Progress and Challenges in Precision Medicine

By Mukesh Verma and Debmalya Barh

Progress and Challenges in Precision Medicine presents an insightful overview to the myriad factors of personalized and precision medicine. The availability of the human genome, large amounts of data on individual genetic variations, environmental interactions, influence of lifestyle, and cutting-edge tools and technologies for big-data analysis have led to the age of personalized and precision medicine.

Bringing together a global range of experts on precision medicine, this book collects previously scattered information into one concise volume which covers the most important developments so far in precision medicine and also suggests the most likely avenues for future development.

The book includes clinical information, informatics, public policy implications, and information on case studies. It is a useful reference and background work for students, researchers, and clinicians working in the biomedical and medical fields, as well as policymakers in the health sciences.

• Provides an overview of the growing field of precision medicine
• Contains chapters from geographically diverse experts in their field
• Explores important aspects of precision medicine, including applications, ethics, and development

• Language: English
• Publisher: Academic Press
• Release date: Dec 22, 2016
• ISBN: 9780128095027

Book preview

Progress and Challenges in Precision Medicine

Editors

Mukesh Verma

Debmalya Barh

Table of Contents

Cover image

Title page

Copyright

List of Contributors

Biography

Dedication

Preface

Chapter 1. Introduction: Every Individual Is Different and Precision Medicine Offers Options for Disease Control and Treatment

1. What Is Precision Medicine? Personalized Medicine Versus Precision Medicine (C. HIzel, P. Hamet, and J. Tremblay)

2. Precision Medicine for Population Health (G. Bartlett)

3. Conclusion

Chapter 2. Clinical Next-Generation Sequencing: Enabling Precision Medicine

1. Introduction

2. Technicalities and Chemistries of NGS

3. NGS Data Analysis

4. Reference Databases for Disease Associations and Drug Response

5. Applications of NGS in Precision Medicine

6. Regulatory Concerns With NGS Clinical Genomics

7. Conclusion

Chapter 3. Phenotyping in Precision Medicine

1. Introduction

2. Deep Phenotyping

3. Expressivity and Penetrance

4. Expressivity

5. Penetrance

6. Phenotypic Variation in Expressivity and Penetrance

7. Pleiotropy

8. Diseases and Phenotypes

9. Cancer

10. Diabetes

11. Respiratory Disorders

12. Encephalopathy

13. Data Mining and Phenotyping

14. Approaches for Phenotyping

15. Future Directions

Chapter 4. Cancer Genetic Screening and Ethical Considerations for Precision Medicine

1. Introduction

2. Genetic Testing in Hereditary Cancers

3. Ethical Issues Related to Cancer Genetic Screening

4. Summary and Conclusions

Chapter 5. Precision Medicine in Primary Health Care

1. The Case for Primary Health Care

2. Precision Medicine in Primary Health Care: Warfarin and Pharmacogenomics

3. Precision Medicine in Primary Health Care: Creating an Informatics System

4. Precision Medicine: A Feasibility Study for Primary Health Care

5. Precision Medicine in Primary Health Care: Feasibility Results

6. Precision Medicine and Implications for Pharmacogenomics in Primary Health Care

Abbreviations

Chapter 6. Population Approach to Precision Medicine

1. Background

2. Examples of Different Tumor Types Where Precision Medicine Was Applied

3. Different Approaches to Address Challenges in Precision Medicine

4. Medical Applications in Health Care Settings

5. Challenges and Potential Solutions

6. Looking Ahead

7. Conclusion

Chapter 7. Regulation of Genomic Testing in the Era of Precision Medicine

1. Genomic Testing in the Era of Precision Medicine

2. Requisite for Regulation of Genomic Testing

3. Perspective of Beyond the Clinic

4. Assuring the Quality of Data

5. Significance of Feedback

6. Requisite of Regulatory Body

Chapter 8. Image-Based Modeling and Precision Medicine

1. Biomedical Visualization

2. Diagnostic Imaging

3. Medical Simulation

4. Multiscale Engineering in Biology

5. Visible Human Project

6. Image-Based Models

7. Functional Anatomy Simulation

8. Cells and Subcellular Systems

9. End User Applications

Chapter 9. Sharing Outside the Sandbox? The Child’s Right to an Open Data Sharing Future in Genomics and Personalized Medicine

