---
OA_place: publisher
OA_type: hybrid
_id: '21503'
abstract:
- lang: eng
text: Currently, pharmacogenetics relies on partially annotated star alleles, leaving
novel variants and complex haplotypes uninterpretable. Computational scoring frameworks
could overcome these limitations. Here, we comprehensively evaluated the ability
of existing (CADD, FATHMM-XF, PROVEAN, MutationAssessor, SIFT, PhyloP100, APF,
APF2) and novel (PharmGScore and PharmMLScore) variant effect predictors to assess
pharmacogenetic alleles in multiple scenarios. Altogether we analyzed 541 PharmVar
alleles, high‑throughput CYP2C9 and CYP2C19 mutational maps, and 200 642 UK Biobank
exomes linked with health records containing antidepressant treatment outcomes.
Many evaluated tools, especially ensemble frameworks, matched or exceeded star
allele classifications (ROC‑AUC up to 0.85 for allele definitions, 0.95 in vitro;
TPR up to 0.99 for exomes) and accurately predicted severe antidepressant adverse
events for carriers of deleterious variants in CYP2C19 (OR 1.20–1.35). Our findings
show that computational predictors deliver star allele accuracy while overcoming
their limitations. With additional validation, computational tools could enhance
clinical decision frameworks by enabling continuous scoring, incorporating previously
unknown variants, and providing genome-wide applicability.
acknowledgement: "This research has been conducted using the UK Biobank Resource under
Application Number 62979. We are grateful to the UK Biobank and all its voluntary
participants. This work used data provided by patients and collected by the NHS
as part of their care and support.\r\n\r\nThis study was funded by the National
Science Center, Poland: PRELUDIUM BIS-3 grant no. 2021/43/O/NZ7/01187 (development
and benchmarking of variant scores) and SONATINA 5 grant 2021/40/C/NZ2/00218 (UKB
analyses). Additional support came from the statutory funds of the Maj Institute
of Pharmacology PAS. We gratefully acknowledge Poland’s high-performance Infrastructure
PLGrid ACK Cyfronet AGH, for providing computer facilities and support within computational
grant no PLG/2022/015861. DMF and GEB were funded by NIH grants NIH R35GM152106
and UM1HG011969."
article_number: '8'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jacek
full_name: Hajto, Jacek
last_name: Hajto
- first_name: Marcin
full_name: Piechota, Marcin
last_name: Piechota
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Paula
full_name: Konowalska, Paula
last_name: Konowalska
- first_name: Gabriel E.
full_name: Boyle, Gabriel E.
last_name: Boyle
- first_name: Douglas M.
full_name: Fowler, Douglas M.
last_name: Fowler
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Michal
full_name: Korostynski, Michal
last_name: Korostynski
citation:
ama: 'Hajto J, Piechota M, Krätschmer I, et al. Computational variant predictors
for pharmacogenomics: From evaluation of single alleles to assessment of adverse
drug reactions to antidepressants. Pharmacogenomics Journal. 2026;26(2).
doi:10.1038/s41397-026-00399-0'
apa: 'Hajto, J., Piechota, M., Krätschmer, I., Konowalska, P., Boyle, G. E., Fowler,
D. M., … Korostynski, M. (2026). Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
Pharmacogenomics Journal. Springer Nature. https://doi.org/10.1038/s41397-026-00399-0'
chicago: 'Hajto, Jacek, Marcin Piechota, Ilse Krätschmer, Paula Konowalska, Gabriel
E. Boyle, Douglas M. Fowler, Malgorzata Borczyk, and Michal Korostynski. “Computational
Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to
Assessment of Adverse Drug Reactions to Antidepressants.” Pharmacogenomics
Journal. Springer Nature, 2026. https://doi.org/10.1038/s41397-026-00399-0.'
ieee: 'J. Hajto et al., “Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants,”
Pharmacogenomics Journal, vol. 26, no. 2. Springer Nature, 2026.'
ista: 'Hajto J, Piechota M, Krätschmer I, Konowalska P, Boyle GE, Fowler DM, Borczyk
M, Korostynski M. 2026. Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
Pharmacogenomics Journal. 26(2), 8.'
mla: 'Hajto, Jacek, et al. “Computational Variant Predictors for Pharmacogenomics:
From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.”
Pharmacogenomics Journal, vol. 26, no. 2, 8, Springer Nature, 2026, doi:10.1038/s41397-026-00399-0.'
short: J. Hajto, M. Piechota, I. Krätschmer, P. Konowalska, G.E. Boyle, D.M. Fowler,
M. Borczyk, M. Korostynski, Pharmacogenomics Journal 26 (2026).
date_created: 2026-03-29T22:07:08Z
date_published: 2026-03-09T00:00:00Z
date_updated: 2026-03-30T07:10:50Z
day: '09'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41397-026-00399-0
external_id:
pmid:
- '41803106'
file:
- access_level: open_access
checksum: 2fd3d7e48b779ac24245f6c35449b89a
content_type: application/pdf
creator: dernst
date_created: 2026-03-30T07:04:08Z
date_updated: 2026-03-30T07:04:08Z
file_id: '21506'
file_name: 2026_PharmacogenomicsJour_Hajto.pdf
file_size: 2618963
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success: 1
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intvolume: ' 26'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Pharmacogenomics Journal
publication_identifier:
eissn:
- 1473-1150
issn:
- ' 1470-269X'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Computational variant predictors for pharmacogenomics: From evaluation of
single alleles to assessment of adverse drug reactions to antidepressants'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '21484'
abstract:
- lang: eng
text: An individual's phenotype reflects a complex interplay of the direct effects
of their DNA, epigenetic modifications of their DNA induced by their parents,
and indirect effects of their parents' DNA. Here, we derive how the genetic variance
within a population is changed under the influence of indirect maternal, paternal
and parent-of-origin effects under random mating. We also consider indirect effects
of a sibling, in particular how the genetic variance is altered when looking at
the phenotypic difference between two siblings. The calculations are then extended
to include assortative mating (AM), which alters the variance by inducing increased
homozygosity and correlations within and across loci. AM likely leads to covariance
of parental genetic effects, a measure of the similarity of parents in the indirect
effects they have on their children. We propose that this assortment for parental
characteristics, where biological parents create similar environments for their
children, can create shared parental effects across traits and the appearance
of cross-trait AM. Our theory shows how the resemblance among relatives increases
under both AM, indirect and parent-of-origin effects. When our model is used to
predict correlations among relatives in human height, we find that explaining
the patterns observed in real data requires both indirect genetic effects and
assortative mating. The degree to which direct, indirect and epigenetic effects
shape the phenotypic variance of complex traits remains an open question that
requires large-scale family data to be resolved.
acknowledgement: We thank members of the Medical Genomics group at ISTA for their
comments, which improved this manuscript. This work was funded by an SNSF Eccellenza
Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science
and Technology Austria.
article_number: iyag042
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Krätschmer I, Robinson MR. A quantitative genetic model for indirect genetic
effects and genomic imprinting under random and assortative mating. Genetics.
2026;232(4). doi:10.1093/genetics/iyag042
apa: Krätschmer, I., & Robinson, M. R. (2026). A quantitative genetic model
for indirect genetic effects and genomic imprinting under random and assortative
mating. Genetics. Oxford University Press. https://doi.org/10.1093/genetics/iyag042
chicago: Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic
Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative
Mating.” Genetics. Oxford University Press, 2026. https://doi.org/10.1093/genetics/iyag042.
ieee: I. Krätschmer and M. R. Robinson, “A quantitative genetic model for indirect
genetic effects and genomic imprinting under random and assortative mating,” Genetics,
vol. 232, no. 4. Oxford University Press, 2026.
ista: Krätschmer I, Robinson MR. 2026. A quantitative genetic model for indirect
genetic effects and genomic imprinting under random and assortative mating. Genetics.
232(4), iyag042.
mla: Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model
for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative
Mating.” Genetics, vol. 232, no. 4, iyag042, Oxford University Press, 2026,
doi:10.1093/genetics/iyag042.
short: I. Krätschmer, M.R. Robinson, Genetics 232 (2026).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "The code used to simulate data can be found at https://github.\r\ncom/medical-genomics-group/familyMC.
Simulations are based\r\non genotype data from 1000 Genomes Project downloaded from\r\nhttps://ftp.1000genomes.ebi.
ac.uk/vol1/ftp/release/20130502/.\r\nSupplemental material available at GENETICS
online."
date_created: 2026-03-23T15:02:54Z
date_published: 2026-04-01T00:00:00Z
date_updated: 2026-07-27T11:56:04Z
day: '01'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1093/genetics/iyag042
external_id:
pmid:
- '41677404'
file:
- access_level: open_access
checksum: 926322c83b02522e96ab7a86e4011eec
content_type: application/pdf
creator: dernst
date_created: 2026-07-27T11:54:00Z
date_updated: 2026-07-27T11:54:00Z
file_id: '22425'
file_name: 2026_Genetics_Kraetschmer.pdf
file_size: 734475
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success: 1
file_date_updated: 2026-07-27T11:54:00Z
has_accepted_license: '1'
intvolume: ' 232'
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genetics
publication_identifier:
issn:
- 1943-2631
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/medical-genomics-group/familyMC
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: A quantitative genetic model for indirect genetic effects and genomic imprinting
under random and assortative mating
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: 232
year: '2026'
...
---
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
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date_created: 2026-07-13T14:56:41Z
date_updated: 2026-07-13T14:56:41Z
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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'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '21488'
abstract:
- lang: eng
text: Human height is a model for the genetic analysis of complex traits, and recent
studies suggest the presence of thousands of common genetic variant associations
and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic
paradigm based on approximate message passing (genomic vector approximate message
passing [gVAMP]) for identifying DNA sequence variants associated with complex
traits and common diseases in large-scale whole-genome sequencing (WGS) data.
We show that gVAMP accurately localizes associations to variants with the correct
frequency and position in the DNA, outperforming existing fine-mapping methods
in selecting the appropriate genetic variants within WGS data. We then apply gVAMP
to jointly model the relationship of tens of millions of WGS variants with human
height in hundreds of thousands of UK Biobank individuals. We identify 59 rare
variants and gene burden scores alongside many hundreds of DNA regions containing
common variant associations and show that understanding the genetic basis of complex
traits will require the joint analysis of hundreds of millions of variables measured
on millions of people. The polygenic risk scores obtained from gVAMP have high
accuracy (including a prediction accuracy of ∼46% for human height) and outperform
current methods for downstream tasks such as mixed linear model association testing
across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation
for a wider range of analyses in WGS data.
acknowledgement: We thank Malgorzata Borczyk for creating the gene burden scores.
We thank Robin Beaumont, Amedeo Roberto Esposito, Gareth Hawkes, Philip Schniter,
Matthew Stephens, Pragya Sur, Peter Visscher, Michael Weedon, and Harry Wright for
providing valuable suggestions and comments on earlier versions of the work. This
project was funded by a Lopez-Loreta Prize to M.M., an SNSF Eccellenza Grant to
M.R.R. (PCEGP3-181181), an ERC Starting Grant to M.M. (INF2, project number 101161364),
and core funding from ISTA. High-performance computing was supported by the Scientific
Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp).
We would like to acknowledge the participants and investigators of the UK Biobank
study. We gratefully acknowledge the All of Us participants for their contributions,
without whom this research would not have been possible. We also thank the National
Institutes of Health All of Us Research Program for making available the participant
data (and/or samples and/or cohort) examined in this study.
article_number: '101162'
article_processing_charge: Yes
article_type: original
author:
- first_name: Al
full_name: Depope, Al
id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
last_name: Depope
- first_name: Jakub
full_name: Bajzik, Jakub
id: b995e25b-8c4b-11ed-a6d8-f71b7bcd6122
last_name: Bajzik
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Depope A, Bajzik J, Mondelli M, Robinson MR. Joint modeling of whole-genome
sequencing data for human height via approximate message passing. Cell Genomics.
