---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '21987'
abstract:
- lang: eng
text: 'We introduce JODIE, a genetic joint modeling approach that estimates how
DNA loci influence human traits by partitioning genetic effects into four components:
direct effects (from a child’s alleles), indirect maternal and paternal effects
(from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental
transmission of alleles), while uniquely accounting for assortative mating. We
analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian
Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood
educational test scores. We find direct effects to be the largest contributor
to trait variation, but combined, indirect parental and PofO effects are similarly
substantial. We support our results by within-family genome-wide association testing
and identify 276 independently associated DNA regions with a complex interplay
between direct, indirect, and PofO effects. By joint modeling, we show that direct,
indirect, and PofO effects collectively shape human phenotypic variation across
loci genome-wide.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik, Peter Visscher, and members of the Robinson
group at ISTA for their comments, which improved this manuscript. This work was
funded by an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181) and by core funding
from the Institute of Science and Technology Austria.\r\nThe Norwegian Mother, Father,
and Child Cohort Study is supported by the Norwegian Ministry of Health and Care
Services and the Ministry of Education and Research. We are grateful to all the
participating families in Norway who take part in this on-going cohort study. We
thank the Norwegian Institute of Public Health (NIPH) for generating high-quality
genomic data. The research is part of the HARVEST collaboration, supported by the
Research Council of Norway (#229624). We also thank the NORMENT Center for providing
genotype data, funded by the Research Council of Norway (#223273), South East Norway
Health Authorities, and Stiftelsen Kristian Gerhard Jebsen, and in collaboration
with deCODE Genetics. We further thank the Center for Diabetes Research, the University
of Bergen for providing genotype data funded by the ERC AdG project SELECTionPREDISPOSED,
Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council
of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western
Norway Health Authorities. The MoBa work was performed on the TSD (Tjeneste for
Sensitive Data) facilities, owned by the University of Oslo, operated and developed
by the TSD service group at the University of Oslo, IT Department (USIT, tsd-drift@usit.uio.no).
E.Y. is supported by the European Union (grant numbers 101045526 and 101073237)
and the Research Council of Norway (grant numbers 336078, 288083, and 331640).\r\nWe
would like to acknowledge the participants and investigators of the Generation Scotland
Cohort study. Generation Scotland received core support from the Chief Scientist
Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish
Funding Council (HR03006). Genotyping and methylation typing of the GS:SFHS samples
was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research
Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK
and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and
Depression Longitudinally” [STRADL] ref. 104036/Z/14/Z).\r\nWe would like to thank
and acknowledge the participants and investigators of the Estonian Biobank (EstBB)
study. The research was conducted using the Estonian Center of Genomics/Roadmap
II funded by the Estonian Research Council (project number TT17).\r\nNorwegian analyses
were performed on resources provided by Sigma2 - the National Infrastructure for
High-Performance Computing and Data Storage in Norway. Estonian Data analysis was
carried out in the High-Performance Computing Center cloud provided by University
of Tartu. Analysis of the Generation Scotland data and the summary statistics obtained
from the other analyses was conducted at IST Austria and is supported by the Scientific
Service Units (SSU) of IST Austria through resources provided by Scientific Computing
(SciComp)."
article_number: '101277'
article_processing_charge: Yes
article_type: original
author:
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Laura
full_name: Hegemann, Laura
last_name: Hegemann
- first_name: Robin J.
full_name: Hofmeister, Robin J.
last_name: Hofmeister
- first_name: Elizabeth C.
full_name: Corfield, Elizabeth C.
last_name: Corfield
- first_name: Mahdi
full_name: Mahmoudi, Mahdi
last_name: Mahmoudi
- first_name: Olivier
full_name: Delaneau, Olivier
last_name: Delaneau
- first_name: Ole A.
full_name: Andreassen, Ole A.
last_name: Andreassen
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
- first_name: Eivind
full_name: Ystrom, Eivind
last_name: Ystrom
- first_name: Alexandra
full_name: Havdahl, Alexandra
last_name: Havdahl
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Krätschmer I, Hegemann L, Hofmeister RJ, et al. Separating direct, indirect,
and parent-of-origin genetic effects in the human population. Cell Genomics.
doi:10.1016/j.xgen.2026.101277
apa: Krätschmer, I., Hegemann, L., Hofmeister, R. J., Corfield, E. C., Mahmoudi,
M., Delaneau, O., … Robinson, M. R. (n.d.). Separating direct, indirect, and parent-of-origin
genetic effects in the human population. Cell Genomics. Elsevier. https://doi.org/10.1016/j.xgen.2026.101277
chicago: Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield,
Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct,
Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” Cell
Genomics. Elsevier, n.d. https://doi.org/10.1016/j.xgen.2026.101277.
