Evidence map›Paper›PMID 34824386›Full record

ArticleThe pharmacogenomics journal2022

Pharmacogenomic analysis of a genetically distinct Indigenous population.

Arvind Jaya Shankar, Sudhir Jadhao, Wendy Hoy, Simon J Foote, Hardip R Patel, Vinod Scaria, Brendan J McMorran, Shivashankar H Nagaraj

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In one paragraph

Article in The pharmacogenomics journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 21% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Genes · 2023
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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

8 authors at 5 institutions in 2 countries.

Arvind Jaya ShankarCentre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
Sudhir JadhaoCentre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
Wendy HoyFaculty of Medicine, University of Queensland, Brisbane, QLD, 4072, Australia.
Simon J FooteJohn Curtin School of Medical Research, Australian National University, Canberra, ACT, 2600, Australia.
Hardip R PatelNational Centre for Indigenous Genomics, Australian National University, Canberra, ACT, 2600, Australia.
Vinod ScariaCSIR Institute of Genomics and Integrative Biology, Mathura Road, Delhi, 110 025, India.
Brendan J McMorranJohn Curtin School of Medical Research, Australian National University, Canberra, ACT, 2600, Australia.
Shivashankar H NagarajCentre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia. shiv.nagaraj@qut.edu.au.ORCID http://orcid.org/0000-0003-3463-6835
Australian National University · AUQueensland University of Technology · AUInstitute of Genomics and Integrative Biology · INThe University of Queensland · AUTranslational Research Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Indigenous Australians face a disproportionately severe burden of chronic disease relative to other Australians, with elevated rates of morbidity and mortality. While genomics technologies are slowly gaining momentum in personalised treatments for many, a lack of pharmacogenomic research in Indigenous peoples could delay adoption. Appropriately implementing pharmacogenomics in clinical care necessitates an understanding of the frequencies of pharmacologically relevant genetic variants within Indigenous populations. We analysed whole-genome sequence data from 187 individuals from the Tiwi Islands and characterised the pharmacogenomic landscape of this population. Specifically, we compared variant profiles and allelic distributions of previously described pharmacologically significant genes and variants with other population groups. We identified 22 translationally relevant pharmacogenomic variants and 18 clinically actionable guidelines with implications for drug dosing and treatment of conditions including heart disease, diabetes and cancer. We specifically observed increased poor and intermediate metabolizer phenotypes in the CYP2C9 (PM:19%, IM:44%) and CYP2C19 (PM:18%, IM:44%) genes.

Indexed as

Indigenous PeoplesPharmacogenomic TestingAustraliaCytochrome P-450 CYP2C9HumansPharmacogenomic VariantsCytochrome P-450 CYP2C9

Identifiers

PMID34824386
OpenAlexW3215650796

What OpenQuestion holds

Textmetadata
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.