1
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Amiri Roudbar M, Mohammadabadi MR, Ayatollahi Mehrgardi A, Abdollahi-Arpanahi R, Momen M, Morota G, Brito Lopes F, Gianola D, Rosa GJM. Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity (Edinb) 2020; 124:658-674. [PMID: 32127659 DOI: 10.1038/s41437-020-0301-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/17/2020] [Accepted: 02/17/2020] [Indexed: 12/16/2022] Open
Abstract
This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.
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Affiliation(s)
- Mahmoud Amiri Roudbar
- Department of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Dezful, Iran.
| | - Mohammad Reza Mohammadabadi
- Department of Animal Science, College of Agriculture, Shahid Bahonar University of Kerman, 76169-133, Kerman, Iran
| | - Ahmad Ayatollahi Mehrgardi
- Department of Animal Science, College of Agriculture, Shahid Bahonar University of Kerman, 76169-133, Kerman, Iran
| | - Rostam Abdollahi-Arpanahi
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, 465, Pakdasht, Tehran, Iran
| | - Mehdi Momen
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Fernando Brito Lopes
- Department of Animal Sciences, Sao Paulo State University, Julio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane, Jaboticabal, SP, 14884-900, Brazil
| | - Daniel Gianola
- Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Guilherme J M Rosa
- Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53792, USA
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Abstract
Obesity and excess weight are a pandemic phenomenon in the modern world. Childhood and adolescent obesity often ends up in obesity in adults. The costs of obesity and its consequences are staggering for any society, crippling for countries in development. Childhood obesity is also widespread in Macedonia. Metabolic syndrome, dyslipidemia and carbohydrate intolerance are found in significant numbers. Parents and grandparents are often obese. Some of the children are either dysmorphic, or slightly retarded. We have already described patients with Prader-Willi syndrome, Bardet-Biedl syndrome or WAGR syndrome. A genetic screening for mutations in monogenic obesity in children with early, rapid-onset or severe obesity, severe hyperphagia, hypogonadism, intestinal dysfunction, hypopigmentation of hair and skin, postprandial hypoglycaemia, diabetes insipidus, abnormal leptin level and coexistence of lean and obese siblings in the family discovers many genetic forms of obesity. There are about 30 monogenic forms of obesity. In addition, obesity is different in ethnic groups, and the types of monogenic obesity differ. In brief, an increasing number of genes and genetic mechanisms in children continue to be discovered. This sheds new light on the molecular mechanisms of obesity and potentially gives a target for new forms of treatment.
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3
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Noell G, Faner R, Agustí A. From systems biology to P4 medicine: applications in respiratory medicine. Eur Respir Rev 2018; 27:27/147/170110. [PMID: 29436404 PMCID: PMC9489012 DOI: 10.1183/16000617.0110-2017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 11/30/2017] [Indexed: 12/22/2022] Open
Abstract
Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field. Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x
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Affiliation(s)
- Guillaume Noell
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain
| | - Rosa Faner
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain
| | - Alvar Agustí
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain .,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain.,Respiratory Institute, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain
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4
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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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Abstract
Obstructive sleep apnea is a common condition, with multiple potential neurocognitive, cardiovascular, and metabolic consequences. Efficacious treatment is available, but patient engagement is typically required for treatment to be effective. Patients with sleep apnea are phenotypically diverse and have individual needs, preferences, and values that impact treatment decisions. There has been a shift in obstructive sleep apnea management from diagnosis to chronic care management. Making treatment decisions that incorporate an individual patient's values and preferences and are personalized for that patient's biology has the potential to improve patient outcomes. A patient-centered care approach in obstructive sleep apnea is reviewed including 1) determining patient-specific needs to guide treatment decisions, 2) understanding patient values, preferences, and other factors impacting treatment decisions and using shared decision-making, 3) enhancing patient education and support to improve treatment adherence, 4) promoting patient engagement, 5) optimizing care coordination, continuity of care, and access to care, and 6) determining and assessing patient-centered outcomes.
