---
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. 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.
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.,
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, iyag042, Oxford University Press, 2026, doi:10.1093/genetics/iyag042.
short: I. Krätschmer, M.R. Robinson, Genetics (2026).
corr_author: '1'
date_created: 2026-03-23T15:02:54Z
date_published: 2026-02-12T00:00:00Z
date_updated: 2026-03-24T06:48:10Z
day: '12'
department:
- _id: MaRo
doi: 10.1093/genetics/iyag042
external_id:
pmid:
- '41677404'
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1093/genetics/iyag042
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genetics
publication_identifier:
issn:
- 1943-2631
publication_status: epub_ahead
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/medical-genomics-group/familyMC
status: public
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
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. 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. 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.,
101162.
mla: Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human
Height via Approximate Message Passing.” Cell Genomics, 101162, Elsevier,
2026, doi:10.1016/j.xgen.2026.101162.
short: A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics (2026).
corr_author: '1'
date_created: 2026-03-23T15:10:03Z
date_published: 2026-02-18T00:00:00Z
date_updated: 2026-04-28T12:08:37Z
day: '18'
ddc:
- '000'
- '570'
department:
- _id: MaMo
- _id: MaRo
doi: 10.1016/j.xgen.2026.101162
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.xgen.2026.101162
month: '02'
oa: 1
oa_version: Published Version
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: epub_ahead
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/
status: public
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2026'
...
---
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'
...
---
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'
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checksum: 7c92919af1b5820d01e91e08906a411f
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creator: dernst
date_created: 2025-12-15T13:18:07Z
date_updated: 2025-12-15T13:18:07Z
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file_name: 2025_GenomeBiology_Robertson.pdf
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month: '12'
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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'
...
---
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
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relation: main_file
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file_date_updated: 2025-01-08T09:26:42Z
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intvolume: ' 112'
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- 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
file_id: '19030'
file_name: 2025_ClinicalEpigenetics_Bernabeu.pdf
file_size: 1170930
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file_date_updated: 2025-02-17T08:44:23Z
has_accepted_license: '1'
intvolume: ' 17'
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: 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: 2025-11-05T07:21:31Z
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: 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: 2026-04-28T22:30:26Z
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: '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: '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: '11733'
abstract:
- lang: eng
text: Genetically informed, deep-phenotyped biobanks are an important research resource
and it is imperative that the most powerful, versatile, and efficient analysis
approaches are used. Here, we apply our recently developed Bayesian grouped mixture
of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest
genomic prediction accuracy reported to date across 21 heritable traits. When
compared to other approaches, GMRM accuracy was greater than annotation prediction
models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
respectively, and was 18% (SE 3%) greater than a baseline BayesR model without
single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage
disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy
R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP.
We then extend our GMRM prediction model to provide mixed-linear model association
(MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which
increased the independent loci detected to 16,162 in unrelated UK Biobank individuals,
compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase,
respectively. The average χ2 value of the leading markers increased by 15.24 (SE
0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR
model across the traits. Thus, we show that modeling genetic associations accounting
for MAF and LD differences among SNP markers, and incorporating prior knowledge
of genomic function, is important for both genomic prediction and discovery in
large-scale individual-level studies.
acknowledgement: This project was funded by Swiss National Science Foundation Eccellenza
Grant PCEGP3-181181(toM.R.R.) and by core funding from the Institute of Science
and Technology Austria. P.M.V. acknowledges funding from the Australian National
Health and Medical Research Council (1113400) and the Australian Research Council
(FL180100072). K.L. and R.M. were supported by the Estonian Research Council Grant
PRG687. Estonian Biobank computations were performed in the High-Performance Computing
Centre, University of Tartu.
article_number: e2121279119
article_processing_charge: No
article_type: original
author:
- first_name: Etienne J.
full_name: Orliac, Etienne J.
last_name: Orliac
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Sven E.
full_name: Ojavee, Sven E.
last_name: Ojavee
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- 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: Orliac EJ, Trejo Banos D, Ojavee SE, et al. Improving GWAS discovery and genomic
prediction accuracy in biobank data. Proceedings of the National Academy of
Sciences of the United States of America. 2022;119(31). doi:10.1073/pnas.2121279119
apa: Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher,
P. M., & Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction
accuracy in biobank data. Proceedings of the National Academy of Sciences of
the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2121279119
chicago: Orliac, Etienne J., Daniel Trejo Banos, Sven E. Ojavee, Kristi Läll, Reedik
Mägi, Peter M. Visscher, and Matthew Richard Robinson. “Improving GWAS Discovery
and Genomic Prediction Accuracy in Biobank Data.” Proceedings of the National
Academy of Sciences of the United States of America. National Academy of Sciences,
2022. https://doi.org/10.1073/pnas.2121279119.
ieee: E. J. Orliac et al., “Improving GWAS discovery and genomic prediction
accuracy in biobank data,” Proceedings of the National Academy of Sciences
of the United States of America, vol. 119, no. 31. National Academy of Sciences,
2022.
ista: Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson
MR. 2022. Improving GWAS discovery and genomic prediction accuracy in biobank
data. Proceedings of the National Academy of Sciences of the United States of
America. 119(31), e2121279119.
mla: Orliac, Etienne J., et al. “Improving GWAS Discovery and Genomic Prediction
Accuracy in Biobank Data.” Proceedings of the National Academy of Sciences
of the United States of America, vol. 119, no. 31, e2121279119, National Academy
of Sciences, 2022, doi:10.1073/pnas.2121279119.
short: E.J. Orliac, D. Trejo Banos, S.E. Ojavee, K. Läll, R. Mägi, P.M. Visscher,
M.R. Robinson, Proceedings of the National Academy of Sciences of the United States
of America 119 (2022).
corr_author: '1'
date_created: 2022-08-07T22:01:56Z
date_published: 2022-07-29T00:00:00Z
date_updated: 2025-06-12T06:22:37Z
day: '29'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1073/pnas.2121279119
external_id:
isi:
- '000881496900003'
pmid:
- '35905320'
file:
- access_level: open_access
checksum: b5d2024e19fbad6f85a5e384e44d0f3b
content_type: application/pdf
creator: dernst
date_created: 2022-08-08T07:31:19Z
date_updated: 2022-08-08T07:31:19Z
file_id: '11745'
file_name: 2022_PNAS_Orliac.pdf
file_size: 1001164
relation: main_file
success: 1
file_date_updated: 2022-08-08T07:31:19Z
has_accepted_license: '1'
intvolume: ' 119'
isi: 1
issue: '31'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
record:
- id: '13064'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Improving GWAS discovery and genomic prediction accuracy in biobank data
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: 119
year: '2022'
...