1. Introduction

2. Children in Research

3. A Changing Landscape for Pediatric Research Participation

4. The Research Ethics Review Process and Implications for Data Sharing

5. Sharing Outside the Sandbox

6. Conclusion

Chapter 10. Lessons Learned From Cohort Studies, and Hospital-Based Studies and Their Implications in Precision Medicine

1. The Pyramid of Evidence: A Useful Construct

2. An Overview of Study Designs

3. Experimental Studies

4. Quasi-Experimental Studies

5. Nonexperimental/Observational Study Designs

6. Cohort Studies

7. Case–Control Studies

8. The STROBE Statement: The Strengthening the Reporting of Observational Studies in Epidemiology Statement

9. Cross-Sectional Studies

10. Case Series

11. Other Study Designs

12. Applications of Clinical Trials, Cohort Studies, and Hospital-Based Studies in Clinical Medicine

13. Future Expectations

Chapter 11. Clinical Trials in Precision Medicine

1. Introduction

2. Phase II Basket Discovery Trials

3. Targeted (Enrichment) Phase III Designs

4. Adaptive Enrichment Designs

5. Conclusion

Chapter 12. Time to Educate Physicians and Hospital Staff in Electronic Medical Records for Precision Medicine

1. Introduction

2. Linking Clinical Information and Bench Science

3. Electronic Medical Record and Clinical Decision Support System

4. Electronic Medical Record as a Foundation for Clinical Decision Support System

5. Precision Medicine and Supercomputers

6. The Use of Bioinformatics and Integrated Knowledge Environments

7. Data Integration Facilitating Medical Research

8. Ethical Issues in Electronic Health Records

9. System Implementation

10. eMERGE (Electronic Medical Records and Genomics) Consortium

11. Conclusion

Chapter 13. Computational Approaches in Precision Medicine

1. Introduction

2. Computational Tools in P4 Medicine I: Hardware and Infrastructure

3. Computational Tools in P4 Medicine II: Software Resources

4. Computational Tools in P4 Medicine III: Biological Databases, Standards, and Ontologies

5. Some Relevant Works on Computational Tools in Precision Medicine

6. Final Considerations

Chapter 14. Handling Big Data in Precision Medicine

1. Introduction

2. From Evidence-Based Medicine to Information-Based Precision Medicine

3. Computational Challenges for Big Data in Precision Medicine

4. Ethical and Legal Challenges for Big Data in Precision Medicine

5. Conclusions and Perspectives

Chapter 15. Trends in Precision Medicine

1. Introduction

2. Phenotyping in Precision Medicine

3. Precision Diagnosis

4. Biomarkers in Precision Medicine

5. Precision Medicine in Diseases

6. Role of Nanomedicine in Precision Medicine

7. Computational Approaches and Handling of Big Data in Precision Medicine

8. Commercial and Market Access Considerations in Precision Medicine

9. Policies and Ethical Issues in Precision Medicine

10. Precision Medicine for Population Health

11. Conclusion

Chapter 16. Personalized Medicine: Interdisciplinary Perspective, World Tidal Wave, and Potential Growth for the Emerging Countries

1. A Social and Epistemologic Reflection That Goes Beyond Medicine Frontier

2. Exportable Virtues of Precision Medicine

3. Conclusion

Index

Copyright

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List of Contributors

A. Ali,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

S.T. Ashraf,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

S.M. Bakhtiar,     Capital University of Science and Technology, Islamabad, Pakistan

G. Bartlett,     McGill University, Montreal, QC, Canada

M. Dawes,     University of British Columbia, Vancouver, BC, Canada

D. Dhawan,     PanGenomics International Pvt. Ltd., Ahmedabad, India

R.G. Dumitrescu,     Distilled Spirits Council, Washington, DC, United States

J. Espinal-Enríquez

National Institute of Genomic Medicine, Mexico, Mexico

Universidad Nacional Autónoma de México, Mexico, Mexico

R. García-Herrera,     National Institute of Genomic Medicine, Mexico, Mexico

P. Hamet

Université de Montréal, Montréal, QC, Canada

International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada

CHUM, Montreal, QC, Canada

A. Hassan,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

E. Hernández-Lemus

National Institute of Genomic Medicine, Mexico, Mexico

Universidad Nacional Autónoma de México, Mexico, Mexico

C. Hizel

OPTI-THERA, Montreal, QC, Canada

Université de Montréal, Montréal, QC, Canada

International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada

Jaspreet Kaur,     Panjab University, Chandigarh, India

Jyotdeep Kaur,     Postgraduate Institute of Medical Education and Research, Chandigarh, India

