---
_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: 2023-08-01T13:38:12Z
day: '28'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13073-023-01161-y
external_id:
isi:
- '000940286600001'
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
license: https://creativecommons.org/licenses/by/4.0/
month: '02'
oa: 1
oa_version: Published Version
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 15
year: '2023'
...
---
_id: '14258'
abstract:
- lang: eng
text: There is currently little evidence that the genetic basis of human phenotype
varies significantly across the lifespan. However, time-to-event phenotypes are
understudied and can be thought of as reflecting an underlying hazard, which is
unlikely to be constant through life when values take a broad range. Here, we
find that 74% of 245 genome-wide significant genetic associations with age at
natural menopause (ANM) in the UK Biobank show a form of age-specific effect.
Nineteen of these replicated discoveries are identified only by our modeling framework,
which determines the time dependency of DNA-variant age-at-onset associations
without a significant multiple-testing burden. Across the range of early to late
menopause, we find evidence for significantly different underlying biological
pathways, changes in the signs of genetic correlations of ANM to health indicators
and outcomes, and differences in inferred causal relationships. We find that DNA
damage response processes only act to shape ovarian reserve and depletion for
women of early ANM. Genetically mediated delays in ANM were associated with increased
relative risk of breast cancer and leiomyoma at all ages and with high cholesterol
and heart failure for late-ANM women. These findings suggest that a better understanding
of the age dependency of genetic risk factor relationships among health indicators
and outcomes is achievable through appropriate statistical modeling of large-scale
biobank data.
acknowledgement: This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181)
and by core funding from the Institute of Science and Technology Austria. K.L. and
R.M. were supported by the Estonian Research Council grant 1911. Estonian Biobank
computations were performed in the High-Performance Computing Center, University
of Tartu. We thank Triin Laisk for her valuable insights and comments that helped
greatly. We would like to acknowledge the participants and investigators of UK Biobank
and Estonian Biobank studies. This project uses UK Biobank data under project number
35520.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Sven E.
full_name: Ojavee, Sven E.
last_name: Ojavee
- first_name: Liza
full_name: Darrous, Liza
last_name: Darrous
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Krista
full_name: Fischer, Krista
last_name: Fischer
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Zoltan
full_name: Kutalik, Zoltan
last_name: Kutalik
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Ojavee SE, Darrous L, Patxot M, et al. Genetic insights into the age-specific
biological mechanisms governing human ovarian aging. American Journal of Human
Genetics. 2023;110(9):1549-1563. doi:10.1016/j.ajhg.2023.07.006
apa: Ojavee, S. E., Darrous, L., Patxot, M., Läll, K., Fischer, K., Mägi, R., …
Robinson, M. R. (2023). Genetic insights into the age-specific biological mechanisms
governing human ovarian aging. American Journal of Human Genetics. Elsevier.
https://doi.org/10.1016/j.ajhg.2023.07.006
chicago: Ojavee, Sven E., Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer,
Reedik Mägi, Zoltan Kutalik, and Matthew Richard Robinson. “Genetic Insights into
the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” American
Journal of Human Genetics. Elsevier, 2023. https://doi.org/10.1016/j.ajhg.2023.07.006.
ieee: S. E. Ojavee et al., “Genetic insights into the age-specific biological
mechanisms governing human ovarian aging,” American Journal of Human Genetics,
vol. 110, no. 9. Elsevier, pp. 1549–1563, 2023.
ista: Ojavee SE, Darrous L, Patxot M, Läll K, Fischer K, Mägi R, Kutalik Z, Robinson
MR. 2023. Genetic insights into the age-specific biological mechanisms governing
human ovarian aging. American Journal of Human Genetics. 110(9), 1549–1563.
mla: Ojavee, Sven E., et al. “Genetic Insights into the Age-Specific Biological
Mechanisms Governing Human Ovarian Aging.” American Journal of Human Genetics,
vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:10.1016/j.ajhg.2023.07.006.
short: S.E. Ojavee, L. Darrous, M. Patxot, K. Läll, K. Fischer, R. Mägi, Z. Kutalik,
M.R. Robinson, American Journal of Human Genetics 110 (2023) 1549–1563.
date_created: 2023-09-03T22:01:15Z
date_published: 2023-09-07T00:00:00Z
date_updated: 2024-01-30T13:21:05Z
day: '07'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2023.07.006
external_id:
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'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2023'
...
---
_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: '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. Proceedings of the 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. Proceedings of the 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. Proceedings of the 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, Proceedings
of the 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).
date_created: 2022-08-07T22:01:56Z
date_published: 2022-07-29T00:00:00Z
date_updated: 2023-08-03T12:40:38Z
day: '29'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1073/pnas.2121279119
external_id:
isi:
- '000881496900003'
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
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '07'
oa: 1
oa_version: Published Version
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: Proceedings of the 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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 119
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).
date_created: 2023-05-23T16:28:13Z
date_published: 2022-09-02T00:00:00Z
date_updated: 2023-08-03T12:40:37Z
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: '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: '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.
date_created: 2023-01-16T09:50:58Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-08-04T09:36:21Z
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: '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: 2023-08-02T14:05:12Z
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: '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'
...
