--- _id: '12719' abstract: - lang: eng text: "Background\r\nEpigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.\r\n\r\nMethods\r\nFirst, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study).\r\n\r\nResults\r\nThrough the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.\r\n\r\nConclusions\r\nThe integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age." acknowledgement: We are grateful to all the families who took part, the general practitioners, and the Scottish School of Primary Care for their help in recruiting them and the whole GS team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants, and nurses. article_number: '12' article_processing_charge: No article_type: original author: - first_name: Elena full_name: Bernabeu, Elena last_name: Bernabeu - first_name: Daniel L. full_name: Mccartney, Daniel L. last_name: Mccartney - first_name: Danni A. full_name: Gadd, Danni A. last_name: Gadd - first_name: Robert F. full_name: Hillary, Robert F. last_name: Hillary - first_name: Ake T. full_name: Lu, Ake T. last_name: Lu - first_name: Lee full_name: Murphy, Lee last_name: Murphy - first_name: Nicola full_name: Wrobel, Nicola last_name: Wrobel - first_name: Archie full_name: Campbell, Archie last_name: Campbell - first_name: Sarah E. full_name: Harris, Sarah E. last_name: Harris - first_name: David full_name: Liewald, David last_name: Liewald - first_name: Caroline full_name: Hayward, Caroline last_name: Hayward - first_name: Cathie full_name: Sudlow, Cathie last_name: Sudlow - first_name: Simon R. full_name: Cox, Simon R. last_name: Cox - first_name: Kathryn L. full_name: Evans, Kathryn L. last_name: Evans - first_name: Steve full_name: Horvath, Steve last_name: Horvath - first_name: Andrew M. full_name: Mcintosh, Andrew M. last_name: Mcintosh - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Catalina A. full_name: Vallejos, Catalina A. last_name: Vallejos - first_name: Riccardo E. full_name: Marioni, Riccardo E. last_name: Marioni citation: ama: Bernabeu E, Mccartney DL, Gadd DA, et al. Refining epigenetic prediction of chronological and biological age. Genome Medicine. 2023;15. doi:10.1186/s13073-023-01161-y apa: Bernabeu, E., Mccartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., … Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. Genome Medicine. Springer Nature. https://doi.org/10.1186/s13073-023-01161-y chicago: Bernabeu, Elena, Daniel L. Mccartney, Danni A. Gadd, Robert F. Hillary, Ake T. Lu, Lee Murphy, Nicola Wrobel, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” Genome Medicine. Springer Nature, 2023. https://doi.org/10.1186/s13073-023-01161-y. ieee: E. Bernabeu et al., “Refining epigenetic prediction of chronological and biological age,” Genome Medicine, vol. 15. Springer Nature, 2023. ista: Bernabeu E, Mccartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, Mcintosh AM, Robinson MR, Vallejos CA, Marioni RE. 2023. Refining epigenetic prediction of chronological and biological age. Genome Medicine. 15, 12. mla: Bernabeu, Elena, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” Genome Medicine, vol. 15, 12, Springer Nature, 2023, doi:10.1186/s13073-023-01161-y. short: E. Bernabeu, D.L. Mccartney, D.A. Gadd, R.F. Hillary, A.T. Lu, L. Murphy, N. Wrobel, A. Campbell, S.E. Harris, D. Liewald, C. Hayward, C. Sudlow, S.R. Cox, K.L. Evans, S. Horvath, A.M. Mcintosh, M.R. Robinson, C.A. Vallejos, R.E. Marioni, Genome Medicine 15 (2023). date_created: 2023-03-12T23:01:02Z date_published: 2023-02-28T00:00:00Z date_updated: 2023-08-01T13:38:12Z day: '28' ddc: - '570' department: - _id: MaRo doi: 10.1186/s13073-023-01161-y external_id: isi: - '000940286600001' file: - access_level: open_access checksum: 833b837910c4db42fb5f0f34125f77a7 content_type: application/pdf creator: cchlebak date_created: 2023-03-14T10:29:47Z date_updated: 2023-03-14T10:29:47Z file_id: '12722' file_name: 2023_GenomeMed_Bernabeu.pdf file_size: 4275987 relation: main_file success: 1 file_date_updated: 2023-03-14T10:29:47Z has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '02' oa: 1 oa_version: Published Version publication: Genome Medicine publication_identifier: eissn: - 1756-994X publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Refining epigenetic prediction of chronological and biological age tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 15 year: '2023' ... --- _id: '14258' abstract: - lang: eng text: There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data. acknowledgement: This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181) and by core funding from the Institute of Science and Technology Austria. K.L. and R.M. were supported by the Estonian Research Council grant 1911. Estonian Biobank computations were performed in the High-Performance Computing Center, University of Tartu. We thank Triin Laisk for her valuable insights and comments that helped greatly. We would like to acknowledge the participants and investigators of UK Biobank and Estonian Biobank studies. This project uses UK Biobank data under project number 35520. article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Sven E. full_name: Ojavee, Sven E. last_name: Ojavee - first_name: Liza full_name: Darrous, Liza last_name: Darrous - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Kristi full_name: Läll, Kristi last_name: Läll - first_name: Krista full_name: Fischer, Krista last_name: Fischer - first_name: Reedik full_name: Mägi, Reedik last_name: Mägi - first_name: Zoltan full_name: Kutalik, Zoltan last_name: Kutalik - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Ojavee SE, Darrous L, Patxot M, et al. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. American Journal of Human Genetics. 2023;110(9):1549-1563. doi:10.1016/j.ajhg.2023.07.006 apa: Ojavee, S. E., Darrous, L., Patxot, M., Läll, K., Fischer, K., Mägi, R., … Robinson, M. R. (2023). Genetic insights into the age-specific biological mechanisms governing human ovarian aging. American Journal of Human Genetics. Elsevier. https://doi.org/10.1016/j.ajhg.2023.07.006 chicago: Ojavee, Sven E., Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer, Reedik Mägi, Zoltan Kutalik, and Matthew Richard Robinson. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” American Journal of Human Genetics. Elsevier, 2023. https://doi.org/10.1016/j.ajhg.2023.07.006. ieee: S. E. Ojavee et al., “Genetic insights into the age-specific biological mechanisms governing human ovarian aging,” American Journal of Human Genetics, vol. 110, no. 9. Elsevier, pp. 1549–1563, 2023. ista: Ojavee SE, Darrous L, Patxot M, Läll K, Fischer K, Mägi R, Kutalik Z, Robinson MR. 2023. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. American Journal of Human Genetics. 110(9), 1549–1563. mla: Ojavee, Sven E., et al. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” American Journal of Human Genetics, vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:10.1016/j.ajhg.2023.07.006. short: S.E. Ojavee, L. Darrous, M. Patxot, K. Läll, K. Fischer, R. Mägi, Z. Kutalik, M.R. Robinson, American Journal of Human Genetics 110 (2023) 1549–1563. date_created: 2023-09-03T22:01:15Z date_published: 2023-09-07T00:00:00Z date_updated: 2024-01-30T13:21:05Z day: '07' ddc: - '570' department: - _id: MaRo doi: 10.1016/j.ajhg.2023.07.006 external_id: pmid: - '37543033' file: - access_level: open_access checksum: 4108b031dc726ae6b4a5ae7e021ba188 content_type: application/pdf creator: dernst date_created: 2024-01-30T13:20:35Z date_updated: 2024-01-30T13:20:35Z file_id: '14912' file_name: 2023_AJHG_Ojavee.pdf file_size: 2551276 relation: main_file success: 1 file_date_updated: 2024-01-30T13:20:35Z has_accepted_license: '1' intvolume: ' 110' issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 1549-1563 pmid: 1 publication: American Journal of Human Genetics publication_identifier: eissn: - 1537-6605 issn: - 0002-9297 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Genetic insights into the age-specific biological mechanisms governing human ovarian aging tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 110 year: '2023' ... --- _id: '10702' abstract: - lang: eng text: 'Background: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.' acknowledgement: 'GS received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award STratifying Resilience and Depression Longitudinally (STRADL; Reference 104036/Z/14/Z). The DNA methylation data assayed for Generation Scotland was partially funded by a 2018 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (Ref: 27404; awardee: Dr David M Howard) and by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley). LBC1936 MRI brain imaging was supported by Medical Research Council (MRC) grants [G0701120], [G1001245], [MR/M013111/1] and [MR/R024065/1]. Magnetic resonance image acquisition and analyses were conducted at the Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.bric.ed.ac.uk) which is part of SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (www.sinapse.ac.uk) funded by the Scottish