É. Lemarié,     Université François-Rabelais, Tours, France

V. Lemay

Université de Montréal, Montréal, QC, Canada

International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada

R.A. Mejía-Pedroza,     National Institute of Genomic Medicine, Mexico, Mexico

A. Munshi,     Central University of Punjab, Bathinda, India

A. Naz,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

K. Naz,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

Q. Nguyen,     McGill University, Montreal, QC, Canada

A. Obaid,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

R.Z. Paracha,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

M.S. Phillips,     Medicine Advisers Inc., Montreal, QC, Canada

B. Rahat,     Postgraduate Institute of Medical Education and Research, Chandigarh, India

V. Rahimzadeh,     McGill University, Montréal, QC, Canada

S. Rehman,     National University of Sciences and Technology (NUST), Islamabad, Pakistan

S. Sharma,     Indraprastha Apollo Hospital, New Delhi, India

V. Sharma,     Institute of Genetics and Hospital for Genetic Diseases, Hyderabad, India

R. Simon,     National Cancer Institute, Bethesda, MD, United States

N.I. Soomro,     Capital University of Science and Technology, Islamabad, Pakistan

S. Thakur,     Postgraduate Institute of Medical Education and Research, Chandigarh, India

J. Tremblay

OPTI-THERA, Montreal, QC, Canada

International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada

Université de Montréal, Montréal, QC, Canada

CRCHUM, Montreal, QC, Canada

Y. Tremblay

Université Laval, Québec, QC, Canada

International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada

M. Verma,     National Institutes of Health, Rockville, MD, United States

Biography

Mukesh Verma

Branch Chief, Methods and Technologies Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Dr. Mukesh Verma is a Program Director and Chief in the Methods and Technologies Branch (MTB), Epidemiology and Genetics Research Program (EGRP) of the Division of Cancer Control and Population Sciences (DCCPS) at the National Cancer Institute (NCI), National Institutes of Health (NIH). Before coming to the DCCPS, he was a Program Director in the Division of Cancer Prevention (DCP), NCI, providing direction in the areas of biomarkers, early detection, risk assessment and prevention of cancer, epigenetics, epidemiology, and cancers associated with infectious agents. Since joining the NCI, he has sought to champion the visibility of and investment in cancer epigenetics research both within the Institute and across other federal and nongovernmental agencies, and to raise public awareness about controlling cancer. He represents NIH in Common Fund Programs on Epigenomics, Metabolomics, and Molecular Transducers of Physical Activity. Dr. Mukesh Verma holds an M.Sc. from Pantnagar University and a Ph.D. from Banaras Hindu University. He did postdoctoral research at George Washington University and was a faculty member at Georgetown University Medical Center. He has published 161 research articles and reviews and edited five books in cancer biomarkers, epigenetics, and epidemiology field.

Debmalya Barh

Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, India.

Dr. Barh is an M.Sc. (Applied Genetics), M.Tech. and M.Phil. Biotechnology, Ph.D. Genomics, Ph.D. Biotechnology, and Postgraduate in Management. He has blended both academic and industrial research and his research areas include integrative omics-based biomarkers, targeted drugs, personalized and predictive medicine in cancers and various complex diseases. He has over 15 years of leading genomics and personalized diagnostic/ medicine industry experience where his main focus has been to translate academic research into high value commercial products at affordable cost. Dr. Barh has edited 16+ cutting-edge research reference books in the field of omics and has nearly 200 research papers to his credit. He is the founder member of the Institute of Integrative Omics and Applied Biotechnology (IIOAB), India - a global platform for multidisciplinary research and advocacy, and due to his significant contribution in the field, he has been recognized by Who’s Who in the World and Limca Book of Records. He is an external reviewer of various leading research journals and is also the editor of the forthcoming Elsevier title Omics Technologies and Bio-Engineering (May 2017).

Dedication

This book is dedicated to Dr. Edward J. Trapido who encouraged me to explore novel ways to control cancer by applying molecular epidemiologic approaches and improve the survival of cancer patients.

