---
OA_place: publisher
_id: '22258'
abstract:
- lang: eng
  text: "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."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This work was supported in part by the Swiss National Science Foundation
  through the\r\nEccellenza Grant \"Improving estimation and prediction of common
  complex disease risk\"\r\n(grant number PCEGP3_181181); the European Research Council
  through the grant\r\n\"Inference in High Dimensions: Light-speed Algorithms and
  Information Limits\" (grant\r\nnumber 101161364); and the Fondation Jean-Jacques
  et Felicia Lopez-Loreta through the\r\nPrix Lopez-Loretta 2019.\r\n"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Al
  full_name: Depope, Al
  id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  last_name: Depope
citation:
  ama: 'Depope A. From sparse selection to risk prediction: Approximate message passing
    for proteomic survival models and large-scale genomics. 2026. doi:<a href="https://doi.org/10.15479/AT-ISTA-22258">10.15479/AT-ISTA-22258</a>'
  apa: 'Depope, A. (2026). <i>From sparse selection to risk prediction: Approximate
    message passing for proteomic survival models and large-scale genomics</i>. Institute
    of Science and Technology Austria. <a href="https://doi.org/10.15479/AT-ISTA-22258">https://doi.org/10.15479/AT-ISTA-22258</a>'
  chicago: 'Depope, Al. “From Sparse Selection to Risk Prediction: Approximate Message
    Passing for Proteomic Survival Models and Large-Scale Genomics.” Institute of
    Science and Technology Austria, 2026. <a href="https://doi.org/10.15479/AT-ISTA-22258">https://doi.org/10.15479/AT-ISTA-22258</a>.'
  ieee: 'A. Depope, “From sparse selection to risk prediction: Approximate message
    passing for proteomic survival models and large-scale genomics,” Institute of
    Science and Technology Austria, 2026.'
  ista: 'Depope A. 2026. From sparse selection to risk prediction: Approximate message
    passing for proteomic survival models and large-scale genomics. Institute of Science
    and Technology Austria.'
  mla: 'Depope, Al. <i>From Sparse Selection to Risk Prediction: Approximate Message
    Passing for Proteomic Survival Models and Large-Scale Genomics</i>. Institute
    of Science and Technology Austria, 2026, doi:<a href="https://doi.org/10.15479/AT-ISTA-22258">10.15479/AT-ISTA-22258</a>.'
  short: 'A. Depope, From Sparse Selection to Risk Prediction: Approximate Message
    Passing for Proteomic Survival Models and Large-Scale Genomics, Institute of Science
    and Technology Austria, 2026.'
corr_author: '1'
das_tickbox: '1'
date_created: 2026-07-10T13:27:20Z
date_published: 2026-07-11T00:00:00Z
date_updated: 2026-10-20T09:03:21Z
day: '11'
ddc:
- '576'
- '610'
- '006'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
- _id: MaMo
doi: 10.15479/AT-ISTA-22258
doi_confirm: '1'
file:
- access_level: open_access
  checksum: 9ab386790515628d957a194f30a7ccb4
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  creator: adepope
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  date_updated: 2026-07-13T14:52:19Z
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  file_size: 25109878
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  creator: adepope
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file_date_updated: 2026-07-13T14:56:41Z
fulldoi: https://doi.org/10.15479/AT-ISTA-22258
has_accepted_license: '1'
keyword:
- Approximate Message Passing
- GWAS
- Genomics
- Proteomics
- Survival modeling
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '169'
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
- _id: 911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf
  grant_number: '101161364'
  name: 'Inference in High Dimensions: Light-speed Algorithms and Information Limits'
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
  record:
  - id: '21488'
    relation: part_of_dissertation
    status: public
status: public
supervisor:
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
title: 'From sparse selection to risk prediction: Approximate message passing for
  proteomic survival models and large-scale genomics'
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2026'
...
---
OA_place: publisher
_id: '17368'
abstract:
- lang: eng
  text: "Recent advancements in molecular diagnostic techniques have enabled the collection
    of\r\nmultiple types of omics data from patients, including genomics, epigenomics,
    proteomics,\r\nand transcriptomics. However, we lack effective methods for integrating
    all these different\r\ndata types and combining them with clinical outcomes to
    study the molecular mechanisms\r\nthat govern pathological phenotypes. We present
    multi-omics BayesW, a penalized Bayesian\r\nregression method that can handle
    general omics data for survival analysis of time-to-event\r\nphenotypes. Our method
    can: (1) accommodate incomplete data by allowing censored\r\nindividuals, (2)
    use continuous time-to-event data to test associations of markers with a\r\nphenotype
    and (3) estimate effects jointly while allowing for independent groups of biological\r\nmarkers.
