Search

Your search keyword '"Kristi Läll"' showing total 53 results
Start Over Author "Kristi Läll"
53 results on '"Kristi Läll"'

1. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

Author

Bradley Jermy, Kristi Läll, Brooke N. Wolford, Ying Wang, Kristina Zguro, Yipeng Cheng, Masahiro Kanai, Stavroula Kanoni, Zhiyu Yang, Tuomo Hartonen, Remo Monti, Julian Wanner, Omar Youssef, Estonian Biobank research team, FinnGen, Christoph Lippert, David van Heel, Yukinori Okada, Daniel L. McCartney, Caroline Hayward, Riccardo E. Marioni, Simone Furini, Alessandra Renieri, Alicia R. Martin, Benjamin M. Neale, Kristian Hveem, Reedik Mägi, Aarno Palotie, Henrike Heyne, Nina Mars, Andrea Ganna, and Samuli Ripatti

Subjects
Science
Abstract

Abstract Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Published
2024
Full Text
View/download PDF

2. Variability of polygenic prediction for body mass index in Africa

Author

Tinashe Chikowore, Kristi Läll, Lisa K. Micklesfield, Zane Lombard, Julia H. Goedecke, Segun Fatumo, Shane A. Norris, Reedik Magi, Michele Ramsay, Paul W. Franks, Guillaume Pare, and Andrew P. Morris

Subjects
BMI, Variability, Polygenic prediction, Polygenic risk score, Medicine, Genetics, QH426-470
Abstract

Abstract Background Polygenic prediction studies in continental Africans are scarce. Africa’s genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required. Methods Using the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions. Results Polygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa’s East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status. Conclusions Our findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.

Published
2024
Full Text
View/download PDF

3. Polygenic risk scores for cervical HPV infection, neoplasia and cancer show potential for personalised screening: comparison of two methods

Author

Anna Tisler, Anneli Uusküla, Sven Erik Ojavee, Kristi Läll, Estonian Biobank research team, and Triin Laisk

Subjects
Cervical cancer, Polygenic risk score, HPV, Screening, Cervical intraepithelial neoplasia, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Infectious and parasitic diseases, RC109-216
Abstract

Abstract The era of precision medicine requires the achievement of accurate risk assessment. Polygenic risk scores (PRSs) have strong potential for increasing the benefits of nationwide cancer screening programs. The current pool of evidence on the role of a PRS as a risk stratification model in actual practice and implementation is limited. To better understand the impact of possible method-induced variance, we constructed and validated two PRSs for cervical cancer (CC) using the Estonian Biobank female population (691 CC cases and 13,820 controls) and evaluated their utility in predicting incident cervical neoplasia (CIN), cancer, and human papillomavirus (HPV) infection using two methods (LDPred and BayesRR-RC). This study demonstrated that two genetic risk scores were significantly associated with CIN, CC, and HPV infection incidence. Independent of the method, we demonstrated that women with elevated PRS values reached the observed cumulative risk levels of CIN or CC much earlier. Our results indicated that the PRS-based discrimination rules could differ substantially when the PRSs contain similar predictive information. In summary, our analysis indicated that PRSs represent a personalized genetic component that could be an additional tool for cervical cancer risk stratification, and earlier detection of abnormalities provides invaluable information for those at high risk.

Published
2023
Full Text
View/download PDF

4. Advancing our understanding of genetic risk factors and potential personalized strategies for pelvic organ prolapse

Author

Natàlia Pujol-Gualdo, Kristi Läll, Maarja Lepamets, Estonian Biobank Research Team, Henna-Riikka Rossi, Riikka K. Arffman, Terhi T. Piltonen, Reedik Mägi, and Triin Laisk

Subjects
Science
Abstract

Although pelvic organ prolapse is a common gynecological condition, the genetic component of disease risk is not well known. Here the authors find common genetic variants associated with the disease and present a polygenic risk score to enhance individual risk prediction.

Published
2022
Full Text
View/download PDF

5. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Author

Ying Wang, Shinichi Namba, Esteban Lopera, Sini Kerminen, Kristin Tsuo, Kristi Läll, Masahiro Kanai, Wei Zhou, Kuan-Han Wu, Marie-Julie Favé, Laxmi Bhatta, Philip Awadalla, Ben Brumpton, Patrick Deelen, Kristian Hveem, Valeria Lo Faro, Reedik Mägi, Yoshinori Murakami, Serena Sanna, Jordan W. Smoller, Jasmina Uzunovic, Brooke N. Wolford, Cristen Willer, Eric R. Gamazon, Nancy J. Cox, Ida Surakka, Yukinori Okada, Alicia R. Martin, Jibril Hirbo, Kuan-Han H. Wu, Humaira Rasheed, Jibril B. Hirbo, Arjun Bhattacharya, Huiling Zhao, Esteban A. Lopera-Maya, Sinéad B. Chapman, Juha Karjalainen, Mitja Kurki, Maasha Mutaamba, Juulia J. Partanen, Ben M. Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A. Feng, Christopher R. Gignoux, Sarah E. Graham, Whitney E. Hornsby, Nathan Ingold, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y. Millwood, Priit Palta, Anita Pandit, Michael H. Preuss, Unnur Thorsteinsdottir, Matthew Zawistowski, Xue Zhong, Archie Campbell, Kristy Crooks, Geertruida H. de Bock, Nicholas J. Douville, Sarah Finer, Lars G. Fritsche, Christopher J. Griffiths, Yu Guo, Karen A. Hunt, Takahiro Konuma, Riccardo E. Marioni, Jansonius Nomdo, Snehal Patil, Nicholas Rafaels, Anne Richmond, Jonathan A. Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Kathleen C. Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Hilary K. Finucane, Lude Franke, Andrea Ganna, Tom R. Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Jukka T. Koskela, Clara Lajonchere, Matthew H. Law, Liming Li, Cecilia M. Lindgren, Ruth J.F. Loos, Stuart MacGregor, Koichi Matsuda, Catherine M. Olsen, David J. Porteous, Jordan A. Shavit, Harold Snieder, Richard C. Trembath, Judith M. Vonk, David Whiteman, Stephen J. Wicks, Cisca Wijmenga, John Wright, Jie Zheng, Xiang Zhou, Michael Boehnke, Daniel H. Geschwind, Caroline Hayward, Eimear E. Kenny, Yen-Feng Lin, Hilary C. Martin, Sarah E. Medland, Aarno V. Palotie, Bogdan Pasaniuc, Kari Stefansson, David A. van Heel, Robin G. Walters, Sebastian Zöllner, Cristen J. Willer, Mark J. Daly, and Benjamin M. Neale

Subjects
Global-Biobank Meta-analysis Initiative, polygenic risk scores, multi-ancestry genetic prediction, accuracy heterogeneity, Genetics, QH426-470, Internal medicine, RC31-1245
Abstract

Summary: Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Published
2023
Full Text
View/download PDF

6. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Author

Sven E. Ojavee, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J. Orliac, Marion Patxot, Kristi Läll, Reedik Mägi, Krista Fischer, Zoltan Kutalik, and Matthew R. Robinson

Subjects
Science
Abstract

Few genome-wide association studies have explored the genetic architecture of age-of-onset for traits and diseases. Here, the authors develop a Bayesian approach to improve prediction in timing-related phenotypes and perform age-of-onset analyses across complex traits in the UK Biobank.

Published
2021
Full Text
View/download PDF

7. Genome-wide risk prediction of common diseases across ancestries in one million people

Author

Nina Mars, Sini Kerminen, Yen-Chen A. Feng, Masahiro Kanai, Kristi Läll, Laurent F. Thomas, Anne Heidi Skogholt, Pietro della Briotta Parolo, Benjamin M. Neale, Jordan W. Smoller, Maiken E. Gabrielsen, Kristian Hveem, Reedik Mägi, Koichi Matsuda, Yukinori Okada, Matti Pirinen, Aarno Palotie, Andrea Ganna, Alicia R. Martin, and Samuli Ripatti

Subjects
polygenic risk score, health disparities, global health, ancestry, precision medicine, risk prediction, Genetics, QH426-470, Internal medicine, RC31-1245
Abstract

Summary: Polygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.

Published
2022
Full Text
View/download PDF

8. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals

Author

Davide Marnetto, Katri Pärna, Kristi Läll, Ludovica Molinaro, Francesco Montinaro, Toomas Haller, Mait Metspalu, Reedik Mägi, Krista Fischer, and Luca Pagani

Subjects
Science
Abstract

Polygenic scores are believed to hold future promise for trait prediction and personalized medicine, but are sensitive to demographic history. Here, Marnetto et al. develop partial polygenic scores supplemented with local ancestry deconvolution which improves prediction accuracy into recently admixed European populations.

Published
2020
Full Text
View/download PDF

9. Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification

Author

Kristi Läll, Maarja Lepamets, Marili Palover, Tõnu Esko, Andres Metspalu, Neeme Tõnisson, Peeter Padrik, Reedik Mägi, and Krista Fischer

Subjects
Polygenic risk score, Genetic predisposition to disease, Breast cancer, Risk stratification, Personalized medicine, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

Published
2019
Full Text
View/download PDF

10. Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations.

Author

Sulev Reisberg, Tatjana Iljasenko, Kristi Läll, Krista Fischer, and Jaak Vilo

Subjects
Medicine, Science
Abstract

Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population's structure. We also show that if an individual is classified to the wrong population, his/her disease risk can be systematically incorrectly estimated.

Published
2017
Full Text
View/download PDF

11. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Author

Esteban A Lopera-Maya, Jibril Hirbo, Reedik Mägi, Brooke N. Wolford, Kuan-Han H. Wu, Yoshinori Murakami, Sini Kerminen, Yukinori Okada, Valeria Lo Faro, Jordan W. Smoller, Global Biobank Meta-analysis Initiative, Cristen Willer, Philip Awadalla, Patrick Deelen, Marie-Julie Favé, Alicia R. Martin, Nancy J. Cox, Masahiro Kanai, Laxmi Bhatta, Ying Wang, Kristin Tsuo, Shinichi Namba, Ben Michael Brumpton, Eric R. Gamazon, Serena Sanna, Kristian Hveem, Wei Zhou, Ida Surakka, Kristi Läll, Jasmina Uzunovic, and Apollo - University of Cambridge Repository

Subjects
0303 health sciences, Global-Biobank Meta-analysis Initiative, Genomics, Disease, multi-ancestry genetic prediction, Heritability, Biology, Precision medicine, Biobank, Genetic architecture, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, polygenic risk scores, SNP, accuracy heterogeneity, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association, Demography
Abstract

SummaryWith the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, few studies have thoroughly investigated best practices for PRS in global populations across different diseases. In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of individuals from diverse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods. We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels; this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For example, substantial improvements are observed in East-Asian ancestry (EAS) using PRS- CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Published
2023
Full Text
View/download PDF

12. Genetic insights into the age-specific biological mechanisms governing human ovarian ageing

Author

Sven E. Ojavee, Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer, Reedik Mägi, Zoltan Kutalik, and Matthew R. Robinson

Abstract

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 modelling 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 sign 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 modelling of large-scale biobank data.

Published
2023

13. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Author

Masahiro Kanai, Roy Elzur, Wei Zhou, Mark J. Daly, Hilary K. Finucane, Kuan-Han H. Wu, Humaira Rasheed, Kristin Tsuo, Jibril B. Hirbo, Ying Wang, Arjun Bhattacharya, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N. Wolford, Valeria Lo Faro, Esteban A. Lopera-Maya, Kristi Läll, Marie-Julie Favé, Juulia J. Partanen, Sinéad B. Chapman, Juha Karjalainen, Mitja Kurki, Mutaamba Maasha, Ben M. Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A. Feng, Lindsay A. Guare, Christopher R. Gignoux, Sarah E. Graham, Whitney E. Hornsby, Nathan Ingold, Said I. Ismail, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y. Millwood, Sonia Moreno-Grau, Kisung Nam, Priit Palta, Anita Pandit, Michael H. Preuss, Chadi Saad, Shefali Setia-Verma, Unnur Thorsteinsdottir, Jasmina Uzunovic, Anurag Verma, Matthew Zawistowski, Xue Zhong, Nahla Afifi, Kawthar M. Al-Dabhani, Asma Al Thani, Yuki Bradford, Archie Campbell, Kristy Crooks, Geertruida H. de Bock, Scott M. Damrauer, Nicholas J. Douville, Sarah Finer, Lars G. Fritsche, Eleni Fthenou, Gilberto Gonzalez-Arroyo, Christopher J. Griffiths, Yu Guo, Karen A. Hunt, Alexander Ioannidis, Nomdo M. Jansonius, Takahiro Konuma, Ming Ta Michael Lee, Arturo Lopez-Pineda, Yuta Matsuda, Riccardo E. Marioni, Babak Moatamed, Marco A. Nava-Aguilar, Kensuke Numakura, Snehal Patil, Nicholas Rafaels, Anne Richmond, Agustin Rojas-Muñoz, Jonathan A. Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Manvi Vernekar, Yogasudha Veturi, Kathleen C. Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Lude Franke, Eric R. Gamazon, Andrea Ganna, Tom R. Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Nicholas Katsanis, Jukka T. Koskela, Clara Lajonchere, Matthew H. Law, Liming Li, Cecilia M. Lindgren, Ruth J.F. Loos, Stuart MacGregor, Koichi Matsuda, Catherine M. Olsen, David J. Porteous, Jordan A. Shavit, Harold Snieder, Tomohiro Takano, Richard C. Trembath, Judith M. Vonk, David C. Whiteman, Stephen J. Wicks, Cisca Wijmenga, John Wright, Jie Zheng, Xiang Zhou, Philip Awadalla, Michael Boehnke, Carlos D. Bustamante, Nancy J. Cox, Segun Fatumo, Daniel H. Geschwind, Caroline Hayward, Kristian Hveem, Eimear E. Kenny, Seunggeun Lee, Yen-Feng Lin, Hamdi Mbarek, Reedik Mägi, Hilary C. Martin, Sarah E. Medland, Yukinori Okada, Aarno V. Palotie, Bogdan Pasaniuc, Daniel J. Rader, Marylyn D. Ritchie, Serena Sanna, Jordan W. Smoller, Kari Stefansson, David A. van Heel, Robin G. Walters, Sebastian Zöllner, null Biobank of the Americas, null Biobank Japan Project, null BioMe, null BioVU, null CanPath - Ontario Health Study, null China Kadoorie Biobank Collaborative Group, null Colorado Center for Personalized Medicine, null deCODE Genetics, null Estonian Biobank, FinnGen, null Generation Scotland, null Genes & Health Research Team, null LifeLines, null Mass General Brigham Biobank, null Michigan Genomics Initiative, null National Biobank of Korea, null Penn Medicine BioBank, null Qatar Biobank, null The Qskin Sun and Health Study, null Taiwan Biobank, null The Hunt Study, null Ucla Atlas Community Health Initiative, null Uganda Genome Resource, null Uk Biobank, Alicia R. Martin, Cristen J. Willer, Benjamin M. Neale, Institute for Molecular Medicine Finland, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Genomics of Neurological and Neuropsychiatric Disorders, Data Science Genetic Epidemiology Lab, Research Programs Unit, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, and University of Helsinki

Subjects
biobank, meta-analysis, genome-wide association study, fine-mapping, Genetics, 1184 Genetics, developmental biology, physiology, GWAS, 3111 Biomedicine, heterogeneity, summary statistics, Biochemistry, Genetics and Molecular Biology (miscellaneous), linkage disequilibrium, miscalibration
Abstract

Funding Information: We acknowledge all the participants and researchers of the 23 biobanks that have contributed to the GBMI. Biobank-specific acknowledgments are included in the Data S3 . We thank H. Huang, A.R. Martin, B.M. Neale, Y. Okada, K. Tsuo, J.C. Ulirsch, Y. Wang, and all the members of Finucane and Daly labs for their helpful feedback. M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation . H.K.F. was funded by NIH grant DP5 OD024582 . Publisher Copyright: © 2022 The Author(s) Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.

Published
2022

14. Effectiveness and feasibility of cardiovascular disease personalized prevention on high polygenic risk score subjects: a randomized controlled pilot study

Author

Margus Viigimaa, Mikk Jürisson, Heti Pisarev, Ruth Kalda, Helene Alavere, Alar Irs, Aet Saar, Krista Fischer, Kristi Läll, Krista Kruuv-Käo, Nina Mars, Elisabeth Widen, Samuli Ripatti, Andres Metspalu, Faculty Common Matters (Faculty of Medicine), Institute for Molecular Medicine Finland, Centre of Excellence in Complex Disease Genetics, Elisabeth Ingrid Maria Widen / Principal Investigator, Genomic Discoveries and Clinical Translation, Department of Public Health, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, and Faculty Common Matters (Faculty of Social Sciences)

Subjects
3121 General medicine, internal medicine and other clinical medicine
Abstract

Aims The aim of this study was to evaluate the effect of the intervention by proactively sharing a patient’s high polygenic risk score (PRS) for coronary artery disease (CAD). Outcomes included: (i) reduction in cardiovascular disease (CVD) risk factors over 12 months; (ii) difference in purchased prescriptions of lipid-lowering and anti-hypertensive drugs between intervention group and control group subjects; and (iii) opinion of the participating physicians and subjects on PRS usefulness. Methods and results This randomized controlled trial was conducted among middle-aged subjects with a top 20% CAD PRS in a family medicine setting. Participants were selected from 26 953 Estonian Biobank cohort participants. Subjects were informed and counselled about their PRS score and CAD risk using the visual tool at baseline (Visit I), counselling session (Visit II), and on the final Visit III at 12 months. The primary endpoint was not significantly different. However, the intervention group participants had a significantly higher probability of initiating statin treatment compared with the controls. Their levels of LDL-cholesterol (LDL-C) were significantly decreased compared with baseline on Visit III and significantly lower than in the control group. The vast majority of participating family physicians believe that finding out about genetic risks will affect the subject’s lifestyle and medication compliance. Conclusion Most of our outcome measures were in favour of this intervention. Participants achieved larger changes in cholesterol and blood pressure values. The vast majority (98.4%) of family physicians are interested in continuing to use genetic risk assessment in practice.

Published
2022

15. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Author

Kristi Läll, Etienne J. Orliac, Marion Patxot, Zoltán Kutalik, Athanasios Kousathanas, Reedik Mägi, Sven Erik Ojavee, Daniel Trejo Banos, Matthew R. Robinson, and Krista Fischer

Subjects
0301 basic medicine, Multifactorial Inheritance, Statistical methods, Computer science, General Physics and Astronomy, Disease, 030105 genetics & heredity, Genome-wide association studies, Genome, Databases, Genetic, Age of Onset, Event (probability theory), Multidisciplinary, Statistics, Age Factors, Contrast (statistics), Genomics, Variance (accounting), Phenotype, Biobank, Cardiovascular Diseases, Hypertension, Female, Menopause, Algorithms, Estonia, Science, Bayesian probability, Computational biology, Biology, Polymorphism, Single Nucleotide, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Genetic variation, Humans, Computer Simulation, Genetic Association Studies, Menarche, Models, Genetic, Genome, Human, Bayes Theorem, General Chemistry, United Kingdom, Genetic architecture, 030104 developmental biology, Diabetes Mellitus, Type 2, Genomic architecture, Genome-Wide Association Study
Abstract

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., Few genome-wide association studies have explored the genetic architecture of age-of-onset for traits and diseases. Here, the authors develop a Bayesian approach to improve prediction in timing-related phenotypes and perform age-of-onset analyses across complex traits in the UK Biobank.