Mukesh Verma

This book is dedicated to Ms. Mamata Barh, my beloved mother who is the inspiration and key driving force of all my scientific activities.

Debmalya Barh

Preface

Precision medicine is an emerging area with tremendous potential in developing tailor-made preventive diagnostics, therapeutics, and management strategies based on an individual’s omics profiles (genomics, metabolomics, epigenomics, transcriptomics, and glycomics etc.), family history, lifestyle, environmental exposures, and other characteristics in any complex disease. The recent Precision Medicine Initiative (PMI), 2015 by the Govt. of USA, has tremendously boosted the concept. However, there is a long way to go for its disease-specific effective applications!

Progress and Challenges in Precision Medicine is the first-of-its-kind book that provides an overview of various essential components and their applications and challenges related to precision medicine, although the book does not give disease-specific precision medicine approaches. The introductory chapter (Chapter 1) by Dr. Hizel’s group provides the basic concept of precision medicine under the title Every individual is different and precision medicine offers options for disease control and treatment. Identification of highly specific molecular markers is the key component in precision medicine that can be achieved using next-generation sequencing. Therefore Chapter 2 is dedicated to this subject area. In this chapter, Dr. Dhawan has discussed her years of experience in clinical next-generation sequencing and how it is an integral part of precision medicine. In Chapter 3, phenotyping in precision medicine is described by Dr. Amjad Ali. The first phase of PMI is focused on cancer and Chapter 4 written by Dr. Dumitrescu will provide an in-depth account on precision medicine in cancer focusing on genetic screening and ethical considerations. Dr. Bartlett-Esquilant’s group in Chapter 5 has given an important direction on how precision medicine can be used in primary health care. Chapter 6 by Dr. Verma deals with Population approach to precision medicine and Chapter 7 written by Dr. Amjad Ali is on Regulation of genomic testing in the era of precision medicine. Dr. Bakhtiar in Chapter 8 has discussed how image-based modeling is used in precision medicine. Precision medicine research is a multiinstitutional approach and sharing information within different groups is essential and Chapter 9 is therefore allotted to discuss open data sharing. Dr. Rahimizadeh in this chapter has given the insight of how open data sharing can boost the outcomes of genomics and personalized medicine. In Chapter 10, Dr. Munshi and colleagues have documented the Lessons learned from cohort studies, and hospital-based studies and their implications in precision medicine. In the next chapter (Chapter 11), Clinical trials in precision medicine are discussed by Dr. Simon and in Chapter 12 why physicians and hospital staffs’ knowledge of electronic medical records is essential for effective precision medicine is pointed out by Dr. Sharma’s team. The next two chapters (Chapters 13 and 14) are dedicated to computational approaches and big-data handling in precision medicine by Dr. Hernández-Lemus and his research group. Chapter 15 of this book by Dr. Kaur and colleagues will brief you on the Trends in precision medicine. The last chapter (Chapter 16) by Tremblay’s group has summarized the future aspects of precision medicine and when it will be ready for global implication.

Precision medicine is an interdisciplinary approach keeping a disease in the center. Although several research groups are contributing to the field and a large number of publications are coming regularly, there is no book currently available that has summarized the achievements of the challenges we are facing in this field so far. The topic is budding and is difficult to give a detailed account of all the components of precision medicine in a single book. This book is the first effort to give readers the concept and components of precision medicine and where we are standing now. We do hope the contents of the book will be useful to readers in understanding precision medicine approaches and therefore will be helpful in its applications. We welcome your thoughts and suggestions to improve the next edition of the book.

Mukesh Verma, Ph.D.

Debmalya Barh, Ph.D.

(Editors)

Chapter 1

Introduction

Every Individual Is Different and Precision Medicine Offers Options for Disease Control and Treatment

C. Hizel¹,², J. Tremblay¹,²,³,⁴, G. Bartlett⁵, and P. Hamet²,³,⁶     ¹OPTI-THERA, Montreal, QC, Canada     ²International and Interdisciplinary Association on the Pharmaceutical Life Cycle (AIICM/IIAPC), Montréal, QC, Canada     ³Université de Montréal, Montréal, QC, Canada     ⁴CRCHUM, Montreal, QC, Canada     ⁵McGill University, Montreal, QC, Canada     ⁶CHUM, Montreal, QC, Canada

