[{"pmid":1,"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.","date_published":"2023-09-07T00:00:00Z","publication":"American Journal of Human Genetics","status":"public","year":"2023","external_id":{"pmid":["37543033"]},"quality_controlled":"1","page":"1549-1563","ddc":["570"],"date_updated":"2024-01-30T13:21:05Z","_id":"14258","type":"journal_article","doi":"10.1016/j.ajhg.2023.07.006","article_processing_charge":"Yes (via OA deal)","publisher":"Elsevier","issue":"9","citation":{"ieee":"S. E. Ojavee <i>et al.</i>, “Genetic insights into the age-specific biological mechanisms governing human ovarian aging,” <i>American Journal of Human Genetics</i>, vol. 110, no. 9. Elsevier, pp. 1549–1563, 2023.","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.","ama":"Ojavee SE, Darrous L, Patxot M, et al. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. <i>American Journal of Human Genetics</i>. 2023;110(9):1549-1563. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">10.1016/j.ajhg.2023.07.006</a>","apa":"Ojavee, S. E., Darrous, L., Patxot, M., Läll, K., Fischer, K., Mägi, R., … Robinson, M. R. (2023). Genetic insights into the age-specific biological mechanisms governing human ovarian aging. <i>American Journal of Human Genetics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">https://doi.org/10.1016/j.ajhg.2023.07.006</a>","mla":"Ojavee, Sven E., et al. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” <i>American Journal of Human Genetics</i>, vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">10.1016/j.ajhg.2023.07.006</a>.","chicago":"Ojavee, Sven E., Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer, Reedik Mägi, Zoltan Kutalik, and Matthew Richard Robinson. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” <i>American Journal of Human Genetics</i>. Elsevier, 2023. <a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">https://doi.org/10.1016/j.ajhg.2023.07.006</a>.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"MaRo"}],"file":[{"access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2023_AJHG_Ojavee.pdf","checksum":"4108b031dc726ae6b4a5ae7e021ba188","relation":"main_file","creator":"dernst","date_updated":"2024-01-30T13:20:35Z","file_size":2551276,"date_created":"2024-01-30T13:20:35Z","file_id":"14912"}],"month":"09","publication_status":"published","publication_identifier":{"eissn":["1537-6605"],"issn":["0002-9297"]},"file_date_updated":"2024-01-30T13:20:35Z","has_accepted_license":"1","intvolume":"       110","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"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."}],"volume":110,"article_type":"original","date_created":"2023-09-03T22:01:15Z","author":[{"full_name":"Ojavee, Sven E.","last_name":"Ojavee","first_name":"Sven E."},{"first_name":"Liza","last_name":"Darrous","full_name":"Darrous, Liza"},{"first_name":"Marion","full_name":"Patxot, Marion","last_name":"Patxot"},{"last_name":"Läll","full_name":"Läll, Kristi","first_name":"Kristi"},{"last_name":"Fischer","full_name":"Fischer, Krista","first_name":"Krista"},{"first_name":"Reedik","last_name":"Mägi","full_name":"Mägi, Reedik"},{"full_name":"Kutalik, Zoltan","last_name":"Kutalik","first_name":"Zoltan"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"day":"07","scopus_import":"1","title":"Genetic insights into the age-specific biological mechanisms governing human ovarian aging","oa_version":"Published Version"},{"quality_controlled":"1","ddc":["570"],"date_updated":"2023-08-01T13:38:12Z","_id":"12719","type":"journal_article","doi":"10.1186/s13073-023-01161-y","article_processing_charge":"No","publisher":"Springer Nature","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.","date_published":"2023-02-28T00:00:00Z","publication":"Genome Medicine","status":"public","isi":1,"year":"2023","external_id":{"isi":["000940286600001"]},"publication_identifier":{"eissn":["1756-994X"]},"publication_status":"published","file_date_updated":"2023-03-14T10:29:47Z","has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"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.","lang":"eng"}],"intvolume":"        15","volume":15,"article_type":"original","date_created":"2023-03-12T23:01:02Z","author":[{"last_name":"Bernabeu","full_name":"Bernabeu, Elena","first_name":"Elena"},{"last_name":"Mccartney","full_name":"Mccartney, Daniel L.","first_name":"Daniel L."},{"full_name":"Gadd, Danni A.","last_name":"Gadd","first_name":"Danni A."},{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"first_name":"Ake T.","full_name":"Lu, Ake T.","last_name":"Lu"},{"full_name":"Murphy, Lee","last_name":"Murphy","first_name":"Lee"},{"first_name":"Nicola","last_name":"Wrobel","full_name":"Wrobel, Nicola"},{"last_name":"Campbell","full_name":"Campbell, Archie","first_name":"Archie"},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"last_name":"Liewald","full_name":"Liewald, David","first_name":"David"},{"first_name":"Caroline","last_name":"Hayward","full_name":"Hayward, Caroline"},{"full_name":"Sudlow, Cathie","last_name":"Sudlow","first_name":"Cathie"},{"first_name":"Simon R.","full_name":"Cox, Simon R.","last_name":"Cox"},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"first_name":"Steve","full_name":"Horvath, Steve","last_name":"Horvath"},{"first_name":"Andrew M.","last_name":"Mcintosh","full_name":"Mcintosh, Andrew M."},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"first_name":"Catalina A.","last_name":"Vallejos","full_name":"Vallejos, Catalina A."},{"last_name":"Marioni","full_name":"Marioni, Riccardo E.","first_name":"Riccardo E."}],"day":"28","scopus_import":"1","title":"Refining epigenetic prediction of chronological and biological age","oa_version":"Published Version","citation":{"ama":"Bernabeu E, Mccartney DL, Gadd DA, et al. Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. 2023;15. doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>","ieee":"E. Bernabeu <i>et al.</i>, “Refining epigenetic prediction of chronological and biological age,” <i>Genome Medicine</i>, vol. 15. Springer Nature, 2023.","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).","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.","chicago":"Bernabeu, Elena, Daniel L. Mccartney, Danni A. Gadd, Robert F. Hillary, Ake T. Lu, Lee Murphy, Nicola Wrobel, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>.","apa":"Bernabeu, E., Mccartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., … Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>","mla":"Bernabeu, Elena, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>, vol. 15, 12, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"MaRo"}],"file":[{"relation":"main_file","checksum":"833b837910c4db42fb5f0f34125f77a7","file_name":"2023_GenomeMed_Bernabeu.pdf","success":1,"content_type":"application/pdf","access_level":"open_access","file_id":"12722","file_size":4275987,"date_created":"2023-03-14T10:29:47Z","creator":"cchlebak","date_updated":"2023-03-14T10:29:47Z"}],"article_number":"12","month":"02"},{"isi":1,"year":"2022","related_material":{"record":[{"id":"13072","relation":"research_data","status":"public"}],"link":[{"url":"https://doi.org/10.1101/2021.05.24.21257698","relation":"earlier_version"}]},"external_id":{"isi":["000744358300002"]},"date_published":"2022-01-17T00:00:00Z","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.","status":"public","publication":"Genome Biology","project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"_id":"10702","date_updated":"2023-08-02T14:05:13Z","type":"journal_article","article_processing_charge":"No","doi":"10.1186/s13059-021-02596-5","publisher":"Springer Nature","quality_controlled":"1","ddc":["570"],"department":[{"_id":"MaRo"}],"file":[{"date_created":"2022-01-31T13:16:05Z","file_size":1540606,"creator":"cchlebak","date_updated":"2022-01-31T13:16:05Z","file_id":"10708","file_name":"2022_GenomeBio_McCartney.pdf","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"34f10bb2b0594189dcac24d13b691d52"}],"article_number":"26","month":"01","citation":{"apa":"McCartney, D. L., Hillary, R. F., Conole, E. L. S., Banos, D. T., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2022). Blood-based epigenome-wide analyses of cognitive abilities. <i>Genome Biology</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>","mla":"McCartney, Daniel L., et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” <i>Genome Biology</i>, vol. 23, no. 1, 26, Springer Nature, 2022, doi:<a href=\"https://doi.org/10.1186/s13059-021-02596-5\">10.1186/s13059-021-02596-5</a>.","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.","chicago":"McCartney, Daniel L., Robert F. Hillary, Eleanor L.S. Conole, Daniel Trejo Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” <i>Genome Biology</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>.","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities,” <i>Genome Biology</i>, vol. 23, no. 1. Springer Nature, 2022.","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).","ama":"McCartney DL, Hillary RF, Conole ELS, et al. Blood-based epigenome-wide analyses of cognitive abilities. <i>Genome Biology</i>. 