---
_id: '12142'
abstract:
- lang: eng
text: Theory for liability-scale models of the underlying genetic basis of complex
disease provides an important way to interpret, compare, and understand results
generated from biological studies. In particular, through estimation of the liability-scale
heritability (LSH), liability models facilitate an understanding and comparison
of the relative importance of genetic and environmental risk factors that shape
different clinically important disease outcomes. Increasingly, large-scale biobank
studies that link genetic information to electronic health records, containing
hundreds of disease diagnosis indicators that mostly occur infrequently within
the sample, are becoming available. Here, we propose an extension of the existing
liability-scale model theory suitable for estimating LSH in biobank studies of
low-prevalence disease. In a simulation study, we find that our derived expression
yields lower mean square error (MSE) and is less sensitive to prevalence misspecification
as compared to previous transformations for diseases with =< 2% population prevalence
and LSH of =< 0.45, especially if the biobank sample prevalence is less than that
of the wider population. Applying our expression to 13 diagnostic outcomes of =<
3% prevalence in the UK Biobank study revealed important differences in LSH obtained
from the different theoretical expressions that impact the conclusions made when
comparing LSH across disease outcomes. This demonstrates the importance of careful
consideration for estimation and prediction of low-prevalence disease outcomes
and facilitates improved inference of the underlying genetic basis of =< 2% population
prevalence diseases, especially where biobank sample ascertainment results in
a healthier sample population.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181),
core funding from the Institute of Science and Technology Austria, and core funding
from the Department of Computational Biology of the University of Lausanne. Z.K.
was funded by the Swiss National Science Foundation (310030-189147). This research
was supported by the Scientific Service Units (SSUs) of IST Austria through resources
provided by Scientific Computing (SciComp). We would like to thank the participants
of the UK Biobank.
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: 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, Kutalik Z, Robinson MR. Liability-scale heritability estimation
for biobank studies of low-prevalence disease. The American Journal of Human
Genetics. 2022;109(11):2009-2017. doi:10.1016/j.ajhg.2022.09.011
apa: Ojavee, S. E., Kutalik, Z., & Robinson, M. R. (2022). Liability-scale heritability
estimation for biobank studies of low-prevalence disease. The American Journal
of Human Genetics. Elsevier. https://doi.org/10.1016/j.ajhg.2022.09.011
chicago: Ojavee, Sven E., Zoltan Kutalik, and Matthew Richard Robinson. “Liability-Scale
Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” The
American Journal of Human Genetics. Elsevier, 2022. https://doi.org/10.1016/j.ajhg.2022.09.011.
ieee: S. E. Ojavee, Z. Kutalik, and M. R. Robinson, “Liability-scale heritability
estimation for biobank studies of low-prevalence disease,” The American Journal
of Human Genetics, vol. 109, no. 11. Elsevier, pp. 2009–2017, 2022.
ista: Ojavee SE, Kutalik Z, Robinson MR. 2022. Liability-scale heritability estimation
for biobank studies of low-prevalence disease. The American Journal of Human Genetics.
109(11), 2009–2017.
mla: Ojavee, Sven E., et al. “Liability-Scale Heritability Estimation for Biobank
Studies of Low-Prevalence Disease.” The American Journal of Human Genetics,
vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:10.1016/j.ajhg.2022.09.011.
short: S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics
109 (2022) 2009–2017.
corr_author: '1'
date_created: 2023-01-12T12:05:28Z
date_published: 2022-11-03T00:00:00Z
date_updated: 2025-06-11T13:55:19Z
day: '03'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2022.09.011
external_id:
isi:
- '000898683500006'
pmid:
- '36265482'
file:
- access_level: open_access
checksum: 4cd7f12bfe21a8237bb095eedfa26361
content_type: application/pdf
creator: dernst
date_created: 2023-01-24T09:23:01Z
date_updated: 2023-01-24T09:23:01Z
file_id: '12353'
file_name: 2022_AJHG_Ojavee.pdf
file_size: 705195
relation: main_file
success: 1
file_date_updated: 2023-01-24T09:23:01Z
has_accepted_license: '1'
intvolume: ' 109'
isi: 1
issue: '11'
keyword:
- Genetics (clinical)
- Genetics
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 2009-2017
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: The American Journal of Human Genetics
publication_identifier:
issn:
- 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Liability-scale heritability estimation for biobank studies of low-prevalence
disease
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: 109
year: '2022'
...
