---
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:10.15479/at:ista:17368
apa: Villanueva Marijuan, A. (2024). Bayesian linear regression for analyzing
general omics data with time-to-event phenotypes. Institute of Science and
Technology Austria. https://doi.org/10.15479/at:ista:17368
chicago: Villanueva Marijuan, Ariadna. “Bayesian Linear Regression for Analyzing
General Omics Data with Time-to-Event Phenotypes.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/at:ista:17368.
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. Bayesian Linear Regression for Analyzing General
Omics Data with Time-to-Event Phenotypes. Institute of Science and Technology
Austria, 2024, doi:10.15479/at:ista:17368.
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'
date_created: 2024-08-02T10:52:40Z
date_published: 2024-08-13T00:00:00Z
date_updated: 2026-04-07T13:03:41Z
day: '13'
ddc:
- '610'
degree_awarded: MS
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:17368
file:
- access_level: open_access
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
- access_level: closed
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content_type: application/zip
creator: avillanu
date_created: 2024-08-14T11:51:57Z
date_updated: 2025-02-14T23:30:03Z
embargo_to: open_access
file_id: '17434'
file_name: Masters thesis-AriadnaVillanueva.zip
file_size: 45642547
relation: source_file
file_date_updated: 2025-02-14T23:30:03Z
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
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
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2024'
...