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Alfayyadh MM, Maksemous N, Sutherland HG, Lea RA, Griffiths LR. Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches. Genes (Basel) 2024; 15:443. [PMID: 38674378 PMCID: PMC11049430 DOI: 10.3390/genes15040443] [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: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.
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Affiliation(s)
| | | | | | | | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia; (M.M.A.); (N.M.); (H.G.S.); (R.A.L.)
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2
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Barnett EJ, Onete DG, Salekin A, Faraone SV. Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:169-177. [PMID: 38109236 DOI: 10.1109/tcbb.2023.3343808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and other differentiating features in genomic machine learning models. We used these features in linear regressions predicting model performance. We tested for univariate and multivariate associations as well as interactions between features. Of the models reviewed, 46% used feature selection methods that can lead to data leakage. Across our models, the number of hyperparameter optimizations reported, data leakage due to feature selection, model type, and modeling an autoimmune disorder were significantly associated with an increase in reported model performance. We found a significant, negative interaction between data leakage and training size. Our results suggest that methods susceptible to data leakage are prevalent among genomic machine learning research, resulting in inflated reported performance. Best practice guidelines that promote the avoidance and recognition of data leakage may help the field avoid biased results.
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3
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Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
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4
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Alzoubi H, Alzubi R, Ramzan N. Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094439. [PMID: 37177642 PMCID: PMC10181706 DOI: 10.3390/s23094439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.
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Affiliation(s)
- Hadeel Alzoubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Raid Alzubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
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5
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Learning high-order interactions for polygenic risk prediction. PLoS One 2023; 18:e0281618. [PMID: 36763605 PMCID: PMC9916647 DOI: 10.1371/journal.pone.0281618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/27/2023] [Indexed: 02/11/2023] Open
Abstract
Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.
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6
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Shandhi MMH, Dunn JP. AI in medicine: Where are we now and where are we going? Cell Rep Med 2022; 3:100861. [PMID: 36543109 PMCID: PMC9798019 DOI: 10.1016/j.xcrm.2022.100861] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022]
Abstract
Advancements in AI enable personalizing healthcare, for example by investigating disease origins at the genetic or molecular level, understanding intraindividual drug effects, and fusing multi-modal personal physiological, behavioral, laboratory, and clinical data to uncover new aspects of pathophysiology. Future efforts should address equity, fairness, explainability, and generalizability of AI models.
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Affiliation(s)
| | - Jessilyn P. Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA,Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA,Duke Clinical Research Institute, Durham, NC, USA,Corresponding author
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7
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Xu Y, Vuckovic D, Ritchie SC, Akbari P, Jiang T, Grealey J, Butterworth AS, Ouwehand WH, Roberts DJ, Di Angelantonio E, Danesh J, Soranzo N, Inouye M. Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. CELL GENOMICS 2022; 2:None. [PMID: 35072137 PMCID: PMC8758502 DOI: 10.1016/j.xgen.2021.100086] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 08/24/2021] [Accepted: 12/13/2021] [Indexed: 12/13/2022]
Abstract
Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.
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Affiliation(s)
- Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Dragana Vuckovic
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
| | - Parsa Akbari
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tao Jiang
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Jason Grealey
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC 3086, Australia
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Willem H. Ouwehand
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
- Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK
| | - David J. Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Science Research Centre, Human Technopole, Milan 20157, Italy
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
- The Alan Turing Institute, London NW1 2DB, UK
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8
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Raben TG, Lello L, Widen E, Hsu SDH. From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits. Methods Mol Biol 2022; 2467:421-446. [PMID: 35451785 DOI: 10.1007/978-1-0716-2205-6_15] [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: 06/14/2023]
Abstract
Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.
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Affiliation(s)
| | - Louis Lello
- Michigan State University, East Lansing, MI, USA
- Genomic Prediction, North Brunswick, NJ, USA
| | - Erik Widen
- Michigan State University, East Lansing, MI, USA
| | - Stephen D H Hsu
- Michigan State University, East Lansing, MI, USA.
- Genomic Prediction, North Brunswick, NJ, USA.
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9
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Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models. Genes (Basel) 2021; 13:genes13010087. [PMID: 35052430 PMCID: PMC8774935 DOI: 10.3390/genes13010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 11/17/2022] Open
Abstract
Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.
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10
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Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021; 124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/27/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
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Affiliation(s)
- Thibault Gautier
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America.
| | - Leah B Ziegler
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Matthew S Gerber
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Enrique Campos-Náñez
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Stephen D Patek
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
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11
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Abstract
Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine's multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine's "big data", in the context of genetics, genomics, and beyond.
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Affiliation(s)
- Sarah J MacEachern
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Nils D Forkert
- Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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12
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Deep neural network improves the estimation of polygenic risk scores for breast cancer. J Hum Genet 2020; 66:359-369. [PMID: 33009504 DOI: 10.1038/s10038-020-00832-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 08/10/2020] [Indexed: 11/08/2022]
Abstract
Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.
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13
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Pattee J, Pan W. Penalized regression and model selection methods for polygenic scores on summary statistics. PLoS Comput Biol 2020; 16:e1008271. [PMID: 33001975 PMCID: PMC7553329 DOI: 10.1371/journal.pcbi.1008271] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 10/13/2020] [Accepted: 08/18/2020] [Indexed: 11/29/2022] Open
Abstract
Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer.
