---
_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. <i>American Journal of Human
    Genetics</i>. 2023;110(9):1549-1563. doi:<a href="https://doi.org/10.1016/j.ajhg.2023.07.006">10.1016/j.ajhg.2023.07.006</a>
  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. <i>American Journal of Human Genetics</i>. Elsevier.
    <a href="https://doi.org/10.1016/j.ajhg.2023.07.006">https://doi.org/10.1016/j.ajhg.2023.07.006</a>
  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.” <i>American
    Journal of Human Genetics</i>. Elsevier, 2023. <a href="https://doi.org/10.1016/j.ajhg.2023.07.006">https://doi.org/10.1016/j.ajhg.2023.07.006</a>.
  ieee: S. E. Ojavee <i>et al.</i>, “Genetic insights into the age-specific biological
    mechanisms governing human ovarian aging,” <i>American Journal of Human Genetics</i>,
    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.” <i>American Journal of Human Genetics</i>,
    vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:<a href="https://doi.org/10.1016/j.ajhg.2023.07.006">10.1016/j.ajhg.2023.07.006</a>.
  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: '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. <i>Genome Medicine</i>. 2023;15. doi:<a href="https://doi.org/10.1186/s13073-023-01161-y">10.1186/s13073-023-01161-y</a>
  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. <i>Genome Medicine</i>. Springer Nature. <a href="https://doi.org/10.1186/s13073-023-01161-y">https://doi.org/10.1186/s13073-023-01161-y</a>
  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.” <i>Genome Medicine</i>. Springer Nature, 2023.
    <a href="https://doi.org/10.1186/s13073-023-01161-y">https://doi.org/10.1186/s13073-023-01161-y</a>.
  ieee: E. Bernabeu <i>et al.</i>, “Refining epigenetic prediction of chronological
    and biological age,” <i>Genome Medicine</i>, 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.” <i>Genome Medicine</i>, vol. 15, 12, Springer Nature, 2023, doi:<a
    href="https://doi.org/10.1186/s13073-023-01161-y">10.1186/s13073-023-01161-y</a>.
  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
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: '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. <i>Genome Biology</i>. 2022;23(1). doi:<a href="https://doi.org/10.1186/s13059-021-02596-5">10.1186/s13059-021-02596-5</a>
  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. <i>Genome Biology</i>. Springer Nature. <a href="https://doi.org/10.1186/s13059-021-02596-5">https://doi.org/10.1186/s13059-021-02596-5</a>
  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.” <i>Genome Biology</i>. Springer Nature, 2022.
    <a href="https://doi.org/10.1186/s13059-021-02596-5">https://doi.org/10.1186/s13059-021-02596-5</a>.
  ieee: D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive
    abilities,” <i>Genome Biology</i>, 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.” <i>Genome Biology</i>, vol. 23, no. 1, 26, Springer Nature, 2022,
    doi:<a href="https://doi.org/10.1186/s13059-021-02596-5">10.1186/s13059-021-02596-5</a>.
  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:
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  date_updated: 2022-01-31T13:16:05Z
  file_id: '10708'
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file_date_updated: 2022-01-31T13:16:05Z
has_accepted_license: '1'
intvolume: '        23'
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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. <i>Proceedings of the National Academy of
    Sciences of the United States of America</i>. 2022;119(31). doi:<a href="https://doi.org/10.1073/pnas.2121279119">10.1073/pnas.2121279119</a>
  apa: Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher,
    P. M., &#38; Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction
    accuracy in biobank data. <i>Proceedings of the National Academy of Sciences of
    the United States of America</i>. Proceedings of the National Academy of Sciences.
