Applied Genomics and Public Health

By George P. Patrinos

Applied Genomics and Public Health examines the interdisciplinary and growing area of how evidence-based genomic knowledge can be applied to public health, population health, healthcare and health policies. The book gathers experts from a variety of disciplines, including life sciences, social sciences, and health care to develop a comprehensive overview of the field. In addition, the book delves into subjects such as pharmacogenomics, genethics, big data, data translation and analysis, economic evaluation, genomic awareness and education, sociology, pricing and reimbursement, policy measures and economic evaluation in genomic medicine. This book is essential reading for researchers and students exploring applications of genomics to population and public health. In addition, it is ideal for those in the biomedical sciences, medical sociologists, healthcare professionals, nurses, regulatory bodies and health economists interested in learning more about this growing field.

• Explores the growing application of genomics to population and public health
• Features internationally renowned contributors from a variety of related fields
• Contains chapters on important topics such as genomic data sharing, genethics and public health genomics, genomics and sociology, and regulatory aspects of genomic medicine and pharmacogenomics

• Language: English
• Publisher: Academic Press
• Release date: Nov 13, 2019
• ISBN: 9780128136966

Book preview

States

Preface

George P. Patrinos, Department of Pharmacy, Faculty of Health Sciences, University of Patras, Patras, Greece, Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates, Zayed Center of Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates, Department of Pathology - Bioinformatics Unit, Faculty of Medicine and Health Sciences, Erasmus University Medical Center, Rotterdam, The Netherlands

The Translational and Applied Genomics book series was launched in 2018, aiming to provide succinct and comprehensive textbooks pertaining to the various translational and applied genomic disciplines that constitute the cornerstone of personalized medicine.

Unfortunately until now, the significant advancements in the field of personalized medicine, in terms of both genomic technology as well as the findings from genomics discovery research, are not matched with reciprocal advances in the translation of these findings into the clinic. In particular, there are often significant hurdles that decelerate the smooth incorporation of genomics research findings in the daily medical practice. These obstacles are more related to public health genomics disciplines rather than genomics research itself but are of equal importance since they contribute greatly to the transition from genomics research to genomic and personalized medicine. These include ethical, legal, and societal aspects in genomics, genome informatics, improving and harmonizing the genetics education of healthcare professionals and biomedical scientists, raising genetics awareness among the general public, and health economic evaluation in relation to genomic medicine.

Given the fact that there was no textbook in the literature exclusively devoted to applied genomics and public health, and immediately upon the launch of the Translational and Applied Genomics book series, I have submitted a proposal to edit such textbook envisaging to fill in this important literature gap, which Elsevier/Academic Press has agreed to include in the book series.

The contents of this textbook are divided in two parts. The first part starts with genetic epidemiology and continues with genomics applications in key medical specialties, such as cancer genomics and psychiatric genomics. Also, dedicated chapters have been compiled for the genomics of rare diseases and pharmacogenomics; not only this, a chapter on microbial genomics is also included in the textbook. In all the above chapters, emphasis has been given on the impact of genomics in public health. Also, given the fact that all of the above areas are entirely dependent upon genome informatics, two chapters have been devoted to genome informatics pipelines and browsers, and the impact of translational tools and databases in personalized medicine. The last chapter of the first part is dedicated to molecular diagnostics and genetic testing, which constitutes a fundamental part of applied genomics.

The second part of the book addresses the various adjacent, to personalized medicine, disciplines that are important for the implementation of genomics discoveries in the clinic. As such, there are chapters dedicated to the means of assessing of the stakeholder environment and stance toward personalized medicine, the issue of genomics awareness, and education of the general public and healthcare professionals, respectively, the ethical, legal, and regulatory aspects of personalized medicine and the economics and the closely related issue of pricing and reimbursement in genomic and personalized medicine. Elements such as the Internet of Things, artificial intelligence, genetic counseling, and the various promotional strategies for genetic testing delivery are also covered in the contents of this textbook, some of which for the first time in a textbook of this kind. Lastly, the book ends with a chapter dedicated to the application of personalized medicine interventions in emerging economies and developing and low-resource countries.

