Dataset Information

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

ABSTRACT: 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.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.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.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.

SUBMITTER: Abo R

PROVIDER: S-EPMC3303952 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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

Abo Ryan R Hebbring Scott S Ji Yuan Y Zhu Hongjie H Zeng Zhao-Bang ZB Batzler Anthony A Jenkins Gregory D GD Biernacka Joanna J Snyder Karen K Drews Maureen M Fiehn Oliver O Fridley Brooke B Schaid Daniel D Kamatani Naoyuki N Nakamura Yusuke Y Kubo Michiaki M Mushiroda Taisei T Kaddurah-Daouk Rima R Mrazek David A DA Weinshilboum Richard M RM

Pharmacogenetics and genomics 20120401 4

<h4>Objective</h4>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.<h4>Methods</h4>We previously g ...[more]

PMID: 22322242

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Project description:The initial decades of the 21st century have witnessed striking technical advances that have made it possible to detect, identify and quantitatively measure large numbers of plasma or tissue metabolites. In parallel, similar advances have taken place in our ability to sequence DNA and RNA. Those advances have moved us beyond studies of single metabolites and single genetic polymorphisms to the study of hundreds or thousands of metabolites and millions of genomic variants in a single cell or subject. It is now possible to merge and integrate large data sets generated by the use of different "-omics" techniques to increase our understanding of the molecular basis for variation in disease risk and/or drug response phenotypes. This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms associated with variation in drug response phenotypes, with an emphasis on the application of a pharmacometabolomics-informed pharmacogenomic research strategy and with selected examples of the application of that strategy. Studies that used pharmacometabolomics to inform and guide pharmacogenomics were reviewed. Clinical studies that were used as the basis for pharmacometabolomics-informed pharmacogenomic studies, published in five independent manuscripts, are described briefly. Within these five manuscripts, both pharmacokinetic and pharmacodynamic metabolomics approaches were used. Candidate gene and genome-wide approaches that were used in concert with these metabolomic data identified novel metabolite-gene relationships that were associated with drug response phenotypes in these pharmacometabolomics-informed pharmacogenomics studies. This "Brief Review" outlines the emerging discipline of pharmacometabolomics-informed pharmacogenomics in which metabolic profiles are associated with both clinical phenotypes and genetic variants to identify novel genetic variants associated with drug response phenotypes based on metabolic profiles.

Project description:Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina's HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%-93%), but IMPUTE2 had the highest IQS (81%-83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF?2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat?=?0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.

Project description:Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.

Dataset Information

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

Publications

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

Similar Datasets

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