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Affiliation(s)
- Jack Pattee
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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Su C, Tong J, Wang F. Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease. NPJ PARKINSONS DISEASE 2020; 6:24. [PMID: 32964109 PMCID: PMC7481248 DOI: 10.1038/s41531-020-00127-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 08/13/2020] [Indexed: 01/08/2023]
Abstract
High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY USA
| | - Jie Tong
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY USA
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15
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Thomas M, Sakoda LC, Hoffmeister M, Rosenthal EA, Lee JK, van Duijnhoven FJB, Platz EA, Wu AH, Dampier CH, de la Chapelle A, Wolk A, Joshi AD, Burnett-Hartman A, Gsur A, Lindblom A, Castells A, Win AK, Namjou B, Van Guelpen B, Tangen CM, He Q, Li CI, Schafmayer C, Joshu CE, Ulrich CM, Bishop DT, Buchanan DD, Schaid D, Drew DA, Muller DC, Duggan D, Crosslin DR, Albanes D, Giovannucci EL, Larson E, Qu F, Mentch F, Giles GG, Hakonarson H, Hampel H, Stanaway IB, Figueiredo JC, Huyghe JR, Minnier J, Chang-Claude J, Hampe J, Harley JB, Visvanathan K, Curtis KR, Offit K, Li L, Le Marchand L, Vodickova L, Gunter MJ, Jenkins MA, Slattery ML, Lemire M, Woods MO, Song M, Murphy N, Lindor NM, Dikilitas O, Pharoah PDP, Campbell PT, Newcomb PA, Milne RL, MacInnis RJ, Castellví-Bel S, Ogino S, Berndt SI, Bézieau S, Thibodeau SN, Gallinger SJ, Zaidi SH, Harrison TA, Keku TO, Hudson TJ, Vymetalkova V, Moreno V, Martín V, Arndt V, Wei WQ, Chung W, Su YR, Hayes RB, White E, Vodicka P, Casey G, Gruber SB, Schoen RE, Chan AT, Potter JD, Brenner H, Jarvik GP, Corley DA, Peters U, Hsu L. Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. Am J Hum Genet 2020; 107:432-444. [PMID: 32758450 PMCID: PMC7477007 DOI: 10.1016/j.ajhg.2020.07.006] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 07/13/2020] [Indexed: 02/08/2023] Open
Abstract
Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
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Affiliation(s)
- Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA
| | - Jeffrey K Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Franzel J B van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen 176700, the Netherlands
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA 90089, USA
| | - Christopher H Dampier
- Department of Surgery, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Albert de la Chapelle
- Department of Cancer Biology and Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna 1090, Austria
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm 17177, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm 17177, Sweden
| | - Antoni Castells
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona 08007, Spain
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Cincinnati VA Medical Center, Cincinnati, OH 45229, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå 90187, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå 90187, Sweden
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Clemens Schafmayer
- Department of General Surgery, University Hospital Rostock, Rostock 18051, Germany
| | - Corinne E Joshu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds LS2 9JT, UK
| | - Daniel D Buchanan
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC 3010, Australia; Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3010, Australia; Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3010, Australia
| | - Daniel Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - David C Muller
- School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - David Duggan
- Translational Genomics Research Institute - An Affiliate of City of Hope, Phoenix, AZ 85003, USA
| | - David R Crosslin
- Department of Bioinformatics and Medical Education, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02108, USA
| | - Eric Larson
- Kaiser Permanente Washington Research Institute, Seattle, WA 98101, USA
| | - Flora Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Frank Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Ian B Stanaway
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jessica Minnier
- School of Public Health, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, 69120 Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg 20246, Germany
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden 01062, Germany
| | - John B Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Cincinnati VA Medical Center, Cincinnati, OH 45229, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Keith R Curtis
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medical College, NY 10065, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | | | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - Mathieu Lemire
- PanCuRx Translational Research Initiative, Ontario, Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, NL A1B 3R7, Canada
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Neil Murphy
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Noralane M Lindor
- Department of Health Science Research, Mayo Clinic, Scottsdale, AZ 85260, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona 08007, Spain
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes 44093, France
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 85054, USA
| | - Steven J Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5G1X5, Canada
| | - Syed H Zaidi
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Thomas J Hudson
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona 08908, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08907, Spain; ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona 08908, Spain
| | - Vicente Martín
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain; Biomedicine Institute (IBIOMED), University of León, León 24071, Spain
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Wendy Chung
- Office of Research & Development, Department of Veterans Affairs, Washington, DC 20420, USA; Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Stephen B Gruber
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Centre for Public Health Research, Massey University, Wellington 6140, New Zealand
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA; Genome Sciences, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington, Seattle, WA 98195, USA.
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
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Song L, Liu A, Shi J. SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics. Bioinformatics 2020; 35:4038-4044. [PMID: 30911754 DOI: 10.1093/bioinformatics/btz176] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 02/08/2019] [Accepted: 03/22/2019] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training datasets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases. RESULTS We develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically <0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive. AVAILABILITY AND IMPLEMENTATION https://github.com/lsncibb/SummaryAUC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lei Song
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.,Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Aiyi Liu
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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17
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Macesic N, Bear Don't Walk OJ, Pe'er I, Tatonetti NP, Peleg AY, Uhlemann AC. Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data. mSystems 2020; 5:e00656-19. [PMID: 32457240 PMCID: PMC7253370 DOI: 10.1128/msystems.00656-19] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 05/01/2020] [Indexed: 02/06/2023] Open
Abstract
Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance.IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.
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Affiliation(s)
- Nenad Macesic
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, New York, USA
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, Australia
| | | | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, New York, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Anton Y Peleg
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, Australia
- Infection and Immunity Program, Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton, Victoria, Australia
| | - Anne-Catrin Uhlemann
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, New York, USA
- Microbiome & Pathogen Genomics Core, Columbia University Irving Medical Center, New York, New York, USA
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18
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Romero-Rosales BL, Tamez-Pena JG, Nicolini H, Moreno-Treviño MG, Trevino V. Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling. PLoS One 2020; 15:e0232103. [PMID: 32324812 PMCID: PMC7179850 DOI: 10.1371/journal.pone.0232103] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 04/07/2020] [Indexed: 01/14/2023] Open
Abstract
Late-onset Alzheimer’s Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer’s disease.
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Affiliation(s)
| | - Jose-Gerardo Tamez-Pena
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, Mexico
| | - Humberto Nicolini
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | | | - Victor Trevino
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, Mexico
- * E-mail:
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Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med 2020; 3:30. [PMID: 32195365 PMCID: PMC7062883 DOI: 10.1038/s41746-020-0229-3] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/17/2020] [Indexed: 02/07/2023] Open
Abstract
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
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Affiliation(s)
- I. S. Stafford
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - M. Kellermann
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - E. Mossotto
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - R. M. Beattie
- Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - B. D. MacArthur
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - S. Ennis
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
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20
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Padilla-Martínez F, Collin F, Kwasniewski M, Kretowski A. Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes. Int J Mol Sci 2020; 21:E1703. [PMID: 32131491 PMCID: PMC7084489 DOI: 10.3390/ijms21051703] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 02/28/2020] [Accepted: 02/28/2020] [Indexed: 02/07/2023] Open
Abstract
Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.
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Affiliation(s)
- Felipe Padilla-Martínez
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland;
| | - Francois Collin
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
| | - Miroslaw Kwasniewski
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland;
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276 Bialystok, Poland
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21
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22
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Ho DSW, Schierding W, Wake M, Saffery R, O’Sullivan J. Machine Learning SNP Based Prediction for Precision Medicine. Front Genet 2019; 10:267. [PMID: 30972108 PMCID: PMC6445847 DOI: 10.3389/fgene.2019.00267] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 03/11/2019] [Indexed: 12/17/2022] Open
Abstract
In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.
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Affiliation(s)
| | | | - Melissa Wake
- Murdoch Children Research Institute, Melbourne, VIC, Australia
| | - Richard Saffery
- Murdoch Children Research Institute, Melbourne, VIC, Australia
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23
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Efficient Implementation of Penalized Regression for Genetic Risk Prediction. Genetics 2019; 212:65-74. [PMID: 30808621 PMCID: PMC6499521 DOI: 10.1534/genetics.119.302019] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 02/22/2019] [Indexed: 12/14/2022] Open
Abstract
Polygenic risk scores (PRS) combine many single-nucleotide polymorphisms into a score reflecting the genetic risk of developing a disease. Privé, Aschard, and Blum present an efficient implementation of penalized logistic regression... Polygenic Risk Scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The “Clumping+Thresholding” (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T. In this paper, we present an efficient method for the joint estimation of SNP effects using individual-level data, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. We also provide an implementation of penalized linear regression for quantitative traits. We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. Overall, we find that PLR achieves equal or higher predictive performance than C+T in most scenarios considered, while being scalable to biobank data. In particular, we find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, in simulations, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC values of 89% and of 82.5%. Applying penalized linear regression to 350,000 individuals of the UK Biobank, we predict height with a larger correlation than with the best prediction of C+T (∼65% instead of ∼55%), further demonstrating its scalability and strong predictive power, even for highly polygenic traits. Moreover, using 150,000 individuals of the UK Biobank, we are able to predict breast cancer better than C+T, fitting PLR in a few minutes only. In conclusion, this paper demonstrates the feasibility and relevance of using penalized regression for PRS computation when large individual-level datasets are available, thanks to the efficient implementation available in our R package bigstatsr.
