk, Rima

AU - Mrazek, David A.

AU - Weinshilboum, Richard M.

PY - 2012/4/1

Y1 - 2012/4/1

N2 - OBJECTIVE: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. METHODS: We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. RESULTS: Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. CONCLUSION: These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.

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