    <a href="https://doi.org/10.1073/pnas.2121279119">https://doi.org/10.1073/pnas.2121279119</a>
  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.” <i>Proceedings of the National
    Academy of Sciences of the United States of America</i>. Proceedings of the National
    Academy of Sciences, 2022. <a href="https://doi.org/10.1073/pnas.2121279119">https://doi.org/10.1073/pnas.2121279119</a>.
  ieee: E. J. Orliac <i>et al.</i>, “Improving GWAS discovery and genomic prediction
    accuracy in biobank data,” <i>Proceedings of the National Academy of Sciences
    of the United States of America</i>, 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.” <i>Proceedings of the National Academy of Sciences
    of the United States of America</i>, vol. 119, no. 31, e2121279119, Proceedings
    of the National Academy of Sciences, 2022, doi:<a href="https://doi.org/10.1073/pnas.2121279119">10.1073/pnas.2121279119</a>.
  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:<a href="https://doi.org/10.5061/DRYAD.GTHT76HMZ">10.5061/DRYAD.GTHT76HMZ</a>
  apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P.,
    &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and
    genomic prediction accuracy in biobank data. Dryad. <a href="https://doi.org/10.5061/DRYAD.GTHT76HMZ">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>
  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. <a href="https://doi.org/10.5061/DRYAD.GTHT76HMZ">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>.
  ieee: E. Orliac <i>et al.</i>, “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, <a href="https://doi.org/10.5061/DRYAD.GTHT76HMZ">10.5061/DRYAD.GTHT76HMZ</a>.
  mla: Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and
    Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href="https://doi.org/10.5061/DRYAD.GTHT76HMZ">10.5061/DRYAD.GTHT76HMZ</a>.
  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. <i>The American Journal of Human
    Genetics</i>. 2022;109(11):2009-2017. doi:<a href="https://doi.org/10.1016/j.ajhg.2022.09.011">10.1016/j.ajhg.2022.09.011</a>
  apa: Ojavee, S. E., Kutalik, Z., &#38; Robinson, M. R. (2022). Liability-scale heritability
    estimation for biobank studies of low-prevalence disease. <i>The American Journal
    of Human Genetics</i>. Elsevier. <a href="https://doi.org/10.1016/j.ajhg.2022.09.011">https://doi.org/10.1016/j.ajhg.2022.09.011</a>
  chicago: Ojavee, Sven E., Zoltan Kutalik, and Matthew Richard Robinson. “Liability-Scale
    Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The
    American Journal of Human Genetics</i>. Elsevier, 2022. <a href="https://doi.org/10.1016/j.ajhg.2022.09.011">https://doi.org/10.1016/j.ajhg.2022.09.011</a>.
  ieee: S. E. Ojavee, Z. Kutalik, and M. R. Robinson, “Liability-scale heritability
    estimation for biobank studies of low-prevalence disease,” <i>The American Journal
    of Human Genetics</i>, 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.” <i>The American Journal of Human Genetics</i>,
    vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:<a href="https://doi.org/10.1016/j.ajhg.2022.09.011">10.1016/j.ajhg.2022.09.011</a>.
  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. <i>European Journal
    of Haematology</i>. 2022;109(5):566-575. doi:<a href="https://doi.org/10.1111/ejh.13844">10.1111/ejh.13844</a>'
  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. <i>European Journal of Haematology</i>.
    Wiley. <a href="https://doi.org/10.1111/ejh.13844">https://doi.org/10.1111/ejh.13844</a>'
  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.”
    <i>European Journal of Haematology</i>. Wiley, 2022. <a href="https://doi.org/10.1111/ejh.13844">https://doi.org/10.1111/ejh.13844</a>.'
  ieee: 'M. Patxot <i>et al.</i>, “Haematological changes from conception to childbirth:
    An indicator of major pregnancy complications,” <i>European Journal of Haematology</i>,
    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.” <i>European Journal of Haematology</i>,
    vol. 109, no. 5, Wiley, 2022, pp. 566–75, doi:<a href="https://doi.org/10.1111/ejh.13844">10.1111/ejh.13844</a>.'
  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: '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. <i>Nature
    Communications</i>. 2021;12(1). doi:<a href="https://doi.org/10.1038/s41467-021-27258-9">10.1038/s41467-021-27258-9</a>
  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. <i>Nature Communications</i>.