2022;23(1). doi:<a href=\"https://doi.org/10.1186/s13059-021-02596-5\">10.1186/s13059-021-02596-5</a>"},"issue":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"volume":23,"date_created":"2022-01-30T23:01:33Z","article_type":"original","scopus_import":"1","day":"17","author":[{"full_name":"McCartney, Daniel L.","last_name":"McCartney","first_name":"Daniel L."},{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"full_name":"Conole, Eleanor L.S.","last_name":"Conole","first_name":"Eleanor L.S."},{"first_name":"Daniel Trejo","full_name":"Banos, Daniel Trejo","last_name":"Banos"},{"first_name":"Danni A.","last_name":"Gadd","full_name":"Gadd, Danni A."},{"first_name":"Rosie M.","last_name":"Walker","full_name":"Walker, Rosie M."},{"first_name":"Cliff","full_name":"Nangle, Cliff","last_name":"Nangle"},{"full_name":"Flaig, Robin","last_name":"Flaig","first_name":"Robin"},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"full_name":"Murray, Alison D.","last_name":"Murray","first_name":"Alison D."},{"full_name":"Maniega, Susana Muñoz","last_name":"Maniega","first_name":"Susana Muñoz"},{"first_name":"María Del C.","last_name":"Valdés-Hernández","full_name":"Valdés-Hernández, María Del C."},{"last_name":"Harris","full_name":"Harris, Mathew A.","first_name":"Mathew A."},{"last_name":"Bastin","full_name":"Bastin, Mark E.","first_name":"Mark E."},{"full_name":"Wardlaw, Joanna M.","last_name":"Wardlaw","first_name":"Joanna M."},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"last_name":"Porteous","full_name":"Porteous, David J.","first_name":"David J."},{"first_name":"Elliot M.","last_name":"Tucker-Drob","full_name":"Tucker-Drob, Elliot M."},{"last_name":"McIntosh","full_name":"McIntosh, Andrew M.","first_name":"Andrew M."},{"full_name":"Evans, Kathryn L.","last_name":"Evans","first_name":"Kathryn L."},{"first_name":"Ian J.","full_name":"Deary, Ian J.","last_name":"Deary"},{"last_name":"Cox","full_name":"Cox, Simon R.","first_name":"Simon R."},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"},{"first_name":"Riccardo E.","full_name":"Marioni, Riccardo E.","last_name":"Marioni"}],"title":"Blood-based epigenome-wide analyses of cognitive abilities","oa_version":"Published Version","file_date_updated":"2022-01-31T13:16:05Z","publication_status":"published","publication_identifier":{"eissn":["1474-760X"],"issn":["1474-7596"]},"has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        23","abstract":[{"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.","lang":"eng"}]},{"doi":"10.1073/pnas.2121279119","article_processing_charge":"No","publisher":"Proceedings of the National Academy of Sciences","date_updated":"2023-08-03T12:40:38Z","_id":"11733","type":"journal_article","ddc":["570"],"quality_controlled":"1","year":"2022","isi":1,"external_id":{"isi":["000881496900003"]},"related_material":{"record":[{"id":"13064","status":"public","relation":"research_data"}]},"status":"public","publication":"Proceedings of the National Academy of Sciences of the United States of America","date_published":"2022-07-29T00:00:00Z","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.","author":[{"first_name":"Etienne J.","full_name":"Orliac, Etienne J.","last_name":"Orliac"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"full_name":"Ojavee, Sven E.","last_name":"Ojavee","first_name":"Sven E."},{"first_name":"Kristi","full_name":"Läll, Kristi","last_name":"Läll"},{"full_name":"Mägi, Reedik","last_name":"Mägi","first_name":"Reedik"},{"full_name":"Visscher, Peter M.","last_name":"Visscher","first_name":"Peter M."},{"last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"}],"day":"29","scopus_import":"1","oa_version":"Published Version","title":"Improving GWAS discovery and genomic prediction accuracy in biobank data","volume":119,"article_type":"original","date_created":"2022-08-07T22:01:56Z","has_accepted_license":"1","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."}],"tmp":{"image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"intvolume":"       119","publication_identifier":{"eissn":["1091-6490"]},"publication_status":"published","file_date_updated":"2022-08-08T07:31:19Z","month":"07","department":[{"_id":"MaRo"}],"article_number":"e2121279119","file":[{"file_size":1001164,"date_created":"2022-08-08T07:31:19Z","date_updated":"2022-08-08T07:31:19Z","creator":"dernst","file_id":"11745","file_name":"2022_PNAS_Orliac.pdf","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"b5d2024e19fbad6f85a5e384e44d0f3b"}],"oa":1,"language":[{"iso":"eng"}],"issue":"31","citation":{"apa":"Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher, P. M., &#38; Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction accuracy in biobank data. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. Proceedings of the National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2121279119\">https://doi.org/10.1073/pnas.2121279119</a>","mla":"Orliac, Etienne J., et al. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 119, no. 31, e2121279119, Proceedings of the National Academy of Sciences, 2022, doi:<a href=\"https://doi.org/10.1073/pnas.2121279119\">10.1073/pnas.2121279119</a>.","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.","chicago":"Orliac, Etienne J., Daniel Trejo Banos, Sven E. Ojavee, Kristi Läll, Reedik Mägi, Peter M. Visscher, and Matthew Richard Robinson. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. Proceedings of the National Academy of Sciences, 2022. <a href=\"https://doi.org/10.1073/pnas.2121279119\">https://doi.org/10.1073/pnas.2121279119</a>.","ieee":"E. J. Orliac <i>et al.</i>, “Improving GWAS discovery and genomic prediction accuracy in biobank data,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 119, no. 31. Proceedings of the National Academy of Sciences, 2022.","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).","ama":"Orliac EJ, Trejo Banos D, Ojavee SE, et al. Improving GWAS discovery and genomic prediction accuracy in biobank data. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2022;119(31). doi:<a href=\"https://doi.org/10.1073/pnas.2121279119\">10.1073/pnas.2121279119</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8"},{"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"abstract":[{"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.","lang":"eng"}],"ddc":["570"],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.gtht76hmz"}],"oa_version":"Published Version","publisher":"Dryad","title":"Improving genome-wide association discovery and genomic prediction accuracy in biobank data","doi":"10.5061/DRYAD.GTHT76HMZ","author":[{"full_name":"Orliac, Etienne","last_name":"Orliac","first_name":"Etienne"},{"first_name":"Daniel","full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos"},{"last_name":"Ojavee","full_name":"Ojavee, Sven","first_name":"Sven"},{"first_name":"Kristi","last_name":"Läll","full_name":"Läll, Kristi"},{"first_name":"Reedik","full_name":"Mägi, Reedik","last_name":"Mägi"},{"first_name":"Peter","last_name":"Visscher","full_name":"Visscher, Peter"},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson"}],"day":"02","article_processing_charge":"No","type":"research_data_reference","date_created":"2023-05-23T16:28:13Z","date_updated":"2023-08-03T12:40:37Z","_id":"13064","status":"public","oa":1,"date_published":"2022-09-02T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>","short":"E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022).","ieee":"E. Orliac <i>et al.