2026;6(5). doi:10.1016/j.xgen.2026.101162
apa: Depope, A., Bajzik, J., Mondelli, M., & Robinson, M. R. (2026). Joint modeling
of whole-genome sequencing data for human height via approximate message passing.
Cell Genomics. Elsevier. https://doi.org/10.1016/j.xgen.2026.101162
chicago: Depope, Al, Jakub Bajzik, Marco Mondelli, and Matthew Richard Robinson.
“Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate
Message Passing.” Cell Genomics. Elsevier, 2026. https://doi.org/10.1016/j.xgen.2026.101162.
ieee: A. Depope, J. Bajzik, M. Mondelli, and M. R. Robinson, “Joint modeling of
whole-genome sequencing data for human height via approximate message passing,”
Cell Genomics, vol. 6, no. 5. Elsevier, 2026.
ista: Depope A, Bajzik J, Mondelli M, Robinson MR. 2026. Joint modeling of whole-genome
sequencing data for human height via approximate message passing. Cell Genomics.
6(5), 101162.
mla: Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human
Height via Approximate Message Passing.” Cell Genomics, vol. 6, no. 5,
101162, Elsevier, 2026, doi:10.1016/j.xgen.2026.101162.
short: A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics 6 (2026).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "This project uses the UK Biobank data under project number
35520. UK Biobank genotypic and phenotypic data are available through a formal request
at http://www.ukbiobank.ac.uk. It also uses genotypic and phenotypic data from the
All of Us study, which are also available through a formal request at https://www.researchallofus.org/data-tools/data-access/.
All summary statistic estimates are released publicly on Dryad: https://doi.org/10.5061/dryad.cz8w9gjjc.\r\n•\r\nThe
gVAMP code developed in this work is open source and has been deposited on GitHub,
where it is publicly available at https://github.com/medical-genomics-group/gVAMP,
and the code used to generate the data in the manuscript are available from Zenodo
https://doi.org/10.5281/zenodo.17935521. The URLs of other software used are listed
in the key resources table."
date_created: 2026-03-23T15:10:03Z
date_published: 2026-05-13T00:00:00Z
date_updated: 2026-07-28T07:08:15Z
day: '13'
ddc:
- '000'
- '570'
department:
- _id: MaMo
- _id: MaRo
doi: 10.1016/j.xgen.2026.101162
external_id:
pmid:
- '41713425'
file:
- access_level: open_access
checksum: 6b59686f8d9733add4f23d23f3dd9df0
content_type: application/pdf
creator: dernst
date_created: 2026-07-28T07:06:26Z
date_updated: 2026-07-28T07:06:26Z
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success: 1
file_date_updated: 2026-07-28T07:06:26Z
has_accepted_license: '1'
intvolume: ' 6'
issue: '5'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
pmid: 1
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: Cell Genomics
publication_identifier:
eissn:
- 2666-979X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
link:
- description: News on ISTA website
relation: press_release
url: https://ista.ac.at/en/news/big-data-and-human-height/
record:
- id: '22258'
relation: dissertation_contains
status: public
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Joint modeling of whole-genome sequencing data for human height via approximate
message passing
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '22614'
abstract:
- lang: eng
text: "This study presents a sustainable and non-destructive recycling strategy
for synthetic fibres, specifically polyethylene terephthalate (PET), polyamide
6 (PA6), polyamide 66 (PA66), and elastane (EL). Two deep eutectic solvents (DESs)
were synthesised, enabling selective dissolution of EL without degrading the surrounding
polymer matrices. The dissolved EL phase was subsequently recovered by centrifugation,
demonstrating its potential for material reuse. Comprehensive characterisation
confirmed that the process preserves fibre integrity. SEM imaging revealed no
detectable changes in fibre morphology, while DSC and TGA analyses indicated that
the thermal properties of both EL and synthetic polymers were preserved. ATR-FTIR
spectroscopy verified the absence of measurable chemical modifications. Mechanical
testing showed that solvent treatment did not compromise the tensile performance
of PET, PA6, or PA66 fibres, confirming their suitability for fibre-to-fibre recycling.\r\nThe
recovered fibres were successfully re-extruded into continuous filaments, confirming
their melt processability and enabling potential applications in textile yarns,
technical fibres, nonwovens, and polymer components via melt-spinning or moulding.
These results demonstrate that the proposed DES-based method offers a promising
approach for the circular recycling of complex fibre blends and provides a viable
pathway towards industrial implementation."
acknowledgement: "The financial support by the Austrian Federal Ministry of Economy,
Energy and Tourism, the National Foundation for Research, Technology and Development
and the Christian Doppler Research Association is gratefully acknowledged. The authors
acknowledge “Open Access Funding by TU Wien” for financial support through its Open
Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER
MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input
throughout the development of this study."
article_number: '115736'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Nika
full_name: Depope, Nika
last_name: Depope
- first_name: Al
full_name: Depope, Al
id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
last_name: Depope
- first_name: Vasiliki Maria
full_name: Archodoulaki, Vasiliki Maria
last_name: Archodoulaki
- first_name: Alicja
full_name: Dabrowska, Alicja
last_name: Dabrowska
- first_name: Bernhard
full_name: Lendl, Bernhard
last_name: Lendl
- first_name: Andreas
full_name: Mautner, Andreas
last_name: Mautner
- first_name: Wolfgang
full_name: Ipsmiller, Wolfgang
last_name: Ipsmiller
- first_name: Andreas
full_name: Bartl, Andreas
last_name: Bartl
citation:
ama: Depope N, Depope A, Archodoulaki VM, et al. DES formulation for recycling synthetic
fibre textile waste containing elastane. Waste Management. 2026;224. doi:10.1016/j.wasman.2026.115736
apa: Depope, N., Depope, A., Archodoulaki, V. M., Dabrowska, A., Lendl, B., Mautner,
A., … Bartl, A. (2026). DES formulation for recycling synthetic fibre textile
waste containing elastane. Waste Management. Elsevier. https://doi.org/10.1016/j.wasman.2026.115736
chicago: Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Alicja Dabrowska,
Bernhard Lendl, Andreas Mautner, Wolfgang Ipsmiller, and Andreas Bartl. “DES Formulation
for Recycling Synthetic Fibre Textile Waste Containing Elastane.” Waste Management.
Elsevier, 2026. https://doi.org/10.1016/j.wasman.2026.115736.
ieee: N. Depope et al., “DES formulation for recycling synthetic fibre textile
waste containing elastane,” Waste Management, vol. 224. Elsevier, 2026.
ista: Depope N, Depope A, Archodoulaki VM, Dabrowska A, Lendl B, Mautner A, Ipsmiller
W, Bartl A. 2026. DES formulation for recycling synthetic fibre textile waste
containing elastane. Waste Management. 224, 115736.
mla: Depope, Nika, et al. “DES Formulation for Recycling Synthetic Fibre Textile
Waste Containing Elastane.” Waste Management, vol. 224, 115736, Elsevier,
2026, doi:10.1016/j.wasman.2026.115736.
short: N. Depope, A. Depope, V.M. Archodoulaki, A. Dabrowska, B. Lendl, A. Mautner,
W. Ipsmiller, A. Bartl, Waste Management 224 (2026).
das_tickbox: '1'
dataavailabilitystatement: Data will be made available on request.
date_created: 2026-08-02T22:01:51Z
date_published: 2026-07-15T00:00:00Z
date_updated: 2026-08-03T06:35:45Z
day: '15'
ddc:
- '540'
department:
- _id: MaRo
doi: 10.1016/j.wasman.2026.115736
external_id:
pmid:
- '42456595'
has_accepted_license: '1'
intvolume: ' 224'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.wasman.2026.115736
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: Waste Management
publication_identifier:
eissn:
- 1879-2456
issn:
- 0956-053X
publication_status: epub_ahead
publisher: Elsevier
quality_controlled: '1'
researchdata_availability: upon request
scopus_import: '1'
status: public
supplementarymaterial: yes
title: DES formulation for recycling synthetic fibre textile waste containing elastane
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: 224
year: '2026'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '21987'
abstract:
- lang: eng
text: 'We introduce JODIE, a genetic joint modeling approach that estimates how
DNA loci influence human traits by partitioning genetic effects into four components:
direct effects (from a child’s alleles), indirect maternal and paternal effects
(from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental
transmission of alleles), while uniquely accounting for assortative mating. We
analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian
Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood
educational test scores. We find direct effects to be the largest contributor
to trait variation, but combined, indirect parental and PofO effects are similarly
substantial. We support our results by within-family genome-wide association testing
and identify 276 independently associated DNA regions with a complex interplay
between direct, indirect, and PofO effects. By joint modeling, we show that direct,
indirect, and PofO effects collectively shape human phenotypic variation across
loci genome-wide.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik, Peter Visscher, and members of the Robinson
group at ISTA for their comments, which improved this manuscript. This work was
funded by an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181) and by core funding
from the Institute of Science and Technology Austria.\r\nThe Norwegian Mother, Father,
and Child Cohort Study is supported by the Norwegian Ministry of Health and Care
Services and the Ministry of Education and Research. We are grateful to all the
participating families in Norway who take part in this on-going cohort study. We
thank the Norwegian Institute of Public Health (NIPH) for generating high-quality
genomic data. The research is part of the HARVEST collaboration, supported by the
Research Council of Norway (#229624). We also thank the NORMENT Center for providing
genotype data, funded by the Research Council of Norway (#223273), South East Norway
Health Authorities, and Stiftelsen Kristian Gerhard Jebsen, and in collaboration
with deCODE Genetics. We further thank the Center for Diabetes Research, the University
of Bergen for providing genotype data funded by the ERC AdG project SELECTionPREDISPOSED,
Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council
of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western
Norway Health Authorities. The MoBa work was performed on the TSD (Tjeneste for
Sensitive Data) facilities, owned by the University of Oslo, operated and developed
by the TSD service group at the University of Oslo, IT Department (USIT, tsd-drift@usit.uio.no).
E.Y. is supported by the European Union (grant numbers 101045526 and 101073237)
and the Research Council of Norway (grant numbers 336078, 288083, and 331640).\r\nWe
would like to acknowledge the participants and investigators of the Generation Scotland
Cohort study. Generation Scotland received core support from the Chief Scientist
Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish
Funding Council (HR03006). Genotyping and methylation typing of the GS:SFHS samples
was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research
Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK
and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and
Depression Longitudinally” [STRADL] ref. 104036/Z/14/Z).\r\nWe would like to thank
and acknowledge the participants and investigators of the Estonian Biobank (EstBB)
study. The research was conducted using the Estonian Center of Genomics/Roadmap
II funded by the Estonian Research Council (project number TT17).\r\nNorwegian analyses
were performed on resources provided by Sigma2 - the National Infrastructure for
High-Performance Computing and Data Storage in Norway. Estonian Data analysis was
carried out in the High-Performance Computing Center cloud provided by University
of Tartu. Analysis of the Generation Scotland data and the summary statistics obtained
from the other analyses was conducted at IST Austria and is supported by the Scientific
Service Units (SSU) of IST Austria through resources provided by Scientific Computing
(SciComp)."
article_number: '101277'
article_processing_charge: Yes
article_type: original
author:
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Laura
full_name: Hegemann, Laura
last_name: Hegemann
- first_name: Robin J.
full_name: Hofmeister, Robin J.
last_name: Hofmeister
- first_name: Elizabeth C.
full_name: Corfield, Elizabeth C.
last_name: Corfield
- first_name: Mahdi
full_name: Mahmoudi, Mahdi
last_name: Mahmoudi
- first_name: Olivier
full_name: Delaneau, Olivier
last_name: Delaneau
- first_name: Ole A.
full_name: Andreassen, Ole A.
last_name: Andreassen
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
- first_name: Eivind
full_name: Ystrom, Eivind
last_name: Ystrom
- first_name: Alexandra
full_name: Havdahl, Alexandra
last_name: Havdahl
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Krätschmer I, Hegemann L, Hofmeister RJ, et al. Separating direct, indirect,
and parent-of-origin genetic effects in the human population. Cell Genomics.