Funding Council and the Chief Scientist Office. This work was supported by the European Union Horizon 2020 (PHC.03.15, project No 666881), SVDs@Target, the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [ref no. 16 CVD 05]. We thank the LBC1936 participants and team members who contributed to these studies. The LBC1936 is supported by Age UK (Disconnected Mind project, which supports S.E.H.), the Medical Research Council (G0701120, G1001245, MR/M013111/1, MR/R024065/1) and the University of Edinburgh. Methylation typing of LBC1936 was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. Genotyping was funded by the Biotechnology and Biological Sciences Research Council (BB/F019394/1). Proteomic analyses in LBC1936 were supported by the Age UK grant and NIH Grants R01AG054628 and R01AG05462802S1. M.V.H. is funded by the Row Fogo Charitable Trust (Grant no. BROD.FID3668413). J.M.W is supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimers Society and Alzheimers Research UK. R.F.H., E.L.S.C and D.A.G. are supported by funding from the Wellcome Trust 4 year PhD in Translational Neuroscience: training the next generation of basic neuroscientists to embrace clinical research [108890/Z/15/Z]. E.M.T.D. was supported by the National Institutes of Health (NIH) grants R01AG054628, R01MH120219, R01HD083613, P2CHD042849 and P30AG066614. S.R.C. was also supported by a National Institutes of Health (NIH) research grant R01AG054628 and is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). D.L.Mc.C. and R.E.M. are supported by Alzheimers Research UK major project grant ARUK/PG2017B/10. R.E.M. is supported by Alzheimer’s Society major project grant AS-PG-19b-010. This research was funded in whole, or in part, by Wellcome [104036/Z/14/Z and 108890/Z/15/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.' article_number: '26' article_processing_charge: No article_type: original author: - first_name: Daniel L. full_name: McCartney, Daniel L. last_name: McCartney - first_name: Robert F. full_name: Hillary, Robert F. last_name: Hillary - first_name: Eleanor L.S. full_name: Conole, Eleanor L.S. last_name: Conole - first_name: Daniel Trejo full_name: Banos, Daniel Trejo last_name: Banos - first_name: Danni A. full_name: Gadd, Danni A. last_name: Gadd - first_name: Rosie M. full_name: Walker, Rosie M. last_name: Walker - first_name: Cliff full_name: Nangle, Cliff last_name: Nangle - first_name: Robin full_name: Flaig, Robin last_name: Flaig - first_name: Archie full_name: Campbell, Archie last_name: Campbell - first_name: Alison D. full_name: Murray, Alison D. last_name: Murray - first_name: Susana Muñoz full_name: Maniega, Susana Muñoz last_name: Maniega - first_name: María Del C. full_name: Valdés-Hernández, María Del C. last_name: Valdés-Hernández - first_name: Mathew A. full_name: Harris, Mathew A. last_name: Harris - first_name: Mark E. full_name: Bastin, Mark E. last_name: Bastin - first_name: Joanna M. full_name: Wardlaw, Joanna M. last_name: Wardlaw - first_name: Sarah E. full_name: Harris, Sarah E. last_name: Harris - first_name: David J. full_name: Porteous, David J. last_name: Porteous - first_name: Elliot M. full_name: Tucker-Drob, Elliot M. last_name: Tucker-Drob - first_name: Andrew M. full_name: McIntosh, Andrew M. last_name: McIntosh - first_name: Kathryn L. full_name: Evans, Kathryn L. last_name: Evans - first_name: Ian J. full_name: Deary, Ian J. last_name: Deary - first_name: Simon R. full_name: Cox, Simon R. last_name: Cox - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Riccardo E. full_name: Marioni, Riccardo E. last_name: Marioni citation: ama: McCartney DL, Hillary RF, Conole ELS, et al. Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology. 2022;23(1). doi:10.1186/s13059-021-02596-5 apa: McCartney, D. L., Hillary, R. F., Conole, E. L. S., Banos, D. T., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2022). Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology. Springer Nature. https://doi.org/10.1186/s13059-021-02596-5 chicago: McCartney, Daniel L., Robert F. Hillary, Eleanor L.S. Conole, Daniel Trejo Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Genome Biology. Springer Nature, 2022. https://doi.org/10.1186/s13059-021-02596-5. ieee: D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive abilities,” Genome Biology, vol. 23, no. 1. Springer Nature, 2022. ista: McCartney DL, Hillary RF, Conole ELS, Banos DT, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Maniega SM, Valdés-Hernández MDC, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2022. Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology. 23(1), 26. mla: McCartney, Daniel L., et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Genome Biology, vol. 23, no. 1, 26, Springer Nature, 2022, doi:10.1186/s13059-021-02596-5. short: D.L. McCartney, R.F. Hillary, E.L.S. Conole, D.T. Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S.M. Maniega, M.D.C. Valdés-Hernández, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, Genome Biology 23 (2022). date_created: 2022-01-30T23:01:33Z date_published: 2022-01-17T00:00:00Z date_updated: 2023-08-02T14:05:13Z day: '17' ddc: - '570' department: - _id: MaRo doi: 10.1186/s13059-021-02596-5 external_id: isi: - '000744358300002' file: - access_level: open_access checksum: 34f10bb2b0594189dcac24d13b691d52 content_type: application/pdf creator: cchlebak date_created: 2022-01-31T13:16:05Z date_updated: 2022-01-31T13:16:05Z file_id: '10708' file_name: 2022_GenomeBio_McCartney.pdf file_size: 1540606 relation: main_file success: 1 file_date_updated: 2022-01-31T13:16:05Z has_accepted_license: '1' intvolume: ' 23' isi: 1 issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version project: - _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A grant_number: PCEGP3_181181 name: Improving estimation and prediction of common complex disease risk publication: Genome Biology publication_identifier: eissn: - 1474-760X issn: - 1474-7596 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: earlier_version url: https://doi.org/10.1101/2021.05.24.21257698 record: - id: '13072' relation: research_data status: public scopus_import: '1' status: public title: Blood-based epigenome-wide analyses of cognitive abilities tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 23 year: '2022' ... --- _id: '11733' abstract: - lang: eng text: Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. acknowledgement: This project was funded by Swiss National Science Foundation Eccellenza Grant PCEGP3-181181(toM.R.R.) and by core funding from the Institute of Science and Technology Austria. P.M.V. acknowledges funding from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). K.L. and R.M. were supported by the Estonian Research Council Grant PRG687. Estonian Biobank computations were performed in the High-Performance Computing Centre, University of Tartu. article_number: e2121279119 article_processing_charge: No article_type: original author: - first_name: Etienne J. full_name: Orliac, Etienne J. last_name: Orliac - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Sven E. full_name: Ojavee, Sven E. last_name: Ojavee - first_name: Kristi full_name: Läll, Kristi last_name: Läll - first_name: Reedik full_name: Mägi, Reedik last_name: Mägi - first_name: Peter M. full_name: Visscher, Peter M. last_name: Visscher - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Orliac EJ, Trejo Banos D, Ojavee SE, et al. Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences of the United States of America. 2022;119(31). doi:10.1073/pnas.2121279119 apa: Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher, P. M., & Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences of the United States of America. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2121279119 chicago: Orliac, Etienne J., Daniel Trejo Banos, Sven E. Ojavee, Kristi Läll, Reedik Mägi, Peter M. Visscher, and Matthew Richard Robinson. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” Proceedings of the National Academy of Sciences of the United States of America. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2121279119. ieee: E. J. Orliac et al., “Improving GWAS discovery and genomic prediction accuracy in biobank data,” Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 31. Proceedings of the National Academy of Sciences, 2022. ista: Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson MR. 2022. Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences of the United States of America. 119(31), e2121279119. mla: Orliac, Etienne J., et al. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 31, e2121279119, Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2121279119. short: E.J. Orliac, D. Trejo Banos, S.E. Ojavee, K. Läll, R. Mägi, P.M. Visscher, M.R. Robinson, Proceedings of the National Academy of Sciences of the United States of America 119 (2022). date_created: 2022-08-07T22:01:56Z date_published: 2022-07-29T00:00:00Z date_updated: 2023-08-03T12:40:38Z day: '29' ddc: - '570' department: - _id: MaRo doi: 10.1073/pnas.2121279119 external_id: isi: - '000881496900003' file: - access_level: open_access checksum: b5d2024e19fbad6f85a5e384e44d0f3b content_type: application/pdf creator: dernst date_created: 2022-08-08T07:31:19Z date_updated: 2022-08-08T07:31:19Z file_id: '11745' file_name: 2022_PNAS_Orliac.pdf file_size: 1001164 relation: main_file success: 1 file_date_updated: 2022-08-08T07:31:19Z has_accepted_license: '1' intvolume: ' 119' isi: 1 issue: '31' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '07' oa: 1 oa_version: Published Version publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - 1091-6490 publication_status: published publisher: Proceedings of the National Academy of Sciences quality_controlled: '1' related_material: record: - id: '13064' relation: research_data status: public scopus_import: '1' status: public title: Improving GWAS discovery and genomic prediction accuracy in biobank data tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 119 year: '2022' ... --- _id: '13064' abstract: - lang: eng text: Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. article_processing_charge: No author: - first_name: Etienne full_name: Orliac, Etienne last_name: Orliac - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Sven full_name: Ojavee, Sven last_name: Ojavee - first_name: Kristi full_name: Läll, Kristi last_name: Läll - first_name: Reedik full_name: Mägi, Reedik last_name: Mägi - first_name: Peter full_name: Visscher, Peter last_name: Visscher - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., & Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ chicago: Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. https://doi.org/10.5061/DRYAD.GTHT76HMZ. ieee: E. Orliac et al., “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022. ista: Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ. mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ. short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022). date_created: 2023-05-23T16:28:13Z date_published: 2022-09-02T00:00:00Z date_updated: 2023-08-03T12:40:37Z day: '02' ddc: - '570' department: - _id: MaRo doi: 10.5061/DRYAD.GTHT76HMZ license: https://creativecommons.org/publicdomain/zero/1.0/ main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.gtht76hmz month: '09' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '11733' relation: used_in_publication status: public status: public title: Improving genome-wide association discovery and genomic prediction accuracy in biobank data tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '12142' abstract: - lang: eng text: Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with =< 2% population prevalence and LSH of =< 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of =< 3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of =< 2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population. acknowledged_ssus: - _id: ScienComp acknowledgement: This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181), core funding from the Institute of Science and Technology Austria, and core funding from the Department of Computational Biology of the University of Lausanne. Z.K. was funded by the Swiss National Science Foundation (310030-189147). This research was supported by the Scientific Service Units (SSUs) of IST Austria through resources provided by Scientific Computing (SciComp). We would like to thank the participants of the UK Biobank. article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Sven E. full_name: Ojavee, Sven E. last_name: Ojavee - first_name: Zoltan full_name: Kutalik, Zoltan last_name: Kutalik - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Ojavee SE, Kutalik Z, Robinson MR. Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. 2022;109(11):2009-2017. doi:10.1016/j.ajhg.2022.09.011 apa: Ojavee, S. E., Kutalik, Z., & Robinson, M. R. (2022). Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. Elsevier. https://doi.org/10.1016/j.ajhg.2022.09.011 chicago: Ojavee, Sven E., Zoltan Kutalik, and Matthew Richard Robinson. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” The American Journal of Human Genetics. Elsevier, 2022. https://doi.org/10.1016/j.ajhg.2022.09.011. ieee: S. E. Ojavee, Z. Kutalik, and M. R. Robinson, “Liability-scale heritability estimation for biobank studies of low-prevalence disease,” The American Journal of Human Genetics, vol. 109, no. 11. Elsevier, pp. 2009–2017, 2022. ista: Ojavee SE, Kutalik Z, Robinson MR. 2022. Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. 109(11), 2009–2017. mla: Ojavee, Sven E., et al. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” The American Journal of Human Genetics, vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:10.1016/j.ajhg.2022.09.011. short: S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics 109 (2022) 2009–2017. date_created: 2023-01-12T12:05:28Z date_published: 2022-11-03T00:00:00Z date_updated: 2023-08-04T08:56:46Z day: '03' ddc: - '570' department: - _id: MaRo doi: 10.1016/j.ajhg.2022.09.011 external_id: isi: - '000898683500006' file: - access_level: open_access checksum: 4cd7f12bfe21a8237bb095eedfa26361 content_type: application/pdf creator: dernst date_created: 2023-01-24T09:23:01Z date_updated: 2023-01-24T09:23:01Z file_id: '12353' file_name: 2022_AJHG_Ojavee.pdf file_size: 705195 relation: main_file success: 1 file_date_updated: 2023-01-24T09:23:01Z has_accepted_license: '1' intvolume: ' 109' isi: 1 issue: '11' keyword: - Genetics (clinical) - Genetics language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 2009-2017 project: - _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A grant_number: PCEGP3_181181 name: Improving estimation and prediction of common complex disease risk publication: The American Journal of Human Genetics publication_identifier: issn: - 0002-9297 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Liability-scale heritability estimation for biobank studies of low-prevalence disease tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 109 year: '2022' ... --- _id: '12235' abstract: - lang: eng text: "Background: About 800 women die every day worldwide from pregnancy-related complications, including excessive blood loss, infections and high-blood pressure (World Health Organization, 2019). To improve screening for high-risk pregnancies, we set out to identify patterns of maternal hematological changes associated with future pregnancy complications.\r\n\r\nMethods: Using mixed effects models, we established changes in 14 complete blood count (CBC) parameters for 1710 healthy pregnancies and compared them to measurements from 98 pregnancy-induced hypertension, 106 gestational diabetes and 339 postpartum hemorrhage cases.\r\n\r\nResults: Results show interindividual variations, but good individual repeatability in CBC values during physiological pregnancies, allowing the identification of specific alterations in women with obstetric complications. For example, in women with uncomplicated pregnancies, haemoglobin count decreases of 0.12 g/L (95% CI −0.16, −0.09) significantly per gestation week (p value <.001). Interestingly, this decrease is three times more pronounced in women who will develop pregnancy-induced hypertension, with an additional decrease of 0.39 g/L (95% CI −0.51, −0.26). We also confirm that obstetric complications and white CBC predict the likelihood of giving birth earlier during pregnancy.\r\n\r\nConclusion: We provide a comprehensive description of the associations between haematological changes through pregnancy and three major obstetric complications to support strategies for prevention, early-diagnosis and maternal care." acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria. We would like to thank the participants of the study and all the midwives and doctors involved for the computerized obstetrical data from the CHUV Maternity Hospital. Open access funding provided by Universite de Lausanne. article_processing_charge: No article_type: original author: - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Miloš full_name: Stojanov, Miloš last_name: Stojanov - first_name: Sven Erik full_name: Ojavee, Sven Erik last_name: Ojavee - first_name: Rosanna Pescini full_name: Gobert, Rosanna Pescini last_name: Gobert - first_name: Zoltán full_name: Kutalik, Zoltán last_name: Kutalik - first_name: Mathilde full_name: Gavillet, Mathilde last_name: Gavillet - first_name: David full_name: Baud, David last_name: Baud - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: 'Patxot M, Stojanov M, Ojavee SE, et al. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. 2022;109(5):566-575. doi:10.1111/ejh.13844' apa: 'Patxot, M., Stojanov, M., Ojavee, S. E., Gobert, R. P., Kutalik, Z., Gavillet, M., … Robinson, M. R. (2022). Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. Wiley. https://doi.org/10.1111/ejh.13844' chicago: 'Patxot, Marion, Miloš Stojanov, Sven Erik Ojavee, Rosanna Pescini Gobert, Zoltán Kutalik, Mathilde Gavillet, David Baud, and Matthew Richard Robinson. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” European Journal of Haematology. Wiley, 2022. https://doi.org/10.1111/ejh.13844.' ieee: 'M. Patxot et al., “Haematological changes from conception to childbirth: An indicator of major pregnancy complications,” European Journal of Haematology, vol. 109, no. 5. Wiley, pp. 566–575, 2022.' ista: 'Patxot M, Stojanov M, Ojavee SE, Gobert RP, Kutalik Z, Gavillet M, Baud D, Robinson MR. 2022. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. 109(5), 566–575.' mla: 'Patxot, Marion, et al. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” European Journal of Haematology, vol. 109, no. 5, Wiley, 2022, pp. 566–75, doi:10.1111/ejh.13844.' short: M. Patxot, M. Stojanov, S.E. Ojavee, R.P. Gobert, Z. Kutalik, M. Gavillet, D. Baud, M.R. Robinson, European Journal of Haematology 109 (2022) 566–575. date_created: 2023-01-16T09:50:58Z date_published: 2022-11-01T00:00:00Z date_updated: 2023-08-04T09:36:21Z day: '01' ddc: - '570' - '610' department: - _id: MaRo doi: 10.1111/ejh.13844 external_id: isi: - '000849690500001' pmid: - '36059200' file: - access_level: open_access checksum: a676d732f67c2990197e34f96b219370 content_type: application/pdf creator: dernst date_created: 2023-01-27T11:42:43Z date_updated: 2023-01-27T11:42:43Z file_id: '12426' file_name: 2022_EuropJourHaematology_Patxot.pdf file_size: 1225073 relation: main_file success: 1 file_date_updated: 2023-01-27T11:42:43Z has_accepted_license: '1' intvolume: ' 109' isi: 1 issue: '5' keyword: - Hematology - General Medicine language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 566-575 pmid: 1 publication: European Journal of Haematology publication_identifier: eissn: - 1600-0609 issn: - 0902-4441 publication_status: published publisher: Wiley quality_controlled: '1' scopus_import: '1' status: public title: 'Haematological changes from conception to childbirth: An indicator of major pregnancy complications' tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 109 year: '2022' ... --- _id: '13072' abstract: - lang: eng text: CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits) article_processing_charge: No author: - first_name: Daniel L full_name: McCartney, Daniel L last_name: McCartney - first_name: Robert F full_name: Hillary, Robert F last_name: Hillary - first_name: Eleanor LS full_name: Conole, Eleanor LS last_name: Conole - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Danni A full_name: Gadd, Danni A last_name: Gadd - first_name: Rosie M full_name: Walker, Rosie M last_name: Walker - first_name: Cliff full_name: Nangle, Cliff last_name: Nangle - first_name: Robin full_name: Flaig, Robin last_name: Flaig - first_name: Archie full_name: Campbell, Archie last_name: Campbell - first_name: Alison D full_name: Murray, Alison D last_name: Murray - first_name: Susana full_name: Munoz Maniega, Susana last_name: Munoz Maniega - first_name: Maria full_name: del C Valdes-Hernandez, Maria last_name: del C Valdes-Hernandez - first_name: Mathew A full_name: Harris, Mathew A last_name: Harris - first_name: Mark E full_name: Bastin, Mark E last_name: Bastin - first_name: Joanna M full_name: Wardlaw, Joanna M last_name: Wardlaw - first_name: Sarah E full_name: Harris, Sarah E last_name: Harris - first_name: David J full_name: Porteous, David J last_name: Porteous - first_name: Elliot M full_name: Tucker-Drob, Elliot M last_name: Tucker-Drob - first_name: Andrew M full_name: McIntosh, Andrew M last_name: McIntosh - first_name: Kathryn L full_name: Evans, Kathryn L last_name: Evans - first_name: Ian J full_name: Deary, Ian J last_name: Deary - first_name: Simon R full_name: Cox, Simon R last_name: Cox - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Riccardo E full_name: Marioni, Riccardo E last_name: Marioni citation: ama: McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:10.5281/ZENODO.5794028 apa: McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. https://doi.org/10.5281/ZENODO.5794028 chicago: McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5794028. ieee: D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021. ista: McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, 10.5281/ZENODO.5794028. mla: McCartney, Daniel L., et al. Blood-Based Epigenome-Wide Analyses of Cognitive Abilities. Zenodo, 2021, doi:10.5281/ZENODO.5794028. short: D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, (2021). date_created: 2023-05-23T16:46:20Z date_published: 2021-12-20T00:00:00Z date_updated: 2023-08-02T14:05:12Z day: '20' ddc: - '570' department: - _id: MaRo doi: 10.5281/ZENODO.5794028 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.5794029 month: '12' oa: 1 oa_version: Published Version publisher: Zenodo related_material: record: - id: '10702' relation: used_in_publication status: public status: public title: Blood-based epigenome-wide analyses of cognitive abilities tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8430' abstract: - lang: eng text: While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches. acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria and the University of Lausanne; the work of KF was supported by the grant PUT1665 by the Estonian Research Council. We would like to thank Mike Goddard for comments which greatly improved the work, the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support. article_number: '2337' article_processing_charge: No author: - first_name: Sven E full_name: Ojavee, Sven E last_name: Ojavee - first_name: Athanasios full_name: Kousathanas, Athanasios last_name: Kousathanas - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Etienne J full_name: Orliac, Etienne J last_name: Orliac - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Kristi full_name: Lall, Kristi last_name: Lall - first_name: Reedik full_name: Magi, Reedik last_name: Magi - first_name: Krista full_name: Fischer, Krista last_name: Fischer - first_name: Zoltan full_name: Kutalik, Zoltan last_name: Kutalik - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Ojavee SE, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-22538-w apa: Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. Nature Research. https://doi.org/10.1038/s41467-021-22538-w chicago: Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac, Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” Nature Communications. Nature Research, 2021. https://doi.org/10.1038/s41467-021-22538-w. ieee: S. E. Ojavee et al., “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” Nature Communications, vol. 12, no. 1. Nature Research, 2021. ista: Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 12(1), 2337. mla: Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” Nature Communications, vol. 12, no. 1, 2337, Nature Research, 2021, doi:10.1038/s41467-021-22538-w. short: S.E. Ojavee, A. Kousathanas, D. Trejo Banos, E.J. Orliac, M. Patxot, K. Lall, R. Magi, K. Fischer, Z. Kutalik, M.R. Robinson, Nature Communications 12 (2021). date_created: 2020-09-17T10:53:00Z date_published: 2021-04-20T00:00:00Z date_updated: 2023-08-04T11:00:17Z day: '20' ddc: - '570' department: - _id: MaRo doi: 10.1038/s41467-021-22538-w external_id: isi: - '000642509600006' file: - access_level: open_access checksum: eca8b9ae713835c5b785211dd08d8a2e content_type: application/pdf creator: kschuh date_created: 2021-05-04T15:07:50Z date_updated: 2021-05-04T15:07:50Z file_id: '9372' file_name: 2021_nature_communications_Ojavee.pdf file_size: 6474239 relation: main_file success: 1 file_date_updated: 2021-05-04T15:07:50Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '1' language: - iso: eng month: '04' oa: 1 oa_version: Published Version project: - _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A grant_number: PCEGP3_181181 name: Improving estimation and prediction of common complex disease risk publication: Nature Communications publication_identifier: eissn: - '20411723' publication_status: published publisher: Nature Research quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/predicting-the-onset-of-diseases/ scopus_import: '1' status: public title: Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2021' ... --- _id: '10069' abstract: - lang: eng text: 'The extent to which women differ in the course of blood cell counts throughout pregnancy, and the importance of these changes to pregnancy outcomes has not been well defined. Here, we develop a series of statistical analyses of repeated measures data to reveal the degree to which women differ in the course of pregnancy, predict the changes that occur, and determine the importance of these changes for post-partum hemorrhage (PPH) which is one of the leading causes of maternal mortality. We present a prospective cohort of 4082 births recorded at the University Hospital, Lausanne, Switzerland between 2009 and 2014 where full labour records could be obtained, along with complete blood count data taken at hospital admission. We find significant differences, at a [Formula: see text] level, among women in how blood count values