    Springer Nature. <a href="https://doi.org/10.1038/s41467-021-27258-9">https://doi.org/10.1038/s41467-021-27258-9</a>
  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.”
    <i>Nature Communications</i>. Springer Nature, 2021. <a href="https://doi.org/10.1038/s41467-021-27258-9">https://doi.org/10.1038/s41467-021-27258-9</a>.
  ieee: M. Patxot <i>et al.</i>, “Probabilistic inference of the genetic architecture
    underlying functional enrichment of complex traits,” <i>Nature Communications</i>,
    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.” <i>Nature Communications</i>,
    vol. 12, no. 1, 6972, Springer Nature, 2021, doi:<a href="https://doi.org/10.1038/s41467-021-27258-9">10.1038/s41467-021-27258-9</a>.
  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: '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. <i>Nature
    Communications</i>. 2021;12(1). doi:<a href="https://doi.org/10.1038/s41467-021-22538-w">10.1038/s41467-021-22538-w</a>
  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. <i>Nature Communications</i>.
    Nature Research. <a href="https://doi.org/10.1038/s41467-021-22538-w">https://doi.org/10.1038/s41467-021-22538-w</a>
  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.” <i>Nature Communications</i>.
    Nature Research, 2021. <a href="https://doi.org/10.1038/s41467-021-22538-w">https://doi.org/10.1038/s41467-021-22538-w</a>.
  ieee: S. E. Ojavee <i>et al.</i>, “Genomic architecture and prediction of censored
    time-to-event phenotypes with a Bayesian genome-wide analysis,” <i>Nature Communications</i>,
    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.” <i>Nature Communications</i>,
    vol. 12, no. 1, 2337, Nature Research, 2021, doi:<a href="https://doi.org/10.1038/s41467-021-22538-w">10.1038/s41467-021-22538-w</a>.
  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: '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 &gt;95%
    probability of contributing &gt;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:<a href="https://doi.org/10.5061/dryad.sqv9s4n51">10.5061/dryad.sqv9s4n51</a>
  apa: Robinson, M. R. (2021). Probabilistic inference of the genetic architecture
    of functional enrichment of complex traits. Dryad. <a href="https://doi.org/10.5061/dryad.sqv9s4n51">https://doi.org/10.5061/dryad.sqv9s4n51</a>
  chicago: Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture
    of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href="https://doi.org/10.5061/dryad.sqv9s4n51">https://doi.org/10.5061/dryad.sqv9s4n51</a>.
  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, <a href="https://doi.org/10.5061/dryad.sqv9s4n51">10.5061/dryad.sqv9s4n51</a>.
  mla: Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture
    of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href="https://doi.org/10.5061/dryad.sqv9s4n51">10.5061/dryad.sqv9s4n51</a>.
  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: '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:<a href="https://doi.org/10.5281/ZENODO.5794028">10.5281/ZENODO.5794028</a>
  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. <a href="https://doi.org/10.5281/ZENODO.5794028">https://doi.org/10.5281/ZENODO.5794028</a>
  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. <a href="https://doi.org/10.5281/ZENODO.5794028">https://doi.org/10.5281/ZENODO.5794028</a>.
  ieee: D. L. McCartney <i>et al.</i>, “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, <a href="https://doi.org/10.5281/ZENODO.5794028">10.5281/ZENODO.5794028</a>.
  mla: McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive
    Abilities</i>. Zenodo, 2021, doi:<a href="https://doi.org/10.5281/ZENODO.5794028">10.5281/ZENODO.5794028</a>.
  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: '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. <i>Scientific Reports</i>.
    2021;11. doi:<a href="https://doi.org/10.1038/s41598-021-98411-z">10.1038/s41598-021-98411-z</a>
  apa: Robinson, M. R., Patxot, M., Stojanov, M., Blum, S., &#38; Baud, D. (2021).
    Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy.