2026;6(7). doi:10.1016/j.xgen.2026.101277
apa: Krätschmer, I., Hegemann, L., Hofmeister, R. J., Corfield, E. C., Mahmoudi,
M., Delaneau, O., … Robinson, M. R. (2026). Separating direct, indirect, and parent-of-origin
genetic effects in the human population. Cell Genomics. Elsevier. https://doi.org/10.1016/j.xgen.2026.101277
chicago: Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield,
Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct,
Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” Cell
Genomics. Elsevier, 2026. https://doi.org/10.1016/j.xgen.2026.101277.
ieee: I. Krätschmer et al., “Separating direct, indirect, and parent-of-origin
genetic effects in the human population,” Cell Genomics, vol. 6, no. 7.
Elsevier, 2026.
ista: Krätschmer I, Hegemann L, Hofmeister RJ, Corfield EC, Mahmoudi M, Delaneau
O, Andreassen OA, Campbell A, Hayward C, Marioni RE, Ystrom E, Havdahl A, Robinson
MR. 2026. Separating direct, indirect, and parent-of-origin genetic effects in
the human population. Cell Genomics. 6(7), 101277.
mla: Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin
Genetic Effects in the Human Population.” Cell Genomics, vol. 6, no. 7,
101277, Elsevier, 2026, doi:10.1016/j.xgen.2026.101277.
short: I. Krätschmer, L. Hegemann, R.J. Hofmeister, E.C. Corfield, M. Mahmoudi,
O. Delaneau, O.A. Andreassen, A. Campbell, C. Hayward, R.E. Marioni, E. Ystrom,
A. Havdahl, M.R. Robinson, Cell Genomics 6 (2026).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "Information on how to access the MoBaPsychGen post-imputation
QC data are available here: https://www.fhi.no/en/me/the-psychgen-centre-for-genetic-epidemiology-and-mental-health/access-to-genetic-data-after-quality-control-by-the-mobapsychgen-pipeline-v/.\r\nEstonian
Biobank data (https://genomics.ut.ee/en/content/estonian-biobank) were used in this
project. For access to be granted to the Estonian Biobank genotypic and corresponding
phenotypic data, a preliminary application must be presented to the oversight committee,
who must first approve the project. Ethics permission must then be obtained from
the Estonian Committee on Bioethics and Human Research. Finally, a full project
must be submitted and approved by the Estonian Biobank.\r\nAccess to the Generation
Scotland data is available with appropriate permission from the Generation Scotland
Access Committee. Applications should be made to access@generationscotland.org (https://genscot.ed.ac.uk/).\r\nThe
code for JODIE developed in this work is open source and is publicly available on
zenodo (https://doi.org/10.5281/zenodo.19593928) and GitHub (https://github.com/medical-genomics-group/JODIE).\r\nHaplotype
Reference Consortium Release 1.1 data (https://ega-archive.org/datasets/EGAD00001002729)
are available by application to a Data Access Committee (DAC) of the Wellcome Trust
Sanger Institute.\r\nThe Common Metabolic Diseases Atlas can be accessed here: https://cmdga.org."
date_created: 2026-06-10T07:39:08Z
date_published: 2026-07-08T00:00:00Z
date_updated: 2026-08-04T09:34:08Z
day: '08'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.xgen.2026.101277
external_id:
pmid:
- '40909755'
file:
- access_level: open_access
checksum: f896b510480d2d4e4a7fd46c2e2761f4
content_type: application/pdf
creator: dernst
date_created: 2026-07-28T07:24:50Z
date_updated: 2026-07-28T07:24:50Z
file_id: '22597'
file_name: 2026_CellGenomics_Kraetschmer.pdf
file_size: 3679297
relation: main_file
success: 1
file_date_updated: 2026-07-28T07:24:50Z
has_accepted_license: '1'
intvolume: ' 6'
issue: '7'
keyword:
- direct genetic effects
- DGE
- indirect genetic effects
- IGE
- parent-of-origin effects
- phenotypic variation
- assortative mating
- within-family GWAS
- MoBa
- EstBB
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: Cell Genomics
publication_identifier:
eissn:
- 2666-979X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
link:
- description: News on ISTA website
relation: press_release
url: https://ista.ac.at/en/news/human-traits-beyond-inherited-genes/
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Separating direct, indirect, and parent-of-origin genetic effects in the human
population
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 6
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18754'
abstract:
- lang: eng
text: 'Exploring the molecular correlates of metabolic health measures may identify
their shared and unique biological processes and pathways. Molecular proxies of
these traits may also provide a more objective approach to their measurement.
Here, DNA methylation (DNAm) data were used in epigenome-wide association studies
(EWASs) and for training epigenetic scores (EpiScores) of six metabolic traits:
body mass index (BMI), body fat percentage, waist-hip ratio, and blood-based measures
of glucose, high-density lipoprotein cholesterol, and total cholesterol in >17,000
volunteers from the Generation Scotland (GS) cohort. We observed a maximum of
12,033 significant findings (p < 3.6 × 10−8) for BMI in a marginal linear regression
EWAS. By contrast, a joint and conditional Bayesian penalized regression approach
yielded 27 high-confidence associations with BMI. EpiScores trained in GS performed
well in both Scottish and Singaporean test cohorts (Lothian Birth Cohort 1936
[LBC1936] and Health for Life in Singapore [HELIOS]). The EpiScores for BMI and
total cholesterol performed best in HELIOS, explaining 20.8% and 7.1% of the variance
in the measured traits, respectively. The corresponding results in LBC1936 were
14.4% and 3.2%, respectively. Differences were observed in HELIOS for body fat,
where the EpiScore explained ∼9% of the variance in Chinese and Malay -subgroups
but ∼3% in the Indian subgroup. The EpiScores also correlated with cognitive function
in LBC1936 (standardized βrange: 0.08–0.12, false discovery rate p [pFDR] < 0.05).
Accounting for the correlation structure across the methylome can vastly affect
the number of lead findings in EWASs. The EpiScores of metabolic traits are broadly
applicable across populations and can reflect differences in cognition.'
article_processing_charge: No
article_type: original
author:
- first_name: Hannah M.
full_name: Smith, Hannah M.
last_name: Smith
- first_name: Hong Kiat
full_name: Ng, Hong Kiat
last_name: Ng
- first_name: Joanna E.
full_name: Moodie, Joanna E.
last_name: Moodie
- first_name: Danni A.
full_name: Gadd, Danni A.
last_name: Gadd
- first_name: Daniel L.
full_name: Mccartney, Daniel L.
last_name: Mccartney
- first_name: Elena
full_name: Bernabeu, Elena
last_name: Bernabeu
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Paul
full_name: Redmond, Paul
last_name: Redmond
- first_name: Adele
full_name: Taylor, Adele
last_name: Taylor
- first_name: Danielle
full_name: Page, Danielle
last_name: Page
- first_name: Janie
full_name: Corley, Janie
last_name: Corley
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Darwin
full_name: Tay, Darwin
last_name: Tay
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- 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: John C.
full_name: Chambers, John C.
last_name: Chambers
- first_name: Marie
full_name: Loh, Marie
last_name: Loh
- first_name: Simon R.
full_name: Cox, Simon R.
last_name: Cox
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
citation:
ama: Smith HM, Ng HK, Moodie JE, et al. DNA methylation-based predictors of metabolic
traits in Scottish and Singaporean cohorts. American Journal of Human Genetics.
2025;112(1):106-115. doi:10.1016/j.ajhg.2024.11.012
apa: Smith, H. M., Ng, H. K., Moodie, J. E., Gadd, D. A., Mccartney, D. L., Bernabeu,
E., … Hillary, R. F. (2025). DNA methylation-based predictors of metabolic traits
in Scottish and Singaporean cohorts. American Journal of Human Genetics.
Elsevier. https://doi.org/10.1016/j.ajhg.2024.11.012
chicago: Smith, Hannah M., Hong Kiat Ng, Joanna E. Moodie, Danni A. Gadd, Daniel
L. Mccartney, Elena Bernabeu, Archie Campbell, et al. “DNA Methylation-Based Predictors
of Metabolic Traits in Scottish and Singaporean Cohorts.” American Journal
of Human Genetics. Elsevier, 2025. https://doi.org/10.1016/j.ajhg.2024.11.012.
ieee: H. M. Smith et al., “DNA methylation-based predictors of metabolic
traits in Scottish and Singaporean cohorts,” American Journal of Human Genetics,
vol. 112, no. 1. Elsevier, pp. 106–115, 2025.
ista: Smith HM, Ng HK, Moodie JE, Gadd DA, Mccartney DL, Bernabeu E, Campbell A,
Redmond P, Taylor A, Page D, Corley J, Harris SE, Tay D, Deary IJ, Evans KL, Robinson
MR, Chambers JC, Loh M, Cox SR, Marioni RE, Hillary RF. 2025. DNA methylation-based
predictors of metabolic traits in Scottish and Singaporean cohorts. American Journal
of Human Genetics. 112(1), 106–115.
mla: Smith, Hannah M., et al. “DNA Methylation-Based Predictors of Metabolic Traits
in Scottish and Singaporean Cohorts.” American Journal of Human Genetics,
vol. 112, no. 1, Elsevier, 2025, pp. 106–15, doi:10.1016/j.ajhg.2024.11.012.
short: H.M. Smith, H.K. Ng, J.E. Moodie, D.A. Gadd, D.L. Mccartney, E. Bernabeu,
A. Campbell, P. Redmond, A. Taylor, D. Page, J. Corley, S.E. Harris, D. Tay, I.J.
Deary, K.L. Evans, M.R. Robinson, J.C. Chambers, M. Loh, S.R. Cox, R.E. Marioni,
R.F. Hillary, American Journal of Human Genetics 112 (2025) 106–115.
date_created: 2025-01-05T23:01:56Z
date_published: 2025-01-02T00:00:00Z
date_updated: 2025-02-27T12:38:23Z
day: '02'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2024.11.012
external_id:
isi:
- '001412498600001'
pmid:
- '39706196'
file:
- access_level: open_access
checksum: 891d120554f07da2c35d38388c29a690
content_type: application/pdf
creator: dernst
date_created: 2025-01-08T09:26:42Z
date_updated: 2025-01-08T09:26:42Z
file_id: '18776'
file_name: 2025_AJHG_Smith.pdf
file_size: 2266488
relation: main_file
success: 1
file_date_updated: 2025-01-08T09:26:42Z
has_accepted_license: '1'
intvolume: ' 112'
isi: 1
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 106-115
pmid: 1
publication: American Journal of Human Genetics
publication_identifier:
eissn:
- 1537-6605
issn:
- 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/marioni-group/Metabolic_trait
scopus_import: '1'
status: public
title: DNA methylation-based predictors of metabolic traits in Scottish and Singaporean
cohorts
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: 112
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '19023'
abstract:
- lang: eng
text: 'Alcohol consumption is an important risk factor for multiple diseases. It
is typically assessed via self-report, which is open to measurement error through
recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm)
could be used to derive a more objective measure of alcohol consumption by incorporating
information from cytosine-phosphate-guanine (CpG) sites known to be linked to
the trait. Here, we explore the epigenetic architecture of self-reported weekly
units of alcohol consumption in the Generation Scotland study. We first create
a blood-based epigenetic score (EpiScore) of alcohol consumption using elastic
net penalized linear regression. We explore the effect of pre-filtering for CpG
features ahead of elastic net, as well as differential patterns by sex and by
units consumed in the last week relative to an average week. The final EpiScore
was trained on 16,717 individuals and tested in four external cohorts: the Lothian
Birth Cohorts (LBC) of 1921 and 1936, the Sister Study, and the Avon Longitudinal
Study of Parents and Children (total N across studies > 10,000). The maximum Pearson
correlation between the EpiScore and self-reported alcohol consumption within
cohort ranged from 0.41 to 0.53. In LBC1936, higher EpiScore levels had significant
associations with poorer global brain imaging metrics, whereas self-reported alcohol
consumption did not. Finally, we identified two novel CpG loci via a Bayesian
penalized regression epigenome-wide association study of alcohol consumption.
