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Melton HJ, Zhang Z, Deng HW, Wu L, Wu C. MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations. Epigenetics 2024; 19:2370542. [PMID: 38963888 PMCID: PMC11225927 DOI: 10.1080/15592294.2024.2370542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 06/12/2024] [Indexed: 07/06/2024] Open
Abstract
Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.
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Affiliation(s)
- Hunter J. Melton
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Zichen Zhang
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Lang Wu
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Chong Wu
- Cancer Epidemiology Division, University of Hawaii Cancer Center, Honolulu, HI, USA
- Institute for Data Science in Oncology, The UT MD Anderson Cancer Center
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Kim Y, Jo J, Ji Y, Bae E, Lee K, Paek JH, Jin K, Han S, Lee JP, Kim DK, Lim CS, Won S, Lee J. Impact of hyperuricemia on CKD risk beyond genetic predisposition in a population-based cohort study. Sci Rep 2024; 14:18466. [PMID: 39122851 PMCID: PMC11316130 DOI: 10.1038/s41598-024-69420-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 08/05/2024] [Indexed: 08/12/2024] Open
Abstract
The bidirectional effect of hyperuricemia on chronic kidney disease (CKD) underscores the importance of hyperuricemia as a risk factor for CKD. We evaluated the effect of hyperuricemia on the presence and development of CKD after considering genetic background by calculating polygenic risk scores (PRSs). We employed genome-wide association study summary statistics-excluding the United Kingdom Biobank (UKB) datasets among published CKD Gen Consortium papers-to calculate the PRSs for CKD in white background subjects. To validate PRS performance, we divided the UKB into two datasets to validate and test the data. We used logistic regression analysis to evaluate the association between hyperuricemia and CKD, and performed Kaplan-Meier survival analysis exclusively for subjects with available follow-up data. In total, 438,253 clinical data and 4,307,940 single nucleotide polymorphisms from 459,155 samples were included. We observed a significant positive association between PRS and CKD and the presence and development of CKD. Hyperuricemia significantly increased CKD risk (adjusted odds ratio 1.55, 95% confidence interval 1.48-1.61). The impact of hyperuricemia on CKD was maintained irrespective of PRS range. In addition, negative interaction between hyperuricemia and PRS for CKD was found. Survival analysis indicates that the presence of hyperuricemia significantly increased the risk of CKD development. The PRS for CKD thoroughly reflects the risk of CKD development. Hyperuricemia is a significant indicator of CKD risk, even after incorporating the genetic risk score for CKD. Irrespective of genetic risk, patients with a prospective risk of developing CKD require uric acid monitoring and management.
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Affiliation(s)
- Yaerim Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Eunjin Bae
- Department of Internal Medicine, Gyeongsang National University College of Medicine, Jinju, Republic of Korea
| | - Kwangbae Lee
- Korea Medical Institute, Seoul, Republic of Korea
| | - Jin Hyuk Paek
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Kyubok Jin
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Seungyeup Han
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea.
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.
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Yang J, Zeng Y, Yang H, Qu Y, Han X, Chen W, Sun Y, Hu Y, Ying Z, Liu D, Song H. Cardiovascular Disease, Genetic Susceptibility, and Risk of Psychiatric Disorders and Suicide Attempt: A Community-Based Matched Cohort Study Based on the UK Biobank. J Am Heart Assoc 2024; 13:e031280. [PMID: 39082195 DOI: 10.1161/jaha.123.031280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 05/22/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND The associations between cardiovascular disease (CVD) and multiple psychiatric disorders and suicide attempt, and whether different genetic susceptibilities affect such links, have not been investigated clearly. METHODS AND RESULTS Based on the UK Biobank, we conducted a matched cohort study involving 63 923 patients who were first hospitalized with a CVD diagnosis between 1997 and 2020, and their 127 845 matched unexposed individuals. Cox models were used to examine the subsequent risk of psychiatric disorders and suicide attempt (ie, anxiety, depression, stress-related disorder, substance misuse, psychotic disorder, and suicide behaviors) following CVD. We further performed stratified analyses by polygenic risk score for each studied psychiatric condition to detect the possible effects of genetic susceptibility on the observed associations. We found an increased risk of any psychiatric disorders and suicide attempt among CVD patients, compared with matched unexposed individuals, particularly within 1 year following the CVD (fully adjusted hazard ratio [HR] within 1 year, 1.83 [95% CI, 1.58-2.12]; HR after 1 year, 1.24 [95% CI, 1.16-1.32]). By subtype, the risk elevations existed for any psychiatric disorders and suicide attempt following most categories of CVDs. Analyses stratified by polygenic risk score revealed little impact of genetic predisposition to studied psychiatric conditions on these observed links. CONCLUSIONS Patients hospitalized for CVD were at increased subsequent risk of multiple types of psychiatric disorders and suicide attempt, especially in the first year after hospitalization, irrespective of their genetic susceptibilities to studied psychiatric conditions, and these findings underscore the necessity of developing timely psychological interventions for this vulnerable population.
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Affiliation(s)
- Jie Yang
- Department of Critical Care Medicine and West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
| | - Yu Zeng
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Huazhen Yang
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Xin Han
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Wenwen Chen
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Yajing Sun
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Yao Hu
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Zhiye Ying
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
| | - Di Liu
- Sichuan University-Pittsburgh Institute Sichuan University Chengdu China
| | - Huan Song
- West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China
- Med-X Center for Informatics Sichuan University Chengdu China
- Center of Public Health Sciences Faculty of Medicine University of Iceland Reykjavík Iceland
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Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, Mosley JD, El Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci 2024; 17:e13893. [PMID: 39078255 PMCID: PMC11287822 DOI: 10.1111/cts.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024] Open
Abstract
Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.
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Affiliation(s)
- Sonal Singh
- Merck & Co., IncSouth San FranciscoCaliforniaUSA
| | - Gabriele Stocco
- Department of Medical, Surgical and Health SciencesUniversity of TriesteTriesteItaly
- Institute for Maternal and Child Health IRCCS Burlo GarofoloTriesteItaly
| | - Katherine N. Theken
- Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alyson Dickson
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - QiPing Feng
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, R. Ken Coit College of PharmacyUniversity of ArizonaTucsonArizonaUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jonathan D. Mosley
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Nihal El Rouby
- Division of Pharmacy Practice and Adminstrative Sciences, James L Winkle College of PharmacyUniversity of CincinnatiCincinnatiOhioUSA
- St. Elizabeth HealthcareEdgewoodKentuckyUSA
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Burt CH. Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation. SOCIOLOGICAL METHODOLOGY 2024; 54:300-350. [PMID: 39091537 PMCID: PMC11293310 DOI: 10.1177/00811750241236482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.
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Akiyama M, Tamiya G, Fujiwara K, Shiga Y, Yokoyama Y, Hashimoto K, Sato M, Sato K, Narita A, Hashimoto S, Ueda E, Furuta Y, Hata J, Miyake M, Ikeda HO, Suda K, Numa S, Mori Y, Morino K, Murakami Y, Shimokawa S, Nakamura S, Yawata N, Fujisawa K, Yamana S, Mori K, Ikeda Y, Miyata K, Mori K, Ogino K, Koyanagi Y, Kamatani Y, Ninomiya T, Sonoda KH, Nakazawa T. Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals. Ophthalmology 2024:S0161-6420(24)00362-2. [PMID: 39023470 DOI: 10.1016/j.ophtha.2024.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 07/20/2024] Open
Abstract
PURPOSE To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals. DESIGN Cross-sectional analysis. PARTICIPANTS Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study). METHODS We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements. MAIN OUTCOME MEASURE Proportion of patients with POAG after stratification according to the GRS. RESULTS We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10-4) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10-6). CONCLUSIONS The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kohta Fujiwara
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yukihiro Shiga
- Department of Neuroscience, Université de Montréal, Montréal, Canada; Neuroscience Division, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Canada; Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yu Yokoyama
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazuki Hashimoto
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masataka Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kota Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Sawako Hashimoto
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Emi Ueda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Miyake
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hanako O Ikeda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Suda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shogo Numa
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuki Mori
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kazuya Morino
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yusuke Murakami
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sakurako Shimokawa
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shun Nakamura
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Nobuyo Yawata
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kimihiko Fujisawa
- Department of Ophthalmology, Japan Community Healthcare Organization Kyushu Hospital, Fukuoka, Japan
| | - Satoshi Yamana
- Department of Ophthalmology, National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Kenichiro Mori
- Department of Ophthalmology, Aso Iizuka Hospital, Iizuka, Japan
| | - Yasuhiro Ikeda
- Department of Ophthalmology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | | | - Keisuke Mori
- Department of Ophthalmology, International University of Health and Welfare, Nasu-shiobara, Tochigi, Japan
| | - Ken Ogino
- Department of Ophthalmology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Yoshito Koyanagi
- Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koh-Hei Sonoda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan; Department of Retinal Disease Control, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan.
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7
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Santiago-Lamelas L, Dos Santos-Sobrín R, Carracedo Á, Castro-Santos P, Díaz-Peña R. Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases. Best Pract Res Clin Rheumatol 2024:101973. [PMID: 38997822 DOI: 10.1016/j.berh.2024.101973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024]
Abstract
Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.