As with all edited textbooks, my effort has been assisted by many internationally renowned experts in their field, who have kindly accepted my invitation to compile the 21 chapters of this book and share with me and the readers their expertise, experience, and results, making effort to formulate the contents of the book such that the notions described therein are explained in a simple language and terminology, so that the book is useful not only to experienced physicians and healthcare specialists and academics but also to undergraduate medical and life sciences students. The numerous self-explanatory illustrations clearly contributes to this end, making this book an ideal reference material for courses related to applied genomics and public health. After all, the contents of this textbook are structured in such way so that it covers specialized courses in the field of public health genomics.

I am grateful to the colleagues who provided constructive comments and criticisms from the proposal stage and during the preparation of this textbook. I expect that some points in this book can be further improved and, therefore, I would welcome comments and constructive criticism from attentive readers, which will contribute to improve the contents of this book in its future editions. I am also indebted to the editors, Mr. Peter Linsley and Ms. Kattie Washington, Kristi Anderson, and Kiruthika Govindaraju at Elsevier, who helped in close collaboration to overcome encountered difficulties at the various stages of this project. I also express my gratitude to all contributors for delivering outstanding compilations that summarize their experience and many years of hard work in their field of research. I am also indebted to Mark Rogers who was responsible for the design and the cover of this book and to the copy editor, Jayaprakash, who has refined the final manuscript prior to publication. Furthermore, I owe my special thanks to the academic reviewers of the proposal for this textbook for their constructive criticisms on the chapters and their positive evaluation.

Last, but not least, I wish to cordially thank my wonderful family for their patience and continuous support over the years, who has significantly contributed to my scientific advancement and, most importantly, personal improvement and from whom I have taken considerable amount of time to devote to this project.

August 2019

Chapter 1

Applied Genomics and Public Health

George P. Patrinos¹,²,³,    ¹Department of Pharmacy, School of Health Sciences, University of Patras, University Campus, Patras, Greece,    ²Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates,    ³Zayed Center of Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates

Abstract

Personalized medicine aims to translate genomic findings into modern health care to inform medical decision-making and to individualize therapeutic interventions. The overall goal of personalized medicine is to improve the quality of life of the patients and, at the same time, reduce the overall cost of the health-care expenditure. While genomic discoveries are progressing at a very rapid pace, there have been much slower steps in the adjacent public health genomics disciplines, which would help catalyzing integration of these genomic discoveries into modern health care. This chapter provides an overview of the various public health genomics disciplines that are discussed in the various chapters in this textbook.

Keywords

Personalized medicine; applied genomics; public health; genome informatics; genomics awareness and education; ethical, legal, and societal issues; genetic counseling; emerging economies

Doctors have always recognized that every patient is unique, and doctors have always tried to tailor their treatments as best they can to individuals. You can match a blood transfusion to a blood type—that was an important discovery. What if matching a cancer cure to our genetic code was just as easy, just as standard? What if figuring out the right dose of medicine was as simple as taking our temperature?

–Former US President Barack H. Obama, January 30, 2015

1.1 Introduction

Personalized medicine exploits an individual’s genomic profile to support the clinical decision-making process and to individualize drug treatment modalities.¹ The concept of personalized medicine has gained momentum in the last decade; however, its founders have described the concept many centuries ago. Around 400 BCE, Hippocrates of Kos (460–370 BCE) stated, … it is more important to know what kind of person suffers from a disease than to know the disease a person suffers. Also, Ibn Sina (c. CE 980–1037) mentioned, … in the make-up of most people there is somewhere a natural tendency to get out of order, some congenital weakness in one particular organ, tissue or system. In particular, he termed this a personal disposition and put forward the view that each patient should be looked upon as a distinct and separate case. Interestingly, the Talmud (Yevamot 64b; 2nd century BCE) mentions that if a woman’s first two children had died from blood loss after circumcision, the third son should be excused from circumcision, hence indicating that the abnormal bleeding tendency was hereditary. These ancient statements and examples, if put together, may encapsulate the essence of modern, personalized, genomic medicine.