Together, these findings show how DNAm can objectively characterize patterns of
alcohol consumption that associate with brain health, unlike self-reported estimates.'
acknowledgement: 'Generation Scotland: Generation Scotland received core support from
the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6)
and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling
of the Generation Scotland samples were carried out by the Genetics Core Laboratory
at the Edinburgh Clinical Research Facility, Edinburgh, Scotland, and were funded
by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic
Award STratifying Resilience and Depression Longitudinally (STRADL; Reference 104036/Z/14/Z)
and 220857/Z/20/Z. The DNA methylation data assayed for Generation Scotland were
partially funded by a 2018 NARSAD Young Investigator Grant from the Brain & Behavior
Research Foundation (Ref: 27404; awardee: Dr David M Howard) and by a JMAS SIM fellowship
from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley).
Lothian Birth Cohorts: We thank the LBC1921 and LBC1936 participants and team members
who contributed to these studies. The LBC1921 was supported by the UK’s Biotechnology
and Biological Sciences Research Council (BBSRC), The Royal Society, and The Chief
Scientist Office of the Scottish Government. The LBC1936 is supported by the BBSRC,
and the Economic and Social Research Council [BB/W008793/1] (which supports S.E.H.),
Age UK (Disconnected Mind project), the Milton Damerel Trust, the Medical Research
Council (MR/M01311/1), and the University of Edinburgh. Methylation typing of LBC1936
was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot
Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The
University of Edinburgh, and The University of Queensland. Genotyping was funded
by the BBSRC (BB/F019394/1). S.R.C. is supported by a Sir Henry Dale Fellowship
jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z).
ALSPAC: The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z)
and the University of Bristol provide core support for ALSPAC. This publication
is the work of the authors and Matthew Suderman will serve as guarantors for the
contents of this paper. A comprehensive list of grants funding is available on the
ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).
Funding for ALSPAC DNAm measurements was supported by the Wellcome (102215/2/13/2);
the University of Bristol; the UK Economic and Social Research Council (ES/N000498/1);
the UK Medical Research Council (MC_UU_12013/1, MC_UU_12013/2); and the John Templeton
Foundation (60828). MS and PY work within the MRC Integrative Epidemiology Unit
at the University of Bristol, which is supported by the Medical Research Council
(MC_UU_00011/5). Sister Study: This research was supported by the Intramural Research
Program of the National Institutes of Health (Z01-ES049033, Z01-ES049032, Z01-ES044005).
A.D.C. was supported by a Medical Research Council PhD Studentship in Precision
Medicine with funding from the Medical Research Council Doctoral Training Program
and the University of Edinburgh College of Medicine and Veterinary Medicine. R.F.H
is supported by an MRC IEU Fellowship. M.R.R. was funded by Swiss National Science
Foundation Eccellenza Grant PCEGP3-181181 and by core funding from the Institute
of Science and Technology Austria. E.B. and R.E.M. are supported by Alzheimer’s
Society major project grant AS-PG-19b-010. This research was funded in whole, or
in part, by the Wellcome Trust (104036/Z/14/Z, 220857/Z/20/Z, and 221890/Z/20/Z).
For the purpose of open access, the author has applied a CC BY public copyright
licence to any Author Accepted Manuscript version arising from this submission.'
article_number: '14'
article_processing_charge: Yes
article_type: original
author:
- first_name: Elena
full_name: Bernabeu, Elena
last_name: Bernabeu
- first_name: Aleksandra D.
full_name: Chybowska, Aleksandra D.
last_name: Chybowska
- first_name: Jacob K.
full_name: Kresovich, Jacob K.
last_name: Kresovich
- first_name: Matthew
full_name: Suderman, Matthew
last_name: Suderman
- first_name: Daniel L.
full_name: Mccartney, Daniel L.
last_name: Mccartney
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Janie
full_name: Corley, Janie
last_name: Corley
- first_name: Maria Del C.
full_name: Valdés-Hernández, Maria Del C.
last_name: Valdés-Hernández
- first_name: Susana Muñoz
full_name: Maniega, Susana Muñoz
last_name: Maniega
- first_name: Mark E.
full_name: Bastin, Mark E.
last_name: Bastin
- first_name: Joanna M.
full_name: Wardlaw, Joanna M.
last_name: Wardlaw
- first_name: Zongli
full_name: Xu, Zongli
last_name: Xu
- first_name: Dale P.
full_name: Sandler, Dale P.
last_name: Sandler
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Andrew M.
full_name: Mcintosh, Andrew M.
last_name: Mcintosh
- first_name: Jack A.
full_name: Taylor, Jack A.
last_name: Taylor
- first_name: Paul
full_name: Yousefi, Paul
last_name: Yousefi
- first_name: Simon R.
full_name: Cox, Simon R.
last_name: Cox
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- 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: Catalina A.
full_name: Vallejos, Catalina A.
last_name: Vallejos
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
citation:
ama: Bernabeu E, Chybowska AD, Kresovich JK, et al. Blood-based epigenome-wide association
study and prediction of alcohol consumption. Clinical Epigenetics. 2025;17.
doi:10.1186/s13148-025-01818-y
apa: Bernabeu, E., Chybowska, A. D., Kresovich, J. K., Suderman, M., Mccartney,
D. L., Hillary, R. F., … Marioni, R. E. (2025). Blood-based epigenome-wide association
study and prediction of alcohol consumption. Clinical Epigenetics. Springer
Nature. https://doi.org/10.1186/s13148-025-01818-y
chicago: Bernabeu, Elena, Aleksandra D. Chybowska, Jacob K. Kresovich, Matthew Suderman,
Daniel L. Mccartney, Robert F. Hillary, Janie Corley, et al. “Blood-Based Epigenome-Wide
Association Study and Prediction of Alcohol Consumption.” Clinical Epigenetics.
Springer Nature, 2025. https://doi.org/10.1186/s13148-025-01818-y.
ieee: E. Bernabeu et al., “Blood-based epigenome-wide association study and
prediction of alcohol consumption,” Clinical Epigenetics, vol. 17. Springer
Nature, 2025.
ista: Bernabeu E, Chybowska AD, Kresovich JK, Suderman M, Mccartney DL, Hillary
RF, Corley J, Valdés-Hernández MDC, Maniega SM, Bastin ME, Wardlaw JM, Xu Z, Sandler
DP, Campbell A, Harris SE, Mcintosh AM, Taylor JA, Yousefi P, Cox SR, Evans KL,
Robinson MR, Vallejos CA, Marioni RE. 2025. Blood-based epigenome-wide association
study and prediction of alcohol consumption. Clinical Epigenetics. 17, 14.
mla: Bernabeu, Elena, et al. “Blood-Based Epigenome-Wide Association Study and Prediction
of Alcohol Consumption.” Clinical Epigenetics, vol. 17, 14, Springer Nature,
2025, doi:10.1186/s13148-025-01818-y.
short: E. Bernabeu, A.D. Chybowska, J.K. Kresovich, M. Suderman, D.L. Mccartney,
R.F. Hillary, J. Corley, M.D.C. Valdés-Hernández, S.M. Maniega, M.E. Bastin, J.M.
Wardlaw, Z. Xu, D.P. Sandler, A. Campbell, S.E. Harris, A.M. Mcintosh, J.A. Taylor,
P. Yousefi, S.R. Cox, K.L. Evans, M.R. Robinson, C.A. Vallejos, R.E. Marioni,
Clinical Epigenetics 17 (2025).
date_created: 2025-02-16T23:02:33Z
date_published: 2025-01-25T00:00:00Z
date_updated: 2025-09-30T10:31:08Z
day: '25'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13148-025-01818-y
external_id:
isi:
- '001406495600001'
pmid:
- '39863868'
file:
- access_level: open_access
checksum: c32511f2d09e6c164116793e784944b8
content_type: application/pdf
creator: dernst
date_created: 2025-02-17T08:44:23Z
date_updated: 2025-02-17T08:44:23Z
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file_name: 2025_ClinicalEpigenetics_Bernabeu.pdf
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isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: Clinical Epigenetics
publication_identifier:
eissn:
- 1868-7083
issn:
- 1868-7075
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Blood-based epigenome-wide association study and prediction of alcohol consumption
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 17
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20479'
abstract:
- lang: eng
text: 'Genetic variation is generally regarded as a prerequisite for evolution.
In principle, epigenetic information inherited independently of DNA sequence can
also enable evolution, but whether this occurs in natural populations is unknown.
Here we show that single-nucleotide and epigenetic gene body DNA methylation (gbM)
polymorphisms explain comparable amounts of expression variance in Arabidopsis
thaliana populations. We genetically demonstrate that gbM regulates
transcription, and we identify and genetically validate many associations between
gbM polymorphism and the variation of complex traits: fitness under heat and drought,
flowering time and accumulation of diverse minerals. Epigenome-wide association
studies pinpoint trait-relevant genes with greater precision than genetic association
analyses, probably due to reduced linkage disequilibrium between gbM variants.
Finally, we identify numerous associations between gbM epialleles and diverse
environmental conditions in native habitats, suggesting that gbM facilitates adaptation.
Overall, our results indicate that epigenetic methylation variation fundamentally
shapes phenotypic diversity in a natural population.'
acknowledgement: We thank P. Baduel and V. Colot for sharing SV data, A. Muyle for
gbM conservation data and X. Feng, C. Dean, E. Coen and Zilberman lab members for
constructive comments on the paper. This work was supported by a European Research
Council grant (725746) to D.Z., LUMS Startup grant (STG-188) to Z.S. and US National
Science Foundation grant (MCB-2334561) to H.R. This study would not have been possible
without Arabidopsis 1001 genome, methylome and transcriptome resources. Open access
funding provided by Institute of Science and Technology (IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Zaigham
full_name: Shahzad, Zaigham
last_name: Shahzad
- first_name: Elizabeth
full_name: Hollwey, Elizabeth
id: b8c4f54b-e484-11eb-8fdc-a54df64ef6dd
last_name: Hollwey
- first_name: Jonathan D.
full_name: Moore, Jonathan D.
last_name: Moore
- first_name: Jaemyung
full_name: Choi, Jaemyung
last_name: Choi
- first_name: Gaëlle
full_name: Cassin-Ross, Gaëlle
last_name: Cassin-Ross
- first_name: Hatem
full_name: Rouached, Hatem
last_name: Rouached
- 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: Daniel
full_name: Zilberman, Daniel
id: 6973db13-dd5f-11ea-814e-b3e5455e9ed1
last_name: Zilberman
orcid: 0000-0002-0123-8649
citation:
ama: Shahzad Z, Hollwey E, Moore JD, et al. Gene body methylation regulates gene
expression and mediates phenotypic diversity in natural Arabidopsis populations.
Nature Plants. 2025;11:2084-2099. doi:10.1038/s41477-025-02108-4
apa: Shahzad, Z., Hollwey, E., Moore, J. D., Choi, J., Cassin-Ross, G., Rouached,
H., … Zilberman, D. (2025). Gene body methylation regulates gene expression and
mediates phenotypic diversity in natural Arabidopsis populations. Nature Plants.
Springer Nature. https://doi.org/10.1038/s41477-025-02108-4
chicago: Shahzad, Zaigham, Elizabeth Hollwey, Jonathan D. Moore, Jaemyung Choi,
Gaëlle Cassin-Ross, Hatem Rouached, Matthew Richard Robinson, and Daniel Zilberman.
