Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine

By Henry H. Heng

Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine transports readers from Mendelian Genetics to 4D-genomics, building a case for genes and genomes as distinct biological entities, and positing that the genome, rather than individual genes, defines system inheritance and represents a clear unit of selection for macro-evolution. In authoring this thought-provoking text, Dr. Heng invigorates fresh discussions in genome theory and helps readers reevaluate their current understanding of human genetics, evolution, and new pathways for advancing molecular and precision medicine.

• Bridges basic research and clinical application and provides a foundation for re-examining the results of large-scale omics studies and advancing molecular medicine
• Gathers the most pressing questions in genomic and cytogenomic research
• Offers alternative explanations to timely puzzles in the field
• Contains eight evidence-based chapters that discuss 4d-genomics, genes and genomes as distinct biological entities, genome chaos and macro-cellular evolution, evolutionary cytogenetics and cancer, chromosomal coding and fuzzy inheritance, and more

• Language: English
• Publisher: Academic Press
• Release date: May 25, 2019
• ISBN: 9780128136362

Henry H. Heng

Dr. Henry H. Heng has coauthored over 200 publications and serves on the editorial boards of seven international, peer-reviewed journals. Using single-cell analysis of in vitro and in vivo models, Dr. Heng’s group has illustrated the evolutionary dynamics of cancer progression by directly observing evolution in action. Such experiments revealed that cancer evolution involves two phases: punctuated genome alteration-mediated macroevolution (which creates a new system), followed by stepwise gene and epigenetic-mediated microevolution (which leads to population growth). By applying this concept to organismal evolution, he discovered that the main function of sex is to reduce genetic diversity at the genome level in order to preserve genome-defined species identity information. Heng introduced the Genome Architecture Theory, a new genome-based conceptual framework of genomics and evolution.

Book preview

Genome Chaos

Rethinking Genetics, Evolution, and Molecular Medicine

Henry H. Heng

Wayne State University School of Medicine, Detroit, MI, United States

Table of Contents

Cover image

Title page

Copyright

Dedication

Preface

Acknowledgments

Chapter 1. From Mendelian Genetics to 4D Genomics

1.1. Summary

1.2. The Emergence of Genomics

1.3. Diminishing Power of Gene-Based Genomics

1.4. New Genomic Science on the Horizon

Chapter 2. Genes and Genomes Represent Different Biological Entities

2.1. Summary

2.2. The Definition of the Genome

2.3. Parts Versus the Whole: The Emergent Relationship (Which Challenges Reductionism)

2.4. ReExamining Gene Theory Predictions

2.5. The Conflicting Relationship Between the Gene and the Genome

2.6. Genome Context Determines Gene Function

2.7. Action Needed

Chapter 3. Genome Chaos and Macrocellular Evolution: How Evolutionary Cytogenetics Unravels the Mystery of Cancer

3.1. Summary

3.2. SOS: We Need a New Conceptual Framework for Cancer Research

3.3. Genome Chaos: Rediscovery of the Importance of the Karyotype in Cancer

3.4. A New Genomic Model for Cancer Evolution

Chapter 4. Chromosomal Coding and Fuzzy Inheritance: The Genomic Basis of Bio-information and Heterogeneity

4.1. Summary

4.2. Chromosomal or Karyotype Coding

4.3. Fuzzy Inheritance

4.4. Overlooked Genome Variations

Chapter 5. Why Sex? Genome Reinterpretation Dethrones the Queen

5.1. Summary

5.2. What Is the Purpose of Sex? The Answer Is Not Obvious

5.3. Surprise! Asexual Reproduction Does Not Generate Clonal Progenitors!

5.4. The Search for the Main Function and Common Mechanism of Sex

5.5. The Battle Is On: Changing Concepts

5.6. Simulation: Ask the Simplest Question About the Function of Sex

5.7. Case Studies: Reinterpretation Using New Framework

5.8. Lessons Learned

Chapter 6. Breaking the Genome Constraint: The Mechanism of Macroevolution

6.1. Summary

6.2. Pattern of Cellular Evolution Challenges Current Evolutionary Theory

6.3. Artificial Selection and Natural Selection Are Fundamentally Different

6.4. Both Isolated Cases and Isolated Natural Environments Represent Exceptions That Fail to Demonstrate the Relationship Between Micro- and Macroevolution

6.5. Maintaining Genome Integrity: The Major Evolutionary Constraint

6.6. Implications of Genome Theory to Evolutionary Concepts

6.7. Evolution Is True but Its Mechanism Must Be Reexamined

6.8. Implications: Creating Artificial Species by Shattering the Genome Followed by Artificial Mating/Genome Selection

Chapter 7. The Genome Theory: A New Framework

7.1. Summary

7.2. The Rationale for Establishing a Genome-Based Genomic Theory

7.3. Unique Considerations for Genome Theory

7.4. Outline of the Genome Theory

7.5. The Predictions, Implications, Limitations, and Falsifiability of the Genome Theory

7.6. Challenges Ahead

Chapter 8. The Rationale and Challenges of Molecular Medicine

8.1. Summary

8.2. A Brief History: The Promises of Molecular Medicine

8.3. The Challenges and Opportunities for Precision medicine

8.4. Future Direction

Epilogue (or Why We Did What We Did)

Bibliography

Index

Copyright

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Dedication

To Julie, Eric, and Christine and those scientists who are brave enough to climb out of the shadows of giants.

Preface

This is not a typical book about genomics, evolution, and molecular medicine where you are provided with comprehensive information reinforcing what you already know. Rather, this book will invite you to rethink the fundamental theories of genetics and evolution you have held for so long—knowledge that has formed the style of your scientific thinking and practicing and that has sculpted you into who you are today.