In recent years, significant advances have been made in understanding the genetic etiology of a wide range of human-inherited diseases. These advances have been made possible thanks to the significant breakthroughs and rapid pace of development of the genomic technology, aiding clinicians in their task of estimating disease risk as well as individualizing treatment modalities.²

Although there have been major leaps in genomics research and discovery work, facilitated by the genomic technology revolution,³ the pace of these discoveries has not met reciprocal advances in the translation of these findings into the clinic. In other words, there are often significant barriers that hamper the smooth incorporation of genomics research findings into daily medical practice, which have to do more with disciplines related to public health genomics rather than genomics research itself. These disciplines are of utmost importance since they contribute to the transition from genomics research to genomic and personalized medicine. These include ethical, legal, and societal aspects in genomics, also termed ELSI, genome informatics, improving and harmonizing the genetics education of health-care professionals and biomedical scientists, raising genetics awareness among the general public, and health economic evaluation in relation to genomic medicine. These disciplines can be exemplified as the supporting pillars that need to be erected, from the solid bedrock of genomics research, to firmly hold the superstructure of genomic and personalized medicine (Fig. 1.1). Presently, although the foundations of genomics research are becoming stronger with ever-increasing hopes and expectations, the pillars themselves are still largely under construction.⁴

Figure 1.1 Illustrative depiction of the transition of genomics research findings into personalized medicine interventions. Genomics research is metaphorically shown as the foundations of an ancient Greek temple. The rooftop of the temple depicts the various genomic and personalized medicine interventions. For the rooftop to hold, strong pillars need to be erected, each one representing the various public health disciplines, each one described in detail in the various chapters of this textbook (see also the text for details).

This textbook is a collection of timely contributions related to the various disciplines touching upon the implementation of genomic and personalized medicine and are closely related to public health genomics.

1.2 Genomics in Health Care

The contribution of germline gene variants to disease etiology is also known as genetic epidemiology. The genetic basis of several Mendelian diseases is elucidated through family-based studies and subsequent functional cloning. As highlighted in Chapter 2, From Genetic Epidemiology to Exposome and Systems Epidemiology, which describes this emerging discipline, although genome-wide association studies identified thousands of variants that are associated with complex genetic traits and conditions, the variants that are actually held responsible for these conditions largely remain undeciphered. As such, although genetic tests for high-penetrance gene variants have clinical utility for individuals, such as in preimplantation, prenatal, or postnatal diagnostics, and in preventive cascade screening of biological relatives (see also Chapter 10: Genetic Testing), genetic testing for complex phenotypes has utility only for research and public health rather than individual testing. Such complex phenotypes include rare diseases, cancer, cardiovascular and psychiatric diseases to name a few.

Rare diseases are defined by their prevalence and although individually infrequent, they affect almost half billion persons globally. Most of these rare diseases have a strong genetic component but till date, their underlying genetic etiology remains elusive. Chapter 3, Rare Diseases: Genomics and Public Health, summarizes the impact of genomic discoveries in accelerating clinical diagnosis, discoveries, and therapeutic interventions in rare diseases.

Of equal importance, cancer is one of the leading causes of death and a global public-health burden. Carcinogenesis originates from a cascade of tumor-promoting events, which result from a genomic-variant overload. Also, despite the emergence of new therapeutic modalities as the standard of care for several cancers, our ability to predict a patient’s response to these therapies, or the duration of these responses, is still lagging behind. Chapter 4, Applied Genomics and Public Health Cancer Genomics, addresses our current understanding of the contribution of various genetic aberrations and different regulatory pathways, genomics, and epigenomics in carcinogenesis and their impact in the era of precision medicine. This chapter also attempts to highlight how various genomics and integrated bioinformatics tools are being used to address many of the crucial questions in cancer medicine.

Similarly, psychiatric illnesses are also characterized by vast phenotypic heterogeneity, resulting from their underlying complex genetic basis, which largely remains unknown. The same is true for the genetic basis of interindividual differences in psychiatric drug-treatment response and toxicity, namely, for antipsychotics, antidepressants, and mood stabilizers. Chapter 5, Genomic Basis of Psychiatric Illnesses and Response to Psychiatric Drug Treatment Modalities, overviews psychiatric genomics and its implications for public health and care. It also attempts to highlight differences in psychiatric drug-treatment response and the genetic basis for the development of adverse drug reactions in these patients.

Chapter 6, Pharmacogenomics in Clinical Care: Implications for Public Health, goes a step further and attempts to summarize, more broadly, our current knowledge on the genomic etiology of variable drug treatment, both in terms of efficacy and also toxicity, namely, the development of adverse drug reactions. This chapter outlines the genetic basis of interindividual drug response for different medical specialties, namely, cardiology, oncology, psychiatry, neurology, and antiinfectious agents, and focuses on these drugs that are approved by the major regulatory bodies. Lastly, the chapter attempts to expand on the various applications of pharmacogenomics, in public health–related disciplines, in line with the overall aims of the textbook.