“Gene Body Methylation Regulates Gene Expression and Mediates Phenotypic Diversity
in Natural Arabidopsis Populations.” Nature Plants. Springer Nature, 2025.
https://doi.org/10.1038/s41477-025-02108-4.
ieee: Z. Shahzad et al., “Gene body methylation regulates gene expression
and mediates phenotypic diversity in natural Arabidopsis populations,” Nature
Plants, vol. 11. Springer Nature, pp. 2084–2099, 2025.
ista: Shahzad Z, Hollwey E, Moore JD, Choi J, Cassin-Ross G, Rouached H, Robinson
MR, Zilberman D. 2025. Gene body methylation regulates gene expression and mediates
phenotypic diversity in natural Arabidopsis populations. Nature Plants. 11, 2084–2099.
mla: Shahzad, Zaigham, et al. “Gene Body Methylation Regulates Gene Expression and
Mediates Phenotypic Diversity in Natural Arabidopsis Populations.” Nature Plants,
vol. 11, Springer Nature, 2025, pp. 2084–99, doi:10.1038/s41477-025-02108-4.
short: Z. Shahzad, E. Hollwey, J.D. Moore, J. Choi, G. Cassin-Ross, H. Rouached,
M.R. Robinson, D. Zilberman, Nature Plants 11 (2025) 2084–2099.
corr_author: '1'
date_created: 2025-10-16T13:11:21Z
date_published: 2025-09-12T00:00:00Z
date_updated: 2025-12-01T14:59:10Z
day: '12'
ddc:
- '580'
department:
- _id: MaRo
- _id: DaZi
doi: 10.1038/s41477-025-02108-4
ec_funded: 1
external_id:
isi:
- '001570197600001'
pmid:
- '40940427'
file:
- access_level: open_access
checksum: 6a3f6cffdc934b8a2015c3c247f5a92a
content_type: application/pdf
creator: dernst
date_created: 2025-10-23T11:13:58Z
date_updated: 2025-10-23T11:13:58Z
file_id: '20524'
file_name: 2025_NaturePlants_Shahzad.pdf
file_size: 7746662
relation: main_file
success: 1
file_date_updated: 2025-10-23T11:13:58Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 2084-2099
pmid: 1
project:
- _id: 62935a00-2b32-11ec-9570-eff30fa39068
call_identifier: H2020
grant_number: '725746'
name: Quantitative analysis of DNA methylation maintenance with chromatin
publication: Nature Plants
publication_identifier:
issn:
- 2055-0278
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Gene body methylation regulates gene expression and mediates phenotypic diversity
in natural Arabidopsis populations
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: 11
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20491'
abstract:
- lang: eng
text: Global fibre production has expanded rapidly, with polyester and cotton dominating,
significantly contributing to textile waste and increasing demand for sustainable
solutions. This study presents innovative method to recycle polyester/cotton (PET/CO)
blends using hydrophobic deep eutectic solvents (DESs), eliminating the need for
toxic chemicals while achieving high dissolution yields. PET was completely dissolved
within 5 min, substantially outperforming state-of-the-art methods and facilitating
the efficient and selective recovery of both components, PET (97%) and CO (100%).
SEM imaging confirmed no morphological changes in cotton fibres after treatment.
The thermal stability of the recovered materials was validated using DSC and TGA
analyses, while ATR-FTIR spectroscopy indicated no chemical changes. Mechanical
testing confirmed recovered cotton’s tenacity and elongation are within expected
ranges despite showing a decrease of 28% in tenacity and 34% in elongation. Hence,
the proposed process provides an efficient and sustainable recycling solution
for PET/CO blends, retaining both polymers in a condition similar to virgin materials
used in textile manufacturing with minimal processing time.
acknowledgement: "This study was conducted at the Josef Ressel Centre for Recovery
Strategies of Textiles which is funded by the Christian Doppler Research Society
on behalf of the Austrian Federal Ministry of Labor and Economic Affairs and the
National Foundation for Research, Technology. The authors acknowledge “Open Access
Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial
thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger
& Co GmbH for their material support and valuable input throughout the development
of this study."
article_number: '115177'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Nika
full_name: Depope, Nika
last_name: Depope
- first_name: Al
full_name: Depope, Al
id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
last_name: Depope
- first_name: Vasiliki Maria
full_name: Archodoulaki, Vasiliki Maria
last_name: Archodoulaki
- first_name: Wolfgang
full_name: Ipsmiller, Wolfgang
last_name: Ipsmiller
- first_name: Andreas
full_name: Bartl, Andreas
last_name: Bartl
citation:
ama: Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. Deep eutectic solvent
as a solution for polyester/cotton textile recycling. Waste Management.
2025;208. doi:10.1016/j.wasman.2025.115177
apa: Depope, N., Depope, A., Archodoulaki, V. M., Ipsmiller, W., & Bartl, A.
(2025). Deep eutectic solvent as a solution for polyester/cotton textile recycling.
Waste Management. Elsevier. https://doi.org/10.1016/j.wasman.2025.115177
chicago: Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Wolfgang Ipsmiller,
and Andreas Bartl. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile
Recycling.” Waste Management. Elsevier, 2025. https://doi.org/10.1016/j.wasman.2025.115177.
ieee: N. Depope, A. Depope, V. M. Archodoulaki, W. Ipsmiller, and A. Bartl, “Deep
eutectic solvent as a solution for polyester/cotton textile recycling,” Waste
Management, vol. 208. Elsevier, 2025.
ista: Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. 2025. Deep eutectic
solvent as a solution for polyester/cotton textile recycling. Waste Management.
208, 115177.
mla: Depope, Nika, et al. “Deep Eutectic Solvent as a Solution for Polyester/Cotton
Textile Recycling.” Waste Management, vol. 208, 115177, Elsevier, 2025,
doi:10.1016/j.wasman.2025.115177.
short: N. Depope, A. Depope, V.M. Archodoulaki, W. Ipsmiller, A. Bartl, Waste Management
208 (2025).
date_created: 2025-10-19T22:01:31Z
date_published: 2025-11-01T00:00:00Z
date_updated: 2025-12-01T12:58:17Z
day: '01'
ddc:
- '572'
department:
- _id: MaRo
doi: 10.1016/j.wasman.2025.115177
external_id:
isi:
- '001594629200003'
pmid:
- '41066876'
file:
- access_level: open_access
checksum: c232aae0ef7ed653813a835013f25bae
content_type: application/pdf
creator: dernst
date_created: 2025-10-20T10:57:36Z
date_updated: 2025-10-20T10:57:36Z
file_id: '20501'
file_name: 2025_WasteMgmt_Depope.pdf
file_size: 4511527
relation: main_file
success: 1
file_date_updated: 2025-10-20T10:57:36Z
has_accepted_license: '1'
intvolume: ' 208'
isi: 1
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
pmid: 1
publication: Waste Management
publication_identifier:
eissn:
- 1879-2456
issn:
- 0956-053X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Deep eutectic solvent as a solution for polyester/cotton textile recycling
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: 208
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '20816'
abstract:
- lang: eng
text: "Background: DNA methylation (DNAm) can regulate gene expression, and its
genome-wide patterns (epigenetic scores or EpiScores) can act as biomarkers for
complex traits. The relative stability of methylation profiles may enable better
assessment of chronic exposures compared to single time-point protein measures.
We present the first large-scale epigenetic study of the highly-abundant serum
proteome measured via ultra-high throughput mass spectrometry in 14,671 samples
from the Generation Scotland cohort. We further demonstrate the first large-scale
comparison of protein EpiScores and their respective proteins as predictors of
incident cardiovascular disease.\r\n\r\nResults: Marginal epigenome-wide association
models, adjusting for age, sex, measurement batch, estimated white cell proportions,
BMI, smoking and methylation principal components, reveal 15,855 significant CpG
– protein associations across 125 of 133 proteins PBonferroni < 2.71 × 10-10.
Bayesian epigenome-wide association studies of the same 133 proteins reveal 697
CpG-Protein associations (posterior inclusion probability > 0.95). 112 protein
EpiScores correlate significantly with their respective protein in a holdout test-set.
Of these, sixteen associate significantly with incident all-cause cardiovascular
disease (Nevents=191) compared to one measured protein.\r\n\r\nConclusions: We
highlight a complex interplay between the blood-based methylome and proteome.
Importantly, we show that protein EpiScores correlate with measured proteins and
demonstrate that the, as-yet understudied, high-abundance proteome may yield clinically
relevant biomarkers. The protein EpiScores demonstrate more significant associations
with cardiovascular disease than directly measured proteins, suggesting their
potential as clinical biomarkers for monitoring or predicting disease risk. We
suggest that biomarker development could be enhanced by the consideration of protein
EpiScores alongside measured proteins."
acknowledgement: "Generation Scotland received core support from the Chief Scientist
Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish
Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z].
Genotyping of the Generation Scotland samples was carried out by the Genetics Core
Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh,
Scotland and was funded by the Medical Research Council UK and the Wellcome Trust
(Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally”
(STRADL) Reference 104036/Z/14/Z). The DNA methylation profiling and analysis was
supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI:
Prof AM McIntosh) and through funding from NARSAD (Ref: 27404; awardee: Dr DM Howard)
and the Royal College of Physicians of Edinburgh (Sim Fellowship; Awardee: Prof
HC Whalley).\r\nJAR is a University of Edinburgh Clinical Academic Track PhD student,
supported by the Wellcome Trust (319878/Z/24/Z). ADC was supported by a Medical
Research Council PhD Studentship in Precision Medicine with funding from the Medical
Research Council Doctoral Training Program and the University of Edinburgh College
of Medicine and Veterinary Medicine. HMS is a student on the University of Edinburgh
Translational Neuroscience PhD programme funded by the Wellcome Trust (218493/Z/19/Z).
CH was funded by MRC Human Genetics Unit program (QTL in Health and Disease) (grant
U.MC_UU_00007/10). S.R.C. is supported by a Sir Henry Dale Fellowship jointly funded
by the Wellcome Trust and the Royal Society (221890/Z/20/Z). JM and REM were supported
by Alzheimer’s Society project grant AS-PG-19b-010."
article_number: '417'
article_processing_charge: Yes
article_type: original
author:
- first_name: Josephine A.
full_name: Robertson, Josephine A.
last_name: Robertson
- first_name: Jakub
full_name: Bajzik, Jakub
id: b995e25b-8c4b-11ed-a6d8-f71b7bcd6122
last_name: Bajzik
- first_name: Spyros
full_name: Vernardis, Spyros
last_name: Vernardis
- first_name: Aleksandra D.
full_name: Chybowska, Aleksandra D.
last_name: Chybowska
- first_name: Daniel L.
full_name: Mccartney, Daniel L.
last_name: Mccartney
- first_name: Arturas
full_name: Grauslys, Arturas
last_name: Grauslys
- first_name: Jure
full_name: Mur, Jure
last_name: Mur
- first_name: Hannah M.
full_name: Smith, Hannah M.
last_name: Smith
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Camilla
full_name: Drake, Camilla
last_name: Drake
- first_name: Hannah
full_name: Grant, Hannah
last_name: Grant
- first_name: Jamie
full_name: Pearce, Jamie
last_name: Pearce
- first_name: Tom C.
full_name: Russ, Tom C.
last_name: Russ
- first_name: Poppy
full_name: Adkin, Poppy
last_name: Adkin
- first_name: Matthew
full_name: White, Matthew
last_name: White
- first_name: Charles
full_name: Brigden, Charles
last_name: Brigden
- first_name: Christoph B.
full_name: Messner, Christoph B.
last_name: Messner
- first_name: David J.
full_name: Porteous, David J.
last_name: Porteous
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: Simon R.
full_name: Cox, Simon R.
last_name: Cox
- first_name: Aleksej
full_name: Zelezniak, Aleksej
last_name: Zelezniak
- first_name: Markus
full_name: Ralser, Markus
last_name: Ralser
- 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: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
citation:
ama: Robertson JA, Bajzik J, Vernardis S, et al. Methylome-wide association studies
and epigenetic biomarker development for 133 mass spectrometry-assessed circulating
proteins in 14,671 Generation Scotland participants. Genome Biology. 2025;26.
doi:10.1186/s13059-025-03892-0
apa: Robertson, J. A., Bajzik, J., Vernardis, S., Chybowska, A. D., Mccartney, D.