</i>, “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022.","chicago":"Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>.","ista":"Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","mla":"Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","apa":"Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>"},"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"11733"}]},"month":"09","year":"2022","department":[{"_id":"MaRo"}]},{"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"ieee":"S. E. Ojavee, Z. Kutalik, and M. R. Robinson, “Liability-scale heritability estimation for biobank studies of low-prevalence disease,” <i>The American Journal of Human Genetics</i>, vol. 109, no. 11. Elsevier, pp. 2009–2017, 2022.","short":"S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics 109 (2022) 2009–2017.","ama":"Ojavee SE, Kutalik Z, Robinson MR. Liability-scale heritability estimation for biobank studies of low-prevalence disease. <i>The American Journal of Human Genetics</i>. 2022;109(11):2009-2017. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>","apa":"Ojavee, S. E., Kutalik, Z., &#38; Robinson, M. R. (2022). Liability-scale heritability estimation for biobank studies of low-prevalence disease. <i>The American Journal of Human Genetics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">https://doi.org/10.1016/j.ajhg.2022.09.011</a>","mla":"Ojavee, Sven E., et al. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The American Journal of Human Genetics</i>, vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>.","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.","chicago":"Ojavee, Sven E., Zoltan Kutalik, and Matthew Richard Robinson. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The American Journal of Human Genetics</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">https://doi.org/10.1016/j.ajhg.2022.09.011</a>."},"issue":"11","month":"11","file":[{"relation":"main_file","checksum":"4cd7f12bfe21a8237bb095eedfa26361","success":1,"file_name":"2022_AJHG_Ojavee.pdf","access_level":"open_access","content_type":"application/pdf","file_id":"12353","file_size":705195,"date_created":"2023-01-24T09:23:01Z","creator":"dernst","date_updated":"2023-01-24T09:23:01Z"}],"department":[{"_id":"MaRo"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"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."}],"tmp":{"image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"intvolume":"       109","has_accepted_license":"1","file_date_updated":"2023-01-24T09:23:01Z","publication_identifier":{"issn":["0002-9297"]},"publication_status":"published","oa_version":"Published Version","title":"Liability-scale heritability estimation for biobank studies of low-prevalence disease","day":"03","scopus_import":"1","author":[{"first_name":"Sven E.","full_name":"Ojavee, Sven E.","last_name":"Ojavee"},{"first_name":"Zoltan","last_name":"Kutalik","full_name":"Kutalik, Zoltan"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"date_created":"2023-01-12T12:05:28Z","article_type":"original","volume":109,"status":"public","publication":"The American Journal of Human Genetics","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"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.","date_published":"2022-11-03T00:00:00Z","external_id":{"isi":["000898683500006"]},"year":"2022","isi":1,"keyword":["Genetics (clinical)","Genetics"],"ddc":["570"],"page":"2009-2017","quality_controlled":"1","publisher":"Elsevier","article_processing_charge":"Yes (via OA deal)","doi":"10.1016/j.ajhg.2022.09.011","type":"journal_article","_id":"12142","date_updated":"2023-08-04T08:56:46Z"},{"month":"11","department":[{"_id":"MaRo"}],"file":[{"relation":"main_file","checksum":"a676d732f67c2990197e34f96b219370","success":1,"file_name":"2022_EuropJourHaematology_Patxot.pdf","access_level":"open_access","content_type":"application/pdf","file_id":"12426","date_created":"2023-01-27T11:42:43Z","file_size":1225073,"creator":"dernst","date_updated":"2023-01-27T11:42:43Z"}],"oa":1,"language":[{"iso":"eng"}],"issue":"5","citation":{"ama":"Patxot M, Stojanov M, Ojavee SE, et al. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. <i>European Journal of Haematology</i>. 2022;109(5):566-575. doi:<a href=\"https://doi.org/10.1111/ejh.13844\">10.1111/ejh.13844</a>","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.","ieee":"M. Patxot <i>et al.</i>, “Haematological changes from conception to childbirth: An indicator of major pregnancy complications,” <i>European Journal of Haematology</i>, vol. 109, no. 5. Wiley, pp. 566–575, 2022.","ista":"Patxot M, Stojanov M, Ojavee SE, Gobert RP, Kutalik Z, Gavillet M, Baud D, Robinson MR. 2022. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. 109(5), 566–575.","chicago":"Patxot, Marion, Miloš Stojanov, Sven Erik Ojavee, Rosanna Pescini Gobert, Zoltán Kutalik, Mathilde Gavillet, David Baud, and Matthew Richard Robinson. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” <i>European Journal of Haematology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/ejh.13844\">https://doi.org/10.1111/ejh.13844</a>.","mla":"Patxot, Marion, et al. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” <i>European Journal of Haematology</i>, vol. 109, no. 5, Wiley, 2022, pp. 566–75, doi:<a href=\"https://doi.org/10.1111/ejh.13844\">10.1111/ejh.13844</a>.","apa":"Patxot, M., Stojanov, M., Ojavee, S. E., Gobert, R. P., Kutalik, Z., Gavillet, M., … Robinson, M. R. (2022). Haematological changes from conception to childbirth: An indicator of major pregnancy complications. <i>European Journal of Haematology</i>. Wiley. <a href=\"https://doi.org/10.1111/ejh.13844\">https://doi.org/10.1111/ejh.13844</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","author":[{"first_name":"Marion","last_name":"Patxot","full_name":"Patxot, Marion"},{"full_name":"Stojanov, Miloš","last_name":"Stojanov","first_name":"Miloš"},{"full_name":"Ojavee, Sven Erik","last_name":"Ojavee","first_name":"Sven Erik"},{"last_name":"Gobert","full_name":"Gobert, Rosanna Pescini","first_name":"Rosanna Pescini"},{"first_name":"Zoltán","last_name":"Kutalik","full_name":"Kutalik, Zoltán"},{"full_name":"Gavillet, Mathilde","last_name":"Gavillet","first_name":"Mathilde"},{"first_name":"David","last_name":"Baud","full_name":"Baud, David"},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"scopus_import":"1","day":"01","oa_version":"Published Version","title":"Haematological changes from conception to childbirth: An indicator of major pregnancy complications","volume":109,"article_type":"original","date_created":"2023-01-16T09:50:58Z","has_accepted_license":"1","tmp":{"image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"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."