Monitoring an extended spectrum of agents, which has the potential of becoming pathogenic and virulent, is of major concern in public health. Also, new microbiota has emerged, requiring them to be included in the mainstream monitoring and surveillance practice to prevent more aggressive outbreaks. Chapter 7, Microbial Genomics in Public Health: A Translational Risk-Response Aspect, touches upon microbiomics and related applications including genomic-related approaches for adaptive and massive testing.

All the aforementioned genomics approaches yield a huge amount of data that need to be processed for them to be correlated with the underlying phenotype. In particular, genome-wide association studies and next generation–sequencing approaches rely on big-data analysis and hence, bioinformatics plays a vital role in the analysis and interpretation of these genomic data.⁵ Chapter 8, Genome Informatics Pipelines and Genome Browsers, attempts to summarize the various bioinformatics pipelines used in translational research and figure out how these data, from the genomic analysis at the level of individuals or populations, can be gradually integrated into the therapeutic and preventative guidelines of modern health-care systems. The chapter also addresses the various computational barriers and challenges, which arise from analyzing massive volumes of genetic data, such as annotation and quantitative data and read alignments. Similarly, genome informatics and genomic data analysis rely on the development and expert curation of genomic databases and translational tools that convert genomics data, which are often difficult to be understood by the treating physicians, to a clinically meaningful format. Chapter 9, Translational Tools and Databases in Genomic Medicine, provides an update on the main genomics databases that are developed to accommodate and curate the huge volume of data resulting from biomarker discovery and next generation–sequencing analysis. It also touches upon the translational tools that facilitate the practice of pharmacogenomics and personalized medicine for pharmacogenomic data interpretation.

As mentioned previously, there are several genetic tests with clinical utility in preimplantation, prenatal, postnatal, and even preventive molecular diagnostics, which resulted from genetic epidemiology and genome-wide association studies, and are developed for high-penetrance gene variants.⁶ Chapter 10, Genetic Testing, provides an overview on the various aspects of molecular diagnostics, the main types of genetic tests, their advantages and limitations, and their usefulness in personalized medicine, clinical practice, and disease diagnosis.

1.3 Personalized Medicine and Public Health

As mentioned earlier, the smooth incorporation of the genomic discoveries into modern medical practices relies on addressing a number of obstacles, one of which is the incomplete mapping of the key stakeholders involved in this translation process, namely, the major players, their power of intervention and policy positions, their interests and networks, and coalitions that connect them. Also, such comprehensive mapping should be complemented with the proper understanding of the policies, opinions, and overall policy content. Chapter 11, Assessing the Stakeholder Landscape and Stance Point on Genomic and Personalized Medicine, aims to provide an overview of the process for assessing the views and opinions of stakeholders toward personalized medicine and an example of implementing such an approach in a health-care environment, so that adoption of genomics into the mainstream medical interventions is expedited.

Also, a crucial bottleneck that requires rectification is the poor genomics education and literacy among health-care professionals and biomedical scientists. Chapter 12, Health-Care Professionals’ Awareness and Understanding of Genomics, critically examines the level of genomics literacy among general physicians, specialists, and other relevant health-care professionals. In particular, this chapter provides a holistic, critical overview of the level of genomics education and the extent of health-care professionals’ understanding toward genomics applications into their medical practices, and highlights a significant gap in the required genomics knowledge and understanding in order to explain the related technologies and their value to the general public. Also, in this chapter the conclusions from various related studies are critically evaluated in an attempt to raise awareness about future educational needs.

As previously indicated, apart from the various societal challenges, there are ethical and legal issues that are related to personalized medicine interventions. Chapter 13, Genethics and Public Health Genomics, and Chapter 14, Legal Aspects of Genomic and Personalized Medicine, address ethical and legal issues related to personalized medicine, respectively. Chapter 13, Genethics and Public Health Genomics, presents ethical and societal issues in genomics, focusing in particular on public health, and also discusses the ethical issues related to the actual implementation of genomic technologies with focus on genome sequencing used for diagnosis, screening, and in the context of reproductive technologies and genome editing. Most importantly, a fundamental issue, to ensure ethical use of genomics in health care, is the provision of a frank and objective view of the present limitations and risks of these modern technologies. Similarly, Chapter 14, Legal Aspects of Genomic and Personalized Medicine, attempts to summarize the existing legal framework that oversees personalized medicine–related issues in various countries worldwide, such as genetic testing, genome editing, and informed consent, to determine possible inconsistencies among different countries, and to highlight eventual legislative gaps, possibly allowing for future harmonization of these legal measures.

