@phdthesis{22258,
  abstract     = {Uncovering the genetic architecture of complex traits and pinpointing causal molecular drivers require the ability to distinguish true signals from noise within massive, high-dimensional omics datasets. To extract meaningful biological insights from these datasets, such as identifying causal genetic variants and proteins, scalable and accurate inference methods are essential. To this end, this thesis develops novel Bayesian inference frameworks based on Vector Approximate Message Passing and demonstrates their effectiveness in the modeling of disease onset times and quantitative physical and clinical measures.

First, we introduce gVAMP, a Bayesian framework tailored for Genome-Wide Association Studies that enables the joint modeling of quantitative complex traits across millions of genetic variants. gVAMP demonstrates superior accuracy in variable selection and out-of-sample polygenic risk prediction compared to state-of-the-art approaches. We model human height using 17 million whole-genome sequence variants from the UK Biobank, incorporating a vast number of rare variants and revealing novel associations. gVAMP achieves a prediction accuracy of approximately 46% for human height, representing the highest reported performance for this trait to date. 

Second, we present vampW, a Bayesian framework for survival analysis applied to proteomic data. By effectively handling right-censoring and complex protein dependencies within the UK Biobank Pharma Proteomics Project dataset, vampW identifies 219 protein associations across 24 disease outcomes, the majority of which are not among the top marginal discoveries. We further adjust protein levels for exponential age effects, yielding 1,308 associations and highlighting the sensitivity of the analysis to the chosen age-correction methodology. Finally, vampW improves upon the variable selection capabilities of the commonly used (penalized) variants of the Cox proportional hazards model and delivers state-of-the-art out-of-sample prediction of disease onset times.

Collectively, these methods provide powerful tools for dissecting the genetic architecture of complex traits and the proteomic drivers of disease onset. Furthermore, by delivering accurate polygenic risk scores and precise predictions of onset times, this work advances the capabilities of personalized medicine and clinical risk stratification.},
  author       = {Depope, Al},
  issn         = {2663-337X},
  keywords     = {Approximate Message Passing, GWAS, Genomics, Proteomics, Survival modeling},
  pages        = {169},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics}},
  doi          = {10.15479/AT-ISTA-22258},
  year         = {2026},
}

@article{10015,
  abstract     = {Auxin plays a dual role in growth regulation and, depending on the tissue and concentration of the hormone, it can either promote or inhibit division and expansion processes in plants. Recent studies have revealed that, beyond transcriptional reprogramming, alternative auxincontrolled mechanisms regulate root growth. Here, we explored the impact of different concentrations of the synthetic auxin NAA that establish growth-promoting and -repressing conditions on the root tip proteome and phosphoproteome, generating a unique resource. From the phosphoproteome data, we pinpointed (novel) growth regulators, such as the RALF34-THE1 module. Our results, together with previously published studies, suggest that auxin, H+-ATPases, cell wall modifications and cell wall sensing receptor-like kinases are tightly embedded in a pathway regulating cell elongation. Furthermore, our study assigned a novel role to MKK2 as a regulator of primary root growth and a (potential) regulator of auxin biosynthesis and signalling, and suggests the importance of the MKK2
Thr31 phosphorylation site for growth regulation in the Arabidopsis root tip.},
  author       = {Nikonorova, N and Murphy, E and Fonseca de Lima, CF and Zhu, S and van de Cotte, B and Vu, LD and Balcerowicz, D and Li, Lanxin and Kong, X and De Rop, G and Beeckman, T and Friml, Jiří and Vissenberg, K and Morris, PC and Ding, Z and De Smet, I},
  issn         = {2073-4409},
  journal      = {Cells},
  keywords     = {primary root, (phospho)proteomics, auxin, (receptor) kinase},
  publisher    = {MDPI},
  title        = {{The Arabidopsis root tip (phospho)proteomes at growth-promoting versus growth-repressing conditions reveal novel root growth regulators}},
  doi          = {10.3390/cells10071665},
  volume       = {10},
  year         = {2021},
}