Evidence map›Paper›PMID 42064965›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Evaluating Individual Level Performance of Polygenic Risk Scores Using Early Onset High Genetic Risk Coronary Artery Disease as a Benchmark.

Shengxin Liang, Min Seo Kim, Yang Sui, Youxin Tan, Linke Li, So Mi Jemma Cho, Satoshi Koyama, Yixuan Liu, Kaavya Paruchuri, Andrew Chan and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Shengxin LiangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Min Seo KimProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Yang SuiProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Youxin TanProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Linke LiProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
So Mi Jemma ChoProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Satoshi KoyamaProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Yixuan LiuProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Kaavya ParuchuriProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Andrew ChanClinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Michael C HonigbergProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Pradeep NatarajanProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Akl C FahedProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Zhi YuProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Funding

Coronary plaque changes with statin and colchicine among people with high polygenic risk- a mechanistic pilot studyR01HL164629 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed, Michael TseYin Lu · 2023 to 2026
$3.3M
Integrating genomic and nongenomic risk for coronary artery diseaseK08HL161448 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed · 2022 to 2026
$751k
Multi-omic dissection of clonal hematopoiesis-associated diseasesR00HG012956 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI Zhi Yu · 2024 to 2026
$747k
Multi-omic dissection of clonal hematopoiesis-associated diseasesK99HG012956 · NHGRI · BROAD INSTITUTE, INC. · PI YU, ZHI · 2023 to 2023
$137k
NHGRI NIH HHS K99 HG012956NHGRI NIH HHS R00 HG012956NHLBI NIH HHS K08 HL161448NHLBI NIH HHS R01 HL164629
6 · The paper itself

Abstract

Polygenic risk scores (PRSs) are typically validated using population-level metrics, masking variability in individual-level risk prediction and hindering clinical translation. To address this, we introduced a novel framework using a "benchmark" cohort (N=1184) of "unexpected coronary artery disease (CAD)": early-onset patients (<55 years) with a clinical profile-low 10-year risk, no diabetes or severe hypercholesterolemia-that excludes therapy indications. The occurrence of early CAD in these clinically low-risk individuals establishes a "ground truth" for high genetic risk. We evaluated 58 published CAD PRSs and demonstrated a disconnection between population-level performance and individual-level accuracy (proportion of benchmark patients captured). The proportion captured by 58 PRSs varied from 10.8% to 33.1%, and the top-performing score was 2-fold more effective at identifying the benchmark group than established non-genetic biomarkers, such as lipoprotein(a). Furthermore, benchmark patients never captured by any score exhibited significantly healthier lipid profiles. Our framework provides an essential method for validating clinical readiness of PRSs.

Identifiers

PMID42064965
PMCPMC13127537

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.