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Affiliation(s)
- Janet Hilbert
- Yale University School of Medicine, Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, New Haven, CT, USA.
| | - Henry K Yaggi
- Yale University School of Medicine, Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, New Haven, CT, USA
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6
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Sohani ZN, Sarma S, Alyass A, de Souza RJ, Robiou-du-Pont S, Li A, Mayhew A, Yazdi F, Reddon H, Lamri A, Stryjecki C, Ishola A, Lee YK, Vashi N, Anand SS, Meyre D. Empirical evaluation of the Q-Genie tool: a protocol for assessment of effectiveness. BMJ Open 2016; 6:e010403. [PMID: 27288371 PMCID: PMC4908888 DOI: 10.1136/bmjopen-2015-010403] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Meta-analyses of genetic association studies are affected by biases and quality shortcomings of the individual studies. We previously developed and validated a risk of bias tool for use in systematic reviews of genetic association studies. The present study describes a larger empirical evaluation of the Q-Genie tool. METHODS AND ANALYSIS MEDLINE, Embase, Global Health and the Human Genome Epidemiology Network will be searched for published meta-analyses of genetic association studies. Twelve reviewers in pairs will apply the Q-Genie tool to all studies in included meta-analyses. The Q-Genie will then be evaluated on its ability to (i) increase precision after exclusion of low quality studies, (ii) decrease heterogeneity after exclusion of low quality studies and (iii) good agreement with experts on quality rating by Q-Genie. A qualitative assessment of the tool will also be conducted using structured questionnaires. DISCUSSION This systematic review will quantitatively and qualitatively assess the Q-Genie's ability to identify poor quality genetic association studies. This information will inform the selection of studies for inclusion in meta-analyses, conduct sensitivity analyses and perform metaregression. Results of this study will strengthen our confidence in estimates of the effect of a gene on an outcome from meta-analyses, ultimately bringing us closer to deliver on the promise of personalised medicine. ETHICS AND DISSEMINATION An updated Q-Genie tool will be made available from the Population Genomics Program website and the results will be submitted for a peer-reviewed publication.
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Affiliation(s)
- Z N Sohani
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - S Sarma
- DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - A Alyass
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - R J de Souza
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - S Robiou-du-Pont
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - A Li
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - A Mayhew
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - F Yazdi
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - H Reddon
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - A Lamri
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - C Stryjecki
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - A Ishola
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - Y K Lee
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - N Vashi
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
| | - S S Anand
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - D Meyre
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Chanchalani Research Centre, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Faculté de Médecine, Inserm U-954, University of Lorraine and University Hospital Center of Nancy, Nancy, France
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7
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Delaney SK, Christman MF. Encouraging physician adoption of genetic testing for precision medicine. Per Med 2016; 13:201-204. [DOI: 10.2217/pme-2016-0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Susan K Delaney
- Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Michael F Christman
- Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
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8
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Ibrahim R, Pasic M, Yousef GM. Omics for personalized medicine: defining the current we swim in. Expert Rev Mol Diagn 2016; 16:719-22. [PMID: 26959799 DOI: 10.1586/14737159.2016.1164601] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Rania Ibrahim
- a Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute , St. Michael's Hospital , Toronto , Canada.,b Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , Canada
| | - Maria Pasic
- a Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute , St. Michael's Hospital , Toronto , Canada.,b Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , Canada
| | - George M Yousef
- a Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute , St. Michael's Hospital , Toronto , Canada.,b Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , Canada
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9
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Lamont A, Lyons MD, Jaki T, Stuart E, Feaster DJ, Tharmaratnam K, Oberski D, Ishwaran H, Wilson DK, Van Horn ML. Identification of predicted individual treatment effects in randomized clinical trials. Stat Methods Med Res 2016; 27:142-157. [DOI: 10.1177/0962280215623981] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.
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Affiliation(s)
- Andrea Lamont
- Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA
| | - Michael D Lyons
- Department of Psychology, University of Houston, Houston, USA
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Elizabeth Stuart
- Department of Mental Health, Department of Biostatistics, and Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins, Baltimore, MD, USA
| | - Daniel J Feaster
- Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA
| | | | - Daniel Oberski
- Department of Methodology and Statistics, Tilburg University, Tilburg, the Netherlands
| | - Hemant Ishwaran
- Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA
| | - Dawn K Wilson
- Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA
| | - M Lee Van Horn
- Department of Individual, Family and Community Education, University of New Mexico, Albuquerque, NM, USA
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10
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Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics 2015; 8:33. [PMID: 26112054 PMCID: PMC4482045 DOI: 10.1186/s12920-015-0108-y] [Citation(s) in RCA: 224] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 06/15/2015] [Indexed: 02/07/2023] Open
Abstract
Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.
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Affiliation(s)
- Akram Alyass
- Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
| | - Michelle Turcotte
- Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
| | - David Meyre
- Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
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