---
_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).
date_created: 2021-10-03T22:01:21Z
date_published: 2021-09-28T00:00:00Z
date_updated: 2023-08-14T07:05:15Z
day: '28'
ddc:
- '618'
department:
- _id: MaRo
doi: 10.1038/s41598-021-98411-z
external_id:
isi:
- '000701575500083'
pmid:
- '34584125'
file:
- access_level: open_access
checksum: f002ec22f609f58e1263b79e7f79601e
content_type: application/pdf
creator: cchlebak
date_created: 2021-10-05T14:56:48Z
date_updated: 2021-10-05T14:56:48Z
file_id: '10091'
file_name: 2021_ScientificReports_Robinson.pdf
file_size: 6970368
relation: main_file
success: 1
file_date_updated: 2021-10-05T14:56:48Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: Scientific Reports
publication_identifier:
eissn:
- 2045-2322
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Postpartum hemorrhage risk is driven by changes in blood composition through
pregnancy
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: 11
year: '2021'
...
---
_id: '8429'
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: 2023-09-26T10:36:14Z
day: '30'
ddc:
- '610'
department:
- _id: MaRo
doi: 10.1038/s41467-021-27258-9
external_id:
isi:
- '000724450600023'
file:
- access_level: open_access
checksum: 384681be17aff902c149a48f52d13d4f
content_type: application/pdf
creator: cchlebak
date_created: 2021-12-06T07:47:11Z
date_updated: 2021-12-06T07:47:11Z
file_id: '10419'
file_name: 2021_NatComm_Paxtot.pdf
file_size: 6519771
relation: main_file
success: 1
file_date_updated: 2021-12-06T07:47:11Z
has_accepted_license: '1'
intvolume: ' 12'
isi: 1
issue: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '13063'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Probabilistic inference of the genetic architecture underlying functional enrichment
of complex traits
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'
...
---
_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).
date_created: 2023-05-23T16:20:16Z
date_published: 2021-11-04T00:00:00Z
date_updated: 2023-09-26T10:36:15Z
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: '7708'
abstract:
- lang: eng
text: We conducted DNA methylation association analyses using Illumina 450K data
from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control
cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented
in the OSCA software. We found a significantly higher proportion of neutrophils
in cases compared to controls which replicated in an independent cohort from the
Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model
has been shown in simulations to best account for confounders. When combined in
a methylation profile score, the 25 most-associated probes identified by MOMENT
significantly classified case–control status in the Netherlands sample (area under
the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC
achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion
was included in the predictor.
article_number: '10'
article_processing_charge: No
article_type: original
author:
- first_name: Marta F.
full_name: Nabais, Marta F.
last_name: Nabais
- first_name: Tian
full_name: Lin, Tian
last_name: Lin
- first_name: Beben
full_name: Benyamin, Beben
last_name: Benyamin
- first_name: Kelly L.
full_name: Williams, Kelly L.
last_name: Williams
- first_name: Fleur C.
full_name: Garton, Fleur C.
last_name: Garton
- first_name: Anna A. E.
full_name: Vinkhuyzen, Anna A. E.
last_name: Vinkhuyzen
- first_name: Futao
full_name: Zhang, Futao
last_name: Zhang
- first_name: Costanza L.
full_name: Vallerga, Costanza L.
last_name: Vallerga
- first_name: Restuadi
full_name: Restuadi, Restuadi
last_name: Restuadi
- first_name: Anna
full_name: Freydenzon, Anna
last_name: Freydenzon
- first_name: Ramona A. J.
full_name: Zwamborn, Ramona A. J.
last_name: Zwamborn
- first_name: Paul J.
full_name: Hop, Paul J.
last_name: Hop
- 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: Jacob
full_name: Gratten, Jacob
last_name: Gratten
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Eilis
full_name: Hannon, Eilis
last_name: Hannon
- first_name: Jonathan
full_name: Mill, Jonathan
last_name: Mill
- first_name: Matthew A.
full_name: Brown, Matthew A.
last_name: Brown
- first_name: Nigel G.
full_name: Laing, Nigel G.
last_name: Laing
- first_name: Karen A.
full_name: Mather, Karen A.
last_name: Mather
- first_name: Perminder S.
full_name: Sachdev, Perminder S.
last_name: Sachdev
- first_name: Shyuan T.
full_name: Ngo, Shyuan T.
last_name: Ngo
- first_name: Frederik J.
full_name: Steyn, Frederik J.
last_name: Steyn
- first_name: Leanne
full_name: Wallace, Leanne
last_name: Wallace
- first_name: Anjali K.
full_name: Henders, Anjali K.
last_name: Henders
- first_name: Merrilee
full_name: Needham, Merrilee
last_name: Needham
- first_name: Jan H.
full_name: Veldink, Jan H.
last_name: Veldink
- first_name: Susan
full_name: Mathers, Susan
last_name: Mathers
- first_name: Garth
full_name: Nicholson, Garth
last_name: Nicholson
- first_name: Dominic B.
full_name: Rowe, Dominic B.
last_name: Rowe
- first_name: Robert D.
full_name: Henderson, Robert D.
last_name: Henderson
- first_name: Pamela A.
full_name: McCombe, Pamela A.
last_name: McCombe
- first_name: Roger
full_name: Pamphlett, Roger
last_name: Pamphlett
- first_name: Jian
full_name: Yang, Jian
last_name: Yang
- first_name: Ian P.
full_name: Blair, Ian P.
last_name: Blair
- first_name: Allan F.
full_name: McRae, Allan F.
last_name: McRae
- first_name: Naomi R.
full_name: Wray, Naomi R.
last_name: Wray
citation:
ama: Nabais MF, Lin T, Benyamin B, et al. Significant out-of-sample classification
from methylation profile scoring for amyotrophic lateral sclerosis. npj Genomic
Medicine. 2020;5. doi:10.1038/s41525-020-0118-3
apa: Nabais, M. F., Lin, T., Benyamin, B., Williams, K. L., Garton, F. C., Vinkhuyzen,
A. A. E., … Wray, N. R. (2020). Significant out-of-sample classification from
methylation profile scoring for amyotrophic lateral sclerosis. Npj Genomic
Medicine. Springer Nature. https://doi.org/10.1038/s41525-020-0118-3
chicago: Nabais, Marta F., Tian Lin, Beben Benyamin, Kelly L. Williams, Fleur C.
