[{"article_processing_charge":"Yes (via OA deal)","department":[{"_id":"MaRo"}],"date_created":"2023-09-03T22:01:15Z","publication_status":"published","intvolume":"       110","title":"Genetic insights into the age-specific biological mechanisms governing human ovarian aging","scopus_import":"1","pmid":1,"_id":"14258","issue":"9","author":[{"full_name":"Ojavee, Sven E.","last_name":"Ojavee","first_name":"Sven E."},{"full_name":"Darrous, Liza","last_name":"Darrous","first_name":"Liza"},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"last_name":"Läll","first_name":"Kristi","full_name":"Läll, Kristi"},{"full_name":"Fischer, Krista","first_name":"Krista","last_name":"Fischer"},{"first_name":"Reedik","last_name":"Mägi","full_name":"Mägi, Reedik"},{"full_name":"Kutalik, Zoltan","first_name":"Zoltan","last_name":"Kutalik"},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"publisher":"Elsevier","article_type":"original","quality_controlled":"1","page":"1549-1563","file_date_updated":"2024-01-30T13:20:35Z","day":"07","doi":"10.1016/j.ajhg.2023.07.006","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."}],"citation":{"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.","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.","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>.","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.","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>","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>"},"year":"2023","date_updated":"2024-01-30T13:21:05Z","external_id":{"pmid":["37543033"]},"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.","volume":110,"ddc":["570"],"oa_version":"Published Version","month":"09","has_accepted_license":"1","publication":"American Journal of Human Genetics","language":[{"iso":"eng"}],"publication_identifier":{"issn":["0002-9297"],"eissn":["1537-6605"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2023-09-07T00:00:00Z","file":[{"access_level":"open_access","success":1,"relation":"main_file","file_id":"14912","creator":"dernst","date_created":"2024-01-30T13:20:35Z","checksum":"4108b031dc726ae6b4a5ae7e021ba188","file_size":2551276,"date_updated":"2024-01-30T13:20:35Z","content_type":"application/pdf","file_name":"2023_AJHG_Ojavee.pdf"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public"},{"language":[{"iso":"eng"}],"keyword":["Genetics (clinical)","Genetics"],"month":"11","acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"publication":"The American Journal of Human Genetics","has_accepted_license":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","file":[{"content_type":"application/pdf","file_name":"2022_AJHG_Ojavee.pdf","date_updated":"2023-01-24T09:23:01Z","checksum":"4cd7f12bfe21a8237bb095eedfa26361","file_size":705195,"date_created":"2023-01-24T09:23:01Z","creator":"dernst","file_id":"12353","success":1,"relation":"main_file","access_level":"open_access"}],"oa":1,"publication_identifier":{"issn":["0002-9297"]},"date_published":"2022-11-03T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png"},"article_type":"original","publisher":"Elsevier","file_date_updated":"2023-01-24T09:23:01Z","page":"2009-2017","quality_controlled":"1","title":"Liability-scale heritability estimation for biobank studies of low-prevalence disease","intvolume":"       109","publication_status":"published","article_processing_charge":"Yes (via OA deal)","date_created":"2023-01-12T12:05:28Z","department":[{"_id":"MaRo"}],"author":[{"last_name":"Ojavee","first_name":"Sven E.","full_name":"Ojavee, Sven E."},{"full_name":"Kutalik, Zoltan","first_name":"Zoltan","last_name":"Kutalik"},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"issue":"11","_id":"12142","scopus_import":"1","ddc":["570"],"volume":109,"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.","abstract":[{"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.","lang":"eng"}],"doi":"10.1016/j.ajhg.2022.09.011","day":"03","isi":1,"external_id":{"isi":["000898683500006"]},"date_updated":"2023-08-04T08:56:46Z","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.","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>.","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>","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>","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.","short":"S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics 109 (2022) 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>."},"year":"2022"}]