The Internet of Things is a new concept, which refers to a pervasive computing environment that is producing a digital replica of all living things and inanimate objects worldwide. This, together with artificial intelligence–assisted data analysis, may constitute an innovative approach pertaining to genomics and public health in particular. Chapter 15, Genomics, The Internet of Things, Artificial Intelligence and Society, discusses this modern concept and provides interesting examples resulting from the application of this innovative approach, such as remote phenotypic data capture, genotype–phenotype association, and multiomics data integration, while it also highlights the sociotechnical aspects of this digital connectivity and their implications for personalized medicine.

An important pillar in the personalized medicine superstructure (Fig. 1.1) is health economics in genomic medicine.⁷ The progress we have witnessed in personalized medicine interventions is tightly linked with the economic viability of these innovative interventions. Chapter 16, Economic Evaluation of Genomic and Personalized Medicine Interventions: Implications in Public Health, describes in an illustrative manner the decision-making process within the context of personalized medicine. First, the chapter describes the methods employed for economic evaluation, together with the challenges for researchers, accompanied by practical examples of use. Also, the chapter describes new economic models to be used in evaluating personalized medicine interventions that incorporate public health aspects, such as economic affordability, innovation, social preferences, personal utility, and clinical ethics, hence providing further insights in the resource-allocation process of modern health-care systems. In the same vein, Chapter 17, Pricing, Budget Allocation, and Reimbursement of Personalized Medicine Interventions, refers to the concept of pricing and reimbursement of genomics technologies and personalized medicine interventions, and attempts to summarize the pricing and reimbursement policies as well as being closely related to pricing issues pertaining to, for example, the balance between price and access to innovative testing, monitoring, and evaluation for cost-effectiveness and safety, and the development of research capacity.

Genetic counseling is a multiskill process, which employs genomics knowledge and communication and psychological skills and expertise to support patients, caregivers, and their families in understanding and coping with genetic diseases. Chapter 18, Genetic Counseling, touches upon the crucial role of genetic counselors in communicating genomic information through consultation with individual patients and their families. This chapter also highlights the key role played by genetic counselors in the translation of the findings derived from genomic sequencing technology and emphasizes that the number of the existing genetic-counseling graduate programs is inadequate to support the role that genetic counselors are expected to play in promoting the benefit of public health genomics.

As summarized in Chapter 10, Genetic Testing, a considerable number of genetic-testing laboratories have emerged, which offer a plethora of different prenatal and postnatal genetic tests. However, the means of communicating and advertising the genetic testing services to the interested parties, namely health-care professionals and the general public, is still poorly developed, which also poses a critical ethical and legal challenge to overcome toward provision of personalized medicine interventions. Chapter 19, Defining Genetic Testing Delivery and Promotional Strategies for Personalized Medicine, discusses the existing models for the delivery of genetic testing services, presents the steps to be undertaken when defining the optimal promotional strategy for genetic testing services, and the various means to promote these services to the interested stakeholders.

As with every public health system, regulation is needed to ensure the highest possible level of confidence between health-care providers and patients/consumers for the provision of health services to function properly. The accelerated pace of discoveries in the field of personalized medicine dictates that acquisition and appraisal of genomic information has to be suitably incorporated in new and/or revised legislation, regulatory guidance, and medical practice. Chapter 20, Regulatory Aspects of Genomic Medicine and Pharmacogenomics, addresses the regulatory aspects of personalized medicine interventions and discusses issues, such as incentives, conditional marketing, and authorization measures, and a balanced mix of incentives and sanctions to encourage corporate and other entities to pursue potential new uses of their approved products along with the evolution of personalized medicine.

Lastly, implementation of personalized medicine often lags behind in developing countries and low-resource environments compared to developed countries, such as those in Northwestern Europe and the United States.⁸ Chapter 21, Genomic Medicine in Emerging Economies, explores the various advances made in genomics and the impact of these technologies in clinical settings across resource-limited countries and emerging economies. In particular, this chapter outlines some of the challenges related to the clinical implementation of genomics in emerging economies, with examples from Latin America and Africa, such as capacity building, lack of genomics education, and qualified laboratory personnel, and discusses opportunities that arise in emerging economies, such as the fast-second winner model,⁹ where emerging economies have the potential to implement genomics faster. Lastly, the chapter provides a list of international organizations, such as the Golden Helix Foundation (www.goldenhelix.org) and the Global Genomic Medicine Collaborative (www.g2mc.org), which are developing standards for the global implementation of personalized medicine.