    <i>Scientific Reports</i>. Springer Nature. <a href="https://doi.org/10.1038/s41598-021-98411-z">https://doi.org/10.1038/s41598-021-98411-z</a>
  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.” <i>Scientific Reports</i>. Springer Nature, 2021. <a href="https://doi.org/10.1038/s41598-021-98411-z">https://doi.org/10.1038/s41598-021-98411-z</a>.
  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,”
    <i>Scientific Reports</i>, 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.” <i>Scientific Reports</i>, vol.
    11, 19238, Springer Nature, 2021, doi:<a href="https://doi.org/10.1038/s41598-021-98411-z">10.1038/s41598-021-98411-z</a>.
  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: '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. <i>Nature Communications</i>. 2020;11. doi:<a
    href="https://doi.org/10.1038/s41467-020-16520-1">10.1038/s41467-020-16520-1</a>
  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. <i>Nature Communications</i>. Springer Nature. <a href="https://doi.org/10.1038/s41467-020-16520-1">https://doi.org/10.1038/s41467-020-16520-1</a>
  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.” <i>Nature Communications</i>. Springer Nature, 2020. <a href="https://doi.org/10.1038/s41467-020-16520-1">https://doi.org/10.1038/s41467-020-16520-1</a>.
  ieee: D. Trejo Banos <i>et al.</i>, “Bayesian reassessment of the epigenetic architecture
    of complex traits,” <i>Nature Communications</i>, 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.” <i>Nature Communications</i>, vol. 11, 2865, Springer Nature,
    2020, doi:<a href="https://doi.org/10.1038/s41467-020-16520-1">10.1038/s41467-020-16520-1</a>.
  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. <i>Genome Medicine</i>.
    2020;12(1). doi:<a href="https://doi.org/10.1186/s13073-020-00754-1">10.1186/s13073-020-00754-1</a>
  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. <i>Genome Medicine</i>.
    Springer Nature. <a href="https://doi.org/10.1186/s13073-020-00754-1">https://doi.org/10.1186/s13073-020-00754-1</a>
  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.” <i>Genome Medicine</i>. Springer Nature, 2020. <a href="https://doi.org/10.1186/s13073-020-00754-1">https://doi.org/10.1186/s13073-020-00754-1</a>.
  ieee: R. F. Hillary <i>et al.</i>, “Multi-method genome- and epigenome-wide studies
    of inflammatory protein levels in healthy older adults,” <i>Genome Medicine</i>,
    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.” <i>Genome Medicine</i>,
    vol. 12, no. 1, 60, Springer Nature, 2020, doi:<a href="https://doi.org/10.1186/s13073-020-00754-1">10.1186/s13073-020-00754-1</a>.
  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: '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. <i>Nature Communications</i>.
    2020;11. doi:<a href="https://doi.org/10.1038/s41467-020-15107-0">10.1038/s41467-020-15107-0</a>
  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. <i>Nature Communications</i>. Springer
    Nature. <a href="https://doi.org/10.1038/s41467-020-15107-0">https://doi.org/10.1038/s41467-020-15107-0</a>
  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.” <i>Nature Communications</i>. Springer Nature, 2020.
    <a href="https://doi.org/10.1038/s41467-020-15107-0">https://doi.org/10.1038/s41467-020-15107-0</a>.
  ieee: J. Sulc <i>et al.</i>, “Quantification of the overall contribution of gene-environment
    interaction for obesity-related traits,” <i>Nature Communications</i>, 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.” <i>Nature Communications</i>, vol. 11,
    1385, Springer Nature, 2020, doi:<a href="https://doi.org/10.1038/s41467-020-15107-0">10.1038/s41467-020-15107-0</a>.
  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: '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. <i>npj Genomic
    Medicine</i>. 2020;5. doi:<a href="https://doi.org/10.1038/s41525-020-0118-3">10.1038/s41525-020-0118-3</a>
  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. <i>Npj Genomic
    Medicine</i>. Springer Nature. <a href="https://doi.org/10.1038/s41525-020-0118-3">https://doi.org/10.1038/s41525-020-0118-3</a>
  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.”