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Affiliation(s)
- Lucía Santiago-Lamelas
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Raquel Dos Santos-Sobrín
- Reumatología, Hospital Clínico Universitario, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Ángel Carracedo
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Grupo de Medicina Xenómica, CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Patricia Castro-Santos
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
| | - Roberto Díaz-Peña
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
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8
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Yang Y, Wang Q, Wang C, Buxbaum J, Ionita-Laza I. KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. Am J Hum Genet 2024; 111:1448-1461. [PMID: 38821058 PMCID: PMC11267528 DOI: 10.1016/j.ajhg.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024] Open
Abstract
Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.
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Affiliation(s)
- Yi Yang
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
| | - Qi Wang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Joseph Buxbaum
- Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY 10032, USA; Department of Statistics, Lund University, Lund, Sweden
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9
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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10
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Eunjin B, Ji Y, Jo J, Kim Y, Lee JP, Won S, Lee J. Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study. Hypertens Res 2024:10.1038/s41440-024-01784-7. [PMID: 38982292 DOI: 10.1038/s41440-024-01784-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/11/2024]
Abstract
Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.
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Affiliation(s)
- Bae Eunjin
- Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
- Department of Internal Medicine, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
- Institute of Medical Science, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
| | - Yaerim Kim
- Department of Internal Medicine, College of Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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11
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Hahn G, Prokopenko D, Hecker J, Lutz SM, Mullin K, Tanzi RE, DeSantis S, Lange C. Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model. Genet Epidemiol 2024. [PMID: 38982682 DOI: 10.1002/gepi.22581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 04/23/2024] [Accepted: 06/25/2024] [Indexed: 07/11/2024]
Abstract
The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.
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Affiliation(s)
- Georg Hahn
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sharon M Lutz
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kristina Mullin
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stacia DeSantis
- The University of Texas Health Science Center, Houston, Texas, USA
| | - Christoph Lange
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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12
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Fitzgerald E, Pokhvisneva I, Patel S, Yu Chan S, Peng Tan A, Chen H, Pelufo Silveira P, Meaney MJ. Microglial function interacts with the environment to affect sex-specific depression risk. Brain Behav Immun 2024; 119:597-606. [PMID: 38670238 DOI: 10.1016/j.bbi.2024.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/02/2024] [Accepted: 04/22/2024] [Indexed: 04/28/2024] Open
Abstract
There is a two-fold higher incidence of depression in females compared to men with recent studies suggesting a role for microglia in conferring this sex-dependent depression risk. In this study we investigated the nature of this relation. Using GWAS enrichment, gene-set enrichment analysis and Mendelian randomization, we found minimal evidence for a direct relation between genes functionally related to microglia and sex-dependent genetic risk for depression. We then used expression quantitative trait loci and single nucleus RNA-sequencing resources to generate polygenic scores (PGS) representative of individual variation in microglial function in the adult (UK Biobank; N = 54753-72682) and fetal (ALSPAC; N = 1452) periods. The adult microglial PGS moderated the association between BMI (UK Biobank; beta = 0.001, 95 %CI 0.0009 to 0.003, P = 7.74E-6) and financial insecurity (UK Biobank; beta = 0.001, 95 %CI 0.005 to 0.015, P = 2E-4) with depressive symptoms in females. The fetal microglia PGS moderated the association between maternal prenatal depressive symptoms and offspring depressive symptoms at 24 years in females (ALSPAC; beta = 0.04, 95 %CI 0.004 to 0.07, P = 0.03). We found no evidence for an interaction between the microglial PGS and depression risk factors in males. Our results illustrate a role for microglial function in the conferral of sex-dependent depression risk following exposure to a depression risk factor.
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Affiliation(s)
- Eamon Fitzgerald
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada.
| | - Irina Pokhvisneva
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
| | - Sachin Patel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
| | - Shi Yu Chan
- Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore
| | - Ai Peng Tan
- Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Diagnostic Imaging, National University Health System, Singapore; Brain - Body Initiative, Agency for Science, Technology & Research (A*STAR), Singapore
| | - Helen Chen
- Department of Psychological Medicine, KK Women's and Children's Hospital, Singapore; Duke-National University of Singapore, Singapore
| | - Patricia Pelufo Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael J Meaney
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada; Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Brain - Body Initiative, Agency for Science, Technology & Research (A*STAR), Singapore.
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13
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Kentistou KA, Kaisinger LR, Stankovic S, Vaudel M, Mendes de Oliveira E, Messina A, Walters RG, Liu X, Busch AS, Helgason H, Thompson DJ, Santoni F, Petricek KM, Zouaghi Y, Huang-Doran I, Gudbjartsson DF, Bratland E, Lin K, Gardner EJ, Zhao Y, Jia RY, Terao C, Riggan MJ, Bolla MK, Yazdanpanah M, Yazdanpanah N, Bradfield JP, Broer L, Campbell A, Chasman DI, Cousminer DL, Franceschini N, Franke LH, Girotto G, He C, Järvelin MR, Joshi PK, Kamatani Y, Karlsson R, Luan J, Lunetta KL, Mägi R, Mangino M, Medland SE, Meisinger C, Noordam R, Nutile T, Concas MP, Polašek O, Porcu E, Ring SM, Sala C, Smith AV, Tanaka T, van der Most PJ, Vitart V, Wang CA, Willemsen G, Zygmunt M, Ahearn TU, Andrulis IL, Anton-Culver H, Antoniou AC, Auer PL, Barnes CLK, Beckmann MW, Berrington de Gonzalez A, Bogdanova NV, Bojesen SE, Brenner H, Buring JE, Canzian F, Chang-Claude J, Couch FJ, Cox A, Crisponi L, Czene K, Daly MB, Demerath EW, Dennis J, Devilee P, De Vivo I, Dörk T, Dunning AM, Dwek M, Eriksson JG, Fasching PA, Fernandez-Rhodes L, Ferreli L, Fletcher O, Gago-Dominguez M, García-Closas M, García-Sáenz JA, González-Neira A, Grallert H, Guénel P, Haiman CA, Hall P, Hamann U, Hakonarson H, Hart RJ, Hickey M, Hooning MJ, Hoppe R, Hopper JL, Hottenga JJ, Hu FB, Huebner H, Hunter DJ, Jernström H, John EM, Karasik D, Khusnutdinova EK, Kristensen VN, Lacey JV, Lambrechts D, Launer LJ, Lind PA, Lindblom A, Magnusson PKE, Mannermaa A, McCarthy MI, Meitinger T, Menni C, Michailidou K, Millwood IY, Milne RL, Montgomery GW, Nevanlinna H, Nolte IM, Nyholt DR, Obi N, O'Brien KM, Offit K, Oldehinkel AJ, Ostrowski SR, Palotie A, Pedersen OB, Peters A, Pianigiani G, Plaseska-Karanfilska D, Pouta A, Pozarickij A, Radice P, Rennert G, Rosendaal FR, Ruggiero D, Saloustros E, Sandler DP, Schipf S, Schmidt CO, Schmidt MK, Small K, Spedicati B, Stampfer M, Stone J, Tamimi RM, Teras LR, Tikkanen E, Turman C, Vachon CM, Wang Q, Winqvist R, Wolk A, Zemel BS, Zheng W, van Dijk KW, Alizadeh BZ, Bandinelli S, Boerwinkle E, Boomsma DI, Ciullo M, Chenevix-Trench G, Cucca F, Esko T, Gieger C, Grant SFA, Gudnason V, Hayward C, Kolčić I, Kraft P, Lawlor DA, Martin NG, Nøhr EA, Pedersen NL, Pennell CE, Ridker PM, Robino A, Snieder H, Sovio U, Spector TD, Stöckl D, Sudlow C, Timpson NJ, Toniolo D, Uitterlinden A, Ulivi S, Völzke H, Wareham NJ, Widen E, Wilson JF, Pharoah PDP, Li L, Easton DF, Njølstad PR, Sulem P, Murabito JM, Murray A, Manousaki D, Juul A, Erikstrup C, Stefansson K, Horikoshi M, Chen Z, Farooqi IS, Pitteloud N, Johansson S, Day FR, Perry JRB, Ong KK. Understanding the genetic complexity of puberty timing across the allele frequency spectrum. Nat Genet 2024; 56:1397-1411. [PMID: 38951643 PMCID: PMC11250262 DOI: 10.1038/s41588-024-01798-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 05/13/2024] [Indexed: 07/03/2024]
Abstract
Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.