1.4 Conclusion

As outlined in the previous sections, with this content composition, this textbook aims to highlight the importance of the closely related, to genomics research, public health genomics disciplines toward maximizing the usefulness of the genomics research findings for the benefit of the patients. It is anticipated that the indicative aspects of public health, described in this textbook, and their role in expediting implementation of personalized medicine interventions, could stimulate large-scale collaborative studies and encourage related multicenter efforts, as a part of large, international public health genomics–research consortia, to catalyze integration of applied genomics into health care and public health.

Acknowledgments

I wish to thank all the authors of the chapters of this textbook for their contributions, highlighting the importance of implementing public health genomics approaches to facilitate and expedite the implementation of personalized medicine interventions in modern medicine.

References

1. Manolio TA, Chisholm RL, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15(4):258–267.

2. Manolio TA, Abramowicz M, Al-Mulla F, et al. Global implementation of genomic medicine: we are not alone. Sci Transl Med. 2015;7:290ps13.

3. Gullapalli RR, Lyons-Weiler M, Petrosko P, et al. Clinical integration of next-generation sequencing technology. Clin Lab Med. 2012;32(4):585–599.

4. Cooper DN, Brand A, Dolzan V, et al. Bridging genomics research between developed and developing countries: the Genomic Medicine Alliance. Per Med. 2014;11(7):615–623.

5. Lambert C, Baker D, Patrinos GP, eds. Human Genome Informatics: Translating Genes Into Health. Burlington, CA: Elsevier/Academic Press; 2018.

6. Patrinos GP, Danielson P, Ansorge W, eds. Molecular Diagnostics. 3rd ed. Burlington, CA: Elsevier/Academic Press; 2016.

7. Fragoulakis V, Mitropoulou C, Williams MS, et al. Economic Evaluation in Genomic Medicine Burlington, CA: Elsevier/Academic Press; 2015.

8. Correa-Lopez C, Patrinos GP, eds. Genomic Medicine in Developing Countries. Burlington, CA: Elsevier/Academic Press; 2018.

9. Mitropoulos K, Cooper DN, Mitropoulou C, et al. Genomic medicine without borders: which strategies should developing countries employ to invest in precision medicine? A new fast-second winner strategy. OMICS. 2017;21(11):647–657.

Part I

Genomics in Healthcare

Outline

Chapter 2 From Genetic Epidemiology to Exposome and Systems Epidemiology

Chapter 3 Rare Diseases: Genomics and Public Health

Chapter 4 Applied Genomics and Public Health Cancer Genomics

Chapter 5 Genomic Basis of Psychiatric Illnesses and Response to Psychiatric Drug Treatment Modalities

Chapter 6 Pharmacogenomics in Clinical Care: Implications for Public Health

Chapter 7 Microbial Genomics in Public Health: A Translational Risk-Response Aspect

Chapter 8 Genome Informatics Pipelines and Genome Browsers

Chapter 9 Translational Tools and Databases in Genomic Medicine

Chapter 10 Genetic Testing

Chapter 2

From Genetic Epidemiology to Exposome and Systems Epidemiology

Nicole Probst-Hensch,    Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Swiss TPH, Basel, Switzerland University of Basel, Basel, Switzerland

Abstract

Genetic epidemiology investigates the contribution of germline gene variants to disease etiology. The genetic architecture of many diseases exhibiting Mendelian inheritance patterns was decoded through family-based studies and subsequent functional cloning. Tests for high-penetrance gene variants have clinical utility for individuals, such as in preimplantation, prenatal, or postnatal diagnostics, and in preventive cascade screening of biological relatives. Genome-wide association studies thousands of identified variants associated with complex traits. Yet, the causal variants explaining the replicated statistical associations and their functional effects have rarely been deciphered. A missing heritability gap remains. Genetic testing for complex phenotypes has utility for research and public health rather than individuals. Genetics integrated with other biomarkers and information in the context of large citizen cohorts enables systems epidemiology approaches. The utility of genomic biomarkers is in studying molecular pathways mediating effects of the exposome on the phenome in order to improve causal understanding of modifiable risk factors. The goal is to strengthen the primary prevention of noncommunicable diseases.