    Extensive simulations using planted signals on real data demonstrate that our
    model\r\naccurately retrieves the true parameters of the model while controlling
    for false discoveries\r\nand maintaining the expected prediction accuracy. We
    address data correlations by estimating\r\nthe effects jointly, even between omic
    groups, while also estimating the individual variance\r\nexplained by each group.
    We apply our model to two datasets. Using 18,000 individuals from\r\nthe Generation
    Scotland study we model the association of time at onset of Type 2 Diabetes,\r\nStroke,
    Ischemic Disease, and Osteoarthritis from baseline study entry, with 831,724 CpG\r\nmethylation
    probes. We find that large proportions of variation in disease onset times can\r\nbe
    attributed to methylation as measured in whole blood at baseline in individuals
    without\r\ndisease symptoms. We then apply our model to The Cancer Genome Atlas
    (TCGA) pan-cancer\r\ndataset, in which we use 5 types of omics: copy number variation,
    epigenetics, somatic\r\nmutations, miRNA, and gene expression. For cancer survival
    age-at-onset we find that, when\r\nfitting the 5 groups together, almost all variation
    attributable to \"omics\" data is explained by\r\nDNA methylation. When considering
    progression times, both methylation and gene expression\r\nexplain a large part
    of the variance. We found 2 genes that are significantly associated (95%\r\nposterior
    inclusion probability) with cancer survival time, conditional on all other genome-wide\r\nomics
    data variation. Owing to the vast variability of mechanisms characterizing different\r\ncancers,
    there are likely few specific genes with a strong signal in a pan-cancer setting.
    Taken\r\ntogether, we showed the applicability of our multi-omics BayesW model
    to a wide-range of\r\nbiological questions in multi-omics data.\r\n"
alternative_title:
- ISTA Master's Thesis
article_processing_charge: No
author:
- first_name: Ariadna
  full_name: Villanueva Marijuan, Ariadna
  id: e0ae4864-133f-11ed-8f02-adaa8dd27540
  last_name: Villanueva Marijuan
citation:
  ama: Villanueva Marijuan A. Bayesian linear regression for analyzing general omics
    data with time-to-event phenotypes. 2024. doi:<a href="https://doi.org/10.15479/at:ista:17368">10.15479/at:ista:17368</a>
  apa: Villanueva Marijuan, A. (2024). <i>Bayesian linear regression for analyzing
    general omics data with time-to-event phenotypes</i>. Institute of Science and
    Technology Austria. <a href="https://doi.org/10.15479/at:ista:17368">https://doi.org/10.15479/at:ista:17368</a>
  chicago: Villanueva Marijuan, Ariadna. “Bayesian Linear Regression for Analyzing
    General Omics Data with Time-to-Event Phenotypes.” Institute of Science and Technology
    Austria, 2024. <a href="https://doi.org/10.15479/at:ista:17368">https://doi.org/10.15479/at:ista:17368</a>.
  ieee: A. Villanueva Marijuan, “Bayesian linear regression for analyzing general
    omics data with time-to-event phenotypes,” Institute of Science and Technology
    Austria, 2024.
  ista: Villanueva Marijuan A. 2024. Bayesian linear regression for analyzing general
    omics data with time-to-event phenotypes. Institute of Science and Technology
    Austria.
  mla: Villanueva Marijuan, Ariadna. <i>Bayesian Linear Regression for Analyzing General
    Omics Data with Time-to-Event Phenotypes</i>. Institute of Science and Technology
    Austria, 2024, doi:<a href="https://doi.org/10.15479/at:ista:17368">10.15479/at:ista:17368</a>.