Published
2021

16. Lessons learned during the process of reporting individual genomic results to participants of a population-based biobank

Author

Liis Leitsalu, Anu Reigo, Marili Palover, Tiit Nikopensius, Kristi Läll, Kristi Krebs, Sulev Reisberg, Reedik Mägi, Mart Kals, Helene Alavere, Margit Nõukas, Anneli Kolk, Ivi Normet, Mari-Liis Tammesoo, Ene Käärik, Mairo Puusepp, Kristjan Metsalu, Annely Allik, Lili Milani, Krista Fischer, Neeme Tõnisson, and Andres Metspalu

Subjects
Genetics, Genetics (clinical)
Abstract

The return of individual genomic results (ROR) to research participants is still in its early phase, and insight on how individuals respond to ROR is scarce. Studies contributing to the evidence base for best practices are crucial before these can be established. Here, we describe a ROR procedure conducted at a population-based biobank, followed by surveying the responses of almost 3000 participants to a range of results, and discuss lessons learned from the process, with the aim of facilitating large-scale expansion. Overall, participants perceived the information that they received with counseling as valuable, even when the reporting of high risks initially caused worry. The face-to-face delivery of results limited the number of participants who received results. Although the participants highly valued this type of communication, additional means of communication need to be considered to improve the feasibility of large-scale ROR. The feedback collected sheds light on the value judgements of the participants and on potential responses to the receipt of genetic risk information. Biobanks in other countries are planning or conducting similar projects, and the sharing of lessons learned may provide valuable insight and aid such endeavors.

Published
2022

17. Genetic and modifiable risk factors combine multiplicatively in common disease

Author

Shichao Pang, Loic Yengo, Christopher P. Nelson, Felix Bourier, Lingyao Zeng, Ling Li, Thorsten Kessler, Jeanette Erdmann, Reedik Mägi, Kristi Läll, Andres Metspalu, Bertram Mueller-Myhsok, Nilesh J. Samani, Peter M. Visscher, and Heribert Schunkert

Subjects
General Medicine, Cardiology and Cardiovascular Medicine
Abstract

Background The joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized. Objectives We modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors. Methods We analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions. Results In UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p p 0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer. Conclusions Alleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed. Graphical Abstract

Published
2022

18. Novel discoveries and enhanced genomic prediction from modelling genetic risk of cancer age-at-onset

Author

Sven E. Ojavee, Ekaterina S. Maksimova, Kristi Läll, Marie C. Sadler, Reedik Mägi, Zoltan Kutalik, and Matthew R. Robinson

Abstract

Genome-wide association studies seek to attribute disease risk to DNA regions and facilitate subject-specific prediction and patient stratification. For later-life disease, inference from case-control studies is hampered by the uncertainty that control group subjects might later be diagnosed. Time-to-event analysis treats controls as right-censored, making no additional assumptions about future disease occurrence and represent a more sound conceptual alternative for more accurate inference. Here, using data on 11 common cancers from the UK and Estonian Biobank studies, we provide empirical evidence that discovery and genomic prediction are greatly improved by analysing age-at-diagnosis, compared to a case-control model of association. We replicate previous findings from large-scale case-control studies and find an additional 59 previously unreported independent genomic regions, out of which 16 replicated in independent data (an increase of 18% and 6% over current findings). Our novel discoveries provide new insights into underlying cancer pathways, and our model yields a better understanding of the polygenicity and genetic architecture of the 11 tumours. We find that heritable germline genetic variation plays a vital role in cancer occurrence, with risk attributable to many thousands of underlying genomic regions. Finally, we show that Bayesian modelling strategies utilising time-to-event data increase prediction accuracy by an average of 20% compared to a recent summary statistic approach (LDpred-funct). As sample sizes increase, incorporating time-to-event data should be commonplace, improving case-control studies by using richer information about the disease process.

Published
2022

19. Contribution of schizophrenia polygenic burden to longitudinal phenotypic variance in 22q11.2 deletion syndrome

Author

Maris, Alver, Valentina, Mancini, Kristi, Läll, Maude, Schneider, Luciana, Romano, Reedik, Mägi, Emmanouil T, Dermitzakis, Stephan, Eliez, and Andres, Metspalu

Subjects
Male, Cellular and Molecular Neuroscience, Psychiatry and Mental health, Multifactorial Inheritance, Adolescent, Psychotic Disorders, Schizophrenia, DiGeorge Syndrome, Humans, Female, Cognitive Dysfunction, Molecular Biology
Abstract

While the recurrent 22q11.2 deletion is one of the strongest genetic risk factors for schizophrenia (SCZ), variability of its associated neuropsychiatric endophenotypes reflects its incomplete penetrance for psychosis development. To assess whether this phenotypic variability is linked to common variants associated with SCZ, we studied the association between SCZ polygenic risk score (PRS) and longitudinally acquired phenotypic information of the Swiss 22q11.2DS cohort (n = 97, 50% females, mean age 17.7 yr, mean visit interval 3.8 yr). The SCZ PRS with the best predictive performance was ascertained in the Estonian Biobank (n = 201,146) with LDpred. The infinitesimal SCZ PRS model showed the strongest capacity in discriminating SCZ cases from controls with one SD difference in SCZ PRS corresponding to an odds ratio (OR) of 1.73 (95% CI 1.57–1.90, P = 1.47 × 10−29). In 22q11.2 patients, random-effects ordinal regression modelling using longitudinal data showed SCZ PRS to have the strongest effect on social anhedonia (OR = 2.09, P = 0.0002), and occupational functioning (OR = 1.82, P = 0.0003) within the negative symptoms course, and dysphoric mood (OR = 2.00, P = 0.002) and stress intolerance (OR = 1.76, P = 0.0002) within the general symptoms course. Genetic liability for SCZ was additionally associated with full scale cognitive decline (β = –0.25, P = 0.02) and with longitudinal volumetric reduction of the right and left hippocampi (β = –0.28, P = 0.005; β = –0.23, P = 0.02, respectively). Our results indicate that the polygenic contribution to SCZ acts upon the threshold-lowering first hit (i.e., the deletion). It modifies the endophenotypes of 22q11.2DS and augments the derailment of developmental trajectories of negative and general symptoms, cognition, and hippocampal volume.

Published
2022

20. Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts

Author

Krista Fischer, Katri Pärna, Ilja M. Nolte, Harold Snieder, Kristi Läll, and Life Course Epidemiology (LCE)

Subjects
personalized prediction, Adult, Estonia, Male, Adolescent, Epidemiology, Single-nucleotide polymorphism, Type 2 diabetes, genetic risk score, Polymorphism, Single Nucleotide, Cohort Studies, Young Adult, 03 medical and health sciences, Risk Factors, medicine, Humans, Genetic Predisposition to Disease, Clinical significance, Research Articles, Genetics (clinical), Aged, Biological Specimen Banks, 030304 developmental biology, Aged, 80 and over, 0303 health sciences, Framingham Risk Score, Models, Genetic, business.industry, Incidence, Incidence (epidemiology), 030305 genetics & heredity, Hazard ratio, Reproducibility of Results, Middle Aged, medicine.disease, Biobank, Confidence interval, Diabetes Mellitus, Type 2, Female, type 2 diabetes, business, Research Article, Demography
Abstract

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high-risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single-weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5-year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p =.009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10−16 EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.

Published
2020

21. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals

Author

Kristi Läll, Luca Pagani, Davide Marnetto, Francesco Montinaro, Katri Pärna, Krista Fischer, Reedik Mägi, Ludovica Molinaro, Toomas Haller, and Mait Metspalu

Subjects
0301 basic medicine, Multifactorial Inheritance, Genotype, Science, Population, Predictive medicine, General Physics and Astronomy, GENETIC RISK, Biology, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, 0302 clinical medicine, Genetic, Models, Genetics, Humans, Genetic Predisposition to Disease, Genetic variation, Allele, education, lcsh:Science, Genetic association study, education.field_of_study, Multidisciplinary, Models, Genetic, RISK PREDICTION, General Chemistry, ASSOCIATION, eye diseases, 3. Good health, 030104 developmental biology, Genetics, Population, Phenotype, Evolutionary biology, Trait, lcsh:Q, Deconvolution, 030217 neurology & neurosurgery
Abstract

Polygenic Scores (PSs) describe the genetic component of an individual’s quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied., Polygenic scores are believed to hold future promise for trait prediction and personalized medicine, but are sensitive to demographic history. Here, Marnetto et al. develop partial polygenic scores supplemented with local ancestry deconvolution which improves prediction accuracy into recently admixed European populations.

Published
2020

22. Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases

Author

Sarah Finer, Yen-Feng Lin, Michigan Genomics Initiative, Valeria Lo Faro, Benjamin M. Neale, Nathan Ingold, Estonian Biobank, Peter Straub, Jasmina Uzunovic, Triin Laisk, Whitney E. Hornsby, Sebastian Zoellner, Takahiro Konuma, Jordan A. Shavit, Masahiro Kanai, Xue Zhong, Stephen J. Wicks, Taiwan Biobank, Arjun Bhattacharya, Nicholas J. Douville, Yi Ding, BioVU, Jennifer E. Huffman, Christopher R. Gignoux, Kristian Hveem, Serena Sanna, Brooke N. Wolford, Mitja I. Kurki, Aarno Palotie, Yukinori Okada, FinnGen, Clara Lajonchere, Ida Surakka, Mass General Brigham Biobank, Jie Zheng, Caroline Hayward, Riccardo E. Marioni, Chris Griffiths, Lars G. Fritsche, Rasheed Humaira, Ben Michael Brumpton, Kristi Läll, Kuang Lin, Jordan W. Smoller, Lude Franke, Michelle Daya, David C. Whiteman, Karen A. Hunt, Harold Snieder, Jun Lv, Stuart MacGregor, Alicia R. Martin, Juha Karjalainen, Jonathan A. Shortt, Kuan-Han H. Wu, Jibril B Hirbo, Sameer Chavan, Marie-Julie Favé, Snehal Patil, Kristy Crooks, Sarah E. Graham, Tzu-Ting Chen, Michael Preuss, Matthew Zawistowski, Yen-Chen Anne Feng, Iona Y Millwood, Cisca Wijmenga, Cristen J. Willer, Jansonius Nomdo, Kristin Tsuo, Qimr Berghofer Biobank, Koichi Matsuda, LifeLines, Shinichi Namba, Nicholas M. Rafaels, Priit Palta, Unnur Thorsteinsdottir, Chia-Yen Chen, Generation Scotland, Cecilia M. Lindgren, Huiling Zhao, Andrea Ganna, Bogdan Pasaniuc, Maasha Mutaamba, Nancy J. Cox, Zhengming Chen, George Davey Smith, Mark J. Daly, Sarah E. Medland, Yu Guo, Daniel H. Geschwind, Matthew Law, Judith M. Vonk, Eimear E. Kenny, David J. Porteous, Tian Ge, Judy H. Cho, UK Biobank, Ruth J. F. Loos, Eric R. Gamazon, Wei Zhou, Richard C. Trembath, Philip Awadalla, Kathleen C. Barnes, David A. van Heel, Kari Stefansson, Archie Campbell, Hilary C. Martin, Tom R. Gaunt, Ruth E. Johnson, Sinéad B. Chapman, Esteban A Lopera-Maya, Michael Boehnke, Brett Vanderwerff, Catherine M. Olsen, Marike Boezen, Anita Pandit, BioMe, Ran Tao, Hilary K. Finucane, Anne Richmond, Ying Wang, Liming Li, Geertruida H. de Bock, John Wright, Xiang Zhou, Robin G. Walters, Reedik Mägi, and Hailiang Huang

Subjects
Disease gene, 0303 health sciences, South asia, Collaborative network, Genome-wide association study, Computational biology, Biology, Biobank, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Baseline characteristics, Meta-analysis, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

SummaryBiobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries - with most biobanks and preceding GWAS studies composed of individuals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.

Published
2021

23. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Author

Wei Zhou, Masahiro Kanai, Kuan-Han H. Wu, Humaira Rasheed, Kristin Tsuo, Jibril B. Hirbo, Ying Wang, Arjun Bhattacharya, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N. Wolford, Valeria Lo Faro, Esteban A. Lopera-Maya, Kristi Läll, Marie-Julie Favé, Juulia J. Partanen, Sinéad B. Chapman, Juha Karjalainen, Mitja Kurki, Mutaamba Maasha, Ben M. Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A. Feng, Lindsay A. Guare, Christopher R. Gignoux, Sarah E. Graham, Whitney E. Hornsby, Nathan Ingold, Said I. Ismail, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y. Millwood, Sonia Moreno-Grau, Kisung Nam, Priit Palta, Anita Pandit, Michael H. Preuss, Chadi Saad, Shefali Setia-Verma, Unnur Thorsteinsdottir, Jasmina Uzunovic, Anurag Verma, Matthew Zawistowski, Xue Zhong, Nahla Afifi, Kawthar M. Al-Dabhani, Asma Al Thani, Yuki Bradford, Archie Campbell, Kristy Crooks, Geertruida H. de Bock, Scott M. Damrauer, Nicholas J. Douville, Sarah Finer, Lars G. Fritsche, Eleni Fthenou, Gilberto Gonzalez-Arroyo, Christopher J. Griffiths, Yu Guo, Karen A. Hunt, Alexander Ioannidis, Nomdo M. Jansonius, Takahiro Konuma, Ming Ta Michael Lee, Arturo Lopez-Pineda, Yuta Matsuda, Riccardo E. Marioni, Babak Moatamed, Marco A. Nava-Aguilar, Kensuke Numakura, Snehal Patil, Nicholas Rafaels, Anne Richmond, Agustin Rojas-Muñoz, Jonathan A. Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Manvi Vernekar, Yogasudha Veturi, Kathleen C. Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Hilary K. Finucane, Lude Franke, Eric R. Gamazon, Andrea Ganna, Tom R. Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Nicholas Katsanis, Jukka T. Koskela, Clara Lajonchere, Matthew H. Law, Liming Li, Cecilia M. Lindgren, Ruth J.F. Loos, Stuart MacGregor, Koichi Matsuda, Catherine M. Olsen, David J. Porteous, Jordan A. Shavit, Harold Snieder, Tomohiro Takano, Richard C. Trembath, Judith M. Vonk, David C. Whiteman, Stephen J. Wicks, Cisca Wijmenga, John Wright, Jie Zheng, Xiang Zhou, Philip Awadalla, Michael Boehnke, Carlos D. Bustamante, Nancy J. Cox, Segun Fatumo, Daniel H. Geschwind, Caroline Hayward, Kristian Hveem, Eimear E. Kenny, Seunggeun Lee, Yen-Feng Lin, Hamdi Mbarek, Reedik Mägi, Hilary C. Martin, Sarah E. Medland, Yukinori Okada, Aarno V. Palotie, Bogdan Pasaniuc, Daniel J. Rader, Marylyn D. Ritchie, Serena Sanna, Jordan W. Smoller, Kari Stefansson, David A. van Heel, Robin G. Walters, Sebastian Zöllner, Alicia R. Martin, Cristen J. Willer, Mark J. Daly, Benjamin M. Neale, Samuli Olli Ripatti / Principal Investigator, University of Helsinki, Institute for Molecular Medicine Finland, Complex Disease Genetics, Genomics of Neurological and Neuropsychiatric Disorders, Data Science Genetic Epidemiology Lab, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Life Course Epidemiology (LCE), Damage and Repair in Cancer Development and Cancer Treatment (DARE), Perceptual and Cognitive Neuroscience (PCN), Groningen Research Institute for Asthma and COPD (GRIAC), Stem Cell Aging Leukemia and Lymphoma (SALL), and Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI)

Subjects
biobank, meta-analysis, genetic association studies, 1184 Genetics, developmental biology, physiology, GWAS, 3111 Biomedicine, biobank meta-analysis, ancestry diversity, phenotype harmonization
Abstract

Funding Information: The work of the contributing biobanks was supported by numerous grants from governmental and charitable bodies. Biobank-specific acknowledgments and more detailed acknowledgments are included in Data S2. Initiative management, S.B.C. J.C. N.J.C. M.J.D. E.E.K. A.R.M. B.M.N. Y.O. A.V.P. D.A.v.H. R.G.W. C.J.W. W.Z. and S.Z.; individual biobank analysis, A.B. Y.B. B.M.B. C.D.B. S.C. T.-T.C. K.C. S.M.D. M.D. G.H.d.B. Y.D. N.J.D. M.-J.F. Y.-C.A.F. S.F. V.L.F. L.G.F. E.R.G. T.R.G. D.H.G. C.R.G. G.G.-A. S.E.G. L.A.G. C.H. J.B.H. W.E.H. H.H. K.H. N.I. A.I. R.J. M. Kurki, J.K. N.K. E.E.K. J.T.K. M. Kanai, T.L. K.L. M.H.L. S.L. K.L. Y.-F.L. V.L.F. R.J.F.L. E.A.L.-M. A.R.-M. S.M.-G. R.M. R.E.M. H.C.M. A.R.M. Y.M. H.M. S.E.M. I.Y.M. B.M. S.M. K.N. S.N. M.A.N.-A. K.N. Y.O. P.P. A.L.-P. A.P. B.P. S.P. M.H.P. D.J.R. N.R. M.D.R. A.R. C.S. S.S. S.S.S. J.A.S. P.S. I.S. T.T. R.T. K.T. J.U. D.A.v.H. B.V. M.V. Y.V. J.M.V. R.G.W. Y.W. S.J.W. B.N.W. K.-H.H.W. M.Z. X.Z. and S.Z.; individual biobank management, N.A. A.A.T. K.M.A.-D. P.A. K.C.B. M. Boehnke, M. Boezen, C.D.B. A.C. Z.C. C.-Y.C. J.C. N.J.C. S.M.D. S.F. Y.-C.A.F. S.F. E.F. T.G. C.R.G. C.J.G. Y.G. H.H. K.A.H. K.H. S.I.I. N.M.J. N.K. E.E.K. J.T.K. C.L. M.H.L. M.T.M.L. L.L. K.L. Y.-F.L. R.J.F.L. J.L. S.M. Y.M. K.M. I.Y.M. Y.O. C.M.O. A.V.P. B.P. D.J.P. D.J.R. M.D.R. S.S. J.W.S. H.S. K.S. T.T. U.T. R.C.T. D.A.v.H. M.V. R.G.W. D.C.W. C.W. J.W. M.Z. X.Z. and S.Z.; study design and interpretation of results, A.B. M. Boehnke, M. Boezen, B.M.B. T.-T.C. C.-Y.C. M.J.D. G.D.S. N.J.D. S.F. M.-J.F. H.K.F. E.R.G. A.G. T.G. J.B.H. J.H. K.H. R.J. M.K. E.E.K. T.K. C.M.L. V.L.F. E.A.L.-M. A.R.M. S.N. B.M.N. C.M.O. J.J.P. B.P. N.R. H.R. J.A.S. I.S. K.T. D.A.v.H. R.G.W. Y.W. D.C.W. S.J.W. C.J.W. B.N.W. J.W. K.-H.H.W. M.Z. H.Z. J.Z. W.Z. X.Z. and S.Z.; drafted and edited the paper, A.B. M. Boehnke, M. Boezen, M.J.D. G.H.d.B. N.J.D. T.R.G. J.B.H. N.I. N.M.J. M.K. V.L.F. S.M. A.R.M. H.M. S.N. B.M.N. C.M.O. B.P. H.R. C.S. J.A.S. J.W.S. K.T. Y.W. D.C.W. C.J.W. K.-H.H.W. H.Z. J.Z. W.Z. and S.Z.; primary meta-analysis and quality control, M.J.D. H.K.F. M. Kanai, J.K. J.T.K. M. Kurki, M.M. B.M.N. C.J.W. K.-H.H.W. and W.Z.; drug discovery: S.N. T.K. K.-H.H.W. W.Z. and Y.O.; fine mapping, M. Kanai, W.Z. M.J.D. and H.K.F.; polygenic risk score, Y.W. S.N. E.A.L.-M. S.K. K.T. K.L. M. Kanai, W.Z. K.W. M.-J.F. L.B. P.A. P.D. V.L.F. R.M. Y.M. B.B. S.S. J.U. E.R.G. N.J.C. I.S. Y.O. A.R.M. and J.B.H.; proteome-wide Mendelian randomization, H.Z. H.R. A.B. G.H. G.D.S. B.M.B. W.Z. B.M.N. T.R.G. and J.Z.; transcriptome-wide association study, A.B. J.B.H. W.Z. J.Z. M. Kanai, B.P. E.R.G. and N.J.C.; asthma, K.T. W.Z. Y.W. M. Kanai, S.N. Y.O. B.M.N. M.J.D. and A.R.M.; heart failure, K.-H.H.W. N.J.D. B.N.W. I.S. S.E.G. J.B.H. N.J.C. M.P. R.J.F.L. M.J.D. B.M.N. W.Z. W.E.H. and C.J.W.; idiopathic pulmonary fibrosis, J.J.P. W.Z. M.J.D. J.T.K. N.J.C. and J.B.H.; primary open-angle glaucoma, V.L.F. A.B. W.Z. Y.W. K.L. M. Kanai, E.A.L.-M. P.S. R.T. X.Z. S.N. S.S. Y.O. N.I. S.M. H.S. I.S. C.W. A.R.M. E.R.G. N.M.J. N.J.C. and J.B.H.; stroke, I.S. K.-H.H.W. W.H. B.N.W. W.Z. J.E.H. A.P. B.B. A.H.S. M.E.G. R.G.W. K.H. C.K. S.Z. M.J.D. B.M.N. and C.J.W.; venous thromboembolism, B.N.W. I.S. K.-H.H.W. B.B. V.L.F. K.T. M.D. B.N. W.Z. J.A.S. and C.J.W. All authors reviewed the manuscript. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. The spouse of C.J.W. works at Regeneron Pharmaceuticals. C.-Y.C. is employed by Biogen. C.R.G. owns stock in 23andMe, Inc. T.R.G. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. E.E.K. has received speaker fees from Regeneron, Illumina, and 23andMe and is a member of the advisory board for Galateo Bio. R.E.M. has received speaker fees from Illumina and is a scientific advisor to the Epigenetic Clock Development Foundation. G.D.S. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. K.S. and U.T. are employed by deCODE Genetics/Amgen, Inc. J.Z. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. S.M. is a co-founder of and holds stock in Seonix Bio. Publisher Copyright: © 2022 Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Published
2022

