Evidence map›Paper›PMID 33547344›Full record

ArticleCommunications biology2021

Genetic ancestry plays a central role in population pharmacogenomics.

Hsin-Chou Yang, Chia-Wei Chen, Yu-Ting Lin, Shih-Kai Chu

Abstract read
In one paragraph

Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. A systematic review of real-world evidence on the clinical relevance, characterization, and utility ofJournal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2025
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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

4 authors.

Hsin-Chou YangInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw.ORCID 0000-0001-6853-7881
Chia-Wei ChenInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Yu-Ting LinInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Shih-Kai ChuInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD ( http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/ ).

Indexed as

PharmacogeneticsBiomarkers, PharmacologicalBiotransformationDatabases, GeneticGene-Environment InteractionGene FrequencyGenetic VariationGenome, HumanHomozygoteHumansInactivation, MetabolicLinkage DisequilibriumPolymorphism, Single NucleotidePrecision MedicinePrincipal Component AnalysisProteomeBiomarkers, PharmacologicalProteome

Identifiers

PMID33547344
PMCPMC7864978

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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.