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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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
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