@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{21987,
  abstract     = {We introduce JODIE, a genetic joint modeling approach that estimates how DNA loci influence human traits by partitioning genetic effects into four components: direct effects (from a child’s alleles), indirect maternal and paternal effects (from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental transmission of alleles), while uniquely accounting for assortative mating. We analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood educational test scores. We find direct effects to be the largest contributor to trait variation, but combined, indirect parental and PofO effects are similarly substantial. We support our results by within-family genome-wide association testing and identify 276 independently associated DNA regions with a complex interplay between direct, indirect, and PofO effects. By joint modeling, we show that direct, indirect, and PofO effects collectively shape human phenotypic variation across loci genome-wide.},
  author       = {Krätschmer, Ilse and Hegemann, Laura and Hofmeister, Robin J. and Corfield, Elizabeth C. and Mahmoudi, Mahdi and Delaneau, Olivier and Andreassen, Ole A. and Campbell, Archie and Hayward, Caroline and Marioni, Riccardo E. and Ystrom, Eivind and Havdahl, Alexandra and Robinson, Matthew Richard},
  issn         = {2666-979X},
  journal      = {Cell Genomics},
  keywords     = {direct genetic effects, DGE, indirect genetic effects, IGE, parent-of-origin effects, phenotypic variation, assortative mating, within-family GWAS, MoBa, EstBB},
  number       = {7},
  publisher    = {Elsevier},
  title        = {{Separating direct, indirect, and parent-of-origin genetic effects in the human population}},
  doi          = {10.1016/j.xgen.2026.101277},
  volume       = {6},
  year         = {2026},
}