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Affiliation(s)
- Katherine A Kentistou
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Stasa Stankovic
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Marc Vaudel
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Edson Mendes de Oliveira
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Andrea Messina
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Alexander S Busch
- Department of General Pediatrics, University of Münster, Münster, Germany
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Hannes Helgason
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Federico Santoni
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Konstantin M Petricek
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pharmacology, Berlin, Germany
| | - Yassine Zouaghi
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Isabel Huang-Doran
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Eirik Bratland
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Raina Y Jia
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Marjorie J Riggan
- Department of Gynecology, Duke University Medical Center, Durham, NC, USA
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mojgan Yazdanpanah
- Research Center of the Sainte-Justine University Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Nahid Yazdanpanah
- Research Center of the Sainte-Justine University Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Jonathan P Bradfield
- Quantinuum Research, Wayne, PA, USA
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Linda Broer
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Diana L Cousminer
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Lude H Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Giorgia Girotto
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Chunyan He
- Department of Internal Medicine, Division of Medical Oncology, University of Kentucky College of Medicine, Lexington, KY, USA
- Cancer Prevention and Control Research Program, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Reedik Mägi
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St. Thomas' Foundation Trust, London, UK
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Christa Meisinger
- Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Augsburg, Germany
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Eleonora Porcu
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffele Hospital, Milano, Italy
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Toshiko Tanaka
- National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Marek Zygmunt
- Clinic of Gynaecology and Obstetrics, University Medicine Greifswald, Greifswald, Germany
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services Bethesda, Bethesda, MD, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Catriona L K Barnes
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julie E Buring
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Angela Cox
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Laura Crisponi
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Ellen W Demerath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Healthcare, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
- Yong Loo Lin School of Medicine, Department of Obstetrics and Gynecology and Human Potential Translational Research Programme, National University Singapore, Singapore City, Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore City, Singapore
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | - Liana Ferreli
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, International Cancer Genetics and Epidemiology Group Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS Santiago de Compostela, Coruña, Spain
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services Bethesda, Bethesda, MD, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Pascal Guénel
- Team 'Exposome and Heredity', CESP, Gustave Roussy INSERM, University Paris-Saclay, UVSQ, Orsay, France
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Roger J Hart
- Division of Obstetrics and Gynaecology, University of Western Australia, Crawley, Western Australia, Australia
| | - Martha Hickey
- Department of Obstetrics and Gynaecology, University of Melbourne and The Royal Women's Hospital, Parkville, Victoria, Australia
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Reiner Hoppe
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health School of Public Health, Boston, MA, USA
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Helena Jernström
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine Stanford, Stanford, CA, USA
- Department of Medicine, Division of Oncology Stanford Cancer Institute, Stanford University School of Medicine Stanford, Stanford, CA, USA
| | - David Karasik
- Hebrew SeniorLife Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Elza K Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Vessela N Kristensen
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - James V Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Thomas Meitinger
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, School of Medicine, Munich, Germany
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dale R Nyholt
- School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Nadia Obi
- Institute for Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet-University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Aarno Palotie
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Giulia Pianigiani
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', MASA, Skopje, Republic of North Macedonia
| | - Anneli Pouta
- National Institute for Health and Welfare, Helsinki, Finland
| | - Alfred Pozarickij
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Paolo Radice
- Unit of Preventive Medicine: Molecular Bases of Genetic Risk, Department of Experimental Oncology, Fondazione IRCCS, Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Gad Rennert
- Faculty of Medicine, Clalit National Cancer Control Center, Carmel Medical Center and Technion, Haifa, Israel
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
- IRCCS Neuromed, Isernia, Italy
| | - Emmanouil Saloustros
- Division of Oncology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Carsten O Schmidt
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Kerrin Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Meir Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia Perth, Perth, Western Australia, Australia
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Lauren R Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Emmi Tikkanen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Babette S Zemel
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ko W van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Marina Ciullo
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
- IRCCS Neuromed, Isernia, Italy
| | | | - Francesco Cucca
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Struan F A Grant
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ivana Kolčić
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ellen A Nøhr
- Institute of Clinical Research, University of Southern Denmark, Department of Obstetrics and Gynecology, Odense University Hospital, Odense, Denmark
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Antonietta Robino
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ulla Sovio
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Doris Stöckl
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Cathie Sudlow
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nic J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffele Hospital, Milano, Italy
| | - André Uitterlinden
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Sheila Ulivi
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Adolescent Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Joanne M Murabito
- NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Boston University Chobanian and Avedisian School of Medicine, Department of Medicine, Section of General Internal Medicine, Boston, MA, USA
| | - Anna Murray
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, RILD Level 3, Royal Devon and Exeter Hospital, Exeter, UK
| | - Despoina Manousaki
- Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Center, University of Montreal, Montreal, Quebec, Canada
- Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Anders Juul
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Nelly Pitteloud
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Stefan Johansson
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
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Tran JH, Kang J, Han E, Gupta U, Seresirikachorn K, Vy HMT, Zhao Y, Rocheleau G, Luo Y, Lee R, Do R, Friedman DS, Kang JH, Wiggs JL, Pasquale LR, Segrè AV, Zebardast N. Use of Diagnostic Codes for Primary Open-Angle Glaucoma Polygenic Risk Score Construction in Electronic Health Record-Linked Biobanks. Am J Ophthalmol 2024; 267:204-212. [PMID: 38906208 DOI: 10.1016/j.ajo.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 06/03/2024] [Accepted: 06/03/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE Polygenic risk scores (PRSs) likely predict risk and prognosis of glaucoma. We compared the PRS performance for primary open-angle glaucoma (POAG), defined using International Classification of Diseases (ICD) codes vs manual medical record review. DESIGN Retrospective cohort study. METHODS We identified POAG cases in the Mount Sinai BioMe and Mass General Brigham (MGB) biobanks using ICD codes. We confirmed POAG based on optical coherence tomograms and visual fields. In a separate 5% sample, the absence of POAG was confirmed with intraocular pressure and cup-disc ratio criteria. We used genotype data and either self-reported glaucoma diagnoses or ICD-10 codes for glaucoma diagnoses from the UK Biobank and the lassosum method to compute a genome-wide POAG PRS. We compared the area under the curve (AUC) for POAG prediction based on ICD codes vs medical records. RESULTS We reviewed 804 of 996 BioMe and 367 of 1006 MGB ICD-identified cases. In BioMe and MGB, respectively, positive predictive value was 53% and 55%; negative predictive value was 96% and 97%; sensitivity was 97% and 97%; and specificity was 44% and 53%. Adjusted PRS AUCs for POAG using ICD codes vs manual record review in BioMe were not statistically different (P ≥.21) by ancestry: 0.77 vs 0.75 for African, 0.80 vs 0.80 for Hispanic, and 0.81 vs 0.81 for European. Results were similar in MGB (P ≥.18): 0.72 vs 0.80 for African, 0.83 vs 0.86 for Hispanic, and 0.74 vs 0.73 for European. CONCLUSIONS A POAG PRS performed similarly using either manual review or ICD codes in 2 electronic health record-linked biobanks; manual assessment of glaucoma status might not be necessary for some PRS studies. However, caution should be exercised when using ICD codes for glaucoma diagnosis given their low specificity (44%-53%) for manually confirmed cases of glaucoma.
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Affiliation(s)
- Jessica H Tran
- From the Department of Ophthalmology (J.H.T., E.H., R.L., L.R.P.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Joyce Kang
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Elaine Han
- From the Department of Ophthalmology (J.H.T., E.H., R.L., L.R.P.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Urvi Gupta
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Kasem Seresirikachorn
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Ha My T Vy
- The Charles Bronfman Institute for Personalized Medicine (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yan Zhao
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yuyang Luo
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel Lee
- From the Department of Ophthalmology (J.H.T., E.H., R.L., L.R.P.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences (H.M.T.V., G.R., R.D.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David S Friedman
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Jae H Kang
- Channing Division of Network Medicine (J.H.K.), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Janey L Wiggs
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Louis R Pasquale
- From the Department of Ophthalmology (J.H.T., E.H., R.L., L.R.P.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ayellet V Segrè
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Nazlee Zebardast
- Department of Ophthalmology (J.K., U.G., K.S., Y.Z., Y.L., D.S.F., J.L.W., A.V.S., N.Z.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
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15
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Ohta R, Tanigawa Y, Suzuki Y, Kellis M, Morishita S. A polygenic score method boosted by non-additive models. Nat Commun 2024; 15:4433. [PMID: 38811555 DOI: 10.1038/s41467-024-48654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
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Affiliation(s)
- Rikifumi Ohta
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
| | - Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Yuta Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Shinichi Morishita
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
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16
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Kim SB, Kang JH, Cheon M, Kim DJ, Lee BC. Stacked neural network for predicting polygenic risk score. Sci Rep 2024; 14:11632. [PMID: 38773257 PMCID: PMC11109142 DOI: 10.1038/s41598-024-62513-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies.