Garton, Anna A. E. Vinkhuyzen, Futao Zhang, et al. “Significant Out-of-Sample
Classification from Methylation Profile Scoring for Amyotrophic Lateral Sclerosis.”
Npj Genomic Medicine. Springer Nature, 2020. https://doi.org/10.1038/s41525-020-0118-3.
ieee: M. F. Nabais et al., “Significant out-of-sample classification from
methylation profile scoring for amyotrophic lateral sclerosis,” npj Genomic
Medicine, vol. 5. Springer Nature, 2020.
ista: Nabais MF, Lin T, Benyamin B, Williams KL, Garton FC, Vinkhuyzen AAE, Zhang
F, Vallerga CL, Restuadi R, Freydenzon A, Zwamborn RAJ, Hop PJ, Robinson MR, Gratten
J, Visscher PM, Hannon E, Mill J, Brown MA, Laing NG, Mather KA, Sachdev PS, Ngo
ST, Steyn FJ, Wallace L, Henders AK, Needham M, Veldink JH, Mathers S, Nicholson
G, Rowe DB, Henderson RD, McCombe PA, Pamphlett R, Yang J, Blair IP, McRae AF,
Wray NR. 2020. Significant out-of-sample classification from methylation profile
scoring for amyotrophic lateral sclerosis. npj Genomic Medicine. 5, 10.
mla: Nabais, Marta F., et al. “Significant Out-of-Sample Classification from Methylation
Profile Scoring for Amyotrophic Lateral Sclerosis.” Npj Genomic Medicine,
vol. 5, 10, Springer Nature, 2020, doi:10.1038/s41525-020-0118-3.
short: M.F. Nabais, T. Lin, B. Benyamin, K.L. Williams, F.C. Garton, A.A.E. Vinkhuyzen,
F. Zhang, C.L. Vallerga, R. Restuadi, A. Freydenzon, R.A.J. Zwamborn, P.J. Hop,
M.R. Robinson, J. Gratten, P.M. Visscher, E. Hannon, J. Mill, M.A. Brown, N.G.
Laing, K.A. Mather, P.S. Sachdev, S.T. Ngo, F.J. Steyn, L. Wallace, A.K. Henders,
M. Needham, J.H. Veldink, S. Mathers, G. Nicholson, D.B. Rowe, R.D. Henderson,
P.A. McCombe, R. Pamphlett, J. Yang, I.P. Blair, A.F. McRae, N.R. Wray, Npj Genomic
Medicine 5 (2020).
date_created: 2020-04-30T10:39:54Z
date_published: 2020-02-27T00:00:00Z
date_updated: 2021-01-12T08:14:59Z
day: '27'
doi: 10.1038/s41525-020-0118-3
extern: '1'
intvolume: ' 5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41525-020-0118-3
month: '02'
oa: 1
oa_version: Published Version
publication: npj Genomic Medicine
publication_identifier:
issn:
- 2056-7944
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Significant out-of-sample classification from methylation profile scoring for
amyotrophic lateral sclerosis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2020'
...
---
_id: '7707'
abstract:
- lang: eng
text: The growing sample size of genome-wide association studies has facilitated
the discovery of gene-environment interactions (GxE). Here we propose a maximum
likelihood method to estimate the contribution of GxE to continuous traits taking
into account all interacting environmental variables, without the need to measure
any. Extensive simulations demonstrate that our method provides unbiased interaction
estimates and excellent coverage. We also offer strategies to distinguish specific
GxE from general scale effects. Applying our method to 32 traits in the UK Biobank
reveals that while the genetic risk score (GRS) of 376 variants explains 5.2%
of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless,
this interaction holds for any variable with identical correlation to BMI as the
GRS, hence may not be GRS-specific. Still, we observe that the global contribution
of specific GRSxE to complex traits is substantial for nine obesity-related measures
(including leg impedance and trunk fat-free mass).
article_number: '1385'
article_processing_charge: No
article_type: original
author:
- first_name: Jonathan
full_name: Sulc, Jonathan
last_name: Sulc
- first_name: Ninon
full_name: Mounier, Ninon
last_name: Mounier
- first_name: Felix
full_name: Günther, Felix
last_name: Günther
- first_name: Thomas
full_name: Winkler, Thomas
last_name: Winkler
- first_name: Andrew R.
full_name: Wood, Andrew R.
last_name: Wood
- first_name: Timothy M.
full_name: Frayling, Timothy M.
last_name: Frayling
- first_name: Iris M.
full_name: Heid, Iris M.
last_name: Heid
- 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: Zoltán
full_name: Kutalik, Zoltán
last_name: Kutalik
citation:
ama: Sulc J, Mounier N, Günther F, et al. Quantification of the overall contribution
of gene-environment interaction for obesity-related traits. Nature Communications.
2020;11. doi:10.1038/s41467-020-15107-0
apa: Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T.
