ArticleNature communications2025
Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Genetic influences on haematopoiesis.Nature reviews. Genetics · 2026Review
- Detecting gene-environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Environmental perturbation increases gene expression variability and unmasks genetic regulation for transcriptional robustness.bioRxiv : the preprint server for biology · 2026Article
- Polygenic modeling of genetic effects on both phenotypic mean and variance: distributional regression for BMI, blood and urine biomarkers in the UK Biobank.Frontiers in bioinformatics · 2026Article
- Integrative causal inference and predictive modeling reveal the iron-related gene SLC17A4 as a key biomarker in chronic rhinosinusitis.Frontiers in immunology · 2025Article
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Abstract
Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.
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