Multi-Trait GWAS Analyses of Blood Pressure Traits Provide Limited Gains in Locus Discovery or Polygenic Risk Score Performance Compared to Single-Trait GWAS.
5 affiliations
- 1Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- 2Department of Non-communicable Chronic Disease Control and Prevention, Beijing Center for Disease Prevention and Control, Beijing, China.
- 3Guangzhou National Laboratory, Guangdong Province, Guangzhou, China.
- 4William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
- 5National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK.
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.
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BibTeX
@article{ma2026multitrait,
title = {{Multi-Trait GWAS Analyses of Blood Pressure Traits Provide Limited Gains in Locus Discovery or Polygenic Risk Score Performance Compared to Single-Trait GWAS.}},
author = {Yujia Ma and Anna D Argoty-Pantoja and Tian Xie and Michael R Barnes and Patricia B Munroe and Claudia P Cabrera and Harold Snieder and Helen R Warren and UMCG Genetics Lifelines Initiative (UGLI) group author: LifeLines Cohort Study},
journal = {Genetic epidemiology},
year = {2026},
month = oct,
doi = {10.1002/gepi.70062},
url = {https://europepmc.org/article/MED/42910470},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-Trait GWAS Analyses of Blood Pressure Traits Provide Limited Gains in Locus Discovery or Polygenic Risk Score Performance Compared to Single-Trait GWAS.
AU - Yujia Ma
AU - Anna D Argoty-Pantoja
AU - Tian Xie
AU - Michael R Barnes
AU - Patricia B Munroe
AU - Claudia P Cabrera
AU - Harold Snieder
AU - Helen R Warren
AU - UMCG Genetics Lifelines Initiative (UGLI) group author: LifeLines Cohort Study
PY - 2026
DA - 2026/10/30
JO - Genetic epidemiology
DO - 10.1002/gepi.70062
UR - https://europepmc.org/article/MED/42910470
AB - 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.
SN - 0741-0395
SN - 1098-2272
ER -