M., … Kutalik, Z. (2020). Quantification of the overall contribution of gene-environment
interaction for obesity-related traits. Nature Communications. Springer
Nature. https://doi.org/10.1038/s41467-020-15107-0
chicago: Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R.
Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán
Kutalik. “Quantification of the Overall Contribution of Gene-Environment Interaction
for Obesity-Related Traits.” Nature Communications. Springer Nature, 2020.
https://doi.org/10.1038/s41467-020-15107-0.
ieee: J. Sulc et al., “Quantification of the overall contribution of gene-environment
interaction for obesity-related traits,” Nature Communications, vol. 11.
Springer Nature, 2020.
ista: Sulc J, Mounier N, Günther F, Winkler T, Wood AR, Frayling TM, Heid IM, Robinson
MR, Kutalik Z. 2020. Quantification of the overall contribution of gene-environment
interaction for obesity-related traits. Nature Communications. 11, 1385.
mla: Sulc, Jonathan, et al. “Quantification of the Overall Contribution of Gene-Environment
Interaction for Obesity-Related Traits.” Nature Communications, vol. 11,
1385, Springer Nature, 2020, doi:10.1038/s41467-020-15107-0.
short: J. Sulc, N. Mounier, F. Günther, T. Winkler, A.R. Wood, T.M. Frayling, I.M.
Heid, M.R. Robinson, Z. Kutalik, Nature Communications 11 (2020).
date_created: 2020-04-30T10:39:33Z
date_published: 2020-03-20T00:00:00Z
date_updated: 2021-01-12T08:14:59Z
day: '20'
doi: 10.1038/s41467-020-15107-0
extern: '1'
intvolume: ' 11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41467-020-15107-0
month: '03'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Quantification of the overall contribution of gene-environment interaction
for obesity-related traits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2020'
...
---
_id: '7999'
abstract:
- lang: eng
text: 'Linking epigenetic marks to clinical outcomes improves insight into molecular
processes, disease prediction, and therapeutic target identification. Here, a
statistical approach is presented to infer the epigenetic architecture of complex
disease, determine the variation captured by epigenetic effects, and estimate
phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe
correlations, data structure (cell-count or relatedness), and single-nucleotide
polymorphism (SNP) marker effects, improves association estimates and in 9,448
individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and
45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole
blood methylation array data. Pathway-linked probes of blood cholesterol, lipid
transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking,
showed >1.5 times larger associations with >95% posterior inclusion probability.
Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO
model, with age-, and tissue-specificity, implying associations are a phenotypic
consequence rather than causal. '
article_number: '2865'
article_processing_charge: No
article_type: original
author:
- first_name: D
full_name: Trejo Banos, D
last_name: Trejo Banos
- first_name: DL
full_name: McCartney, DL
last_name: McCartney
- first_name: M
full_name: Patxot, M
last_name: Patxot
- first_name: L
full_name: Anchieri, L
last_name: Anchieri
- first_name: T
full_name: Battram, T
last_name: Battram
- first_name: C
full_name: Christiansen, C
last_name: Christiansen
- first_name: R
full_name: Costeira, R
last_name: Costeira
- first_name: RM
full_name: Walker, RM
last_name: Walker
- first_name: SW
full_name: Morris, SW
last_name: Morris
- first_name: A
full_name: Campbell, A
last_name: Campbell
- first_name: Q
full_name: Zhang, Q
last_name: Zhang
- first_name: DJ
full_name: Porteous, DJ
last_name: Porteous
- first_name: AF
full_name: McRae, AF
last_name: McRae
- first_name: NR
full_name: Wray, NR
last_name: Wray
- first_name: PM
full_name: Visscher, PM
last_name: Visscher
- first_name: CS
full_name: Haley, CS
last_name: Haley
- first_name: KL
full_name: Evans, KL
last_name: Evans
- first_name: IJ
full_name: Deary, IJ
last_name: Deary
- first_name: AM
full_name: McIntosh, AM
last_name: McIntosh
- first_name: G
full_name: Hemani, G
last_name: Hemani
- first_name: JT
full_name: Bell, JT
last_name: Bell
- first_name: RE
full_name: Marioni, RE
last_name: Marioni
- 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: Trejo Banos D, McCartney D, Patxot M, et al. Bayesian reassessment of the epigenetic
architecture of complex traits. Nature Communications. 2020;11. doi:10.1038/s41467-020-16520-1
apa: Trejo Banos, D., McCartney, D., Patxot, M., Anchieri, L., Battram, T., Christiansen,
C., … Robinson, M. R. (2020). Bayesian reassessment of the epigenetic architecture
of complex traits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-16520-1
chicago: Trejo Banos, D, DL McCartney, M Patxot, L Anchieri, T Battram, C Christiansen,
R Costeira, et al. “Bayesian Reassessment of the Epigenetic Architecture of Complex
Traits.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-16520-1.
ieee: D. Trejo Banos et al., “Bayesian reassessment of the epigenetic architecture
of complex traits,” Nature Communications, vol. 11. Springer Nature, 2020.
ista: Trejo Banos D, McCartney D, Patxot M, Anchieri L, Battram T, Christiansen
C, Costeira R, Walker R, Morris S, Campbell A, Zhang Q, Porteous D, McRae A, Wray
N, Visscher P, Haley C, Evans K, Deary I, McIntosh A, Hemani G, Bell J, Marioni
R, Robinson MR. 2020. Bayesian reassessment of the epigenetic architecture of
complex traits. Nature Communications. 11, 2865.
