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


CONCLUSIONS
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.
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
Ryan
R
Abo
Scott
S
Hebbring
Yuan
Y
Ji
Hongjie
H
Zhu
Zhao-Bang
ZB
Zeng
Anthony
A
Batzler
Gregory D
GD
Jenkins
Joanna
J
Biernacka
Karen
K
Snyder
Maureen
M
Drews
Oliver
O
Fiehn
Brooke
B
Fridley
Daniel
D
Schaid
Naoyuki
N
Kamatani
Yusuke
Y
Nakamura
Michiaki
M
Kubo
Taisei
T
Mushiroda
Rima
R
Kaddurah-Daouk
David A
DA
Mrazek
Richard M
RM
Weinshilboum
D015283:Citalopram / Q000009:adverse effects / Q000627:therapeutic use; D003866:Depressive Disorder / Q000188:drug therapy / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods; D005998:Glycine / Q000096:biosynthesis* / Q000235:genetics* / Q000378:metabolism; D050789:Glycine Dehydrogenase / Q000235:genetics*; D060148:HapMap Project; D006239:Haplotypes; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D055432:Metabolomics; D010597:Pharmacogenetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D017367:Selective Serotonin Reuptake Inhibitors / Q000378:metabolism*
D015283:Citalopram; D005998:Glycine; D050789:Glycine Dehydrogenase; D017367:Selective Serotonin Reuptake Inhibitors