24. Improving GWAS discovery and genomic prediction accuracy in Biobank data

Author

Kristi Läll, Etienne J. Orliac, Sven Erik Ojavee, Peter M. Visscher, Reedik Mägi, Daniel Trejo Banos, and Matthew R. Robinson

Subjects
Estonia, Multidisciplinary, Genotype, Bayesian probability, Bayes Theorem, Genome-wide association study, Genomics, Computational biology, Biology, Polymorphism, Single Nucleotide, Biobank, Phenotype, Quantitative Trait, Heritable, England, SNP annotation, Databases, Genetic, Humans, SNP, Polygenic risk score, Precision Medicine, Genotyping, Predictive modelling, Genome-Wide Association Study
Abstract

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 χ 2 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.

Published
2021

25. Stratification of Type 2 Diabetes by Age of Diagnosis in the UK Biobank Reveals Subgroup-Specific Genetic Associations and Causal Risk Profiles

Author

Raymond, Noordam, Kristi, Läll, Roelof A J, Smit, Triin, Laisk, Andres, Metspalu, Tõnu, Esko, Lili, Milani, Ruth J F, Loos, Reedik, Mägi, Ko, Willems van Dijk, and Diana, van Heemst

Subjects
Adult, Male, medicine.medical_specialty, Genotype, endocrine system diseases, Endocrinology, Diabetes and Metabolism, Single-nucleotide polymorphism, Genome-wide association study, Type 2 diabetes, Risk profile, Polymorphism, Single Nucleotide, Internal medicine, Internal Medicine, medicine, Humans, Genetic Predisposition to Disease, Genetic association, Aged, Biological Specimen Banks, Aged, 80 and over, business.industry, Age Factors, nutritional and metabolic diseases, Middle Aged, medicine.disease, Control subjects, Biobank, United Kingdom, Diabetes Mellitus, Type 2, Female, business, Transcription Factor 7-Like 2 Protein, Genome-Wide Association Study
Abstract

The pathogenesis of type 2 diabetes (T2D) might change with increasing age. Here, we used a stratification based on age of diagnosis to gain insight into the genetics and causal risk factors of T2D across different age-groups. We performed genome-wide association studies (GWAS) on T2D and T2D subgroups based on age of diagnosis (70 years) (total of 24,986 cases). As control subjects, participants were at least 70 years of age at the end of follow-up without developing T2D (N =187,130). GWAS identified 208 independent lead single nucleotide polymorphism (SNPs) mapping to 69 loci associated with T2D (P < 1.0e−8). Among others, SNPs mapped to CDKN2B-AS1 and multiple independent SNPs mapped to TCF7L2 were more strongly associated with cases diagnosed after age 70 years than with cases diagnosed before age 50 years. Based on the different case groups, we performed two-sample Mendelian randomization. Most notably, we observed that of the investigated risk factors, the association between BMI and T2D attenuated with increasing age of diagnosis. Collectively, our results indicate that stratification of T2D based on age of diag-nosis reveals subgroup-specific genetics and causal determinants, supporting the hypothesis that the pathogenesis of T2D changes with increasing age.

Published
2021

26. Advancing our understanding of genetic risk factors and potential personalized strategies in pelvic organ prolapse: largest GWAS to date reveals 19 novel associated loci

Author

Natàlia Pujol-Gualdo, Riikka K Arffman, Kristi Läll, Terhi Piltonen, Henna-Riikka Rossi, Reedik Mägi, Triin Laisk, and Maarja Lepamets

Subjects
Linkage (software), medicine.medical_specialty, business.industry, Genitourinary system, Genome-wide association study, medicine.disease, Biobank, Internal medicine, Medicine, Genetic risk, business, Body mass index, Asthma, Genetic association
Abstract

ObjectivesTo identify the genetic determinants of pelvic organ prolapse (POP) and assess the predictive ability of polygenic risk scores (PRS) alone or in combination with clinical risk factors.DesignMeta-analysis of genome-wide association studies (GWAS) and PRS construction and validation.SettingGWAS summary statistics from three European datasets and individual-level data from Estonian Biobank, including phenotype questionnaire and measurement panel, together with follow-up data from linkage with national health-related registries.ParticipantsA total of 28,086 women with POP and 546,321 controls of European ancestry. Genetic risk scores were derived from a dataset of 20,118 cases and 427,426 controls of European ancestry and validated in a target dataset of 7,896 cases and 118,895 controls. Cases were defined using ICD codes and classical risk factors were derived from questionnaire data and ICD10 codes.ResultsThe identified novel loci reinforce the role of connective tissue abnormalities, urogenital tract development and point towards association with a range of cardiometabolic traits. A novel PRS combining 3,242,959 variants demonstrated that women in the top 5% have 1.63 (95% CI: 1.37 to 1.93) times the hazard of developing POP compared to the rest of the women. When analyzing PRS in incident POP, it showed similar predictive ability (Harrell C-statistic 0.583, sd=0.007) than five established clinical risk factors (number of children, body mass index (BMI), ever smoked, constipation and asthma) combined (Harrell C-statistic 0.588, sd=0.007) and demonstrated its incremental value in combination with these (Harrell C-statistic 0.630, sd=0.007).ConclusionsThe largest GWAS meta-analysis in POP to date identified 26 genetic loci which establish links between POP and connective tissue abnormalities, urogenital development and cardiometabolic health. We present a PRS for POP which provides the first potential tool for preventive strategies and early detection of higher risk susceptibility to POP including genetic risk factors.

Published
2021

27. O-116 Genetic association analyses identify links between pelvic prolapse (PP) and connective tissue biology, cardiovascular and reproductive health

Author

Terhi Piltonen, N Pujol Gualdo, Maarja Lepamets, Riikka K Arffman, Reedik Mägi, Triin Laisk, and Kristi Läll

Subjects
medicine.anatomical_structure, Reproductive Medicine, Pelvic prolapse, business.industry, Rehabilitation, medicine, Obstetrics and Gynecology, Connective tissue, Biology, business, Bioinformatics, Reproductive health, Genetic association
Abstract

Study question Can genome-wide association analysis unravel the biological underpinnings of PP and facilitate personalized risk assessment via genetic risk scores construction? Summary answer We unravel novel links with urogenital development and vascular health in PP and present polygenic risk score as a tool to stratify PP risk. What is known already Prolapse is characterized by a descent of the pelvic organs into the vaginal cavity. PP affects around 40% of women after menopause and is the main indication for major gynecological surgery, having an important health, social and economic burden. Although the etiology and biological mechanisms underlying PP remain poorly understood, prior studies suggest genetic factors might play a role. Recently, a genome-wide association study (GWAS) identified seven genome-wide significant loci, located in or near genes involved in connective tissue metabolism and estrogen exposure in the etiology of PP. Study design, size, duration We conducted a three-stage case-control genome-wide association study. Firstly, in the discovery phase, we meta-analyzed Icelandic, UK Biobank and the FinnGen R3 datasets, comprising a total of 20118 cases and 427426 controls of European ancestry. For replication we used an independent dataset from Estonian Biobank (7968 cases and 118895 controls). Finally, we conducted a joint meta-analysis, containing 28086 cases and 546321 controls, which is the largest GWAS of PP to date. Participants/materials, setting, methods We performed functional annotation on genetic variants unraveled by GWAS and integrated these with expression quantitative trait loci and chromatin interaction data. In addition, we looked at enrichment of association signal on gene-set, tissue and cell type level and analyzed associations with other phenotypes both on genetic and phenotypic level. Colocalisation analyses were conducted to help pinpoint causal genes. We further constructed polygenic risk scores to explore options for personalized risk assessment and prevention. Main results and the role of chance In the discovery phase, we identified 18 genetic loci and 20 genetic variants significantly associated with POP (p Limitations, reasons for caution This GWAS meta-analyses focused on European ancestry populations, which challenges the generalizability of GWAS findings to non-European populations. Moreover, this study included women with PP from population-based biobanks identified using the ICD-10 code N81, which limits analyses considering different disease stages and severity. Wider implications of the findings Our study provides genetic evidence to improve the current understanding of PP pathogenesis and serves as basis for further functional studies. Moreover, we provide a genetic tool for personalized risk stratification, which could help prevent PP development and improve the quality of a vast quantity of women. Trial registration number not applicable

Published
2021

28. Stratification of Type 2 Diabetes Mellitus by Age of Diagnosis in the UK Biobank Reveals Subgroup-Specific Genetic Associations and Causal Risk Profiles

Author

Estonian Biobank research team, Diana van Heemst, Ko Willems van Dijk, Reedik Mägi, Ruth JF Loos, Triin Laisk, Roelof AJ Smit, Kristi Läll, and Raymond Noordam

Subjects
endocrine system diseases, nutritional and metabolic diseases
Abstract

The pathogenesis of type 2 diabetes mellitus (T2D) might change with increasing age. Here, we used a stratification based on age of diagnosis, to gain insight in the genetics and causal risk factors of T2D across different age groups. We performed genome-wide association studies (GWAS) on T2D and T2D subgroups based on age of diagnosis (70 years; total of 24,986 cases). As controls, we used participants who were at least 70 years of age at the end of follow-up without developing T2D (N = 187,130). GWAS identified 208 independent lead SNPs mapping to 69 loci associated with T2D (pCDKN2B-AS1 and multiple independent SNPs mapped to TCF7L2 were more strongly associated to cases diagnosed after age 70 years than to cases diagnosed before age 50 years. Based on the different case groups, we performed two-sample Mendelian Randomization. Most notably, we observed that of the investigated risk factors the association between body mass index and T2D attenuated with increasing age of diagnosis. Collectively, stratification of T2D based on age of diagnosis reveals subgroup-specific genetics and causal determinants, supporting the hypothesis that the pathogenesis of T2D changes with increasing age.

Published
2021

29. Estimating the performance of three cardiovascular disease risk scores: the Estonian Biobank cohort study

Author

Maris Alver, Tiia Ainla, Kristi Läll, Krista Fischer, Aet Saar, Jaan Eha, Toomas Marandi, and Andres Metspalu

Subjects
Adult, Estonia, Male, Epidemiology, 030204 cardiovascular system & hematology, Risk Assessment, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Predictive Value of Tests, Risk Factors, Humans, Medicine, 030212 general & internal medicine, Aged, business.industry, Atherosclerotic cardiovascular disease, Incidence, Incidence (epidemiology), Public Health, Environmental and Occupational Health, Middle Aged, Atherosclerosis, Biobank, Cardiovascular Diseases, Predictive value of tests, Cohort, Disease risk, Female, business, Risk assessment, Demography, Cohort study
Abstract

BackgroundWe aim to investigate the predictive ability of PCE (Pooled Cohort Equations), QRISK2 and SCORE (Systematic COronary Risk Estimation) scoring systems for atherosclerotic cardiovascular disease (ASCVD) risk prediction in Estonia, a country with one of the highest ASCVD event rates in Europe.MethodsSeven-year risk estimates were calculated in risk score–specific subsets of the Estonian Biobank cohort. Calibration was assessed by standardised incidence ratios (SIRs) and discrimination by Harrell’s C-statistics. In addition, a head-to-head comparison of the scores was performed in the intersection of the three score-specific subcohorts.ResultsPCE, QRISK2 and SCORE risk estimates were calculated for 4356, 7191 and 3987 eligible individuals, respectively. During the 7-year follow-up, 220 hard ASCVD events (PCE outcome), 671 ASCVD events (QRISK2 outcome) and 94 ASCVD deaths (SCORE outcome) occurred among the score-specific subsets of the cohort. While PCE (SIR 1.03, 95% CI 0.90 to 1.18) and SCORE (SIR 0.99, 95% CI 0.81 to 1.21) were calibrated well for the cohort, QRISK2 underestimated the risk by 48% (SIR 0.52, 95% CI 0.48 to 0.56). In terms of discrimination, PCE (C-statistic 0.778) was inferior to QRISK2 (C-statistic 0.812) and SCORE (C-statistic 0.865). All three risk scores performed at similar level in the head-to-head comparison.ConclusionOf three widely used ASCVD risk scores, PCE and SCORE performed at acceptable level, while QRISK2 underestimated ASCVD risk markedly. These results highlight the need for evaluating the accuracy of ASCVD risk scores prior to use in high-risk populations.