---
_id: '12235'
abstract:
- lang: eng
text: "Background: About 800 women die every day worldwide from pregnancy-related
complications, including excessive blood loss, infections and high-blood pressure
(World Health Organization, 2019). To improve screening for high-risk pregnancies,
we set out to identify patterns of maternal hematological changes associated with
future pregnancy complications.\r\n\r\nMethods: Using mixed effects models, we
established changes in 14 complete blood count (CBC) parameters for 1710 healthy
pregnancies and compared them to measurements from 98 pregnancy-induced hypertension,
106 gestational diabetes and 339 postpartum hemorrhage cases.\r\n\r\nResults:
Results show interindividual variations, but good individual repeatability in
CBC values during physiological pregnancies, allowing the identification of specific
alterations in women with obstetric complications. For example, in women with
uncomplicated pregnancies, haemoglobin count decreases of 0.12 g/L (95% CI −0.16,
−0.09) significantly per gestation week (p value <.001). Interestingly, this decrease
is three times more pronounced in women who will develop pregnancy-induced hypertension,
with an additional decrease of 0.39 g/L (95% CI −0.51, −0.26). We also confirm
that obstetric complications and white CBC predict the likelihood of giving birth
earlier during pregnancy.\r\n\r\nConclusion: We provide a comprehensive description
of the associations between haematological changes through pregnancy and three
major obstetric complications to support strategies for prevention, early-diagnosis
and maternal care."
acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by core funding from the Institute of Science and Technology Austria. We would
like to thank the participants of the study and all the midwives and doctors involved
for the computerized obstetrical data from the CHUV Maternity Hospital. Open access
funding provided by Universite de Lausanne.
article_processing_charge: No
article_type: original
author:
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Miloš
full_name: Stojanov, Miloš
last_name: Stojanov
- first_name: Sven Erik
full_name: Ojavee, Sven Erik
last_name: Ojavee
- first_name: Rosanna Pescini
full_name: Gobert, Rosanna Pescini
last_name: Gobert
- first_name: Zoltán
full_name: Kutalik, Zoltán
last_name: Kutalik
- first_name: Mathilde
full_name: Gavillet, Mathilde
last_name: Gavillet
- first_name: David
full_name: Baud, David
last_name: Baud
- 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: 'Patxot M, Stojanov M, Ojavee SE, et al. Haematological changes from conception
to childbirth: An indicator of major pregnancy complications. European Journal
of Haematology. 2022;109(5):566-575. doi:10.1111/ejh.13844'
apa: 'Patxot, M., Stojanov, M., Ojavee, S. E., Gobert, R. P., Kutalik, Z., Gavillet,
M., … Robinson, M. R. (2022). Haematological changes from conception to childbirth:
An indicator of major pregnancy complications. European Journal of Haematology.
Wiley. https://doi.org/10.1111/ejh.13844'
chicago: 'Patxot, Marion, Miloš Stojanov, Sven Erik Ojavee, Rosanna Pescini Gobert,
Zoltán Kutalik, Mathilde Gavillet, David Baud, and Matthew Richard Robinson. “Haematological
Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.”
European Journal of Haematology. Wiley, 2022. https://doi.org/10.1111/ejh.13844.'
ieee: 'M. Patxot et al., “Haematological changes from conception to childbirth:
An indicator of major pregnancy complications,” European Journal of Haematology,
vol. 109, no. 5. Wiley, pp. 566–575, 2022.'
ista: 'Patxot M, Stojanov M, Ojavee SE, Gobert RP, Kutalik Z, Gavillet M, Baud D,
Robinson MR. 2022. Haematological changes from conception to childbirth: An indicator
of major pregnancy complications. European Journal of Haematology. 109(5), 566–575.'
mla: 'Patxot, Marion, et al. “Haematological Changes from Conception to Childbirth:
An Indicator of Major Pregnancy Complications.” European Journal of Haematology,
vol. 109, no. 5, Wiley, 2022, pp. 566–75, doi:10.1111/ejh.13844.'
short: M. Patxot, M. Stojanov, S.E. Ojavee, R.P. Gobert, Z. Kutalik, M. Gavillet,
D. Baud, M.R. Robinson, European Journal of Haematology 109 (2022) 566–575.
corr_author: '1'
date_created: 2023-01-16T09:50:58Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2024-10-09T21:03:49Z
day: '01'
ddc:
- '570'
- '610'
department:
- _id: MaRo
doi: 10.1111/ejh.13844
external_id:
isi:
- '000849690500001'
pmid:
- '36059200'
file:
- access_level: open_access
checksum: a676d732f67c2990197e34f96b219370
content_type: application/pdf
creator: dernst
date_created: 2023-01-27T11:42:43Z
date_updated: 2023-01-27T11:42:43Z
file_id: '12426'
file_name: 2022_EuropJourHaematology_Patxot.pdf
file_size: 1225073
relation: main_file
success: 1
file_date_updated: 2023-01-27T11:42:43Z
has_accepted_license: '1'
intvolume: ' 109'
isi: 1
issue: '5'
keyword:
- Hematology
- General Medicine
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 566-575
pmid: 1
publication: European Journal of Haematology
publication_identifier:
eissn:
- 1600-0609
issn:
- 0902-4441
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Haematological changes from conception to childbirth: An indicator of major
pregnancy complications'
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
year: '2022'
...
---
_id: '13064'
abstract:
- lang: eng
text: Genetically informed, deep-phenotyped biobanks are an important research resource
and it is imperative that the most powerful, versatile, and efficient analysis
approaches are used. Here, we apply our recently developed Bayesian grouped mixture
of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest
genomic prediction accuracy reported to date across 21 heritable traits. When
compared to other approaches, GMRM accuracy was greater than annotation prediction
models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
respectively, and was 18% (SE 3%) greater than a baseline BayesR model without
single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage
disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy
R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP
2 . We then extend our GMRM prediction model to provide mixed-linear model association
(MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which
increased the independent loci detected to 16,162 in unrelated UK Biobank individuals,
compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase,
respectively. The average χ2 value of the leading markers increased by 15.24 (SE
0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR
model across the traits. Thus, we show that modeling genetic associations accounting
for MAF and LD differences among SNP markers, and incorporating prior knowledge
of genomic function, is important for both genomic prediction and discovery in
large-scale individual-level studies.
article_processing_charge: No
author:
- first_name: Etienne
full_name: Orliac, Etienne
last_name: Orliac
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Sven
full_name: Ojavee, Sven
last_name: Ojavee
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Peter
full_name: Visscher, Peter
last_name: Visscher
- 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: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association
discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ
apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P.,
& Robinson, M. R. (2022). Improving genome-wide association discovery and
genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ
chicago: Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi,
Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association
Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. https://doi.org/10.5061/DRYAD.GTHT76HMZ.
ieee: E. Orliac et al., “Improving genome-wide association discovery and
genomic prediction accuracy in biobank data.” Dryad, 2022.
ista: Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR.