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Affiliation(s)
- Sun Bin Kim
- Genoplan Korea Inc., Seoul, Republic of Korea
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17
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Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, Zhan X, Carrel L, Liu DJ, Jiang B. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 2024; 15:4260. [PMID: 38769300 DOI: 10.1038/s41467-024-48143-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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Affiliation(s)
- Lida Wang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Chachrit Khunsriraksakul
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Havell Markus
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Dieyi Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fan Zhang
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fang Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xiaowei Zhan
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, US
- Center for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, US
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US.
| | - Bibo Jiang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
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18
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Yang H, Yang L, Chen W, Zeng Y, Zhang Y, Tang Y, Zeng H, Yang D, Qu Y, Hu Y, Liu D, Song J, Fang F, Valdimarsdóttir UA, Li Q, Song H. Association of pre-existing depression and anxiety with Omicron variant infection. Mol Psychiatry 2024:10.1038/s41380-024-02594-6. [PMID: 38755244 DOI: 10.1038/s41380-024-02594-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Pre-existing psychiatric disorders were linked to an increased susceptibility to COVID-19 during the initial outbreak of the pandemic, while evidence during Omicron prevalence is lacking. Leveraging data from two prospective cohorts in China, we identified incident Omicron infections between January 2023 and April 2023. Participants with a self-reported history or self-rated symptoms of depression or anxiety before the Omicron pandemic were considered the exposed group, whereas the others were considered unexposed. We employed multivariate logistic regression models to examine the association of pre-existing depression or anxiety with the risk of any or severe Omicron infection indexed by medical interventions or severe symptoms. Further, we stratified the analyses by polygenic risk scores (PRSs) for COVID-19 and repeated the analyses using the UK Biobank data. We included 10,802 individuals from the Chinese cohorts (mean age = 51.1 years, 45.6% male), among whom 7841 (72.6%) were identified as cases of Omicron infection. No association was found between any pre-existing depression or anxiety and the overall risk of Omicron infection (odds ratio [OR] =1.04, 95% confidence interval [CI] 0.95-1.14). However, positive associations were noted for severe Omicron infection, either as infections requiring medical interventions (1.26, 1.02-1.54) or with severe symptoms (≥3: 1.73, 1.51-1.97). We obtained comparable estimates when stratified by COVID-19 PRS level. Additionally, using clustering method, we identified eight distinct symptom patterns and found associations between pre-existing depression or anxiety and the patterns characterized by multiple or complex severe symptoms including cough and taste and smell decline (ORs = 1.42-2.35). The results of the UK Biobank analyses corroborated findings of the Chinese cohorts. In conclusion, pre-existing depression and anxiety was not associated with the risk of Omicron infection overall but an elevated risk of severe Omicron infection, supporting the continued efforts on monitoring and possible early intervention in this high-risk population during Omicron prevalence.
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Affiliation(s)
- Huazhen Yang
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Lei Yang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wenwen Chen
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yanan Zhang
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuling Tang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Huolin Zeng
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Di Yang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Yuanyuan Qu
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yao Hu
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Di Liu
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Sichuan University - Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Jie Song
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Unnur A Valdimarsdóttir
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Qian Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.
| | - Huan Song
- Department of Anesthesiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
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Mews MA, Naj AC, Griswold AJ, Below JE, Bush WS. Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305737. [PMID: 38699333 PMCID: PMC11065015 DOI: 10.1101/2024.04.17.24305737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
INTRODUCTION Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS. METHODS We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944). RESULTS We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 . DISCUSSION Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.
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20
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Chen YJ, Hsiao TH, Lin YC, Jeng WJ, Mao CL, Wei CY, Hsieh YC, Huang CJ, Pan MH, Chen IC, Lin CH, Chen YM, Yang HI. Polygenic Risk Score Predicts Earlier-Onset Adult Systemic Lupus Erythematosus and First-Year Renal Diseases in a Taiwanese Cohort. RMD Open 2024; 10:e003293. [PMID: 38637112 PMCID: PMC11146410 DOI: 10.1136/rmdopen-2023-003293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/26/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES This study aimed to develop a predictive model using polygenic risk score (PRS) to forecast renal outcomes for adult systemic lupus erythematosus (SLE) in a Taiwanese population. METHODS Patients with SLE (n=2782) and matched non-SLE controls (n=11 128) were genotyped using Genome-Wide TWB 2.0 single-nucleotide polymorphism (SNP) array. PRS models (C+T, LDpred2, Lassosum, PRSice-2, PRS-continuous shrinkage (CS)) were constructed for predicting SLE susceptibility. Logistic regression was assessed for C+T-based PRS association with renal involvement in patients with SLE. RESULTS In the training set, C+T-based SLE-PRS, only incorporating 27 SNPs, outperformed other models with area under the curve (AUC) values of 0.629, surpassing Lassosum (AUC=0.621), PRSice-2 (AUC=0.615), LDpred2 (AUC=0.609) and PRS-CS (AUC=0.602). Additionally, C+T-based SLE-PRS demonstrated consistent predictive capacity in the testing set (AUC=0.620). Individuals in the highest quartile exhibited earlier SLE onset (39.06 vs 44.22 years, p<0.01), higher Systemic Lupus Erythematosus Disease Activity Index scores (3.00 vs 2.37, p=0.04), elevated risks of renal involvement within the first year of SLE diagnosis, including WHO class III-IV lupus nephritis (OR 2.36, 95% CI 1.47 to 3.80, p<0.01), estimated glomerular filtration rate <60 mL/min/1.73m2 (OR 1.49, 95% CI 1.18 to 1.89, p<0.01) and urine protein-to-creatinine ratio >150 mg/day (OR 2.07, 95% CI 1.49 to 2.89, p<0.01), along with increased seropositivity risks, compared with those in the lowest quartile. Furthermore, among patients with SLE with onset before 50 years, the highest PRS quartile was significantly associated with more serious renal diseases within the first year of SLE diagnosis. CONCLUSIONS PRS of SLE is associated with earlier onset, renal involvement within the first year of SLE diagnosis and seropositivity in Taiwanese patients. Integrating PRS with clinical decision-making may enhance lupus nephritis screening and early treatment to improve renal outcomes in patients with SLE.
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Affiliation(s)
- Yen-Ju Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Ying-Cheng Lin
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Wen-Juei Jeng
- Division of Hepatogastroenterology, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Chien-Lin Mao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chia-Yi Wei
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yi-Chung Hsieh
- Division of Hepatogastroenterology, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Chih-Jen Huang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Mei-Hung Pan
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ching-Heng Lin
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Health Care Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan
- Institute of Public Health and Community Medicine Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Hwai-I Yang
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
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Alireza Z, Maleeha M, Kaikkonen M, Fortino V. Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection. J Transl Med 2024; 22:356. [PMID: 38627847 PMCID: PMC11020205 DOI: 10.1186/s12967-024-05090-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024] Open
Abstract
Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.
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Affiliation(s)
- Z Alireza
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland
| | - M Maleeha
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland
| | - M Kaikkonen
- A.I.Virtanen Institute, University of Eastern Finland, 70210, Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland.
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22
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Weon B, Jang Y, Jo J, Jin W, Ha S, Ko A, Oh YK, Lim CS, Lee JP, Won S, Lee J. Association between dyslipidemia and the risk of incident chronic kidney disease affected by genetic susceptibility: Polygenic risk score analysis. PLoS One 2024; 19:e0299605. [PMID: 38626061 PMCID: PMC11020804 DOI: 10.1371/journal.pone.0299605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 02/13/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The effect of dyslipidemia on kidney disease outcomes has been inconclusive, and it requires further clarification. Therefore, we aimed to investigate the effects of genetic factors on the association between dyslipidemia and the risk of chronic kidney disease (CKD) using polygenic risk score (PRS). METHODS We analyzed data from 373,523 participants from the UK Biobank aged 40-69 years with no history of CKD. Baseline data included plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride, as well as genome-wide genotype data for PRS. Our primary outcome, incident CKD, was defined as a composite of estimated glomerular filtration rate < 60 ml/min/1.73 m2 and CKD diagnosis according to International Classification of Disease-10 codes. The effects of the association between lipid levels and PRS on incident CKD were assessed using the Cox proportional hazards model. To investigate the effect of this association, we introduced multiplicative interaction terms into a multivariate analysis model and performed subgroup analysis stratified by PRS tertiles. RESULTS In total, 4,424 participants developed CKD. In the multivariable analysis, PRS was significantly predictive of the risk of incident CKD as both a continuous variable and a categorized variable. In addition, lower total cholesterol, LDL-C, HDL-C, and higher triglyceride levels were significantly associated with the risk of incident CKD. There were interactions between triglycerides and intermediate and high PRS, and the interactions were inversely associated with the risk of incident CKD. CONCLUSIONS This study showed that PRS presented significant predictive power for incident CKD and individuals in the low-PRS group had a higher risk of triglyceride-related incident CKD.
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Affiliation(s)
- Boram Weon
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | | | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Wencheng Jin
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seounguk Ha
- Korea Medical Institute, Seoul, Republic of Korea
| | - Ara Ko
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun Kyu Oh
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Rexsoft Corporation, Seoul, Republic of Korea
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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23
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O'Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An ensemble penalized regression method for multi-ancestry polygenic risk prediction. Nat Commun 2024; 15:3238. [PMID: 38622117 PMCID: PMC11271575 DOI: 10.1038/s41467-024-47357-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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24
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Yun JS, Jung SH, Lee SN, Jung SM, Won HH, Kim D, Choi JA. Polygenic risk score-based phenome-wide association for glaucoma and its impact on disease susceptibility in two large biobanks. J Transl Med 2024; 22:355. [PMID: 38622600 PMCID: PMC11020996 DOI: 10.1186/s12967-024-05152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/01/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Glaucoma is a leading cause of worldwide irreversible blindness. Considerable uncertainty remains regarding the association between a variety of phenotypes and the genetic risk of glaucoma, as well as the impact they exert on the glaucoma development. METHODS We investigated the associations of genetic liability for primary open angle glaucoma (POAG) with a wide range of potential risk factors and to assess its impact on the risk of incident glaucoma. The phenome-wide association study (PheWAS) approach was applied to determine the association of POAG polygenic risk score (PRS) with a wide range of phenotypes in 377, 852 participants from the UK Biobank study and 43,623 participants from the Penn Medicine Biobank study, all of European ancestry. Participants were stratified into four risk tiers: low, intermediate, high, and very high-risk. Cox proportional hazard models assessed the relationship of POAG PRS and ocular factors with new glaucoma events. RESULTS In both discovery and replication set in the PheWAS, a higher genetic predisposition to POAG was specifically correlated with ocular disease phenotypes. The POAG PRS exhibited correlations with low corneal hysteresis, refractive error, and ocular hypertension, demonstrating a strong association with the onset of glaucoma. Individuals carrying a high genetic burden exhibited a 9.20-fold, 11.88-fold, and 28.85-fold increase in glaucoma incidence when associated with low corneal hysteresis, high myopia, and elevated intraocular pressure, respectively. CONCLUSION Genetic susceptibility to POAG primarily influences ocular conditions, with limited systemic associations. Notably, the baseline polygenic risk for POAG robustly associates with new glaucoma events, revealing a large combined effect of genetic and ocular risk factors on glaucoma incidents.