mla: Trejo Banos, D., et al. “Bayesian Reassessment of the Epigenetic Architecture
of Complex Traits.” Nature Communications, vol. 11, 2865, Springer Nature,
2020, doi:10.1038/s41467-020-16520-1.
short: D. Trejo Banos, D. McCartney, M. Patxot, L. Anchieri, T. Battram, C. Christiansen,
R. Costeira, R. Walker, S. Morris, A. Campbell, Q. Zhang, D. Porteous, A. McRae,
N. Wray, P. Visscher, C. Haley, K. Evans, I. Deary, A. McIntosh, G. Hemani, J.
Bell, R. Marioni, M.R. Robinson, Nature Communications 11 (2020).
date_created: 2020-06-22T11:18:25Z
date_published: 2020-06-08T00:00:00Z
date_updated: 2023-08-22T07:13:09Z
day: '08'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41467-020-16520-1
external_id:
isi:
- '000541702400004'
pmid:
- '32513961'
file:
- access_level: open_access
checksum: 4c96babd4cfb0d153334f6c598c0bacb
content_type: application/pdf
creator: dernst
date_created: 2020-06-22T11:24:32Z
date_updated: 2020-07-14T12:48:07Z
file_id: '8000'
file_name: 2020_NatureComm_Bayesian.pdf
file_size: 1475657
relation: main_file
file_date_updated: 2020-07-14T12:48:07Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s41467-020-19099-9
scopus_import: '1'
status: public
title: Bayesian reassessment of the epigenetic architecture of complex traits
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: 11
year: '2020'
...
---
_id: '8133'
abstract:
- lang: eng
text: The molecular factors which control circulating levels of inflammatory proteins
are not well understood. Furthermore, association studies between molecular probes
and human traits are often performed by linear model-based methods which may fail
to account for complex structure and interrelationships within molecular datasets.In
this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS)
on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy
older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel).
We employ a Bayesian framework (BayesR+) which can account for issues pertaining
to data structure and unknown confounding variables (with sensitivity analyses
using ordinary least squares- (OLS) and mixed model-based approaches). We identified
13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and
Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG
each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged
genetic variants accounted for up to 45% of variance in protein levels (for MCP2,
36% of variance alone attributable to 1 polymorphism). Methylation data accounted
for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation
in protein levels (for VEGFA) was explained using genetic and epigenetic data
combined. We demonstrated putative causal relationships between CD6 and IL18R1
with inflammatory bowel disease and between IL12B and Crohn’s disease. Our data
may aid understanding of the molecular regulation of the circulating inflammatory
proteome as well as causal relationships between inflammatory mediators and disease.
article_number: '60'
article_processing_charge: No
article_type: original
author:
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Daniel
full_name: Trejo-Banos, Daniel
last_name: Trejo-Banos
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Daniel L.
full_name: Mccartney, Daniel L.
last_name: Mccartney
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Anna J.
full_name: Stevenson, Anna J.
last_name: Stevenson
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Sven Erik
full_name: Ojavee, Sven Erik
last_name: Ojavee
- first_name: Qian
full_name: Zhang, Qian
last_name: Zhang
- first_name: David C.
full_name: Liewald, David C.
last_name: Liewald
- first_name: Craig W.
full_name: Ritchie, Craig W.
last_name: Ritchie
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Elliot M.
full_name: Tucker-Drob, Elliot M.
last_name: Tucker-Drob
- first_name: Naomi R.
full_name: Wray, Naomi R.
last_name: Wray
- first_name: Allan F.
full_name: Mcrae, Allan F.
last_name: Mcrae
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- 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, Trejo-Banos D, Kousathanas A, et al. Multi-method genome- and epigenome-wide
studies of inflammatory protein levels in healthy older adults. Genome Medicine.
2020;12(1). doi:10.1186/s13073-020-00754-1
apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., Mccartney, D. L., Harris,
S. E., Stevenson, A. J., … Marioni, R. E. (2020). Multi-method genome- and epigenome-wide
studies of inflammatory protein levels in healthy older adults. Genome Medicine.
Springer Nature. https://doi.org/10.1186/s13073-020-00754-1
chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel
L. Mccartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Multi-Method
Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older
Adults.” Genome Medicine. Springer Nature, 2020. https://doi.org/10.1186/s13073-020-00754-1.
ieee: R. F. Hillary et al., “Multi-method genome- and epigenome-wide studies
of inflammatory protein levels in healthy older adults,” Genome Medicine,
vol. 12, no. 1. Springer Nature, 2020.
ista: Hillary RF, Trejo-Banos D, Kousathanas A, Mccartney DL, Harris SE, Stevenson
AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob
EM, Wray NR, Mcrae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. Genome Medicine. 12(1), 60.
mla: Hillary, Robert F., et al. “Multi-Method Genome- and Epigenome-Wide Studies
of Inflammatory Protein Levels in Healthy Older Adults.” Genome Medicine,
vol. 12, no. 1, 60, Springer Nature, 2020, doi:10.1186/s13073-020-00754-1.
short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. Mccartney, S.E. Harris,
A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie,
K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. Mcrae, P.M. Visscher, I.J. Deary,
M.R. Robinson, R.E. Marioni, Genome Medicine 12 (2020).
date_created: 2020-07-19T22:00:58Z
date_published: 2020-07-08T00:00:00Z
date_updated: 2023-08-22T07:55:37Z
day: '08'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1186/s13073-020-00754-1
external_id:
isi:
- '000551778400001'
pmid:
- '32641083'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-07-22T06:27:38Z
date_updated: 2020-07-22T06:27:38Z
file_id: '8145'
file_name: 2020_GenomeMedicine_Hillary.pdf
file_size: 1136983
relation: main_file
success: 1
file_date_updated: 2020-07-22T06:27:38Z
has_accepted_license: '1'
intvolume: ' 12'
isi: 1
issue: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genome Medicine
publication_identifier:
eissn:
- 1756994X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '9706'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Multi-method genome- and epigenome-wide studies of inflammatory protein levels
in healthy older adults
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: '2020'
...