2022. Improving genome-wide association discovery and genomic prediction accuracy
in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ.
mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and
Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ.
short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R.
Robinson, (2022).
corr_author: '1'
date_created: 2023-05-23T16:28:13Z
date_published: 2022-09-02T00:00:00Z
date_updated: 2025-06-12T06:22:36Z
day: '02'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5061/DRYAD.GTHT76HMZ
license: https://creativecommons.org/publicdomain/zero/1.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.gtht76hmz
month: '09'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '11733'
relation: used_in_publication
status: public
status: public
title: Improving genome-wide association discovery and genomic prediction accuracy
in biobank data
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '10702'
abstract:
- lang: eng
text: 'Background: Blood-based markers of cognitive functioning might provide an
accessible way to track neurodegeneration years prior to clinical manifestation
of cognitive impairment and dementia. Results: Using blood-based epigenome-wide
analyses of general cognitive function, we show that individual differences in
DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function
(g). A DNAm predictor explains ~4% of the variance, independently of a polygenic
score, in two external cohorts. It also associates with circulating levels of
neurology- and inflammation-related proteins, global brain imaging metrics, and
regional cortical volumes. Conclusions: As sample sizes increase, the ability
to assess cognitive function from DNAm data may be informative in settings where
cognitive testing is unreliable or unavailable.'
acknowledgement: 'GS 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 GS samples was carried
out by the Genetics Core Laboratory at the Edinburgh 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; Reference 104036/Z/14/Z). The DNA methylation data assayed for Generation
Scotland was 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). LBC1936 MRI brain imaging was supported by Medical Research
Council (MRC) grants [G0701120], [G1001245], [MR/M013111/1] and [MR/R024065/1].
Magnetic resonance image acquisition and analyses were conducted at the Brain Research
Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.bric.ed.ac.uk)
which is part of SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence)
collaboration (www.sinapse.ac.uk) funded by the Scottish Funding Council and the
Chief Scientist Office. This work was supported by the European Union Horizon 2020
(PHC.03.15, project No 666881), SVDs@Target, the Fondation Leducq Transatlantic
Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease
[ref no. 16 CVD 05]. We thank the LBC1936 participants and team members who contributed
to these studies. The LBC1936 is supported by Age UK (Disconnected Mind project,
which supports S.E.H.), the Medical Research Council (G0701120, G1001245, MR/M013111/1,
MR/R024065/1) 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 Biotechnology
and Biological Sciences Research Council (BB/F019394/1). Proteomic analyses in LBC1936
were supported by the Age UK grant and NIH Grants R01AG054628 and R01AG05462802S1.
M.V.H. is funded by the Row Fogo Charitable Trust (Grant no. BROD.FID3668413). J.M.W
is supported by the UK Dementia Research Institute which receives its funding from
DRI Ltd, funded by the UK Medical Research Council, Alzheimers Society and Alzheimers
Research UK. R.F.H., E.L.S.C and D.A.G. are supported by funding from the Wellcome
Trust 4 year PhD in Translational Neuroscience: training the next generation of
basic neuroscientists to embrace clinical research [108890/Z/15/Z]. E.M.T.D. was
supported by the National Institutes of Health (NIH) grants R01AG054628, R01MH120219,
R01HD083613, P2CHD042849 and P30AG066614. S.R.C. was also supported by a National
Institutes of Health (NIH) research grant R01AG054628 and is supported by a Sir
Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society
(Grant Number 221890/Z/20/Z). D.L.Mc.C. and R.E.M. are supported by Alzheimers Research
UK major project grant ARUK/PG2017B/10. R.E.M. is supported by Alzheimer’s Society
major project grant AS-PG-19b-010. This research was funded in whole, or in part,
by Wellcome [104036/Z/14/Z and 108890/Z/15/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: '26'
article_processing_charge: No
article_type: original
author:
- 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: Eleanor L.S.
full_name: Conole, Eleanor L.S.
last_name: Conole
- first_name: Daniel Trejo
full_name: Banos, Daniel Trejo
last_name: Banos
- first_name: Danni A.
full_name: Gadd, Danni A.
last_name: Gadd
- first_name: Rosie M.
full_name: Walker, Rosie M.
last_name: Walker
- first_name: Cliff
full_name: Nangle, Cliff
last_name: Nangle
- first_name: Robin
full_name: Flaig, Robin
last_name: Flaig
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Alison D.
full_name: Murray, Alison D.
last_name: Murray
- first_name: Susana Muñoz
full_name: Maniega, Susana Muñoz
last_name: Maniega
- first_name: María Del C.
full_name: Valdés-Hernández, María Del C.
last_name: Valdés-Hernández
- first_name: Mathew A.
full_name: Harris, Mathew A.
last_name: Harris
- 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: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: David J.
full_name: Porteous, David J.
last_name: Porteous
- first_name: Elliot M.
full_name: Tucker-Drob, Elliot M.
last_name: Tucker-Drob
- first_name: Andrew M.
full_name: McIntosh, Andrew M.
last_name: McIntosh
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- first_name: Simon R.
full_name: Cox, Simon R.
last_name: Cox
- 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: McCartney DL, Hillary RF, Conole ELS, et al. Blood-based epigenome-wide analyses
of cognitive abilities. Genome Biology. 2022;23(1). doi:10.1186/s13059-021-02596-5
apa: McCartney, D. L., Hillary, R. F., Conole, E. L. S., Banos, D. T., Gadd, D.