Published
2019

30. SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Author

Matteo D’Antonio, Jennifer P. Nguyen, Timothy D. Arthur, Hiroko Matsui, Agnieszka D’Antonio-Chronowska, Kelly A. Frazer, Benjamin M. Neale, Mark Daly, Andrea Ganna, Christine Stevens, Gita A. Pathak, Shea J. Andrews, Masahiro Kanai, Mattia Cordioli, Juha Karjalainen, Renato Polimanti, Matti Pirinen, Nadia Harerimana, Kumar Veerapen, Brooke Wolford, Huy Nguyen, Matthew Solomonson, Rachel G. Liao, Karolina Chwialkowska, Amy Trankiem, Mary K. Balaconis, Caroline Hayward, Anne Richmond, Archie Campbell, Marcela Morris, Chloe Fawns-Ritchie, Joseph T. Glessner, Douglas M. Shaw, Xiao Chang, Hannah Polikowski, Petty E. Lauren, Hung-Hsin Chen, Zhu Wanying, Hakon Hakonarson, David J. Porteous, Jennifer Below, Kari North, Joseph B. McCormick, Paul R.H.J. Timmers, James F. Wilson, Albert Tenesa, Kenton D’Mellow, Shona M. Kerr, Mari E.K. Niemi, Lindokuhle Nkambul, Kathrin Aprile von Hohenstaufen, Ali Sobh, Madonna M. Eltoukhy, Amr M. Yassen, Mohamed A.F. Hegazy, Kamal Okasha, Mohammed A. Eid, Hanteera S. Moahmed, Doaa Shahin, Yasser M. El-Sherbiny, Tamer A. Elhadidy, Mohamed S. Abd Elghafar, Jehan J. El-Jawhari, Attia A.S. Mohamed, Marwa H. Elnagdy, Amr Samir, Mahmoud Abdel-Aziz, Walid T. Khafaga, Walaa M. El-Lawaty, Mohamed S. Torky, Mohamed R. El-shanshory, Chiara Batini, Paul H. Lee, Nick Shrine, Alexander T. Williams, Martin D. Tobin, Anna L. Guyatt, Catherine John, Richard J. Packer, Altaf Ali, Robert C. Free, Xueyang Wang, Louise V. Wain, Edward J. Hollox, Laura D. Venn, Catherine E. Bee, Emma L. Adams, Ahmadreza Niavarani, Bahareh Sharififard, Rasoul Aliannejad, Ali Amirsavadkouhi, Zeinab Naderpour, Hengameh Ansari Tadi, Afshar Etemadi Aleagha, Saeideh Ahmadi, Seyed Behrooz Mohseni Moghaddam, Alireza Adamsara, Morteza Saeedi, Hamed Abdollahi, Abdolmajid Hosseini, Pajaree Chariyavilaskul, Monpat Chamnanphon, Thitima B. Suttichet, Vorasuk Shotelersuk, Monnat Pongpanich, Chureerat Phokaew, Wanna Chetruengchai, Watsamon Jantarabenjakul, Opass Putchareon, Pattama Torvorapanit, Thanyawee Puthanakit, Pintip Suchartlikitwong, Nattiya Hirankarn, Voraphoj Nilaratanakul, Pimpayao Sodsai, Ben M. Brumpton, Kristian Hveem, Cristen Willer, Wei Zhou, Tormod Rogne, Erik Solligard, Bjørn Olav Åsvold, Malak Abedalthagafi, Manal Alaamery, Saleh Alqahtani, Dona Baraka, Fawz Al Harthi, Ebtehal Alsolm, Leen Abu Safieh, Albandary M. Alowayn, Fatimah Alqubaishi, Amal Al Mutairi, Serghei Mangul, Abdulraheem Alshareef, Mona Sawaji, Mansour Almutairi, Nora Aljawini, Nour Albesher, Yaseen M. Arabi, Ebrahim S. Mahmoud, Amin K. Khattab, Roaa T. Halawani, Ziab Z. Alahmadey, Jehad K. Albakri, Walaa A. Felemban, Bandar A. Suliman, Rana Hasanato, Laila Al-Awdah, Jahad Alghamdi, Deema AlZahrani, Sameera AlJohani, Hani Al-Afghani, May Alrashed, Nouf AlDhawi, Hadeel AlBardis, Sarah Alkwai, Moneera Alswailm, Faisal Almalki, Maha Albeladi, Iman Almohammed, Eman Barhoush, Anoud Albader, Salam Massadeh, Abdulaziz AlMalik, Sara Alotaibi, Bader Alghamdi, Junghyun Jung, Mohammad S. Fawzy, Yunsung Lee, Per Magnus, Lill-Iren S. Trogstad, Øyvind Helgeland, Jennifer R. Harris, Massimo Mangino, Tim D. Spector, Duncan Emma, Sandra P. Smieszek, Bartlomiej P. Przychodzen, Christos Polymeropoulos, Vasilios Polymeropoulos, Mihael H. Polymeropoulos, Israel Fernandez-Cadenas, Jordi Perez-Tur, Laia Llucià-Carol, Natalia Cullell, Elena Muiño, Jara Cárcel-Márquez, Marta L. DeDiego, Lara Lloret Iglesias, Anna M. Planas, Alex Soriano, Veronica Rico, Daiana Agüero, Josep L. Bedini, Francisco Lozano, Carlos Domingo, Veronica Robles, Francisca Ruiz-Jaén, Leonardo Márquez, Juan Gomez, Eliecer Coto, Guillermo M. Albaiceta, Marta García-Clemente, David Dalmau, Maria J. Arranz, Beatriz Dietl, Alex Serra-Llovich, Pere Soler, Roger Colobrán, Andrea Martín-Nalda, Alba Parra Martínez, David Bernardo, Silvia Rojo, Aida Fiz-López, Elisa Arribas, Paloma de la Cal-Sabater, Tomás Segura, Esther González-Villa, Gemma Serrano-Heras, Joan Martí-Fàbregas, Elena Jiménez-Xarrié, Alicia de Felipe Mimbrera, Jaime Masjuan, Sebastian García-Madrona, Anna Domínguez-Mayoral, Joan Montaner Villalonga, Paloma Menéndez-Valladares, Daniel I. Chasman, Julie E. Buring, Paul M. Ridker, Giulianini Franco, Howard D. Sesso, JoAnn E. Manson, Joseph R. Glessner, Carolina Medina-Gomez, Andre G. Uitterlinden, M. Arfan Ikram, Kati Kristiansson, Sami Koskelainen, Markus Perola, Kati Donner, Katja Kivinen, Aarno Palotie, Samuli Ripatti, Sanni Ruotsalainen, Mari Kaunisto, null FinnGen, Tomoko Nakanishi, Guillaume Butler-Laporte, Vincenzo Forgetta, David R. Morrison, Biswarup Ghosh, Laetitia Laurent, Alexandre Belisle, Danielle Henry, Tala Abdullah, Olumide Adeleye, Noor Mamlouk, Nofar Kimchi, Zaman Afrasiabi, Nardin Rezk Branka Vulesevic, Meriem Bouab, Charlotte Guzman, Louis Petitjean, Chris Tselios, Xiaoqing Xue, Erwin Schurr, Jonathan Afilalo, Marc Afilalo, Maureen Oliveira, Bluma Brenner, Pierre Lepage, Jiannis Ragoussis, Daniel Auld, Nathalie Brassard, Madeleine Durand, Michaël Chassé, Daniel E. Kaufmann, G. Mark Lathrop, Vincent Mooser, J. Brent Richards, Rui Li, Darin Adra, Souad Rahmouni, Michel Georges, Michel Moutschen, Benoit Misset, Gilles Darcis, Julien Guiot, Julien Guntz, Samira Azarzar, Stéphanie Gofflot, Yves Beguin, Sabine Claassen, Olivier Malaise, Pascale Huynen, Christelle Meuris, Marie Thys, Jessica Jacques, Philippe Léonard, Frederic Frippiat, Jean-Baptiste Giot, Anne-Sophie Sauvage, Christian Von Frenckell, Yasmine Belhaj, Bernard Lambermont, Sara Pigazzini, Lindokuhle Nkambule, Michelle Daya, Jonathan Shortt, Nicholas Rafaels, Stephen J. Wicks, Kristy Crooks, Kathleen C. Barnes, Christopher R. Gignoux, Sameer Chavan, Triin Laisk, Kristi Läll, Maarja Lepamets, Reedik Mägi, Tõnu Esko, Ene Reimann, Lili Milani, Helene Alavere, Kristjan Metsalu, Mairo Puusepp, Andres Metspalu, Paul Naaber, Edward Laane, Jaana Pesukova, Pärt Peterson, Kai Kisand, Jekaterina Tabri, Raili Allos, Kati Hensen, Joel Starkopf, Inge Ringmets, Anu Tamm, Anne Kallaste, Pierre-Yves Bochud, Carlo Rivolta, Stéphanie Bibert, Mathieu Quinodoz, Dhryata Kamdar, Noémie Boillat, Semira Gonseth Nussle, Werner Albrich, Noémie Suh, Dionysios Neofytos, Véronique Erard, Cathy Voide, null FHoGID, null RegCOVID, null P-PredictUs, null SeroCOVID, null CRiPSI, Rafael de Cid, Iván Galván-Femenía, Natalia Blay, Anna Carreras, Beatriz Cortés, Xavier Farré, Lauro Sumoy, Victor Moreno, Josep Maria Mercader, Marta Guindo-Martinez, David Torrents, Manolis Kogevinas, Judith Garcia-Aymerich, Gemma Castaño-Vinyals, Carlota Dobaño, Alessandra Renieri, Francesca Mari, Chiara Fallerini, Sergio Daga, Elisa Benetti, Margherita Baldassarri, Francesca Fava, Elisa Frullanti, Floriana Valentino, Gabriella Doddato, Annarita Giliberti, Rossella Tita, Sara Amitrano, Mirella Bruttini, Susanna Croci, Ilaria Meloni, Maria Antonietta Mencarelli, Caterina Lo Rizzo, Anna Maria Pinto, Giada Beligni, Andrea Tommasi, Laura Di Sarno, Maria Palmieri, Miriam Lucia Carriero, Diana Alaverdian, Stefano Busani, Raffaele Bruno, Marco Vecchia, Mary Ann Belli, Nicola Picchiotti, Maurizio Sanarico, Marco Gori, Simone Furini, Stefania Mantovani, Serena Ludovisi, Mario Umberto Mondelli, Francesco Castelli, Eugenia Quiros-Roldan, Melania Degli Antoni, Isabella Zanella, Massimo Vaghi, Stefano Rusconi, Matteo Siano, Francesca Montagnani, Arianna Emiliozzi, Massimiliano Fabbiani, Barbara Rossetti, Elena Bargagli, Laura Bergantini, Miriana D’Alessandro, Paolo Cameli, David Bennett, Federico Anedda, Simona Marcantonio, Sabino Scolletta, Federico Franchi, Maria Antonietta Mazzei, Susanna Guerrini, Edoardo Conticini, Luca Cantarini, Bruno Frediani, Danilo Tacconi, Chiara Spertilli, Marco Feri, Alice Donati, Raffaele Scala, Luca Guidelli, Genni Spargi, Marta Corridi, Cesira Nencioni, Leonardo Croci, Maria Bandini, Gian Piero Caldarelli, Paolo Piacentini, Elena Desanctis, Silvia Cappelli, Anna Canaccini, Agnese Verzuri, Valentina Anemoli, Agostino Ognibene, Alessandro Pancrazzi, Maria Lorubbio, Antonella D’Arminio Monforte, Federica Gaia Miraglia, Massimo Girardis, Sophie Venturelli, Andrea Cossarizza, Andrea Antinori, Alessandra Vergori, Arianna Gabrieli, Agostino Riva, Daniela Francisci, Elisabetta Schiaroli, Francesco Paciosi, Pier Giorgio Scotton, Francesca Andretta, Sandro Panese, Renzo Scaggiante, Francesca Gatti, Saverio Giuseppe Parisi, Stefano Baratti, Matteo Della Monica, Carmelo Piscopo, Mario Capasso, Roberta Russo, Immacolata Andolfo, Achille Iolascon, Giuseppe Fiorentino, Massimo Carella, Marco Castori, Giuseppe Merla, Gabriella Maria Squeo, Filippo Aucella, Pamela Raggi, Carmen Marciano, Rita Perna, Matteo Bassetti, Antonio Di Biagio, Maurizio Sanguinetti, Luca Masucci, Serafina Valente, Marco Mandalà, Alessia Giorli, Lorenzo Salerni, Patrizia Zucchi, Pierpaolo Parravicini, Elisabetta Menatti, Tullio Trotta, Ferdinando Giannattasio, Gabriella Coiro, Fabio Lena, Domenico A. Coviello, Cristina Mussini, Enrico Martinelli, Sandro Mancarella, Luisa Tavecchia, Lia Crotti, Chiara Gabbi, Marco Rizzi, Franco Maggiolo, Diego Ripamonti, Tiziana Bachetti, Maria Teresa La Rovere, Simona Sarzi-Braga, Maurizio Bussotti, Stefano Ceri, Pietro Pinoli, Francesco Raimondi, Filippo Biscarini, Alessandra Stella, Kristina Zguro, Katia Capitani, Claudia Suardi, Simona Dei, Gianfranco Parati, Sabrina Ravaglia, Rosangela Artuso, Giordano Bottà, Paolo Di Domenico, Ilaria Rancan, Antonio Perrella Francesco Bianchi, Davide Romani, Paola Bergomi, Emanuele Catena, Riccardo Colombo, Marco Tanfoni, Antonella Vincenti, Claudio Ferri, Davide Grassi, Gloria Pessina, Mario Tumbarello, Massimo Di Pietro, Ravaglia Sabrina, Sauro Luchi, Chiara Barbieri, Donatella Acquilini, Elena Andreucci, Francesco Vladimiro Segala, Giusy Tiseo, Marco Falcone, Mirjam Lista, Monica Poscente, Oreste De Vivo, Paola Petrocelli, Alessandra Guarnaccia, Silvia Baroni, Albert V. Smith, Andrew P. Boughton, Kevin W. Li, Jonathon LeFaive, Aubrey Annis, Anne E. Justice, Tooraj Mirshahi, Geetha Chittoor, Navya Shilpa Josyula, Jack A. Kosmicki, Manuel A.R. Ferreira, Joseph B. Leader, Dave J. Carey, Matthew C. Gass, Julie E. Horowitz, Michael N. Cantor, Ashish Yadav, Aris Baras, Goncalo R. Abecasis, David A. van Heel, Karen A. Hunt, Dan Mason, Qin Qin Huang, Sarah Finer, null Genes & Health Research Team, Bhavi Trivedi, Christopher J. Griffiths, Hilary C. Martin, John Wright, Richard C. Trembath, Nicole Soranzo, Jing Hua Zhao, Adam S. Butterworth, John Danesh, Emanuele Di Angelantonio, Lude Franke Marike Boezen, Patrick Deelen, Annique Claringbould, Esteban Lopera, Robert Warmerdam, Judith.M. Vonk, Irene van Blokland, Pauline Lanting, Anil P.S. Ori, Brooke Wolford Sebastian Zöllner, Jiongming Wang, Andrew Beck, Gina Peloso, Yuk-Lam Ho, Yan V. Sun, Jennifer E. Huffman, Christopher J. O’Donnell, Kelly Cho, Phil Tsao, J. Michael Gaziano, Michel (M.G.) Nivard, Eco (E.J.C.) de geus, Meike Bartels, Jouke Jan Hottenga, Scott T. Weiss, Elizabeth W. Karlson, Jordan W. Smoller, Robert C. Green, Yen-Chen Anne Feng, Josep Mercader, Shawn N. Murphy, James B. Meigs, Ann E. Woolley, Emma F. Perez, Daniel Rader, Anurag Verma, Marylyn D. Ritchie, Binglan Li, Shefali S. Verma, Anastasia Lucas, Yuki Bradford, Hugo Zeberg, Robert Frithiof, Michael Hultström, Miklos Lipcsey, Lindo Nkambul, Nicolas Tardif, Olav Rooyackers, Jonathan Grip, Tomislav Maricic, Konrad J. Karczewski, Elizabeth G. Atkinson, Kristin Tsuo, Nikolas Baya, Patrick Turley, Rahul Gupta, Shawneequa Callier, Raymond K. Walters, Duncan S. Palmer, Gopal Sarma, Nathan Cheng, Wenhan Lu, Sam Bryant, Claire Churchhouse, Caroline Cusick, Jacqueline I. Goldstein, Daniel King, Cotton Seed, Hilary Finucane, Alicia R. Martin, F. Kyle Satterstrom, Daniel J. Wilson, Jacob Armstrong, Justine K. Rudkin, Gavin Band, Sarah G. Earle, Shang-Kuan Lin, Nicolas Arning, Derrick W. Crook, David H. Wyllie, Anne Marie O’Connell, Chris C.A. Spencer, Nils Koelling, Mark J. Caulfield, Richard H. Scott, Tom Fowler, Loukas Moutsianas, Athanasios Kousathanas, Dorota Pasko, Susan Walker, Augusto Rendon, Alex Stuckey, Christopher A. Odhams, Daniel Rhodes, Georgia Chan, Prabhu Arumugam, Catherine A. Ball, Eurie L. Hong, Kristin Rand, Ahna Girshick, Harendra Guturu, Asher Haug Baltzell, Genevieve Roberts, Danny Park, Marie Coignet, Shannon McCurdy, Spencer Knight, Raghavendran Partha, Brooke Rhead, Miao Zhang, Nathan Berkowitz, Michael Gaddis, Keith Noto, Luong Ruiz, Milos Pavlovic, Laura G. Sloofman, Alexander W. Charney, Noam D. Beckmann, Eric E. Schadt, Daniel M. Jordan, Ryan C. Thompson, Kyle Gettler, Noura S. Abul-Husn, Steven Ascolillo, Joseph D. Buxbaum, Kumardeep Chaudhary, Judy H. Cho, Yuval Itan, Eimear E. Kenny, Gillian M. Belbin, Stuart C. Sealfon, Robert P. Sebra, Irene Salib, Brett L. Collins, Tess Levy, Bari Britvan, Katherine Keller, Lara Tang, Michael Peruggia, Liam L. Hiester, Kristi Niblo, Alexandra Aksentijevich, Alexander Labkowsky, Avromie Karp, Menachem Zlatopolsky, Michael Preuss, Ruth J.F. Loos, Girish N. Nadkarni, Ron Do, Clive Hoggart, Sam Choi, Slayton J. Underwood, Paul O’Reilly, Laura M. Huckins, Marissa Zyndorf, AII - Infectious diseases, Amsterdam Neuroscience - Neuroinfection & -inflammation, and Neurology

Subjects
Medical Physiology, Gene Expression, Genome-wide association study, Genome, Severity of Illness Index, colocalization, Gene expression, Databases, Genetic, Ethnicity, 2.1 Biological and endogenous factors, GWAS, Aetiology, Biology (General), Lung, Genetics, Chromosome Mapping, Single Nucleotide, Organ Specificity, Biotechnology, Cell type, QH301-705.5, Quantitative Trait Loci, Single-nucleotide polymorphism, Biology, eQTL, Polymorphism, Single Nucleotide, Article, General Biochemistry, Genetics and Molecular Biology, Databases, Genetic, SNP, Humans, Genetic Predisposition to Disease, COVID-19 Host Genetics Initiative, Polymorphism, Gene, COVID-19, SARS-CoV-2, Gene Expression Profiling, Prevention, Human Genome, Computational Biology, Genetic Variation, Good Health and Well Being, Expression quantitative trait loci, Biochemistry and Cell Biology, Transcriptome, Genome-Wide Association Study
Abstract

