Evidence map›Paper›PMID 35176049›Full record

ArticlePloS one2022

Genotypic and phenotypic landscapes of 51 pharmacogenes derived from whole-genome sequencing in a Thai population.

Natnicha Wankaew, Pajaree Chariyavilaskul, Monpat Chamnanphon, Adjima Assawapitaksakul, Wanna Chetruengchai, Monnat Pongpanich, Vorasuk Shotelersuk

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Frontiers in pharmacology · 2025
    Pooled it
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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

7 authors at 2 institutions in 1 country.

Natnicha WankaewProgram in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.
Pajaree ChariyavilaskulClinical Pharmacokinetics and Pharmacogenomics Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Monpat ChamnanphonClinical Pharmacokinetics and Pharmacogenomics Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.ORCID 0000-0003-3917-6567
Adjima AssawapitaksakulCenter of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Wanna ChetruengchaiCenter of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Monnat PongpanichDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.ORCID 0000-0003-3228-3351
Vorasuk ShotelersukCenter of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Chulalongkorn University · THThai Red Cross Society · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differences in drug responses in individuals are partly due to genetic variations in pharmacogenes, which differ among populations. Here, genome sequencing of 171 unrelated Thai individuals from all regions of Thailand was used to call star alleles of 51 pharmacogenes by Stargazer, determine allele and genotype frequencies, predict phenotype and compare high-impact variant frequencies between Thai and other populations. Three control genes, EGFR, VDR, and RYR1, were used, giving consistent results. Every individual had at least three genes with variant or altered phenotype. Forty of the 51 pharmacogenes had at least one individual with variant or altered phenotype. Moreover, thirteen genes had at least 25% of individuals with variant or altered phenotype including SLCO1B3 (97.08%), CYP3A5 (88.3%), CYP2C19 (60.82%), CYP2A6 (60.2%), SULT1A1 (56.14%), G6PD (54.39%), CYP4B1 (50.00%), CYP2D6 (48.65%), CYP2F1 (46.41%), NAT2 (40.35%), SLCO2B1 (28.95%), UGT1A1 (28.07%), and SLCO1B1 (26.79%). Allele frequencies of high impact variants from our samples were most similar to East Asian. Remarkably, we identified twenty predicted high impact variants which have not previously been reported. Our results provide information that contributes to the implementation of pharmacogenetic testing in Thailand and other Southeast Asian countries, bringing a step closer to personalized medicine.

Indexed as

Genetic Association StudiesGenetic MarkersPolymorphism, Single NucleotideAsian PeopleFemaleGene FrequencyHealthy VolunteersHigh-Throughput Nucleotide SequencingHumansMalePharmacogeneticsThailandWhole Genome SequencingGenetic Markers

Identifiers

PMID35176049
PMCPMC8853512
OpenAlexW4212888640

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.