---
_id: '9706'
abstract:
- lang: eng
text: 'Additional file 2: Supplementary Tables. The association of pre-adjusted
protein levels with biological and technical covariates. Protein levels were adjusted
for age, sex, array plate and four genetic principal components (population structure)
prior to analyses. Significant associations are emboldened. (Table S1). pQTLs
associated with inflammatory biomarker levels from Bayesian penalised regression
model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated
with inflammatory biomarker levels from ordinary least squares regression model
(P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary
least squares GWAS and EWAS performed on inflammatory protein levels (n = 70)
in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs
associated with inflammatory biomarker levels from ordinary least squares regression
model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary
least squares and Bayesian penalised regression models for concordantly identified
SNPs. (Table S6). Estimate of heritability for blood protein levels as well as
proportion of variance explained attributable to different prior mixtures. (Table
S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood)
and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant
SNPs identified by linear model and Bayesian penalised regression and whether
they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation
for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression
are putatively associated with circulating inflammatory proteins that harbour
pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified
by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table
S12). CpGs associated with inflammatory protein biomarkers as identified by linear
model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory
protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10.
(Table S14). Estimate of variance explained for blood protein levels by DNA methylation
as well as proportion of explained attributable to different prior mixtures -
BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide
DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression
model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker
levels explained by common genetic and methylation data (joint and conditional
estimates from BayesR+). Ordered by combined variance explained by genetic and
epigenetic data - smallest to largest. Significant results from t-tests comparing
distributions for variance explained by methylation or genetics alone versus combined
estimate are emboldened. (Table S17). Genetic and epigenetic factors identified
by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian
Randomisation analyses to assess whether proteins with concordantly identified
genetic signals are causally associated with Alzheimer’s disease risk. (Table
S19).'
article_processing_charge: No
author:
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Daniel
full_name: Trejo-Banos, Daniel
last_name: Trejo-Banos
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Daniel L.
full_name: McCartney, Daniel L.
last_name: McCartney
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Anna J.
full_name: Stevenson, Anna J.
last_name: Stevenson
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Sven Erik
full_name: Ojavee, Sven Erik
last_name: Ojavee
- first_name: Qian
full_name: Zhang, Qian
last_name: Zhang
- first_name: David C.
full_name: Liewald, David C.
last_name: Liewald
- first_name: Craig W.
full_name: Ritchie, Craig W.
last_name: Ritchie
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Elliot M.
full_name: Tucker-Drob, Elliot M.
last_name: Tucker-Drob
- first_name: Naomi R.
full_name: Wray, Naomi R.
last_name: Wray
- first_name: 'Allan F. '
full_name: 'McRae, Allan F. '
last_name: McRae
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- 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, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. 2020. doi:10.6084/m9.figshare.12629697.v1
apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris,
S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. Springer Nature. https://doi.org/10.6084/m9.figshare.12629697.v1
chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel
L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional
File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein
Levels in Healthy Older Adults.” Springer Nature, 2020. https://doi.org/10.6084/m9.figshare.12629697.v1.
ieee: R. F. Hillary et al., “Additional file 2 of multi-method genome- and
epigenome-wide studies of inflammatory protein levels in healthy older adults.”
Springer Nature, 2020.
ista: Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson
AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob
EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional
file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein
levels in healthy older adults, Springer Nature, 10.6084/m9.figshare.12629697.v1.
mla: Hillary, Robert F., et al. Additional File 2 of Multi-Method Genome- and
Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.
Springer Nature, 2020, doi:10.6084/m9.figshare.12629697.v1.
short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris,
A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie,
K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary,
M.R. Robinson, R.E. Marioni, (2020).
date_created: 2021-07-23T08:59:15Z
date_published: 2020-07-09T00:00:00Z
date_updated: 2023-08-22T07:55:36Z
day: '09'
department:
- _id: MaRo
doi: 10.6084/m9.figshare.12629697.v1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.12629697.v1
month: '07'
oa: 1
oa_version: Published Version
other_data_license: CC0 + CC BY (4.0)
publisher: Springer Nature
related_material:
record:
- id: '8133'
relation: used_in_publication
status: public
status: public
title: Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory
protein levels in healthy older adults
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: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '7710'
abstract:
- lang: eng
text: 'The number of human genomes being genotyped or sequenced increases exponentially
and efficient haplotype estimation methods able to handle this amount of data
are now required. Here we present a method, SHAPEIT4, which substantially improves
upon other methods to process large genotype and high coverage sequencing datasets.
