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Nicholls HL, John CR, Watson DS, Munroe PB, Barnes MR, Cabrera CP. Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci. Front Genet 2020; 11:350. [PMID: 32351543 PMCID: PMC7174742 DOI: 10.3389/fgene.2020.00350] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/23/2020] [Indexed: 12/21/2022] Open
Abstract
Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.
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Affiliation(s)
- Hannah L. Nicholls
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Christopher R. John
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - David S. Watson
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
| | - Patricia B. Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Michael R. Barnes
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- The Alan Turing Institute, British Library, London, United Kingdom
| | - Claudia P. Cabrera
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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Drubay D, Gautheret D, Michiels S. A benchmark study of scoring methods for non-coding mutations. Bioinformatics 2019; 34:1635-1641. [PMID: 29340599 DOI: 10.1093/bioinformatics/bty008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 01/09/2018] [Indexed: 01/06/2023] Open
Abstract
Motivation Detailed knowledge of coding sequences has led to different candidate models for pathogenic variant prioritization. Several deleteriousness scores have been proposed for the non-coding part of the genome, but no large-scale comparison has been realized to date to assess their performance. Results We compared the leading scoring tools (CADD, FATHMM-MKL, Funseq2 and GWAVA) and some recent competitors (DANN, SNP and SOM scores) for their ability to discriminate assumed pathogenic variants from assumed benign variants (using the ClinVar, COSMIC and 1000 genomes project databases). Using the ClinVar benchmark, CADD was the best tool for detecting the pathogenic variants that are mainly located in protein coding gene regions. Using the COSMIC benchmark, FATHMM-MKL, GWAVA and SOMliver outperformed the other tools for pathogenic variants that are typically located in lincRNAs, pseudogenes and other parts of the non-coding genome. However, all tools had low precision, which could potentially be improved by future non-coding genome feature discoveries. These results may have been influenced by the presence of potential benign variants in the COSMIC database. The development of a gold standard as consistent as ClinVar for these regions will be necessary to confirm our tool ranking. Availability and implementation The Snakemake, C++ and R codes are freely available from https://github.com/Oncostat/BenchmarkNCVTools and supported on Linux. Contact damien.drubay@gustaveroussy.fr or stefan.michiels@gustaveroussy.fr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Damien Drubay
- INSERM U1018, CESP, Fac. de Médecine-Univ. Paris-Sud-UVSQ, INSERM, Université Paris-Saclay, 94807 Villejuif cedex, France.,Gustave Roussy, Service de Biostatistique et d'Epidémiologie, Villejuif F-94805, France
| | - Daniel Gautheret
- Institute for Integrative Biology of the Cell, Université Paris-Sud, CNRS, CEA, 91198 Gif-sur-Yvette, France
| | - Stefan Michiels
- INSERM U1018, CESP, Fac. de Médecine-Univ. Paris-Sud-UVSQ, INSERM, Université Paris-Saclay, 94807 Villejuif cedex, France.,Gustave Roussy, Service de Biostatistique et d'Epidémiologie, Villejuif F-94805, France
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Woo HJ, Reifman J. Collective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis. BMC Psychiatry 2018; 18:175. [PMID: 29871603 PMCID: PMC5989392 DOI: 10.1186/s12888-018-1753-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 05/21/2018] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes. These traits, however, are often highly polygenic, with individual loci contributing only small effects to the overall association. The polygenicity makes it challenging to explain, for example, the widely observed comorbidity between stress and cardiac disease. METHODS We present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait. Using simulated data, we demonstrate that by taking into account the non-uniform distribution of genotypes within a cohort, we can achieve greater power than regression-based methods for high-dimensional inference. RESULTS We analyzed genome-wide data sets of outbred mice and pet dogs, and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects: γ-carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear, consistent with our previous finding in human post-traumatic stress disorder (PTSD) data. Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis, and noise-induced fear in dogs with hemostasis. CONCLUSIONS Our findings suggest a novel explanation for the observed comorbidity between PTSD/anxiety and cardiovascular diseases: key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear.
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Affiliation(s)
- Hyung Jun Woo
- 0000 0001 0036 4726grid.420210.5Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD USA
| | - Jaques Reifman
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, USA.