Keywords

Genetic variant; genome-wide association; penetrance; complex disease; cohort; biobank; Mendelian randomization; exposome; gene–environment interaction; systems epidemiology

2.1 Genetic Variation

The haploid human genome is the complete DNA sequence consisting of approximately 3.3 billion base pairs. Many different types of genetic variation exist. They can be classified in different ways, for example, by their physical nature, by their biological consequence, or by their effect on disease risk. Common physical types of sequence variation are differences in the number of sequence repeats of different length and single-nucleotide polymorphisms (SNPs). The biological relevance of these genetic variants ranges from functionally irrelevant or silent to the abolishment of protein production, with differences in the penetrance and clinical relevance associated with respective variants.¹,² Assessing the functional relevance of a genetic variant and assigning it a causal role in the etiology of a specific phenotype or disease remains the biggest challenge of genetic epidemiology, particularly with regard to common complex diseases. Two major aspects are at the heart of this challenge. First, only a very minor part of the DNA sequence is coding for proteins.³–⁶ Elucidating the biological relevance of the DNA sequence, which is not protein-coding, is the focus of intense international research efforts. Second, DNA variants in proximity of each other on the same chromosome are not transmitted to subsequent generations independently of each other. Crossover of homologous chromosome segments during meiosis, when haploid gametes are formed, is less likely to separate genetic variants lying close to each other. As a result, genetic variants can be in complete or partial linkage disequilibrium (LD) over many subsequent generations. It is therefore not possible to differentiate with family-based or genetic association studies alone, which, of several disease-associated genetic variants, is causally responsible for the disease association.

2.2 What is Genetic Epidemiology?

Genetic epidemiology focuses on the effect of genetic variation on health and disease.¹,² To maximize causal inference and understanding of disease etiology, it ideally integrates the current understanding of biology as well as nongenetic disease determinants. Genetic epidemiology has evolved in parallel to technological advances from studying patterns of disease aggregation and distribution in families to assessing the health effects of single and combined genetic variants across the whole genome.

Genetic epidemiology has successfully identified the cause of many monogenetic disorders, for example, disease exhibiting a clear Mendelian inheritance pattern.¹ These mostly rare genetic syndromes are linked to the inheritance of high-penetrance gene variants. The penetrance of a gene variant reflects the likelihood that its carrier will develop the specific disease. Carriers of such variants face a very high, sometimes up to 100%, likelihood of developing the specific disease before a certain age, irrespective of environment, behavior, or other genetic variants. An example for a genetic disease with 100% penetrance is Huntington disease.

The identification of the genetic variants contributing to common, noncommunicable diseases (NCDs), such as cardiovascular, respiratory, metabolic, or neurological disorders, is more challenging.¹,⁷,⁸ These diseases develop against a background of complex risk patterns consisting of several genetic variants and biological pathways as well as exogenous factors to which a person is exposed, that is, lifestyle, physical environment, social environment. The contribution to disease risk for each factor separately is usually small, and each new diagnosis develops from a personalized risk profile.

2.3 Approaches Toward Identifying Genetic Variants

The following four questions are at the heart of genetic epidemiology⁹: Does a phenotypic trait show familial aggregation? Does it show genetic segregation? Does it show cosegregation in families with a genetic marker? Does it show association in the population with a genetic marker? Answering the last two questions in the context of linkage studies and genetic association studies, respectively, requires access to DNA and genotyping for appropriate genetic markers. The advances in molecular genotyping technology combined with progress made in computational efficiency have considerably improved the power of linkage and association studies at a reasonable cost over the recent decades.

The approach toward identifying disease-causing gene variants depends importantly on their penetrance (Table 2.1). While high-penetrance gene variants, which tend to be rare, are usually first studied with the help of family studies, the common family study approaches are often not efficient for identifying low-penetrance gene variants, which are often prevalent. On the contrary the two approaches can also be combined to benefit from the advantages of both methods.¹⁰

Table 2.1

Adapted from Ott J, Kamatani Y, Lathrop M. Family-based designs for genome-wide association studies. Nat Rev Genet. 2011;12(7):465–474.