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Affiliation(s)
- Jae-Seung Yun
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Su-Nam Lee
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Min Jung
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jin A Choi
- Department of Ophthalmology, College of Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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25
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Zhang T, Zhou G, Klei L, Liu P, Chouldechova A, Zhao H, Roeder K, G'Sell M, Devlin B. Evaluating and improving health equity and fairness of polygenic scores. HGG ADVANCES 2024; 5:100280. [PMID: 38402414 PMCID: PMC10937319 DOI: 10.1016/j.xhgg.2024.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024] Open
Abstract
Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.
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Affiliation(s)
- Tianyu Zhang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Geyu Zhou
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peng Liu
- Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexandra Chouldechova
- Microsoft Research NYC, New York, NY 10012, USA; Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Max G'Sell
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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26
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O’Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.15.532652. [PMID: 36993331 PMCID: PMC10055041 DOI: 10.1101/2023.03.15.532652] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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27
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Jin J, Zhan J, Zhang J, Zhao R, O'Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. CELL GENOMICS 2024; 4:100539. [PMID: 38604127 PMCID: PMC11019365 DOI: 10.1016/j.xgen.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/07/2023] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Eimear E Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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28
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Zhao Y, Chukanova M, Kentistou KA, Fairhurst-Hunter Z, Siegert AM, Jia RY, Dowsett GKC, Gardner EJ, Lawler K, Day FR, Kaisinger LR, Tung YCL, Lam BYH, Chen HJC, Wang Q, Berumen-Campos J, Kuri-Morales P, Tapia-Conyer R, Alegre-Diaz J, Barroso I, Emberson J, Torres JM, Collins R, Saleheen D, Smith KR, Paul DS, Merkle F, Farooqi IS, Wareham NJ, Petrovski S, O'Rahilly S, Ong KK, Yeo GSH, Perry JRB. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease. Nat Genet 2024; 56:579-584. [PMID: 38575728 PMCID: PMC11018524 DOI: 10.1038/s41588-024-01694-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024]
Abstract
Obesity is a major risk factor for many common diseases and has a substantial heritable component. To identify new genetic determinants, we performed exome-sequence analyses for adult body mass index (BMI) in up to 587,027 individuals. We identified rare loss-of-function variants in two genes (BSN and APBA1) with effects substantially larger than those of well-established obesity genes such as MC4R. In contrast to most other obesity-related genes, rare variants in BSN and APBA1 were not associated with normal variation in childhood adiposity. Furthermore, BSN protein-truncating variants (PTVs) magnified the influence of common genetic variants associated with BMI, with a common variant polygenic score exhibiting an effect twice as large in BSN PTV carriers than in noncarriers. Finally, we explored the plasma proteomic signatures of BSN PTV carriers as well as the functional consequences of BSN deletion in human induced pluripotent stem cell-derived hypothalamic neurons. Collectively, our findings implicate degenerative processes in synaptic function in the etiology of adult-onset obesity.
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Affiliation(s)
- Yajie Zhao
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maria Chukanova
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Zammy Fairhurst-Hunter
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Maria Siegert
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Raina Y Jia
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Georgina K C Dowsett
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine Lawler
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yi-Chun Loraine Tung
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Brian Yee Hong Lam
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Hsiao-Jou Cortina Chen
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jaime Berumen-Campos
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Pablo Kuri-Morales
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
- Instituto Tecnológico de Estudios Superiores de Monterrey, Tecnológico, Monterrey, Mexico
| | - Roberto Tapia-Conyer
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Jesus Alegre-Diaz
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Jonathan Emberson
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jason M Torres
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Florian Merkle
- Institute of Metabolic Science and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nick J Wareham
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Giles S H Yeo
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
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Hu T, Dai Q, Epstein MP, Yang J. Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305044. [PMID: 38585769 PMCID: PMC10996749 DOI: 10.1101/2024.03.28.24305044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.
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Affiliation(s)
- Tingyang Hu
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qile Dai
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
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30
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Jung SH, Lee YC, Shivakumar M, Kim J, Yun JS, Park WY, Won HH, Kim D. Association between genetic risk and adherence to healthy lifestyle for developing age-related hearing loss. BMC Med 2024; 22:141. [PMID: 38532472 DOI: 10.1186/s12916-024-03364-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Previous studies have shown that lifestyle/environmental factors could accelerate the development of age-related hearing loss (ARHL). However, there has not yet been a study investigating the joint association among genetics, lifestyle/environmental factors, and adherence to healthy lifestyle for risk of ARHL. We aimed to assess the association between ARHL genetic variants, lifestyle/environmental factors, and adherence to healthy lifestyle as pertains to risk of ARHL. METHODS This case-control study included 376,464 European individuals aged 40 to 69 years, enrolled between 2006 and 2010 in the UK Biobank (UKBB). As a replication set, we also included a total of 26,523 individuals considered of European ancestry and 9834 individuals considered of African-American ancestry through the Penn Medicine Biobank (PMBB). The polygenic risk score (PRS) for ARHL was derived from a sensorineural hearing loss genome-wide association study from the FinnGen Consortium and categorized as low, intermediate, high, and very high. We selected lifestyle/environmental factors that have been previously studied in association with hearing loss. A composite healthy lifestyle score was determined using seven selected lifestyle behaviors and one environmental factor. RESULTS Of the 376,464 participants, 87,066 (23.1%) cases belonged to the ARHL group, and 289,398 (76.9%) individuals comprised the control group in the UKBB. A very high PRS for ARHL had a 49% higher risk of ARHL than those with low PRS (adjusted OR, 1.49; 95% CI, 1.36-1.62; P < .001), which was replicated in the PMBB cohort. A very poor lifestyle was also associated with risk of ARHL (adjusted OR, 3.03; 95% CI, 2.75-3.35; P < .001). These risk factors showed joint effects with the risk of ARHL. Conversely, adherence to healthy lifestyle in relation to hearing mostly attenuated the risk of ARHL even in individuals with very high PRS (adjusted OR, 0.21; 95% CI, 0.09-0.52; P < .001). CONCLUSIONS Our findings of this study demonstrated a significant joint association between genetic and lifestyle factors regarding ARHL. In addition, our analysis suggested that lifestyle adherence in individuals with high genetic risk could reduce the risk of ARHL.
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Affiliation(s)
- Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Young Chan Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jaeyoung Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Jae-Seung Yun
- Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA.
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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32
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Song M, Kwak SH, Kim J. Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population. Sci Rep 2024; 14:6790. [PMID: 38514700 PMCID: PMC10957984 DOI: 10.1038/s41598-024-55945-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 02/29/2024] [Indexed: 03/23/2024] Open
Abstract
Joint modelling of genetic and environmental risk factors can provide important information to predict the risk of type 2 diabetes (T2D). Therefore, to predict the genetic risk of T2D, we constructed a polygenic risk score (PRS) using genotype data of one Korean cohort, KARE (745 cases and 2549 controls), and the genome-wide association study summary statistics of Biobank Japan. We evaluated the performance of PRS in an independent Korean cohort, HEXA (5684 cases and 35,703 controls). Individuals with T2D had a significantly higher mean PRS than controls (0.492 vs. - 0.078, p ≈ 0 ). PRS predicted the risk of T2D with an AUC of 0.658 (95% CI 0.651-0.666). We also evaluated interaction between PRS and waist circumference (WC) in the HEXA cohort. PRS exhibited a significant sub-multiplicative interaction with WC (ORinteraction 0.991, 95% CI 0.987-0.995, pinteraction = 4.93 × 10-6) in T2D. The effect of WC on T2D decreased as PRS increased. The sex-specific analyses produced similar interaction results, revealing a decreased WC effect on T2D as the PRS increased. In conclusion, the risk of WC for T2D may differ depending on PRS and those with a high PRS might develop T2D with a lower WC threshold. Our findings are expected to improve risk prediction for T2D and facilitate the identification of individuals at an increased risk of T2D.
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Affiliation(s)
- Minsun Song
- Department of Statistics & Research Institute of Natural Sciences, Sookmyung Women's University, Seoul, 04310, Korea
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Korea.
| | - Jihyun Kim
- Department of Statistics, Sookmyung Women's University, Seoul, 04310, Korea
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33
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Sekimitsu S, Shweikh Y, Shareef S, Zhao Y, Elze T, Segrè A, Wiggs J, Zebardast N. Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline. Br J Ophthalmol 2024; 108:599-606. [PMID: 36990674 DOI: 10.1136/bjo-2022-322762] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment. METHODS Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted. RESULTS Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements. CONCLUSIONS AND RELEVANCE Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.