Perhaps you are an undergraduate student or a fresh graduate, younger than the age of genomics, who grew up amid large-scale -omics technical platforms and promises. You dreamed of becoming a hero by finally curing cancer in the age of big data and personalized medicine. Is this necessary? you may wonder.

Or perhaps you are established in the field. Why bother? you may ask. Why do we need to reexamine the frameworks in a blossoming field such as genomics and molecular medicine? We have headline after headline trumpeting the success of the Human Genome Project and many the large-scale -omics projects that followed. Your career is going well; you are widely published, well-funded, and respected. Yes, there are many big surprises, you argue, but science is naturally a half-full or half-empty story. The logical way to advance the field is by continuing to improve technologies to collect more data. With more funding, the mystery of life will be solved, and all diseases will be eradicated.

After reading report after report on the major failures of molecular targeting-based clinical applications or after witnessing the disappointment of large-scale genome-wide association studies or after the cancer genome project bluntly questions the value of your personal research (when the list of the top 100 most mutated genes in cancer does not include your favorite gene), occasionally, in the back of your mind, you might have asked yourself, Geez, how did we miss it again? So many years of hard work and experimentation had clearly already proved the importance of this gene mutation in cancer.

Then came a series of shocking and nerve-racking report: most landmark studies in oncology (nearly 90%) are unrepeatable. The success rate of translational research is extremely low, akin to crossing the valley of death. Genomics is good for research, but not for medicine. Current US biomedical research needs to be rescued from its systemic flaws.

These conclusions are based on solid analyses and can no longer be ignored. How does one reconcile positive and negative news in the genomic era? What are the key implications of these surprises? How should the fields of genomics and molecular medicine move forward? And more profoundly, do those stunning reports reflect the increased anomalies and paradoxes that are crying out for a paradigm shift in biomedical research?

In this book, these issues will be systematically and candidly discussed. In particular, you will be introduced to a different conceptual framework of biological reasoning, exposing holes in traditional ways of thinking and conducting research. By reexamining many paradoxes through the lens of the genome theory, rather than the gene theory you are so familiar with, this book will lead you to discover a new way to appreciate how genomics and evolution work in the context of heterogeneity/emergence-defined reality. Perhaps you will realize that many well-known milestone findings in classic genetics and evolution are in fact less important as they represent exceptions rather than generalities. These new discoveries and further syntheses, many of which may initially seem counterintuitive and controversial, will surely challenge your own knowledge and beliefs. At the start, they may make you uncomfortable. But hopefully, at the end of the day, many of these proposed concepts and statements will make sense to you.

We explore this paradigm shift in eight chapters:

In Chapter 1, From Mendelian Genetics to 4D Genomics, a brief review of the birth of genomics and its relationship with the Human Genome Project is presented, which illustrates how the concept of the gene has changed during the genomics era. Further discussions highlight some key limitations of traditional genetic/genomic research and call for a new genomic paradigm.

Chapter 2, Genes and Genomes Represent Different Biological Entities, supports the concept that the emergent genome, rather than isolated genes, defines a biosystem. Many gene-centric concepts and their limitations are briefly reviewed. Experimental observations are presented to illustrate the conflicting relationship between genes and the genome, as the genome-level operation is not simply a matter of adding up the functions of individual genes.

Chapter 3, Genome Chaos and Macrocellular Evolution: How Evolutionary Cytogenetics Unravels the Mystery of Cancer, discusses why a new conceptual framework of cancer research is urgently needed and describes the journey of searching for a genome-based cancer evolutionary theory. This journey has led to many important discoveries, including two-phased cancer evolution (macro- and microcellular evolution), the importance of nonclonal chromosome aberrations, the key function of genome chaos, and the evolutionary mechanism of cancer. A new genomic model for cancer evolution is proposed to relate the contributions of genome and gene in cancer evolution.

In Chapter 4, Chromosomal Coding and Fuzzy Inheritance: The Genomic Basis of Bio-information and Heterogeneity, the many genomic surprises observed from the first three chapters are explained by the novel concepts of system inheritance and fuzzy inheritance. Unlike gene-defined parts inheritance, chromosome-encoded system inheritance defines the genomic blueprint. In addition, the multiple levels of genomic and nongenomic information are fuzzy, which allows them to code for a spectrum of potential genotypes for the environment to select. Furthermore, fuzzy inheritance is the genomic mechanism of bio-heterogeneity, which is the key to understanding many common and complex diseases.

In Chapter 5, Why Sex: Genome Reinterpretation Dethrones the Queen, an unexpected story likely solves the century-long mystery behind the main function of sexual reproduction, the queen of problems in evolutionary biology. Initially, the two phases of cellular evolution suggested that asexual reproduction can produce highly diverse genomes, whereas sexual reproduction should produce identical genomes. Ample evidence from different organisms now supports this new concept. Furthermore, meiosis has been identified as the mechanism to maintain species identity by preserving system inheritance. Thus, the primary function of sex is to preserve the genome-defined system (the species).

In Chapter 6, Breaking the Genome Constraint: The Mechanism of Macroevolution, following a brief review of the fundamental differences between artificial selection, highly isolated natural selection, and natural selection in general, the maintenance of genome integrity is linked to major evolutionary constraints. After emphasizing the importance of evolutionary constraint, a new model of speciation is proposed. In this model, speciation is characterized by genome reorganization-mediated macroevolution, mating with a partner of a similar genome to produce fertile offspring. Finally, microevolution might promote the formation of lasting species with large populations. This model drastically departs from the explanation of speciation through natural selection, where the accumulation of the small changes over long period of time is key.

