Evidence map›Paper›PMID 30281645›Full record

ArticlePloS one2018

Validation of a genetic risk score for Arkansas women of color.

Athena Starlard-Davenport, Richard Allman, Gillian S Dite, John L Hopper, Erika Spaeth Tuff, Stewart Macleod, Susan Kadlubar, Michael Preston, Ronda Henry-Tillman

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

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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

9 authors at 5 institutions in 2 countries.

Athena Starlard-DavenportDepartment of Genetics, Genomics & Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.ORCID 0000-0002-8938-237X
Richard AllmanGenetic Technologies Ltd, Fitzroy, Victoria, Australia.
Gillian S DiteCentre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia.
John L HopperCentre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia.
Erika Spaeth TuffPhenogen Sciences Inc, Charlotte, North Carolina, United States of America.ORCID 0000-0002-5397-3493
Stewart MacleodGenomics Core, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
Susan KadlubarDivision of Medical Genetics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
Michael PrestonCenter for Diversity Affairs and Inclusion, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
Ronda Henry-TillmanDepartment of Surgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
The University of Melbourne · AUUniversity of Arkansas for Medical Sciences · USWinthrop Rockefeller Foundation · USGenetic Technologies (Australia) · AUUniversity of Tennessee Health Science Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

African American women in the state of Arkansas have high breast cancer mortality rates. Breast cancer risk assessment tools developed for African American underestimate breast cancer risk. Combining African American breast cancer associated single-nucleotide polymorphisms (SNPs) into breast cancer risk algorithms may improve individualized estimates of a woman's risk of developing breast cancer and enable improved recommendation of screening and chemoprevention for women at high risk. The goal of this study was to confirm with an independent dataset consisting of Arkansas women of color, whether a genetic risk score derived from common breast cancer susceptibility SNPs can be combined with a clinical risk estimate provided by the Breast Cancer Risk Assessment Tool (BCRAT) to produce a more accurate individualized breast cancer risk estimate. A population-based cohort of African American women representative of Arkansas consisted of 319 cases and 559 controls for this study. Five-year and lifetime risks from the BCRAT were measured and combined with a risk score based on 75 independent susceptibility SNPs in African American women. We used the odds ratio (OR) per adjusted standard deviation to evaluate the improvement in risk estimates produced by combining the polygenic risk score (PRS) with 5-year and lifetime risk scores estimated using BCRAT. For 5-year risk OR per standard deviation increased from 1.84 to 2.08 with the addition of the polygenic risk score and from 1.79 to 2.07 for the lifetime risk score. Reclassification analysis indicated that 13% of cases had their 5-year risk increased above the 1.66% guideline threshold (NRI = 0.020 (95% CI -0.040, 0.080)) and 6.3% of cases had their lifetime risk increased above the 20% guideline threshold by the addition of the polygenic risk score (NRI = 0.034 (95% CI 0.000, 0.070)). Our data confirmed that discriminatory accuracy of BCRAT is improved for African American women in Arkansas with the inclusion of specific SNP breast cancer risk alleles.

Indexed as

Polymorphism, Single NucleotideAdultAgedAged, 80 and overArkansasBlack or African AmericanBreast NeoplasmsCase-Control StudiesFemaleGenetic Predisposition to DiseaseHumansLogistic ModelsMiddle AgedOdds RatioPrecision MedicineRisk Assessment

Identifiers

PMID30281645
PMCPMC6169938
OpenAlexW2894677813

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.