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Affiliation(s)
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Sussex Eye Hospital, University Hospitals Sussex NHS Foundation Trust, Sussex, UK
| | - Sarah Shareef
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Yan Zhao
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Tobias Elze
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Ayellet Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Janey Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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Melton HJ, Zhang Z, Wu C. SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations. Hum Mol Genet 2024; 33:624-635. [PMID: 38129112 PMCID: PMC10954367 DOI: 10.1093/hmg/ddad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.
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Affiliation(s)
- Hunter J Melton
- Department of Statistics, Florida State University, 214 Rogers Building, 117 N. Woodward Avenue, Tallahassee, FL 32306, United States
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
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35
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Kim NY, Lee H, Kim S, Kim YJ, Lee H, Lee J, Kwak SH, Lee S. The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population. Sci Rep 2024; 14:5749. [PMID: 38459065 PMCID: PMC10923897 DOI: 10.1038/s41598-024-55313-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 02/22/2024] [Indexed: 03/10/2024] Open
Abstract
The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E-02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β -cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel's C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.
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Affiliation(s)
- Na Yeon Kim
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Haekyung Lee
- Division of Nephrology, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, South Korea
| | - Sehee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul, South Korea
| | - Ye-Jee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul, South Korea
| | - Hyunsuk Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Junhyeong Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea.
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36
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Aw AJ, Spence JP, Song YS. A SIMPLE AND FLEXIBLE TEST OF SAMPLE EXCHANGEABILITY WITH APPLICATIONS TO STATISTICAL GENOMICS. Ann Appl Stat 2024; 18:858-881. [PMID: 38784669 PMCID: PMC11115382 DOI: 10.1214/23-aoas1817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these considerations are fundamental to downstream tasks such as demographic inference and the construction of polygenic risk scores. We propose a non-parametric approach, which we call the V test, to address these two questions, namely, a test of sample exchangeability given dependency structure of features, and a test of feature independence given sample exchangeability. Our test is conceptually simple, yet fast and flexible. It controls the Type I error across realistic scenarios, and handles data of arbitrary dimensions by leveraging large-sample asymptotics. Through extensive simulations and a comparison against unsupervised tests of stratification based on random matrix theory, we find that our test compares favorably in various scenarios of interest. We apply the test to data from the 1000 Genomes Project, demonstrating how it can be employed to assess exchangeability of the genetic sample, or find optimal linkage disequilibrium (LD) splits for downstream analysis. For exchangeability assessment, we find that removing rare variants can substantially increase the p -value of the test statistic. For optimal LD splitting, the V test reports different optimal splits than previous approaches not relying on hypothesis testing. Software for our methods is available in R (CRAN: flintyR) and Python (PyPI: flintyPy).
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Affiliation(s)
- Alan J Aw
- Department of Statistics, University of California, Berkeley
| | | | - Yun S Song
- Department of Statistics and Computer Science Division, University of California, Berkeley
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37
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Kar A, Alvarez M, Garske KM, Huang H, Lee SHT, Deal M, Das SS, Koka A, Jamal Z, Mohlke KL, Laakso M, Heinonen S, Pietiläinen KH, Pajukanta P. Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions. Genome Med 2024; 16:19. [PMID: 38297378 PMCID: PMC10829214 DOI: 10.1186/s13073-024-01291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
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Affiliation(s)
- Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Huiling Huang
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Milena Deal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Sankha Subhra Das
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Zoeb Jamal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA.
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA.
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, USA.
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Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. Biometrics 2024; 80:ujad039. [PMID: 38470257 PMCID: PMC10928990 DOI: 10.1093/biomtc/ujad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 03/13/2024]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.
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Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, IA 50011, United States
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
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Goddard TR, Brookes KJ, Sharma R, Moemeni A, Rajkumar AP. Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells 2024; 13:223. [PMID: 38334615 PMCID: PMC10854541 DOI: 10.3390/cells13030223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.
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Affiliation(s)
- Thomas R. Goddard
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| | - Keeley J. Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Riddhi Sharma
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
- UK Health Security Agency, Radiation Effects Department, Radiation Protection Science Division, Harwell Science Campus, Didcot, Oxfordshire OX11 0RQ, UK
| | - Armaghan Moemeni
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Anto P. Rajkumar
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
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40
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Deng WQ, Belisario K, Gray JC, Levitt EE, MacKillop J. A high-resolution PheWAS approach to alcohol-related polygenic risk scores reveals mechanistic influences of alcohol reinforcing value and drinking motives. Alcohol Alcohol 2024; 59:agad093. [PMID: 38261344 DOI: 10.1093/alcalc/agad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/08/2023] [Accepted: 12/16/2023] [Indexed: 01/24/2024] Open
Abstract
AIMS This study uses a high-resolution phenome-wide approach to evaluate the motivational mechanisms of polygenic risk scores (PRSs) that have been robustly associated with coarse alcohol phenotypes in large-scale studies. METHODS In a community-based sample of 1534 Europeans, we examined genome-wide PRSs for the Alcohol Use Disorders Identification Test (AUDIT), drinks per week, alcohol use disorder (AUD), problematic alcohol use (PAU), and general addiction, in relation to 42 curated phenotypes. The curated phenotypes were in seven categories: alcohol consumption, alcohol reinforcing value, drinking motives, other addictive behaviors, commonly comorbid psychiatric syndromes, impulsivity, and personality traits. RESULTS The PRS for each alcohol phenotype was validated via its within-sample association with the corresponding phenotype (adjusted R2s = 0.35-1.68%, Ps = 0.012-3.6 × 10-7) with the exception of AUD. All PRSs were positively associated with alcohol reinforcing value and drinking motives, with the strongest effects from AUDIT-consumption (adjusted R2s = 0.45-1.33%, Ps = 0.006-3.6 × 10-5) and drinks per week PRSs (adjusted R2s = 0.52-2.28%, Ps = 0.004-6.6 × 10-9). Furthermore, the PAU and drinks per week PRSs were positively associated with adverse childhood experiences (adjusted R2s = 0.6-0.7%, Ps = 0.0001-4.8 × 10-4). CONCLUSIONS These results implicate alcohol reinforcing value and drinking motives as genetically-influenced mechanisms using PRSs for the first time. The findings also highlight the value of dissecting genetic influence on alcohol involvement through diverse phenotypic risk pathways but also the need for future studies with both phenotypic richness and larger samples.
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Affiliation(s)
- Wei Q Deng
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 3K7, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8N 3K7, Canada
| | - Kyla Belisario
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 3K7, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8N 3K7, Canada
| | - Joshua C Gray
- Department of Medical and Clinical Psychology, Uniformed Services University, Bethesda, MD 20814, United States
| | - Emily E Levitt
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 3K7, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8N 3K7, Canada
| | - James MacKillop
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 3K7, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8N 3K7, Canada
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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Uffelmann E, Price AL, Posthuma D, Peyrot WJ. Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301157. [PMID: 38260678 PMCID: PMC10802765 DOI: 10.1101/2024.01.12.24301157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.
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Affiliation(s)
- Emil Uffelmann
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
| | | | | | - Alkes L. Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex, Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Psychiatry, Amsterdam UMC, The Netherlands
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Zhang X, Gomez L, Below JE, Naj AC, Martin ER, Kunkle BW, Bush WS. An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease. J Alzheimers Dis 2024; 98:1053-1067. [PMID: 38489177 DOI: 10.3233/jad-231075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD). Objective To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
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Affiliation(s)
- Xueyi Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam C Naj
- Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Brian W Kunkle
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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46
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Mo C, Ye Z, Pan Y, Zhang Y, Wu Q, Bi C, Liu S, Mitchell B, Kochunov P, Hong LE, Ma T, Chen S. An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping. Mol Cell Neurosci 2023; 127:103895. [PMID: 37634742 PMCID: PMC11128188 DOI: 10.1016/j.mcn.2023.103895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/21/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023] Open
Abstract
In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yuan Zhang
- Department of Statistics, College of Arts and Sciences, Ohio State University, Columbus, Ohio, United States
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Braxton Mitchell
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States
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Chen S, Lin Z, Shen X, Li L, Pan W. Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data. Genet Epidemiol 2023; 47:585-599. [PMID: 37573486 PMCID: PMC10840616 DOI: 10.1002/gepi.22535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.