In Chapter 7, The Genome Theory: A New Framework, the rationale for integrating genome-based genomics with evolutionary concepts is laid out alongside the key assumptions that validate them (treating the genome as an information unit, evolutionary selection unit, and platform of bio-emergence). The genome theory is cohesively outlined with 12 principles. In addition, the genome theory's key predictions and limitations, as well as its falsifiability, are discussed.

In Chapter 8, The Rationale and Challenges for Molecular Medicine, the history of precision medicine, as well as its challenges and opportunities, is briefly traced. The future direction of molecular medicine is discussed, including the relationship between big data and theory, the increase in bio-uncertainty, and how education can play an important role in the future of biomedical science.

Although the chapters are arranged in a logical order that reflects our journey of thinking and searching, each chapter has been written as a potential standalone unit for the ease of readers. Therefore, there is some minimal overlap among chapters. The commonly shared message that threads all eight chapters together is the importance of genome-defined genomic information and its implications for evolution and molecular medicine. More specifically, the often-ignored role of genome constraint in evolution is emphasized. Within this new perspective, both the system's variable features (reflected as short-term adaptive dynamics) and the existence of the system itself (reflected as long-term stasis) are essential components, marking a departure from current genomic and evolutionary theories. Such new theories based on real-world complexity will not only challenge current genomic and evolutionary mechanisms, but also explain the relationships between micro-adaptation and macro-speciation, germline stability and somatic dynamics, and fuzzy inheritance encoded phenotype potentials and environment-selected realities.

By analyzing initial confusions, identifying paradoxes, thoroughly reinterpreting key data, rethinking ignored phenomena, introducing new discoveries, and searching for new frameworks, this book invites you to join us on this journey of rediscovery. If you are a genome-based reader, help us to improve the genome theory and establish a technological platform to study human diseases. If you are a hard-core gene-based reader, bear with us and momentarily set aside the ideas you know best to have a conversation about ideas that you may have dismissed, ignored, or even disliked. We are always searching for a better theory, after all. Maybe you will come up with a strong argument to convince us to join you.

No matter what, the only goal of a true scientist should be to search for truth. In that light, I hope this process, no matter how difficult, will be enjoyable. Sometimes the truth hurts. Often, it is not easily appreciated, especially when an improper framework has previously dominated our thinking. But ultimately, truth will prevail. When you finish reading and ponder our message, you will likely start to ask a few questions. Is this really real? you may wonder. And If so, how did I miss something this big for so long? And hopefully, What should I do next?

Acknowledgments

First, let me thank Julie Heng, Barbara Spyropoulos, Sarah Regan, Sarah Alemara, Steven Horne, and Batoul Abdallah for editing the manuscript. I also would like to thank all the members of my research team from the Wayne State University School of Medicine for believing in me and my work on the genome theory when others were highly skeptical: Gao Liu, Joshua Stevens, Steve Bremer, Karen Ye, Lesley Lawrenson, Steve Horne, Batoul Abdallah, Sarah Regan, Wei Lu, and Christine Ye.

Second, my sincere appreciation belongs to many of my mentors for supporting my efforts to characterize genome level alterations when most people consider them insignificant: Lap-Chee Tsui, Peter Moens, F. T. Kao, Clement Markert, Y. C. Wang, and W. Y. Chen. Their support allowed me to develop new methods such as high-resolution fiber FISH and DNA/chromosome/protein in situ codetection.

Third, I must thank many thinkers and scientists for their encouragement, suggestions, and candid opinions to improve our concepts and to articulate our message: Bill Brinkley, Mina Bissell, Linda Cannizzaro, Don Coffey, Jim Crow, Peter Duesberg, Wayt Gibbs, Arny Glazier, Morris Goodman, Root Gorelick, Dean Hamer, Gloria Heppner, Sui Huang, Steve Krawetz, Rong Li, Thomas Liehr, Larry Loeb, Carlo Maley, O. J. Miller, Brian Reid, Harry Rubin, Bill Shields, L. -J. Shi, Jeremy Squire, Gary Stein, Joachim Sturmberg, David Ward, Douglas Wallace, Adam Wilkins, and T. H. Yosida. Thanks also to my friends, colleagues, and collaborators, with whom I shared many interesting discussions regarding 4-D genomics and evolution: Ping Ao, Rodrigo Fernandez-Valdivia, Jing-Bing Fan, Y. -B. Fu, Rafael Fridman, Markus Friedrich, Rafe Furst, Edward Golenberg, Alex Gow, Larry Grossman, Weilong Hao, ZhuoCheng Hou, Markku Kurkinen, Joshua Liao, R. Lin, J. S. Liu, Fred Miller, William Moore, Avraham Raz, Zachary Sharpe, Sureyya Savasan, John Tomkiel, Jeffrey Tseng, Derek Wildman, Alan Wang, J. Wang, H. -Y. Wu, G. -S. Wu, Y. -M. Xie, Ping Xue, Yang Yang, Weining Yang, Hao Ying, Holly Yu, J. -W. Yu, Kezhong Zhang, and Ren Zhang.

Fourth, I own my gratitude to my editors from Elsevier: Peter Linsley for his initiation of this project, and Timothy Bennett for his valuable editorial help.

Finally, I would like to thank my wife, Christine, and children, Julie and Eric. It takes a family to write this book. In addition to their unconditional support, their enthusiasm made the writing process a highly enjoyable journey. From breakfast to bedtime and in between, my discussions with them have motivated me to climb out of the shadows of giants as much as I hope to inspire them to.