ieee: I. Krätschmer et al., “Separating direct, indirect, and parent-of-origin
genetic effects in the human population,” Cell Genomics. Elsevier.
ista: Krätschmer I, Hegemann L, Hofmeister RJ, Corfield EC, Mahmoudi M, Delaneau
O, Andreassen OA, Campbell A, Hayward C, Marioni RE, Ystrom E, Havdahl A, Robinson
MR. Separating direct, indirect, and parent-of-origin genetic effects in the human
population. Cell Genomics., 101277.
mla: Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin
Genetic Effects in the Human Population.” Cell Genomics, 101277, Elsevier,
doi:10.1016/j.xgen.2026.101277.
short: I. Krätschmer, L. Hegemann, R.J. Hofmeister, E.C. Corfield, M. Mahmoudi,
O. Delaneau, O.A. Andreassen, A. Campbell, C. Hayward, R.E. Marioni, E. Ystrom,
A. Havdahl, M.R. Robinson, Cell Genomics (n.d.).
corr_author: '1'
date_created: 2026-06-10T07:39:08Z
date_published: 2026-06-09T00:00:00Z
date_updated: 2026-06-19T07:00:47Z
day: '09'
department:
- _id: MaRo
doi: 10.1016/j.xgen.2026.101277
external_id:
pmid:
- '40909755'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.xgen.2026.101277
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: Cell Genomics
publication_identifier:
eissn:
- 2666-979X
publication_status: inpress
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Separating direct, indirect, and parent-of-origin genetic effects in the human
population
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
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'
...
---
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
relation: main_file
success: 1
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: publisher
_id: '18642'
abstract:
- lang: eng
text: "This thesis consists of two pieces of work in the broader feld of computational
biology,\r\nboth of which are methods for the analysis of large scale biological
data, implemented in\r\nefcient software.\r\nChapter 2 introduces a statistical
software for causal discovery and inference from observed\r\ngenetic marker and
phenotypic trait data. We explore in simulation how well the method\r\ncan fne-map
genetic efects, fnd the correct causal structure among tens of traits and\r\nmillions
of genetic markers, and infer the causal efect size for the discovered causal\r\nrelations.
We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank
and show that many relationships found with other methods are likely due to\r\nthe
efects of hidden confounders.\r\nChapter 3 describes how this method can be applied
to longitudinal data. I show how one\r\ncan incorporate the background knowledge
present in the known order of measurements to\r\nimprove the accuracy of the causal
discovery process, and explore the method’s ability to\r\nidentify age specifc
genetic efects, and how the error rates of this recovery are infuenced\r\nby missing
data due to diferent censoring mechanisms.\r\nChapter 4 introduces a statistical
software for the comparison of chromatin contact maps\r\nbased on the structural
similarity index. We explore the robustness of the method to\r\nnoise and size
diferences of the compared maps, show how it can measure evolutionary\r\nconservation
of topological features by providing a similarity ranking of syntenic regions,\r\nand
fnally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin
samples of medical relevance.\r\n"
acknowledgement: "I would like to thank the Swiss National Science Foundation for
funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation
and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
citation:
ama: Machnik NN. Algorithms for causal learning and comparative analysis for genomic
data. 2024. doi:10.15479/at:ista:18642
apa: Machnik, N. N. (2024). Algorithms for causal learning and comparative analysis
for genomic data. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18642
chicago: Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis
for Genomic Data.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18642.
ieee: N. N. Machnik, “Algorithms for causal learning and comparative analysis for
genomic data,” Institute of Science and Technology Austria, 2024.
ista: Machnik NN. 2024. Algorithms for causal learning and comparative analysis
for genomic data. Institute of Science and Technology Austria.
mla: Machnik, Nick N. Algorithms for Causal Learning and Comparative Analysis
for Genomic Data. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18642.
short: N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for
Genomic Data, Institute of Science and Technology Austria, 2024.