}],"intvolume":"       109","publication_identifier":{"issn":["0902-4441"],"eissn":["1600-0609"]},"publication_status":"published","file_date_updated":"2023-01-27T11:42:43Z","isi":1,"year":"2022","external_id":{"pmid":["36059200"],"isi":["000849690500001"]},"keyword":["Hematology","General Medicine"],"publication":"European Journal of Haematology","status":"public","pmid":1,"date_published":"2022-11-01T00:00:00Z","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.","doi":"10.1111/ejh.13844","article_processing_charge":"No","publisher":"Wiley","date_updated":"2023-08-04T09:36:21Z","_id":"12235","type":"journal_article","page":"566-575","ddc":["570","610"],"quality_controlled":"1"},{"date_updated":"2023-09-26T10:36:14Z","_id":"8429","type":"journal_article","doi":"10.1038/s41467-021-27258-9","article_processing_charge":"No","publisher":"Springer Nature","quality_controlled":"1","ddc":["610"],"isi":1,"year":"2021","external_id":{"isi":["000724450600023"]},"related_material":{"record":[{"relation":"research_data","status":"public","id":"13063"}]},"date_published":"2021-11-30T00:00:00Z","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.","publication":"Nature Communications","status":"public","volume":12,"article_type":"original","date_created":"2020-09-17T10:52:38Z","author":[{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Daniel","full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos"},{"first_name":"Athanasios","last_name":"Kousathanas","full_name":"Kousathanas, Athanasios"},{"full_name":"Orliac, Etienne J","last_name":"Orliac","first_name":"Etienne J"},{"full_name":"Ojavee, Sven E","last_name":"Ojavee","first_name":"Sven E"},{"first_name":"Gerhard","full_name":"Moser, Gerhard","last_name":"Moser"},{"first_name":"Julia","last_name":"Sidorenko","full_name":"Sidorenko, Julia"},{"last_name":"Kutalik","full_name":"Kutalik, Zoltan","first_name":"Zoltan"},{"first_name":"Reedik","full_name":"Magi, Reedik","last_name":"Magi"},{"first_name":"Peter M","last_name":"Visscher","full_name":"Visscher, Peter M"},{"first_name":"Lars","full_name":"Ronnegard, Lars","last_name":"Ronnegard"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"}],"day":"30","scopus_import":"1","title":"Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits","oa_version":"Published Version","publication_identifier":{"eissn":["2041-1723"]},"publication_status":"published","file_date_updated":"2021-12-06T07:47:11Z","has_accepted_license":"1","intvolume":"        12","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"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."}],"department":[{"_id":"MaRo"}],"article_number":"6972","file":[{"checksum":"384681be17aff902c149a48f52d13d4f","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2021_NatComm_Paxtot.pdf","file_id":"10419","creator":"cchlebak","date_updated":"2021-12-06T07:47:11Z","file_size":6519771,"date_created":"2021-12-06T07:47:11Z"}],"month":"11","issue":"1","citation":{"mla":"Patxot, Marion, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” <i>Nature Communications</i>, vol. 12, no. 1, 6972, Springer Nature, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-27258-9\">10.1038/s41467-021-27258-9</a>.","apa":"Patxot, M., Trejo Banos, D., Kousathanas, A., Orliac, E. J., Ojavee, S. E., Moser, G., … Robinson, M. R. (2021). Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-021-27258-9\">https://doi.org/10.1038/s41467-021-27258-9</a>","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.","chicago":"Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” <i>Nature Communications</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41467-021-27258-9\">https://doi.org/10.1038/s41467-021-27258-9</a>.","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).","ieee":"M. Patxot <i>et al.</i>, “Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits,” <i>Nature Communications</i>, vol. 12, no. 1. Springer Nature, 2021.","ama":"Patxot M, Trejo Banos D, Kousathanas A, et al. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-27258-9\">10.1038/s41467-021-27258-9</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}]},{"volume":12,"date_created":"2020-09-17T10:53:00Z","day":"20","scopus_import":"1","author":[{"first_name":"Sven E","last_name":"Ojavee","full_name":"Ojavee, Sven E"},{"first_name":"Athanasios","last_name":"Kousathanas","full_name":"Kousathanas, Athanasios"},{"full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos","first_name":"Daniel"},{"first_name":"Etienne J","last_name":"Orliac","full_name":"Orliac, Etienne J"},{"last_name":"Patxot","full_name":"Patxot, Marion","first_name":"Marion"},{"last_name":"Lall","full_name":"Lall, Kristi","first_name":"Kristi"},{"first_name":"Reedik","full_name":"Magi, Reedik","last_name":"Magi"},{"last_name":"Fischer","full_name":"Fischer, Krista","first_name":"Krista"},{"last_name":"Kutalik","full_name":"Kutalik, Zoltan","first_name":"Zoltan"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"}],"title":"Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis","oa_version":"Published Version","file_date_updated":"2021-05-04T15:07:50Z","publication_status":"published","publication_identifier":{"eissn":["20411723"]},"has_accepted_license":"1","intvolume":"        12","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"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."}],"department":[{"_id":"MaRo"}],"article_number":"2337","file":[{"creator":"kschuh","date_updated":"2021-05-04T15:07:50Z","date_created":"2021-05-04T15:07:50Z","file_size":6474239,"file_id":"9372","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2021_nature_communications_Ojavee.pdf","checksum":"eca8b9ae713835c5b785211dd08d8a2e","relation":"main_file"}],"month":"04","citation":{"apa":"Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. Nature Research. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>","mla":"Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>, vol. 12, no. 1, 2337, Nature Research, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>.","chicago":"Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac, Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>. Nature Research, 2021. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>.","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.","ieee":"S. E. Ojavee <i>et al.</i>, “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” <i>Nature Communications</i>, vol. 12, no. 1. Nature Research, 2021.","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).","ama":"Ojavee SE, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>"},"issue":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"_id":"8430","date_updated":"2023-08-04T11:00:17Z","type":"journal_article","article_processing_charge":"No","doi":"10.1038/s41467-021-22538-w","publisher":"Nature Research","quality_controlled":"1","ddc":["570"],"year":"2021","isi":1,"related_material":{"link":[{"relation":"press_release","description":"News on IST Homepage","url":"https://ist.ac.at/en/news/predicting-the-onset-of-diseases/"}]},"external_id":{"isi":["000642509600006"]},"date_published":"2021-04-20T00:00:00Z","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.","publication":"Nature Communications","status":"public","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}]},{"status":"public","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-11-04T00:00:00Z","citation":{"chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>.","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>","ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>","short":"M.R. Robinson, (2021).","ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021."},"month":"11","related_material":{"link":[{"url":"https://github.com/medical-genomics-group/gmrm","relation":"software"}],"record":[{"id":"8429","relation":"used_in_publication","status":"public"}]},"year":"2021","department":[{"_id":"MaRo"}],"ddc":["570"],"abstract":[{"text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.","lang":"eng"}],"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"main_file_link":[{"url":"https://doi.org/10.5061/dryad.sqv9s4n51","open_access":"1"}],"publisher":"Dryad","title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","oa_version":"Published Version","day":"04","article_processing_charge":"No","author":[{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"doi":"10.5061/dryad.sqv9s4n51","date_created":"2023-05-23T16:20:16Z","type":"research_data_reference","_id":"13063","date_updated":"2023-09-26T10:36:15Z"},{"ddc":["570"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5794029"}],"article_processing_charge":"No","day":"20","doi":"10.5281/ZENODO.5794028","author":[{"last_name":"McCartney","full_name":"McCartney, Daniel L","first_name":"Daniel L"},{"full_name":"Hillary, Robert F","last_name":"Hillary","first_name":"Robert F"},{"first_name":"Eleanor LS","last_name":"Conole","full_name":"Conole, Eleanor LS"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"full_name":"Gadd, Danni A","last_name":"Gadd","first_name":"Danni A"},{"first_name":"Rosie M","last_name":"Walker","full_name":"Walker, Rosie M"},{"first_name":"Cliff","full_name":"Nangle, Cliff","last_name":"Nangle"},{"first_name":"Robin","full_name":"Flaig, Robin","last_name":"Flaig"},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"full_name":"Murray, Alison D","last_name":"Murray","first_name":"Alison D"},{"last_name":"Munoz