change through pregnancy for mean corpuscular hemoglobin, mean corpuscular volume, mean platelet volume, platelet count and red cell distribution width. We find evidence that almost all complete blood count values show trimester-specific associations with PPH. For example, high platelet count (OR 1.20, 95% CI 1.01-1.53), high mean platelet volume (OR 1.58, 95% CI 1.04-2.08), and high erythrocyte levels (OR 1.36, 95% CI 1.01-1.57) in trimester 1 increased PPH, but high values in trimester 3 decreased PPH risk (OR 0.85, 0.79, 0.67 respectively). We show that differences among women in the course of blood cell counts throughout pregnancy have an important role in shaping pregnancy outcome and tracking blood count value changes through pregnancy improves identification of women at increased risk of postpartum hemorrhage. This study provides greater understanding of the complex changes in blood count values that occur through pregnancy and provides indicators to guide the stratification of patients into risk groups.' acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria. We would like to thank the participants of the study and all the midwives and doctors for the computerized obstetrical data. article_number: '19238' article_processing_charge: Yes article_type: original author: - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Miloš full_name: Stojanov, Miloš last_name: Stojanov - first_name: Sabine full_name: Blum, Sabine last_name: Blum - first_name: David full_name: Baud, David last_name: Baud citation: ama: Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. Scientific Reports. 2021;11. doi:10.1038/s41598-021-98411-z apa: Robinson, M. R., Patxot, M., Stojanov, M., Blum, S., & Baud, D. (2021). Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-021-98411-z chicago: Robinson, Matthew Richard, Marion Patxot, Miloš Stojanov, Sabine Blum, and David Baud. “Postpartum Hemorrhage Risk Is Driven by Changes in Blood Composition through Pregnancy.” Scientific Reports. Springer Nature, 2021. https://doi.org/10.1038/s41598-021-98411-z. ieee: M. R. Robinson, M. Patxot, M. Stojanov, S. Blum, and D. Baud, “Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy,” Scientific Reports, vol. 11. Springer Nature, 2021. ista: Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. 2021. Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. Scientific Reports. 11, 19238. mla: Robinson, Matthew Richard, et al. “Postpartum Hemorrhage Risk Is Driven by Changes in Blood Composition through Pregnancy.” Scientific Reports, vol. 11, 19238, Springer Nature, 2021, doi:10.1038/s41598-021-98411-z. short: M.R. Robinson, M. Patxot, M. Stojanov, S. Blum, D. Baud, Scientific Reports 11 (2021). date_created: 2021-10-03T22:01:21Z date_published: 2021-09-28T00:00:00Z date_updated: 2023-08-14T07:05:15Z day: '28' ddc: - '618' department: - _id: MaRo doi: 10.1038/s41598-021-98411-z external_id: isi: - '000701575500083' pmid: - '34584125' file: - access_level: open_access checksum: f002ec22f609f58e1263b79e7f79601e content_type: application/pdf creator: cchlebak date_created: 2021-10-05T14:56:48Z date_updated: 2021-10-05T14:56:48Z file_id: '10091' file_name: 2021_ScientificReports_Robinson.pdf file_size: 6970368 relation: main_file success: 1 file_date_updated: 2021-10-05T14:56:48Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version pmid: 1 publication: Scientific Reports publication_identifier: eissn: - 2045-2322 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 11 year: '2021' ... --- _id: '8429' abstract: - lang: eng text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria. We would like to thank the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support. P.M.V. acknowledges funding from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). L.R. acknowledges funding from the Kjell & Märta Beijer Foundation (Stockholm, Sweden). We also would like to acknowledge Simone Rubinacci, Oliver Delanau, Alexander Terenin, Eleonora Porcu, and Mike Goddard for their useful comments and suggestions. article_number: '6972' article_processing_charge: No article_type: original author: - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Athanasios full_name: Kousathanas, Athanasios last_name: Kousathanas - first_name: Etienne J full_name: Orliac, Etienne J last_name: Orliac - first_name: Sven E full_name: Ojavee, Sven E last_name: Ojavee - first_name: Gerhard full_name: Moser, Gerhard last_name: Moser - first_name: Julia full_name: Sidorenko, Julia last_name: Sidorenko - first_name: Zoltan full_name: Kutalik, Zoltan last_name: Kutalik - first_name: Reedik full_name: Magi, Reedik last_name: Magi - first_name: Peter M full_name: Visscher, Peter M last_name: Visscher - first_name: Lars full_name: Ronnegard, Lars last_name: Ronnegard - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Patxot M, Trejo Banos D, Kousathanas A, et al. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-27258-9 apa: Patxot, M., Trejo Banos, D., Kousathanas, A., Orliac, E. J., Ojavee, S. E., Moser, G., … Robinson, M. R. (2021). Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-021-27258-9 chicago: Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” Nature Communications. Springer Nature, 2021. https://doi.org/10.1038/s41467-021-27258-9. ieee: M. Patxot et al., “Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits,” Nature Communications, vol. 12, no. 1. Springer Nature, 2021. ista: Patxot M, Trejo Banos D, Kousathanas A, Orliac EJ, Ojavee SE, Moser G, Sidorenko J, Kutalik Z, Magi R, Visscher PM, Ronnegard L, Robinson MR. 2021. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 12(1), 6972. mla: Patxot, Marion, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” Nature Communications, vol. 12, no. 1, 6972, Springer Nature, 2021, doi:10.1038/s41467-021-27258-9. short: M. Patxot, D. Trejo Banos, A. Kousathanas, E.J. Orliac, S.E. Ojavee, G. Moser, J. Sidorenko, Z. Kutalik, R. Magi, P.M. Visscher, L. Ronnegard, M.R. Robinson, Nature Communications 12 (2021). date_created: 2020-09-17T10:52:38Z date_published: 2021-11-30T00:00:00Z date_updated: 2023-09-26T10:36:14Z day: '30' ddc: - '610' department: - _id: MaRo doi: 10.1038/s41467-021-27258-9 external_id: isi: - '000724450600023' file: - access_level: open_access checksum: 384681be17aff902c149a48f52d13d4f content_type: application/pdf creator: cchlebak date_created: 2021-12-06T07:47:11Z date_updated: 2021-12-06T07:47:11Z file_id: '10419' file_name: 2021_NatComm_Paxtot.pdf file_size: 6519771 relation: main_file success: 1 file_date_updated: 2021-12-06T07:47:11Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '1' language: - iso: eng month: '11' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '13063' relation: research_data status: public scopus_import: '1' status: public title: Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2021' ... --- _id: '13063' abstract: - lang: eng text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having >95% probability of contributing >0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. article_processing_charge: No author: - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:10.5061/dryad.sqv9s4n51 apa: Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. https://doi.org/10.5061/dryad.sqv9s4n51 chicago: Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. https://doi.org/10.5061/dryad.sqv9s4n51. ieee: M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021. ista: Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, 10.5061/dryad.sqv9s4n51. mla: Robinson, Matthew Richard. Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits. Dryad, 2021, doi:10.5061/dryad.sqv9s4n51. short: M.R. Robinson, (2021). date_created: 2023-05-23T16:20:16Z date_published: 2021-11-04T00:00:00Z date_updated: 2023-09-26T10:36:15Z day: '04' ddc: - '570' department: - _id: MaRo doi: 10.5061/dryad.sqv9s4n51 main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.sqv9s4n51 month: '11' oa: 1 oa_version: Published Version publisher: Dryad related_material: link: - relation: software url: https://github.com/medical-genomics-group/gmrm record: - id: '8429' relation: used_in_publication status: public status: public title: Probabilistic inference of the genetic architecture of functional enrichment of complex traits tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '7708' abstract: - lang: eng text: We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor. article_number: '10' article_processing_charge: No article_type: original author: - first_name: Marta F. full_name: Nabais, Marta F. last_name: Nabais - first_name: Tian full_name: Lin, Tian last_name: Lin - first_name: Beben full_name: Benyamin, Beben last_name: Benyamin - first_name: Kelly L. full_name: Williams, Kelly L. last_name: Williams - first_name: Fleur C. full_name: Garton, Fleur C. last_name: Garton - first_name: Anna A. E. full_name: Vinkhuyzen, Anna A. E. last_name: Vinkhuyzen - first_name: Futao full_name: Zhang, Futao last_name: Zhang - first_name: Costanza L. full_name: Vallerga, Costanza