Chapter 1

From Mendelian Genetics to 4D Genomics

Abstract

The gene frames much of modern genetics by acting as an independent unit of genetic information. The gene-defined genotype–phenotype relationship has been demonstrated by classical studies linking genes to specific genetic traits and Mendelian diseases. However, it is now apparent that most genetic traits cannot be explained by single genes or even a combination of many. Genomics was positioned to solve this challenge by searching for more genetic variants and quantitatively illustrating their combinatorial mechanisms. Although this approach appears promising to many, genomics has failed to identify common mechanisms of most complex traits. Where then do genetics and genomics fall short? A review of the field reveals that most genes do not, in reality, have independent functions, leading to a great deal of confusion about the role of genes in determining the phenotype. One could say that Mendel’s original pea experiments, which formed the foundation of modern genetics, should have already generated such confusion upon close analysis. In this chapter, the transition from genetics to genomics is briefly reviewed, as reflected by how the concept of the gene has changed during the genomics era. The initial enthusiasm and subsequent disappointment of the Human Genome Project is addressed, as well as the lack of fundamental progress despite overwhelming data accumulation, which slows down bio-industry and medicine. This journey has now brought us to an urgent need for a new biological paradigm, which focuses on genome and evolution-based genomics and incorporates both emergent properties and cytogenetic organization.

Keywords

4-D Genomics; Genetic determinism; Human Genome Project; Limitations of gene; Reevaluating Mendelian genetics

1.1. Summary

The gene frames much of modern genetics by acting as an independent unit of genetic information. The gene-defined genotype–phenotype relationship has been demonstrated by classical studies linking genes to specific genetic traits and Mendelian diseases. However, it is now apparent that most genetic traits cannot be explained by single genes or even a combination of many. Genomics was positioned to solve this challenge by searching for more genetic variants and quantitatively illustrating their combinatorial mechanisms. Although this approach appears promising to many, genomics has failed to identify common mechanisms of most complex traits. Where then do genetics and genomics fall short? A review of the field reveals that most genes do not, in reality, have independent functions, leading to a great deal of confusion about the role of genes in determining the phenotype. One could say that Mendel’s original pea experiments, which formed the foundation of modern genetics, should have already generated such confusion upon close analysis. In this chapter, the transition from genetics to genomics is briefly reviewed, as reflected by how the concept of the gene has changed during the genomics era. The initial enthusiasm and subsequent disappointment of the Human Genome Project is addressed, as well as the lack of fundamental progress despite overwhelming data accumulation, which slows down bio-industry and medicine. This journey has now brought us to an urgent need for a new biological paradigm, which focuses on genome and evolution-based genomics and incorporates both emergent properties and cytogenetic organization.

1.2. The Emergence of Genomics

Genetics had already come a long way when British botanist William Bateson coined the term at the first International Congress on Genetics in 1906 to describe a new science that explored heredity and variation as initiated by Mendel's (1866) publication of heredity in peas (Mendel, 1866). In the past 150   years, to understand the mechanism of Mendelian inheritance, researchers have zoomed in from the nucleus to chromosomes, from chromosomes to genes, and then from genes to DNA motifs. Such reductionist analyses have triumphed, leading to our understanding of the physical and chemical properties and structure of the gene, the mechanism of gene coding RNAs and proteins, the various models of gene regulation, protein modifications/degradation, macromolecule assembly, and the link between gene mutations and phenotypic variants, including many human diseases. We also understand how to identify and manipulate specific genes and apply this knowledge to produce genetically modified foods and improve human health through molecular medicine.

The introduction of the double-helix model of DNA in 1953 and recombinant DNA technology in 1972 changed genetics forever (Watson and Crick, 1953a; Jackson et al., 1972). Molecular genetics has become the go-to field for new generations of biologists. Many bio-disciplines that were not gene-based withered. Moreover, the power of the gene has become a cultural phenomenon by capturing the general population's imagination, thanks to many popular ideas. Richard Dawkins's The Selfish Gene marked the onset of the gene-era hype in which everything was apparently controlled by genes—from individual proteins to specific biological traits and from evolutionary history to current health and behavior (Dawkins, 1976). This mode of thought assumed that all biological systems, including humans, serve the gene masters. We are merely the unwitting vehicles of genes. Genes are dominant, powerful, selfish, and mysterious. Such gene-centric concepts have shaped modern biology, generating a great deal of excitement and expectation within science, medicine, bio-industry, and society in general. If only the path of future genetics was as clear and simple as just following the gene!

1.2.1. A Brief History of Genomics

Naturally, the ultimate goal of human genetics became hunting down all disease genes by molecular cloning and then correcting them by genetic manipulation such as gene therapy or eliminating them through prenatal screening. Suddenly, gene-based molecular genetics became the flagship of science, and the success of identifying gene defects responsible for human diseases further validated gene-based genetic approaches. Positional cloning initiated an exciting wave of gene hunting. Following the first gene cloning success in 1986 for X-linked chronic granulomatous diseases by Harvard Medical School's Stuart Orkin, gene after gene associated with many important disorders have been cloned, including Duchenne muscular dystrophy (cloned by Louis Kunkel at Boston Children's Hospital and Ronald Worton from the Hospital for Sick Children in Toronto), cystic fibrosis (cloned by Lap-Chee Tsui from the Hospital for Sick Children in Toronto in cooperation with Francis Collins from the University of Michigan), Huntington disease, adult polycystic kidney disease, certain forms of colorectal cancer, and breast cancer. By 1995, about 50 inherited disease genes had been identified, highlighting the triumphant era of human molecular genetics (Collins, 1995).

