---
OA_place: publisher
OA_type: hybrid
_id: '18754'
abstract:
- lang: eng
  text: 'Exploring the molecular correlates of metabolic health measures may identify
    their shared and unique biological processes and pathways. Molecular proxies of
    these traits may also provide a more objective approach to their measurement.
    Here, DNA methylation (DNAm) data were used in epigenome-wide association studies
    (EWASs) and for training epigenetic scores (EpiScores) of six metabolic traits:
    body mass index (BMI), body fat percentage, waist-hip ratio, and blood-based measures
    of glucose, high-density lipoprotein cholesterol, and total cholesterol in >17,000
    volunteers from the Generation Scotland (GS) cohort. We observed a maximum of
    12,033 significant findings (p < 3.6 × 10−8) for BMI in a marginal linear regression
    EWAS. By contrast, a joint and conditional Bayesian penalized regression approach
    yielded 27 high-confidence associations with BMI. EpiScores trained in GS performed
    well in both Scottish and Singaporean test cohorts (Lothian Birth Cohort 1936
    [LBC1936] and Health for Life in Singapore [HELIOS]). The EpiScores for BMI and
    total cholesterol performed best in HELIOS, explaining 20.8% and 7.1% of the variance
    in the measured traits, respectively. The corresponding results in LBC1936 were
    14.4% and 3.2%, respectively. Differences were observed in HELIOS for body fat,
    where the EpiScore explained ∼9% of the variance in Chinese and Malay -subgroups
    but ∼3% in the Indian subgroup. The EpiScores also correlated with cognitive function
    in LBC1936 (standardized βrange: 0.08–0.12, false discovery rate p [pFDR] < 0.05).
    Accounting for the correlation structure across the methylome can vastly affect
    the number of lead findings in EWASs. The EpiScores of metabolic traits are broadly
    applicable across populations and can reflect differences in cognition.'
article_processing_charge: No
article_type: original
author:
- first_name: Hannah M.
  full_name: Smith, Hannah M.
  last_name: Smith
- first_name: Hong Kiat
  full_name: Ng, Hong Kiat
  last_name: Ng
- first_name: Joanna E.
  full_name: Moodie, Joanna E.
  last_name: Moodie
- first_name: Danni A.
  full_name: Gadd, Danni A.
  last_name: Gadd
- first_name: Daniel L.
  full_name: Mccartney, Daniel L.
  last_name: Mccartney
- first_name: Elena
  full_name: Bernabeu, Elena
  last_name: Bernabeu
- first_name: Archie
  full_name: Campbell, Archie
  last_name: Campbell
- first_name: Paul
  full_name: Redmond, Paul
  last_name: Redmond
- first_name: Adele
  full_name: Taylor, Adele
  last_name: Taylor
- first_name: Danielle
  full_name: Page, Danielle
  last_name: Page
- first_name: Janie
  full_name: Corley, Janie
  last_name: Corley
- first_name: Sarah E.
  full_name: Harris, Sarah E.
  last_name: Harris
- first_name: Darwin
  full_name: Tay, Darwin
  last_name: Tay
- first_name: Ian J.
  full_name: Deary, Ian J.
  last_name: Deary
- first_name: Kathryn L.
  full_name: Evans, Kathryn L.
  last_name: Evans
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
- first_name: John C.
  full_name: Chambers, John C.
  last_name: Chambers
- first_name: Marie
  full_name: Loh, Marie
  last_name: Loh
- first_name: Simon R.
  full_name: Cox, Simon R.
  last_name: Cox
- first_name: Riccardo E.
  full_name: Marioni, Riccardo E.
  last_name: Marioni
- first_name: Robert F.
  full_name: Hillary, Robert F.
  last_name: Hillary
citation:
  ama: Smith HM, Ng HK, Moodie JE, et al. DNA methylation-based predictors of metabolic
    traits in Scottish and Singaporean cohorts. <i>American Journal of Human Genetics</i>.
    2025;112(1):106-115. doi:<a href="https://doi.org/10.1016/j.ajhg.2024.11.012">10.1016/j.ajhg.2024.11.012</a>
  apa: Smith, H. M., Ng, H. K., Moodie, J. E., Gadd, D. A., Mccartney, D. L., Bernabeu,
    E., … Hillary, R. F. (2025). DNA methylation-based predictors of metabolic traits
    in Scottish and Singaporean cohorts. <i>American Journal of Human Genetics</i>.
    Elsevier. <a href="https://doi.org/10.1016/j.ajhg.2024.11.012">https://doi.org/10.1016/j.ajhg.2024.11.012</a>
  chicago: Smith, Hannah M., Hong Kiat Ng, Joanna E. Moodie, Danni A. Gadd, Daniel
    L. Mccartney, Elena Bernabeu, Archie Campbell, et al. “DNA Methylation-Based Predictors
    of Metabolic Traits in Scottish and Singaporean Cohorts.” <i>American Journal
    of Human Genetics</i>. Elsevier, 2025. <a href="https://doi.org/10.1016/j.ajhg.2024.11.012">https://doi.org/10.1016/j.ajhg.2024.11.012</a>.
  ieee: H. M. Smith <i>et al.</i>, “DNA methylation-based predictors of metabolic
    traits in Scottish and Singaporean cohorts,” <i>American Journal of Human Genetics</i>,
    vol. 112, no. 1. Elsevier, pp. 106–115, 2025.
