---
_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).
date_created: 2022-01-30T23:01:33Z
date_published: 2022-01-17T00:00:00Z
date_updated: 2023-08-02T14:05:13Z
day: '17'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13059-021-02596-5
external_id:
isi:
- '000744358300002'
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
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 23
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.
date_created: 2023-01-12T12:05:28Z
date_published: 2022-11-03T00:00:00Z
date_updated: 2023-08-04T08:56:46Z
day: '03'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2022.09.011
external_id:
isi:
- '000898683500006'
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
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
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: 2023-08-04T11:00:17Z
day: '20'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41467-021-22538-w
external_id:
isi:
- '000642509600006'
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
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:
- '20411723'
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 12
year: '2021'
...