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Zhang S, Bamakan SMH, Qu Q, Li S. Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective. IEEE Rev Biomed Eng 2019; 12:194-208. [DOI: 10.1109/rbme.2018.2864254] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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25
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Rashkin SR, Chua KC, Ho C, Mulkey F, Jiang C, Mushiroda T, Kubo M, Friedman PN, Rugo HS, McLeod HL, Ratain MJ, Castillos F, Naughton M, Overmoyer B, Toppmeyer D, Witte JS, Owzar K, Kroetz DL. A Pharmacogenetic Prediction Model of Progression-Free Survival in Breast Cancer using Genome-Wide Genotyping Data from CALGB 40502 (Alliance). Clin Pharmacol Ther 2018; 105:738-745. [PMID: 30260474 DOI: 10.1002/cpt.1241] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/30/2018] [Indexed: 01/10/2023]
Abstract
Genome-wide genotyping data are increasingly available for pharmacogenetic association studies, but application of these data for development of prediction models is limited. Prediction methods, such as elastic net regularization, have recently been applied to genetic studies but only limitedly to pharmacogenetic outcomes. An elastic net was applied to a pharmacogenetic study of progression-free survival (PFS) of 468 patients with advanced breast cancer in a clinical trial of paclitaxel, nab-paclitaxel, and ixabepilone. A final model included 13 single nucleotide polymorphisms (SNPs) in addition to clinical covariates (prior taxane status, hormone receptor status, disease-free interval, and presence of visceral metastases) with an area under the curve (AUC) integrated over time of 0.81, an increase compared to an AUC of 0.64 for a model with clinical covariates alone. This model may be of value in predicting PFS with microtubule targeting agents and may inform reverse translational studies to understand differential response to these drugs.
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Affiliation(s)
- Sara R Rashkin
- Department of Biostatistics and Epidemiology, University of California San Francisco, San Francisco, California, USA
| | - Katherina C Chua
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA
| | - Carol Ho
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA
| | - Flora Mulkey
- Alliance Statistics and Data Center, Duke University, Durham, North Carolina, USA
| | - Chen Jiang
- Alliance Statistics and Data Center, Duke University, Durham, North Carolina, USA
| | - Tasei Mushiroda
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Paula N Friedman
- Department of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Hope S Rugo
- Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Howard L McLeod
- DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center, Tampa, Florida, USA
| | - Mark J Ratain
- Department of Medicine, The University of Chicago, Chicago, Illinois, USA
| | | | - Michael Naughton
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Beth Overmoyer
- Dana-Farber/Partners Cancer Care, Boston, Massachusetts, USA
| | - Deborah Toppmeyer
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
| | - John S Witte
- Department of Biostatistics and Epidemiology, University of California San Francisco, San Francisco, California, USA
| | - Kouros Owzar
- Alliance Statistics and Data Center, Duke University, Durham, North Carolina, USA.,Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA
| | - Deanna L Kroetz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA
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26
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Jordan DM, Do R. Using Full Genomic Information to Predict Disease: Breaking Down the Barriers Between Complex and Mendelian Diseases. Annu Rev Genomics Hum Genet 2018; 19:289-301. [PMID: 29641912 DOI: 10.1146/annurev-genom-083117-021136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.
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Affiliation(s)
- Daniel M Jordan
- Charles Bronfman Institute for Personalized Medicine and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
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27
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Maciukiewicz M, Marshe VS, Hauschild AC, Foster JA, Rotzinger S, Kennedy JL, Kennedy SH, Müller DJ, Geraci J. GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. J Psychiatr Res 2018; 99:62-68. [PMID: 29407288 DOI: 10.1016/j.jpsychires.2017.12.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/31/2017] [Accepted: 12/14/2017] [Indexed: 12/22/2022]
Abstract
Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction.
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Affiliation(s)
- Malgorzata Maciukiewicz
- Pharmacogenetic Research Clinic, Center for Addiction and Mental Health, Toronto, ON, Canada
| | - Victoria S Marshe
- Pharmacogenetic Research Clinic, Center for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anne-Christin Hauschild
- IBM Life Sciences Discovery Centre, Princess Margaret Cancer Centre, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; University Health Network, Toronto, ON, Canada
| | - Jane A Foster
- University Health Network, Toronto, ON, Canada; Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Susan Rotzinger
- University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - James L Kennedy
- Pharmacogenetic Research Clinic, Center for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada
| | - Daniel J Müller
- Pharmacogenetic Research Clinic, Center for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Joseph Geraci
- Department of Molecular Medicine, Queen's University, Kingston, ON, Canada
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28
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Morassut RE, Langlois C, Alyass A, Ishola AF, Yazdi FT, Mayhew AJ, Reddon H, MacKillop J, Pigeyre M, Meyre D. Rationale and design of GENEiUS: a prospective observational study on the genetic and environmental determinants of body mass index evolution in Canadian undergraduate students. BMJ Open 2017; 7:e019365. [PMID: 29229660 PMCID: PMC5778320 DOI: 10.1136/bmjopen-2017-019365] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Obesity is a global epidemic and is a risk factor for developing other comorbidities. Young adulthood is a critical period for body weight change and establishing healthy lifestyle behaviours. The 'Freshman 15' suggests that undergraduate students gain 15 lbs (6.8 kg) during their first year of university, although evidence estimates a more modest weight gain of approximately 3-5 lbs (1.4-2.3 kg). Previous studies have only investigated weight change in the first year and do not study potential risk factors. Genetic and EnviroNmental Effects on weight in University Students (GENEiUS) is a prospective observational study which will investigate the environmental and biological determinants of weight change in undergraduate students over 4 years. METHODS AND ANALYSIS The GENEiUS study will recruit 2500 multiethnic undergraduates aged 17-25 years at McMaster University at the start of their first year and will follow them every 6 months for 4 years. Primary outcomes are obesity traits: body mass index, waist circumference, waist-to-hip ratio, body fat mass and body fat percentage. The contribution of well-established and novel genetic variants for obesity traits and heritability values will be derived from whole-genome single-nucleotide polymorphism genotyping arrays. Civil status, age, sex, ethnicity, length of residence in Canada, religiosity, energy intake, physical activity, exercise motivation, electronic screen time, sleep patterns, history of assault, smoking status, alcohol consumption, medication and drug use, stress, impulsivity, body image perception, self-esteem, anxiety, eating disorders and depression will be investigated for their effect on obesity traits. The findings of the GENEiUS study will be used to help design obesity prevention programme in North American universities with multiethnic populations. ETHICS AND DISSEMINATION Ethical approval of the study protocol has been obtained from the Hamilton Integrated Research Ethics Board. Study results will be disseminated through scientific publications, scholarly meetings, and collaborative meetings with university administration and student groups.
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Affiliation(s)
- Rita E Morassut
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Christine Langlois
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Adeola F Ishola
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Fereshteh T Yazdi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Alexandra J Mayhew
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Hudson Reddon
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - James MacKillop
- Peter Boris Centre for Addictions Research, McMaster University/St. Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Nutrition, University of Lille, CHRU Lille, Lille, France
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
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29
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Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data. Genetics 2017; 207:1147-1155. [PMID: 28899997 DOI: 10.1534/genetics.117.300283] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 08/31/2017] [Indexed: 12/30/2022] Open
Abstract
Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.