Interestingly, even before the gene hunting movement reached its peak in the late 80s to early 90s, there were increasing concerns about the gene-centric reductionist approach, which lead to calls for genome-based research, notably by Barbara McClintock and a number of evolutionary biologists and scientists who questioned genetic determinism. McClintock, the Nobel laureate who greatly recognized the importance of the genome in biology, specifically emphasized this in her 1983 Nobel Prize acceptance lecture at the Karolinska Institute in Stockholm.

In the future, attention undoubtedly will be centered on the genome, with greater appreciation of its significance as a highly sensitive organ of the cell that monitors genomic activities and corrects common errors, senses unusual and unexpected events and response to them, often by restructuring the genome. We know about the components of genomes that could be made available for such restructuring. We know nothing, however, about how the cell senses danger and instigates response to it that often are truly remarkable.

McClintock, 1984

It gradually became obvious that most genes do not have dominant phenotypes that display high penetration in populations. Researchers also realized that even though it is possible to identify specific gene mutations in many single-gene Mendelian diseases, this success might not be transferable to many common and complex diseases because of the large number of potential genes involved. Clearly, a better strategy was to search for more genes throughout the entire genome, which was the rationale to move from single-gene hunting to whole genome searches. For many, the advantage of focusing on the genome was merely to include more genes.

In the mid-80s, some key technologies became capable of analyzing more genes, such as DNA panels of rodent-human somatic cell hybrids for physical mapping, DNA restriction fragment length polymorphism or RFLPs as variation markers for genetic mapping, polymerase chain reaction, automated DNA sequencing, and partial sequencing or mapping of several small genomes of microbes. These methodologies and the increased use of computers for data storage and analysis served as the necessary platforms for this new frontier of genetics. Then, the perfect storm came.

In May 1985, Robert Sinsheimer, the Chancellor of the University of California–Santa Cruz, held a workshop there titled Can we sequence the human genome? Sinsheimer organized this workshop to present a stronger argument that such a project was significant and feasible following an unsuccessful attempt to extract funding from his University. Many leading researchers attended, including David Botstein, George Church, Ron Davis, Walter Gilbert, Lee Hood, and John Sulston, and they discussed potential problems, technologies, and a timeline as well as costs for the genome project. Despite the success of this workshop, Sinsheimer still failed to obtain any funding for his project. However, the meeting initiated a chain reaction (Sinsheimer, 2006).

In March 1986, new on the job and eager to establish a novel megaproject to bolster the genetic programs within the US Department of Energy (DoE), Charles DeLisi, the Director of the Office of Health and Environmental Research of the DoE, organized a conference at Santa Fe. Influenced by Sinsheimer's workshop, this meeting also sought to determine the complete sequence of the human genome and map the location of each gene. Most significantly, in addition to discussing the desirability and feasibility of implementing a Human Genome Project, this meeting was crucial to pushing the idea of a full genome sequence onto the national scientific stage and converting it into a reality. DeLisi and others were able to begin the key task of garnering support from the DoE, the Reagan administration, and Congress (DeLisi, 2008).

At the same time, Renato Dulbecco, a Nobel winner for discoveries concerning the interaction between tumor viruses and the genetic materials of the cell, published an influential editorial piece in Science urging that sequencing the entire human genome was the best way to solve the puzzles of cancer. His argument has often been used as the rationale for genome sequencing, especially in later cancer genome sequencing. Another meeting worth mentioning is the 1986 Cold Spring Harbor symposium The Molecular Biology of Homo Sapiens where the Human Genome Project was also debated in a rump session moderated by Paul Berg and Walter Gilbert. Despite the fact that there were more voices urging caution, the discussion among many molecular geneticists in attendance was essential to maturing this idea (Robertson, 1986). Also in late 1986, the National Academy of Science/National Research Council formed a committee on mapping and sequencing the human genome. Collectively, all these events led to the Human Genome Project becoming a reality. The genome research center was established in 1987 and included three National Laboratories of the Energy Department. An office of Human Genome Research at the NIH opened its doors in 1988. Finally, an international organization named the Human Genome Organization (HUGO) was established in 1988, and the rest is history.

It is interesting to ask what caused Sinsheimer to act? He says he was influenced by other Big Science projects outside biology.

… As Chancellor, I had been involved in the conception of several large-scale scientific enterprises–involving telescopes (the TMT project) and accelerators–which were Big Science, scientific projects requiring, in some instances, billions of dollars and the joint efforts of many scientists and engineers. It was thus evident to me that physicists and astronomers were not hesitant to ask for large sums of money to support programs they believed to be essential to advance their science. Biology was still very much a cottage industry, which was fine, but I wondered if we were missing some possibilities of major advances because we did not think on a large enough scale …

Sinsheimer, 2006

Similarly, why did the DoE initially play the leading role rather than the NIH? The NIH was correctly concerned about the potential shift of money away from investigator-initiated proposals to this big science project. Despite the fact that the DoE had funded studies of the biological effects of radiation for years, perhaps its historical link to some big projects like the construction of the atomic bomb in the Manhattan Project influenced the Department to undertake this gigantic project. The idea of sequencing the human genome to bolster the DoE's research program was already circulated before DeLisi's arrival. The report titled Technologies for Detecting Heritable Mutations in Human Beings by the Office of Technology Assessment hinted at the idea of sequencing the whole genome. A new wave of big science was coming. Nevertheless, the birth of such an enormous initiative like the Human Genome Project meant that genetics and biology would never be the same. It certainly marked the maturation of genetics and it also transformed genetics into genomics.