Abstract

With the availability of high-throughput genomics technologies and the completion of the Human Genome Project in 2003, we are now in the postgenomics era. The concept of precision medicine evolved over time and was popularized only recently. It became a hot topic in the medical community as well as in public sphere due to president Obama's announcement of the Precision Medicine Initiative at the beginning of 2015. In principle, the term precision medicine referring to multiple omics profiles, which include genomics, pharmacogenomics, proteomics, metabolomics, transcriptomics, epigenomics, and metagenomics, takes into account family history and lifestyles to make more tailored diagnostic and therapeutic strategies to a particular patient with different monogenic and multifactorial polygenic complex diseases, such as diabetes. The term personalized medicine is wider, more inclusive of subjects' environment, exposure, and socioeconomic status. However, besides technical issues, as a cornerstone of individual approach, precision medicine can fulfill its promise and build its sustainable existence by addressing and asking precision questions regarding structural deficiencies in health care system for the most vulnerable patients and pathologies including neglected diseases and global epidemic of complex noncommunicable diseases, such as diabetes in the society of not only high-income but also in low- and middle-income countries.

Keywords

Diabetes; Low- and middle-income countries (LMICs); Noncommunicable diseases (CNCDs); Omics; Precision medicine

1. What Is Precision Medicine? Personalized Medicine Versus Precision Medicine (C. HIzel, P. Hamet, and J. Tremblay)

The push to appeal precision medicine for patient health care as individual approach received a major boost with the announcement of a $215  million for Precision Medicine Initiative by President Obama earlier on January 2015 (www.whitehouse.gov/precision-medicine). There is a lot of overlap between the terms Precision and personalized medicine and sometimes are used interchangeably with very subtle difference in life sciences. The more popularized term precision medicine, emphasizes more the stratification of molecular-level information, whereas personalized medicine is more defined with the ability to tailor treatments, as well as prevention strategies, to the unique characteristics of each person (Jameson and Longo, 2015; Mirnezami et al., 2012).

Even if with both terms genomic studies are one of the principal components for identifying the Achilles heel of the subject affected by the disease, it is only one piece of the puzzle. Thereby, the creation of robust data from genomic risk to the exposome together with medical histories, social factors, and lifestyle factors is pivotal and they cannot act in isolation, but in concert to fulfill prior engagement of both terms (personalized/precision) in drug effectiveness and safety as much as in susceptibility to common multifactorial complex diseases for more precise personalized health care (Pashayan et al., 2011; Kittles, 2012). Like personalized medicine, precision medicine is neither parallel nor perpendicular! This is a highly interactive field that requires a synthesis in medicine, biochemistry, molecular biology, genetics, and even sociology and politics making a call to the informatics and biostatistics. That is to say, it is a matter of interdisciplinarity. Since the completion of Human Genome Project in 2003 symbolically announced the postgenomic era, determining the precise molecular structure of DNA and understanding the genome structure revolutionized our concept of health due to rapid development of molecular medicine specifically genetics, informatics, and other high-throughput technologies (e.g., nanotechnology, proteomics, metabolomics) which has led scientists and physicians to have an avant-garde thinking about how to detect and finally treat disease precisely (Naidoo et al., 2011). Upon the arrival of postgenomics era medicine with the completion of the Human Genome Project (HGP) in 2003, an important conceptual shift is done on prediction/prevention of future health outcomes (e.g., disease susceptibility, response to health interventions) with the use of individual genetic/genomics information. A corollary is that preventive and customized interventions and diagnostic tests may now be conceptualized (and in some cases implemented) during the presymptomatic phase of a disease or before pharmacotherapy is initiated (Hizel et al., 2009; Aydin Son et al., 2013). The concept of prevention represents the next step in the development of the predictive medicine as conceived by Jean Dausset, one of the three winners of the Nobel Prize in Physiology/Medicine in 1980. Dausset has suggested the term predictive medicine as a prerequisite step for preventive medicine. Subsequently in 1993, Jacques Ruffie offered a more comprehensive definition for the term and laid the philosophical basis for this new field in a book entitled Naissance de la Médecine Prédictive (Birth of Predictive Medicine) (Ruffié, 1993). Today, the term predictive medicine is replaced by one that is more precise: personalized medicine and precision medicine enabled by the introduction and availability of high-throughput genomics technologies (Hizel et al., 2009; Aydin Son et al., 2013). As a consequence of genetic diversity and the existence of our genes in different forms in different environments with different lifestyles, the genetic basis of individual approach is resumed as we are all different, which reflects the presence of genetic diversity (Cavalli-Sforza and Piazza, 1993; Cavalli-Sforza, 1997). Behind this expression, there is a scientific phenomenon called polymorphism. Our genes exist in different forms which determine the differences in gene activity. More than 93% of genes are polymorphic such as single nucleotide polymorphism (SNP) (Chakravarti, 2001). Polymorphism, especially SNP has the power to predict enzyme activity encoded by this or that gene. SNP detection enables us to understand better metabolic peculiarities in each case (Lai, 2001). So, precision medicine as a part of new genetics (Sutton, 1995) is based on the polymorphism phenomenon which now can be applied even in routine medical practice. In the light of new knowledge from genome studies and their by-products, matching molecular and genetic profiling with clinical–pathological data is important to create precise individual approach in disease predisposition (who is at risk), diagnosis (what is the cause), prognostic (who to treat), therapeutic response (how to treat) as treatment decision-making and prevention strategies in day-to-day personalized health care (Ginsburg and McCarthy, 2001; McCarthy et al., 2013). However, the activity of enzymes is not only under the control of genetic factors but also environmental factors which change gene activity–gene expression leading to different phenotypic expression for different diseases. Cells and tissue are in continuous communication across different interacting layers, such as DNA, RNA, and protein to maintain homeostasis and regulate biological processes in response to external environmental stimuli (Hausman et al., 2009). Accordingly, the main principle for the development of predictive and personalized/precision medicine as presented in Fig. 1.1, all of us as open system are the result of constant communication between our genes and our environment—biological psychological, electromagnetic (Prigogine, Nobel Prize for Physics in 1977), and the impact of each factor (genetic and environmental) in disease interaction development (Prigogine et al., 1974). Moreover, the biostatistical and informatics approaches play a pivotal role in the measuring of relative risk (also as odds ratio, etc.) of any disorder in each particular case. We cannot change the structure of our genes, but we can influence the results of their expression for instance, via epigenetic modulation of transcription (disease development/protection, drug metabolism) through our lifestyle and gene expression changes according to the feedback principle to prevent or delay the onset of disorders (Dempfle et al., 2008).

