| Title: | Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction |
| Journal: | Nature Communications |
| Published: | 7 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40335489/ |
| DOI: | https://doi.org/10.1038/s41467-025-59525-4 |
| URL: | https://www.nature.com/articles/s41467-025-59525-4.pdf |
Publication 15412
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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.</p>
13 Keywords
- Alcohol Drinking
- Blood Cells
- Female
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Male
- Mendelian Randomization Analysis
- Multifactorial Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
- United Kingdom
9 Authors
- Ruidong Xiang
- Chief Ben-Eghan
- Yang Liu
- David Roberts
- Scott Ritchie
- Samuel A. Lambert
- Yu Xu
- Fumihiko Takeuchi
- Michael Inouye
1 Application
| Application ID | Title |
|---|---|
| 30418 | Biomarker profiling by NMR metabolomics for the study of chronic disease risk and underlying risk factors |
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