There are different opinions regarding the relationship between the birth of the Human Genome Project and genomics. Some believe the Human Genome Project spawned a new science called genomics, while others think the birth of genomics was a gradual process that began from earlier efforts of gene mapping and sequencing that led to the Human Genome Project as it represented a necessary preliminary step before considering the feasibility of the Human Genome Project. Just as genetic research predated the use of the term genetics, genomics research predated the creation of its official name. It is hard to determine a defined timeline for the official birth of genomics compared with the Human Genome Project as they are intimately intertwined in both research context and historical timing. One thing was certain, however: the Human Genome Project became the primary goal and a major challenge for the young field of genomics.

The journal Genomics was launched in 1987 by Victor McKusick, a medical genetics pioneer who published a catalog of all known genes and genetic disorders called Mendelian Inheritance in Man (MIM), and Frank Ruddle, a gene mapping pioneer. In their introduction of Genomics, A new discipline, a new name, a new journal, they stated the following:

For the newly developing discipline of mapping/sequencing (including analysis of the information) we have adopted the term genomics … The new discipline is born from a marriage of molecular and cell biology with classical genetics and is fostered by computational science. Genomics involves workers competent in constructing and interpreting various types of genetic maps and interested in learning their biologic significance. Genetic mapping and nucleic acid sequencing should be viewed as parts of the same analytic process–a process intertwined with our efforts to understand development and diseases.

The initial focus of the journal reflected the focus of the field of genomics which was well-laid out by McKusick and Ruddle in their first editorial piece (McKusick and Ruddle, 1987). It included the following topics: chromosomal mapping of genes, DNA fragments and gene families; sequence characterization of cloned genes and/or other interesting portions of genomes; comparative analyses of genomes to understand structural, regulatory, functional, developmental or evolutionary mechanisms; methods for large-scale genomic cloning, restriction mapping, and DNA sequencing; computational platforms/methods and algorithms to illuminate DNA and protein sequence data; understanding the hierarchy of chromosome structure; analysis of genetic linkage data related to inherited disorders; development of a genomic database; and parallel studies on genomes from different organisms…

Thomas Roderick of the Jackson Laboratory coined the term genomics that would become the name of the new journal as well as for the new scientific field. According to Roderick, while attending a 1986 meeting with future editors in chief, McKusick and Ruddle:

One evening, about 10 of us were at a raw bar, drinking beer and discussing possible titles for the new journal. We were on our second or third pitcher when I suggested 'genomics'. Little did we know then that it would become such a widely used term.

Keim, 2008

1.2.2. Genetics or Genomics?

Since the emergence of genomics, the terms genetics and genomics have been associated with diverse definitions within literature. Despite some definitional overlap, genetics is generally defined as the science of individual genes, heredity, and variation in living organisms, whereas genomics is a new discipline that studies the genomes of organisms. The main difference between genetics and genomics is that genetics scrutinizes the functions and composition of single genes to illustrate how individual traits are transmitted from parent to offspring, whereas genomics addresses the structure, organization, and function (inheritance) of a genome by dealing with a large number (or the complete set) of genes and noncoding sequences and their nuclear topological and/or biological interrelationships (see Chapters 2 and 4). As no gene is an island and most genetic traits involve multiple genes and their complex interactions within environments, the scope of genomic research is drastically increasing. In particular, because the genome is not just a bag of genes (see Chapter 2), genomics has expanded past its genetic roots. Now, genomic concepts and methodologies generally dominate biological science. Single-gene research no longer fits under the genomics umbrella unless the aim of a specific study is to incorporate the gene or its associated pathway and elucidate its effect on the entire genome's network (Genome.gov). It would be safe to say that genomics represents a new phase of genetics. Some scholars even refer to genomics as 21st century genetics. Knowing that the future studying of genetic information will undoubtedly involve the genome system rather than individual genes in isolation, the holistic platform of genomics might someday replace genetics altogether.

The emergence of genomics is of ultimate significance to genetic research. First and foremost, it turned traditionally highly selective genetic research into less selective genomic investigation. Such a transition is reflected at both the research subject level and the system used. New genomics research focuses on large regions of the genome or the entire genome rather than specific and isolated genes of interest. Equally important, genomics allows a new research approach more amenable to direct analyses of natural populations rather than traditional genetics studies that are mainly dependent on highly specific model systems under controlled laboratory conditions. In fact, most genetics laboratories focus on model organisms as experimental systems, including Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis, various inbred mice strains, and established cell lines. These model organisms/systems clearly lack the diversity and heterogeneity of natural populations. Although some classic genetics studies have analyzed some natural populations, the scale is incomparable with genomics studies in terms of the whole genome approach and the size of the natural populations that are studied.

Second, the birth of Big Science in biology has transformed genetics, challenging the previous small-scale hypothesis-driven system that was best suited to studying causative relationships in a defined linear system. By revealing the true complexity of biological systems, researchers will likely begin to question the genetic traditions of searching for causal isolated genes or well-defined molecular pathways. The big science projects approach has brought both enthusiasm and uneasiness to the scientific community, as this is a change from traditional biological science where individual researchers carve out their own unique niche, testing their own hypotheses, sometimes for many years. A key challenge of large-scale genomic projects is using the correct framework to best integrate technologies (Heng and Regan, 2017). These large-scale genomic projects will likely conflict with traditional genetic knowledge, as when increased variants are involved, different principles are often applied. For example, when dealing with a complex adaptive system involving many different factors, the correlation study becomes more important as it is too hard to identify true causation.