L., Grauslys, A., … Marioni, R. E. (2025). Methylome-wide association studies
and epigenetic biomarker development for 133 mass spectrometry-assessed circulating
proteins in 14,671 Generation Scotland participants. Genome Biology. Springer
Nature. https://doi.org/10.1186/s13059-025-03892-0
chicago: Robertson, Josephine A., Jakub Bajzik, Spyros Vernardis, Aleksandra D.
Chybowska, Daniel L. Mccartney, Arturas Grauslys, Jure Mur, et al. “Methylome-Wide
Association Studies and Epigenetic Biomarker Development for 133 Mass Spectrometry-Assessed
Circulating Proteins in 14,671 Generation Scotland Participants.” Genome Biology.
Springer Nature, 2025. https://doi.org/10.1186/s13059-025-03892-0.
ieee: J. A. Robertson et al., “Methylome-wide association studies and epigenetic
biomarker development for 133 mass spectrometry-assessed circulating proteins
in 14,671 Generation Scotland participants,” Genome Biology, vol. 26. Springer
Nature, 2025.
ista: Robertson JA, Bajzik J, Vernardis S, Chybowska AD, Mccartney DL, Grauslys
A, Mur J, Smith HM, Campbell A, Drake C, Grant H, Pearce J, Russ TC, Adkin P,
White M, Brigden C, Messner CB, Porteous DJ, Hayward C, Cox SR, Zelezniak A, Ralser
M, Robinson MR, Marioni RE. 2025. Methylome-wide association studies and epigenetic
biomarker development for 133 mass spectrometry-assessed circulating proteins
in 14,671 Generation Scotland participants. Genome Biology. 26, 417.
mla: Robertson, Josephine A., et al. “Methylome-Wide Association Studies and Epigenetic
Biomarker Development for 133 Mass Spectrometry-Assessed Circulating Proteins
in 14,671 Generation Scotland Participants.” Genome Biology, vol. 26, 417,
Springer Nature, 2025, doi:10.1186/s13059-025-03892-0.
short: J.A. Robertson, J. Bajzik, S. Vernardis, A.D. Chybowska, D.L. Mccartney,
A. Grauslys, J. Mur, H.M. Smith, A. Campbell, C. Drake, H. Grant, J. Pearce, T.C.
Russ, P. Adkin, M. White, C. Brigden, C.B. Messner, D.J. Porteous, C. Hayward,
S.R. Cox, A. Zelezniak, M. Ralser, M.R. Robinson, R.E. Marioni, Genome Biology
26 (2025).
date_created: 2025-12-14T23:02:04Z
date_published: 2025-12-08T00:00:00Z
date_updated: 2025-12-15T13:19:41Z
day: '08'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13059-025-03892-0
external_id:
pmid:
- '41361833'
file:
- access_level: open_access
checksum: 7c92919af1b5820d01e91e08906a411f
content_type: application/pdf
creator: dernst
date_created: 2025-12-15T13:18:07Z
date_updated: 2025-12-15T13:18:07Z
file_id: '20825'
file_name: 2025_GenomeBiology_Robertson.pdf
file_size: 2206991
relation: main_file
success: 1
file_date_updated: 2025-12-15T13:18:07Z
has_accepted_license: '1'
intvolume: ' 26'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genome Biology
publication_identifier:
eissn:
- 1474-760X
issn:
- 1474-7596
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Methylome-wide association studies and epigenetic biomarker development for
133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland
participants
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: 26
year: '2025'
...
---
_id: '14932'
abstract:
- lang: eng
text: The huge antlers of the extinct Irish elk have invited evolutionary speculation
since Darwin. In the 1970s, Stephen Jay Gould presented the first extensive data
on antler size in the Irish elk and combined these with comparative data from
other deer to test the hypothesis that the gigantic antlers were the outcome of
a positive allometry that constrained large-bodied deer to have proportionally
even larger antlers. He concluded that the Irish elk had antlers as predicted
for its size and interpreted this within his emerging framework of developmental
constraints as an explanatory factor in evolution. Here we reanalyze antler allometry
based on new morphometric data for 57 taxa of the family Cervidae. We also present
a new phylogeny for the Cervidae, which we use for comparative analyses. In contrast
to Gould, we find that the antlers of Irish elk were larger than predicted from
the allometry within the true deer, Cervini, as analyzed by Gould, but follow
the allometry across Cervidae as a whole. After dissecting the discrepancy, we
reject the allometric-constraint hypothesis because, contrary to Gould, we find
no similarity between static and evolutionary allometries, and because we document
extensive non-allometric evolution of antler size across the Cervidae.
acknowledgement: "Open access funding provided by University of Oslo (incl Oslo University
Hospital).\r\nWe thank Adrian Lister, Louis Tomsett, Roberto Portela Miguez and
Roula Pappa (NHMUK), Brian O'Toole and Eileen Westwig (AMNH), Daniela Kalthoff (NHRM),
Alexander Bibl and Zachos Frank (NHMW), Darrin Lunde and John Ososky (NMNH), Matthew
Parkes and Nigel Monaghan (NMI), Elizabetta Cioppi and Luca Bellucci (IGF), and
Yoshihiro Tanaka and Hiroyuki Taruno (OMNH), who helped us in obtaining the museum
data, and a special thanks to Jørgen Sikkeland (NTNU NHM) for assistance in obtaining
the ontogenetic data for the red deer. We thank Olja Toljagic and Kjetil L. Voje
for discussions, Ayumu Tsuboi for assistance with data collection, and Jean-Michel
Gaillard and the anonymous reviewers for comments on the manuscript. We thank the
Centre of Advanced Study (CAS) at the Norwegian Academy of Sciences and Letters
for hosting us during the academic year of 2019/2020 when much of the analysis and
writing were done. MT was funded by JSPS Research Fellowship for Young Scientists
(201603238)."
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Masahito
full_name: Tsuboi, Masahito
last_name: Tsuboi
- first_name: Bjørn Tore
full_name: Kopperud, Bjørn Tore
last_name: Kopperud
- first_name: Michael
full_name: Matschiner, Michael
last_name: Matschiner
- first_name: Mark
full_name: Grabowski, Mark
last_name: Grabowski
- first_name: Chrsitine
full_name: Syrowatka, Chrsitine
id: 205ffb76-7fe7-11eb-aa17-958bd11b99ad
last_name: Syrowatka
- first_name: Christophe
full_name: Pélabon, Christophe
last_name: Pélabon
- first_name: Thomas F.
full_name: Hansen, Thomas F.
last_name: Hansen
citation:
ama: Tsuboi M, Kopperud BT, Matschiner M, et al. Antler allometry, the Irish elk
and Gould revisited. Evolutionary Biology. 2024;51:149-165. doi:10.1007/s11692-023-09624-1
apa: Tsuboi, M., Kopperud, B. T., Matschiner, M., Grabowski, M., Syrowatka, C.,
Pélabon, C., & Hansen, T. F. (2024). Antler allometry, the Irish elk and Gould
revisited. Evolutionary Biology. Springer Nature. https://doi.org/10.1007/s11692-023-09624-1
chicago: Tsuboi, Masahito, Bjørn Tore Kopperud, Michael Matschiner, Mark Grabowski,
Chrsitine Syrowatka, Christophe Pélabon, and Thomas F. Hansen. “Antler Allometry,
the Irish Elk and Gould Revisited.” Evolutionary Biology. Springer Nature,
2024. https://doi.org/10.1007/s11692-023-09624-1.
ieee: M. Tsuboi et al., “Antler allometry, the Irish elk and Gould revisited,”
Evolutionary Biology, vol. 51. Springer Nature, pp. 149–165, 2024.
ista: Tsuboi M, Kopperud BT, Matschiner M, Grabowski M, Syrowatka C, Pélabon C,
Hansen TF. 2024. Antler allometry, the Irish elk and Gould revisited. Evolutionary
Biology. 51, 149–165.
mla: Tsuboi, Masahito, et al. “Antler Allometry, the Irish Elk and Gould Revisited.”
Evolutionary Biology, vol. 51, Springer Nature, 2024, pp. 149–65, doi:10.1007/s11692-023-09624-1.
short: M. Tsuboi, B.T. Kopperud, M. Matschiner, M. Grabowski, C. Syrowatka, C. Pélabon,
T.F. Hansen, Evolutionary Biology 51 (2024) 149–165.
date_created: 2024-02-04T23:00:53Z
date_published: 2024-03-01T00:00:00Z
date_updated: 2025-09-04T11:54:55Z
day: '01'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1007/s11692-023-09624-1
external_id:
isi:
- '001151285200001'
file:
- access_level: open_access
checksum: 416ab4dac751c443b9de51fb58278ff2
content_type: application/pdf
creator: dernst
date_created: 2024-07-22T11:53:43Z
date_updated: 2024-07-22T11:53:43Z
file_id: '17311'
file_name: 2024_EvolutionaryBio_Tsuboi.pdf
file_size: 1705974
relation: main_file
success: 1
file_date_updated: 2024-07-22T11:53:43Z
has_accepted_license: '1'
intvolume: ' 51'
isi: 1
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 149-165
publication: Evolutionary Biology
publication_identifier:
eissn:
- 1934-2845
issn:
- 0071-3260
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Antler allometry, the Irish elk and Gould revisited
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 51
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '17147'
abstract:
- lang: eng
text: Efficient utilization of large-scale biobank data is crucial for inferring
the genetic basis of disease and predicting health outcomes from the DNA. Yet
we lack efficient, accurate methods that scale to data where electronic health
records are linked to whole genome sequence information. To address this issue,
our paper develops a new algorithmic paradigm based on Approximate Message Passing
(AMP), which is specifically tailored for genomic prediction and association testing.
Our method yields comparable out-of-sample prediction accuracy to the state of
the art on UK Biobank traits, whilst dramatically improving computational complexity,
with a 8x-speed up in the run time. In addition, AMP theory provides a joint association
testing framework, which outperforms the currently used REGENIE method, in roughly
a third of the compute time. This first, truly large-scale application of the
AMP framework lays the foundations for a far wider range of statistical analyses
for hundreds of millions of variables measured on millions of people.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This work was supported by a Lopez-Loreta Prize to MM, an SNSF Eccellenza
Grant to MRR (PCEGP3-181181), and core funding from ISTA. The authors thank Philip
Schniter, Matthew Stephens and Pragya Sur for valuable suggestions on an early version
of the work. The authors acknowledge the participants and investigators of the UK
Biobank study. High-performance\r\ncomputing was supported by the Scientific Service
Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp)."
article_processing_charge: No
author:
- first_name: Al
full_name: Depope, Al
id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
last_name: Depope
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: 'Depope A, Mondelli M, Robinson MR. Inference of genetic effects via approximate
message passing. In: 2024 IEEE International Conference on Acoustics, Speech,
and Signal Processing. IEEE; 2024:13151-13155. doi:10.1109/ICASSP48485.2024.10447198'
apa: 'Depope, A., Mondelli, M., & Robinson, M. R. (2024). Inference of genetic
effects via approximate message passing. In 2024 IEEE International Conference
on Acoustics, Speech, and Signal Processing (pp. 13151–13155). Seoul, Korea:
IEEE. https://doi.org/10.1109/ICASSP48485.2024.10447198'
chicago: Depope, Al, Marco Mondelli, and Matthew Richard Robinson. “Inference of
Genetic Effects via Approximate Message Passing.” In 2024 IEEE International
Conference on Acoustics, Speech, and Signal Processing, 13151–55. IEEE, 2024.
https://doi.org/10.1109/ICASSP48485.2024.10447198.
ieee: A. Depope, M. Mondelli, and M. R. Robinson, “Inference of genetic effects
via approximate message passing,” in 2024 IEEE International Conference on
Acoustics, Speech, and Signal Processing, Seoul, Korea, 2024, pp. 13151–13155.
ista: 'Depope A, Mondelli M, Robinson MR. 2024. Inference of genetic effects via
approximate message passing. 2024 IEEE International Conference on Acoustics,
Speech, and Signal Processing. ICASSP: International Conference on Acoustics,
Speech and Signal Processing, 13151–13155.'
mla: Depope, Al, et al. “Inference of Genetic Effects via Approximate Message Passing.”