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

Published
2022

31. The trans-ancestral genomic architecture of glycemic traits

Author

Albertine J. Oldehinkel, Wieland Kiess, Xueling Sim, Norihiro Kato, Philippe Froguel, Astrid van Hylckama Vlieg, Josée Dupuis, Nanette R. Lee, Symen Ligthart, Harry Campbell, Marta E. Alarcón-Riquelme, P. Eline Slagboom, Massimo Mangino, Tian Xie, Niek Verweij, James B. Meigs, Chaolong Wang, Michael Y. Tsai, Erik Ingelsson, Colin N. A. Palmer, Erik B. van den Akker, Fumihiko Matsuda, Rainer Rauramaa, Yi-Cheng Chang, Lars Lind, Stefan R. Bornstein, Mandy Vogel, Sven Bergmann, Ya X. Wang, Ching-Ti Liu, Annette Schürmann, Michael Boehnke, David J. Porteous, Kazuya Setoh, Qibin Qi, Ayse Demirkan, Francesco Cucca, Allan Linneberg, Claire J. Steves, Jun Liu, Leslie A. Lange, Noël P. Burtt, Diana Kuh, Cassandra N. Spracklen, Ken K. Ong, Charumathi Sabanayagam, Jost B. Jonas, Ele Ferrannini, Lawrence J. Beilin, Qing Duan, Blair H. Smith, Isobel D. Stewart, Alexander P. Reiner, Simon P. Mooijaart, Tim D. Spector, Paul W. Franks, E. Shyong Tai, Mark I. McCarthy, Anna L. Gloyn, D.I. Boomsma, Dennis Raven, Nicholas J. Timpson, Rona J. Strawbridge, George Dedoussis, Susan Redline, Jaeyoung Hong, Harald Grallert, Jagadish Vangipurapu, Rico Rueedi, Diane M. Becker, Marian Beekman, Claudia P. Cabrera, Johannes Waage, Jin Fang Chai, Yii-Der Ida Chen, Graciela E. Delgado, Thibaud S. Boutin, Yang Hai, Yoriko Heianza, Wei Zhao, Andres Metspalu, Tien Yin Wong, Mila Desi Anasanti, Inger Njølstad, Hans Bisgaard, Valeriya Lyssenko, Denis Rybin, Wanqing Wen, Torben Hansen, James F. Wilson, Sameline Grimsgaard, Annette Peters, Michele K. Evans, Damia Noce, Sarah C. Nelson, May E. Montasser, Nan Wang, Geltrude Mingrone, Gudny Eiriksdottir, Nicholas J. Wareham, Fouad Kandeel, Linda S. Adair, Kelvin Lam, Jaana Lindström, Eco J. C. de Geus, Debbie A Lawlor, Sara M. Willems, Xu Lin, Harold Snieder, Matt J. Neville, Naveed Sattar, Chelsea K. Raulerson, Paul M. Ridker, Jer-Yuarn Wu, Weihua Zhang, H. Janaka de Silva, Jana V. van Vliet-Ostaptchouk, Elena Tremoli, Toru Nabika, Jing Hua Zhao, Vilmundur Gudnason, Tao Huang, Robert C. Kaplan, Sohee Han, Mohammad Hadi Zafarmand, Aaron Leong, Yen-Feng Chiu, Kumaraswamy Naidu Chitrala, Ivana Kolcic, Franco Giulianini, Tao Wang, Lu Qi, Stephan J. L. Bakker, Laura J Corbin, Zoltán Kutalik, Bruna Gigante, Willa A. Hsueh, Peter J. van der Most, Tin Louie, Yujie Wang, Stella Trompet, Fernando Rivideneira, Yasumasa Ohyagi, Lynne E. Wagenknecht, Jerry L. Nadler, Michael Stumvoll, Mark O. Goodarzi, Sahoko Ichihara, Jeffrey R. O'Connell, Tomohiro Katsuya, Giorgio Pistis, Alice Stanton, Sirkka Keinänen-Kiukaanniemi, Momoko Horikoshi, Honglan Li, Tanja G. M. Vrijkotte, Caroline Hayward, Karen L. Mohlke, Carola Marzi, Girish N. Nadkarni, Laura J. Rasmussen-Torvik, Alain G. Bertoni, Andrew R. Wood, Annique Claringbould, Mi Yeong Hwang, Hugh Watkins, Heikki A. Koistinen, Mattias Frånberg, Jani Heikkinen, Elizabeth Selvin, Donald W. Bowden, Abbas Dehghan, Christian Fuchsberger, Audrey Y. Chu, Kent D. Taylor, Katherine A. Kentistou, Johanna Kuusisto, Jingyi Tan, Huaixing Li, Eric Boerwinkle, Catharina A. Hartman, Archie Campbell, Kari E. North, Oluf Pedersen, Sölve Elmståhl, Emil V. R. Appel, Chang-Hsun Hsieh, Dennis O. Mook-Kanamori, Rob M. van Dam, Pontiano Kaleebu, Corri Black, Jennifer A. Brody, Bengt Sennblad, Shaofeng Huo, M. Larissa Avilés-Santa, Ruth J. F. Loos, Patricia B. Munroe, Chien-Hsiun Chen, Liang Sun, Zorayr Arzumanyan, Rebecca Rohde, Yasuharu Tabara, Albert V. Smith, Betina H. Thuesen, Niels Grarup, Jorgen Engmann, Tatijana Zemunik, M. Arfan Ikram, Marit E. Jørgensen, Christian Herder, Ching-Yu Cheng, Serena Sanna, Damiano Baldassarre, Tarunveer S. Ahluwalia, Mark J. Caulfield, Anne Ndungu, Carl D. Langefeld, Lisa R. Yanek, Luigi Ferrucci, Ananda R. Wickremasinghe, Raymond Noordam, Trevor A. Mori, Tom Wilsgaard, Mika Kivimäki, Rita R. Kalyani, Alan B. Zonderman, Veronique Vitart, Patricia A. Peyser, Shuiqing Lai, Richa Saxena, Li-Ching Chang, Karin Leander, Wei Huang, Peter Vollenweider, Tanya M. Teslovich, Ying Wu, Shufa Du, Brian E. Cade, Patrik K. E. Magnusson, John C. Chambers, Stephen C. J. Parker, Tamar Sofer, Winfried März, Sharon L.R. Kardia, Peter K. Joshi, Neil R. Robertson, Anny H. Xiang, Fumihiko Takeuchi, N. Amin, Jouke-Jan Hottenga, Carol A. Wang, Stefan Gustafsson, Jung Ho Gong, Penny Gordon-Larsen, Yu-Tang Gao, Abhishek Nag, Gonneke Willemsen, Michael A. Province, Aliki-Eleni Farmaki, Segun Fatumo, Antje Körner, Pim van der Harst, Marie Loh, Kei Hang Katie Chan, Gonçalo R. Abecasis, Nicholette D. Palmer, Simin Liu, Ishminder K. Kooner, Javier Gayán, Arne Astrup, Laura J. Scott, Erwin P. Bottinger, Andrew Wong, Inga Prokopenko, Ping An, Markku Laakso, Matthias Blüher, Susan R. Heckbert, Thomas A. Buchanan, Tatsuaki Matsubara, Andrew P. Morris, Brian H. Chen, Kristi Läll, Teresa Tusie, Timo A. Lakka, Jie Yao, Michael Preuss, Teemu Kuulasmaa, Carlos Lorenzo, Stephen S. Rich, Marie Lauzon, Laura M. Raffield, Pankow S. James, Takahisa Kawaguchi, Kathleen A. Ryan, Wei Zheng, Igor Rudan, Thomas Sparsø, Hugoline G. de Haan, Sandosh Padmanabhan, Richard M. Watanabe, Alicia Huerta-Chagoya, Anette P. Gjesing, Andrew A. Hicks, Richard N. Bergman, Mitsuhiro Yokota, Heather M. Stringham, Bruce M. Psaty, Jian'an Luan, Anuj Goel, Eleanor Wheeler, Masahiro Nakatochi, Young-Jin Kim, Xiao-Ou Shu, Mickaël Canouil, Robert A. Scott, Marika Kaakinen, Mari Nelis, Adolfo Correa, Jaspal S. Kooner, Michiya Igase, Anubha Mahajan, Peter E. H. Schwarz, Craig E. Pennell, Claudia Schurmann, Xiaoran Chai, Ji Chen, Lori L. Bonnycastle, Peter S. Sever, Thorkild I. A. Sørensen, André G. Uitterlinden, Ilja M. Nolte, Gaëlle Marenne, Timothy M. Frayling, Bong-Jo Kim, Kerrin S. Small, Cecilia M. Lindgren, Bernhard O. Böhm, Shih-Yi Lin, Katharina E. Schraut, Cornelia M. van Duijn, Sanghoon Moon, Mark Walker, Chiea Chuen Khor, Ruifang Li-Gao, Qiuyin Cai, Neil Schneiderman, Ko Willems van Dijk, Ozren Polasek, W. Craig Johnson, Dermot F. Reilly, Inês Barroso, Anke Tönjes, Manjinder S. Sandhu, Wen B. Wei, Jose C. Florez, Lorraine Southam, Leif Groop, Lawrence F. Bielak, Peter Kovacs, Jianjun Liu, Jouko Saramies, Helen R. Warren, Man Li, Daniel I. Chasman, Eleftheria Zeggini, Xiaoshuai Zhang, Loic Yengo, Shi Jinxiu, Jirong Long, Xiuqing Guo, Meena Kumari, Leslie J. Raffel, Jill M. Norris, Henrik Vestergaard, Jing He, Peter P. Pramstaller, Diana van Heemst, Kevin Sandow, Marjo-Ritta Jarvelin, Carlos A. Aguilar-Salinas, Peitao Wu, Hortensia Moreno-Macías, Jerome I. Rotter, Kathryn Roll, Frits R. Rosendaal, Bernardo L. Horta, Heming Wang, Fernando Pires Hartwig, Richard A. Jensen, Matti Uusitupa, Rozenn N. Lemaitre, Paul R. H. J. Timmers, Timo Saaristo, Jaakko Tuomilehto, Reedik Mägi, Debashree Ray, J. Wouter Jukema, Claudia Langenberg, Marcus E. Kleber, Francis S. Collins, Klaus Bønnelykke, Lenore J. Launer, Arushi Varshney, Anders Hamsten, European Commission, Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO)-Université de Brest (UBO)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM), Sanger Institute, Wellcome Trust, Marenne, Gaëlle [0000-0002-4363-7170], Varshney, Arushi [0000-0001-9177-9707], Corbin, Laura J [0000-0002-4032-9500], Parker, Stephen CJ [0000-0001-8122-0117], Langenberg, Claudia [0000-0002-5017-7344], Wheeler, Eleanor [0000-0002-8616-6444], Morris, Andrew P [0000-0002-6805-6014], Barroso, Inês [0000-0001-5800-4520], Apollo - University of Cambridge Repository, Lifelines Cohort Study, Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), de Haan, H.G., van den Akker, E., van der Most, P.J., de Geus, EJC, van Dam, R.M., van Heemst, D., van Hylckama Vlieg, A., van Willems van Dijk, K., de Silva, H.J., van der Harst, P., van Duijn, C., Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Leif Groop Research Group, HUS Internal Medicine and Rehabilitation, Department of Medicine, Department of Biochemistry and Developmental Biology, Helsinki University Hospital Area, University of Helsinki, Clinicum, Department of Public Health, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Physiology, AMS - Musculoskeletal Health, AMS - Tissue Function & Regeneration, APH - Mental Health, Nutrition and Health, APH - Methodology, Epidemiology and Data Science, ACS - Atherosclerosis & ischemic syndromes, APH - Aging & Later Life, ARD - Amsterdam Reproduction and Development, Public and occupational health, Epidemiology, Internal Medicine, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Center for Liver, Digestive and Metabolic Diseases (CLDM), and Cardiovascular Centre (CVC)

Subjects
Blood Glucose, Disease risk, Multifactorial Inheritance, Glycated Hemoglobin A, [SDV]Life Sciences [q-bio], Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), LOCI, Genome-wide association study, Type 2 diabetes, VARIANTS, GLUCOSE, Epigenesis, Genetic, chemistry.chemical_compound, 0302 clinical medicine, Mechanisms, WIDE ASSOCIATION, genetics, Gene-expression, HEMOGLOBIN, ComputingMilieux_MISCELLANEOUS, 11 Medical and Health Sciences, GENE-EXPRESSION, Genetics, Genetics & Heredity, 0303 health sciences, INSULIN-RESISTANCE, Genome, Loci, 1184 Genetics, developmental biology, physiology, Variants, ALSPAC, Physical Chromosome Mapping, Life Sciences & Biomedicine, Human, Quantitative Trait Loci, Wide association study, Biology, Quantitative trait locus, Article, White People, diseases, MECHANISMS, Quantitative Trait, 03 medical and health sciences, Insulin resistance, Quantitative Trait, Heritable, SDG 3 - Good Health and Well-being, Genetic, Lifelines Cohort Study, Diabetes mellitus, medicine, Humans, Hemoglobin, Heritable, METAANALYSIS, Alleles, 030304 developmental biology, Genetic association, Glycemic, Glycated Hemoglobin, Science & Technology, Genome, Human, Whites, Gene Expression Profiling, DISEASE RISK, Settore MED/13 - ENDOCRINOLOGIA, Insulin-resistance, 06 Biological Sciences, medicine.disease, Glucose, chemistry, Blood Glucose/genetics, European Continental Ancestry Group/genetics, Genome-Wide Association Study, Glycated Hemoglobin A/metabolism, Multifactorial Inheritance/genetics, Quantitative Trait Loci/genetics, Glycated hemoglobin, 030217 neurology & neurosurgery, Meta analysis, Epigenesis, Developmental Biology
Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

Published
2021

32. Population Bias in Polygenic Risk Prediction Models for Coronary Artery Disease

Author

Jeanette Erdmann, Kristi Läll, Damian Gola, Bertram Müller-Myhsok, Inke R. König, Heribert Schunkert, and Reedik Mägi

Subjects
Multifactorial Inheritance, Population, Coronary Artery Disease, Biology, Individual risk, Population stratification, Genome, Coronary artery disease, 03 medical and health sciences, 0302 clinical medicine, Bias, Risk Factors, medicine, Prevalence, Humans, Genetic Predisposition to Disease, 030212 general & internal medicine, education, 030304 developmental biology, Biological Specimen Banks, Genetics, 0303 health sciences, education.field_of_study, Models, Genetic, Genetic variants, Reproducibility of Results, General Medicine, medicine.disease, Genetics, Population, Area Under Curve, Polygenic risk score, Predictive modelling, Genome-Wide Association Study
Abstract

Background: Individual risk prediction based on genome-wide polygenic risk scores (PRSs) using millions of genetic variants has attracted much attention. It is under debate whether PRS models can be applied—without loss of precision—to populations of similar ethnic but different geographic background than the one the scores were trained on. Here, we examine how PRS trained in population-specific but European data sets perform in other European subpopulations in distinguishing between coronary artery disease patients and healthy individuals. Methods: We use data from UK and Estonian biobanks (UKB, EB) as well as case-control data from the German population (DE) to develop and evaluate PRS in the same and different populations. Results: PRSs have the highest performance in their corresponding population testing data sets, whereas their performance significantly drops if applied to testing data sets from different European populations. Models trained on DE data revealed area under the curves in independent testing sets in DE: 0.6752, EB: 0.6156, and UKB: 0.5989; trained on EB and tested on EB: 0.6565, DE: 0.5407, and UKB: 0.6043; trained on UKB and tested on UKB: 0.6133, DE: 0.5143, and EB: 0.6049. Conclusions: This result has a direct impact on the clinical usability of PRS for risk prediction models using PRS: a population effect must be kept in mind when applying risk estimation models, which are based on additional genetic information even for individuals from different European populations of the same ethnicity.

Published
2020

33. Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Author

Anubha Mahajan, Cassandra N Spracklen, Weihua Zhang, Maggie CY Ng, Lauren E Petty, Hidetoshi Kitajima, Grace Z Yu, Sina Rüeger, Leo Speidel, Young Jin Kim, Momoko Horikoshi, Josep M Mercader, Daniel Taliun, Sanghoon Moon, Soo-Heon Kwak, Neil R Robertson, Nigel W Rayner, Marie Loh, Bong-Jo Kim, Joshua Chiou, Irene Miguel-Escalada, Pietro della Briotta Parolo, Kuang Lin, Fiona Bragg, Michael H Preuss, Fumihiko Takeuchi, Jana Nano, Xiuqing Guo, Amel Lamri, Masahiro Nakatochi, Robert A Scott, Jung-Jin Lee, Alicia Huerta-Chagoya, Mariaelisa Graff, Jin-Fang Chai, Esteban J Parra, Jie Yao, Lawrence F Bielak, Yasuharu Tabara, Yang Hai, Valgerdur Steinthorsdottir, James P Cook, Mart Kals, Niels Grarup, Ellen M Schmidt, Ian Pan, Tamar Sofer, Matthias Wuttke, Chloe Sarnowski, Christian Gieger, Darryl Nousome, Stella Trompet, Jirong Long, Meng Sun, Lin Tong, Wei-Min Chen, Meraj Ahmad, Raymond Noordam, Victor JY Lim, Claudia HT Tam, Yoonjung Yoonie Joo, Chien-Hsiun Chen, Laura M Raffield, Cécile Lecoeur, Nisa M Maruthur, Bram Peter Prins, Aude Nicolas, Lisa R Yanek, Guanjie Chen, Richard A Jensen, Salman Tajuddin, Edmond Kabagambe, Ping An, Anny H Xiang, Hyeok Sun Choi, Brian E Cade, Jingyi Tan, Fernando Abaitua, Linda S Adair, Adebowale Adeyemo, Carlos A Aguilar-Salinas, Masato Akiyama, Sonia S Anand, Alain Bertoni, Zheng Bian, Jette Bork-Jensen, Ivan Brandslund, Jennifer A Brody, Chad M Brummett, Thomas A Buchanan, Mickaël Canouil, Juliana CN Chan, Li-Ching Chang, Miao-Li Chee, Ji Chen, Shyh-Huei Chen, Yuan-Tsong Chen, Zhengming Chen, Lee-Ming Chuang, Mary Cushman, Swapan K Das, H. Janaka de Silva, George Dedoussis, Latchezar Dimitrov, Ayo P Doumatey, Shufa Du, Qing Duan, Kai-Uwe Eckardt, Leslie S Emery, Daniel S Evans, Michele K Evans, Krista Fischer, James S Floyd, Ian Ford, Myriam Fornage, Oscar H Franco, Timothy M Frayling, Barry I Freedman, Christian Fuchsberger, Pauline Genter, Hertzel C Gerstein, Vilmantas Giedraitis, Clicerio González-Villalpando, Maria Elena González-Villalpando, Mark O Goodarzi, Penny Gordon-Larsen, David Gorkin, Myron Gross, Yu Guo, Sophie Hackinger, Sohee Han, Andrew T Hattersley, Christian Herder, Annie-Green Howard, Willa Hsueh, Mengna Huang, Wei Huang, Yi-Jen Hung, Mi Yeong Hwang, Chii-Min Hwu, Sahoko Ichihara, Mohammad Arfan Ikram, Martin Ingelsson, Md. Tariqul Islam, Masato Isono, Hye-Mi Jang, Farzana Jasmine, Guozhi Jiang, Jost B Jonas, Marit E Jørgensen, Torben Jørgensen, Yoichiro Kamatani, Fouad R Kandeel, Anuradhani Kasturiratne, Tomohiro Katsuya, Varinderpal Kaur, Takahisa Kawaguchi, Jacob M Keaton, Abel N Kho, Chiea-Chuen Khor, Muhammad G Kibriya, Duk-Hwan Kim, Katsuhiko Kohara, Jennifer Kriebel, Florian Kronenberg, Johanna Kuusisto, Kristi Läll, Leslie A Lange, Myung-Shik Lee, Nanette R Lee, Aaron Leong, Liming Li, Yun Li, Ruifang Li-Gao, Symen Ligthart, Cecilia M Lindgren, Allan Linneberg, Ching-Ti Liu, Jianjun Liu, Adam E Locke, Tin Louie, Jian’an Luan, Andrea O Luk, Xi Luo, Jun Lv, Valeriya Lyssenko, Vasiliki Mamakou, K Radha Mani, Thomas Meitinger, Andres Metspalu, Andrew D Morris, Girish N. Nadkarni, Jerry L Nadler, Michael A Nalls, Uma Nayak, Ioanna Ntalla, Yukinori Okada, Lorena Orozco, Sanjay R Patel, Mark A Pereira, Annette Peters, Fraser J Pirie, Bianca Porneala, Gauri Prasad, Sebastian Preissl, Laura J Rasmussen-Torvik, Alexander P Reiner, Michael Roden, Rebecca Rohde, Katheryn Roll, Charumathi Sabanayagam, Maike Sander, Kevin Sandow, Naveed Sattar, Sebastian Schönherr, Claudia Schurmann, Mohammad Shahriar, Jinxiu Shi, Dong Mun Shin, Daniel Shriner, Jennifer A Smith, Wing Yee So, Alena Stančáková, Adrienne M Stilp, Konstantin Strauch, Ken Suzuki, Atsushi Takahashi, Kent D Taylor, Barbara Thorand, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Brian Tomlinson, Jason M Torres, Fuu-Jen Tsai, Jaakko Tuomilehto, Teresa Tusie-Luna, Miriam S Udler, Adan Valladares-Salgado, Rob M van Dam, Jan B van Klinken, Rohit Varma, Marijana Vujkovic, Niels Wacher-Rodarte, Ellie Wheeler, Eric A Whitsel, Ananda R Wickremasinghe, Konstantin Willems van Dijk, Daniel R Witte, Chittaranjan S Yajnik, Ken Yamamoto, Toshimasa Yamauchi, Loïc Yengo, Kyungheon Yoon, Canqing Yu, Jian-Min Yuan, Salim Yusuf, Liang Zhang, Wei Zheng, null FinnGen, Leslie J Raffel, Michiya Igase, Eli Ipp, Susan Redline, Yoon Shin Cho, Lars Lind, Michael A Province, Craig L Hanis, Patricia A Peyser, Erik Ingelsson, Alan B Zonderman, Bruce M Psaty, Ya-Xing Wang, Charles N Rotimi, Diane M Becker, Fumihiko Matsuda, Yongmei Liu, Eleftheria Zeggini, Mitsuhiro Yokota, Stephen S Rich, Charles Kooperberg, James S Pankow, James C Engert, Yii-Der Ida Chen, Philippe Froguel, James G Wilson, Wayne HH Sheu, Sharon LR Kardia, Jer-Yuarn Wu, M Geoffrey Hayes, Ronald CW Ma, Tien-Yin Wong, Leif Groop, Dennis O Mook-Kanamori, Giriraj R Chandak, Francis S Collins, Dwaipayan Bharadwaj, Guillaume Paré, Michèle M Sale, Habibul Ahsan, Ayesha A Motala, Xiao-Ou Shu, Kyong-Soo Park, J Wouter Jukema, Miguel Cruz, Roberta McKean-Cowdin, Harald Grallert, Ching-Yu Cheng, Erwin P Bottinger, Abbas Dehghan, E-Shyong Tai, Josee Dupuis, Norihiro Kato, Markku Laakso, Anna Köttgen, Woon-Puay Koh, Colin NA Palmer, Simin Liu, Goncalo Abecasis, Jaspal S Kooner, Ruth JF Loos, Kari E North, Christopher A Haiman, Jose C Florez, Danish Saleheen, Torben Hansen, Oluf Pedersen, Reedik Mägi, Claudia Langenberg, Nicholas J Wareham, Shiro Maeda, Takashi Kadowaki, Juyoung Lee, Iona Y Millwood, Robin G Walters, Kari Stefansson, Simon R Myers, Jorge Ferrer, Kyle J Gaulton, James B Meigs, Karen L Mohlke, Anna L Gloyn, Donald W Bowden, Jennifer E Below, John C Chambers, Xueling Sim, Michael Boehnke, Jerome I Rotter, Mark I McCarthy, and Andrew P Morris