Figure 1.1  Gene–environment interaction (GxE).

The application of precision medicine will certainly in stepwise fashion broaden in most areas of medicine for diverse health conditions particularly in multifactorial affections for which we are currently witnessing a distinct increase in the incidence of these complex multifactorial noncommunicable diseases connected with the environment such as cancer, cardio vascular disease, and diabetes (Mirnezami et al., 2012). One of the essential ideas underpinning precision medicine is to determine information about the genetic risk factors for different diseases to inform patients and to introduce proactive changes in behavior such as lifestyle factors. Accordingly, with a better understanding of our initial genetic capacity we can prevent the development of diseases induced by the environment (most cancers, multifactorial diseases such as diabetes, high blood pressure, cardiovascular risk) and of course the secondary drug reactions with the application of knowledge in pharmacogenetics. For example an individual with a high gene risk score for diabetes but who has not developed the disease yet could be advised to change his lifestyle factors such as losing weight and starting exercise (Huggins et al., 2015). So, in the context of precision medicine which aims at developing targeted medical treatments and interventions, each person should be analyzed and examined according to his proper context, in relation with the person’s environment, genetics, lifestyle, and even the socioeconomic and family interactions since all these informations which are used in routine practice contributes to the individual approach (Goryakin et al., 2015). Accordingly, understanding gene–environment interactions relevant to genetic variations for common and complex human diseases is an important challenge for precision medicine. Eventually, with the aid of pharmacogenetics, precision medicine could assure that patients get the right drug at the right dose at the right time, with minimum adverse drug reactions (ADRs) and maximum efficacy by translating functional genomics into rational therapeutics (Ingelman-Sundberg, 2001; Aydin Son et al., 2013). As a promising new era of personalized interventions, translation of pharmacogenomic knowledge into clinical practice by optimizing drugs and drug combinations according to each individual’s unique genetic makeup remains the first priority in implementing personalized health care as an important component for precision medicine (Aydin Son et al., 2013; Sharma, 2014). Henceforth, translation of pharmacogenomic knowledge into clinical practice by optimizing drugs and drug combinations according to each individual’s unique genetic makeup remains the first priority in individualized health care as an important component for evidence-based precision medicine (Aydin Son et al., 2013; Sharma, 2014). Since exponential growth of genetic data together with information on environmental factors is becoming an essential tool in health care, for personalized/precision medicine to be useful at the clinical level implementation of health records (EHRs) storing comprehensive, individual-specific data is crucial (Scheuner et al., 2009). Moreover, precision medicine relies on the ability to accurately identify and stratify relevant patient subgroups from extensive clinical and genomic datasets (Marsolo and Spooner, 2013; Ritchie et al., 2015; Laper et al., 2016). Clinical decision support system as translation software undoubtedly could offer opportunities for combining heterogenous clinical phenotype data with genomic data to generate more actionable information at the clinical level for an individual approach, such as interpretation of PGx results for more actionable highly personalized reports.