L. last_name: Vallerga - first_name: Restuadi full_name: Restuadi, Restuadi last_name: Restuadi - first_name: Anna full_name: Freydenzon, Anna last_name: Freydenzon - first_name: Ramona A. J. full_name: Zwamborn, Ramona A. J. last_name: Zwamborn - first_name: Paul J. full_name: Hop, Paul J. last_name: Hop - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Jacob full_name: Gratten, Jacob last_name: Gratten - first_name: Peter M. full_name: Visscher, Peter M. last_name: Visscher - first_name: Eilis full_name: Hannon, Eilis last_name: Hannon - first_name: Jonathan full_name: Mill, Jonathan last_name: Mill - first_name: Matthew A. full_name: Brown, Matthew A. last_name: Brown - first_name: Nigel G. full_name: Laing, Nigel G. last_name: Laing - first_name: Karen A. full_name: Mather, Karen A. last_name: Mather - first_name: Perminder S. full_name: Sachdev, Perminder S. last_name: Sachdev - first_name: Shyuan T. full_name: Ngo, Shyuan T. last_name: Ngo - first_name: Frederik J. full_name: Steyn, Frederik J. last_name: Steyn - first_name: Leanne full_name: Wallace, Leanne last_name: Wallace - first_name: Anjali K. full_name: Henders, Anjali K. last_name: Henders - first_name: Merrilee full_name: Needham, Merrilee last_name: Needham - first_name: Jan H. full_name: Veldink, Jan H. last_name: Veldink - first_name: Susan full_name: Mathers, Susan last_name: Mathers - first_name: Garth full_name: Nicholson, Garth last_name: Nicholson - first_name: Dominic B. full_name: Rowe, Dominic B. last_name: Rowe - first_name: Robert D. full_name: Henderson, Robert D. last_name: Henderson - first_name: Pamela A. full_name: McCombe, Pamela A. last_name: McCombe - first_name: Roger full_name: Pamphlett, Roger last_name: Pamphlett - first_name: Jian full_name: Yang, Jian last_name: Yang - first_name: Ian P. full_name: Blair, Ian P. last_name: Blair - first_name: Allan F. full_name: McRae, Allan F. last_name: McRae - first_name: Naomi R. full_name: Wray, Naomi R. last_name: Wray citation: ama: Nabais MF, Lin T, Benyamin B, et al. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. npj Genomic Medicine. 2020;5. doi:10.1038/s41525-020-0118-3 apa: Nabais, M. F., Lin, T., Benyamin, B., Williams, K. L., Garton, F. C., Vinkhuyzen, A. A. E., … Wray, N. R. (2020). Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. Npj Genomic Medicine. Springer Nature. https://doi.org/10.1038/s41525-020-0118-3 chicago: Nabais, Marta F., Tian Lin, Beben Benyamin, Kelly L. Williams, Fleur C. Garton, Anna A. E. Vinkhuyzen, Futao Zhang, et al. “Significant Out-of-Sample Classification from Methylation Profile Scoring for Amyotrophic Lateral Sclerosis.” Npj Genomic Medicine. Springer Nature, 2020. https://doi.org/10.1038/s41525-020-0118-3. ieee: M. F. Nabais et al., “Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis,” npj Genomic Medicine, vol. 5. Springer Nature, 2020. ista: Nabais MF, Lin T, Benyamin B, Williams KL, Garton FC, Vinkhuyzen AAE, Zhang F, Vallerga CL, Restuadi R, Freydenzon A, Zwamborn RAJ, Hop PJ, Robinson MR, Gratten J, Visscher PM, Hannon E, Mill J, Brown MA, Laing NG, Mather KA, Sachdev PS, Ngo ST, Steyn FJ, Wallace L, Henders AK, Needham M, Veldink JH, Mathers S, Nicholson G, Rowe DB, Henderson RD, McCombe PA, Pamphlett R, Yang J, Blair IP, McRae AF, Wray NR. 2020. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. npj Genomic Medicine. 5, 10. mla: Nabais, Marta F., et al. “Significant Out-of-Sample Classification from Methylation Profile Scoring for Amyotrophic Lateral Sclerosis.” Npj Genomic Medicine, vol. 5, 10, Springer Nature, 2020, doi:10.1038/s41525-020-0118-3. short: M.F. Nabais, T. Lin, B. Benyamin, K.L. Williams, F.C. Garton, A.A.E. Vinkhuyzen, F. Zhang, C.L. Vallerga, R. Restuadi, A. Freydenzon, R.A.J. Zwamborn, P.J. Hop, M.R. Robinson, J. Gratten, P.M. Visscher, E. Hannon, J. Mill, M.A. Brown, N.G. Laing, K.A. Mather, P.S. Sachdev, S.T. Ngo, F.J. Steyn, L. Wallace, A.K. Henders, M. Needham, J.H. Veldink, S. Mathers, G. Nicholson, D.B. Rowe, R.D. Henderson, P.A. McCombe, R. Pamphlett, J. Yang, I.P. Blair, A.F. McRae, N.R. Wray, Npj Genomic Medicine 5 (2020). date_created: 2020-04-30T10:39:54Z date_published: 2020-02-27T00:00:00Z date_updated: 2021-01-12T08:14:59Z day: '27' doi: 10.1038/s41525-020-0118-3 extern: '1' intvolume: ' 5' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1038/s41525-020-0118-3 month: '02' oa: 1 oa_version: Published Version publication: npj Genomic Medicine publication_identifier: issn: - 2056-7944 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 5 year: '2020' ... --- _id: '7707' abstract: - lang: eng text: The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). article_number: '1385' article_processing_charge: No article_type: original author: - first_name: Jonathan full_name: Sulc, Jonathan last_name: Sulc - first_name: Ninon full_name: Mounier, Ninon last_name: Mounier - first_name: Felix full_name: Günther, Felix last_name: Günther - first_name: Thomas full_name: Winkler, Thomas last_name: Winkler - first_name: Andrew R. full_name: Wood, Andrew R. last_name: Wood - first_name: Timothy M. full_name: Frayling, Timothy M. last_name: Frayling - first_name: Iris M. full_name: Heid, Iris M. last_name: Heid - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Zoltán full_name: Kutalik, Zoltán last_name: Kutalik citation: ama: Sulc J, Mounier N, Günther F, et al. Quantification of the overall contribution of gene-environment interaction for obesity-related traits. Nature Communications. 2020;11. doi:10.1038/s41467-020-15107-0 apa: Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T. M., … Kutalik, Z. (2020). Quantification of the overall contribution of gene-environment interaction for obesity-related traits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-15107-0 chicago: Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R. Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán Kutalik. “Quantification of the Overall Contribution of Gene-Environment Interaction for Obesity-Related Traits.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-15107-0. ieee: J. Sulc et al., “Quantification of the overall contribution of gene-environment interaction for obesity-related traits,” Nature Communications, vol. 11. Springer Nature, 2020. ista: Sulc J, Mounier N, Günther F, Winkler T, Wood AR, Frayling TM, Heid IM, Robinson MR, Kutalik Z. 2020. Quantification of the overall contribution of gene-environment interaction for obesity-related traits. Nature Communications. 11, 1385. mla: Sulc, Jonathan, et al. “Quantification of the Overall Contribution of Gene-Environment Interaction for Obesity-Related Traits.” Nature Communications, vol. 11, 1385, Springer Nature, 2020, doi:10.1038/s41467-020-15107-0. short: J. Sulc, N. Mounier, F. Günther, T. Winkler, A.R. Wood, T.M. Frayling, I.M. Heid, M.R. Robinson, Z. Kutalik, Nature Communications 11 (2020). date_created: 2020-04-30T10:39:33Z date_published: 2020-03-20T00:00:00Z date_updated: 2021-01-12T08:14:59Z day: '20' doi: 10.1038/s41467-020-15107-0 extern: '1' intvolume: ' 11' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1038/s41467-020-15107-0 month: '03' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: issn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Quantification of the overall contribution of gene-environment interaction for obesity-related traits type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 11 year: '2020' ... --- _id: '7999' abstract: - lang: eng text: 'Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal. ' article_number: '2865' article_processing_charge: No article_type: original author: - first_name: D full_name: Trejo Banos, D last_name: Trejo Banos - first_name: DL full_name: McCartney, DL last_name: McCartney - first_name: M full_name: Patxot, M last_name: Patxot - first_name: L full_name: Anchieri, L last_name: Anchieri - first_name: T full_name: Battram, T last_name: Battram - first_name: C full_name: Christiansen, C last_name: Christiansen - first_name: R full_name: Costeira, R last_name: Costeira - first_name: RM full_name: Walker, RM last_name: Walker - first_name: SW full_name: Morris, SW last_name: Morris - first_name: A full_name: Campbell, A last_name: Campbell - first_name: Q full_name: Zhang, Q last_name: Zhang - first_name: DJ full_name: Porteous, DJ last_name: Porteous - first_name: AF full_name: McRae, AF last_name: McRae - first_name: NR full_name: Wray, NR last_name: Wray - first_name: PM full_name: Visscher, PM last_name: Visscher - first_name: CS full_name: Haley, CS last_name: Haley - first_name: KL full_name: Evans, KL last_name: Evans - first_name: IJ full_name: Deary, IJ last_name: Deary - first_name: AM full_name: McIntosh, AM last_name: McIntosh - first_name: G full_name: Hemani, G last_name: Hemani - first_name: JT full_name: Bell, JT last_name: Bell - first_name: RE full_name: Marioni, RE last_name: Marioni - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Trejo Banos D, McCartney D, Patxot M, et al. Bayesian reassessment of the epigenetic architecture of complex traits. Nature Communications. 