Subjects
0303 health sciences, Transferability, Translation (biology), Type 2 diabetes, Biology, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, Evolutionary biology, Global health, medicine, Genetic risk, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p-8), including 237 attaining a more stringent trans-ancestry threshold (p-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

Published
2020

34. The Trans-Ancestral Genomic Architecture of Glycaemic Traits

Author

Hugoline G. de Haan, Andrew A. Hicks, Achilleas Pitsilides, Fernando Pires Hartwig, Richard A. Jensen, Matti Uusitupa, Anders Hamsten, Dennis O. Mook-Kanamori, Zorayr Arzumanyan, Betina H. Thuesen, Karin Leander, Fernando Rivideneira, Lynne E. Wagenknecht, Andrew R. Wood, Annique Claringbould, Ele Ferranni, Sölve Elmståhl, Eleanor Wheeler, Sharon L.R. Kardia, Richa Saxena, Tatijana Zemunik, Cassandra N. Spracklen, Ken K. Ong, Xiao-Ou Shu, Johannes Waage, Blair H. Smith, Rozenn N. Lemaitre, Torben Hansen, Peter K. Joshi, Lisa R. Yanek, Neil R. Robertson, Sven Bergmann, Mila Desi Anasanti, Inger Njølstad, Ananda R. Wickremasinghe, Xu Lin, Harold Snieder, Wanqing Wen, Veronique Vitart, Paul R. H. J. Timmers, Timo Saaristo, James F. Wilson, Tian Xie, Tao Huang, Rainer Rauramaa, Kei Hang, Rebecca Rohde, Li-Ching Chang, Jing Hua Zhao, Kazuya Setoh, Yasuharu Tabara, Michael Stumvoll, Mark O. Goodarzi, Igor Rudan, James B. Meigs, Jaakko Tuomilehto, Richard M. Watanabe, Ruth J. F. Loos, Reedik Mägi, Jouke-Jan Hottenga, Ozren Polasek, Michael Y. Tsai, Donald W. Bowden, Diana Kuh, Erik B. van den Akker, Yii-Der Ida Chen, Daniel I. Chasman, Weihua Zhang, Nicholette D. Palmer, Marcus E. Kleber, Anny H. Xiang, Chang-Hsun Hsieh, Alan B. Zonderman, Stefan Gustafsson, Timo A. Lakka, Brian H. Chen, Dermot F. Reilly, Francis S. Collins, Oluf Pedersen, Corri Black, Yang Hai, Zoltán Kutalik, Yoriko Heianza, Willa A. Hsueh, Vilmundur Gudnason, Robert C. Kaplan, Jun Liu, Michael A. Province, Aliki-Eleni Farmaki, Stephen S. Rich, Jian'an Luan, Erik Ingelsson, Marie Loh, Michael Preuss, Lars Lind, Stefan R. Bornstein, Mandy Vogel, Colin N. A. Palmer, Fumihiko Matsuda, Takahisa Kawaguchi, Sohee Han, Ching-Ti Liu, Young-Jin Kim, L. Southam, Sara M. Willems, Mickaël Canouil, Robert A. Scott, Marika Kaakinen, Stephan J. L. Bakker, Momoko Horikoshi, Tarunveer S. Ahluwalia, Annette Schürmann, Graciela E. Delgado, Thibaud S. Boutin, Thomas Sparsø, Sandosh Padmanabhan, Fouad Kandeel, Eco J. C. de Geus, Anubha Mahajan, Claudia Schurmann, Klaus Bønnelykke, Leslie A. Lange, Qing Duan, Rona J. Strawbridge, Dennis Raven, Gonçalo R. Abecasis, Mitsuhiro Yokota, Jani Heikkinen, Elizabeth Selvin, Audrey Y. Chu, Anke Tönjes, Marta E. Alarcón-Riquelme, Hans Bisgaard, P. Eline Slagboom, Eric Boerwinkle, Massimo Mangino, Catharina A. Hartman, Geltrude Mingrone, Lenore J. Launer, Michael Boehnke, Emil V. R. Appel, Niels Grarup, Arushi Varshney, Archie Campbell, Kari E. North, W. Craig Johnson, Inês Barroso, Ya X. Wang, Carola Marzi, Anuj Goel, Eleftheria Zeggini, Lu Qi, Yasumasa Ohyagi, Tien Yin Wong, Tanja G. M. Vrijkotte, Gudny Eiriksdottir, Harald Grallert, Ishminder K. Kooner, Trevor A. Mori, Jagadish Vangipurapu, Laura J Corbin, Tomohiro Katsuya, Wen B. Wei, Segun Fatumo, Debashree Ray, Annette Peters, Lori L. Bonnycastle, Ilja M. Nolte, M. Arfan Ikram, Manjinder S. Sandhu, Marit E. Jørgensen, Christian Herder, Damia Noce, Sarah C. Nelson, Chien-Hsiun Chen, Heather M. Stringham, Yong-Bing Xiang, Bruce M. Psaty, Alain G. Bertoni, Gaëlle Marenne, Timothy M. Frayling, Jose C. Florez, Penny Gordon-Larsen, Yu-Tang Gao, Abhishek Nag, Damiano Baldassarre, J. Wouter Jukema, Wei Huang, Yi-Cheng Chang, Albertine J. Oldehinkel, Xiaoshuai Zhang, Yujie Wang, Shaofeng Huo, Xueling Sim, Norihiro Kato, Bernhard O. Böhm, Lorraine Southam, Mari Nelis, Gonneke Willemsen, Laura J. Rasmussen-Torvik, Philippe Froguel, Charumathi Sabanayagam, Leif Groop, Loic Yengo, Shi Jinxiu, Adolfo Correa, Serena Sanna, Arne Astrup, Teemu Kuulasmaa, Symen Ligthart, Shih-Yi Lin, David J. Porteous, Harry Campbell, Peter Vollenweider, Mark J. Caulfield, Kristi Läll, Anne Ndungu, Carl D. Langefeld, Tanya M. Teslovich, Heikki A. Koistinen, Ying Wu, Mattias Frånberg, D.I. Boomsma, Lawrence F. Bielak, Diana van Heemst, Peter Kovacs, Markku Laakso, Leslie J. Raffel, Katharina E. Schraut, Noël P. Burtt, Michiya Igase, Craig E. Pennell, Claudia Langenberg, Huaixing Li, Teresa Tusie, Laura M. Raffield, Jorgen Engmann, Stephen C. J. Parker, Michele K. Evans, Chaolong Wang, Rico Rueedi, Jianjun Liu, Pankow S. James, Hortensia Moreno-Macías, Fumihiko Takeuchi, Cornelia M. van Duijn, Sanghoon Moon, Susan R. Heckbert, Thomas A. Buchanan, Ko Willems van Dijk, Toru Nabika, May E. Montasser, Caroline Hayward, Jie Yao, Aaron Leong, Antje Körner, Jouko Saramies, Jost B. Jonas, Pim van der Harst, Naveed Sattar, Helen R. Warren, Alice Stanton, Yen-Feng Chiu, Kumaraswamy Naidu Chitrala, Mi Yeong Hwang, Jin Fang Chai, Alicia Huerta-Chagoya, Anette P. Gjesing, Ching-Yu Cheng, Debbie A Lawlor, Simin Liu, Man Li, Ivana Kolcic, Erwin P. Bottinger, Andrew Wong, Stella Trompet, Heming Wang, Jirong Long, Xiuqing Guo, Jeffrey R. O'Connell, Meena Kumari, Sirkka Keinänen-Kiukaanniemi, Rita R. Kalyani, Bengt Sennblad, Mohammad Hadi Zafarmand, Kent D. Taylor, Katherine A. Kentistou, Carol A. Wang, Shuiqing Lai, Patricia B. Munroe, Patricia A. Peyser, Lawrence J. Beilin, Niek Verweij, Inga Prokopenko, Brian E. Cade, Patrik K. E. Magnusson, John C. Chambers, Tamar Sofer, Ping An, Matthias Blüher, Isobel D. Stewart, Alexander P. Reiner, Anna L. Gloyn, Simon P. Mooijaart, Tim D. Spector, Paul W. Franks, Wei Zhao, Andres Metspalu, Wieland Kiess, Kathleen A. Ryan, Astrid van Hylckama Vlieg, Jaana Lindström, Wei Zheng, E. Shyong Tai, Josée Dupuis, Nanette R. Lee, Laura J. Scott, Nicholas J. Timpson, George Dedoussis, Mark I. McCarthy, Tatsuaki Matsubara, Carlos Lorenzo, Denis Rybin, Luigi Ferruci, Chelsea K. Raulerson, Mika Kivimäki, Paul M. Ridker, Jer-Yuarn Wu, Shufa Du, Jaeyoung Hong, Linda S. Adair, Tin Louie, Valeriya Lyssenko, Susan Redline, Kelvin Lam, Qibin Qi, H. Janaka de Silva, Jana V. van Vliet-Ostaptchouk, Peter J. van der Most, Sahoko Ichihara, Nicholas J. Wareham, Ayse Demirkan, Francesco Cucca, Allan Linneberg, Rob M. van Dam, Claire J. Steves, Liang Sun, Albert V. Smith, Raymond Noordam, Tom Wilsgaard, Winfried März, Jung Ho Gong, Matt J. Neville, Jerry L. Nadler, Giorgio Pistis, Karen L. Mohlke, Bruna Gigante, Jennifer A. Brody, Andrew P. Morris, Marie Lauzon, Peter E. H. Schwarz, Bernardo L. Horta, Xiaoran Chai, Ji Chen, Peter S. Sever, Thorkild I. A. Sørensen, André G. Uitterlinden, Javier Gayán, Elena Tremoli, Girish N. Nadkarni, Najaf Amin, Hugh Watkins, Johanna Kuusisto, Jingyi Tan, Sameline Grimsgaard, Bong-Jo Kim, Kerrin S. Small, Jill M. Norris, Cecilia M. Lindgren, Richard N. Bergman, Mark Walker, Henrik Vestergaard, Larissa Aviles-Santa, Jing He, Masahiro Nakatochi, Peter P. Pramstaller, Chiea Chuen Khor, Ruifang Li-Gao, Qiuyin Cai, Neil Schneiderman, Kevin Sandow, Jaspal S. Kooner, Carlos A. Aguilar-Salinas, Peitao Wu, Jerome I. Rotter, Kathryn Roll, Frits R. Rosendaal, Diane M. Becker, Marian Beekman, Claudia P. Cabrera, Nan Wang, Franco Giulianini, Tao Wang, Honglan Li, Abbas Dehghan, Christian Fuchsberger, and Pontiano Kaleebu

Subjects
0303 health sciences, medicine.medical_specialty, business.industry, Type 2 diabetes, medicine.disease, Fasting insulin, Fasting glucose, 03 medical and health sciences, 0302 clinical medicine, Endocrinology, Internal medicine, Diabetes mellitus, medicine, Genomic architecture, business, Glycated haemoglobin, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

Published
2020

35. Development and validation of two SCORE-based cardiovascular risk prediction models for Eastern Europe : a multicohort study

Author

Taavi Tillmann, Sofia Malyutina, Anne Peasey, Martin Bobak, Mika Kivimäki, Yuri Nikitin, Andrzej Pajak, Magdalena Kozela, Oliver Dukes, Tõnu Esko, Kristi Läll, Krista Fischer, Andres Metspalu, Giovanni Veronesi, Hynek Pikhart, and Ruzena Kubinova

Subjects
Cardiovascular diseases, Eastern Europe, Psychosocial deprivation, Risk prediction, Sensitivity and specificity, Socioeconomic factors, 030204 cardiovascular system & hematology, Risk prediction models, 03 medical and health sciences, 0302 clinical medicine, Medicine, 030212 general & internal medicine, 2. Zero hunger, business.industry, Derivation cohort, Confidence interval, 3. Good health, Eastern european, Marital status, Cardiology and Cardiovascular Medicine, business, Body mass index, Psychosocial, Demography, Cohort study
Abstract

Aims Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model. Methods and results We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02–0.11] in the derivation cohort and 0.14 (95% CI 0.04–0.25) in the validation cohort. Conclusion Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.

Published
2020

36. Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification

Author

Neeme Tõnisson, Maarja Lepamets, Tõnu Esko, Krista Fischer, Marili Palover, Peeter Padrik, Kristi Läll, Reedik Mägi, and Andres Metspalu

Subjects
0301 basic medicine, Multifactorial Inheritance, Cancer Research, Cohort Studies, Breast cancer screening, Breast cancer, 0302 clinical medicine, Medicine, Early Detection of Cancer, education.field_of_study, medicine.diagnostic_test, Hazard ratio, Middle Aged, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Prognosis, Biobank, 3. Good health, Oncology, 030220 oncology & carcinogenesis, Cohort, Female, Research Article, Adult, Risk, Genotype, Population, Genetic predisposition to disease, Breast Neoplasms, Single-nucleotide polymorphism, lcsh:RC254-282, Polymorphism, Single Nucleotide, 03 medical and health sciences, Polygenic risk score, Population Groups, Predictive Value of Tests, Biomarkers, Tumor, Genetics, Humans, Genetic Testing, education, Risk stratification, Aged, Genetic association, business.industry, medicine.disease, Personalized medicine, 030104 developmental biology, Case-Control Studies, business, Genome-Wide Association Study, Demography
Abstract

Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women. Electronic supplementary material The online version of this article (10.1186/s12885-019-5783-1) contains supplementary material, which is available to authorized users.

Published
2019

37. Advantages of genotype imputation with ethnically matched reference panel for rare variant association analyses

Author

A. Metspalu, T Esko, Samuli Ripatti, V. Salomaa, Reedik Mägi, Tiit Nikopensius, Kristi Läll, Jaana Suvisaari, Timo Tõnis Sikka, K. Mart, A. Palotie, Priit Palta, and Kalle Pärn

Subjects
Genetics, 0303 health sciences, 03 medical and health sciences, Genotype imputation, 030305 genetics & heredity, Biology, Rare variant association, 030304 developmental biology
Abstract

Genotype imputation has become a standard procedure prior genome-wide association studies (GWASs). For common and low-frequency variants, genotype imputation can be performed sufficiently accurately with publicly available and ethnically heterogeneous reference datasets like 1000 Genomes Project (1000G) and Haplotype Reference Consortium panels. However, the imputation of rare variants has been shown to be significantly more accurate when ethnically matched reference panel is used. Even more, greater genetic similarity between reference panel and target samples facilitates the detection of rare (or even population-specific) causal variants. Notwithstanding, the genome-wide downstream consequences and differences of using ethnically mixed and matched reference panels have not been yet comprehensively explored.We determined and quantified these differences by performing several comparative evaluations of the discovery-driven analysis scenarios. A variant-wise GWAS was performed on seven complex diseases and body mass index by using genome-wide genotype data of ∼37,000 Estonians imputed with ethnically mixed 1000G and ethnically matched imputation reference panels. Although several previously reported common (minor allele frequency; MAF > 5%) variant associations were replicated in both resulting imputed datasets, no major differences were observed among the genome-wide significant findings or in the fine-mapping effort. In the analysis of rare (MAF < 1%) coding variants, 46 significantly associated genes were identified in the ethnically matched imputed data as compared to four genes in the 1000G panel based imputed data. All resulting genes were consequently studied in the UK Biobank data.These associations provide a solid example of how rare variants can be efficiently analysed to discover novel, potentially functional genetic variants in relevant phenotypes. Furthermore, our work serves as proof of a cost-efficient study design, demonstrating that the usage of ethnically matched imputation reference panels can enable substantially improved imputation of rare variants, facilitating novel high-confidence findings in rare variant GWAS scans.Author summaryOver the last decade, genome-wide association studies (GWASs) have been widely used for detecting genetic biomarkers in a wide range of traits. Typically, GWASs are carried out using chip-based genotyping data, which are then combined with a more densely genotyped reference panel to infer untyped genetic variants in chip-typed individuals. The latter method is called genotype imputation and its accuracy depends on multiple factors. Publicly available and ethnically heterogeneous imputation reference panels (IRPs) such as 1000 Genomes Project (1000G) are sufficiently accurate for imputation of common and low-frequency variants, but custom ethnically matched IRPs outperform these in case of rare variants. In this work, we systematically compare downstream association analysis effects on eight complex traits in ∼37,000 Estonians imputed with ethnically mixed and ethnically matched IRPs. We do not observe major differences in the single variant analysis, where both imputed datasets replicate previously reported significant loci. But in the gene-based analysis of rare protein-coding variants we show that ethnically matched panel clearly outperforms 1000G panel based imputation, providing 10-fold increase in significant gene-trait associations. Our study demonstrates empirically that imputed data based on ethnically matched panel is very promising for rare variant analysis – it captures more population-specific variants and makes it possible to efficiently identify novel findings.