Maniega","full_name":"Munoz Maniega, Susana","first_name":"Susana"},{"last_name":"del C Valdes-Hernandez","full_name":"del C Valdes-Hernandez, Maria","first_name":"Maria"},{"first_name":"Mathew A","last_name":"Harris","full_name":"Harris, Mathew A"},{"first_name":"Mark E","last_name":"Bastin","full_name":"Bastin, Mark E"},{"full_name":"Wardlaw, Joanna M","last_name":"Wardlaw","first_name":"Joanna M"},{"first_name":"Sarah E","last_name":"Harris","full_name":"Harris, Sarah E"},{"first_name":"David J","full_name":"Porteous, David J","last_name":"Porteous"},{"full_name":"Tucker-Drob, Elliot M","last_name":"Tucker-Drob","first_name":"Elliot M"},{"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"},{"full_name":"Cox, Simon R","last_name":"Cox","first_name":"Simon R"},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"first_name":"Riccardo E","full_name":"Marioni, Riccardo E","last_name":"Marioni"}],"publisher":"Zenodo","oa_version":"Published Version","title":"Blood-based epigenome-wide analyses of cognitive abilities","_id":"13072","date_updated":"2023-08-02T14:05:12Z","date_created":"2023-05-23T16:46:20Z","type":"research_data_reference","oa":1,"status":"public","citation":{"mla":"McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive Abilities</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","apa":"McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>","ista":"McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","chicago":"McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>.","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).","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.","ama":"McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-12-20T00:00:00Z","year":"2021","related_material":{"record":[{"id":"10702","relation":"used_in_publication","status":"public"}]},"month":"12","department":[{"_id":"MaRo"}]},{"title":"Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy","oa_version":"Published Version","author":[{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"first_name":"Marion","full_name":"Patxot, Marion","last_name":"Patxot"},{"first_name":"Miloš","full_name":"Stojanov, Miloš","last_name":"Stojanov"},{"first_name":"Sabine","last_name":"Blum","full_name":"Blum, Sabine"},{"first_name":"David","last_name":"Baud","full_name":"Baud, David"}],"day":"28","scopus_import":"1","article_type":"original","date_created":"2021-10-03T22:01:21Z","volume":11,"intvolume":"        11","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"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.","lang":"eng"}],"has_accepted_license":"1","publication_status":"published","publication_identifier":{"eissn":["2045-2322"]},"file_date_updated":"2021-10-05T14:56:48Z","month":"09","article_number":"19238","file":[{"date_updated":"2021-10-05T14:56:48Z","creator":"cchlebak","file_size":6970368,"date_created":"2021-10-05T14:56:48Z","file_id":"10091","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2021_ScientificReports_Robinson.pdf","checksum":"f002ec22f609f58e1263b79e7f79601e","relation":"main_file"}],"department":[{"_id":"MaRo"}],"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"chicago":"Robinson, Matthew Richard, Marion Patxot, Miloš Stojanov, Sabine Blum, and David Baud. “Postpartum Hemorrhage Risk Is Driven by Changes in Blood Composition through Pregnancy.” <i>Scientific Reports</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41598-021-98411-z\">https://doi.org/10.1038/s41598-021-98411-z</a>.","ista":"Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. 2021. Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. Scientific Reports. 11, 19238.","mla":"Robinson, Matthew Richard, et al. “Postpartum Hemorrhage Risk Is Driven by Changes in Blood Composition through Pregnancy.” <i>Scientific Reports</i>, vol. 11, 19238, Springer Nature, 2021, doi:<a href=\"https://doi.org/10.1038/s41598-021-98411-z\">10.1038/s41598-021-98411-z</a>.","apa":"Robinson, M. R., Patxot, M., Stojanov, M., Blum, S., &#38; Baud, D. (2021). Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. <i>Scientific Reports</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41598-021-98411-z\">https://doi.org/10.1038/s41598-021-98411-z</a>","ama":"Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. <i>Scientific Reports</i>. 2021;11. doi:<a href=\"https://doi.org/10.1038/s41598-021-98411-z\">10.1038/s41598-021-98411-z</a>","short":"M.R. Robinson, M. Patxot, M. Stojanov, S. Blum, D. Baud, Scientific Reports 11 (2021).","ieee":"M. R. Robinson, M. Patxot, M. Stojanov, S. Blum, and D. Baud, “Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy,” <i>Scientific Reports</i>, vol. 11. Springer Nature, 2021."},"publisher":"Springer Nature","doi":"10.1038/s41598-021-98411-z","article_processing_charge":"Yes","type":"journal_article","date_updated":"2023-08-14T07:05:15Z","_id":"10069","ddc":["618"],"quality_controlled":"1","external_id":{"pmid":["34584125"],"isi":["000701575500083"]},"year":"2021","isi":1,"publication":"Scientific Reports","status":"public","date_published":"2021-09-28T00:00:00Z","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.","pmid":1},{"quality_controlled":"1","ddc":["570"],"_id":"7999","date_updated":"2023-08-22T07:13:09Z","type":"journal_article","article_processing_charge":"No","doi":"10.1038/s41467-020-16520-1","publisher":"Springer Nature","pmid":1,"date_published":"2020-06-08T00:00:00Z","status":"public","publication":"Nature Communications","isi":1,"year":"2020","related_material":{"link":[{"url":"https://doi.org/10.1038/s41467-020-19099-9","relation":"erratum"}]},"external_id":{"isi":["000541702400004"],"pmid":["32513961"]},"file_date_updated":"2020-07-14T12:48:07Z","publication_status":"published","publication_identifier":{"issn":["2041-1723"]},"has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        11","abstract":[{"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. ","lang":"eng"}],"volume":11,"date_created":"2020-06-22T11:18:25Z","article_type":"original","scopus_import":"1","day":"08","author":[{"first_name":"D","last_name":"Trejo Banos","full_name":"Trejo Banos, D"},{"last_name":"McCartney","full_name":"McCartney, DL","first_name":"DL"},{"full_name":"Patxot, M","last_name":"Patxot","first_name":"M"},{"full_name":"Anchieri, L","last_name":"Anchieri","first_name":"L"},{"first_name":"T","last_name":"Battram","full_name":"Battram, T"},{"first_name":"C","last_name":"Christiansen","full_name":"Christiansen, C"},{"full_name":"Costeira, R","last_name":"Costeira","first_name":"R"},{"first_name":"RM","full_name":"Walker, RM","last_name":"Walker"},{"first_name":"SW","last_name":"Morris","full_name":"Morris, SW"},{"full_name":"Campbell, A","last_name":"Campbell","first_name":"A"},{"last_name":"Zhang","full_name":"Zhang, Q","first_name":"Q"},{"last_name":"Porteous","full_name":"Porteous, DJ","first_name":"DJ"},{"last_name":"McRae","full_name":"McRae, AF","first_name":"AF"},{"full_name":"Wray, NR","last_name":"Wray","first_name":"NR"},{"full_name":"Visscher, PM","last_name":"Visscher","first_name":"PM"},{"first_name":"CS","last_name":"Haley","full_name":"Haley, CS"},{"first_name":"KL","last_name":"Evans","full_name":"Evans, KL"},{"first_name":"IJ","last_name":"Deary","full_name":"Deary, IJ"},{"first_name":"AM","full_name":"McIntosh, AM","last_name":"McIntosh"},{"last_name":"Hemani","full_name":"Hemani, G","first_name":"G"},{"first_name":"JT","last_name":"Bell","full_name":"Bell, JT"},{"last_name":"Marioni","full_name":"Marioni, RE","first_name":"RE"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson"}],"oa_version":"Published Version","title":"Bayesian reassessment of the epigenetic architecture of complex traits","citation":{"ieee":"D. Trejo Banos <i>et al.</i>, “Bayesian reassessment of the epigenetic architecture of complex traits,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","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).","ama":"Trejo Banos D, McCartney D, Patxot M, et al. Bayesian reassessment of the epigenetic architecture of complex traits. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-16520-1\">10.1038/s41467-020-16520-1</a>","apa":"Trejo Banos, D., McCartney, D., Patxot, M., Anchieri, L., Battram, T., Christiansen, C., … Robinson, M. R. (2020). Bayesian reassessment of the epigenetic architecture of complex traits. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-16520-1\">https://doi.org/10.1038/s41467-020-16520-1</a>","mla":"Trejo Banos, D., et al. “Bayesian Reassessment of the Epigenetic Architecture of Complex Traits.” <i>Nature Communications</i>, vol. 11, 2865, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-16520-1\">10.1038/s41467-020-16520-1</a>.","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.","chicago":"Trejo Banos, D, DL McCartney, M Patxot, L Anchieri, T Battram, C Christiansen, R Costeira, et al. “Bayesian Reassessment of the Epigenetic Architecture of Complex Traits.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-16520-1\">https://doi.org/10.1038/s41467-020-16520-1</a>."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"MaRo"}],"file":[{"file_name":"2020_NatureComm_Bayesian.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file","checksum":"4c96babd4cfb0d153334f6c598c0bacb","date_created":"2020-06-22T11:24:32Z","file_size":1475657,"creator":"dernst","date_updated":"2020-07-14T12:48:07Z","file_id":"8000"}],"article_number":"2865","month":"06"},{"oa":1,"language":[{"iso":"eng"}],"citation":{"apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., Mccartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. <i>Genome Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13073-020-00754-1\">https://doi.org/10.1186/s13073-020-00754-1</a>","mla":"Hillary, Robert F., et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” <i>Genome Medicine</i>, vol. 12, no. 1, 60, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1186/s13073-020-00754-1\">10.1186/s13073-020-00754-1</a>.","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.","chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. Mccartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” <i>Genome Medicine</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1186/s13073-020-00754-1\">https://doi.org/10.1186/s13073-020-00754-1</a>.","ieee":"R. F. Hillary <i>et al.</i>, “Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults,” <i>Genome Medicine</i>, vol. 12, no. 1. Springer Nature, 2020.","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).","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. <i>Genome Medicine</i>. 2020;12(1). doi:<a href=\"https://doi.org/10.1186/s13073-020-00754-1\">10.1186/s13073-020-00754-1</a>"},"issue":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"07","department":[{"_id":"MaRo"}],"file":[{"file_size":1136983,"date_created":"2020-07-22T06:27:38Z","date_updated":"2020-07-22T06:27:38Z","creator":"dernst","file_id":"8145","file_name":"2020_GenomeMedicine_Hillary.pdf","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"article_number":"60","has_accepted_license":"1","abstract":[{"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.","lang":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        12","file_date_updated":"2020-07-22T06:27:38Z","publication_status":"published","publication_identifier":{"eissn":["1756994X"]},"scopus_import":"1","day":"08","author":[{"full_name":"Hillary, Robert F.","last_name":"Hillary","first_name":"Robert F."},{"first_name":"Daniel","last_name":"Trejo-Banos","full_name":"Trejo-Banos, Daniel"},{"first_name":"Athanasios","last_name":"Kousathanas","full_name":"Kousathanas, Athanasios"},{"full_name":"Mccartney, Daniel L.","last_name":"Mccartney","first_name":"Daniel L."},{"full_name":"Harris, Sarah E.","last_name":"Harris","first_name":"Sarah E."},{"full_name":"Stevenson, Anna J.","last_name":"Stevenson","first_name":"Anna J."},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Sven Erik","last_name":"Ojavee","full_name":"Ojavee, Sven Erik"},{"first_name":"Qian","last_name":"Zhang","full_name":"Zhang, Qian"},{"full_name":"Liewald, David C.","last_name":"Liewald","first_name":"David C."},{"full_name":"Ritchie, Craig W.","last_name":"Ritchie","first_name":"Craig W."},{"first_name":"Kathryn L.","full_name":"Evans, Kathryn L.","last_name":"Evans"},{"full_name":"Tucker-Drob, Elliot M.","last_name":"Tucker-Drob","first_name":"Elliot M."},{"first_name":"Naomi R.","full_name":"Wray, Naomi R.","last_name":"Wray"},{"first_name":"Allan F.","last_name":"Mcrae","full_name":"Mcrae, Allan F."},{"first_name":"Peter M.","full_name":"Visscher, Peter M.","last_name":"Visscher"},{"full_name":"Deary, Ian J.","last_name":"Deary","first_name":"Ian J."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"full_name":"Marioni, Riccardo E.","last_name":"Marioni","first_name":"Riccardo E."}],"title":"Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","oa_version":"Published Version","volume":12,"date_created":"2020-07-19T22:00:58Z","article_type":"original","status":"public","publication":"Genome Medicine","pmid":1,"date_published":"2020-07-08T00:00:00Z","year":"2020","isi":1,"related_material":{"record":[{"id":"9706","relation":"research_data","status":"public"}]},"external_id":{"isi":["000551778400001"],"pmid":["32641083"]},"ddc":["570"],"quality_controlled":"1","article_processing_charge":"No","doi":"10.1186/s13073-020-00754-1","publisher":"Springer Nature","_id":"8133","date_updated":"2023-08-22T07:55:37Z","type":"journal_article"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2020-03-20T00:00:00Z","citation":{"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.","chicago":"Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R. Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán Kutalik. “Quantification of the Overall Contribution of Gene-Environment Interaction for Obesity-Related Traits.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-15107-0\">https://doi.org/10.1038/s41467-020-15107-0</a>.","mla":"Sulc, Jonathan, et al. “Quantification of the Overall Contribution of Gene-Environment Interaction for Obesity-Related Traits.” <i>Nature Communications</i>, vol. 11, 1385, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-15107-0\">10.1038/s41467-020-15107-0</a>.","apa":"Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T. M., … Kutalik, Z. (2020). Quantification of the overall contribution of gene-environment interaction for obesity-related traits. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-15107-0\">https://doi.org/10.1038/s41467-020-15107-0</a>","ama":"Sulc J, Mounier N, Günther F, et al. Quantification of the overall contribution of gene-environment interaction for obesity-related traits. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-15107-0\">10.1038/s41467-020-15107-0</a>","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).","ieee":"J. Sulc <i>et al.</i>, “Quantification of the overall contribution of gene-environment interaction for obesity-related traits,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020."},"status":"public","language":[{"iso":"eng"}],"extern":"1","publication":"Nature Communications","oa":1,"article_number":"1385","month":"03","year":"2020","publication_status":"published","publication_identifier":{"issn":["2041-1723"]},"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41467-020-15107-0"}],"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)."}],"intvolume":"        11","date_created":"2020-04-30T10:39:33Z","type":"journal_article","article_type":"original","_id":"7707","volume":11,"date_updated":"2021-01-12T08:14:59Z","oa_version":"Published Version","title":"Quantification of the overall contribution of gene-environment interaction for obesity-related traits","publisher":"Springer Nature","article_processing_charge":"No","day":"20","doi":"10.1038/s41467-020-15107-0","author":[{"full_name":"Sulc, Jonathan","last_name":"Sulc","first_name":"Jonathan"},{"full_name":"Mounier, Ninon","last_name":"Mounier","first_name":"Ninon"},{"first_name":"Felix","full_name":"Günther, Felix","last_name":"Günther"},{"full_name":"Winkler, Thomas","last_name":"Winkler","first_name":"Thomas"},{"full_name":"Wood, Andrew R.","last_name":"Wood","first_name":"Andrew R."},{"first_name":"Timothy M.","full_name":"Frayling, Timothy M.","last_name":"Frayling"},{"first_name":"Iris M.","last_name":"Heid","full_name":"Heid, Iris M."