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30
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Mayhew AJ, Meyre D. Assessing the Heritability of Complex Traits in Humans: Methodological Challenges and Opportunities. Curr Genomics 2017; 18:332-340. [PMID: 29081689 PMCID: PMC5635617 DOI: 10.2174/1389202918666170307161450] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 12/29/2016] [Accepted: 01/29/2017] [Indexed: 02/08/2023] Open
Abstract
The goal of this review article is to provide a conceptual based summary of how heritability estimates for complex traits such as obesity are determined and to explore the future directions of research in the heritability field. The target audience are researchers who use heritability data rather than those conducting heritability studies. The article provides an introduction to key concepts critical to understanding heritability studies including: i) definitions of heritability: broad sense versus narrow sense heritability; ii) how data for heritability studies are collected: twin, adoption, family and population-based studies; and iii) analytical techniques: path analysis, structural equations and mixed or regressive models of complex segregation analysis. For each section, a discussion of how the different definitions and methodologies influence heritability estimates is provided. The general limitations of heritability studies are discussed including the issue of “missing heritability” in which heritability estimates are significantly higher than the variance explained by known genetic variants. Potential causes of missing heritability include restriction of many genetic association studies to single nucleotide polymorphisms, gene by gene interactions, epigenetics, and gene by environment interactions. Innovative strategies of accounting for missing heritability including modeling techniques and improved software are discussed.
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Affiliation(s)
- Alexandra J Mayhew
- Department of Health Research Methods, McMaster University, Evidence and Impact, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Health Research Methods, McMaster University, Evidence and Impact, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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31
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Isobe N, Keshavan A, Gourraud PA, Zhu AH, Datta E, Schlaeger R, Caillier SJ, Santaniello A, Lizée A, Himmelstein DS, Baranzini SE, Hollenbach J, Cree BAC, Hauser SL, Oksenberg JR, Henry RG. Association of HLA Genetic Risk Burden With Disease Phenotypes in Multiple Sclerosis. JAMA Neurol 2017; 73:795-802. [PMID: 27244296 DOI: 10.1001/jamaneurol.2016.0980] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
IMPORTANCE Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. OBJECTIVES To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. MAIN OUTCOMES AND MEASURES Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. RESULTS Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [-0.3 to 0.5], respectively; P = 1.8 × 10-27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = -1.20 × 10-1; P = 1.7 × 10-2 and standard β = -1.67 × 10-1; P = 2.3 × 10-4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = -1.28 × 10-1; P = 5.1 × 10-3 and standard β = 9.52 × 10-2; P = 3.6 × 10-2, respectively). CONCLUSIONS AND RELEVANCE We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.
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Affiliation(s)
- Noriko Isobe
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Anisha Keshavan
- Department of Neurology, School of Medicine, University of California, San Francisco
| | | | - Alyssa H Zhu
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Esha Datta
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Regina Schlaeger
- Department of Neurology, School of Medicine, University of California, San Francisco2Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Stacy J Caillier
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Adam Santaniello
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Antoine Lizée
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Daniel S Himmelstein
- Department of Neurology, School of Medicine, University of California, San Francisco3Biological and Medical Informatics, University of California, San Francisco
| | - Sergio E Baranzini
- Department of Neurology, School of Medicine, University of California, San Francisco3Biological and Medical Informatics, University of California, San Francisco
| | - Jill Hollenbach
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Bruce A C Cree
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Stephen L Hauser
- Department of Neurology, School of Medicine, University of California, San Francisco4Institute of Human Genetics, University of California, San Francisco
| | - Jorge R Oksenberg
- Department of Neurology, School of Medicine, University of California, San Francisco4Institute of Human Genetics, University of California, San Francisco
| | - Roland G Henry
- Department of Neurology, School of Medicine, University of California, San Francisco5Bioengineering Graduate Group, University of California, San Francisco and Berkeley6Department of Radiology and Biomedical Imaging, University of California, San Francisc
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Lam HV, Nguyen DT, Nguyen CD. Sibling method increases risk assessment estimates for type 1 diabetes. PLoS One 2017; 12:e0176341. [PMID: 28510587 PMCID: PMC5433695 DOI: 10.1371/journal.pone.0176341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 04/10/2017] [Indexed: 12/29/2022] Open
Abstract
We presented a risk assessment model to distinguish between type 1 diabetes (T1D) affected and unaffected siblings using only three single nucleotide polymorphism (SNP) genotypes. In addition we calculated the heritability from genome-wide identity-by-descent (IBD) sharing between full siblings. We analyzed 1,253 pairs of affected individuals and their unaffected siblings (750 pairs from a discovery set and 503 pairs from a validation set) from the T1D Genetics Consortium (T1DGC), applying a logistic regression to analyze the area under the receiver operator characteristic (ROC) curve (AUC). To calculate the heritability of T1D we used the Haseman-Elston regression analysis of the squared difference between the phenotypes of the pairs of siblings on the estimate of their genome-wide IBD proportion. The model with only 3 SNPs achieving an AUC of 0.75 in both datasets outperformed the model using the presence of the high-risk DR3/4 HLA genotype, namely AUC of 0.60. The heritability on the liability scale of T1D was approximately from 0.53 to 0.92, close to the results obtained from twin studies, ranging from 0.4 to 0.88.
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Affiliation(s)
- Hoang V. Lam
- Department of Endocrinology, Cho Ray Hospital, Ho Chi Minh City, Vietnam
| | - Dat T. Nguyen
- Department of Science and Technology, Hoa Sen University, Ho Chi Minh City, Vietnam
| | - Cao D. Nguyen
- Department of Business and Information System, Economics University, Ho Chi Minh City, Vietnam
- Clinical Analysis and Modelling—Department of Health, Western Australia, Australia
- * E-mail:
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Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol 2017; 41:469-480. [DOI: 10.1002/gepi.22050] [Citation(s) in RCA: 186] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 02/20/2017] [Accepted: 03/14/2017] [Indexed: 01/01/2023]
Affiliation(s)
| | | | - Shing Wan Choi
- Department of Psychiatry; University of Hong Kong; Hong Kong
| | - Xueya Zhou
- Department of Psychiatry; University of Hong Kong; Hong Kong
| | - Pak Chung Sham
- Centre for Genomic Sciences; University of Hong Kong; Hong Kong
- Department of Psychiatry; University of Hong Kong; Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences; University of Hong Kong; Hong Kong
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Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci (Lond) 2017; 130:943-86. [PMID: 27154742 DOI: 10.1042/cs20160136] [Citation(s) in RCA: 227] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/24/2016] [Indexed: 12/19/2022]
Abstract
In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.
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Lee SH, Weerasinghe WMSP, Wray NR, Goddard ME, van der Werf JHJ. Using information of relatives in genomic prediction to apply effective stratified medicine. Sci Rep 2017; 7:42091. [PMID: 28181587 PMCID: PMC5299615 DOI: 10.1038/srep42091] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 01/05/2017] [Indexed: 01/14/2023] Open
Abstract
Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.