Published
2019

38. Author response: Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

Author

Uwe Völker, Ninon Mounier, J. M. van Dongen, Martina Müller-Nurasyid, Peter K. Joshi, David W. Clark, Jaanika Kronberg-Guzman, Holger Kirsten, Viktorija Kukushkina, Xia Shen, Patrick Deelen, Mawusse Agbessi, Alexis Battle, Susan M. Ring, Marie-Julie Favé, Anke Tönjes, Tõnu Esko, Z. Kutalik, Alexander Teumer, Biao Zeng, Michael Stumvoll, Katharina Schramm, Natalia Pervjakova, Greg Gibson, Hanieh Yaghootkar, Yungil Kim, Annique Claringbould, Patrick F. Sullivan, Habibul Ahsan, Lin Tong, Markus Scholz, Philip Awadalla, Mark W. Christiansen, Brenda W.J.H. Penninx, Ashis Saha, Urmo Võsa, Urko M. Marigorta, D.I. Boomsma, Peter Kovacs, Isabel Alves, Gibran Hemani, Knut Krohn, Jarno Kettunen, Krista Fischer, Anand Kumar Andiappan, James F. Wilson, Holger Prokisch, Timothy M. Frayling, Joost Verlouw, Anette Kalnapenkis, Markus Perola, Andrew D. Bretherick, Jian Yang, Peter M. Visscher, Markus Loeffler, Joseph E. Powell, Lude Franke, Marc Jan Bonder, Zoltán Kutalik, Silva Kasela, Kristi Läll, Sina A. Gharib, Paul R. H. J. Timmers, Lili Milani, Olli T. Raitakari, T Esko, Zheng Ning, Ilkka Seppälä, Andres Metspalu, Bernett Lee, Michel G. Nivard, Matthias Nauck, Brandon L. Pierce, Joachim Thiery, J.B. van Meurs, Sina Rüeger, Olaf Rötzschke, Samuli Ripatti, Mika Kähönen, Bruce M. Psaty, Rick Jansen, Futao Zhang, Terho Lehtimäki, and Xiao Feng

Subjects
Evolutionary biology, Genomics, Biology
Published
2018

39. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Author

George Dedoussis, M. Arfan Ikram, Marit E. Jørgensen, Christian Herder, Gudmar Thorleifsson, Oscar H. Franco, Xueling Sim, Philippe Froguel, Symen Ligthart, Ellen M. Schmidt, Jason M. Torres, Karen L. Mohlke, Markku Laakso, Barbara Thorand, Erwin P. Bottinger, Matthias Thurner, Torben Hansen, Jerome I. Rotter, Weihua Zhang, Ching-Ti Liu, Michael Boehnke, Kristi Läll, Konstantin Strauch, Kent D. Taylor, Andrew D. Morris, Daniel R. Witte, Anna L. Gloyn, Jonathan Marchini, Andres Metspalu, Florian Kronenberg, Anubha Mahajan, Claudia Schurmann, Jennifer Kriebel, James S. Pankow, Matthias Wuttke, Valeriya Lyssenko, Jose C. Florez, Josée Dupuis, Patricia A. Peyser, Leif Groop, John C. Chambers, Torben Jørgensen, Timothy M. Frayling, Lawrence F. Bielak, Nicholas J. Wareham, Thomas Meitinger, Alena Stančáková, Vasiliki Mamakou, Cramer Christensen, Michael Preuss, Bram P. Prins, Niels Grarup, Amanda J. Bennett, Vilmantas Giedraitis, Jian'an Luan, Young-Jin Kim, Mickaël Canouil, Robert A. Scott, Kari Stefansson, Jana Nano, Donald W. Bowden, Oluf Pedersen, Cécile Lecoeur, Anna Köttgen, Jette Bork-Jensen, Francis S. Collins, Unnur Thorsteinsdottir, Naveed Sattar, Stella Trompet, Martin Ingelsson, Maggie C.Y. Ng, Jaakko Tuomilehto, Reedik Mägi, James B. Meigs, Lauren E. Petty, J. Wouter Jukema, Andrew T. Hattersley, Claudia Langenberg, Harald Grallert, Christian Gieger, Chad M. Brummett, Ian Ford, Andrew P. Morris, James P. Cook, Vibe Nylander, Sebastian Schönherr, Daniel Taliun, Xiuqing Guo, Valgerdur Steinthorsdottir, Cecilia M. Lindgren, Jennifer E. Below, Eleftheria Zeggini, Anthony Payne, Loic Yengo, Sharon L.R. Kardia, Neil R. Robertson, Abbas Dehghan, Ivan Brandslund, Erik Ingelsson, Colin N. A. Palmer, Ruth J. F. Loos, Girish N. Nadkarni, Gonçalo R. Abecasis, Adam E. Locke, Johanna Kuusisto, Lars Lind, Krista Fischer, Annette Peters, Kai-Uwe Ec Kardt, Ioanna Ntalla, Chloé Sarnowski, Mark I. McCarthy, N. William Rayner, Allan Linneberg, Sophie Hackinger, Centre of Excellence in Complex Disease Genetics, Institute for Molecular Medicine Finland, University of Helsinki, HUS Abdominal Center, Mahajan, Anubha [0000-0001-5585-3420], Morris, Andrew P [0000-0002-6805-6014], Boehnke, Michael [0000-0002-6442-7754], McCarthy, Mark I [0000-0002-4393-0510], Apollo - University of Cambridge Repository, Lee Kong Chian School of Medicine (LKCMedicine), and Epidemiology

Subjects
0301 basic medicine, Male, Linkage disequilibrium, endocrine system diseases, Genome-wide association study, DISEASE, Linkage Disequilibrium, Body Mass Index, Epigenesis, Genetic, 0302 clinical medicine, Gene Frequency, High-Throughput Screening Assays/methods, European Continental Ancestry Group/genetics, Genetics & Heredity, 0303 health sciences, Chromosome Mapping, 11 Medical And Health Sciences, Functional Genomics, 3. Good health, Genetic Loci/genetics, Medical genetics, Female, Islets of Langerhans/metabolism, Life Sciences & Biomedicine, TRAITS, EXPRESSION, medicine.medical_specialty, SUSCEPTIBILITY LOCI, Genomics, 030209 endocrinology & metabolism, PHENOTYPES, Computational biology, Biology, Polymorphism, Single Nucleotide, White People, Article, GENETIC ARCHITECTURE, Islets of Langerhans, 03 medical and health sciences, Sex Factors, SDG 3 - Good Health and Well-being, Meta-Analysis as Topic, SCORE, Genetics, medicine, Humans, Medicine [Science], Genetic Predisposition to Disease, Allele, GENOME-WIDE ASSOCIATION, Genotyping, Allele frequency, METAANALYSIS, 030304 developmental biology, Science & Technology, IDENTIFICATION, Genome, Human, Haplotype, 06 Biological Sciences, Human genetics, Genetic architecture, High-Throughput Screening Assays, Minor allele frequency, 030104 developmental biology, Diabetes Mellitus, Type 2, Diabetes Mellitus, Type 2/epidemiology, Sample size determination, Genetic Loci, Case-Control Studies, Genome, Human/genetics, Genome-wide Association Studies, 3111 Biomedicine, Chromosome Mapping/methods, 030217 neurology & neurosurgery, Imputation (genetics), Developmental Biology, Genome-Wide Association Study
Abstract

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ˜27M variants (˜21M with minor allele frequency [MAF]p−8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

Published
2018

40. Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

Author

Kristi Läll, Krista Fischer, Peeter Padrik, Neeme Tõnisson, A. Metspalu, Reedik Mägi, Maarja Lepamets, Tõnu Esko, and Marili Palover

Subjects
0303 health sciences, education.field_of_study, medicine.diagnostic_test, business.industry, Hazard ratio, Population, Single-nucleotide polymorphism, Odds ratio, medicine.disease, Biobank, 03 medical and health sciences, Breast cancer screening, 0302 clinical medicine, Breast cancer, 030220 oncology & carcinogenesis, Cohort, Medicine, business, education, 030304 developmental biology, Demography
Abstract

BackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

Published
2018

41. Cardiovascular Risk Factors and Ischemic Heart Disease

Author

Arto Pietilä, Antonio Cabrera de León, Olle Mellander, Maris Alver, Veikko Salomaa, Kristi Läll, Krista Fischer, Isaac Subirana, Mariano Sentí, David S. Siscovick, Gavin Lucas, Carla Lluis-Ganella, Sergi Sayols-Baixeras, Jaume Marrugat, Roberto Elosua, and Aki S. Havulinna

Subjects
Genetic Markers, 0301 basic medicine, Oncology, medicine.medical_specialty, Myocardial Ischemia, Blood Pressure, Disease, 030204 cardiovascular system & hematology, Polymorphism, Single Nucleotide, Risk Assessment, Article, Body Mass Index, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Cor -- Malalties -- Aspectes genètics, Internal medicine, Diabetes mellitus, Genetics, Humans, Medicine, Genetic Predisposition to Disease, Obesity, Risk factor, Genetic Association Studies, Genetics (clinical), Dyslipidemias, 2. Zero hunger, business.industry, Case-control study, Malalties -- Factors de risc, medicine.disease, Lipids, 3. Good health, Infart de miocardi, Phenotype, 030104 developmental biology, Diabetes Mellitus, Type 2, Case-Control Studies, Hypertension, Waist Circumference, Cardiology and Cardiovascular Medicine, business, Risk assessment, Body mass index, Cohort study
Abstract

Background— Cardiovascular risk factors tend to aggregate. The biological and predictive value of this aggregation is questioned and genetics could shed light on this debate. Our aims were to reappraise the impact of risk factor confluence on ischemic heart disease (IHD) risk by testing whether genetic risk scores (GRSs) associated with these factors interact on an additive or multiplicative scale, and to determine whether these interactions provide additional value for predicting IHD risk. Methods and Results— We selected genetic variants associated with blood pressure, body mass index, waist circumference, triglycerides, type-2 diabetes mellitus, high-density lipoprotein and low-density lipoprotein cholesterol, and IHD to create GRSs for each factor. We tested and meta-analyzed the impact of additive (synergy index) and multiplicative (β interaction ) interactions between each GRS pair in 1 case–control (n=6042) and 4 cohort studies (n=17 794) and evaluated the predictive value of these interactions. We observed 2 multiplicative interactions: GRS LDL ·GRS Triglycerides (β interaction =–0.096; SE=0.028) and nonpleiotropic GRS IHD ·GRS LDL (β interaction =0.091; SE=0.028). Inclusion of these interaction terms did not improve predictive capacity. Conclusions— The confluence of low-density lipoprotein cholesterol and triglycerides genetic risk load has an additive effect on IHD risk. The interaction between low-density lipoprotein cholesterol and IHD genetic load is more than multiplicative, supporting the hazardous impact on atherosclerosis progression of the combination of inflammation and increased lipid levels. The capacity of risk factor confluence to improve IHD risk prediction is questionable. Further studies in larger samples are warranted to confirm and expand our results.

Published
2016

42. Genomic underpinnings of lifespan allow prediction and reveal basis in modern risks

Author

Paul RHJ Timmers, Ninon Mounier, Kristi Läll, Krista Fischer, Zheng Ning, Xiao Feng, Andrew Bretherick, David W Clark, eQTLGen Consortium, Xia Shen, Tōnu Esko, Zoltán Kutalik, James F Wilson, and Peter K Joshi

Subjects
Genetics, Dorsolateral prefrontal cortex, medicine.anatomical_structure, ABO blood group system, medicine, GADD45G, Cancer, Dementia, Genomics, Disease, Biology, medicine.disease, Gene
Abstract

We use a multi-stage genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near GADD45G, KCNK3, LDLR, POM121C, ZC3HC1, and ABO. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and tissue-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer –but not other cancers-explain the most variance, possibly reflecting modern susceptibilities, whilst cancer may act through many rare variants, or the environment. Resultant polygenic scores predict a mean lifespan difference of around five years of life across the deciles.

Published
2018

43. Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia

Author

Mart Kals, Marili Palover, Stacey Gabriel, Tõnu Esko, Anthony A. Philippakis, Jaan Eha, Pradeep Natarajan, Neeme Tõnisson, Liis Leitsalu, Kristi Läll, Reedik Mägi, Tiit Nikopensius, Aet Saar, Anu Reigo, Toomas Marandi, Tiia Ainla, Seyedeh M. Zekavat, Sekar Kathiresan, Alar Irs, Maris Alver, Eric S. Lander, and Andres Metspalu

Subjects
0301 basic medicine, Proband, Estonia, Male, Genotype, cascade screening, Genetic counseling, Population, population-based biobank, genomics-guided disease management, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, Article, Hyperlipoproteinemia Type II, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Mass Screening, education, Exome, Genetics (clinical), Biological Specimen Banks, Genetics, education.field_of_study, familial hypercholesterolemia, business.industry, PCSK9, recall by genotype, Sequence Analysis, DNA, medicine.disease, Biobank, 3. Good health, 030104 developmental biology, Receptors, LDL, Apolipoprotein B-100, Mutation, Female, Proprotein Convertase 9, business
Abstract

Purpose Large-scale, population-based biobanks integrating health records and genomic profiles may provide a platform to identify individuals with disease-predisposing genetic variants. Here, we recall probands carrying familial hypercholesterolemia (FH)-associated variants, perform cascade screening of family members, and describe health outcomes affected by such a strategy. Methods The Estonian Biobank of Estonian Genome Center, University of Tartu, comprises 52,274 individuals. Among 4776 participants with exome or genome sequences, we identified 27 individuals who carried FH-associated variants in the LDLR, APOB, or PCSK9 genes. Cascade screening of 64 family members identified an additional 20 carriers of FH-associated variants. Results Via genetic counseling and clinical management of carriers, we were able to reclassify 51% of the study participants from having previously established nonspecific hypercholesterolemia to having FH and identify 32% who were completely unaware of harboring a high-risk disease-associated genetic variant. Imaging-based risk stratification targeted 86% of the variant carriers for statin treatment recommendations. Conclusion Genotype-guided recall of probands and subsequent cascade screening for familial hypercholesterolemia is feasible within a population-based biobank and may facilitate more appropriate clinical management.

Published
2018

44. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Author

Ching-Ti Liu, Michael Boehnke, George Dedoussis, Sophie V. Eastwood, Ruth J. F. Loos, Keng-Hung Lin, Denis Rybin, Fredrik Karpe, Martina Müller-Nurasyid, Michael A. Province, Alison D. Murray, Bo Isomaa, Eirini Marouli, Konstantin Strauch, Michael Preuss, Paul M. Ridker, Jette Bork-Jensen, Albert V. Smith, Hugoline G. de Haan, Wayne Huey-Herng Sheu, Barbara Thorand, Wolfgang Rathmann, Lawrence F. Bielak, Peter Kovacs, Marit E. Jørgensen, Jennifer Wessel, Danish Saleheen, Jung-Jin Lee, James B. Meigs, Veikko Salomaa, Alena Stančáková, Tibor V. Varga, Hidetoshi Kitajima, Inês Barroso, Kent D. Taylor, Claudia Langenberg, Yoon Shin Cho, Joanna M. M. Howson, Andrew T. Hattersley, Marie-France Hivert, Markku Laakso, Kai-Uwe Eckardt, Bram P. Prins, Matthias B. Schulze, Andrew D. Morris, Susanne Jäger, Francis S. Collins, Kristi Läll, Xu Lin, Anette Varbo, Benjamin Lehne, Girish N. Nadkarni, Jonathan Marchini, Daniel I. Chasman, Michael Stumvoll, Mark O. Goodarzi, Cécile Lecoeur, Philippe M. Frossard, Noël P. Burtt, Frank Kee, Jasmina Kravic, Alain G. Bertoni, Ivan Brandslund, Najaf Amin, Lenore J. Launer, Oluf Pedersen, Johanna Kuusisto, Line Rode, Eleftheria Zeggini, Yingchang Lu, Markus Perola, Helen R. Warren, André G. Uitterlinden, Hanieh Yaghootkar, Torben Hansen, Harald Grallert, Annemari Käräjämäki, Abbas Dehghan, Gina M. Peloso, Yii-Der Ida Chen, Man Li, Shaofeng Huo, Lars Lind, Karen L. Mohlke, Adrienne Tin, Yang Hai, Renée de Mutsert, Gudmar Thorleifsson, Marie Moitry, Sune F. Nielsen, Sara M. Willems, Matthias Wuttke, Weihua Zhang, Young-Jin Kim, Giovanni Malerba, Richard A. Jensen, Loic Yengo, Mickaël Canouil, Kurt Lohman, Robert A. Scott, Tamara B. Harris, Ruifang Li-Gao, Florian Kronenberg, Anke Tönjes, Bok-Ghee Han, Krista Fischer, Thomas Meitinger, James S. Pankow, Jaakko Tuomilehto, Adam S. Butterworth, Jerome I. Rotter, Olov Rolandsson, Xiuqing Guo, Cramer Christensen, Marie Loh, Elizabeth Selvin, Bong-Jo Kim, Audrey Y. Chu, Reedik Mägi, Josée Dupuis, Anna Köttgen, Jean Ferrières, Jin Li, Robert Sladek, Leslie A. Lange, Niels Grarup, Roberta McKean-Cowdin, Cristen J. Willer, Jose C. Florez, Valgerdur Steinthorsdottir, Karina Meidtner, Annette Peters, Børge G. Nordestgaard, Rajiv Chowdhury, Ioanna Ntalla, Emma Ahlqvist, Leif Groop, Nicholas J. Wareham, Kerrin S. Small, Tiinamaija Tuomi, Cecilia M. Lindgren, Katharine R. Owen, Giovanni Gambaro, Cornelia M. van Duijn, Dennis O. Mook-Kanamori, Kenneth Rice, Erik Ingelsson, Colin N. A. Palmer, Sharon L.R. Kardia, Neil R. Robertson, Dajiang J. Liu, Sebastian Schönherr, Daniel Taliun, Sekar Kathiresan, James G. Wilson, Ping An, Patricia A. Peyser, Matthias Blüher, Frits R. Rosendaal, John C. Chambers, Caroline Hayward, Shoaib Afzal, Fernando Rivadineira, Marielisa Graff, Pranav Yajnik, Vasiliki Mamakou, Juan Fernandez Tajes, Stefan Gustafsson, Heather M. Highland, Vilmantas Giedraitis, Andrew R. Wood, Saima Afaq, Jaspal S. Kooner, Megan L. Grove, Jennifer A. Brody, Andrew P. Morris, James P. Cook, Praveen Surendran, Jennifer Kriebel, Heikki A. Koistinen, Kari Stefansson, Anders Rosengren, Rainer Rauramaa, Satu Männistö, Oscar H. Franco, Yongmei Liu, N. William Rayner, Blair H. Smith, Erwin P. Bottinger, Ayse Demirkan, Allan Linneberg, Jonathan Marten, Huaixing Li, Sung Soo Kim, Sophie Hackinger, Cristina Bombieri, Lia B. Bang, Jun Liu, Asif Rasheed, Tim D. Spector, Paul W. Franks, Mark I. McCarthy, Heiner Boeing, Anne E. Justice, Vilmundur Gudnason, Sohee Han, Unnur Thorsteinsdottir, Panos Deloukas, Naveed Sattar, Eric Boerwinkle, Martin Ingelsson, John Danesh, Vassily Trubetskoy, Marco M Ferrario, Marju Orho-Melander, Wei Gan, Philippe Froguel, Symen Ligthart, Susan R. Heckbert, Jie Yao, Anne Tybjærg-Hansen, Robin Young, Daniel R. Witte, Anubha Mahajan, Peter Almgren, Timothy M. Frayling, Tanya M. Teslovich, Matt Neville, Philippe Amouyel, Wei Zhao, Andres Metspalu, Yao Hu, Olle Melander, Kari Kuulasmaa, Jason Flannick, Torben Jørgensen, Stephen S. Rich, Nicole Soranzo, Bruce M. Psaty, Rohit Varma, Epidemiology, and Internal Medicine

Subjects
0301 basic medicine, Male, Inference, Genome-wide association study, Whole Exome Sequencing, 0302 clinical medicine, type 2 diabetes, coding variant associations signals, mechanistic inference, fine mapping, Coding region, Chromosome Mapping/statistics & numerical data, European Continental Ancestry Group/genetics, CONFERS SUSCEPTIBILITY, Exome sequencing, 11 Medical and Health Sciences, Genetics, Genetics & Heredity, 0303 health sciences, MAGIC Consortium, Chromosome Mapping, Whole Exome Sequencing/statistics & numerical data, Identification (information), RARE VARIANTS, LOW-FREQUENCY, Female, ExomeBP Consortium, Life Sciences & Biomedicine, SUSCEPTIBILITY LOCI, Posterior probability, European Continental Ancestry Group, 030209 endocrinology & metabolism, Context (language use), Computational biology, Biology, GENOTYPE IMPUTATION, Article, White People, GENETIC ARCHITECTURE, 03 medical and health sciences, SDG 3 - Good Health and Well-being, QUALITY-CONTROL, Exome Sequencing, Genome-Wide Association Study/statistics & numerical data, Journal Article, GIANT Consortium, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Alleles, Genetic association, 030304 developmental biology, FATTY LIVER-DISEASE, Science & Technology, Genetic Variation, 06 Biological Sciences, Genetic architecture, Minor allele frequency, BODY-MASS INDEX, 030104 developmental biology, Diabetes Mellitus, Type 2, Diabetes Mellitus, Type 2/classification, 030217 neurology & neurosurgery, Coding (social sciences), Genome-Wide Association Study, Developmental Biology
Abstract

Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated coding variant data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance (p−7). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency 1.29. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls of European ancestry to fine-map these associated coding variants in their regional context, with and without additional weighting to account for the global enrichment of complex trait association signals in coding exons. At the 37 signals for which we attempted fine-mapping, we demonstrate convincing support (posterior probability >80% under the “annotation-weighted” model) that coding variants are causal for the association at 16 (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, at 13 of the 37 loci, the associated coding variants represent “false leads” and naïve analysis could have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.

