---
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:10.15479/AT-ISTA-22258'
apa: '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. https://doi.org/10.15479/AT-ISTA-22258'
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. https://doi.org/10.15479/AT-ISTA-22258.'
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. From Sparse Selection to Risk Prediction: Approximate Message
Passing for Proteomic Survival Models and Large-Scale Genomics. Institute
of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-22258.'
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: 2026-07-28T07:08:15Z
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
content_type: application/pdf
creator: adepope
date_created: 2026-07-13T14:52:19Z
date_updated: 2026-07-13T14:52:19Z
file_id: '22316'
file_name: 2026_Depope_Al_Thesis.pdf
file_size: 25109878
relation: main_file
- access_level: closed
checksum: 8ed8fb63f76a695d5b6fec35343f4b90
content_type: application/zip
creator: adepope
date_created: 2026-07-13T14:56:41Z
date_updated: 2026-07-13T14:56:41Z
file_id: '22317'
file_name: 2026_Depope_Al_Thesis.zip
file_size: 1203199939
relation: source_file
file_date_updated: 2026-07-13T14:56:41Z
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'
...