2020;11. doi:10.1038/s41467-020-16520-1 apa: Trejo Banos, D., McCartney, D., Patxot, M., Anchieri, L., Battram, T., Christiansen, C., … Robinson, M. R. (2020). Bayesian reassessment of the epigenetic architecture of complex traits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-16520-1 chicago: Trejo Banos, D, DL McCartney, M Patxot, L Anchieri, T Battram, C Christiansen, R Costeira, et al. “Bayesian Reassessment of the Epigenetic Architecture of Complex Traits.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-16520-1. ieee: D. Trejo Banos et al., “Bayesian reassessment of the epigenetic architecture of complex traits,” Nature Communications, vol. 11. Springer Nature, 2020. ista: Trejo Banos D, McCartney D, Patxot M, Anchieri L, Battram T, Christiansen C, Costeira R, Walker R, Morris S, Campbell A, Zhang Q, Porteous D, McRae A, Wray N, Visscher P, Haley C, Evans K, Deary I, McIntosh A, Hemani G, Bell J, Marioni R, Robinson MR. 2020. Bayesian reassessment of the epigenetic architecture of complex traits. Nature Communications. 11, 2865. mla: Trejo Banos, D., et al. “Bayesian Reassessment of the Epigenetic Architecture of Complex Traits.” Nature Communications, vol. 11, 2865, Springer Nature, 2020, doi:10.1038/s41467-020-16520-1. short: D. Trejo Banos, D. McCartney, M. Patxot, L. Anchieri, T. Battram, C. Christiansen, R. Costeira, R. Walker, S. Morris, A. Campbell, Q. Zhang, D. Porteous, A. McRae, N. Wray, P. Visscher, C. Haley, K. Evans, I. Deary, A. McIntosh, G. Hemani, J. Bell, R. Marioni, M.R. Robinson, Nature Communications 11 (2020). date_created: 2020-06-22T11:18:25Z date_published: 2020-06-08T00:00:00Z date_updated: 2023-08-22T07:13:09Z day: '08' ddc: - '570' department: - _id: MaRo doi: 10.1038/s41467-020-16520-1 external_id: isi: - '000541702400004' pmid: - '32513961' file: - access_level: open_access checksum: 4c96babd4cfb0d153334f6c598c0bacb content_type: application/pdf creator: dernst date_created: 2020-06-22T11:24:32Z date_updated: 2020-07-14T12:48:07Z file_id: '8000' file_name: 2020_NatureComm_Bayesian.pdf file_size: 1475657 relation: main_file file_date_updated: 2020-07-14T12:48:07Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '06' oa: 1 oa_version: Published Version pmid: 1 publication: Nature Communications publication_identifier: issn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1038/s41467-020-19099-9 scopus_import: '1' status: public title: Bayesian reassessment of the epigenetic architecture of complex traits tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 11 year: '2020' ... --- _id: '8133' abstract: - lang: eng text: The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn’s disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease. article_number: '60' article_processing_charge: No article_type: original author: - first_name: Robert F. full_name: Hillary, Robert F. last_name: Hillary - first_name: Daniel full_name: Trejo-Banos, Daniel last_name: Trejo-Banos - first_name: Athanasios full_name: Kousathanas, Athanasios last_name: Kousathanas - first_name: Daniel L. full_name: Mccartney, Daniel L. last_name: Mccartney - first_name: Sarah E. full_name: Harris, Sarah E. last_name: Harris - first_name: Anna J. full_name: Stevenson, Anna J. last_name: Stevenson - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Sven Erik full_name: Ojavee, Sven Erik last_name: Ojavee - first_name: Qian full_name: Zhang, Qian last_name: Zhang - first_name: David C. full_name: Liewald, David C. last_name: Liewald - first_name: Craig W. full_name: Ritchie, Craig W. last_name: Ritchie - first_name: Kathryn L. full_name: Evans, Kathryn L. last_name: Evans - first_name: Elliot M. full_name: Tucker-Drob, Elliot M. last_name: Tucker-Drob - first_name: Naomi R. full_name: Wray, Naomi R. last_name: Wray - first_name: Allan F. full_name: Mcrae, Allan F. last_name: Mcrae - first_name: Peter M. full_name: Visscher, Peter M. last_name: Visscher - first_name: Ian J. full_name: Deary, Ian J. last_name: Deary - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Riccardo E. full_name: Marioni, Riccardo E. last_name: Marioni citation: ama: Hillary RF, Trejo-Banos D, Kousathanas A, et al. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Medicine. 2020;12(1). doi:10.1186/s13073-020-00754-1 apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., Mccartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Medicine. Springer Nature. https://doi.org/10.1186/s13073-020-00754-1 chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. Mccartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Genome Medicine. Springer Nature, 2020. https://doi.org/10.1186/s13073-020-00754-1. ieee: R. F. Hillary et al., “Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults,” Genome Medicine, vol. 12, no. 1. Springer Nature, 2020. ista: Hillary RF, Trejo-Banos D, Kousathanas A, Mccartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, Mcrae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Medicine. 12(1), 60. mla: Hillary, Robert F., et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Genome Medicine, vol. 12, no. 1, 60, Springer Nature, 2020, doi:10.1186/s13073-020-00754-1. short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. Mccartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. Mcrae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, Genome Medicine 12 (2020). date_created: 2020-07-19T22:00:58Z date_published: 2020-07-08T00:00:00Z date_updated: 2023-08-22T07:55:37Z day: '08' ddc: - '570' department: - _id: MaRo doi: 10.1186/s13073-020-00754-1 external_id: isi: - '000551778400001' pmid: - '32641083' file: - access_level: open_access content_type: application/pdf creator: dernst date_created: 2020-07-22T06:27:38Z date_updated: 2020-07-22T06:27:38Z file_id: '8145' file_name: 2020_GenomeMedicine_Hillary.pdf file_size: 1136983 relation: main_file success: 1 file_date_updated: 2020-07-22T06:27:38Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 publication: Genome Medicine publication_identifier: eissn: - 1756994X publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '9706' relation: research_data status: public scopus_import: '1' status: public title: Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2020' ... --- _id: '9706' abstract: - lang: eng text: 'Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19).' article_processing_charge: No author: - first_name: Robert F. full_name: Hillary, Robert F. last_name: Hillary - first_name: Daniel full_name: Trejo-Banos, Daniel last_name: Trejo-Banos - first_name: Athanasios full_name: Kousathanas, Athanasios last_name: Kousathanas - first_name: Daniel L. full_name: McCartney, Daniel L. last_name: McCartney - first_name: Sarah E. full_name: Harris, Sarah E. last_name: Harris - first_name: Anna J. full_name: Stevenson, Anna J. last_name: Stevenson - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Sven Erik full_name: Ojavee, Sven Erik last_name: Ojavee - first_name: Qian full_name: Zhang, Qian last_name: Zhang - first_name: David C. full_name: Liewald, David C. last_name: Liewald - first_name: Craig W. full_name: Ritchie, Craig W. last_name: Ritchie - first_name: Kathryn L. full_name: Evans, Kathryn L. last_name: Evans - first_name: Elliot M. full_name: Tucker-Drob, Elliot M. last_name: Tucker-Drob - first_name: Naomi R. full_name: Wray, Naomi R. last_name: Wray - first_name: 'Allan F. ' full_name: 'McRae, Allan F. ' last_name: McRae - first_name: Peter M. full_name: Visscher, Peter M. last_name: Visscher - first_name: Ian J. full_name: Deary, Ian J. last_name: Deary - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: 'Riccardo E. ' full_name: 'Marioni, Riccardo E. ' last_name: Marioni citation: ama: Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:10.6084/m9.figshare.12629697.v1 apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Springer Nature. https://doi.org/10.6084/m9.figshare.12629697.v1 chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. https://doi.org/10.6084/m9.figshare.12629697.v1. ieee: R. F. Hillary et al., “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020. ista: Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults, Springer Nature, 10.6084/m9.figshare.12629697.v1. mla: Hillary, Robert F., et al. Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults. Springer Nature, 2020, doi:10.6084/m9.figshare.12629697.v1. short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, (2020). date_created: 2021-07-23T08:59:15Z date_published: 2020-07-09T00:00:00Z date_updated: 2023-08-22T07:55:36Z day: '09' department: - _id: MaRo doi: 10.6084/m9.figshare.12629697.v1 has_accepted_license: '1' main_file_link: - open_access: '1' url: https://doi.org/10.6084/m9.figshare.12629697.v1 month: '07' oa: 1 oa_version: Published Version other_data_license: CC0 + CC BY (4.0) publisher: Springer Nature related_material: record: - id: '8133' relation: used_in_publication status: public status: public title: Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2020' ... --- _id: '7710' abstract: - lang: eng text: 'The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.' article_number: '5436' article_processing_charge: No article_type: original author: - first_name: Olivier full_name: Delaneau, Olivier last_name: Delaneau - first_name: Jean-François full_name: Zagury, Jean-François last_name: Zagury - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Jonathan L. full_name: Marchini, Jonathan L. last_name: Marchini - first_name: Emmanouil T. full_name: Dermitzakis, Emmanouil T. last_name: Dermitzakis citation: ama: Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. Accurate, scalable and integrative haplotype estimation. Nature Communications. 