Third, the biomedical industry requires pragmatic reality checks more often than traditional academic institutions require. They require vigorous reviews to select molecular targets derived from basic research. The failure of any clinical trial could be devastating to a company regardless of how solid the lab-based research is. Such reality checks, which are now fed back into the research community, influence the direction of basic research, as policy makers and researchers are increasingly paying attention. For instance, it was industrial researchers who reported that the majority of representative high-quality cancer research papers are unrepeatable (Mullard, 2011; Begley, 2012). There is no incentive for academic research to carry out such analyses. However, it is crucial for pharmaceutical companies to make sure that their billion dollar drug development effort has a solid basis.

Finally, because of the scale of funding, public interest is now a key component in genomic research. It is no longer enough to just explore billion dollar hypotheses for curiosity's sake. Many basic genetic researchers are not happy with this new trend. They firmly believe that basic research takes time and will ultimately pay off in the long run and that scientific progress should not be unduly influenced by factors outside of science. However, the good old days of doing science purely for the accumulation of academic knowledge will likely not return. The days of moderate research budgets supporting individual labs and their genetic discoveries have given way to the megaprojects of the genomics age. This large-scale approach requires more public support and associated scrutiny. Understanding the new reality of genomics is critical, as the research community must educate the general public and be careful to avoid harmful overreaching promises. For this reason, many previously off-topic issues have become inseparable parts of genomics itself. Science policy, ethical issues, and public interest are often on the agenda of most scientific conferences of genomics.

1.2.3. Fundamental Limitations of Traditional Genetics

Throughout the history of modern genetics, a chain of many brilliant experimental designs has generated our core knowledge of genetics, which formed the backbone of the gene theory. An interesting open secret is, though, that most of these famous milestone experiments are actually based on exceptional cases that can only be effectively demonstrated using specific model systems and under well-defined experimental conditions. For example, it is well known that Mendel's classic paper, which perfectly illustrated the genotype–phenotype relationship between parent and offspring, demonstrated that genes function as defined independent informational units. This is still the basis of current genetic theory. However, it is much less appreciated that there were many preconditions or limitations for his beautiful illustrations.

First, it is difficult to replicate Mendel's clear-cut patterns using most other species. In fact, Mendel himself had failed to confirm his hypothesis in his own hands when he used hawkweed (as suggested by Karl von Nageli, one of the leading scientists at that time who had read Mendel's seminal paper) and beans. Rather, some upsetting data began to appear: only for certain characteristics did the flowers follow the same pattern as his peas. The drastically increased data diversity presented in these other systems clearly caused increased confusion for Mendel.

Second, Mendel only selected 7 traits among 34 initially studied traits in peas to demonstrate his points. The rationale of reporting seven selected traits was likely because only these seven traits produced the most appropriate results to support his concept. It would be interesting to know what the data looked like for majority of the traits unreported by Mendel. We know today that the phenotypes of most genes do not follow the Mendelian 3:1 pattern because a majority of genes do not truly function as straightforward independent units. Instead, the expression of a genotype often involves multiple genes and complicated genomic and environmental interactions.

Now, Mendel's seven genetic factors have been linked to seven genes with molecular characterization. These famous characteristics are likely involved in a range of genetic causes (including simple base substitutions, changes to splice sites, and the insertion of a transposon-like element). Interestingly, these seven genes were either not linked or if linked, possibly not subject to his analysis (Reid and Ross, 2011), which allowed Mendel to see a distinct pattern of segregation. Clearly, in contrast to the popular viewpoint, it was not by luck that Mendel chose these seven perfect characteristics, but by extreme trait selection. Indeed, Mendel's character selection was described in his paper:

Some of the characters noted do not permit of a sharp and certain separation, since the difference is of a more or less nature, which is often difficult to define. Such characters could not be utilized for the separate experiments ….

Mendel, 1866

Third, Mendel had a strict selection criterion for each sample. He had purposely avoided collecting average data by using exceptional samples in his experiments. For example, to comparing the difference in the stem length (one of his 7 traits), a long axis of 6–7   ft was always crossed with a short one of 0.75–1.5   ft. By pushing extreme cases rather than using average long and short populations, the certainty of data becomes much more impressive. Paradoxically, however, the pattern he discovered based on selection will not represent the majority of the data he ignored.

Fourth, Mendel had tried his best to reduce environmental variations that could influence the data, such as growth conditions, timing of experiments, and the effect of all foreign pollen, which invariably created ideal systems with minimal environmental influences.

Together, Mendel had created a perfect yet highly exceptional system. Perfect for a manipulated linear model with reduced variants, exceptional for the reality of genetics where most genetic traits do not contribute by a single gene and heterogeneity dominates within a population.

Mendel's approach might be the reason why many scientists have had trouble replicating the same simple ratios he reported for these carefully selected traits. For example, when the sweet pea (a closely related species of the garden pea that Mendel used) was examined, the pattern of heredity was considerably more complicated than Mendel's results (Bateson and Saunders, 1902). In fact, the independence of genes can be diluted when passed them among generations. Furthermore, the rationale of classifying genes into dominant or recessive status has been challenged back to beginning of the last century, when data showed that genetic traits can be dominant, recessive, neither (Weldon, 1902; Radick, 2015), both, or one of many statuses in between. The effect of a gene is constrained or defined by the hereditary background (ancestry) and environments, and the determinist's viewpoint of the gene might be an illusion for majority of species. After carefully analyzing data from Mendel and other well-known researchers working on related systems, Weldon concluded the following:

… I think we can only conclude that segregation of seed-characters is not of universal occurrence among cross-bred Peas, and that when it does occur, it may or may not follow Mendel's law. The law of segregation, like the law of dominance, appears therefore to hold only for races of particular ancestry. In special cues, other formulae expressing segregation have been offered, especially by De Vries and by Tschermak for other plants, but these seem as little likely to prove generally valid as Mendel's formula itself.

