[{"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"1","citation":{"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>.","apa":"McCartney, D. L., Hillary, R. F., Conole, E. L. S., Banos, D. T., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2022). Blood-based epigenome-wide analyses of cognitive abilities. <i>Genome Biology</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>","chicago":"McCartney, Daniel L., Robert F. Hillary, Eleanor L.S. Conole, Daniel Trejo Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” <i>Genome Biology</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>.","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.","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).","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.","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>"},"language":[{"iso":"eng"}],"oa":1,"article_number":"26","file":[{"checksum":"34f10bb2b0594189dcac24d13b691d52","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"2022_GenomeBio_McCartney.pdf","success":1,"file_id":"10708","date_updated":"2022-01-31T13:16:05Z","creator":"cchlebak","file_size":1540606,"date_created":"2022-01-31T13:16:05Z"}],"department":[{"_id":"MaRo"}],"month":"01","publication_status":"published","publication_identifier":{"issn":["1474-7596"],"eissn":["1474-760X"]},"file_date_updated":"2022-01-31T13:16:05Z","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"}],"has_accepted_license":"1","article_type":"original","date_created":"2022-01-30T23:01:33Z","volume":23,"oa_version":"Published Version","title":"Blood-based epigenome-wide analyses of cognitive abilities","author":[{"full_name":"McCartney, Daniel L.","last_name":"McCartney","first_name":"Daniel L."},{"first_name":"Robert F.","last_name":"Hillary","full_name":"Hillary, Robert F."},{"first_name":"Eleanor L.S.","full_name":"Conole, Eleanor L.S.","last_name":"Conole"},{"full_name":"Banos, Daniel Trejo","last_name":"Banos","first_name":"Daniel Trejo"},{"first_name":"Danni A.","full_name":"Gadd, Danni A.","last_name":"Gadd"},{"full_name":"Walker, Rosie M.","last_name":"Walker","first_name":"Rosie M."},{"first_name":"Cliff","full_name":"Nangle, Cliff","last_name":"Nangle"},{"first_name":"Robin","last_name":"Flaig","full_name":"Flaig, Robin"},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"last_name":"Murray","full_name":"Murray, Alison D.","first_name":"Alison D."},{"last_name":"Maniega","full_name":"Maniega, Susana Muñoz","first_name":"Susana Muñoz"},{"first_name":"María Del C.","last_name":"Valdés-Hernández","full_name":"Valdés-Hernández, María Del C."},{"first_name":"Mathew A.","last_name":"Harris","full_name":"Harris, Mathew A."},{"first_name":"Mark E.","last_name":"Bastin","full_name":"Bastin, Mark E."},{"first_name":"Joanna M.","last_name":"Wardlaw","full_name":"Wardlaw, Joanna M."},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"full_name":"Porteous, David J.","last_name":"Porteous","first_name":"David J."},{"first_name":"Elliot M.","full_name":"Tucker-Drob, Elliot M.","last_name":"Tucker-Drob"},{"full_name":"McIntosh, Andrew M.","last_name":"McIntosh","first_name":"Andrew M."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"last_name":"Deary","full_name":"Deary, Ian J.","first_name":"Ian J."},{"last_name":"Cox","full_name":"Cox, Simon R.","first_name":"Simon R."},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E.","first_name":"Riccardo E."}],"scopus_import":"1","day":"17","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.","date_published":"2022-01-17T00:00:00Z","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"publication":"Genome Biology","status":"public","external_id":{"isi":["000744358300002"]},"related_material":{"record":[{"relation":"research_data","status":"public","id":"13072"}],"link":[{"url":"https://doi.org/10.1101/2021.05.24.21257698","relation":"earlier_version"}]},"isi":1,"year":"2022","quality_controlled":"1","ddc":["570"],"type":"journal_article","date_updated":"2023-08-02T14:05:13Z","_id":"10702","publisher":"Springer Nature","doi":"10.1186/s13059-021-02596-5","article_processing_charge":"No"},{"has_accepted_license":"1","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."}],"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","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","file_date_updated":"2023-01-24T09:23:01Z","publication_identifier":{"issn":["0002-9297"]},"publication_status":"published","day":"03","scopus_import":"1","author":[{"first_name":"Sven E.","last_name":"Ojavee","full_name":"Ojavee, Sven E."