2019;10. doi:10.1038/s41467-019-13225-y apa: Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L., & Dermitzakis, E. T. (2019). Accurate, scalable and integrative haplotype estimation. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-019-13225-y chicago: Delaneau, Olivier, Jean-François Zagury, Matthew Richard Robinson, Jonathan L. Marchini, and Emmanouil T. Dermitzakis. “Accurate, Scalable and Integrative Haplotype Estimation.” Nature Communications. Springer Nature, 2019. https://doi.org/10.1038/s41467-019-13225-y. ieee: O. Delaneau, J.-F. Zagury, M. R. Robinson, J. L. Marchini, and E. T. Dermitzakis, “Accurate, scalable and integrative haplotype estimation,” Nature Communications, vol. 10. Springer Nature, 2019. ista: Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. 2019. Accurate, scalable and integrative haplotype estimation. Nature Communications. 10, 5436. mla: Delaneau, Olivier, et al. “Accurate, Scalable and Integrative Haplotype Estimation.” Nature Communications, vol. 10, 5436, Springer Nature, 2019, doi:10.1038/s41467-019-13225-y. short: O. Delaneau, J.-F. Zagury, M.R. Robinson, J.L. Marchini, E.T. Dermitzakis, Nature Communications 10 (2019). date_created: 2020-04-30T10:40:32Z date_published: 2019-11-28T00:00:00Z date_updated: 2021-01-12T08:15:01Z day: '28' doi: 10.1038/s41467-019-13225-y extern: '1' intvolume: ' 10' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1038/s41467-019-13225-y month: '11' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: issn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Accurate, scalable and integrative haplotype estimation type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 10 year: '2019' ... --- _id: '7711' abstract: - lang: eng text: The nature and extent of mitochondrial DNA variation in a population and how it affects traits is poorly understood. Here we resequence the mitochondrial genomes of 169 Drosophila Genetic Reference Panel lines, identifying 231 variants that stratify along 12 mitochondrial haplotypes. We identify 1,845 cases of mitonuclear allelic imbalances, thus implying that mitochondrial haplotypes are reflected in the nuclear genome. However, no major fitness effects are associated with mitonuclear imbalance, suggesting that such imbalances reflect population structure at the mitochondrial level rather than genomic incompatibilities. Although mitochondrial haplotypes have no direct impact on mitochondrial respiration, some haplotypes are associated with stress- and metabolism-related phenotypes, including food intake in males. Finally, through reciprocal swapping of mitochondrial genomes, we demonstrate that a mitochondrial haplotype associated with high food intake can rescue a low food intake phenotype. Together, our findings provide new insight into population structure at the mitochondrial level and point to the importance of incorporating mitochondrial haplotypes in genotype–phenotype relationship studies. article_processing_charge: No article_type: original author: - first_name: Roel P. J. full_name: Bevers, Roel P. J. last_name: Bevers - first_name: Maria full_name: Litovchenko, Maria last_name: Litovchenko - first_name: Adamandia full_name: Kapopoulou, Adamandia last_name: Kapopoulou - first_name: Virginie S. full_name: Braman, Virginie S. last_name: Braman - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Johan full_name: Auwerx, Johan last_name: Auwerx - first_name: Brian full_name: Hollis, Brian last_name: Hollis - first_name: Bart full_name: Deplancke, Bart last_name: Deplancke citation: ama: Bevers RPJ, Litovchenko M, Kapopoulou A, et al. Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. Nature Metabolism. 2019;1(12):1226-1242. doi:10.1038/s42255-019-0147-3 apa: Bevers, R. P. J., Litovchenko, M., Kapopoulou, A., Braman, V. S., Robinson, M. R., Auwerx, J., … Deplancke, B. (2019). Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. Nature Metabolism. Springer Nature. https://doi.org/10.1038/s42255-019-0147-3 chicago: Bevers, Roel P. J., Maria Litovchenko, Adamandia Kapopoulou, Virginie S. Braman, Matthew Richard Robinson, Johan Auwerx, Brian Hollis, and Bart Deplancke. “Mitochondrial Haplotypes Affect Metabolic Phenotypes in the Drosophila Genetic Reference Panel.” Nature Metabolism. Springer Nature, 2019. https://doi.org/10.1038/s42255-019-0147-3. ieee: R. P. J. Bevers et al., “Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel,” Nature Metabolism, vol. 1, no. 12. Springer Nature, pp. 1226–1242, 2019. ista: Bevers RPJ, Litovchenko M, Kapopoulou A, Braman VS, Robinson MR, Auwerx J, Hollis B, Deplancke B. 2019. Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. Nature Metabolism. 1(12), 1226–1242. mla: Bevers, Roel P. J., et al. “Mitochondrial Haplotypes Affect Metabolic Phenotypes in the Drosophila Genetic Reference Panel.” Nature Metabolism, vol. 1, no. 12, Springer Nature, 2019, pp. 1226–42, doi:10.1038/s42255-019-0147-3. short: R.P.J. Bevers, M. Litovchenko, A. Kapopoulou, V.S. Braman, M.R. Robinson, J. Auwerx, B. Hollis, B. Deplancke, Nature Metabolism 1 (2019) 1226–1242. date_created: 2020-04-30T10:40:56Z date_published: 2019-12-09T00:00:00Z date_updated: 2021-01-12T08:15:01Z day: '09' doi: 10.1038/s42255-019-0147-3 extern: '1' intvolume: ' 1' issue: '12' language: - iso: eng month: '12' oa_version: None page: 1226-1242 publication: Nature Metabolism publication_identifier: issn: - 2522-5812 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1038/s42255-020-0202-0 status: public title: Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 1 year: '2019' ... --- _id: '7782' abstract: - lang: eng text: As genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance Embedded Image is significantly (P < 10−56) larger than it would be expected for a similarly correlated variable Embedded Image. We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures. article_processing_charge: No author: - first_name: Jonathan full_name: Sulc, Jonathan last_name: Sulc - first_name: Ninon full_name: Mounier, Ninon last_name: Mounier - first_name: Felix full_name: Günther, Felix last_name: Günther - first_name: Thomas full_name: Winkler, Thomas last_name: Winkler - first_name: Andrew R. full_name: Wood, Andrew R. last_name: Wood - first_name: Timothy M. full_name: Frayling, Timothy M. last_name: Frayling - first_name: Iris M. full_name: Heid, Iris M. last_name: Heid - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Zoltán full_name: Kutalik, Zoltán last_name: Kutalik citation: ama: 'Sulc J, Mounier N, Günther F, et al. Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. bioRxiv. 2019.' apa: 'Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T. M., … Kutalik, Z. (2019). Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. bioRxiv. Cold Spring Harbor Laboratory.' chicago: 'Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R. Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán Kutalik. “Maximum Likelihood Method Quantifies the Overall Contribution of Gene-Environment Interaction to Continuous Traits: An Application to Complex Traits in the UK Biobank.” BioRxiv. Cold Spring Harbor Laboratory, 2019.' ieee: 'J. Sulc et al., “Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank,” bioRxiv. Cold Spring Harbor Laboratory, 2019.' ista: 'Sulc J, Mounier N, Günther F, Winkler T, Wood AR, Frayling TM, Heid IM, Robinson MR, Kutalik Z. 2019. Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. bioRxiv, .' mla: 'Sulc, Jonathan, et al. “Maximum Likelihood Method Quantifies the Overall Contribution of Gene-Environment Interaction to Continuous Traits: An Application to Complex Traits in the UK Biobank.” BioRxiv, Cold Spring Harbor Laboratory, 2019.' short: J. Sulc, N. Mounier, F. Günther, T. Winkler, A.R. Wood, T.M. Frayling, I.M. Heid, M.R. Robinson, Z. Kutalik, BioRxiv (2019). date_created: 2020-04-30T13:04:26Z date_published: 2019-06-14T00:00:00Z date_updated: 2021-01-12T08:15:30Z day: '14' extern: '1' language: - iso: eng main_file_link: - open_access: '1' url: 'https://doi.org/10.1101/632380 ' month: '06' oa: 1 oa_version: Preprint page: '20' publication: bioRxiv publication_status: published publisher: Cold Spring Harbor Laboratory status: public title: 'Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank' type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2019' ...