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Affiliation(s)
- S. Hong Lee
- School of Environmental and Rural Science, University of New England, NSW 2351, Australia
| | | | - Naomi R. Wray
- The Centre of Neurogenetics and Statistical Genomics, Queensland Brain Institute, The University of Queensland, QLD 4072, Australia
| | - Michael E. Goddard
- Faculty of Land and Food Resources, University of Melbourne, Melbourne, Australia
- Department of Primary Industries, Biosciences Research Division, Bundoora, Australia
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Woo HJ, Yu C, Reifman J. Collective Genetic Interaction Effects and the Role of Antigen-Presenting Cells in Autoimmune Diseases. PLoS One 2017; 12:e0169918. [PMID: 28081217 PMCID: PMC5231276 DOI: 10.1371/journal.pone.0169918] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 12/23/2016] [Indexed: 12/20/2022] Open
Abstract
Autoimmune diseases occur when immune cells fail to develop or lose their tolerance toward self and destroy body's own tissues. Both insufficient negative selection of self-reactive T cells and impaired development of regulatory T cells preventing effector cell activation are believed to contribute to autoimmunity. Genetic predispositions center around the major histocompatibility complex (MHC) class II loci involved in antigen presentation, the key determinant of CD4+ T cell activation. Recent studies suggested that variants in the MHC region also exhibit significant non-additive interaction effects. However, collective interactions involving large numbers of single nucleotide polymorphisms (SNPs) contributing to such effects are yet to be characterized. In addition, relatively little is known about the cell-type-specificity of such interactions in the context of cellular pathways. Here, we analyzed type 1 diabetes (T1D) and rheumatoid arthritis (RA) genome-wide association data sets via large-scale, high-performance computations and inferred collective interaction effects involving MHC SNPs using the discrete discriminant analysis. Despite considerable differences in the details of SNP interactions in T1D and RA data, the enrichment pattern of interacting pairs in reference epigenomes was remarkably similar: statistically significant interactions were epigenetically active in cell-type combinations connecting B cells to T cells and intestinal epithelial cells, with both helper and regulatory T cells showing strong disease-associated interactions with B cells. Our results provide direct genetic evidence pointing to the important roles B cells play as antigen-presenting cells toward CD4+ T cells in the context of central and peripheral tolerance. In addition, they are consistent with recent experimental studies suggesting that the repertoire of B cell-specific self-antigens in the thymus are critical to the effective control of corresponding autoimmune activation in peripheral tissues.
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MESH Headings
- Antigen-Presenting Cells/cytology
- Antigen-Presenting Cells/immunology
- Antigen-Presenting Cells/metabolism
- Area Under Curve
- Arthritis, Rheumatoid/genetics
- Arthritis, Rheumatoid/pathology
- Autoimmune Diseases/genetics
- Autoimmune Diseases/pathology
- B-Lymphocytes/cytology
- B-Lymphocytes/immunology
- B-Lymphocytes/metabolism
- CD4-Positive T-Lymphocytes/cytology
- CD4-Positive T-Lymphocytes/immunology
- CD4-Positive T-Lymphocytes/metabolism
- Diabetes Mellitus, Type 1/genetics
- Diabetes Mellitus, Type 1/pathology
- Epigenomics
- Gene Regulatory Networks
- Genome-Wide Association Study
- Histocompatibility Antigens Class II/genetics
- Humans
- Linkage Disequilibrium
- Polymorphism, Single Nucleotide
- ROC Curve
- T-Lymphocytes, Helper-Inducer/cytology
- T-Lymphocytes, Helper-Inducer/immunology
- T-Lymphocytes, Helper-Inducer/metabolism
- T-Lymphocytes, Regulatory/cytology
- T-Lymphocytes, Regulatory/immunology
- T-Lymphocytes, Regulatory/metabolism
- Thymus Gland/cytology
- Thymus Gland/metabolism
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Affiliation(s)
- Hyung Jun Woo
- Biotechnology High-Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Maryland, United States of America
- * E-mail: (HJW); (JR)
| | - Chenggang Yu
- Biotechnology High-Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Maryland, United States of America
| | - Jaques Reifman
- Biotechnology High-Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Maryland, United States of America
- * E-mail: (HJW); (JR)
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Mieth B, Kloft M, Rodríguez JA, Sonnenburg S, Vobruba R, Morcillo-Suárez C, Farré X, Marigorta UM, Fehr E, Dickhaus T, Blanchard G, Schunk D, Navarro A, Müller KR. Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies. Sci Rep 2016; 6:36671. [PMID: 27892471 PMCID: PMC5125008 DOI: 10.1038/srep36671] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 10/06/2016] [Indexed: 12/21/2022] Open
Abstract
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.
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Affiliation(s)
- Bettina Mieth
- Machine Learning Group, Technische Universität Berlin, Berlin, 10587, Germany
| | - Marius Kloft
- Department of Computer Science, Humboldt University of Berlin, Berlin, 10099, Germany
| | - Juan Antonio Rodríguez
- Institut de Biología Evolutiva (CSIC-UPF). Departament de Ciències Experimentals i de la Salut. Universitat Pompeu Fabra, Barcelona, 08003, Spain
| | | | - Robin Vobruba
- Machine Learning Group, Technische Universität Berlin, Berlin, 10587, Germany
| | - Carlos Morcillo-Suárez
- Institut de Biología Evolutiva (CSIC-UPF). Departament de Ciències Experimentals i de la Salut. Universitat Pompeu Fabra, Barcelona, 08003, Spain
| | - Xavier Farré
- Institut de Biología Evolutiva (CSIC-UPF). Departament de Ciències Experimentals i de la Salut. Universitat Pompeu Fabra, Barcelona, 08003, Spain
| | - Urko M. Marigorta
- School of Biology, Georgia Institute of Technology, Atlanta, 30332, GA, USA
| | - Ernst Fehr
- Department of Economics, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, 8006, Switzerland
| | - Thorsten Dickhaus
- Institute for Statistics (FB 3), University of Bremen, Bremen, 28359, Germany
| | - Gilles Blanchard
- Department of Mathematics, University of Potsdam, Potsdam, 14476, Germany
| | - Daniel Schunk
- Department of Economics, University of Mainz, Mainz, 55099, Germany
| | - Arcadi Navarro
- Institut de Biología Evolutiva (CSIC-UPF). Departament de Ciències Experimentals i de la Salut. Universitat Pompeu Fabra, Barcelona, 08003, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, 08010, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, 08003, Spain
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Berlin, 10587, Germany
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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Carapito R, Radosavljevic M, Bahram S. Next-Generation Sequencing of the HLA locus: Methods and impacts on HLA typing, population genetics and disease association studies. Hum Immunol 2016; 77:1016-1023. [DOI: 10.1016/j.humimm.2016.04.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 03/15/2016] [Accepted: 04/04/2016] [Indexed: 12/30/2022]
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Kang H, Wang Y, Peng S, Zhang Y, Xiao Y, Wang D, Qu S, Li Z, Yan S, Wang Z, Liu W, Ning Y, Korniliev P, Leung H, Mezey J, McCouch SR, Wang GL. Dissection of the genetic architecture of rice resistance to the blast fungus Magnaporthe oryzae. MOLECULAR PLANT PATHOLOGY 2016; 17:959-72. [PMID: 26574735 PMCID: PMC6638458 DOI: 10.1111/mpp.12340] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 10/22/2015] [Accepted: 10/26/2015] [Indexed: 05/12/2023]
Abstract
Resistance in rice cultivars to the rice blast fungus Magnaporthe oryzae is complex and is controlled by both major genes and quantitative trait loci (QTLs). We undertook a genome-wide association study (GWAS) using the rice diversity panel 1 (RDP1) that was genotyped using a high-density (700 000 single nucleotide polymorphisms) array and inoculated with five diverse M. oryzae isolates. We identified 97 loci associated with blast resistance (LABRs). Among them, 82 were new regions and 15 co-localized with known blast resistance loci. The top 72 LABRs explained up to 98% of the phenotypic variation. The candidate genes in the LABRs encode nucleotide-binding site leucine-rich repeat (NBS-LRR) resistance proteins, receptor-like protein kinases, transcription factors and defence-related proteins. Among them, LABR_64 was strongly associated with resistance to all five isolates. We analysed the function of candidate genes underlying LABR_64 using RNA interference (RNAi) technology and identified two new resistance alleles at the Pi5 locus. We demonstrate an efficient strategy for rapid allele discovery using the power of GWAS, coupled with RNAi technology, for the dissection of complex blast resistance in rice.