  short: A. Villanueva Marijuan, Bayesian Linear Regression for Analyzing General
    Omics Data with Time-to-Event Phenotypes, Institute of Science and Technology
    Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-08-02T10:52:40Z
date_published: 2024-08-13T00:00:00Z
date_updated: 2026-10-02T11:35:19Z
day: '13'
ddc:
- '610'
degree_awarded: MS
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:17368
doi_confirm: '1'
file:
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  checksum: 0c2daa174609f0c00919dccc5701d375
  content_type: application/pdf
  creator: avillanu
  date_created: 2024-08-14T11:51:24Z
  date_updated: 2025-02-14T23:30:03Z
  embargo: 2025-02-14
  file_id: '17433'
  file_name: Masters_thesis_AriadnaVillanueva.pdf
  file_size: 13052436
  relation: main_file
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  date_updated: 2025-02-14T23:30:03Z
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  file_id: '17434'
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  relation: source_file
file_date_updated: 2025-02-14T23:30:03Z
fulldoi: https://doi.org/10.15479/at:ista:17368
has_accepted_license: '1'
keyword:
- Epigenetics
- Multi-omics
- Bayesian regression
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '08'
oa: 1
oa_version: Published Version
page: '60'
publication_identifier:
  issn:
  - 2791-4585
publication_status: published
publisher: Institute of Science and Technology Austria
researchdata_availability: no
status: public
supervisor:
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
supplementarymaterial: no
title: Bayesian linear regression for analyzing general omics data with time-to-event
  phenotypes
tmp:
  image: /images/cc_by_nc_sa.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
    BY-NC-SA 4.0)
  short: CC BY-NC-SA (4.0)
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
_id: '18642'
abstract:
- lang: eng
  text: "This thesis consists of two pieces of work in the broader field of computational
    biology,\r\nboth of which are methods for the analysis of large scale biological
    data, implemented in\r\nefficient software.\r\nChapter 2 introduces a statistical
    software for causal discovery and inference from observed\r\ngenetic marker and
    phenotypic trait data. We explore in simulation how well the method\r\ncan fine-map
    genetic effects, find the correct causal structure among tens of traits and\r\nmillions
    of genetic markers, and infer the causal effect size for the discovered causal\r\nrelations.
    We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank
    and show that many relationships found with other methods are likely due to\r\nthe
    effects of hidden confounders.\r\nChapter 3 describes how this method can be applied
    to longitudinal data. I show how one\r\ncan incorporate the background knowledge
    present in the known order of measurements to\r\nimprove the accuracy of the causal
    discovery process, and explore the method’s ability to\r\nidentify age specific
    genetic effects, and how the error rates of this recovery are influenced\r\nby
    missing data due to different censoring mechanisms.\r\nChapter 4 introduces a
    statistical software for the comparison of chromatin contact maps\r\nbased on
    the structural similarity index. We explore the robustness of the method to\r\nnoise
    and size differences of the compared maps, show how it can measure evolutionary\r\nconservation
    of topological features by providing a similarity ranking of syntenic regions,\r\nand
    finally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin
    samples of medical relevance.\r\n"
acknowledgement: "I would like to thank the Swiss National Science Foundation for
  funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation
  and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nick N
  full_name: Machnik, Nick N
  id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
  last_name: Machnik
  orcid: 0000-0001-6617-9742
citation:
  ama: Machnik NN. Algorithms for causal learning and comparative analysis for genomic
    data. 2024. doi:<a href="https://doi.org/10.15479/at:ista:18642">10.15479/at:ista:18642</a>
  apa: Machnik, N. N. (2024). <i>Algorithms for causal learning and comparative analysis
    for genomic data</i>. Institute of Science and Technology Austria. <a href="https://doi.org/10.15479/at:ista:18642">https://doi.org/10.15479/at:ista:18642</a>
  chicago: Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis
    for Genomic Data.” Institute of Science and Technology Austria, 2024. <a href="https://doi.org/10.15479/at:ista:18642">https://doi.org/10.15479/at:ista:18642</a>.
  ieee: N. N. Machnik, “Algorithms for causal learning and comparative analysis for
    genomic data,” Institute of Science and Technology Austria, 2024.
  ista: Machnik NN. 2024. Algorithms for causal learning and comparative analysis
    for genomic data. Institute of Science and Technology Austria.
  mla: Machnik, Nick N. <i>Algorithms for Causal Learning and Comparative Analysis
    for Genomic Data</i>. Institute of Science and Technology Austria, 2024, doi:<a
    href="https://doi.org/10.15479/at:ista:18642">10.15479/at:ista:18642</a>.
  short: N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for
    Genomic Data, Institute of Science and Technology Austria, 2024.
corr_author: '1'
date_created: 2024-12-10T13:49:15Z
date_published: 2024-12-11T00:00:00Z
date_updated: 2026-10-16T11:01:16Z
day: '11'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:18642
doi_confirm: '1'
file:
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  date_created: 2024-12-11T11:59:34Z
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has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '138'
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
  record:
  - id: '8707'
    relation: part_of_dissertation
    status: public
  - id: '18648'
    relation: part_of_dissertation
    status: public
researchdata_availability: unclear
status: public
supervisor:
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
supplementarymaterial: unclear
title: Algorithms for causal learning and comparative analysis for genomic data
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...