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Dennis J, Medina-Rivera A, Truong V, Antounians L, Zwingerman N, Carrasco G, Strug L, Wells P, Trégouët DA, Morange PE, Wilson MD, Gagnon F. Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels. Genet Epidemiol 2017; 41:455-466. [PMID: 28421636 DOI: 10.1002/gepi.22049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Revised: 03/07/2017] [Accepted: 03/14/2017] [Indexed: 11/10/2022]
Abstract
Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome-wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French-Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis-driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI. Our GWAS identified 131 SNPs with suggestive evidence of association (P-value < 5 × 10-8 ), but no SNPs reached the genome-wide threshold for statistical significance. Hemostasis pathway genes were not enriched for TFPI plasma level associated SNPs (global hypothesis test P-value = 0.147), but EC regulatory regions contained more TFPI plasma level associated SNPs than expected by chance (global hypothesis test P-value = 0.046). We therefore stratified our genome-wide SNPs, prioritizing those in EC regulatory regions via stratified false discovery rate (sFDR) control, and reranked the SNPs by q-value. The minimum q-value was 0.27, and the top-ranked SNPs did not show association evidence in the MARTHA replication sample of 1,033 unrelated VTE cases. Although this study did not result in new loci for TFPI, our work lays out a strategy to utilize epigenomic data in prioritization schemes for future GWAS studies.
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Affiliation(s)
- Jessica Dennis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Alejandra Medina-Rivera
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Vinh Truong
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Lina Antounians
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Nora Zwingerman
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Giovana Carrasco
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Lisa Strug
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Phil Wells
- Ottawa Hospital Research Institute, Ottawa, Canada
| | - David-Alexandre Trégouët
- Sorbonne Universités, UPMC Univ Paris 06, Paris, France.,INSERM, UMR_S 1166, Paris, France.,ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - Pierre-Emmanuel Morange
- INSERM, UMR_S 1062, Marseille, France.,Inra, UMR_INRA 1260, Marseille, France.,Aix Marseille Université, Marseille, France
| | - Michael D Wilson
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada.,Heart & Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, Toronto, Canada
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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Zai G, Alberry B, Arloth J, Bánlaki Z, Bares C, Boot E, Camilo C, Chadha K, Chen Q, Cole CB, Cost KT, Crow M, Ekpor I, Fischer SB, Flatau L, Gagliano S, Kirli U, Kukshal P, Labrie V, Lang M, Lett TA, Maffioletti E, Maier R, Mihaljevic M, Mittal K, Monson ET, O'Brien NL, Østergaard SD, Ovenden E, Patel S, Peterson RE, Pouget JG, Rovaris DL, Seaman L, Shankarappa B, Tsetsos F, Vereczkei A, Wang C, Xulu K, Yuen RKC, Zhao J, Zai CC, Kennedy JL. Rapporteur summaries of plenary, symposia, and oral sessions from the XXIIIrd World Congress of Psychiatric Genetics Meeting in Toronto, Canada, 16-20 October 2015. Psychiatr Genet 2016; 26:229-257. [PMID: 27606929 PMCID: PMC5134913 DOI: 10.1097/ypg.0000000000000148] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The XXIIIrd World Congress of Psychiatric Genetics meeting, sponsored by the International Society of Psychiatric Genetics, was held in Toronto, ON, Canada, on 16-20 October 2015. Approximately 700 participants attended to discuss the latest state-of-the-art findings in this rapidly advancing and evolving field. The following report was written by trainee travel awardees. Each was assigned one session as a rapporteur. This manuscript represents the highlights and topics that were covered in the plenary sessions, symposia, and oral sessions during the conference, and contains major notable and new findings.
