Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Ruidong Xiang (), Chief Ben-Eghan, Yang Liu, David Roberts, Scott Ritchie, Samuel A. Lambert, Yu Xu, Fumihiko Takeuchi and Michael Inouye ()
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Ruidong Xiang: Baker Heart and Diabetes Institute
Chief Ben-Eghan: University of Cambridge
Yang Liu: Baker Heart and Diabetes Institute
David Roberts: University of Cambridge
Scott Ritchie: Baker Heart and Diabetes Institute
Samuel A. Lambert: Baker Heart and Diabetes Institute
Yu Xu: University of Cambridge
Fumihiko Takeuchi: Baker Heart and Diabetes Institute
Michael Inouye: Baker Heart and Diabetes Institute

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-59525-4

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