It notably exhibits sub-linear running times with sample size, provides highly
accurate haplotypes and allows integrating external phasing information such as
large reference panels of haplotypes, collections of pre-phased variants and long
sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate
its performance in terms of accuracy and running times on two gold standard datasets:
the UK Biobank data and the Genome In A Bottle.'
article_number: '5436'
article_processing_charge: No
article_type: original
author:
- first_name: Olivier
full_name: Delaneau, Olivier
last_name: Delaneau
- first_name: Jean-François
full_name: Zagury, Jean-François
last_name: Zagury
- 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: Jonathan L.
full_name: Marchini, Jonathan L.
last_name: Marchini
- first_name: Emmanouil T.
full_name: Dermitzakis, Emmanouil T.
last_name: Dermitzakis
citation:
ama: Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. Accurate,
scalable and integrative haplotype estimation. Nature Communications. 2019;10.
doi:10.1038/s41467-019-13225-y
apa: Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L., & Dermitzakis,
E. T. (2019). Accurate, scalable and integrative haplotype estimation. Nature
Communications. Springer Nature. https://doi.org/10.1038/s41467-019-13225-y
chicago: Delaneau, Olivier, Jean-François Zagury, Matthew Richard Robinson, Jonathan
L. Marchini, and Emmanouil T. Dermitzakis. “Accurate, Scalable and Integrative
Haplotype Estimation.” Nature Communications. Springer Nature, 2019. https://doi.org/10.1038/s41467-019-13225-y.
ieee: O. Delaneau, J.-F. Zagury, M. R. Robinson, J. L. Marchini, and E. T. Dermitzakis,
“Accurate, scalable and integrative haplotype estimation,” Nature Communications,
vol. 10. Springer Nature, 2019.
ista: Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. 2019. Accurate,
scalable and integrative haplotype estimation. Nature Communications. 10, 5436.
mla: Delaneau, Olivier, et al. “Accurate, Scalable and Integrative Haplotype Estimation.”
Nature Communications, vol. 10, 5436, Springer Nature, 2019, doi:10.1038/s41467-019-13225-y.
short: O. Delaneau, J.-F. Zagury, M.R. Robinson, J.L. Marchini, E.T. Dermitzakis,
Nature Communications 10 (2019).
date_created: 2020-04-30T10:40:32Z
date_published: 2019-11-28T00:00:00Z
date_updated: 2021-01-12T08:15:01Z
day: '28'
doi: 10.1038/s41467-019-13225-y
extern: '1'
intvolume: ' 10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41467-019-13225-y
month: '11'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Accurate, scalable and integrative haplotype estimation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 10
year: '2019'
...
---
_id: '7711'
abstract:
- lang: eng
text: The nature and extent of mitochondrial DNA variation in a population and how
it affects traits is poorly understood. Here we resequence the mitochondrial genomes
of 169 Drosophila Genetic Reference Panel lines, identifying 231 variants that
stratify along 12 mitochondrial haplotypes. We identify 1,845 cases of mitonuclear
allelic imbalances, thus implying that mitochondrial haplotypes are reflected
in the nuclear genome. However, no major fitness effects are associated with mitonuclear
imbalance, suggesting that such imbalances reflect population structure at the
mitochondrial level rather than genomic incompatibilities. Although mitochondrial
haplotypes have no direct impact on mitochondrial respiration, some haplotypes
are associated with stress- and metabolism-related phenotypes, including food
intake in males. Finally, through reciprocal swapping of mitochondrial genomes,
we demonstrate that a mitochondrial haplotype associated with high food intake
can rescue a low food intake phenotype. Together, our findings provide new insight
into population structure at the mitochondrial level and point to the importance
of incorporating mitochondrial haplotypes in genotype–phenotype relationship studies.
article_processing_charge: No
article_type: original
author:
- first_name: Roel P. J.
full_name: Bevers, Roel P. J.
last_name: Bevers
- first_name: Maria
full_name: Litovchenko, Maria
last_name: Litovchenko
- first_name: Adamandia
full_name: Kapopoulou, Adamandia
last_name: Kapopoulou
- first_name: Virginie S.
full_name: Braman, Virginie S.
last_name: Braman
- 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: Johan
full_name: Auwerx, Johan
last_name: Auwerx
- first_name: Brian
full_name: Hollis, Brian
last_name: Hollis
- first_name: Bart
full_name: Deplancke, Bart
last_name: Deplancke
citation:
ama: Bevers RPJ, Litovchenko M, Kapopoulou A, et al. Mitochondrial haplotypes affect
metabolic phenotypes in the Drosophila Genetic Reference Panel. Nature Metabolism.
2019;1(12):1226-1242. doi:10.1038/s42255-019-0147-3
apa: Bevers, R. P. J., Litovchenko, M., Kapopoulou, A., Braman, V. S., Robinson,
M. R., Auwerx, J., … Deplancke, B. (2019). Mitochondrial haplotypes affect metabolic
phenotypes in the Drosophila Genetic Reference Panel. Nature Metabolism.
Springer Nature. https://doi.org/10.1038/s42255-019-0147-3
chicago: Bevers, Roel P. J., Maria Litovchenko, Adamandia Kapopoulou, Virginie S.
Braman, Matthew Richard Robinson, Johan Auwerx, Brian Hollis, and Bart Deplancke.
