---
res:
  bibo_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.\r\n\r\nFirst, 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. \r\n\r\nSecond, 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.\r\n\r\nCollectively, 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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Al
      foaf_name: Depope, Al
      foaf_surname: Depope
      foaf_workInfoHomepage: http://www.librecat.org/personId=0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  bibo_doi: 10.15479/AT-ISTA-22258
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_subject:
  - Approximate Message Passing
  - GWAS
  - Genomics
  - Proteomics
  - Survival modeling
  dct_title: 'From sparse selection to risk prediction: Approximate message passing
    for proteomic survival models and large-scale genomics@'
...