2.4 Implying Genetic Loci in Disease Etiology in the Context of Family Studies—Segregation and Linkage Analysis

The first indication for the contribution of a highly penetrant gene variant to a disease risk comes for the aggregation of specific traits in families as well as from a higher observed trait concordance in monozygotic compared to dizygotic twins.¹ Yet, disease aggregation in families or monozygotic twins is not sufficient proof of a genetic disease cause. Family members and monozygotic twins are also more likely to share environment and lifestyle compared to unrelated persons. Nevertheless, if familial aggregation is observed, it justifies investigating the role of genetics further and searching for disease genes.

In a next step, the patterns of disease distribution within families can be compared to patterns expected, assuming one or more biologically rational models, for example, additive, dominant, recessive, and polygenic genetic effects. This approach of statistically estimating the mode of inheritance of a disease trait based on disease distributions within families is called segregation analysis, which does not require access to genetic material.¹,⁹,¹¹

When DNA is available from several family members across different generations, the cosegregation of genetic variants with disease in these families allows identifying the chromosomal regions within which the disease-causing genetic variant(s) are likely to lie.¹,⁹ In linkage studies, highly polymorphic genetic markers, spread across the chromosomes, are measured in the DNA collected from family members from different generations. Linkage analysis investigates, in many different families, whether a specific polymorphic marker in a chromosomal region is present more often than expected by chance among family members who developed the disease (Fig. 2.1A).

Figure 2.1 (A) Linkage analysis for the discovery of disease susceptibility loci. Linkage reflects the tendency for chromosomal segments to be inherited intact from parent to offspring. Here, all affected offspring share a chromosome segment (blue) inherited from the mother, which is not shared with the unaffected offspring, suggesting a susceptibility locus within the shared chromosome segment. (B) Association analysis for the discovery of disease susceptibility loci. LD is the nonrandom association of alleles at two or more loci. Here, association testing would identify a correlation between SNPs in the block of high LD (blue) and case status, which would suggest the presence of an unobserved disease variant (D). LD, Linkage disequilibrium; SNPs, single-nucleotide polymorphisms. Adapted from Mathias RA. Introduction to genetics and genomics in asthma: genetics of asthma. Adv Exp Med Biol. 2014;795:125–155.¹²

The polymorphic gene variant is not the causal mutation, but rather a proxy indicator for a chromosomal context within which the disease-causing mutation may lie. The utility of polymorphic gene markers as proxy for such chromosomal regions lies in the fact that genetic loci, in chromosomal proximity to each other, tend to be linked, for example, they are not likely to be separated by recombination during meiosis. As statistical methods and computational power for linkage analysis evolved, it became possible to handle complex parametric and nonparametric models and to take into consideration anticipation (the earlier onset of a disease sometimes seen in succeeding generations), imprinting (the phenotype that is observed depends on the sex or the parent transmitting the disease-causing allele), admixture, and missing information.

Linkage analysis approaches can be differentiated into model-based and model-free linkage analysis. Model-based linkage analyses need to assume a genetic model for the phenotype of interest. Model misspecification poses a challenge to model-based linkage analysis. Despite this challenge, model-based linkage analyses successfully led to the eventual cloning of monogenic disease genes.⁹,¹⁰ Model-free linkage analysis methods in contrast, which do not need to specify the model of inheritance, have been developed toward identifying genetic variants involved in more complex disease etiology.

2.5 Implying Genetic Loci in Disease Etiology in the Context of Genetic Association Studies

A limitation of family-based studies is the fact that given the few generations studied in each family, the likelihood for recombination events remains very limited over large chromosomal regions. As a result, genetic variants over extended chromosomal regions are mostly coinherited and their cosegregation with the disease of interest is therefore identical. Zooming in on the disease-causing variant, in order to ultimately clone it, can in part be achieved in the context of association studies of indirectly related individuals. Association studies allow finer mapping of disease-causing variants than family-based linkage analysis. The correlation of genetic variants lying next to each other on the same chromosome is present over much shorter chromosome stretches, because the likelihood of recombination events between polymorphic markers, in proximity to each other, increases over numerous ancestral generations. On the one hand, these shorter distances of linked markers allow narrowing down the chromosomal region within which a disease-causing variant may lie. On the other hand, the fact that even after numerous generations, a disease-causing variant is still present in a specific chromosomal context of other genetic variants, for example, the disease-causing variants is in LD with other genetic variants, supports the efficient search for disease-causing loci (Fig.