corr_author: '1'
date_created: 2024-12-10T13:49:15Z
date_published: 2024-12-11T00:00:00Z
date_updated: 2026-04-07T13:23:06Z
day: '11'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:18642
file:
- access_level: open_access
checksum: d45e4d170f9a70a1f69b44b99bd058e4
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creator: nmachnik
date_created: 2024-12-11T11:59:54Z
date_updated: 2025-06-12T22:30:02Z
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file_name: NickMachnikThesisFinal_pdfa_conv.pdf
file_size: 12845009
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date_created: 2024-12-11T11:59:34Z
date_updated: 2025-06-12T22:30:02Z
embargo_to: open_access
file_id: '18650'
file_name: thesis.zip
file_size: 14189810
relation: source_file
file_date_updated: 2025-06-12T22:30:02Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '138'
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '18648'
relation: part_of_dissertation
status: public
- id: '8707'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
title: Algorithms for causal learning and comparative analysis for genomic data
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
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: 2026-06-24T22:30:25Z
day: '10'
department:
- _id: MaRo
doi: 10.1101/2023.12.06.570392
language:
- iso: eng
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: '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: '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:
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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: '8430'
abstract:
- lang: eng
text: While recent advancements in computation and modelling have improved the analysis
of complex traits, our understanding of the genetic basis of the time at symptom
onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides
probabilistic inference of the genetic architecture of age-at-onset phenotypes
in a sampling scheme that facilitates biobank-scale time-to-event analyses. We
show in extensive simulation work the benefits BayesW provides in terms of number
of discoveries, model performance and genomic prediction. In the UK Biobank, we
find many thousands of common genomic regions underlying the age-at-onset of high
blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for
the genetic basis of onset reflecting the underlying genetic liability to disease.
Age-at-menopause and age-at-menarche are also highly polygenic, but with higher
variance contributed by low frequency variants. Genomic prediction into the Estonian
Biobank data shows that BayesW gives higher prediction accuracy than other approaches.
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 and the
University of Lausanne; the work of KF was supported by the grant PUT1665 by the
Estonian Research Council. We would like to thank Mike Goddard for comments which
greatly improved the work, 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.
article_number: '2337'
article_processing_charge: No
author:
- first_name: Sven E
full_name: Ojavee, Sven E
last_name: Ojavee
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Etienne J
full_name: Orliac, Etienne J
last_name: Orliac
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Kristi
full_name: Lall, Kristi
last_name: Lall
- first_name: Reedik
full_name: Magi, Reedik
last_name: Magi
- first_name: Krista
full_name: Fischer, Krista
last_name: Fischer
- 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, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction
of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature
Communications. 2021;12(1). doi:10.1038/s41467-021-22538-w
apa: Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M.,
Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored
time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications.
Nature Research. https://doi.org/10.1038/s41467-021-22538-w
chicago: Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac,
Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew
Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event
Phenotypes with a Bayesian Genome-Wide Analysis.” Nature Communications.
Nature Research, 2021. https://doi.org/10.1038/s41467-021-22538-w.
ieee: S. E. Ojavee et al., “Genomic architecture and prediction of censored
time-to-event phenotypes with a Bayesian genome-wide analysis,” Nature Communications,
vol. 12, no. 1. Nature Research, 2021.
ista: Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi
R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction
of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature
Communications. 12(1), 2337.
mla: Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event
Phenotypes with a Bayesian Genome-Wide Analysis.” Nature Communications,
vol. 12, no. 1, 2337, Nature Research, 2021, doi:10.1038/s41467-021-22538-w.
short: S.E. Ojavee, A. Kousathanas, D. Trejo Banos, E.J. Orliac, M. Patxot, K. Lall,
R. Magi, K. Fischer, Z. Kutalik, M.R. Robinson, Nature Communications 12 (2021).
date_created: 2020-09-17T10:53:00Z
date_published: 2021-04-20T00:00:00Z
date_updated: 2026-04-03T09:31:17Z
day: '20'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41467-021-22538-w
external_id:
isi:
- '000642509600006'
pmid:
- '33879782'
file:
- access_level: open_access
checksum: eca8b9ae713835c5b785211dd08d8a2e
content_type: application/pdf
creator: kschuh
date_created: 2021-05-04T15:07:50Z
date_updated: 2021-05-04T15:07:50Z
file_id: '9372'
file_name: 2021_nature_communications_Ojavee.pdf
file_size: 6474239
relation: main_file
success: 1
file_date_updated: 2021-05-04T15:07:50Z
has_accepted_license: '1'
intvolume: ' 12'
isi: 1
issue: '1'
language:
- iso: eng
month: '04'
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: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Nature Research
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/predicting-the-onset-of-diseases/
scopus_import: '1'
status: public
title: Genomic architecture and prediction of censored time-to-event phenotypes with
a Bayesian genome-wide analysis
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 12
year: '2021'
...