A., Walker, R. M., … Marioni, R. E. (2022). Blood-based epigenome-wide analyses
of cognitive abilities. Genome Biology. Springer Nature. https://doi.org/10.1186/s13059-021-02596-5
chicago: McCartney, Daniel L., Robert F. Hillary, Eleanor L.S. Conole, Daniel Trejo
Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide
Analyses of Cognitive Abilities.” Genome Biology. Springer Nature, 2022.
https://doi.org/10.1186/s13059-021-02596-5.
ieee: D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive
abilities,” Genome Biology, vol. 23, no. 1. Springer Nature, 2022.
ista: McCartney DL, Hillary RF, Conole ELS, Banos DT, Gadd DA, Walker RM, Nangle
C, Flaig R, Campbell A, Murray AD, Maniega SM, Valdés-Hernández MDC, Harris MA,
Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans
KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2022. Blood-based epigenome-wide
analyses of cognitive abilities. Genome Biology. 23(1), 26.
mla: McCartney, Daniel L., et al. “Blood-Based Epigenome-Wide Analyses of Cognitive
Abilities.” Genome Biology, vol. 23, no. 1, 26, Springer Nature, 2022,
doi:10.1186/s13059-021-02596-5.
short: D.L. McCartney, R.F. Hillary, E.L.S. Conole, D.T. Banos, D.A. Gadd, R.M.
Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S.M. Maniega, M.D.C. Valdés-Hernández,
M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob,
A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni,
Genome Biology 23 (2022).
corr_author: '1'
date_created: 2022-01-30T23:01:33Z
date_published: 2022-01-17T00:00:00Z
date_updated: 2025-06-11T13:54:53Z
day: '17'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13059-021-02596-5
external_id:
isi:
- '000744358300002'
pmid:
- '35039062'
file:
- access_level: open_access
checksum: 34f10bb2b0594189dcac24d13b691d52
content_type: application/pdf
creator: cchlebak
date_created: 2022-01-31T13:16:05Z
date_updated: 2022-01-31T13:16:05Z
file_id: '10708'
file_name: 2022_GenomeBio_McCartney.pdf
file_size: 1540606
relation: main_file
success: 1
file_date_updated: 2022-01-31T13:16:05Z
has_accepted_license: '1'
intvolume: ' 23'
isi: 1
issue: '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: Genome Biology
publication_identifier:
eissn:
- 1474-760X
issn:
- 1474-7596
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: earlier_version
url: https://doi.org/10.1101/2021.05.24.21257698
record:
- id: '13072'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Blood-based epigenome-wide analyses of cognitive abilities
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: 23
year: '2022'
...
---
_id: '17076'
abstract:
- lang: eng
text: "Introduction: The levels of many blood proteins are associated with Alzheimer's
disease (AD) or its pathological hallmarks. Elucidating the molecular factors
that control circulating levels of these proteins may help to identify proteins
associated with disease risk mechanisms.\r\n\r\nMethods: Genome-wide and epigenome-wide
studies (nindividuals ≤1064) were performed on plasma levels of 282 AD-associated
proteins, identified by a structured literature review. Bayesian penalized regression
estimated contributions of genetic and epigenetic variation toward inter-individual
differences in plasma protein levels. Mendelian randomization (MR) and co-localization
tested associations between proteins and disease-related phenotypes.\r\n\r\nResults:
Sixty-four independent genetic and 26 epigenetic loci were associated with 45
proteins. Novel findings included an association between plasma triggering receptor
expressed on myeloid cells 2 (TREM2) levels and a polymorphism and cytosine-phosphate-guanine
(CpG) site within the MS4A4A locus. Higher plasma tubulin-specific chaperone A
(TBCA) and TREM2 levels were significantly associated with lower AD risk.\r\n\r\nDiscussion:
Our data inform the regulation of biomarker levels and their relationships with
AD."
acknowledgement: This research was funded in whole, or in part, by Wellcome [108890/Z/15/Z,
104036/Z/14/Z]. For the purpose of open access, the author has applied a CC BY public
copyright license to any Author Accepted Manuscript version arising from this submission.
The authors are grateful to the families who took part in this study, the general
practitioners, and the Scottish School of Primary Care for their help in recruiting
them and the wider Generation Scotland team. 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 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 (MRC) UK and the Wellcome Trust (Wellcome Trust
Strategic Award “STratifying Resilience and Depression Longitudinally” ([STRADL]
Reference [104036/Z/14/Z]). Andrew M. McIntosh is supported by Wellcome [104036/Z/14/Z,
216767/Z/19/Z, 220857/Z/20/Z], United Kingdom Research and Innovation (UKRI) MRC
[MC_PC_17209, MR/S035818/1] and the European Union H2020 [SEP-210574971]. Ian J.
Deary received support from Age UK, Wellcome, and the Medical Research Council.
David J. Porteous is supported by Wellcome as prinicpal investigator (PI), and MRC
and National Institute for Health Research (NIHR) grants as co-PI, made to the University
of Edinburgh. Robert F. Hillary and Danni A. Gadd are supported by funding from
the Wellcome 4-year PhD in Translational Neuroscience—training the next generation
of basic neuroscientists to embrace clinical research [108890/Z/15/Z]. Daniel L.
McCartney and Riccardo E. Marioni are supported by Alzheimer's Research UK major
project grant ARUK-PG2017B-10. Riccardo E. Marioni is supported by Alzheimer's Society
major project grant AS-PG-19b-010. Proteomic analyses in STRADL were supported by
Dementias Platform UK (DPUK). DPUK funded this work through core grant support from
the Medical Research Council [MR/L023784/2]. Kathryn L. Evans was supported by a
grant from Alzheimer's Research UK, paid to the University of Edinburgh. Alejo J.