    <i>Npj Genomic Medicine</i>. Springer Nature, 2020. <a href="https://doi.org/10.1038/s41525-020-0118-3">https://doi.org/10.1038/s41525-020-0118-3</a>.
  ieee: M. F. Nabais <i>et al.</i>, “Significant out-of-sample classification from
    methylation profile scoring for amyotrophic lateral sclerosis,” <i>npj Genomic
    Medicine</i>, 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.” <i>Npj Genomic Medicine</i>,
    vol. 5, 10, Springer Nature, 2020, doi:<a href="https://doi.org/10.1038/s41525-020-0118-3">10.1038/s41525-020-0118-3</a>.
  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: '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:<a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>
  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. <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">https://doi.org/10.6084/m9.figshare.12629697.v1</a>
  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. <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.
  ieee: R. F. Hillary <i>et al.</i>, “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, <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>.
  mla: Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and
    Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>.
    Springer Nature, 2020, doi:<a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>.
  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: '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. <i>bioRxiv</i>. 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. <i>bioRxiv</i>. 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.”
    <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2019.'
  ieee: 'J. Sulc <i>et al.</i>, “Maximum likelihood method quantifies the overall
    contribution of gene-environment interaction to continuous traits: An application
    to complex traits in the UK Biobank,” <i>bioRxiv</i>. 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.” <i>BioRxiv</i>, 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'
...
---
_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. <i>Nature Communications</i>. 2019;10.
    doi:<a href="https://doi.org/10.1038/s41467-019-13225-y">10.1038/s41467-019-13225-y</a>
  apa: Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L., &#38; Dermitzakis,
    E. T. (2019). Accurate, scalable and integrative haplotype estimation. <i>Nature
    Communications</i>. Springer Nature. <a href="https://doi.org/10.1038/s41467-019-13225-y">https://doi.org/10.1038/s41467-019-13225-y</a>
  chicago: Delaneau, Olivier, Jean-François Zagury, Matthew Richard Robinson, Jonathan
    L. Marchini, and Emmanouil T. Dermitzakis. “Accurate, Scalable and Integrative
    Haplotype Estimation.” <i>Nature Communications</i>. Springer Nature, 2019. <a
    href="https://doi.org/10.1038/s41467-019-13225-y">https://doi.org/10.1038/s41467-019-13225-y</a>.
  ieee: O. Delaneau, J.-F. Zagury, M. R. Robinson, J. L. Marchini, and E. T. Dermitzakis,
    “Accurate, scalable and integrative haplotype estimation,” <i>Nature Communications</i>,
    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.”
    <i>Nature Communications</i>, vol. 10, 5436, Springer Nature, 2019, doi:<a href="https://doi.org/10.1038/s41467-019-13225-y">10.1038/s41467-019-13225-y</a>.
  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. <i>Nature Metabolism</i>.
    2019;1(12):1226-1242. doi:<a href="https://doi.org/10.1038/s42255-019-0147-3">10.1038/s42255-019-0147-3</a>
  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. <i>Nature Metabolism</i>.
    Springer Nature. <a href="https://doi.org/10.1038/s42255-019-0147-3">https://doi.org/10.1038/s42255-019-0147-3</a>
  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.” <i>Nature Metabolism</i>. Springer Nature, 2019. <a href="https://doi.org/10.1038/s42255-019-0147-3">https://doi.org/10.1038/s42255-019-0147-3</a>.
  ieee: R. P. J. Bevers <i>et al.</i>, “Mitochondrial haplotypes affect metabolic
    phenotypes in the Drosophila Genetic Reference Panel,” <i>Nature Metabolism</i>,
    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.” <i>Nature Metabolism</i>, vol. 1,
    no. 12, Springer Nature, 2019, pp. 1226–42, doi:<a href="https://doi.org/10.1038/s42255-019-0147-3">10.1038/s42255-019-0147-3</a>.
  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'
...