---
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'
...
---
_id: '10015'
abstract:
- lang: eng
  text: "Auxin plays a dual role in growth regulation and, depending on the tissue
    and concentration of the hormone, it can either promote or inhibit division and
    expansion processes in plants. Recent studies have revealed that, beyond transcriptional
    reprogramming, alternative auxincontrolled mechanisms regulate root growth. Here,
    we explored the impact of different concentrations of the synthetic auxin NAA
    that establish growth-promoting and -repressing conditions on the root tip proteome
    and phosphoproteome, generating a unique resource. From the phosphoproteome data,
    we pinpointed (novel) growth regulators, such as the RALF34-THE1 module. Our results,
    together with previously published studies, suggest that auxin, H+-ATPases, cell
    wall modifications and cell wall sensing receptor-like kinases are tightly embedded
    in a pathway regulating cell elongation. Furthermore, our study assigned a novel
    role to MKK2 as a regulator of primary root growth and a (potential) regulator
    of auxin biosynthesis and signalling, and suggests the importance of the MKK2\r\nThr31
    phosphorylation site for growth regulation in the Arabidopsis root tip."
acknowledgement: We thank the Nottingham Stock Centre for seeds, Frank Van Breusegem
  for the phb3 mutant, and Herman Höfte for the the1 mutant. Open Access Funding by
  the Austrian Science Fund (FWF).
alternative_title:
- Protein Phosphorylation and Cell Signaling in Plants
article_number: '1665 '
article_processing_charge: Yes
article_type: original
author:
- first_name: N
  full_name: Nikonorova, N
  last_name: Nikonorova
- first_name: E
  full_name: Murphy, E
  last_name: Murphy
- first_name: CF
  full_name: Fonseca de Lima, CF
  last_name: Fonseca de Lima
- first_name: S
  full_name: Zhu, S
  last_name: Zhu
- first_name: B
  full_name: van de Cotte, B
  last_name: van de Cotte
- first_name: LD
  full_name: Vu, LD
  last_name: Vu
- first_name: D
  full_name: Balcerowicz, D
  last_name: Balcerowicz
- first_name: Lanxin
  full_name: Li, Lanxin
  id: 367EF8FA-F248-11E8-B48F-1D18A9856A87
  last_name: Li
  orcid: 0000-0002-5607-272X
- first_name: X
  full_name: Kong, X
  last_name: Kong
- first_name: G
  full_name: De Rop, G
  last_name: De Rop
- first_name: T
  full_name: Beeckman, T
  last_name: Beeckman
- first_name: Jiří
  full_name: Friml, Jiří
  id: 4159519E-F248-11E8-B48F-1D18A9856A87
  last_name: Friml
  orcid: 0000-0002-8302-7596
- first_name: K
  full_name: Vissenberg, K
  last_name: Vissenberg
- first_name: PC
  full_name: Morris, PC
  last_name: Morris
- first_name: Z
  full_name: Ding, Z
  last_name: Ding
- first_name: I
  full_name: De Smet, I
  last_name: De Smet
citation:
  ama: Nikonorova N, Murphy E, Fonseca de Lima C, et al. The Arabidopsis root tip
    (phospho)proteomes at growth-promoting versus growth-repressing conditions reveal
    novel root growth regulators. <i>Cells</i>. 2021;10. doi:<a href="https://doi.org/10.3390/cells10071665">10.3390/cells10071665</a>
  apa: Nikonorova, N., Murphy, E., Fonseca de Lima, C., Zhu, S., van de Cotte, B.,
    Vu, L., … De Smet, I. (2021). The Arabidopsis root tip (phospho)proteomes at growth-promoting
    versus growth-repressing conditions reveal novel root growth regulators. <i>Cells</i>.
    MDPI. <a href="https://doi.org/10.3390/cells10071665">https://doi.org/10.3390/cells10071665</a>
  chicago: Nikonorova, N, E Murphy, CF Fonseca de Lima, S Zhu, B van de Cotte, LD
    Vu, D Balcerowicz, et al. “The Arabidopsis Root Tip (Phospho)Proteomes at Growth-Promoting
    versus Growth-Repressing Conditions Reveal Novel Root Growth Regulators.” <i>Cells</i>.
    MDPI, 2021. <a href="https://doi.org/10.3390/cells10071665">https://doi.org/10.3390/cells10071665</a>.
  ieee: N. Nikonorova <i>et al.</i>, “The Arabidopsis root tip (phospho)proteomes
    at growth-promoting versus growth-repressing conditions reveal novel root growth
    regulators,” <i>Cells</i>, vol. 10. MDPI, 2021.
  ista: Nikonorova N, Murphy E, Fonseca de Lima C, Zhu S, van de Cotte B, Vu L, Balcerowicz
    D, Li L, Kong X, De Rop G, Beeckman T, Friml J, Vissenberg K, Morris P, Ding Z,
    De Smet I. 2021. The Arabidopsis root tip (phospho)proteomes at growth-promoting
    versus growth-repressing conditions reveal novel root growth regulators. Cells.
    10, 1665.
  mla: Nikonorova, N., et al. “The Arabidopsis Root Tip (Phospho)Proteomes at Growth-Promoting
    versus Growth-Repressing Conditions Reveal Novel Root Growth Regulators.” <i>Cells</i>,
    vol. 10, 1665, MDPI, 2021, doi:<a href="https://doi.org/10.3390/cells10071665">10.3390/cells10071665</a>.
  short: N. Nikonorova, E. Murphy, C. Fonseca de Lima, S. Zhu, B. van de Cotte, L.
    Vu, D. Balcerowicz, L. Li, X. Kong, G. De Rop, T. Beeckman, J. Friml, K. Vissenberg,
    P. Morris, Z. Ding, I. De Smet, Cells 10 (2021).
date_created: 2021-09-14T11:36:20Z
date_published: 2021-07-02T00:00:00Z
date_updated: 2026-08-14T22:30:48Z
day: '02'
ddc:
- '575'
department:
- _id: JiFr
doi: 10.3390/cells10071665
ec_funded: 1
external_id:
  isi:
  - '000676604700001'
  pmid:
  - '34359847'
file:
- access_level: open_access
  checksum: 2a9f534b9c2200e72e2cde95afaf4eed
  content_type: application/pdf
  creator: cchlebak
  date_created: 2021-09-16T09:07:06Z
  date_updated: 2021-09-16T09:07:06Z
  file_id: '10021'
  file_name: 2021_Cells_Nikonorova.pdf
  file_size: 2667848
  relation: main_file
  success: 1
file_date_updated: 2021-09-16T09:07:06Z
has_accepted_license: '1'
intvolume: '        10'
isi: 1
keyword:
- primary root
- (phospho)proteomics
- auxin
- (receptor) kinase
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '665385'
  name: International IST Doctoral Program
- _id: 3AC91DDA-15DF-11EA-824D-93A3E7B544D1
  call_identifier: FWF
  name: FWF Open Access Fund
publication: Cells
publication_identifier:
  issn:
  - 2073-4409
publication_status: published
publisher: MDPI
quality_controlled: '1'
related_material:
  record:
  - id: '10083'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: The Arabidopsis root tip (phospho)proteomes at growth-promoting versus growth-repressing
  conditions reveal novel root growth regulators
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: 10
year: '2021'
...