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Affiliation(s)
- Siyi Chen
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455
| | - Ling Li
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
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48
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Moll M, Sordillo JE, Ghosh AJ, Hayden LP, McDermott G, McGeachie MJ, Dahlin A, Tiwari A, Manmadkar MG, Abston ED, Pavuluri C, Saferali A, Begum S, Ziniti JP, Gulsvik A, Bakke PS, Aschard H, Iribarren C, Hersh CP, Sparks JA, Hobbs BD, Lasky-Su JA, Silverman EK, Weiss ST, Wu AC, Cho MH. Polygenic risk scores identify heterogeneity in asthma and chronic obstructive pulmonary disease. J Allergy Clin Immunol 2023; 152:1423-1432. [PMID: 37595761 PMCID: PMC10841234 DOI: 10.1016/j.jaci.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND Asthma and chronic obstructive pulmonary disease (COPD) have distinct and overlapping genetic and clinical features. OBJECTIVE We sought to test the hypothesis that polygenic risk scores (PRSs) for asthma (PRSAsthma) and spirometry (FEV1 and FEV1/forced vital capacity; PRSspiro) would demonstrate differential associations with asthma, COPD, and asthma-COPD overlap (ACO). METHODS We developed and tested 2 asthma PRSs and applied the higher performing PRSAsthma and a previously published PRSspiro to research (Genetic Epidemiology of COPD study and Childhood Asthma Management Program, with spirometry) and electronic health record-based (Mass General Brigham Biobank and Genetic Epidemiology Research on Adult Health and Aging [GERA]) studies. We assessed the association of PRSs with COPD and asthma using modified random-effects and binary-effects meta-analyses, and ACO and asthma exacerbations in specific cohorts. Models were adjusted for confounders and genetic ancestry. RESULTS In meta-analyses of 102,477 participants, the PRSAsthma (odds ratio [OR] per SD, 1.16 [95% CI, 1.14-1.19]) and PRSspiro (OR per SD, 1.19 [95% CI, 1.17-1.22]) both predicted asthma, whereas the PRSspiro predicted COPD (OR per SD, 1.25 [95% CI, 1.21-1.30]). However, results differed by cohort. The PRSspiro was not associated with COPD in GERA and Mass General Brigham Biobank. In the Genetic Epidemiology of COPD study, the PRSAsthma (OR per SD: Whites, 1.3; African Americans, 1.2) and PRSspiro (OR per SD: Whites, 2.2; African Americans, 1.6) were both associated with ACO. In GERA, the PRSAsthma was associated with asthma exacerbations (OR, 1.18) in Whites; the PRSspiro was associated with asthma exacerbations in White, LatinX, and East Asian participants. CONCLUSIONS PRSs for asthma and spirometry are both associated with ACO and asthma exacerbations. Genetic prediction performance differs in research versus electronic health record-based cohorts.
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Affiliation(s)
- Matthew Moll
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Joanne E Sordillo
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass
| | - Auyon J Ghosh
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, SUNY Upstate Medical Center, Syracuse, NY
| | - Lystra P Hayden
- Department of Pediatrics, Division of Pulmonary Medicine, Boston Children's Hospital, Harvard Medical School, Massachusetts General Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Gregory McDermott
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Mass
| | - Michael J McGeachie
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Amber Dahlin
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Anshul Tiwari
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Monica G Manmadkar
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Eric D Abston
- Department of Thoracic Surgery, Massachusetts General Hospital, Boston, Mass
| | - Chandan Pavuluri
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Aabida Saferali
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Sofina Begum
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - John P Ziniti
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Amund Gulsvik
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Universit de Paris, Paris, France
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, Calif
| | - Craig P Hersh
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Jeffrey A Sparks
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Mass
| | - Brian D Hobbs
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Jessica A Lasky-Su
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Edwin K Silverman
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Scott T Weiss
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Ann Chen Wu
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass
| | - Michael H Cho
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass.
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49
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Levi H, Carmi S, Rosset S, Yerushalmi R, Zick A, Yablonski-Peretz T, Wang Q, Bolla MK, Dennis J, Michailidou K, Lush M, Ahearn T, Andrulis IL, Anton-Culver H, Antoniou AC, Arndt V, Augustinsson A, Auvinen P, Beane Freeman L, Beckmann M, Behrens S, Bermisheva M, Bodelon C, Bogdanova NV, Bojesen SE, Brenner H, Byers H, Camp N, Castelao J, Chang-Claude J, Chirlaque MD, Chung W, Clarke C, Collee MJ, Colonna S, Couch F, Cox A, Cross SS, Czene K, Daly M, Devilee P, Dork T, Dossus L, Eccles DM, Eliassen AH, Eriksson M, Evans G, Fasching P, Fletcher O, Flyger H, Fritschi L, Gabrielson M, Gago-Dominguez M, García-Closas M, Garcia-Saenz JA, Genkinger J, Giles GG, Goldberg M, Guénel P, Hall P, Hamann U, He W, Hillemanns P, Hollestelle A, Hoppe R, Hopper J, Jakovchevska S, Jakubowska A, Jernström H, John E, Johnson N, Jones M, Vijai J, Kaaks R, Khusnutdinova E, Kitahara C, Koutros S, Kristensen V, Kurian AW, Lacey J, Lambrechts D, Le Marchand L, Lejbkowicz F, Lindblom A, Loibl S, Lori A, Lubinski J, Mannermaa A, Manoochehri M, Mavroudis D, Menon U, Mulligan A, Murphy R, Nevelsteen I, Newman WG, Obi N, O'Brien K, Offit K, Olshan A, Plaseska-Karanfilska D, Olson J, Panico S, Park-Simon TW, Patel A, Peterlongo P, Rack B, Radice P, Rennert G, Rhenius V, Romero A, Saloustros E, Sandler D, Schmidt MK, Schwentner L, Shah M, Sharma P, Simard J, Southey M, Stone J, Tapper WJ, Taylor J, Teras L, Toland AE, Troester M, Truong T, van der Kolk LE, Weinberg C, Wendt C, Yang XR, Zheng W, Ziogas A, Dunning AM, Pharoah P, Easton DF, Ben-Sachar S, Elefant N, Shamir R, Elkon R. Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel. J Med Genet 2023; 60:1186-1197. [PMID: 37451831 PMCID: PMC10715538 DOI: 10.1136/jmg-2023-109185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/28/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. METHODS We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. RESULTS In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). CONCLUSIONS Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.
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Grants
- R01 CA176785 NCI NIH HHS
- NU58DP006344 NCCDPHP CDC HHS
- R37 CA070867 NCI NIH HHS
- HHSN261201800015I NCI NIH HHS
- R01 CA064277 NCI NIH HHS
- P50 CA116201 NCI NIH HHS
- G1000143 Medical Research Council
- P30 CA062203 NCI NIH HHS
- HHSN261201800015C NCI NIH HHS
- R01 CA047305 NCI NIH HHS
- HHSN261201800009I NCI NIH HHS
- R01 CA163353 NCI NIH HHS
- UM1 CA164917 NCI NIH HHS
- U01 CA199277 NCI NIH HHS
- U01 CA179715 NCI NIH HHS
- HHSN261201800032C NCI NIH HHS
- U54 CA156733 NCI NIH HHS
- HHSN261201800009C NCI NIH HHS
- Z01 CP010119 Intramural NIH HHS
- UM1 CA164973 NCI NIH HHS
- P01 CA087969 NCI NIH HHS
- UM1 CA164920 NCI NIH HHS
- NU58DP006320 CDC HHS
- UM1 CA176726 NCI NIH HHS
- R01 CA092447 NCI NIH HHS
- Z01 ES049030 Intramural NIH HHS
- R01 CA058860 NCI NIH HHS
- K07 CA092044 NCI NIH HHS
- HHSN261201800016C NCI NIH HHS
- P50 CA058223 NCI NIH HHS
- R01 CA100374 NCI NIH HHS
- P30 CA008748 NCI NIH HHS
- R01 CA128978 NCI NIH HHS
- R01 CA047147 NCI NIH HHS
- U19 CA148537 NCI NIH HHS
- R01 CA116167 NCI NIH HHS
- R01 CA148667 NCI NIH HHS
- R01 CA063464 NCI NIH HHS
- HHSN261201800016I NCI NIH HHS
- UM1 CA186107 NCI NIH HHS
- P30 CA023100 NCI NIH HHS
- U01 CA063464 NCI NIH HHS
- R01 CA077398 NCI NIH HHS
- R01 CA054281 NCI NIH HHS
- R01 CA132839 NCI NIH HHS
- P30 CA068485 NCI NIH HHS
- U01 CA058860 NCI NIH HHS
- U01 CA164920 NCI NIH HHS
- R35 CA253187 NCI NIH HHS
- 14136 Cancer Research UK
- U19 CA148112 NCI NIH HHS
- HHSN261201800032I NCI NIH HHS
- U01 CA098758 NCI NIH HHS
- Z01 ES044005 Intramural NIH HHS
- U19 CA148065 NCI NIH HHS
- P30 CA033572 NCI NIH HHS
- R01 CA069664 NCI NIH HHS
- Wellcome Trust
- 001 World Health Organization
- Z01 ES049033 Intramural NIH HHS
- R01 CA192393 NCI NIH HHS
- U01 CA164973 NCI NIH HHS
- R37 CA054281 NCI NIH HHS
- Consellería de Industria Programa Sectorial de Investigación Aplicada
- Statistics Netherlands
- South Eastern Norway Health Authority
- Lower Saxonian Cancer Society
- Lise Boserup Fund
- Heidelberger Zentrum für Personalisierte Onkologie Deutsches Krebsforschungszentrum In Der Helmholtz-Gemeinschaft
- Lon V. Smith Foundation
- Scottish Funding Council
- Komen Foundation
- Claudia von Schilling Foundation for Breast Cancer Research
- Russian Foundation for Basic Research
- Ligue Contre le Cancer
- Sigrid Juselius Foundation
- Kuopion Yliopistollinen Sairaala
- Sheffield Experimental Cancer Medicine Centre
- Stockholm läns landsting
- Department of Health and Human Services (USA)
- Department of Defence (USA)
- Stichting Tegen Kanker
- David F. and Margaret T. Grohne Family Foundation
- Sundhed og Sygdom, Det Frie Forskningsråd
- Stavros Niarchos Foundation
- Post-Cancer GWAS initiative
- Institute of the Ruhr University Bochum
- Instituto de Salud Carlos III
- Institute of Cancer Research
- Public Health Institute
- Fondation du cancer du sein du Québec
- Institut National de la Santé et de la Recherche Médicale
- Pink Ribbon
- Institute for Prevention and Occupational Medicine
- K.G. Jebsen Centre for Breast Cancer Research
- Research Centre for Genetic Engineering and Biotechnology
- Center of Excellence (Finland)
- Robert and Kate Niehaus Clinical Cancer Genetics Initiative
- Rudolf Bartling Foundation
- Center for Disease Control and Prevention (USA)
- Karolinska Institutet
- Norges Forskningsråd
- Robert Bosch Stiftung
- Intramural Research Funds of the National Cancer Institute (USA)
- Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC
- Intramural Research Program of the Division of Cancer Epidemiology and Genetics
- Centre International de Recherche sur le Cancer
- Queensland Cancer Fund
- Red Temática de Investigación Cooperativa en Cáncer
- Intramural Research Program of the National Institutes of Health
- National Health Service (UK)
- Ministerie van Volksgezondheid, Welzijn en Sport
- National cancer institute (USA)
- KWF Kankerbestrijding
- Märit and Hans Rausings Initiative Against Breast Cancer
- Associazione Italiana per la Ricerca sul Cancro
- Fundación Científica Asociación Española Contra el Cáncer
- ERC advanced grant
- Australian National Health and Medical Research Council
- Agence Nationale de la Recherche
- Dutch Prevention Funds,
- Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail
- American Cancer Society
- Dutch Zorg Onderzoek
- Alexander von Humboldt-Stiftung
- Ministerio de Economia y Competitividad (Spain)
- Ministère du Développement Économique, de l’Innovation et de l’Exportation
- Susan G. Komen for the Cure
- Minister of Science and Higher Education
- Medical Research Council UK
- Ministry of Science and Higher Education of the Russian Federation
- Ministry of Science and Higher Education (Sweden)
- Against Breast Cancer
- Mutuelle Générale de l’Education Nationale
- Academy of Finland
- Deutsche Krebshilfe e.V.
