Evidence map›Paper›PMID 36068478›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Genetic Ancestry Inference for Pharmacogenomics.

I King Jordan, Shivam Sharma, Shashwat Deepali Nagar, Augusto Valderrama-Aguirre, Leonardo Mariño-Ramírez

Erratum issuedAbstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

I King JordanSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. king.jordan@biology.gatech.edu.
Shivam SharmaNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Shashwat Deepali NagarSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Augusto Valderrama-AguirreDepartment of Biological Sciences, Faculty of Sciences, Universidad de Los Andes, Bogotá, DC, Colombia.
Leonardo Mariño-RamírezNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA. marino@nih.gov.

Funding

Genomics and Biobank Research for Health EquityZIAMD000018 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI MARIÑO-RAMÍREZ, LEONARDO · 2020 to 2025
$2.7M
Epidemiology and Genomics of Health DisparitiesZIAMD000016 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI MARINO-RAMIREZ, LEONARDO · 2019 to 2021
$1.6M
Intramural NIH HHS Z99 MD999999Intramural NIH HHS ZIA MD000016Intramural NIH HHS ZIA MD000018
6 · The paper itself

Abstract

Genetic ancestry inference can be used to stratify patient cohorts and to model pharmacogenomic variation within and between populations. We provide a detailed guide to genetic ancestry inference using genome-wide genetic variant datasets, with an emphasis on two widely used techniques: principal components analysis (PCA) and ADMIXTURE analysis. PCA can be used for patient stratification and categorical ancestry inference, whereas ADMIXTURE is used to characterize genetic ancestry as a continuous variable. Visualization methods are critical for the interpretation of genetic ancestry inference methods, and we provide instructions for how the results of PCA and ADMIXTURE can be effectively visualized.

Indexed as

Genetic TechniquesPharmacogeneticsGenetics, PopulationHumansPolymorphism, Single NucleotidePopulation GroupsPrincipal Component AnalysisAdmixtureGenetic ancestry inferenceGenetic variantsHealth disparitiesPharmacogenomicsPopulation-specific drug efficacy

Identifiers

PMID36068478
PMCPMC9486757

What OpenQuestion holds

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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.