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing,
IEEE, 2024, pp. 13151–55, doi:10.1109/ICASSP48485.2024.10447198.
short: A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference
on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.
conference:
end_date: 2024-04-19
location: Seoul, Korea
name: 'ICASSP: International Conference on Acoustics, Speech and Signal Processing'
start_date: 2024-04-14
corr_author: '1'
date_created: 2024-06-16T22:01:07Z
date_published: 2024-04-19T00:00:00Z
date_updated: 2026-07-13T14:57:55Z
day: '19'
department:
- _id: MaMo
- _id: MaRo
doi: 10.1109/ICASSP48485.2024.10447198
external_id:
isi:
- '001396233806078'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=aQYCDxfZV0
month: '04'
oa: 1
oa_version: Submitted Version
page: 13151-13155
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing
publication_identifier:
isbn:
- '9798350344851'
issn:
- 1520-6149
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inference of genetic effects via approximate message passing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
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
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checksum: e9ed4465dfa539ac4c3a8d4d0b6271a1
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'
...
---
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:10.15479/at:ista:18642
apa: Machnik, N. N. (2024). Algorithms for causal learning and comparative analysis
for genomic data. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18642
chicago: Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis
for Genomic Data.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18642.
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. Algorithms for Causal Learning and Comparative Analysis
for Genomic Data. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18642.
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: 2026-07-29T13:02:44Z
day: '11'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:18642
doi_confirm: '1'
file:
- access_level: open_access
checksum: d45e4d170f9a70a1f69b44b99bd058e4
content_type: application/pdf
creator: nmachnik
date_created: 2024-12-11T11:59:54Z
date_updated: 2025-06-12T22:30:02Z
embargo: 2025-06-12
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file_size: 12845009
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creator: nmachnik
date_created: 2024-12-11T11:59:34Z
date_updated: 2025-06-12T22:30:02Z
embargo_to: open_access
file_id: '18650'
file_name: thesis.zip
file_size: 14189810
relation: source_file
file_date_updated: 2025-06-12T22:30:02Z
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: '18648'
relation: part_of_dissertation
status: public
- id: '8707'
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
title: Algorithms for causal learning and comparative analysis for genomic data
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
OA_place: repository
OA_type: free access
_id: '18648'
abstract:
- lang: eng
text: "Statistical causal learning in genomics relies on the instrumental variable
method of\r\nMendelian Randomization (MR). Currently, an overwhelming number of
MR studies\r\npurport to show causal relationships among a wide range of risk
factors and outcomes.\r\nHere, we show that selecting instrument variables from
genome-wide association study\r\nestimates leads to high false discovery rates
for many MR approaches, which can be\r\ngreatly reduced by employing a graphical
inference approach which: (i) explicitly tests\r\ninstrumental variable assumptions;
(ii) distinguishes direct from indirect factors in very\r\nhigh-dimensional data;
(iii) discriminates pleiotropic from trait-specific markers, controlling for LD
genome-wide; (iv) accommodates rare variants and binary outcomes in a\r\nprincipled
way; and (v) identifies potential unobserved latent confounding. For 17 traits\r\nand
8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that\r\nstandard
MR analysis gives an abundance of findings that disappear under stringent\r\nassumption
checks, with many relationships reflecting potential unmeasured confounding. This
implies that mixtures of temporal precedence and potential for reverse-causality\r\nprohibit
understanding the underlying nature of phenotypic and genetic correlations in\r\nbiobank
data. We propose that well-curated longitudinal records are likely needed and\r\nthat
our approach provides a first-step toward robust principled screening for potential\r\ncausal
links.\r\n"
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik and members of the Robinson group \r\nat
ISTA for their comments, which improved this manuscript. This work was funded \r\nby
a research collaboration agreement between Boehringer Ingelheim and the research
\r\ngroup of MRR at the Institute of Science and Technology Austria. Additional
funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by \r\ncore funding from the Institute of Science and Technology Austria. We
would like \r\nto acknowledge the participants and investigators of the UK Biobank
study. High- \r\nperformance computing was supported by the Scientific Service Units
(SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). "
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
- first_name: Seyed Mahdi
full_name: Mahmoudi, Seyed Mahdi
id: b9f6d5ef-7774-11eb-a47f-df2c75c02ee7
last_name: Mahmoudi
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Markus J.
full_name: Bauer, Markus J.
last_name: Bauer
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal
inference for multiple risk factors and diseases from genomics data. bioRxiv.
2024. doi:10.1101/2023.12.06.570392
apa: Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J.,
& Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases
from genomics data. bioRxiv. https://doi.org/10.1101/2023.12.06.570392
chicago: Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer,
Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple
Risk Factors and Diseases from Genomics Data.” BioRxiv, 2024. https://doi.org/10.1101/2023.12.06.570392.
ieee: N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and
M. R. Robinson, “Causal inference for multiple risk factors and diseases from
genomics data,” bioRxiv. 2024.
ista: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2024.
Causal inference for multiple risk factors and diseases from genomics data. bioRxiv,
10.1101/2023.12.06.570392.
mla: Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases
from Genomics Data.” BioRxiv, 2024, doi:10.1101/2023.12.06.570392.
short: N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R.
Robinson, BioRxiv (2024).
corr_author: '1'
date_created: 2024-12-11T10:42:59Z
date_published: 2024-08-10T00:00:00Z
date_updated: 2026-08-16T22:31:07Z
day: '10'
department:
- _id: MaRo
doi: 10.1101/2023.12.06.570392
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2023.12.06.570392
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
- _id: bd936e6f-d553-11ed-ba76-a82299f63e8c
grant_number: '590359'
name: Advanced statistical modelling to facilitate more accurate characterisation
of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis
generation
publication: bioRxiv
publication_status: published
related_material:
record:
- id: '18642'
relation: dissertation_contains
status: public
status: public
title: Causal inference for multiple risk factors and diseases from genomics data
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '12719'
abstract:
- lang: eng
text: "Background\r\nEpigenetic clocks can track both chronological age (cAge) and
biological age (bAge). The latter is typically defined by physiological biomarkers
and risk of adverse health outcomes, including all-cause mortality. As cohort
sample sizes increase, estimates of cAge and bAge become more precise. Here, we
aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving
our understanding of their epigenomic architecture.\r\n\r\nMethods\r\nFirst, we
perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of
chronological age and all-cause mortality. Next, to create a cAge predictor, we
use methylation data from 24,674 participants from the Generation Scotland study,
the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly
available data. In addition, we train a predictor of time to all-cause mortality
as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths).
For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins
and the 8 component parts of GrimAge, one of the current best epigenetic predictors
of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936,
the Framingham Heart Study and the Women’s Health Initiative study).\r\n\r\nResults\r\nThrough
the inclusion of linear and non-linear age-CpG associations from the EWAS, feature
pre-selection in advance of elastic net regression, and a leave-one-cohort-out
(LOCO) cross-validation framework, we obtain cAge prediction with a median absolute
error equal to 2.3 years. Our bAge predictor was found to slightly outperform
GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47
[1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60).
Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide
CpG-age associations.\r\n\r\nConclusions\r\nThe integration of multiple large
datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection
has facilitated improvements to the blood-based epigenetic prediction of biological
and chronological age."
acknowledgement: We are grateful to all the families who took part, the general practitioners,
and the Scottish School of Primary Care for their help in recruiting them and the
whole GS team that includes interviewers, computer and laboratory technicians, clerical
workers, research scientists, volunteers, managers, receptionists, healthcare assistants,
and nurses.
article_number: '12'
article_processing_charge: No
article_type: original
author:
- first_name: Elena
full_name: Bernabeu, Elena
last_name: Bernabeu
- first_name: Daniel L.
full_name: Mccartney, Daniel L.
last_name: Mccartney
- first_name: Danni A.
full_name: Gadd, Danni A.
last_name: Gadd
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Ake T.
full_name: Lu, Ake T.
last_name: Lu
- first_name: Lee
full_name: Murphy, Lee
last_name: Murphy
- first_name: Nicola
full_name: Wrobel, Nicola
last_name: Wrobel
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: David
full_name: Liewald, David
last_name: Liewald
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: Cathie
full_name: Sudlow, Cathie
last_name: Sudlow
- first_name: Simon R.
full_name: Cox, Simon R.
last_name: Cox
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Steve
full_name: Horvath, Steve
last_name: Horvath
- first_name: Andrew M.
full_name: Mcintosh, Andrew M.
last_name: Mcintosh
- 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: Catalina A.
full_name: Vallejos, Catalina A.
last_name: Vallejos
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
citation:
ama: Bernabeu E, Mccartney DL, Gadd DA, et al. Refining epigenetic prediction of
chronological and biological age. Genome Medicine. 2023;15. doi:10.1186/s13073-023-01161-y
apa: Bernabeu, E., Mccartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy,
L., … Marioni, R. E. (2023). Refining epigenetic prediction of chronological and
biological age. Genome Medicine. Springer Nature. https://doi.org/10.1186/s13073-023-01161-y
chicago: Bernabeu, Elena, Daniel L. Mccartney, Danni A. Gadd, Robert F. Hillary,
Ake T. Lu, Lee Murphy, Nicola Wrobel, et al. “Refining Epigenetic Prediction of
Chronological and Biological Age.” Genome Medicine. Springer Nature, 2023.
https://doi.org/10.1186/s13073-023-01161-y.
ieee: E. Bernabeu et al., “Refining epigenetic prediction of chronological
and biological age,” Genome Medicine, vol. 15. Springer Nature, 2023.
ista: Bernabeu E, Mccartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N,
Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath
S, Mcintosh AM, Robinson MR, Vallejos CA, Marioni RE. 2023. Refining epigenetic
prediction of chronological and biological age. Genome Medicine. 15, 12.
mla: Bernabeu, Elena, et al. “Refining Epigenetic Prediction of Chronological and
Biological Age.” Genome Medicine, vol. 15, 12, Springer Nature, 2023, doi:10.1186/s13073-023-01161-y.
short: E. Bernabeu, D.L. Mccartney, D.A. Gadd, R.F. Hillary, A.T. Lu, L. Murphy,
N. Wrobel, A. Campbell, S.E. Harris, D. Liewald, C. Hayward, C. Sudlow, S.R. Cox,
K.L. Evans, S. Horvath, A.M. Mcintosh, M.R. Robinson, C.A. Vallejos, R.E. Marioni,
Genome Medicine 15 (2023).
date_created: 2023-03-12T23:01:02Z
date_published: 2023-02-28T00:00:00Z
date_updated: 2025-04-23T08:49:38Z
day: '28'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13073-023-01161-y
external_id:
isi:
- '000940286600001'
pmid:
- '36855161'
file:
- access_level: open_access
checksum: 833b837910c4db42fb5f0f34125f77a7
content_type: application/pdf
creator: cchlebak
date_created: 2023-03-14T10:29:47Z
date_updated: 2023-03-14T10:29:47Z
file_id: '12722'
file_name: 2023_GenomeMed_Bernabeu.pdf
file_size: 4275987
relation: main_file
success: 1
file_date_updated: 2023-03-14T10:29:47Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genome Medicine
publication_identifier:
eissn:
- 1756-994X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Refining epigenetic prediction of chronological and biological age
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: 15
year: '2023'
...
---
_id: '12758'
abstract:
- lang: eng
text: AlphaFold changed the field of structural biology by achieving three-dimensional
(3D) structure prediction from protein sequence at experimental quality. The astounding
success even led to claims that the protein folding problem is “solved”. However,
protein folding problem is more than just structure prediction from sequence.