  ista: Smith HM, Ng HK, Moodie JE, Gadd DA, Mccartney DL, Bernabeu E, Campbell A,
    Redmond P, Taylor A, Page D, Corley J, Harris SE, Tay D, Deary IJ, Evans KL, Robinson
    MR, Chambers JC, Loh M, Cox SR, Marioni RE, Hillary RF. 2025. DNA methylation-based
    predictors of metabolic traits in Scottish and Singaporean cohorts. American Journal
    of Human Genetics. 112(1), 106–115.
  mla: Smith, Hannah M., et al. “DNA Methylation-Based Predictors of Metabolic Traits
    in Scottish and Singaporean Cohorts.” <i>American Journal of Human Genetics</i>,
    vol. 112, no. 1, Elsevier, 2025, pp. 106–15, doi:<a href="https://doi.org/10.1016/j.ajhg.2024.11.012">10.1016/j.ajhg.2024.11.012</a>.
  short: H.M. Smith, H.K. Ng, J.E. Moodie, D.A. Gadd, D.L. Mccartney, E. Bernabeu,
    A. Campbell, P. Redmond, A. Taylor, D. Page, J. Corley, S.E. Harris, D. Tay, I.J.
    Deary, K.L. Evans, M.R. Robinson, J.C. Chambers, M. Loh, S.R. Cox, R.E. Marioni,
    R.F. Hillary, American Journal of Human Genetics 112 (2025) 106–115.
date_created: 2025-01-05T23:01:56Z
date_published: 2025-01-02T00:00:00Z
date_updated: 2025-02-27T12:38:23Z
day: '02'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2024.11.012
external_id:
  isi:
  - '001412498600001'
  pmid:
  - '39706196'
file:
- access_level: open_access
  checksum: 891d120554f07da2c35d38388c29a690
  content_type: application/pdf
  creator: dernst
  date_created: 2025-01-08T09:26:42Z
  date_updated: 2025-01-08T09:26:42Z
  file_id: '18776'
  file_name: 2025_AJHG_Smith.pdf
  file_size: 2266488
  relation: main_file
  success: 1
file_date_updated: 2025-01-08T09:26:42Z
fulldoi: https://doi.org/10.1016/j.ajhg.2024.11.012
has_accepted_license: '1'
intvolume: '       112'
isi: 1
issue: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
page: 106-115
pmid: 1
publication: American Journal of Human Genetics
publication_identifier:
  eissn:
  - 1537-6605
  issn:
  - 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/marioni-group/Metabolic_trait
scopus_import: '1'
status: public
title: DNA methylation-based predictors of metabolic traits in Scottish and Singaporean
  cohorts
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2025'
...
---
_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.
corr_author: '1'
date_created: 2023-09-03T22:01:15Z
date_published: 2023-09-07T00:00:00Z
date_updated: 2025-09-09T12:51:20Z
day: '07'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2023.07.006
external_id:
  isi:
  - '001074842500001'
  pmid:
  - '37543033'
file:
- access_level: open_access
  checksum: 4108b031dc726ae6b4a5ae7e021ba188
  content_type: application/pdf
  creator: dernst
  date_created: 2024-01-30T13:20:35Z
  date_updated: 2024-01-30T13:20:35Z
  file_id: '14912'
  file_name: 2023_AJHG_Ojavee.pdf
  file_size: 2551276
  relation: main_file
  success: 1
file_date_updated: 2024-01-30T13:20:35Z
fulldoi: https://doi.org/10.1016/j.ajhg.2023.07.006
has_accepted_license: '1'
intvolume: '       110'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 1549-1563
pmid: 1
publication: American Journal of Human Genetics
publication_identifier:
  eissn:
  - 1537-6605
  issn:
  - 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Genetic insights into the age-specific biological mechanisms governing human
  ovarian aging
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 110
year: '2023'
...
---
_id: '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.
corr_author: '1'
date_created: 2023-01-12T12:05:28Z
date_published: 2022-11-03T00:00:00Z
date_updated: 2025-06-11T13:55:19Z
day: '03'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1016/j.ajhg.2022.09.011
external_id:
  isi:
  - '000898683500006'
  pmid:
  - '36265482'
file:
- access_level: open_access
  checksum: 4cd7f12bfe21a8237bb095eedfa26361
  content_type: application/pdf
  creator: dernst
  date_created: 2023-01-24T09:23:01Z
  date_updated: 2023-01-24T09:23:01Z
  file_id: '12353'
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  file_size: 705195
  relation: main_file
  success: 1
file_date_updated: 2023-01-24T09:23:01Z
fulldoi: https://doi.org/10.1016/j.ajhg.2022.09.011
has_accepted_license: '1'
intvolume: '       109'
isi: 1
issue: '11'
keyword:
- Genetics (clinical)
- Genetics
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '11'
oa: 1
oa_version: Published Version
page: 2009-2017
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication: The American Journal of Human Genetics
publication_identifier:
  issn:
  - 0002-9297
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Liability-scale heritability estimation for biobank studies of low-prevalence
  disease
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    (CC BY-NC-ND 4.0)
  short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 109
year: '2022'
...