---
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: 2026-07-28T07:08:15Z
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'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '22228'
abstract:
- lang: eng
  text: "In a mixed generalized linear model, the goal is to learn multiple signals
    from unlabeled observations: each sample comes from exactly one signal, but it
    is not known which one. We consider the prototypical problem of estimating two
    statistically independent signals in a mixed generalized linear model with Gaussian
    covariates. Spectral methods are a popular class of estimators which output the
    top two eigenvectors of a suitable data-dependent matrix. However, despite the
    wide applicability, their design is still obtained via heuristic considerations,
    and the number of samples \U0001D45B needed to guarantee recovery is superlinear
    in the signal dimension \U0001D451. In this paper, we develop exact asymptotics
    on spectral methods in the challenging proportional regime in which \U0001D45B,\U0001D451
    grow large and their ratio converges to a finite constant. This allows us optimize
    the design of the spectral method, and combine it with a simple linear estimator,
    to minimize the estimation error. Our characterization exploits a mix of tools
    from random matrices, free probability, and the theory of approximate message
    passing algorithms. Numerical simulations for mixed linear regression and phase
    retrieval demonstrate the advantage enabled by our analysis over existing designs
    of spectral methods."
acknowledgement: The first and second authors were partially supported by the 2019
  Lopez-Loreta prize.
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Yihan
  full_name: Zhang, Yihan
  last_name: Zhang
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
- first_name: Ramji
  full_name: Venkataramanan, Ramji
  last_name: Venkataramanan
citation:
  ama: Zhang Y, Mondelli M, Venkataramanan R. Precise asymptotics for spectral methods
    in mixed generalized linear models. <i>SIAM Journal on Mathematics of Data Science</i>.
    2026;8(2):411-439. doi:<a href="https://doi.org/10.1137/24m1702854">10.1137/24m1702854</a>
  apa: Zhang, Y., Mondelli, M., &#38; Venkataramanan, R. (2026). Precise asymptotics
    for spectral methods in mixed generalized linear models. <i>SIAM Journal on Mathematics
    of Data Science</i>. SIAM. <a href="https://doi.org/10.1137/24m1702854">https://doi.org/10.1137/24m1702854</a>
  chicago: Zhang, Yihan, Marco Mondelli, and Ramji Venkataramanan. “Precise Asymptotics
    for Spectral Methods in Mixed Generalized Linear Models.” <i>SIAM Journal on Mathematics
    of Data Science</i>. SIAM, 2026. <a href="https://doi.org/10.1137/24m1702854">https://doi.org/10.1137/24m1702854</a>.
  ieee: Y. Zhang, M. Mondelli, and R. Venkataramanan, “Precise asymptotics for spectral
    methods in mixed generalized linear models,” <i>SIAM Journal on Mathematics of
    Data Science</i>, vol. 8, no. 2. SIAM, pp. 411–439, 2026.
  ista: Zhang Y, Mondelli M, Venkataramanan R. 2026. Precise asymptotics for spectral
    methods in mixed generalized linear models. SIAM Journal on Mathematics of Data
    Science. 8(2), 411–439.
  mla: Zhang, Yihan, et al. “Precise Asymptotics for Spectral Methods in Mixed Generalized
    Linear Models.” <i>SIAM Journal on Mathematics of Data Science</i>, vol. 8, no.
    2, SIAM, 2026, pp. 411–39, doi:<a href="https://doi.org/10.1137/24m1702854">10.1137/24m1702854</a>.
  short: Y. Zhang, M. Mondelli, R. Venkataramanan, SIAM Journal on Mathematics of
    Data Science 8 (2026) 411–439.
corr_author: '1'
das_tickbox: '0'
date_created: 2026-06-30T13:03:41Z
date_published: 2026-06-01T00:00:00Z
date_updated: 2026-08-12T06:23:07Z
day: '01'
ddc:
- '000'
department:
- _id: MaMo
doi: 10.1137/24m1702854
external_id:
  arxiv:
  - '2211.11368'
file:
- access_level: open_access
  checksum: 5cfd350dc64d1476063e959316dbff65
  content_type: application/pdf
  creator: dernst
  date_created: 2026-07-01T06:22:15Z
  date_updated: 2026-07-01T06:22:15Z
  file_id: '22230'
  file_name: 2026_SIAMJourmathDataScience_Zhang.pdf
  file_size: 1210346
  relation: main_file
  success: 1
file_date_updated: 2026-07-01T06:22:15Z
has_accepted_license: '1'
intvolume: '         8'
issue: '2'
keyword:
- spectral estimator
- generalized linear models
- mixed regression
- high-dimensional asymptotics
- random matrix theory
- approximate message passing (AMP)
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
mathsc:
- '62E20'
- 62J05
- 62J12
month: '06'
oa: 1
oa_version: Published Version
page: 411-439
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: SIAM Journal on Mathematics of Data Science
publication_identifier:
  eissn:
  - 2577-0187
publication_status: published
publisher: SIAM
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Precise asymptotics for spectral methods in mixed generalized linear models
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2026'
...