},{"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"}],"oa_version":"Published Version","title":"Liability-scale heritability estimation for biobank studies of low-prevalence disease","volume":109,"date_created":"2023-01-12T12:05:28Z","article_type":"original","oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Ojavee SE, Kutalik Z, Robinson MR. Liability-scale heritability estimation for biobank studies of low-prevalence disease. <i>The American Journal of Human Genetics</i>. 2022;109(11):2009-2017. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>","short":"S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics 109 (2022) 2009–2017.","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.","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>.","ista":"Ojavee SE, Kutalik Z, Robinson MR. 2022. Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. 109(11), 2009–2017.","mla":"Ojavee, Sven E., et al. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The American Journal of Human Genetics</i>, vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>.","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>"},"issue":"11","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"11","department":[{"_id":"MaRo"}],"file":[{"relation":"main_file","checksum":"4cd7f12bfe21a8237bb095eedfa26361","file_name":"2022_AJHG_Ojavee.pdf","success":1,"content_type":"application/pdf","access_level":"open_access","file_id":"12353","file_size":705195,"date_created":"2023-01-24T09:23:01Z","date_updated":"2023-01-24T09:23:01Z","creator":"dernst"}],"page":"2009-2017","ddc":["570"],"quality_controlled":"1","article_processing_charge":"Yes (via OA deal)","doi":"10.1016/j.ajhg.2022.09.011","publisher":"Elsevier","_id":"12142","date_updated":"2023-08-04T08:56:46Z","type":"journal_article","status":"public","publication":"The American Journal of Human Genetics","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"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","isi":1,"year":"2022","external_id":{"isi":["000898683500006"]},"keyword":["Genetics (clinical)","Genetics"]},{"file_date_updated":"2021-05-04T15:07:50Z","publication_identifier":{"eissn":["20411723"]},"publication_status":"published","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","abstract":[{"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.","lang":"eng"}],"has_accepted_license":"1","date_created":"2020-09-17T10:53:00Z","volume":12,"title":"Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis","oa_version":"Published Version","scopus_import":"1","day":"20","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"},{"full_name":"Orliac, Etienne J","last_name":"Orliac","first_name":"Etienne J"},{"first_name":"Marion","last_name":"Patxot","full_name":"Patxot, Marion"},{"last_name":"Lall","full_name":"Lall, Kristi","first_name":"Kristi"},{"first_name":"Reedik","last_name":"Magi","full_name":"Magi, Reedik"},{"first_name":"Krista","last_name":"Fischer","full_name":"Fischer, Krista"},{"first_name":"Zoltan","full_name":"Kutalik, Zoltan","last_name":"Kutalik"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"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>.","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>","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.","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>.","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).","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.","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","language":[{"iso":"eng"}],"oa":1,"file":[{"file_id":"9372","creator":"kschuh","date_updated":"2021-05-04T15:07:50Z","file_size":6474239,"date_created":"2021-05-04T15:07:50Z","checksum":"eca8b9ae713835c5b785211dd08d8a2e","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"2021_nature_communications_Ojavee.pdf","success":1}],"article_number":"2337","department":[{"_id":"MaRo"}],"month":"04","quality_controlled":"1","ddc":["570"],"type":"journal_article","_id":"8430","date_updated":"2023-08-04T11:00:17Z","publisher":"Nature Research","article_processing_charge":"No","doi":"10.1038/s41467-021-22538-w","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.","status":"public","publication":"Nature Communications","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"related_material":{"link":[{"description":"News on IST Homepage","url":"https://ist.ac.at/en/news/predicting-the-onset-of-diseases/","relation":"press_release"}]},"external_id":{"isi":["000642509600006"]},"year":"2021","isi":1}]