Nevado-Holgado was funded by a Horizon 2020 Virtual Brain Cloud project (H2020-SC1-DTH-2018-1),
in addition to funding from the MRC, UK Rosetrees, and King Abdullah University
of Science and Technology, Saudi Arabia. Caroline Hayward is supported by an MRC
University Unit Programme Grant MC_UU_00007/10 (QTL in Health and Disease). Liu
Shi is funded by DPUK through MRC [MR/L023784/2] and the UK Medical Research Council
Award to the University of Oxford [MC_PC_17215]. Liu Shi received support from the
NIHR Biomedical Research Centre at Oxford Health NHS Foundation Trust. Matthew R.
Robinson is funded by a Swiss National Science Foundation Eccellenza Grant [PCEGP3-181181].
article_number: e12280
article_processing_charge: Yes
article_type: original
author:
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- 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: Liu
full_name: Shi, Liu
last_name: Shi
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Rosie M.
full_name: Walker, Rosie M.
last_name: Walker
- first_name: Craig W.
full_name: Ritchie, Craig W.
last_name: Ritchie
- 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: Alejo J.
full_name: Nevado‐Holgado, Alejo J.
last_name: Nevado‐Holgado
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: David J.
full_name: Porteous, David J.
last_name: Porteous
- first_name: Andrew M.
full_name: McIntosh, Andrew M.
last_name: McIntosh
- first_name: Simon
full_name: Lovestone, Simon
last_name: Lovestone
- 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: 'Hillary RF, Gadd DA, McCartney DL, et al. Genome‐ and epigenome‐wide studies
of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2
in disease risk. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease
Monitoring. 2022;14(1). doi:10.1002/dad2.12280'
apa: 'Hillary, R. F., Gadd, D. A., McCartney, D. L., Shi, L., Campbell, A., Walker,
R. M., … Marioni, R. E. (2022). Genome‐ and epigenome‐wide studies of plasma protein
biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk. Alzheimer’s
& Dementia: Diagnosis, Assessment & Disease Monitoring. Wiley. https://doi.org/10.1002/dad2.12280'
chicago: 'Hillary, Robert F., Danni A. Gadd, Daniel L. McCartney, Liu Shi, Archie
Campbell, Rosie M. Walker, Craig W. Ritchie, et al. “Genome‐ and Epigenome‐wide
Studies of Plasma Protein Biomarkers for Alzheimer’s Disease Implicate TBCA and
TREM2 in Disease Risk.” Alzheimer’s & Dementia: Diagnosis, Assessment &
Disease Monitoring. Wiley, 2022. https://doi.org/10.1002/dad2.12280.'
ieee: 'R. F. Hillary et al., “Genome‐ and epigenome‐wide studies of plasma
protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease
risk,” Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring,
vol. 14, no. 1. Wiley, 2022.'
ista: 'Hillary RF, Gadd DA, McCartney DL, Shi L, Campbell A, Walker RM, Ritchie
CW, Deary IJ, Evans KL, Nevado‐Holgado AJ, Hayward C, Porteous DJ, McIntosh AM,
Lovestone S, Robinson MR, Marioni RE. 2022. Genome‐ and epigenome‐wide studies
of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2
in disease risk. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease
Monitoring. 14(1), e12280.'
mla: 'Hillary, Robert F., et al. “Genome‐ and Epigenome‐wide Studies of Plasma Protein
Biomarkers for Alzheimer’s Disease Implicate TBCA and TREM2 in Disease Risk.”
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring,
vol. 14, no. 1, e12280, Wiley, 2022, doi:10.1002/dad2.12280.'
short: 'R.F. Hillary, D.A. Gadd, D.L. McCartney, L. Shi, A. Campbell, R.M. Walker,
C.W. Ritchie, I.J. Deary, K.L. Evans, A.J. Nevado‐Holgado, C. Hayward, D.J. Porteous,
A.M. McIntosh, S. Lovestone, M.R. Robinson, R.E. Marioni, Alzheimer’s & Dementia:
Diagnosis, Assessment & Disease Monitoring 14 (2022).'
date_created: 2024-05-29T06:13:25Z
date_published: 2022-04-20T00:00:00Z
date_updated: 2024-07-31T11:33:50Z
day: '20'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1002/dad2.12280
external_id:
pmid:
- '35475137'
file:
- access_level: open_access
checksum: 49c8597b588ef1c63897703a32b7967b
content_type: application/pdf
creator: dernst
date_created: 2024-07-31T11:27:29Z
date_updated: 2024-07-31T11:27:29Z
file_id: '17356'
file_name: 2023_AlzheimersDementia_Hillary.pdf
file_size: 975181
relation: main_file
success: 1
file_date_updated: 2024-07-31T11:27:29Z
has_accepted_license: '1'
intvolume: ' 14'
issue: '1'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: 'Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring'
publication_identifier:
eissn:
- 2352-8729
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer's
disease implicate TBCA and TREM2 in disease risk
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: 14
year: '2022'
...
---
_id: '13063'
abstract:
- lang: eng
text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability
estimation, an alternative to marker discovery, and accurate genomic prediction,
taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability
parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation
captured for height, body mass index, cardiovascular disease, and type 2 diabetes
is attributable to proximal regulatory regions within 10kb upstream of genes,
while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to
distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each
chromosome are associated with each trait, with over 3,100 independent exonic
and intronic regions and over 5,400 independent regulatory regions having >95%
probability of contributing >0.001% to the genetic variance of these four traits.
