Evidence map›Paper›PMID 25136350›Full record

ArticleFrontiers in genetics2014

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example.

Benjamin A Goldstein, Joshua W Knowles, Elias Salfati, John P A Ioannidis, Themistocles L Assimes

Erratum issuedOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2014. 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 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
6.1field-weighted citation impact, top 3% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.

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  6. Clinical utility of polygenic risk scores for coronary artery disease.Nature reviews. Cardiology · 2022 · on this map
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Benjamin A GoldsteinDepartment of Medicine, Stanford University School of Medicine Stanford, CA, USA.
Joshua W KnowlesDepartment of Medicine, Stanford University School of Medicine Stanford, CA, USA.
Elias SalfatiDepartment of Medicine, Stanford University School of Medicine Stanford, CA, USA.
John P A IoannidisDepartment of Medicine, Stanford University School of Medicine Stanford, CA, USA ; Department of Health Research and Policy, Stanford University School of Medicine Stanford, CA, USA ; Department of Statistics, Stanford University School of Humanities and Sciences Stanford, CA, USA.
Themistocles L AssimesDepartment of Medicine, Stanford University School of Medicine Stanford, CA, USA.
Stanford University · US

Funding

Understanding and predicting cardiac events in HD using real-time EHRsK25DK097279 · NIDDK · STANFORD UNIVERSITY · PI GOLDSTEIN, BENJAMIN ALAN · 2013 to 2017
$797k
NIDDK NIH HHS K25 DK097279
6 · The paper itself

Abstract

purposeGenetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. MATERIALS AND

methodsWe illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.

resultsThe addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.

conclusionThe proposed method facilitates the standardized incorporation of a GRS in risk assessment.

Indexed as

coronary heart diseaseelectronic health recordsgenetic risk scorespersonalized medicine

Identifiers

PMID25136350
PMCPMC4117937
OpenAlexW2101998069

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.