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"first_name":"Zoltán","full_name":"Kutalik, Zoltán","last_name":"Kutalik"}]},{"article_number":"10","month":"02","year":"2020","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2020-02-27T00:00:00Z","citation":{"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).","ieee":"M. F. Nabais <i>et al.</i>, “Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis,” <i>npj Genomic Medicine</i>, vol. 5. Springer Nature, 2020.","ama":"Nabais MF, Lin T, Benyamin B, et al. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. <i>npj Genomic Medicine</i>. 2020;5. doi:<a href=\"https://doi.org/10.1038/s41525-020-0118-3\">10.1038/s41525-020-0118-3</a>","mla":"Nabais, Marta F., et al. “Significant Out-of-Sample Classification from Methylation Profile Scoring for Amyotrophic Lateral Sclerosis.” <i>Npj Genomic Medicine</i>, vol. 5, 10, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41525-020-0118-3\">10.1038/s41525-020-0118-3</a>.","apa":"Nabais, M. F., Lin, T., Benyamin, B., Williams, K. L., Garton, F. C., Vinkhuyzen, A. A. E., … Wray, N. R. (2020). Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. <i>Npj Genomic Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41525-020-0118-3\">https://doi.org/10.1038/s41525-020-0118-3</a>","chicago":"Nabais, Marta F., Tian Lin, Beben Benyamin, Kelly L. Williams, Fleur C. Garton, Anna A. E. Vinkhuyzen, Futao Zhang, et al. “Significant Out-of-Sample Classification from Methylation Profile Scoring for Amyotrophic Lateral Sclerosis.” <i>Npj Genomic Medicine</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41525-020-0118-3\">https://doi.org/10.1038/s41525-020-0118-3</a>.","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."},"extern":"1","language":[{"iso":"eng"}],"status":"public","publication":"npj Genomic Medicine","oa":1,"date_created":"2020-04-30T10:39:54Z","type":"journal_article","article_type":"original","volume":5,"_id":"7708","date_updated":"2021-01-12T08:14:59Z","title":"Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis","oa_version":"Published Version","publisher":"Springer Nature","day":"27","article_processing_charge":"No","author":[{"last_name":"Nabais","full_name":"Nabais, Marta F.","first_name":"Marta F."},{"first_name":"Tian","last_name":"Lin","full_name":"Lin, Tian"},{"last_name":"Benyamin","full_name":"Benyamin, Beben","first_name":"Beben"},{"first_name":"Kelly L.","last_name":"Williams","full_name":"Williams, Kelly L."},{"last_name":"Garton","full_name":"Garton, Fleur C.","first_name":"Fleur C."},{"full_name":"Vinkhuyzen, Anna A. E.","last_name":"Vinkhuyzen","first_name":"Anna A. E."},{"last_name":"Zhang","full_name":"Zhang, Futao","first_name":"Futao"},{"full_name":"Vallerga, Costanza L.","last_name":"Vallerga","first_name":"Costanza L."},{"full_name":"Restuadi, Restuadi","last_name":"Restuadi","first_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"},{"full_name":"Hop, Paul J.","last_name":"Hop","first_name":"Paul J."},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"},{"first_name":"Jacob","last_name":"Gratten","full_name":"Gratten, Jacob"},{"first_name":"Peter M.","last_name":"Visscher","full_name":"Visscher, Peter M."},{"last_name":"Hannon","full_name":"Hannon, Eilis","first_name":"Eilis"},{"first_name":"Jonathan","full_name":"Mill, Jonathan","last_name":"Mill"},{"last_name":"Brown","full_name":"Brown, Matthew A.","first_name":"Matthew A."},{"full_name":"Laing, Nigel G.","last_name":"Laing","first_name":"Nigel G."},{"first_name":"Karen A.","full_name":"Mather, Karen A.","last_name":"Mather"},{"last_name":"Sachdev","full_name":"Sachdev, Perminder S.","first_name":"Perminder S."},{"first_name":"Shyuan T.","last_name":"Ngo","full_name":"Ngo, Shyuan T."},{"full_name":"Steyn, Frederik J.","last_name":"Steyn","first_name":"Frederik J."},{"full_name":"Wallace, Leanne","last_name":"Wallace","first_name":"Leanne"},{"full_name":"Henders, Anjali K.","last_name":"Henders","first_name":"Anjali K."},{"full_name":"Needham, Merrilee","last_name":"Needham","first_name":"Merrilee"},{"full_name":"Veldink, Jan H.","last_name":"Veldink","first_name":"Jan H."},{"first_name":"Susan","last_name":"Mathers","full_name":"Mathers, Susan"},{"first_name":"Garth","full_name":"Nicholson, Garth","last_name":"Nicholson"},{"first_name":"Dominic B.","last_name":"Rowe","full_name":"Rowe, Dominic B."},{"first_name":"Robert D.","last_name":"Henderson","full_name":"Henderson, Robert D."},{"first_name":"Pamela A.","full_name":"McCombe, Pamela A.","last_name":"McCombe"},{"first_name":"Roger","full_name":"Pamphlett, Roger","last_name":"Pamphlett"},{"last_name":"Yang","full_name":"Yang, Jian","first_name":"Jian"},{"full_name":"Blair, Ian P.","last_name":"Blair","first_name":"Ian P."},{"last_name":"McRae","full_name":"McRae, Allan F.","first_name":"Allan F."},{"first_name":"Naomi R.","last_name":"Wray","full_name":"Wray, Naomi R."}],"doi":"10.1038/s41525-020-0118-3","publication_identifier":{"issn":["2056-7944"]},"publication_status":"published","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41525-020-0118-3"}],"abstract":[{"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.","lang":"eng"}],"intvolume":"         5"},{"date_updated":"2023-08-22T07:55:36Z","_id":"9706","type":"research_data_reference","date_created":"2021-07-23T08:59:15Z","doi":"10.6084/m9.figshare.12629697.v1","author":[{"full_name":"Hillary, Robert F.","last_name":"Hillary","first_name":"Robert F."},{"first_name":"Daniel","full_name":"Trejo-Banos, Daniel","last_name":"Trejo-Banos"},{"first_name":"Athanasios","full_name":"Kousathanas, Athanasios","last_name":"Kousathanas"},{"last_name":"McCartney","full_name":"McCartney, Daniel L.","first_name":"Daniel L."},{"full_name":"Harris, Sarah E.","last_name":"Harris","first_name":"Sarah E."},{"last_name":"Stevenson","full_name":"Stevenson, Anna J.","first_name":"Anna J."},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Sven Erik","last_name":"Ojavee","full_name":"Ojavee, Sven Erik"},{"first_name":"Qian","last_name":"Zhang","full_name":"Zhang, Qian"},{"last_name":"Liewald","full_name":"Liewald, David C.","first_name":"David C."},{"full_name":"Ritchie, Craig W.","last_name":"Ritchie","first_name":"Craig W."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"first_name":"Elliot M.","last_name":"Tucker-Drob","full_name":"Tucker-Drob, Elliot M."},{"last_name":"Wray","full_name":"Wray, Naomi R.","first_name":"Naomi R."},{"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"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E. ","first_name":"Riccardo E. "}],"day":"09","article_processing_charge":"No","oa_version":"Published Version","title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","publisher":"Springer Nature","main_file_link":[{"open_access":"1","url":"https://doi.org/10.6084/m9.figshare.12629697.v1"}],"other_data_license":"CC0 + CC BY (4.0)","has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"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)."}],"department":[{"_id":"MaRo"}],"year":"2020","month":"07","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8133"}]},"citation":{"chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.","ista":"Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults, Springer Nature, <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Springer Nature. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>","mla":"Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>. Springer Nature, 2020, doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>","ieee":"R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020.","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_published":"2020-07-09T00:00:00Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa":1,"status":"public"},{"month":"06","year":"2019","date_published":"2019-06-14T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Sulc J, Mounier N, Günther F, et al. Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. <i>bioRxiv</i>. 2019.","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).","ieee":"J. Sulc <i>et al.