“Mitochondrial Haplotypes Affect Metabolic Phenotypes in the Drosophila Genetic
Reference Panel.” Nature Metabolism. Springer Nature, 2019. https://doi.org/10.1038/s42255-019-0147-3.
ieee: R. P. J. Bevers et al., “Mitochondrial haplotypes affect metabolic
phenotypes in the Drosophila Genetic Reference Panel,” Nature Metabolism,
vol. 1, no. 12. Springer Nature, pp. 1226–1242, 2019.
ista: Bevers RPJ, Litovchenko M, Kapopoulou A, Braman VS, Robinson MR, Auwerx J,
Hollis B, Deplancke B. 2019. Mitochondrial haplotypes affect metabolic phenotypes
in the Drosophila Genetic Reference Panel. Nature Metabolism. 1(12), 1226–1242.
mla: Bevers, Roel P. J., et al. “Mitochondrial Haplotypes Affect Metabolic Phenotypes
in the Drosophila Genetic Reference Panel.” Nature Metabolism, vol. 1,
no. 12, Springer Nature, 2019, pp. 1226–42, doi:10.1038/s42255-019-0147-3.
short: R.P.J. Bevers, M. Litovchenko, A. Kapopoulou, V.S. Braman, M.R. Robinson,
J. Auwerx, B. Hollis, B. Deplancke, Nature Metabolism 1 (2019) 1226–1242.
date_created: 2020-04-30T10:40:56Z
date_published: 2019-12-09T00:00:00Z
date_updated: 2021-01-12T08:15:01Z
day: '09'
doi: 10.1038/s42255-019-0147-3
extern: '1'
intvolume: ' 1'
issue: '12'
language:
- iso: eng
month: '12'
oa_version: None
page: 1226-1242
publication: Nature Metabolism
publication_identifier:
issn:
- 2522-5812
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s42255-020-0202-0
status: public
title: Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic
Reference Panel
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2019'
...
---
_id: '7782'
abstract:
- lang: eng
text: As genome-wide association studies (GWAS) increased in size, numerous gene-environment
interactions (GxE) have been discovered, many of which however explore only one
environment at a time and may suffer from statistical artefacts leading to biased
interaction estimates. Here we propose a maximum likelihood method to estimate
the contribution of GxE to complex traits taking into account all interacting
environmental variables at the same time, without the need to measure any. This
is possible because GxE induces fluctuations in the conditional trait variance,
the extent of which depends on the strength of GxE. The approach can be applied
to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive
simulations demonstrated that our method yields unbiased interaction estimates
and excellent confidence interval coverage. We also offer a strategy to distinguish
specific GxE from general heteroscedasticity (scale effects). Applying our method
to 32 complex traits in the UK Biobank reveals that for body mass index (BMI)
the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution.
However, this interaction is not specific to the GRS and holds for any variable
similarly correlated with BMI. On the contrary, the GRSxE interaction effect for
leg impedance Embedded Image is significantly (P < 10−56) larger than it would
be expected for a similarly correlated variable Embedded Image. We showed that
our method could robustly detect the global contribution of GxE to complex traits,
which turned out to be substantial for certain obesity measures.
article_processing_charge: No
author:
- first_name: Jonathan
full_name: Sulc, Jonathan
last_name: Sulc
- first_name: Ninon
full_name: Mounier, Ninon
last_name: Mounier
- first_name: Felix
full_name: Günther, Felix
last_name: Günther
- first_name: Thomas
full_name: Winkler, Thomas
last_name: Winkler
- first_name: Andrew R.
full_name: Wood, Andrew R.
last_name: Wood
- first_name: Timothy M.
full_name: Frayling, Timothy M.
last_name: Frayling
- first_name: Iris M.
full_name: Heid, Iris M.
last_name: Heid
- 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: Zoltán
full_name: Kutalik, Zoltán
last_name: Kutalik
citation:
ama: 'Sulc J, Mounier N, Günther F, et al. Maximum likelihood method quantifies
the overall contribution of gene-environment interaction to continuous traits:
An application to complex traits in the UK Biobank. bioRxiv. 2019.'
apa: 'Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T.
M., … Kutalik, Z. (2019). Maximum likelihood method quantifies the overall contribution
of gene-environment interaction to continuous traits: An application to complex
traits in the UK Biobank. bioRxiv. Cold Spring Harbor Laboratory.'
chicago: 'Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R.
Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán
Kutalik. “Maximum Likelihood Method Quantifies the Overall Contribution of Gene-Environment
Interaction to Continuous Traits: An Application to Complex Traits in the UK Biobank.”
BioRxiv. Cold Spring Harbor Laboratory, 2019.'
ieee: 'J. Sulc et al., “Maximum likelihood method quantifies the overall
contribution of gene-environment interaction to continuous traits: An application
to complex traits in the UK Biobank,” bioRxiv. Cold Spring Harbor Laboratory,
2019.'
ista: 'Sulc J, Mounier N, Günther F, Winkler T, Wood AR, Frayling TM, Heid IM, Robinson
MR, Kutalik Z. 2019. Maximum likelihood method quantifies the overall contribution
of gene-environment interaction to continuous traits: An application to complex
traits in the UK Biobank. bioRxiv, .'
mla: 'Sulc, Jonathan, et al. “Maximum Likelihood Method Quantifies the Overall Contribution
of Gene-Environment Interaction to Continuous Traits: An Application to Complex
Traits in the UK Biobank.” BioRxiv, Cold Spring Harbor Laboratory, 2019.'
short: J. Sulc, N. Mounier, F. Günther, T. Winkler, A.R. Wood, T.M. Frayling, I.M.
Heid, M.R. Robinson, Z. Kutalik, BioRxiv (2019).
date_created: 2020-04-30T13:04:26Z
date_published: 2019-06-14T00:00:00Z
date_updated: 2021-01-12T08:15:30Z
day: '14'
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/632380 '
month: '06'
oa: 1
oa_version: Preprint
page: '20'
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
status: public
title: 'Maximum likelihood method quantifies the overall contribution of gene-environment
interaction to continuous traits: An application to complex traits in the UK Biobank'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...