Our open-source software (GMRM) provides a scalable alternative to current approaches
for biobank data.
article_processing_charge: No
author:
- 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: Robinson MR. Probabilistic inference of the genetic architecture of functional
enrichment of complex traits. 2021. doi:10.5061/dryad.sqv9s4n51
apa: Robinson, M. R. (2021). Probabilistic inference of the genetic architecture
of functional enrichment of complex traits. Dryad. https://doi.org/10.5061/dryad.sqv9s4n51
chicago: Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture
of Functional Enrichment of Complex Traits.” Dryad, 2021. https://doi.org/10.5061/dryad.sqv9s4n51.
ieee: M. R. Robinson, “Probabilistic inference of the genetic architecture of functional
enrichment of complex traits.” Dryad, 2021.
ista: Robinson MR. 2021. Probabilistic inference of the genetic architecture of
functional enrichment of complex traits, Dryad, 10.5061/dryad.sqv9s4n51.
mla: Robinson, Matthew Richard. Probabilistic Inference of the Genetic Architecture
of Functional Enrichment of Complex Traits. Dryad, 2021, doi:10.5061/dryad.sqv9s4n51.
short: M.R. Robinson, (2021).
corr_author: '1'
date_created: 2023-05-23T16:20:16Z
date_published: 2021-11-04T00:00:00Z
date_updated: 2025-06-12T06:54:51Z
day: '04'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5061/dryad.sqv9s4n51
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.sqv9s4n51
month: '11'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
link:
- relation: software
url: https://github.com/medical-genomics-group/gmrm
record:
- id: '8429'
relation: used_in_publication
status: public
status: public
title: Probabilistic inference of the genetic architecture of functional enrichment
of complex traits
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13072'
abstract:
- lang: eng
text: CpGs and corresponding mean weights for DNAm-based prediction of cognitive
abilities (6 traits)
article_processing_charge: No
author:
- 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: Eleanor LS
full_name: Conole, Eleanor LS
last_name: Conole
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Danni A
full_name: Gadd, Danni A
last_name: Gadd
- first_name: Rosie M
full_name: Walker, Rosie M
last_name: Walker
- first_name: Cliff
full_name: Nangle, Cliff
last_name: Nangle
- first_name: Robin
full_name: Flaig, Robin
last_name: Flaig
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Alison D
full_name: Murray, Alison D
last_name: Murray
- first_name: Susana
full_name: Munoz Maniega, Susana
last_name: Munoz Maniega
- first_name: Maria
full_name: del C Valdes-Hernandez, Maria
last_name: del C Valdes-Hernandez
- first_name: Mathew A
full_name: Harris, Mathew A
last_name: Harris
- 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: Sarah E
full_name: Harris, Sarah E
last_name: Harris
- first_name: David J
full_name: Porteous, David J
last_name: Porteous
- first_name: Elliot M
full_name: Tucker-Drob, Elliot M
last_name: Tucker-Drob
- first_name: Andrew M
full_name: McIntosh, Andrew M
last_name: McIntosh
- first_name: Kathryn L
full_name: Evans, Kathryn L
last_name: Evans
- first_name: Ian J
full_name: Deary, Ian J
last_name: Deary
- first_name: Simon R
full_name: Cox, Simon R
last_name: Cox
- 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: McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses
of cognitive abilities. 2021. doi:10.5281/ZENODO.5794028
apa: McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D.
A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses
of cognitive abilities. Zenodo. https://doi.org/10.5281/ZENODO.5794028
chicago: McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo
Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide
Analyses of Cognitive Abilities.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5794028.
ieee: D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive
abilities.” Zenodo, 2021.
ista: McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle
C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M,
Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh
AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide
analyses of cognitive abilities, Zenodo, 10.5281/ZENODO.5794028.
mla: McCartney, Daniel L., et al. Blood-Based Epigenome-Wide Analyses of Cognitive
Abilities. Zenodo, 2021, doi:10.5281/ZENODO.5794028.
short: D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M.
Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del
C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J.
Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R.
Robinson, R.E. Marioni, (2021).
date_created: 2023-05-23T16:46:20Z
date_published: 2021-12-20T00:00:00Z
date_updated: 2025-06-11T13:54:53Z
day: '20'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5281/ZENODO.5794028
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5794029
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '10702'
relation: used_in_publication
status: public
status: public
title: Blood-based epigenome-wide analyses of cognitive abilities
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10069'
abstract:
- lang: eng
text: 'The extent to which women differ in the course of blood cell counts throughout
pregnancy, and the importance of these changes to pregnancy outcomes has not been
well defined. Here, we develop a series of statistical analyses of repeated measures
data to reveal the degree to which women differ in the course of pregnancy, predict
the changes that occur, and determine the importance of these changes for post-partum
hemorrhage (PPH) which is one of the leading causes of maternal mortality. We
present a prospective cohort of 4082 births recorded at the University Hospital,
Lausanne, Switzerland between 2009 and 2014 where full labour records could be
obtained, along with complete blood count data taken at hospital admission. We
find significant differences, at a [Formula: see text] level, among women in how
blood count values change through pregnancy for mean corpuscular hemoglobin, mean
corpuscular volume, mean platelet volume, platelet count and red cell distribution
width. We find evidence that almost all complete blood count values show trimester-specific
associations with PPH. For example, high platelet count (OR 1.20, 95% CI 1.01-1.53),
high mean platelet volume (OR 1.58, 95% CI 1.04-2.08), and high erythrocyte levels
(OR 1.36, 95% CI 1.01-1.57) in trimester 1 increased PPH, but high values in trimester
3 decreased PPH risk (OR 0.85, 0.79, 0.67 respectively). We show that differences
among women in the course of blood cell counts throughout pregnancy have an important
role in shaping pregnancy outcome and tracking blood count value changes through
pregnancy improves identification of women at increased risk of postpartum hemorrhage.
This study provides greater understanding of the complex changes in blood count
values that occur through pregnancy and provides indicators to guide the stratification
of patients into risk groups.'
acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by core funding from the Institute of Science and Technology Austria. We would
like to thank the participants of the study and all the midwives and doctors for
the computerized obstetrical data.
article_number: '19238'
article_processing_charge: Yes
article_type: original
author:
- 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: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Miloš
full_name: Stojanov, Miloš
last_name: Stojanov
- first_name: Sabine
full_name: Blum, Sabine
last_name: Blum
- first_name: David
full_name: Baud, David
last_name: Baud
citation:
ama: Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. Postpartum hemorrhage risk
is driven by changes in blood composition through pregnancy. Scientific Reports.
