Evidence map›Paper›PMID 42910470›Full record

ArticleGenetic epidemiology2026

Multi-Trait GWAS Analyses of Blood Pressure Traits Provide Limited Gains in Locus Discovery or Polygenic Risk Score Performance Compared to Single-Trait GWAS.

Yujia Ma, Anna D Argoty-Pantoja, Tian Xie, Michael R Barnes, Patricia B Munroe, Claudia P Cabrera, Harold Snieder, Helen R Warren, UMCG Genetics Lifelines Initiative (UGLI) group author: LifeLines Cohort Study

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Article in Genetic epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yujia MaDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID https://orcid.org/0009-0007-3307-8874
Anna D Argoty-PantojaDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID https://orcid.org/0000-0001-5211-4497
Tian XieDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Michael R BarnesWilliam Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0001-9097-7381
Patricia B MunroeWilliam Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0002-4176-2947
Claudia P CabreraWilliam Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0002-2205-5315
Harold SniederDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID https://orcid.org/0000-0003-1949-2298
Helen R WarrenWilliam Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0003-0511-1386
UMCG Genetics Lifelines Initiative (UGLI) group author: LifeLines Cohort Study

Funding

Ministerie van Economische Zaken en KlimaatMinisterie van Volksgezondheid, Welzijn en SportNational Institute for Health and Care Research NIHR203330Northern Provinces of the NetherlandsUniversitair Medisch Centrum GroningenUniversity of Groningen
6 · The paper itself

Abstract

High blood pressure (BP) represents a major cause of life-threatening cardiovascular conditions. GWAS have identified >2000 BP-associated signals. BP-GWAS usually analyze each BP trait individually. However, multi-trait GWAS may yield novel loci and improve polygenic risk scores (PRS) prediction. We explored whether multi-trait BP-GWAS can: (i) find novel loci undetected by single-trait GWAS; (ii) increase heritability estimates; (iii) improve PRS prediction of hypertension within the Lifelines Cohort Study. We investigate the utility of multi-trait GWAS approaches of correlated BP traits (systolic BP, diastolic BP, pulse pressure). We tested two multi-trait methods inputting results from the latest N > 1 million single-trait BP-GWAS: (a) Multi-Trait Analysis of GWAS (MTAG); (b) genomic structural equation modeling (GenomicSEM); and compared results with single-trait results. We only identified one novel locus from multi-trait GWAS: ZNF268. Neither heritability estimates nor PRS performance improved using multi-trait GWAS. In this scenario of complete sample overlap across BP-GWAS, we observed little gain in results from multi-trait BP-GWAS over single-trait BP-GWAS. Our findings quantify the limited benefit of multi-trait methods under this specific setting. Future BP genetic discoveries are more likely to come from larger GWAS adding new cohort data. However, conclusions may differ for other traits, depending on trait-specific genetic architecture.

Indexed as

Blood PressureGenome-Wide Association StudyHypertensionMultifactorial InheritanceFemaleGenetic Predisposition to DiseaseGenetic Risk ScoreHumansMaleModels, GeneticPolymorphism, Single NucleotideQuantitative Trait Lociblood pressureGenomicSEMhypertensionmulti‐trait GWAS

Identifiers

PMID42910470
PMCPMC13688232

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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.