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Affiliation(s)
- Houxiang Kang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yue Wang
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of Agronomy, Hunan Agricultural University, Changsha, Hunan, 410128, China
| | - Shasha Peng
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yanli Zhang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yinghui Xiao
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of Agronomy, Hunan Agricultural University, Changsha, Hunan, 410128, China
| | - Dan Wang
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of Agronomy, Hunan Agricultural University, Changsha, Hunan, 410128, China
| | - Shaohong Qu
- Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou, Zhejiang, 310021, China
| | - Zhiqiang Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Shuangyong Yan
- Tianjin Crop Research Institute, Tianjin Academy of Agriculture Sciences, Tianjin, 300112, China
| | - Zhilong Wang
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of Agronomy, Hunan Agricultural University, Changsha, Hunan, 410128, China
| | - Wende Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yuese Ning
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Pavel Korniliev
- Department of Plant Breeding & Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Hei Leung
- International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila 1301, Philippines
| | - Jason Mezey
- Department of Plant Breeding & Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Susan R McCouch
- Department of Plant Breeding & Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Guo-Liang Wang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- Department of Plant Pathology, Ohio State University, Columbus, OH, 43210, USA
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Zhao Y, Chen G, Yu H, Hu L, Bian Y, Yun D, Chen J, Mao Y, Chen H, Lu D. Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances. Oncotarget 2016; 9:8311-8325. [PMID: 29492197 PMCID: PMC5823595 DOI: 10.18632/oncotarget.10882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 06/29/2016] [Indexed: 12/17/2022] Open
Abstract
Over 14 common single nucleotide polymorphisms (SNP) have been consistently identified from genome-wide association studies (GWAS) as associated with glioma risk in European background. The extent to which and how these genetic variants can improve the prediction of glioma risk has was not been investigated. In this study, we employed three independent case-control datasets in Chinese populations, tested GWAS signals in dataset1, validated association results in dataset2, developed prediction models in dataset2 for the consistently replicated SNPs, refined the consistently replicated SNPs in dataset3 and developed tailored models for Chinese populations. For model construction, we aggregated the contribution of multiple SNPs into genetic risk scores (count GRS and weighed GRS) or predicted risks from logistic regression analyses (PRFLR). In dataset2, the area under receiver operating characteristic curves (AUC) of the 5 consistently replicated SNPs by PRFLR(SNPs) was 0.615, higher than those of all GRSs(ranging from 0.607 to 0.611, all P>0.05). The AUC of genetic profile significantly exceeded that of family history (fmc) alone (AUC=0.535, all P<0.001). The best model in our study comprised “PRURA +fmc” (AUC=0.646) in dataset3. Further model assessment analyses provided additional evidence. This study indicates that genetic markers have potential value for risk prediction of glioma.
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Affiliation(s)
- Yingjie Zhao
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Gong Chen
- Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, China
| | - Hongjie Yu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China.,Center for Genetic Epidemiology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lingna Hu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yunmeng Bian
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Dapeng Yun
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Juxiang Chen
- Department of Neurosurgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Ying Mao
- Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, China
| | - Hongyan Chen
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
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Guo Y, Wei Z, Keating BJ, Hakonarson H. Machine learning derived risk prediction of anorexia nervosa. BMC Med Genomics 2016; 9:4. [PMID: 26792494 PMCID: PMC4721143 DOI: 10.1186/s12920-016-0165-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Accepted: 01/15/2016] [Indexed: 12/25/2022] Open
Abstract
Background Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. Methods In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children’s Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Results Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC’s of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. Conclusions To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting. Electronic supplementary material The online version of this article (doi:10.1186/s12920-016-0165-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiran Guo
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Brendan J Keating
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | | | - Hakon Hakonarson
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. .,Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Potenciano V, Abad-Grau MM, Alcina A, Matesanz F. A comparison of genomic profiles of complex diseases under different models. BMC Med Genomics 2016; 9:3. [PMID: 26782991 PMCID: PMC4717655 DOI: 10.1186/s12920-015-0157-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 11/27/2015] [Indexed: 12/15/2022] Open
Abstract
Background Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Víctor Potenciano
- Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain.
| | - María Mar Abad-Grau
- Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain.
| | - Antonio Alcina
- Instituto de Parasitología y Biología Molecular, CSIC, Granada, Spain.
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Roizen JD, Bradfield JP, Hakonarson H. Progress in understanding type 1 diabetes through its genetic overlap with other autoimmune diseases. Curr Diab Rep 2015; 15:102. [PMID: 26454449 PMCID: PMC5585867 DOI: 10.1007/s11892-015-0668-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Type 1 diabetes mellitus (T1DM) is the most common autoimmune disease in pediatrics with a prevalence of roughly 1 in 500 children in the USA. Genome-wide association studies have identified more than 50 variants associated with increased risk for type 1 diabetes. Comparison of these variants with those identified in other autoimmune diseases reveals three important findings: (1) there is a high degree of overlap in implicated variants in diseases with similar pathophysiology, (2) in diseases with differing pathophysiology the same variants are often implicated in opposite roles, (3) in diseases with differing pathophysiology that have many non-overlapping or oppositely implicated variants there are still several variants which are overlapping or shared. Thus, the genetic overlap between T1DM and other autoimmune diseases forms the basis for our understanding of druggable targets in type 1 diabetes.
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Affiliation(s)
- Jeffrey D Roizen
- Division of Endocrinology and Diabetes, The Children's Hospital of Philadelphia, 34th and Civic Center Blvd. 11NW, Philadelphia, PA, 19103, USA.
- Division of Pulmonary Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Jonathan P Bradfield
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, 3615 Civic Center Blvd., Suite 1014H, Philadelphia, PA, 19104, USA.
| | - Hakon Hakonarson
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, 3615 Civic Center Blvd., Suite 1014H, Philadelphia, PA, 19104, USA.