Published
2018

45. Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations

Author

Jaak Vilo, Krista Fischer, Kristi Läll, Tatjana Iljasenko, and Sulev Reisberg

Subjects
0301 basic medicine, Multifactorial Inheritance, lcsh:Medicine, Coronary Disease, Type 2 diabetes, Cardiovascular Medicine, Vascular Medicine, 0302 clinical medicine, Endocrinology, Mathematical and Statistical Techniques, Gene Frequency, Risk Factors, Medicine and Health Sciences, Coronary Heart Disease, Her Disease, lcsh:Science, education.field_of_study, Principal Component Analysis, Multidisciplinary, Framingham Risk Score, Asia, Eastern, Genomics, Type 2 Diabetes, Europe, Cardiovascular Diseases, Risk stratification, Physical Sciences, Cardiology, Statistics (Mathematics), Research Article, Estonia, medicine.medical_specialty, Asia, Endocrine Disorders, Population, Target population, Research and Analysis Methods, Polymorphism, Single Nucleotide, Risk Assessment, 03 medical and health sciences, Internal medicine, medicine, Genetics, Genome-Wide Association Studies, Diabetes Mellitus, Humans, Genetic Predisposition to Disease, Statistical Methods, education, Evolutionary Biology, Models, Genetic, Population Biology, business.industry, lcsh:R, Biology and Life Sciences, Computational Biology, Human Genetics, medicine.disease, Genome Analysis, Coronary heart disease, 030104 developmental biology, Genetics, Population, Diabetes Mellitus, Type 2, Metabolic Disorders, Africa, Genetics of Disease, Multivariate Analysis, Polygenic risk score, lcsh:Q, Americas, business, 030217 neurology & neurosurgery, Mathematics, Population Genetics, Demography, Meta-Analysis
Abstract

Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population's structure. We also show that if an individual is classified to the wrong population, his/her disease risk can be systematically incorrectly estimated.

Published
2017

46. Genetic variants linked to education predict longevity

Author

Chris Power, Gail Davies, Ilaria Gandin, Panagiotis Deloukas, Jennifer E. Huffman, Pascal Timshel, Albert V. Smith, A. Kong, Paul Lichtenstein, Joseph K. Pickrell, Philipp Koellinger, P. L. De Jager, Reedik Mägi, G. B. Chen, Neil Pendleton, B. V. Halldórsson, George Dedoussis, Antti-Pekka Sarin, Natalia Pervjakova, Veikko Salomaa, Simona Vaccargiu, Ozren Polasek, K. H. Jöckel, Elisabeth Steinhagen-Thiessen, Y. Milaneschi, Jessica D. Faul, Patricia A. Boyle, Patrik K. E. Magnusson, Igor Rudan, Christopher P. Nelson, Vilmundur Gudnason, John Attia, Jürgen Wellmann, Kristi Läll, Konstantin Strauch, Stuart J. Ritchie, Markus Perola, Nicola Pirastu, Klaus Bønnelykke, Robert Karlsson, R. de Vlaming, Liisa Keltigangas-Jarvinen, Thomas Meitinger, Riccardo E. Marioni, Anu Loukola, Barbera Franke, Reinhold Schmidt, Maël Lebreton, Sven Oskarsson, E. Mihailov, Harm-Jan Westra, David R. Weir, Aldi T. Kraja, Niek Verweij, Peter M. Visscher, Hans-Jörgen Grabe, Johannes H. Brandsma, Mark Adams, R. J. Scott, G. Thorleifsson, Tõnu Esko, Mika Kähönen, Saskia P. Hagenaars, Patrick Turley, Johannes Waage, Peter Lichtner, Dragana Vuckovic, Antonietta Robino, Henry Völzke, Lydia Quaye, C. de Leeuw, Marika Kaakinen, Wei Zhao, Abdel Abdellaoui, Reka Nagy, Pedro Marques-Vidal, Johan G. Eriksson, Alan F. Wright, Andres Metspalu, Lavinia Paternoster, Momoko Horikoshi, Jan A. Staessen, Tarunveer S. Ahluwalia, Tian Liu, Martin Kroh, Aldo Rustichini, Giorgia Girotto, Cristina Venturini, Lili Milani, Jennifer A. Smith, Ginevra Biino, Tessel E. Galesloot, Michael A. Horan, Gerardus A. Meddens, James F. Wilson, Francesco Cucca, Peter Vollenweider, Erika Salvi, P. J. van der Most, Jari Lahti, Campbell A, David Laibson, Andrew Bakshi, Wolfgang Hoffmann, Tomi Mäki-Opas, Andreas J. Forstner, C M van Duijn, Nicholas G. Martin, Jonathan Marten, Ute Bültmann, Olli T. Raitakari, David A. Bennett, A.G. Uitterlinden, J. E. De Neve, Ingrid B. Borecki, WD Hill, Bo Jacobsson, Antti Latvala, Katri Räikkönen, Michael B. Miller, Jonathan P. Beauchamp, S. J. van der Lee, Ilja Demuth, Stavroula Kanoni, Veronique Vitart, Elina Hyppönen, N. Eklund, Francesco P. Cappuccio, Robert F. Krueger, Maria Pina Concas, Jaime Derringer, F. J.A. Van Rooij, Helena Schmidt, Patrick J. F. Groenen, Valur Emilsson, Rico Rueedi, Aysu Okbay, Georg Homuth, Edith Hofer, W. E. R. Ollier, Hannah Campbell, Paolo Gasparini, Mark Alan Fontana, Magnus Johannesson, Seppo Koskinen, Christopher F. Chabris, Jouke-Jan Hottenga, Christine Meisinger, Kari Stefansson, Jun Ding, Tia Sorensen, Brenda W.J.H. Penninx, Michelle N. Meyer, James J. Lee, Diego Vozzi, Gonneke Willemsen, K. Petrovic, Sarah E. Medland, Mary F. Feitosa, Henning Tiemeier, L. J. Launer, William G. Iacono, Massimo Mangino, Tune H. Pers, S. E. Baumeister, Christopher Oldmeadow, Grant W. Montgomery, Marjo-Riitta Järvelin, Jaakko Kaprio, Catharine R. Gale, S.F.W. Meddens, Kevin Thom, Klaus Berger, Pablo V. Gejman, Lude Franke, Gyda Bjornsdottir, Daniel J. Benjamin, Steven F. Lehrer, Krista Fischer, Alan R. Sanders, S. Ulivi, Katharina E. Schraut, Tim D. Spector, Amy Hofman, Matt McGue, Terho Lehtimäki, D. C. Liewald, Hans Bisgaard, L. Eisele, Astanand Jugessur, George Davey Smith, T.B. Harris, A.R. Thurik, Cornelius A. Rietveld, David Schlessinger, Z. Kutalik, David J. Porteous, Lynne J. Hocking, N J Timpson, A. Palotie, Lambertus A. Kiemeney, Ian J. Deary, Sharon L.R. Kardia, Peter K. Joshi, Nilesh J. Samani, Michael A. Province, Börge Schmidt, Richa Gupta, Carmen Amador, Erin B. Ware, Joyce Y. Tung, Ioanna-Panagiota Kalafati, Lars Bertram, Caroline Hayward, P. van der Harst, Penelope A. Lind, Kadri Kaasik, N.A. Furlotte, Sarah E. Harris, B. St Pourcain, Susan M. Ring, Zhihong Zhu, Alexander Teumer, Behrooz Z. Alizadeh, Judith M. Vonk, Blair H. Smith, A Payton, Wouter J. Peyrot, Jacob Gratten, Douglas F. Levinson, C Gieger, Leanne M. Hall, Andrew Heath, Mario Pirastu, Peter Eibich, Nancy L. Pedersen, Ronny Myhre, Antonio Terracciano, David M. Evans, Raymond A. Poot, Uwe Völker, Dorret I. Boomsma, Clemens Baumbach, Unnur Thorsteinsdottir, Ivana Kolcic, Jia-Shu Yang, Dalton Conley, A. A. Vinkhuyzen, Danielle Posthuma, Karl-Oskar Lindgren, Olga Rostapshova, Jonas Bacelis, Daniele Cusi, Yong Qian, Bjarni Gunnarsson, George McMahon, Elizabeth G. Holliday, Pamela A. F. Madden, David A. Hinds, David Cesarini, Jianxin Shi, Najaf Amin, Dale R. Nyholt, Applied Economics, Epidemiology, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Research Institute for Asthma and COPD (GRIAC), Aletta Jacobs School of Public Health, Public Health Research (PHR), Stem Cell Aging Leukemia and Lymphoma (SALL), Cardiovascular Centre (CVC), Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, EMGO - Mental health, Complex Trait Genetics, Biological Psychology, Marioni, RE, Ritchie, SJ, Joshi, PK, Hagenaars, SP, Hypponen, E, Benjamin, DJ, Social Science Genetic Association Consortium, Marioni, Re, Ritchie, Sj, Joshi, Pk, Hagenaars, Sp, Okbay, A, Fischer, K, Adams, Mj, Hill, Wd, Davies, G, Nagy, R, Amador, C, Läll, K, Metspalu, A, Liewald, Dc, Campbell, A, Wilson, Jf, Hayward, C, Esko, T, Porteous, Dj, Gale, Cr, Deary, Ij, Beauchamp, Jp, Fontana, Ma, Lee, Jj, Pers, Th, Rietveld, Ca, Turley, P, Chen, Gb, Emilsson, V, Meddens, Sf, Oskarsson, S, Pickrell, Jk, Thom, K, Timshel, P, de Vlaming, R, Abdellaoui, A, Ahluwalia, T, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Jh, Concas, MARIA PINA, Derringer, J, Furlotte, Na, Galesloot, Te, Girotto, Giorgia, Gupta, R, Hall, Lm, Harris, Se, Hofer, E, Horikoshi, M, Huffman, Je, Kaasik, K, Kalafati, Ip, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sj, de Leeuw, C, Lind, Pa, Lindgren, Ko, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, Mb, van der Most, Pj, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, Wj, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, Ke, Shi, J, Smith, Av, Poot, Ra, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N, Vuckovic, Dragana, Wellmann, J, Westra, Hj, Yang, J, Zhao, W, Zhu, Z, Alizadeh, Bz, Amin, N, Bakshi, A, Baumeister, Se, Biino, G, Bønnelykke, K, Boyle, Pa, Campbell, H, Cappuccio, Fp, De Neve, Je, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, Dm, Faul, Jd, Feitosa, Mf, Forstner, Aj, Gandin, Ilaria, Gunnarsson, B, Halldórsson, Bv, Harris, Tb, Heath, Ac, Hocking, Lj, Holliday, Eg, Homuth, G, Horan, Ma, Hottenga, Jj, de Jager, Pl, Jugessur, A, Kaakinen, Ma, Kähönen, M, Kanoni, S, Keltigangas Järvinen, L, Kiemeney, La, Kolcic, I, Koskinen, S, Kraja, At, Kroh, M, Kutalik, Z, Latvala, A, Launer, Lj, Lebreton, Mp, Levinson, Df, Lichtenstein, P, Lichtner, P, Loukola, A, Madden, Pa, Mägi, R, Mäki Opas, T, Marques Vidal, P, Meddens, Ga, Mcmahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, Gw, Myhre, R, Nelson, Cp, Nyholt, Dr, Ollier, We, Palotie, A, Paternoster, L, Pedersen, Nl, Petrovic, Ke, Räikkönen, K, Ring, Sm, Robino, Antonietta, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, Ar, Sarin, Ap, Schmidt, H, Scott, Rj, Smith, Bh, Smith, Ja, Staessen, Ja, Steinhagen Thiessen, E, Strauch, K, Terracciano, A, Tobin, Md, Ulivi, Sheila, Vaccargiu, S, Quaye, L, van Rooij, Fj, Venturini, C, Vinkhuyzen, Aa, Völker, U, Völzke, H, Vonk, Jm, Vozzi, Diego, Waage, J, Ware, Eb, Willemsen, G, Attia, Jr, Bennett, Da, Berger, K, Bertram, L, Bisgaard, H, Boomsma, Di, Borecki, Ib, Bultmann, U, Chabris, Cf, Cucca, F, Cusi, D, Dedoussis, Gv, van Duijn, Cm, Eriksson, Jg, Franke, B, Franke, L, Gasparini, Paolo, Gejman, Pv, Gieger, C, Grabe, Hj, Gratten, J, Groenen, Pj, Gudnason, V, van der Harst, P, Hinds, Da, Hoffmann, W, Iacono, Wg, Jacobsson, B, Järvelin, Mr, Jöckel, Kh, Kaprio, J, Kardia, Sl, Lehtimäki, T, Lehrer, Sf, Magnusson, Pk, Martin, Ng, Mcgue, M, Pendleton, N, Penninx, Bw, Perola, M, Pirastu, Nicola, Pirastu, M, Polasek, O, Posthuma, D, Power, C, Province, Ma, Samani, Nj, Schlessinger, D, Schmidt, R, Sørensen, Ti, Spector, Td, Stefansson, K, Thorsteinsdottir, U, Thurik, Ar, Timpson, Nj, Tiemeier, H, Tung, Jy, Uitterlinden, Ag, Vitart, V, Vollenweider, P, Weir, Dr, Wright, Af, Conley, Dc, Krueger, Rf, Smith, Gd, Hofman, A, Laibson, Di, Medland, Se, Meyer, Mn, Johannesson, M, Visscher, Pm, Koellinger, Pd, Cesarini, D, and Benjamin, Dj

Subjects
Netherlands Twin Register (NTR), 0301 basic medicine, Male, Parents, education: longevity: prediction: polygenic score [genetics], Multifactorial Inheritance, polygenic, Lebenserwartung, Cohort Studies, 0302 clinical medicine, Databases, Genetic, Medicine, genetics, polygenic score, longevity, education, gene, Soziales und Gesundheit, media_common, Aged, 80 and over, education, Multidisciplinary, Longevity, Middle Aged, Biobank, humanities, 3. Good health, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Cohort, Educational Status, Female, Cohort study, Estonia, education, longevity, polygenic, Offspring, media_common.quotation_subject, Kultursektor, Prognose, Lernen, Lower risk, Education, 03 medical and health sciences, longevity, SDG 3 - Good Health and Well-being, Commentaries, Polygenic score, Journal Article, Genetics, Humans, Non-Profit-Sektor, Genetic Association Studies, Aged, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, ta1184, Genetic Variation, prediction, Educational attainment, United Kingdom, Gesundheitsstatistik, 030104 developmental biology, Genetic epidemiology, Scotland, Gesundheitszustand, Genetische Forschung, business, Prediction, Bildung, 030217 neurology & neurosurgery, Demography
Abstract

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity. Marioni RE, Ritchie SJ, Joshi PK, Hagenaars SP, Okbay A, Fischer K, Adams MJ, Hill WD, Davies G, Social Science Genetic Association Consortium, Nagy R, Amador C, Läll K, Metspalu A, Liewald DC, Campbell A, Wilson JF, Hayward C, Esko T, Porteous DJ, Proceedings of the National Academy of Sciences of the United States of America, 2016, vol. 113, no. 47, pp. 13366-13371, 2016 Refereed/Peer-reviewed

Published
2016

47. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

Author

Reedik Mägi, Kristi Läll, Andrew P. Morris, Krista Fischer, and Andres Metspalu

Subjects
0301 basic medicine, Male, endocrine system diseases, Genome-wide association study, Type 2 diabetes, genetic risk, Body Mass Index, Cohort Studies, risk prediction, 0302 clinical medicine, Risk Factors, Medicine, Original Research Article, Prospective Studies, Genetic risk, Prospective cohort study, Genetics (clinical), 0303 health sciences, education.field_of_study, Middle Aged, Biobank, 3. Good health, Cohort, Female, type 2 diabetes, Risk assessment, Cohort study, Genotype, precision medicine, Population, 030209 endocrinology & metabolism, Single-nucleotide polymorphism, genetic risk score, Polymorphism, Single Nucleotide, 03 medical and health sciences, Humans, Genetic Predisposition to Disease, Genetic Testing, education, Alleles, 030304 developmental biology, Genetic association, business.industry, fungi, nutritional and metabolic diseases, medicine.disease, 030104 developmental biology, Diabetes Mellitus, Type 2, business, Body mass index, Demography, Genome-Wide Association Study
Abstract

Purpose: The study aims to develop a Genetic Risk Score (GRS) for the prediction of Type 2 Diabetes (T2D) that could be used for risk assessment in general population. Methods: Using the results of genome-wide association studies, we develop a doubly-weighted GRS for the prediction of T2D risk, aiming to capture the effect of 1000 single nucleotide polymorphisms. The GRS is evaluated in the Estonian Biobank cohort (n=10273), analysing its effect on prevalent and incident T2D, while adjusting for other predictors. We assessed the effect of GRS on all-cause and cardiovascular mortality and its association with other T2D risk factors, and conducted the reclassification analysis. Results: The adjusted hazard for incident T2D is 1.90 (95% CI 1.48, 2.44) times higher and for cardiovascular mortality 1.27 (95% CI 1.10, 1.46) times higher in the highest GRS quintile compared to the rest of the cohort. No significant association between BMI and GRS is found in T2D-free individuals. Adding GRS to the prediction model for 5-year T2D risks results in continuous Net Reclassification Improvement of 0.26 (95% CI 0.15, 0.38). Conclusion: The proposed GRS would considerably improve the accuracy of T2D risk prediction when added to the set of predictors used so far. Keywords: genetic risk score, Type 2 Diabetes, risk prediction, genetic risk, precision medicine

Published
2016
Full Text
View/download PDF

50. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

Author

Paul RHJ Timmers, Ninon Mounier, Kristi Lall, Krista Fischer, Zheng Ning, Xiao Feng, Andrew D Bretherick, David W Clark, eQTLGen Consortium, Xia Shen, Tõnu Esko, Zoltán Kutalik, James F Wilson, and Peter K Joshi

Subjects
genomics, longevity, lifespan, complex trait, Medicine, Science, Biology (General), QH301-705.5
Abstract

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results