Evidence map›Paper›PMID 42094046›Full record

ArticleResearch square2026

Widespread and Biologically Driven Sex Disparities in Polygenic Risk Prediction Across Complex Traits.

Akl Fahed, Xingyu Chen, Tingfeng Xu, Yang Sui, Kelvin Supriami, Min Seo Kim, Injeong Shim, Jennifer Halford, Kang Yu, Xinyu Zhou and 19 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 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

29 authors.

Akl FahedMassachusetts General Hospital.ORCID 0000-0002-4849-6389
Xingyu ChenMassachusetts General Hospital/ Broad Institute of MIT and Harvard/ Beijing Institute of Genomics (China National Center for Bioinformation).
Tingfeng XuBeijing Institute of Genomics, Chinese Academy of Sciences. (China National Center for Bioinformation).
Yang SuiBroad Institute of MIT and Harvard.
Kelvin SupriamiMassachusetts General Hospital/ Broad Institute of MIT and Harvard.ORCID 0000-0001-5577-5899
Min Seo KimBroad Institute of MIT and Harvard.
Injeong ShimMassachusetts General Hospital.
Jennifer HalfordMassachusetts General Hospital, Broad Institute of MIT and Harvard.ORCID 0000-0002-1454-3650
Kang YuBeijing Institute of Genomics (China National Center for Bioinformation).
Xinyu ZhouBeijing Institute of Genomics (China National Center for Bioinformation).
Qiuli ChenBeijing Institute of Genomics (China National Center for Bioinformation).
Jing HaoBeijing Institute of Genomics (China National Center for Bioinformation).
Shaoqi WangBeijing Institute of Genomics (China National Center for Bioinformation).
Kewei ZhaoBeijing Institute of Genomics (China National Center for Bioinformation).
Shuangqiao LiaoBeijing Institute of Genomics (China National Center for Bioinformation).
Jichen WenChina National Center for Bioinformation.
Jiaqi DingBeijing Institute of Genomics (China National Center for Bioinformation).
Xinyu ZhuBeijing Institute of Genomics (China National Center for Bioinformation).
Weixiang ZhaoBeijing Institute of Genomics (China National Center for Bioinformation).
Junyu YangBeijing Institute of Genomics (China National Center for Bioinformation).
Yu ZhangBeijing Institute of Genomics (China National Center for Bioinformation).
Meier ChenBeijing Institute of Genomics (China National Center for Bioinformation).
Komal ZaibBeijing Institute of Genomics (China National Center for Bioinformation).
Bitao ZhongBeijing Institute of Genomics (China National Center for Bioinformation).
Fei WangBeijing Institute of Genomics (China National Center for Bioinformation).
Whitney HornsbyMassachusetts General Hospital.
Kaavya ParuchuriBroad Institute.
Pradeep NatarajanMassachusetts General Hospital.ORCID 0000-0001-8402-7435
Minxian WangBeijing Institute of Genomics (China National Center for Bioinformation).

Funding

Using genetic variation to study biology of blood lipids & coronary heart diseaseR01HL127564 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Pradeep Natarajan, Gina Marie Peloso · 2015 to 2026
$7.3M
Enabling improved applicability and transferability of polygenic scores across populationsU01HG011719 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI Alicia Martin · 2021 to 2026
$5.5M
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
Partners Healthcare Training Program in Precision and Genomic MedicineT32HG010464 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI HEIDI L REHM, JORDAN W SMOLLER · 2019 to 2026
$3.1M
Integrating genomic and nongenomic risk for coronary artery diseaseK08HL161448 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed · 2022 to 2026
$751k
NHGRI NIH HHS T32 HG010464NHGRI NIH HHS U01 HG011719NHLBI NIH HHS K08 HL161448NHLBI NIH HHS R01 HL127564NHLBI NIH HHS R01 HL164629
6 · The paper itself

Abstract

Polygenic risk scores (PRS) are increasingly used for disease prediction, yet their performance equity across sexes remains unclear. We evaluated sex differences in PRS performance using 3,263 scores across 145 traits from the PGS Catalog in 409,440 UK Biobank participants. Sex-differential prediction was widespread and trait-specific, affecting 15 of 64 (23%) of diseases and 43 of 81 (53%) of quantitative traits. Female-favoring performance was enriched in autoimmune and endocrine traits, whereas cardiometabolic traits more often favored males. Discovery GWAS sex imbalance partially explained disease-level disparities (R

Identifiers

PMID42094046
PMCPMC13142612

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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.