Development of Human Genome Project high-throughput technologies, such as genomic sequencing and real-time imaging techniques, provide the ability to collect, quantify, and digitize systemwide measurements of different complex pathological conditions with precision. However, the ability to deliver truly precise medicine depends not only on having tools to measure complex and interactive networks underlying human disease but also deepens equitably the expansion in different populations not limited to high-income countries (HICs) as well as low- and middle-income countries (LMICs). To this end, precision medicine should meet the needs of the society and should be consistent with the realities of populations in these countries—the realities regarding the social, cultural, political issues, and local burden in terms of diseases; noncommunicable complex diseases (NCCD) such as diabetes, cardiovascular disease, cancer, as well as neglected diseases such as tuberculosis, HIV, and hepatitis.

1.1. Precision Medicine in Complex Chronic Disease: A Focus on Diabetes

Today, approximately 4000 known pathologies are of monogenic type causally supported by defects in a single gene (Ropers, 2010; Kuehn, 2012). Knowing that we have around 25,000 genes, it represents approximately 16% of all genes. All of these genes, including those causing monogenetic disorders can be involved in complex diseases. By complex diseases, we mean diseases where multiple genes are involved, in an interaction with the environment. Second, the substantive difference of genes involved between monogenic and polygenic disorders is that for most Mendelian diseases the defect implicates structural changes within the coding sequence of the gene, SNP, deletion, CNV, resulting directly in a defect of the gene function. In diabetes, most of monogenetic forms are caused by sequence defect in a gene involved in insulin secretion, glucose transport etc. One of the best examples is polymorphisms in HNF4-α (Hepatic Nuclear Factor 4 Alpha) gene, which codes for a transcription factor being responsible for regulating gene transcription in pancreatic beta cells, as a cause of maturity onset diabetes of the young (MODY 3) (Hellwege et al., 2011). In contrast, in polygenic disease, a large majority of polymorphisms is outside of the coding sequence, in 3′ and 5′ segments, in introns, or completely outside genes, yet usually involved in the control of gene transcription. A complex illustration is UMOD gene, its implication in polygenic disease is best illustrated by large numbers of polymorphisms outside of coding sequence, implicated in diabetic nephropathy (Gorski et al., 2015), while mutations within the gene coding sequence result in an autosomal dominant form of nephropathy, familial interstitial nephropathy, a diabetes-independent monogenic disorder.

For the most part, although complex diseases often cluster in families, they do not obey the standard Mendelian clear-cut patterns of inheritance and they are the result of interactions between genetic and environmental and lifestyle factors (Rannala, 2001). Accordingly, genetic factors represent only one piece of the puzzle as a risk factor associated with complex disease phenotypes (Dempfle et al., 2008; Thomas, 2010; Hamet, 2012). Moreover, the interplay between genetic and environmental factors in these pathologies make the research studies more challenging, since interaction of the gene product and the by-products of environmental insult often occurs at the molecular level interaction of gene functional control (transcription, translations) or its product and the by-products of environmental insult often occurs at the molecular level. Possible confounding factors effecting disease phenotype should be taken into consideration that may obscure the apparent relationship between genotype and disease phenotype being one of the most difficult components of this jigsaw puzzle (Dempfle et al., 2008; Thomas, 2010; Hamet, 2012; Simon et al., 2016). Therefore, predisposition is influenced by the level of certain environmental exposures, personal factors, and so it is very important to understand those extrinsic (environmental, lifestyle) and intrinsic (gender, race and ethnicity, age, weight, renal, or hepatic function and genetic differences in metabolic enzymes or drug transporters) factors and bring together the components when the time comes to interpret the data (Piccolo et al., 2016). To this aim, understanding and qualification of gene–environment interactions related to genetic variations for common and complex human diseases is an important challenge leading the importance of the need to carry out studies in different environmental conditions and populations so as to form the global final picture for developing targeted therapies (Dempfle et al., 2008; Hamet, 2012).