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Affiliation(s)
- Gwyneth Zai
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, ON, Canada
- Frederick W. Thompson Anxiety Disorders Centre, Department of Psychiatry, Sunnybrook Health Sciences Centre
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Bonnie Alberry
- Molecular Genetics Unit, Department of Biology, University of Western Ontario, London, ON, Canada
| | - Janine Arloth
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Translational Research in Psychiatry, Institute of Computational Biology, Helmholtz Zentrum München, Germany
| | - Zsófia Bánlaki
- Department of Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University, Budapest, Hungary
| | - Cristina Bares
- School of Social Work, University of Michigan, Ann Arbor, MI, USA
| | - Erik Boot
- Department of Psychiatry, University of Toronto, ON, Canada
- The Dalglish Family 22q Clinic, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Nuclear Medicine, Academic Medical Centre, Amsterdam, The Netherlands
| | - Caroline Camilo
- Institute and Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Kartikay Chadha
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Qi Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Christopher B. Cole
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Biomedical Sciences Division, Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Katherine Tombeau Cost
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY, USA
| | - Ibene Ekpor
- Department of Psychiatry, University of Calabar Teaching Hospital, Calabar, Nigeria
| | - Sascha B. Fischer
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Laura Flatau
- Institute of Psychiatric Phenomics and Genomics, University of Munich, Munich, Germany
| | - Sarah Gagliano
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Umut Kirli
- Department of Psychiatry, Ege University School of Medicine, Izmir, Turkey
| | - Prachi Kukshal
- Department of Genetics, University of Delhi, South Campus, New Delhi, India
| | - Viviane Labrie
- Department of Psychiatry, University of Toronto, ON, Canada
- Krembil Family Epigenetics Laboratory, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Maren Lang
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | | | | | - Robert Maier
- Queensland Brain Institute, University of Queensland, St. Lucia, Australia
| | | | - Kirti Mittal
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Eric T. Monson
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Niamh L. O'Brien
- Molecular Psychiatric Laboratory, Division of Psychiatry, University College London, London, UK
| | - Søren Dinesen Østergaard
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - Ellen Ovenden
- Human Genetics Lab, Department of Genetics, Stellenbosch University, South Africa
| | - Sejal Patel
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jennie G. Pouget
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Diego Luiz Rovaris
- Department of Genetics, Instituto de Biociências, Federal University of Rio Grande do Sul, Brazil
- ADHD Outpatient Clinic, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Brazil
| | - Lauren Seaman
- Department of Chemistry and Biochemistry, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bhagya Shankarappa
- Molecular Genetics Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Fotis Tsetsos
- Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece
| | - Andrea Vereczkei
- Department of Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University, Budapest, Hungary
| | | | - Khethelo Xulu
- Department of Psychiatry, Stellenbosch University, South Africa
| | - Ryan K. C. Yuen
- The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jingjing Zhao
- School of Psychology, Shaanxi Normal University, Xi'an, China
- School of Psychology, National University of Ireland, Galway, Ireland
| | - Clement C. Zai
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, ON, Canada
| | - James L. Kennedy
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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Gagliano SA, Paterson AD, Weale ME, Knight J. Assessing models for genetic prediction of complex traits: a comparison of visualization and quantitative methods. BMC Genomics 2015; 16:405. [PMID: 25997848 PMCID: PMC4440290 DOI: 10.1186/s12864-015-1616-z] [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: 12/23/2014] [Accepted: 05/05/2015] [Indexed: 11/13/2022] Open
Abstract
Background In silico models have recently been created in order to predict which genetic variants are more likely to contribute to the risk of a complex trait given their functional characteristics. However, there has been no comprehensive review as to which type of predictive accuracy measures and data visualization techniques are most useful for assessing these models. Methods We assessed the performance of the models for predicting risk using various methodologies, some of which include: receiver operating characteristic (ROC) curves, histograms of classification probability, and the novel use of the quantile-quantile plot. These measures have variable interpretability depending on factors such as whether the dataset is balanced in terms of numbers of genetic variants classified as risk variants versus those that are not. Results We conclude that the area under the curve (AUC) is a suitable starting place, and for models with similar AUCs, violin plots are particularly useful for examining the distribution of the risk scores. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1616-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sarah A Gagliano
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. .,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. .,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
| | - Andrew D Paterson
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. .,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. .,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. .,Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. .,Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
| | - Michael E Weale
- Department of Medical & Molecular Genetics, King's College London, Guy's Hospital, London, UK.
| | - Jo Knight
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. .,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. .,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. .,Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
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