2021;11. doi:10.1038/s41598-021-98411-z
apa: Robinson, M. R., Patxot, M., Stojanov, M., Blum, S., & Baud, D. (2021).
Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy.
Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-021-98411-z
chicago: Robinson, Matthew Richard, Marion Patxot, Miloš Stojanov, Sabine Blum,
and David Baud. “Postpartum Hemorrhage Risk Is Driven by Changes in Blood Composition
through Pregnancy.” Scientific Reports. Springer Nature, 2021. https://doi.org/10.1038/s41598-021-98411-z.
ieee: M. R. Robinson, M. Patxot, M. Stojanov, S. Blum, and D. Baud, “Postpartum
hemorrhage risk is driven by changes in blood composition through pregnancy,”
Scientific Reports, vol. 11. Springer Nature, 2021.
ista: Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. 2021. Postpartum hemorrhage
risk is driven by changes in blood composition through pregnancy. Scientific Reports.
11, 19238.
mla: Robinson, Matthew Richard, et al. “Postpartum Hemorrhage Risk Is Driven by
Changes in Blood Composition through Pregnancy.” Scientific Reports, vol.
11, 19238, Springer Nature, 2021, doi:10.1038/s41598-021-98411-z.
short: M.R. Robinson, M. Patxot, M. Stojanov, S. Blum, D. Baud, Scientific Reports
11 (2021).
corr_author: '1'
date_created: 2021-10-03T22:01:21Z
date_published: 2021-09-28T00:00:00Z
date_updated: 2024-10-09T21:00:57Z
day: '28'
ddc:
- '618'
department:
- _id: MaRo
doi: 10.1038/s41598-021-98411-z
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title: Postpartum hemorrhage risk is driven by changes in blood composition through
pregnancy
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---
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abstract:
- lang: eng
text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability
estimation, an alternative to marker discovery, and accurate genomic prediction,
taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability
parameters in the UK Biobank. We find that only ≤10% of the genetic variation
captured for height, body mass index, cardiovascular disease, and type 2 diabetes
is attributable to proximal regulatory regions within 10kb upstream of genes,
while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to
distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each
chromosome are associated with each trait, with over 3,100 independent exonic
and intronic regions and over 5,400 independent regulatory regions having ≥95%
probability of contributing ≥0.001% to the genetic variance of these four traits.
Our open-source software (GMRM) provides a scalable alternative to current approaches
for biobank data.
acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by core funding from the Institute of Science and Technology Austria. We would
like to thank the participants of the cohort studies, and the Ecole Polytechnique
Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity
with their time and the kindness of their support. P.M.V. acknowledges funding from
the Australian National Health and Medical Research Council (1113400) and the Australian
Research Council (FL180100072). L.R. acknowledges funding from the Kjell & Märta
Beijer Foundation (Stockholm, Sweden). We also would like to acknowledge Simone
Rubinacci, Oliver Delanau, Alexander Terenin, Eleonora Porcu, and Mike Goddard for
their useful comments and suggestions.
article_number: '6972'
article_processing_charge: No
article_type: original
author:
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Etienne J
full_name: Orliac, Etienne J
last_name: Orliac
- first_name: Sven E
full_name: Ojavee, Sven E
last_name: Ojavee
- first_name: Gerhard
full_name: Moser, Gerhard
last_name: Moser
- first_name: Julia
full_name: Sidorenko, Julia
last_name: Sidorenko
- first_name: Zoltan
full_name: Kutalik, Zoltan
last_name: Kutalik
- first_name: Reedik
full_name: Magi, Reedik
last_name: Magi
- first_name: Peter M
full_name: Visscher, Peter M
last_name: Visscher
- first_name: Lars
full_name: Ronnegard, Lars
last_name: Ronnegard
- 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: Patxot M, Trejo Banos D, Kousathanas A, et al. Probabilistic inference of the
genetic architecture underlying functional enrichment of complex traits. Nature
Communications. 2021;12(1). doi:10.1038/s41467-021-27258-9
apa: Patxot, M., Trejo Banos, D., Kousathanas, A., Orliac, E. J., Ojavee, S. E.,
Moser, G., … Robinson, M. R. (2021). Probabilistic inference of the genetic architecture
underlying functional enrichment of complex traits. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-021-27258-9
chicago: Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac,
Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference
of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.”
Nature Communications. Springer Nature, 2021. https://doi.org/10.1038/s41467-021-27258-9.
ieee: M. Patxot et al., “Probabilistic inference of the genetic architecture
underlying functional enrichment of complex traits,” Nature Communications,
vol. 12, no. 1. Springer Nature, 2021.
ista: Patxot M, Trejo Banos D, Kousathanas A, Orliac EJ, Ojavee SE, Moser G, Sidorenko
J, Kutalik Z, Magi R, Visscher PM, Ronnegard L, Robinson MR. 2021. Probabilistic
inference of the genetic architecture underlying functional enrichment of complex
traits. Nature Communications. 12(1), 6972.
mla: Patxot, Marion, et al. “Probabilistic Inference of the Genetic Architecture
Underlying Functional Enrichment of Complex Traits.” Nature Communications,
vol. 12, no. 1, 6972, Springer Nature, 2021, doi:10.1038/s41467-021-27258-9.
short: M. Patxot, D. Trejo Banos, A. Kousathanas, E.J. Orliac, S.E. Ojavee, G. Moser,
J. Sidorenko, Z. Kutalik, R. Magi, P.M. Visscher, L. Ronnegard, M.R. Robinson,
Nature Communications 12 (2021).
date_created: 2020-09-17T10:52:38Z
date_published: 2021-11-30T00:00:00Z
date_updated: 2025-06-12T06:54:52Z
day: '30'
ddc:
- '610'
department:
- _id: MaRo
doi: 10.1038/s41467-021-27258-9
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- '34848700'
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title: Probabilistic inference of the genetic architecture underlying functional enrichment
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type: journal_article
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...