- Dietmar-Hopp Foundation,
- Division of Cancer Prevention, National Cancer Institute
- Deutsche Krebshilfe
- World Cancer Research Fund
- Genome Québec
- National Cancer Institute’s Surveillance, Epidemiology and End Results Program
- Breast Cancer Campaign
- National Cancer Research Network
- Berta Kamprad Foundation FBKS
- Bert von Kantzows foundation
- Biomedical Research Centre at Guy’s and St Thomas
- Genome Canada
- Freistaat Sachsen
- Biobanking and Biomolecular Resources Research Infrastructure
- Friends of Hannover Medical School
- Breast Cancer Research Foundation
- California Department of Public Health
- Government of Russian Federation
- Deutsche Forschungsgemeinschaft
- National Institute for Health and Care Research
- National Health and Medical Research Council (Australia)
- German Federal Ministry of Research and Education
- National Institute of Environmental Health Sciences
- Breast Cancer Now
- Seventh Framework Programme
- Transcan
- Centrum för idrottsforskning
- UK National Institute for Health Research Biomedical Research Centre
- University of Crete
- National Breast Cancer Foundation (Finland)
- European Regional Development Fund
- National Breast Cancer Foundation (Australia)
- United States Army Medical Research and Materiel Command
- EU Horizon 2020 Research and Innovation Programme
- Directorate-General XII, Science, Research, and Development
- Baden Württemberg Ministry of Science, Research and Arts
- VicHealth
- Fondo de Investigación Sanitario
- Victorian Breast Cancer Research Consortium.
- Finnish Cancer Foundation
- University of Southern California San Francisco
- Fomento de la Investigación Clínica Independiente
- the Cancer Biology Research Center (CBRC), Djerassi Oncology Center
- Bundesministerium für Bildung und Forschung
- Cancerfonden
- Tel Aviv University Center for AI and Data Science
- University of Oulu
- National Breast Cancer Foundation (JS)
- Safra Center for Bioinformatics
- Fondation de France, Institut National du Cancer
- Israeli Science Foundation
- University of Utah
- National Cancer Center Research and Development Fund (Japan)
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Oak Foundation
- Health Research Fund (FIS)
- Ontario Familial Breast Cancer Registry
- New South Wales Cancer Council
- North Carolina University Cancer Research Fund
- Kreftforeningen
- Northern California Breast Cancer Family Registry
- Institut Gustave Roussy
- Huntsman Cancer Institute, University of Utah
- Ovarian Cancer Research Fund
- NIHR Oxford Biomedical Research Centre
- Hellenic Health Foundation
- Oulun Yliopistollinen Sairaala
- Helmholtz Society
- Herlev and Gentofte Hospital
- PSRSIIRI-701
- Helsinki University Hospital Research Fund
- Cancer Council Victoria
- National Research Council (Italy)
- Cancer Council Tasmania
- Cancer Council Western Australia
- Hamburger Krebsgesellschaft
- Gustav V Jubilee foundation
- National Program of Cancer Registries
- Canadian Cancer Society
- Cancer Council South Australia
- Canadian Institutes of Health Research
- Cancer Council NSW
- Guy's & St. Thomas' NHS Foundation Trust
- Netherlands Organisation of Scientific Research
- Cancer Institute NSW
- National Institutes of Health (USA)
- National Research Foundation of Korea
- Syöpäsäätiö
- Cancer Foundation of Western Australia
- Netherlands Cancer Registry (NKR),
- Cancer Fund of North Savo
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Affiliation(s)
- Hagai Levi
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Department of Human Molecular Genetics and Biochemistry, Tel Aviv University, Tel Aviv, Israel
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Saharon Rosset
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Rinat Yerushalmi
- Institute of Oncology, Davidoff Cancer Center, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Aviad Zick
- Department of oncology, Hadassah Medical Center, Jerusalem, Israel
- Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tamar Yablonski-Peretz
- Department of oncology, Hadassah Medical Center, Jerusalem, Israel
- Hebrew University of Jerusalem, Jerusalem, Israel
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Annelie Augustinsson
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Päivi Auvinen
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
- Department of Oncology, Cancer Center, Kuopio University Hospital, Kuopio, Finland
| | - Laura Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Matthias Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
| | - Clara Bodelon
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Helen Byers
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Nicola Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt lake city, UT, USA
| | - Jose Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Wendy Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Christine Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Margriet J Collee
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Sarah Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt lake city, UT, USA
| | - Fergus Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Angela Cox
- Department of Oncology and Metabolism, Sheffield Institute for Nucleic Acids (SInFoNiA), University of Sheffield, Sheffield, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
- Department of Human Genetics, Leiden University Medical, Leiden, Netherlands
| | - Thilo Dork
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
| | - Laure Dossus
- Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Diana M Eccles
- Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gareth Evans
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Peter Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, International Cancer Genetics and Epidemiology Group, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jeanine Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, New York, New York, USA
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Mark Goldberg
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montreal, QU, Canada
| | - Pascal Guénel
- Team 'Exposome and Heredity', CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Hillemanns
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
| | | | - Reiner Hoppe
- Dr Margarete Fischer Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tubingen, Germany
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Simona Jakovchevska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Skopje, North Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Helena Jernström
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Esther John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Michael Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Joseph Vijai
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Cari Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Vessela Kristensen
- Institute of Clinical Medicine, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Allison W Kurian
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - James Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Flavio Lejbkowicz
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Annika Lindblom
- Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | | | - Adriana Lori
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Jan Lubinski
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College, London, UK
| | - AnnaMarie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | - Rachel Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - Ines Nevelsteen
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - William G Newman
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Nadia Obi
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katie O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Ken Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Olshan
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Janet Olson
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Salvatore Panico
- Dipertimento Di Medicina Clinca e Chirurgia, Federico II University, Naples, Italy
| | | | - Alpa Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Brigitte Rack
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Valerie Rhenius
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Atocha Romero
- Laboratorio de Oncología Molecular, Hospital Clínico San Carlos, Madrid, Spain
| | | | - Dale Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Lukas Schwentner
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, USA
| | - Jacques Simard
- Genomics Center, Molecular Medicine, Université Laval, Quebec, Quebec, Canada
| | - Melissa Southey
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - William J Tapper
- Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jack Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Lauren Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Melissa Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Team 'Exposome and Heredity', CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France
| | | | - Clarice Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Camilla Wendt
- Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden
| | - Xiaohong Rose Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Argyrios Ziogas
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Shay Ben-Sachar
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel
| | - Naama Elefant
- Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Tel Aviv University, Tel Aviv, Israel
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Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528092. [PMID: 38045347 PMCID: PMC10690198 DOI: 10.1101/2023.02.10.528092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.
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Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
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