Diabetes mellitus is worldwide, an ever-growing and alarming public health problem of pandemic noncommunicable disease with a large economic burden on the health care systems for society (Guariguata et al., 2014). According to the Diabetes Atlas published by the International Diabetes Federation and the data compiled by the World Health Organization (WHO) by 2030, it is projected that diabetes mellitus with the growing incidence will be the seventh leading cause of death and approximately 592 million people worldwide will have diabetes (Guariguata et al., 2014). Notably, rising in prevalence of these diabetes burdens will be most probably felt more in LMICs associated with sociodemographic, economic transition, and environmental circumstances (Ali et al., 2011; Guariguata et al., 2014; Zabetian et al., 2014; Goryakin et al., 2015). The most prevalent form is diabetes type 2 (T2D), which is a group of complex metabolic disorder having high morbidity and mortality and like other multifactorial polygenic diseases rarely affects each person similarly. T2D is characterized by impaired insulin secretion and decreased insulin sensitivity (i.e., insulin resistance) and is more frequently diagnosed in late adulthood; however, its prevalence increases in children and young adults which are affected by obesity worldwide (Permutt et al., 2005; Pinhas-Hamiel and Zeitler, 2005; Rosenbloom et al., 2009; Gao et al., 2016). Diabetes type 1 (T1DM) incidence is higher in childhood by an absolute deficiency in insulin secretion with minimal insulin resistance feature (Lipton et al., 2005; Piloya-Were et al., 2016; Gao et al., 2016). Both type 1 and type 2 diabetes are associated with chronic hyperglycemia which has a pathogenic role in microvascular diseases, such as nephropathy, retinopathy, and neuropathy as well as macrovascular complications such as stroke and coronary heart disease (Meeuwisse-Pasterkamp et al., 2008; Boussageon et al., 2011; Green, 2014; Tremblay and Hamet, 2015; Wang et al., 2016). Notably, in patients with diabetes mellitus, with concomitant increase in associated cardiovascular mortality, diabetic nephropathy is among the most dangerous complication affecting approximately one-third of people with both types of diabetes mellitus that has become the most common single cause of end-stage renal disease requiring renal transplantation or dialysis (Reutens and Atkins, 2011; Currie et al., 2014). Even though dozens of genes are now well established for their association with type 2 diabetes, molecular mechanisms linking environmental factors with these genes in type 2 diabetes pathogenesis are not yet clearly understood (Ling and Groop, 2009; Gilbert and Liu, 2012; Gaulton et al., 2015; Simon et al., 2016). As an important epitome of multifactorial complex polygenic condition, T2D is the result of the interaction of lifespan environmental/lifestyle factors, such as obesogenic environment and genetic variation at multiple different chromosomal sites which raise the risk in a synergistic manner. Since patients diagnosed with T2D have a variety of phenotypes and susceptibilities to diabetes-related complications, it is very important to understand this complex gene–environment architecture to gain insight into disease progression and to translate into genomic information for more effective prevention and precise patient management (Franks et al., 2013; Franks and Paré, 2016). Moreover, T2D severity and prevalence could also be correlated with ethnicity and some ethnic groups tend to be more predisposed than others even in similar obesogenic environments (Abate and Chandalia, 2003; Brorsson and Pociot, 2011; Joseph et al., 2016; Maskarinec et al., 2016). Obesity is one the major risk factors for type T2D and in concordance with obesity the effects of environmental exposures may reflect epigenetic processes since T2D may be attributable to susceptibility genes and epigenetic alterations affecting the regulation of tissue-specific gene expression throughout the life course by DNA methylation, posttranslational histone modifications (Turner, 1998), and other changes in noncoding RNA (Peschansky and Wahlestedt, 2014) and chromatin without any alteration in primary DNA sequence itself (Holliday, 2006; Tremblay and Hamet, 2008; Waddington, 2012). Furthermore, epigenetic processes is one