Presently, it is unknown if the AlphaFold-triggered revolution could help to solve
other problems related to protein folding. Here we assay the ability of AlphaFold
to predict the impact of single mutations on protein stability (ΔΔG) and function.
To study the question we extracted the pLDDT and metrics from AlphaFold
predictions before and after single mutation in a protein and correlated the predicted
change with the experimentally known ΔΔG values. Additionally, we correlated the
same AlphaFold pLDDT metrics with the impact of a single mutation on structure
using a large scale dataset of single mutations in GFP with the experimentally
assayed levels of fluorescence. We found a very weak or no correlation between
AlphaFold output metrics and change of protein stability or fluorescence. Our
results imply that AlphaFold may not be immediately applied to other problems
or applications in protein folding.
acknowledgement: The authors acknowledge the use of Zhores supercomputer [28] for
obtaining the results presented in this paper.The authors thank Zimin Foundation
and Petrovax for support of the presented study at the School of Molecular and Theoretical
Biology 2021.
article_number: e0282689
article_processing_charge: No
article_type: original
author:
- first_name: Marina A.
full_name: Pak, Marina A.
last_name: Pak
- first_name: Karina A.
full_name: Markhieva, Karina A.
last_name: Markhieva
- first_name: Mariia S.
full_name: Novikova, Mariia S.
last_name: Novikova
- first_name: Dmitry S.
full_name: Petrov, Dmitry S.
last_name: Petrov
- first_name: Ilya S.
full_name: Vorobyev, Ilya S.
last_name: Vorobyev
- first_name: Ekaterina
full_name: Maksimova, Ekaterina
id: 2FBE0DE4-F248-11E8-B48F-1D18A9856A87
last_name: Maksimova
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Dmitry N.
full_name: Ivankov, Dmitry N.
last_name: Ivankov
citation:
ama: Pak MA, Markhieva KA, Novikova MS, et al. Using AlphaFold to predict the impact
of single mutations on protein stability and function. PLoS ONE. 2023;18(3).
doi:10.1371/journal.pone.0282689
apa: Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I.
S., Maksimova, E., … Ivankov, D. N. (2023). Using AlphaFold to predict the impact
of single mutations on protein stability and function. PLoS ONE. Public
Library of Science. https://doi.org/10.1371/journal.pone.0282689
chicago: Pak, Marina A., Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov,
Ilya S. Vorobyev, Ekaterina Maksimova, Fyodor Kondrashov, and Dmitry N. Ivankov.
“Using AlphaFold to Predict the Impact of Single Mutations on Protein Stability
and Function.” PLoS ONE. Public Library of Science, 2023. https://doi.org/10.1371/journal.pone.0282689.
ieee: M. A. Pak et al., “Using AlphaFold to predict the impact of single
mutations on protein stability and function,” PLoS ONE, vol. 18, no. 3.
Public Library of Science, 2023.
ista: Pak MA, Markhieva KA, Novikova MS, Petrov DS, Vorobyev IS, Maksimova E, Kondrashov
F, Ivankov DN. 2023. Using AlphaFold to predict the impact of single mutations
on protein stability and function. PLoS ONE. 18(3), e0282689.
mla: Pak, Marina A., et al. “Using AlphaFold to Predict the Impact of Single Mutations
on Protein Stability and Function.” PLoS ONE, vol. 18, no. 3, e0282689,
Public Library of Science, 2023, doi:10.1371/journal.pone.0282689.
short: M.A. Pak, K.A. Markhieva, M.S. Novikova, D.S. Petrov, I.S. Vorobyev, E. Maksimova,
F. Kondrashov, D.N. Ivankov, PLoS ONE 18 (2023).
date_created: 2023-03-26T22:01:07Z
date_published: 2023-03-16T00:00:00Z
date_updated: 2025-04-23T08:50:30Z
day: '16'
ddc:
- '570'
department:
- _id: FyKo
- _id: MaRo
doi: 10.1371/journal.pone.0282689
external_id:
isi:
- '000985134400106'
pmid:
- '36928239'
file:
- access_level: open_access
checksum: 0281bdfccf8d76c4e08dd011c603f6b6
content_type: application/pdf
creator: dernst
date_created: 2023-03-27T07:09:08Z
date_updated: 2023-03-27T07:09:08Z
file_id: '12771'
file_name: 2023_PLoSOne_Pak.pdf
file_size: 856625
relation: main_file
success: 1
file_date_updated: 2023-03-27T07:09:08Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
eissn:
- 1932-6203
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Using AlphaFold to predict the impact of single mutations on protein stability
and function
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: 18
year: '2023'
...
---
_id: '14258'
abstract:
- lang: eng
text: There is currently little evidence that the genetic basis of human phenotype
varies significantly across the lifespan. However, time-to-event phenotypes are
understudied and can be thought of as reflecting an underlying hazard, which is
unlikely to be constant through life when values take a broad range. Here, we
find that 74% of 245 genome-wide significant genetic associations with age at
natural menopause (ANM) in the UK Biobank show a form of age-specific effect.
Nineteen of these replicated discoveries are identified only by our modeling framework,
which determines the time dependency of DNA-variant age-at-onset associations
without a significant multiple-testing burden. Across the range of early to late
menopause, we find evidence for significantly different underlying biological
pathways, changes in the signs of genetic correlations of ANM to health indicators
and outcomes, and differences in inferred causal relationships. We find that DNA
damage response processes only act to shape ovarian reserve and depletion for
women of early ANM. Genetically mediated delays in ANM were associated with increased
relative risk of breast cancer and leiomyoma at all ages and with high cholesterol
and heart failure for late-ANM women. These findings suggest that a better understanding
of the age dependency of genetic risk factor relationships among health indicators
and outcomes is achievable through appropriate statistical modeling of large-scale
biobank data.
acknowledgement: This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181)
and by core funding from the Institute of Science and Technology Austria. K.L. and
R.M. were supported by the Estonian Research Council grant 1911. Estonian Biobank
computations were performed in the High-Performance Computing Center, University
of Tartu. We thank Triin Laisk for her valuable insights and comments that helped
greatly. We would like to acknowledge the participants and investigators of UK Biobank
and Estonian Biobank studies. This project uses UK Biobank data under project number
35520.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Sven E.
full_name: Ojavee, Sven E.
last_name: Ojavee
- first_name: Liza
full_name: Darrous, Liza
last_name: Darrous
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Krista
full_name: Fischer, Krista
last_name: Fischer
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Zoltan
full_name: Kutalik, Zoltan
last_name: Kutalik
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Ojavee SE, Darrous L, Patxot M, et al. Genetic insights into the age-specific
biological mechanisms governing human ovarian aging. American Journal of Human
Genetics. 2023;110(9):1549-1563. doi:10.1016/j.ajhg.2023.07.006
apa: Ojavee, S. E., Darrous, L., Patxot, M., Läll, K., Fischer, K., Mägi, R., …
Robinson, M. R. (2023). Genetic insights into the age-specific biological mechanisms
governing human ovarian aging. American Journal of Human Genetics. Elsevier.
https://doi.org/10.1016/j.ajhg.2023.07.006
chicago: Ojavee, Sven E., Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer,
Reedik Mägi, Zoltan Kutalik, and Matthew Richard Robinson. “Genetic Insights into
the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” American
Journal of Human Genetics. Elsevier, 2023. https://doi.org/10.1016/j.ajhg.2023.07.006.
ieee: S. E. Ojavee et al., “Genetic insights into the age-specific biological
mechanisms governing human ovarian aging,” American Journal of Human Genetics,
vol. 110, no. 9. Elsevier, pp. 1549–1563, 2023.
ista: Ojavee SE, Darrous L, Patxot M, Läll K, Fischer K, Mägi R, Kutalik Z, Robinson
MR. 2023. Genetic insights into the age-specific biological mechanisms governing
human ovarian aging. American Journal of Human Genetics. 110(9), 1549–1563.
mla: Ojavee, Sven E., et al. “Genetic Insights into the Age-Specific Biological
Mechanisms Governing Human Ovarian Aging.” American Journal of Human Genetics,
vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:10.1016/j.ajhg.2023.07.006.
short: S.E. Ojavee, L. Darrous, M. Patxot, K. Läll, K. Fischer, R. Mägi, Z. Kutalik,
M.R. Robinson, American Journal of Human Genetics 110 (2023) 1549–1563.
corr_author: '1'
date_created: 2023-09-03T22:01:15Z
date_published: 2023-09-07T00:00:00Z
date_updated: 2025-09-09T12:51:20Z
day: '07'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2023.07.006
external_id:
isi:
- '001074842500001'
pmid:
- '37543033'
file:
- access_level: open_access
checksum: 4108b031dc726ae6b4a5ae7e021ba188
content_type: application/pdf
creator: dernst
date_created: 2024-01-30T13:20:35Z
date_updated: 2024-01-30T13:20:35Z
file_id: '14912'
file_name: 2023_AJHG_Ojavee.pdf
file_size: 2551276
relation: main_file
success: 1
file_date_updated: 2024-01-30T13:20:35Z
has_accepted_license: '1'
intvolume: ' 110'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 1549-1563
pmid: 1
publication: American Journal of Human Genetics
publication_identifier:
eissn:
- 1537-6605
issn:
- 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Genetic insights into the age-specific biological mechanisms governing human
ovarian aging
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 110
year: '2023'
...
---
_id: '14689'
article_processing_charge: No
article_type: letter_note
author:
- first_name: Elizabeth
full_name: Ing-Simmons, Elizabeth
last_name: Ing-Simmons
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Juan M.
full_name: Vaquerizas, Juan M.
last_name: Vaquerizas
citation:
ama: 'Ing-Simmons E, Machnik NN, Vaquerizas JM. Reply to: Revisiting the use of
structural similarity index in Hi-C. Nature Genetics. 2023;55(12):2053-2055.
doi:10.1038/s41588-023-01595-5'
apa: 'Ing-Simmons, E., Machnik, N. N., & Vaquerizas, J. M. (2023). Reply to:
Revisiting the use of structural similarity index in Hi-C. Nature Genetics.
Springer Nature. https://doi.org/10.1038/s41588-023-01595-5'
chicago: 'Ing-Simmons, Elizabeth, Nick N Machnik, and Juan M. Vaquerizas. “Reply
to: Revisiting the Use of Structural Similarity Index in Hi-C.” Nature Genetics.
Springer Nature, 2023. https://doi.org/10.1038/s41588-023-01595-5.'
ieee: 'E. Ing-Simmons, N. N. Machnik, and J. M. Vaquerizas, “Reply to: Revisiting
the use of structural similarity index in Hi-C,” Nature Genetics, vol.
55, no. 12. Springer Nature, pp. 2053–2055, 2023.'
ista: 'Ing-Simmons E, Machnik NN, Vaquerizas JM. 2023. Reply to: Revisiting the
use of structural similarity index in Hi-C. Nature Genetics. 55(12), 2053–2055.'
mla: 'Ing-Simmons, Elizabeth, et al. “Reply to: Revisiting the Use of Structural
Similarity Index in Hi-C.” Nature Genetics, vol. 55, no. 12, Springer Nature,
2023, pp. 2053–55, doi:10.1038/s41588-023-01595-5.'
short: E. Ing-Simmons, N.N. Machnik, J.M. Vaquerizas, Nature Genetics 55 (2023)
2053–2055.
date_created: 2023-12-17T23:00:53Z
date_published: 2023-12-01T00:00:00Z
date_updated: 2025-09-09T14:00:46Z
day: '01'
department:
- _id: MaRo
doi: 10.1038/s41588-023-01595-5
external_id:
isi:
- '001169777400004'
pmid:
- '38052961'
intvolume: ' 55'
isi: 1
issue: '12'
language:
- iso: eng
month: '12'
oa_version: None
page: 2053-2055
pmid: 1
publication: Nature Genetics
publication_identifier:
eissn:
- 1546-1718
issn:
- 1061-4036
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Reply to: Revisiting the use of structural similarity index in Hi-C'
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 55
year: '2023'
...