Evidence map›Paper›PMID 33050895›Full record

ArticleBMC biology2020

Population structure and pharmacogenomic risk stratification in the United States.

Shashwat Deepali Nagar, Andrew B Conley, I King Jordan

Open access · goldAbstract read
In one paragraph

Article in BMC biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia.European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology · 2024
    Article
  8. Article
  9. Review
  10. Article
  11. The frequency of majorPharmacogenomics · 2023
    Article
  12. Article
  13. Article
  14. Genetic Ancestry Inference for Pharmacogenomics.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  15. Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2021
    Article
  16. Article
  17. Review
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 1 institution in 2 countries.

Shashwat Deepali NagarSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Andrew B ConleySchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
I King JordanSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. king.jordan@biology.gatech.edu.ORCID 0000-0003-4996-2203
Georgia Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPharmacogenomic (PGx) variants mediate how individuals respond to medication, and response differences among racial/ethnic groups have been attributed to patterns of PGx diversity. We hypothesized that genetic ancestry (GA) would provide higher resolution for stratifying PGx risk, since it serves as a more reliable surrogate for genetic diversity than self-identified race/ethnicity (SIRE), which includes a substantial social component. We analyzed a cohort of 8628 individuals from the United States (US), for whom we had both SIRE information and whole genome genotypes, with a focus on the three largest SIRE groups in the US: White, Black (African-American), and Hispanic (Latino). Our approach to the question of PGx risk stratification entailed the integration of two distinct methodologies: population genetics and evidence-based medicine. This integrated approach allowed us to consider the clinical implications for the observed patterns of PGx variation found within and between population groups.

resultsWhole genome genotypes were used to characterize individuals' continental ancestry fractions-European, African, and Native American-and individuals were grouped according to their GA profiles. SIRE and GA groups were found to be highly concordant. Continental ancestry predicts individuals' SIRE with > 96% accuracy, and accordingly, GA provides only a marginal increase in resolution for PGx risk stratification. In light of the concordance between SIRE and GA, taken together with the fact that information on SIRE is readily available to clinicians, we evaluated PGx variation between SIRE groups to explore the potential clinical utility of race and ethnicity. PGx variants are highly diverged compared to the genomic background; 82 variants show significant frequency differences among SIRE groups, and genome-wide patterns of PGx variation are almost entirely concordant with SIRE. The vast majority of PGx variation is found within rather than between groups, a well-established fact for almost all genetic variants, which is often taken to argue against the clinical utility of population stratification. Nevertheless, analysis of highly differentiated PGx variants illustrates how SIRE partitions PGx variation based on groups' characteristic ancestry patterns. These cases underscore the extent to which SIRE carries clinically valuable information for stratifying PGx risk among populations, albeit with less utility for predicting individual-level PGx alleles (genotypes), supporting the concept of population pharmacogenomics.

conclusionsPerhaps most interestingly, we show that individuals who identify as Black or Hispanic stand to gain far more from the consideration of race/ethnicity in treatment decisions than individuals from the majority White population.

Indexed as

Genome, HumanGenotypeRisk AssessmentEthnicityGenetics, PopulationHumansPharmacogeneticsUnited StatesEthnicityGenetic ancestryPharmacogenomicsPopulation genomicsPrecision public healthRace

Identifiers

PMID33050895
PMCPMC7557099
OpenAlexW3092466205

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.