- Division of Pulmonary Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Vilhjálmsson B, Yang J, Finucane H, Gusev A, Lindström S, Ripke S, Genovese G, Loh PR, Bhatia G, Do R, Hayeck T, Won HH, Kathiresan S, Pato M, Pato C, Tamimi R, Stahl E, Zaitlen N, Pasaniuc B, Belbin G, Kenny EE, Schierup MH, De Jager P, Patsopoulos NA, McCarroll S, Daly M, Purcell S, Chasman D, Neale B, Goddard M, Visscher PM, Kraft P, Patterson N, Price AL, Ripke S, Neale B, Corvin A, Walters J, Farh KH, Holmans P, Lee P, Bulik-Sullivan B, Collier D, Huang H, Pers T, Agartz I, Agerbo E, Albus M, Alexander M, Amin F, Bacanu S, Begemann M, Belliveau R, Bene J, Bergen S, Bevilacqua E, Bigdeli T, Black D, Bruggeman R, Buccola N, Buckner R, Byerley W, Cahn W, Cai G, Campion D, Cantor R, Carr V, Carrera N, Catts S, Chambert K, Chan R, Chen R, Chen E, Cheng W, Cheung E, Chong S, Cloninger C, Cohen D, Cohen N, Cormican P, Craddock N, Crowley J, Curtis D, Davidson M, Davis K, Degenhardt F, Del Favero J, DeLisi L, Demontis D, Dikeos D, Dinan T, Djurovic S, Donohoe G, Drapeau E, Duan J, Dudbridge F, Durmishi N, Eichhammer P, Eriksson J, Escott-Price V, Essioux L, Fanous A, Farrell M, Frank J, Franke L, Freedman R, Freimer N, Friedl M, Friedman J, Fromer M, Genovese G, Georgieva L, Gershon E, Giegling I, Giusti-Rodrguez P, Godard S, Goldstein J, Golimbet V, Gopal S, Gratten J, Grove J, de Haan L, Hammer C, Hamshere M, Hansen M, Hansen T, Haroutunian V, Hartmann A, Henskens F, Herms S, Hirschhorn J, Hoffmann P, Hofman A, Hollegaard M, Hougaard D, Ikeda M, Joa I, Julia A, Kahn R, Kalaydjieva L, Karachanak-Yankova S, Karjalainen J, Kavanagh D, Keller M, Kelly B, Kennedy J, Khrunin A, Kim Y, Klovins J, Knowles J, Konte B, Kucinskas V, Kucinskiene Z, Kuzelova-Ptackova H, Kahler A, Laurent C, Keong J, Lee S, Legge S, Lerer B, Li M, Li T, Liang KY, Lieberman J, Limborska S, Loughland C, Lubinski J, Lnnqvist J, Macek M, Magnusson P, Maher B, Maier W, Mallet J, Marsal S, Mattheisen M, Mattingsdal M, McCarley R, McDonald C, McIntosh A, Meier S, Meijer C, Melegh B, Melle I, Mesholam-Gately R, Metspalu A, Michie P, Milani L, Milanova V, Mokrab Y, Morris D, Mors O, Mortensen P, Murphy K, Murray R, Myin-Germeys I, Mller-Myhsok B, Nelis M, Nenadic I, Nertney D, Nestadt G, Nicodemus K, Nikitina-Zake L, Nisenbaum L, Nordin A, O’Callaghan E, O’Dushlaine C, O’Neill F, Oh SY, Olincy A, Olsen L, Van Os J, Pantelis C, Papadimitriou G, Papiol S, Parkhomenko E, Pato M, Paunio T, Pejovic-Milovancevic M, Perkins D, Pietilinen O, Pimm J, Pocklington A, Powell J, Price A, Pulver A, Purcell S, Quested D, Rasmussen H, Reichenberg A, Reimers M, Richards A, Roffman J, Roussos P, Ruderfer D, Salomaa V, Sanders A, Schall U, Schubert C, Schulze T, Schwab S, Scolnick E, Scott R, Seidman L, Shi J, Sigurdsson E, Silagadze T, Silverman J, Sim K, Slominsky P, Smoller J, So HC, Spencer C, Stahl E, Stefansson H, Steinberg S, Stogmann E, Straub R, Strengman E, Strohmaier J, Stroup T, Subramaniam M, Suvisaari J, Svrakic D, Szatkiewicz J, Sderman E, Thirumalai S, Toncheva D, Tooney P, Tosato S, Veijola J, Waddington J, Walsh D, Wang D, Wang Q, Webb B, Weiser M, Wildenauer D, Williams N, Williams S, Witt S, Wolen A, Wong E, Wormley B, Wu J, Xi H, Zai C, Zheng X, Zimprich F, Wray N, Stefansson K, Visscher P, Adolfsson R, Andreassen O, Blackwood D, Bramon E, Buxbaum J, Børglum A, Cichon S, Darvasi A, Domenici E, Ehrenreich H, Esko T, Gejman P, Gill M, Gurling H, Hultman C, Iwata N, Jablensky A, Jonsson E, Kendler K, Kirov G, Knight J, Lencz T, Levinson D, Li Q, Liu J, Malhotra A, McCarroll S, McQuillin A, Moran J, Mortensen P, Mowry B, Nthen M, Ophoff R, Owen M, Palotie A, Pato C, Petryshen T, Posthuma D, Rietschel M, Riley B, Rujescu D, Sham P, Sklar P, St. Clair D, Weinberger D, Wendland J, Werge T, Daly M, Sullivan P, O’Donovan M, Kraft P, Hunter DJ, Adank M, Ahsan H, Aittomäki K, Baglietto L, Berndt S, Blomquist C, Canzian F, Chang-Claude J, Chanock SJ, Crisponi L, Czene K, Dahmen N, Silva IDS, Easton D, Eliassen AH, Figueroa J, Fletcher O, Garcia-Closas M, Gaudet MM, Gibson L, Haiman CA, Hall P, Hazra A, Hein R, Henderson BE, Hofman A, Hopper JL, Irwanto A, Johansson M, Kaaks R, Kibriya MG, Lichtner P, Lindström S, Liu J, Lund E, Makalic E, Meindl A, Meijers-Heijboer H, Müller-Myhsok B, Muranen TA, Nevanlinna H, Peeters PH, Peto J, Prentice RL, Rahman N, Sánchez MJ, Schmidt DF, Schmutzler RK, Southey MC, Tamimi R, Travis R, Turnbull C, Uitterlinden AG, van der Luijt RB, Waisfisz Q, Wang Z, Whittemore AS, Yang R, Zheng W. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am J Hum Genet 2015; 97:576-92. [PMID: 26430803 DOI: 10.1016/j.ajhg.2015.09.001] [Citation(s) in RCA: 798] [Impact Index Per Article: 88.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 09/01/2015] [Indexed: 11/24/2022] Open
Abstract
Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.
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Abstract
Colorectal cancer (CRC) is a complex disease that develops as a consequence of both genetic and environmental risk factors. A small proportion (3-5%) of cases arise from hereditary syndromes predisposing to early onset CRC as a result of mutations in over a dozen well defined genes. In contrast, CRC is predominantly a late onset 'sporadic' disease, developing in individuals with no obvious hereditary syndrome. In recent years, genome wide association studies have discovered that over 40 genetic regions are associated with weak effects on sporadic CRC, and it has been estimated that increasingly large genome wide scans will identify many additional novel genetic regions. Subsequent experimental validations have identified the causally related variant(s) in a limited number of these genetic regions. Further biological insight could be obtained through ethnically diverse study populations, larger genetic sequencing studies and development of higher throughput functional experiments. Along with inherited variation, integration of the tumour genome may shed light on the carcinogenic processes in CRC. In addition to summarising the genetic architecture of CRC, this review discusses genetic factors that modify environmental predictors of CRC, as well as examples of how genetic insight has improved clinical surveillance, prevention and treatment strategies. In summary, substantial progress has been made in uncovering the genetic architecture of CRC, and continued research efforts are expected to identify additional genetic risk factors that further our biological understanding of this disease. Subsequently these new insights will lead to improved treatment and prevention of colorectal cancer.
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Affiliation(s)
- Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Stephanie Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Niha Zubair
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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Chen CY, Han J, Hunter DJ, Kraft P, Price AL. Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction. Genet Epidemiol 2015; 39:427-38. [PMID: 25995153 PMCID: PMC4734143 DOI: 10.1002/gepi.21906] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Revised: 03/19/2015] [Accepted: 04/07/2015] [Indexed: 01/14/2023]
Abstract
Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P < 10(-16)), the R(2) for TA increased by 123% (0.0154 to 0.0344; P < 10(-16)), and the liability-scale R(2) for BCC increased by 68% (0.0138-0.0232; P < 10(-16)) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.
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Affiliation(s)
- Chia-Yen Chen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Jiali Han
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Simon Cancer Center, Indiana University, Indianapolis, Indiana 46202
| | - David J. Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
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Mittag F, Römer M, Zell A. Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies. PLoS One 2015; 10:e0135832. [PMID: 26285210 PMCID: PMC4540285 DOI: 10.1371/journal.pone.0135832] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/27/2015] [Indexed: 12/31/2022] Open
Abstract
Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS). However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs) and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM) as they allow for better subsequent interpretation without significant loss of accuracy.