</i>, “Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2019.","chicago":"Sulc, Jonathan, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R. Wood, Timothy M. Frayling, Iris M. Heid, Matthew Richard Robinson, and Zoltán Kutalik. “Maximum Likelihood Method Quantifies the Overall Contribution of Gene-Environment Interaction to Continuous Traits: An Application to Complex Traits in the UK Biobank.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2019.","ista":"Sulc J, Mounier N, Günther F, Winkler T, Wood AR, Frayling TM, Heid IM, Robinson MR, Kutalik Z. 2019. Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. bioRxiv, .","mla":"Sulc, Jonathan, et al. “Maximum Likelihood Method Quantifies the Overall Contribution of Gene-Environment Interaction to Continuous Traits: An Application to Complex Traits in the UK Biobank.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2019.","apa":"Sulc, J., Mounier, N., Günther, F., Winkler, T., Wood, A. R., Frayling, T. M., … Kutalik, Z. (2019). Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank. <i>bioRxiv</i>. Cold Spring Harbor Laboratory."},"language":[{"iso":"eng"}],"status":"public","publication":"bioRxiv","extern":"1","oa":1,"type":"preprint","date_created":"2020-04-30T13:04:26Z","date_updated":"2021-01-12T08:15:30Z","_id":"7782","oa_version":"Preprint","title":"Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank","publisher":"Cold Spring Harbor Laboratory","author":[{"full_name":"Sulc, Jonathan","last_name":"Sulc","first_name":"Jonathan"},{"first_name":"Ninon","full_name":"Mounier, Ninon","last_name":"Mounier"},{"first_name":"Felix","last_name":"Günther","full_name":"Günther, Felix"},{"last_name":"Winkler","full_name":"Winkler, Thomas","first_name":"Thomas"},{"full_name":"Wood, Andrew R.","last_name":"Wood","first_name":"Andrew R."},{"last_name":"Frayling","full_name":"Frayling, Timothy M.","first_name":"Timothy M."},{"first_name":"Iris M.","last_name":"Heid","full_name":"Heid, Iris M."},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"},{"full_name":"Kutalik, Zoltán","last_name":"Kutalik","first_name":"Zoltán"}],"article_processing_charge":"No","day":"14","publication_status":"published","main_file_link":[{"url":"https://doi.org/10.1101/632380 ","open_access":"1"}],"abstract":[{"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.","lang":"eng"}],"page":"20"},{"extern":"1","language":[{"iso":"eng"}],"status":"public","publication":"Nature Communications","oa":1,"date_published":"2019-11-28T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Delaneau, Olivier, Jean-François Zagury, Matthew Richard Robinson, Jonathan L. Marchini, and Emmanouil T. Dermitzakis. “Accurate, Scalable and Integrative Haplotype Estimation.” <i>Nature Communications</i>. Springer Nature, 2019. <a href=\"https://doi.org/10.1038/s41467-019-13225-y\">https://doi.org/10.1038/s41467-019-13225-y</a>.","ista":"Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. 2019. Accurate, scalable and integrative haplotype estimation. Nature Communications. 10, 5436.","apa":"Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L., &#38; Dermitzakis, E. T. (2019). Accurate, scalable and integrative haplotype estimation. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-019-13225-y\">https://doi.org/10.1038/s41467-019-13225-y</a>","mla":"Delaneau, Olivier, et al. “Accurate, Scalable and Integrative Haplotype Estimation.” <i>Nature Communications</i>, vol. 10, 5436, Springer Nature, 2019, doi:<a href=\"https://doi.org/10.1038/s41467-019-13225-y\">10.1038/s41467-019-13225-y</a>.","ama":"Delaneau O, Zagury J-F, Robinson MR, Marchini JL, Dermitzakis ET. Accurate, scalable and integrative haplotype estimation. <i>Nature Communications</i>. 2019;10. doi:<a href=\"https://doi.org/10.1038/s41467-019-13225-y\">10.1038/s41467-019-13225-y</a>","ieee":"O. Delaneau, J.-F. Zagury, M. R. Robinson, J. L. Marchini, and E. T. Dermitzakis, “Accurate, scalable and integrative haplotype estimation,” <i>Nature Communications</i>, vol. 10. Springer Nature, 2019.","short":"O. Delaneau, J.-F. Zagury, M.R. Robinson, J.L. Marchini, E.T. Dermitzakis, Nature Communications 10 (2019)."},"month":"11","year":"2019","article_number":"5436","abstract":[{"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.","lang":"eng"}],"intvolume":"        10","publication_identifier":{"issn":["2041-1723"]},"publication_status":"published","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41467-019-13225-y"}],"title":"Accurate, scalable and integrative haplotype estimation","publisher":"Springer Nature","oa_version":"Published Version","doi":"10.1038/s41467-019-13225-y","author":[{"first_name":"Olivier","last_name":"Delaneau","full_name":"Delaneau, Olivier"},{"first_name":"Jean-François","full_name":"Zagury, Jean-François","last_name":"Zagury"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"},{"full_name":"Marchini, Jonathan L.","last_name":"Marchini","first_name":"Jonathan L."},{"last_name":"Dermitzakis","full_name":"Dermitzakis, Emmanouil T.","first_name":"Emmanouil T."}],"article_processing_charge":"No","day":"28","article_type":"original","type":"journal_article","date_created":"2020-04-30T10:40:32Z","date_updated":"2021-01-12T08:15:01Z","_id":"7710","volume":10},{"page":"1226-1242","intvolume":"         1","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."}],"publication_identifier":{"issn":["2522-5812"]},"quality_controlled":"1","publication_status":"published","day":"09","article_processing_charge":"No","author":[{"first_name":"Roel P. J.","full_name":"Bevers, Roel P. J.","last_name":"Bevers"},{"last_name":"Litovchenko","full_name":"Litovchenko, Maria","first_name":"Maria"},{"full_name":"Kapopoulou, Adamandia","last_name":"Kapopoulou","first_name":"Adamandia"},{"first_name":"Virginie S.","last_name":"Braman","full_name":"Braman, Virginie S."},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"first_name":"Johan","last_name":"Auwerx","full_name":"Auwerx, Johan"},{"last_name":"Hollis","full_name":"Hollis, Brian","first_name":"Brian"},{"last_name":"Deplancke","full_name":"Deplancke, Bart","first_name":"Bart"}],"doi":"10.1038/s42255-019-0147-3","oa_version":"None","title":"Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel","publisher":"Springer Nature","volume":1,"_id":"7711","date_updated":"2021-01-12T08:15:01Z","date_created":"2020-04-30T10:40:56Z","article_type":"original","type":"journal_article","language":[{"iso":"eng"}],"extern":"1","publication":"Nature Metabolism","status":"public","citation":{"mla":"Bevers, Roel P. J., et al. “Mitochondrial Haplotypes Affect Metabolic Phenotypes in the Drosophila Genetic Reference Panel.” <i>Nature Metabolism</i>, vol. 1, no. 12, Springer Nature, 2019, pp. 1226–42, doi:<a href=\"https://doi.org/10.1038/s42255-019-0147-3\">10.1038/s42255-019-0147-3</a>.","apa":"Bevers, R. P. J., Litovchenko, M., Kapopoulou, A., Braman, V. S., Robinson, M. R., Auwerx, J., … Deplancke, B. (2019). Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. <i>Nature Metabolism</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s42255-019-0147-3\">https://doi.org/10.1038/s42255-019-0147-3</a>","chicago":"Bevers, Roel P. J., Maria Litovchenko, Adamandia Kapopoulou, Virginie S. Braman, Matthew Richard Robinson, Johan Auwerx, Brian Hollis, and Bart Deplancke. “Mitochondrial Haplotypes Affect Metabolic Phenotypes in the Drosophila Genetic Reference Panel.” <i>Nature Metabolism</i>. Springer Nature, 2019. <a href=\"https://doi.org/10.1038/s42255-019-0147-3\">https://doi.org/10.1038/s42255-019-0147-3</a>.","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.","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.","ieee":"R. P. J. Bevers <i>et al.</i>, “Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel,” <i>Nature Metabolism</i>, vol. 1, no. 12. Springer Nature, pp. 1226–1242, 2019.","ama":"Bevers RPJ, Litovchenko M, Kapopoulou A, et al. Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. <i>Nature Metabolism</i>. 2019;1(12):1226-1242. doi:<a href=\"https://doi.org/10.1038/s42255-019-0147-3\">10.1038/s42255-019-0147-3</a>"},"issue":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2019-12-09T00:00:00Z","year":"2019","related_material":{"link":[{"url":"https://doi.org/10.1038/s42255-020-0202-0","relation":"erratum"}]},"month":"12"}]