Evidence map›Paper›PMID 22344246›Full record

ArticlePharmacogenetics and genomics2012

The limits of genome-wide methods for pharmacogenomic testing.

Eric R Gamazon, Andrew D Skol, Minoli A Perera

Abstract read
In one paragraph

Article in Pharmacogenetics and genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
6.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 35 citations in OpenAlex.

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  17. Whole genome sequencing in pharmacogenomics.Frontiers in pharmacology · 2015
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Eric R GamazonDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, Illinois, USA.
Andrew D Skol
Minoli A Perera
University of Chicago · USPharmacoGenetics (China) · CN

Funding

Whole GenomeU01GM061393 · NIGMS · UNIVERSITY OF CHICAGO · PI COX, NANCY J, DOLAN, MARY EILEEN · 2000 to 2014
$39.0M
The implementation of a pharmacogenomics-based algorithm for warfarin dosingK23HL089808 · NHLBI · UNIVERSITY OF CHICAGO · PI PERERA, MINOLI A · 2009 to 2013
$601k
Comprehensive studies of novel SNPs affecting warfarin dose in African AmericansR21HL106097 · NHLBI · UNIVERSITY OF CHICAGO · PI PERERA, MINOLI A · 2011 to 2012
$437k
NHLBI NIH HHS 1 K23 HL089808-01A2NHLBI NIH HHS K23 HL089808NHLBI NIH HHS R21 HL106097NIGMS NIH HHS U01GM61393
6 · The paper itself

Abstract

objectiveThe goal of pharmacogenomics is the translation of genomic discoveries into individualized patient care. Recent advances in the means to survey human genetic variation are fundamentally transforming our understanding of the genetic basis of interindividual variation in therapeutic response. The goal of this study was to systematically evaluate high-throughput genotyping technologies for their ability to assay variation in pharmacogenetically important genes (pharmacogenes). These platforms are either being proposed for or are already being widely used for clinical implementation; therefore, knowledge of coverage of pharmacogenes on these platforms would serve to better evaluate current or proposed pharmacogenetic association studies.

methodAmong the genes included in our study are drug-metabolizing enzymes, transporters, receptors, and drug targets, of interest to the entire pharmacogenetic community. We considered absolute and linkage disequilibrium (LD)-informed coverage, minor allele frequency spectrum, and functional annotation for a Caucasian population. We also examined the effect of LD, effect size, and cohort size on the power to detect single nucleotide polymorphism associations.

resultsIn our analysis of 253 pharmacogenes, we found that no platform showed more than 85% coverage of these genes (after accounting for LD). Furthermore, the lack of coverage showed a marked increase at minor allele frequencies of less than 20%. Even after accounting for LD, only 30% of the missense polymorphisms (which are enriched for low-frequency alleles) were covered by HapMap, with still lower coverage on the other platforms.

conclusionWe have conducted the first systematic evaluation of the Axiom Genomic Database, Omni 2.5 M, and the Drug Metabolizing Enzymes and Transporters chip. This study is the first to utilize the 1000 Genomes Project to present a comprehensive evaluative framework. Our results provide a much-needed assessment of microarray-based genotyping and next-generation sequencing technologies' ability to survey fully the variation in genes of particular interest to the pharmacogenetics community. Our findings demonstrate the limitations of genome-wide methods and the challenges of implementing pharmacogenomic tests into the clinical context.

Indexed as

Biomarkers, PharmacologicalGenome, HumanDatabases, GeneticGenetic Association StudiesGenetic VariationGenotypeHapMap ProjectHumansInactivation, MetabolicLinkage DisequilibriumPharmacogeneticsPolymorphism, Single NucleotideWhite PeopleBiomarkers, Pharmacological

Identifiers

PMID22344246
PMCPMC3655533
OpenAlexW2016362575

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.