@article{21488,
  abstract     = {Human height is a model for the genetic analysis of complex traits, and recent studies suggest the presence of thousands of common genetic variant associations and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic paradigm based on approximate message passing (genomic vector approximate message passing [gVAMP]) for identifying DNA sequence variants associated with complex traits and common diseases in large-scale whole-genome sequencing (WGS) data. We show that gVAMP accurately localizes associations to variants with the correct frequency and position in the DNA, outperforming existing fine-mapping methods in selecting the appropriate genetic variants within WGS data. We then apply gVAMP to jointly model the relationship of tens of millions of WGS variants with human height in hundreds of thousands of UK Biobank individuals. We identify 59 rare variants and gene burden scores alongside many hundreds of DNA regions containing common variant associations and show that understanding the genetic basis of complex traits will require the joint analysis of hundreds of millions of variables measured on millions of people. The polygenic risk scores obtained from gVAMP have high accuracy (including a prediction accuracy of ∼46% for human height) and outperform current methods for downstream tasks such as mixed linear model association testing across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation for a wider range of analyses in WGS data.},
  author       = {Depope, Al and Bajzik, Jakub and Mondelli, Marco and Robinson, Matthew Richard},
  issn         = {2666-979X},
  journal      = {Cell Genomics},
  publisher    = {Elsevier},
  title        = {{Joint modeling of whole-genome sequencing data for human height via approximate message passing}},
  doi          = {10.1016/j.xgen.2026.101162},
  year         = {2026},
}

@article{20816,
  abstract     = {Background: DNA methylation (DNAm) can regulate gene expression, and its genome-wide patterns (epigenetic scores or EpiScores) can act as biomarkers for complex traits. The relative stability of methylation profiles may enable better assessment of chronic exposures compared to single time-point protein measures. We present the first large-scale epigenetic study of the highly-abundant serum proteome measured via ultra-high throughput mass spectrometry in 14,671 samples from the Generation Scotland cohort. We further demonstrate the first large-scale comparison of protein EpiScores and their respective proteins as predictors of incident cardiovascular disease.

Results: Marginal epigenome-wide association models, adjusting for age, sex, measurement batch, estimated white cell proportions, BMI, smoking and methylation principal components, reveal 15,855 significant CpG – protein associations across 125 of 133 proteins PBonferroni < 2.71 × 10-10. Bayesian epigenome-wide association studies of the same 133 proteins reveal 697 CpG-Protein associations (posterior inclusion probability > 0.95). 112 protein EpiScores correlate significantly with their respective protein in a holdout test-set. Of these, sixteen associate significantly with incident all-cause cardiovascular disease (Nevents=191) compared to one measured protein.

Conclusions: We highlight a complex interplay between the blood-based methylome and proteome. Importantly, we show that protein EpiScores correlate with measured proteins and demonstrate that the, as-yet understudied, high-abundance proteome may yield clinically relevant biomarkers. The protein EpiScores demonstrate more significant associations with cardiovascular disease than directly measured proteins, suggesting their potential as clinical biomarkers for monitoring or predicting disease risk. We suggest that biomarker development could be enhanced by the consideration of protein EpiScores alongside measured proteins.},
  author       = {Robertson, Josephine A. and Bajzik, Jakub and Vernardis, Spyros and Chybowska, Aleksandra D. and Mccartney, Daniel L. and Grauslys, Arturas and Mur, Jure and Smith, Hannah M. and Campbell, Archie and Drake, Camilla and Grant, Hannah and Pearce, Jamie and Russ, Tom C. and Adkin, Poppy and White, Matthew and Brigden, Charles and Messner, Christoph B. and Porteous, David J. and Hayward, Caroline and Cox, Simon R. and Zelezniak, Aleksej and Ralser, Markus and Robinson, Matthew Richard and Marioni, Riccardo E.},
  issn         = {1474-760X},
  journal      = {Genome Biology},
  publisher    = {Springer Nature},
  title        = {{Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants}},
  doi          = {10.1186/s13059-025-03892-0},
  volume       = {26},
  year         = {2025},
}