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Affiliation(s)
- Florian Mittag
- Cognitive Systems Group, University of Tübingen, Tübingen, Germany
- * E-mail:
| | - Michael Römer
- Cognitive Systems Group, University of Tübingen, Tübingen, Germany
| | - Andreas Zell
- Cognitive Systems Group, University of Tübingen, Tübingen, Germany
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Abstract
Recent advances in genome-wide association studies have stimulated interest in the genomic prediction of disease risk, potentially enabling individual-level risk estimates for early intervention and improved diagnostic procedures. Here, we review recent findings and approaches to genomic prediction model construction and performance, then contrast the potential benefits of such models in two complex human diseases, aiding diagnosis in celiac disease and prospective risk prediction for cardiovascular disease. Early indications are that optimal application of genomic risk scores will differ substantially for each disease depending on underlying genetic architecture as well as current clinical and public health practice. As costs decline, genomic profiles become common, and popular understanding of risk and its communication improves, genomic risk will become increasingly useful for the individual and the clinician.
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Dai Y, Chen Y, Huang H, Zhou W, Niu Y, Zhang M, Bin P, Dong H, Jia Q, Huang J, Yi J, Liao Q, Li H, Teng Y, Zang D, Zhai Q, Duan H, Shen J, He J, Meng T, Sha Y, Shen M, Ye M, Jia X, Xiang Y, Huang H, Wu Q, Shi M, Huang X, Yang H, Luo L, Li S, Li L, Zhao J, Li L, Wang J, Zheng Y. Performance of genetic risk factors in prediction of trichloroethylene induced hypersensitivity syndrome. Sci Rep 2015; 5:12169. [PMID: 26190474 PMCID: PMC4507183 DOI: 10.1038/srep12169] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Accepted: 06/22/2015] [Indexed: 11/09/2022] Open
Abstract
Trichloroethylene induced hypersensitivity syndrome is dose-independent and potentially life threatening disease, which has become one of the serious occupational health issues and requires intensive treatment. To discover the genetic risk factors and evaluate the performance of risk prediction model for the disease, we conducted genomewide association study and replication study with total of 174 cases and 1761 trichloroethylene-tolerant controls. Fifty seven SNPs that exceeded the threshold for genome-wide significance (P < 5 × 10(-8)) were screened to relate with the disease, among which two independent SNPs were identified, that is rs2857281 at MICA (odds ratio, 11.92; P meta = 1.33 × 10(-37)) and rs2523557 between HLA-B and MICA (odds ratio, 7.33; P meta = 8.79 × 10(-35)). The genetic risk score with these two SNPs explains at least 20.9% of the disease variance and up to 32.5-fold variation in inter-individual risk. Combining of two SNPs as predictors for the disease would have accuracy of 80.73%, the area under receiver operator characteristic curves (AUC) scores was 0.82 with sensitivity of 74% and specificity of 85%, which was considered to have excellent discrimination for the disease, and could be considered for translational application for screening employees before exposure.
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Affiliation(s)
- Yufei Dai
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Ying Chen
- BGI-Tech, BGI-Shenzhen, Shenzhen, China
| | - Hanlin Huang
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China
| | - Wei Zhou
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | - Yong Niu
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | | | - Ping Bin
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Haiyan Dong
- Center for Disease Control and Prevention of Yunnan province, Kunming, Yunnan, China
| | - Qiang Jia
- Shandong Academy of Occupational Health and Occupational Medicine, Jinan, China
| | - Jianxun Huang
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China
| | - Juan Yi
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | | | - Haishan Li
- Institute of chemicals safety, Chinese academy of inspection and quarantine, Beijing, China
| | - Yanxia Teng
- Health Supervision Institutionof Dongcheng Health Bureau, Beijing, China
| | - Dan Zang
- Food And Drug Administration Of Beijing Fengtai District, Beijing, China
| | - Qingfeng Zhai
- Weifang Medical University, Weifang, Shandong, China
| | - Huawei Duan
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Juan Shen
- BGI-Tech, BGI-Shenzhen, Shenzhen, China
| | - Jiaxi He
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | - Tao Meng
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Yan Sha
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | - Meili Shen
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Meng Ye
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Xiaowei Jia
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
| | - Yingping Xiang
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | - Huiping Huang
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | - Qifeng Wu
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China
| | | | - Xianqing Huang
- Hospital for Occupational Diseases Control of Shenzhen, Shenzhen, China
| | | | | | - Sai Li
- BGI-Tech, BGI-Shenzhen, Shenzhen, China
| | - Lin Li
- BGI-Tech, BGI-Shenzhen, Shenzhen, China
| | | | - Laiyu Li
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China
| | - Jun Wang
- BGI-Tech, BGI-Shenzhen, Shenzhen, China
| | - Yuxin Zheng
- Key laboratory of Chemical Safety and Health, Chinese Centre for Disease Control and Prevention. National Institute for Occupational Health and Poison Control, Chinese Centre for Disease Control and Prevention, Beijing, 100050, China
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Abraham G, Rohmer A, Tye-Din JA, Inouye M. Genomic prediction of celiac disease targeting HLA-positive individuals. Genome Med 2015; 7:72. [PMID: 26244058 PMCID: PMC4523954 DOI: 10.1186/s13073-015-0196-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Accepted: 07/08/2015] [Indexed: 02/07/2023] Open
Abstract
Background Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk. Methods Using L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with HLA-DQ2.5 risk haplotypes (GRS-DQ2.5) and compared them with the predictive power of the existing CD GRS (GRS14) as well as two haplotype-based approaches, externally validating the results in a North American case-control study. Results Consistent with previous observations, both the existing GRS14 and the GRS-DQ2.5 had better predictive performance than the HLA haplotype approaches. GRS-DQ2.5 models, based on directly genotyped or imputed markers, achieved similar levels of predictive performance (AUC = 0.718-0.73), which were substantially higher than those obtained from the DQ2.5 zygosity alone (AUC = 0.558), the HLA risk haplotype method (AUC = 0.634), or the generic GRS14 (AUC = 0.679). In a screening model of at-risk individuals, the GRS-DQ2.5 lowered the number of unnecessary follow-up tests for CD across most sensitivity levels. Relative to a baseline implicating all DQ2.5-positive individuals for follow-up, the GRS-DQ2.5 resulted in a net saving of 2.2 unnecessary follow-up tests for each justified test while still capturing 90 % of DQ2.5-positive CD cases. Conclusions Genomic risk scores for CD that target genetically at-risk sub-groups improve predictive performance beyond traditional approaches and may represent a useful strategy for prioritizing individuals at increased risk of disease, thus potentially reducing unnecessary follow-up diagnostic tests. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0196-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gad Abraham
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, 3010 Victoria Australia ; Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, 3010 Victoria Australia
| | - Alexia Rohmer
- Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, 3010 Victoria Australia ; Faculty of Life Science, University of Strasbourg, Strasbourg, 67084 CEDEX France
| | - Jason A Tye-Din
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052 Victoria Australia ; Department of Medical Biology, The University of Melbourne, Parkville, 3010 Victoria Australia ; Department of Gastroenterology, The Royal Melbourne Hospital, Grattan St., Parkville, 3050 Victoria Australia ; Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria 3050 Australia
| | - Michael Inouye
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, 3010 Victoria Australia ; Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, 3010 Victoria Australia
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