Weldon, 1902

Interestingly, the above paper systematically challenged the data presentation and legitimacy of Mendel's theory immediately following the rediscovery of the laws of genetics. Based on the understanding of the pea varieties and their pedigrees, Weldon was convinced that Mendel's law had no validity beyond the created artificially purified experimental systems. He not only calculated the chance that getting worse results is 16 to 1 (based on Mendel's data) but also illustrated the challenge of classifying continuous variable characteristics of the pea (green or yellow for seed color, round or wrinkled for seed shape) using binary categories (dominant vs. recessive). His analyses hinted the high possibility of cherry-picked results on Mendel's part.

Ronald Fisher also thought that the data from Mendel were too good to be true. Given Fisher's reputation in data analysis, his viewpoint is more influential than Weldon's. Three decades after Weldon, Fisher published a paper to elaborate on this issue. In his paper, Fisher argued that Mendel knew how his data should be according to his theory, and he carefully planned his experiments to support his theory. Fisher even guessed that some data must have been quietly removed to support the theoretical prediction (Fisher, 1936). This paper, in addition to some later more direct accusations of data falsifying, has formed the so-called Mendel–Fisher controversy. Still, it is now accepted that most accusations and suspicions have turned out to be groundless (Hartl and Fairbanks, 2007; Franklin et al., 2008).

The editor of Classic Papers in Genetics, James A. Peters, wrote the following introduction to Mendel's original paper that laid the foundation of modern genetics (Peters, 1959):

… There have been comments made that Mendel was either very lucky or tampered with his data, because his results are almost miraculously close to perfect … As to the second charge, that he might have arranged his data so as to shed the best possible light on his conclusions, I believe that the only way he might have manipulated his data is through omission of certain results that would have led to unnecessary complications.…Mendel probably knew of these interrelationships … The fact he chose to utilize only those characteristics that fitted his concepts cannot be interpreted as an act of dishonesty on his part …

Peters, 1959

Judging by Mendel's candid presentation in his publication with all the details of data selection and, in particular, knowing that he was increasingly puzzled when he worked on other species, it is clear to us that his extreme selective reporting was not because of his dishonesty but the natural unconscious bias that comes with science research, as these improbably perfect data can only be generated from highly selected artificial systems that he created.

The dilemma Mendel faced was how to balance the art of selecting beautiful but exceptional data to unveil hidden scientific principles while avoiding the fundamental misunderstanding by ignoring the general feature of the system under study. The majority of genetic researchers favor Mendel's approaches. They argue that it is absolutely necessary and sometimes the only option to select specific conditions or unique models to illustrate certain aspects of nature, which is the rationale of using models to simplify nature and eliminate variables. In fact, the selection of an appropriate system to address the right questions is a key to success in science. Only when we discover the mechanisms in a specific and often the simplest system, can we further add more elements to modify our theory that best fits reality. Of course, they often cite Mendel as an excellent example. Mendel's law fits single-gene heredity best and thus provides the basis for understanding the heredity of multiple genes in major complex cases.

However, scientists must be aware of the fundamental limitations of this approach, as the more unique and elegant the model system used, the more limited the conclusions will be for generalization. For example, the following questions need to be addressed to understand the limitations of Mendel's laws: should we classify genes based on a dominant–recessive relationship while knowing that a large amount of genetic variants cannot be explained by such binary categories of genes? If there is no clear-cut relationship between most individual genes and phenotypes, should we still consider Mendel's law the law of genetics? What if laws based on a simplified system (like the single gene–phenotype relationship detected by Mendel) are drastically different from real-world complex systems (the majority of biological cases where all genes are connected and environmental variation plays an important role)? Moreover, should we clearly point out that Mendel's hypotheses only represent exceptions before crowning them as the law of genetics?

Such a dilemma has occurred throughout the modern history of genetics, yet most textbooks fail to warn readers that many well-accepted concepts of genetics are fundamentally limited because of key differences between simplified models and reality. By comparing classical model systems and their derived laws of genetics, common features can be summarized using single-factor analysis to link a single genetic element to limited phenotypes by ignoring links with high complexity. Often, the selected model system ideally illustrates a causative relationship of an investigator's favorite concept, as these model systems become more or less linear with artificially enhanced certainty. In a sense, each model system offers some low-hanging fruit, but they are the exceptions in the real world as these pure systems artificially amplify given genetic contributions by eliminating other important factors that exist in natural systems.

This way of thinking in genetics has lasted for over a century without any serious challenge. We often validate data using artificial models rather than real-world situations. A major and unfortunate trend in the field is to publish positive data and not report negative data or data that do not make sense. Many clear-cut stories have been published. Although these stories are academically interesting, they have limited practical implications.

With the advances of human genetics and medical genetics and the increased popularity of the gene in society, there has been high hope to fix gene mutations to fight many common and complex diseases. For the first time in the history of genetics, theories can be directly examined using various molecular genetic methods on many human diseases. Paradoxically, however, the progress has been slow, and the knowledge gap has been drastically increased between genetic concepts and clinical realities. First, it has been challenging to link non-Mendelian diseases to specific genes. Second, the genetic heterogeneity is overwhelming. Third, environment–gene interaction plays dominant roles for disease phenotypes. Fourth, the disease progression/response to treatment is an adaptive system where the power of genetic prediction is drastically reduced. All of these features raise some profound questions: if Mendel's law is correct, and if many diseases are caused by multiple genes, then why is it so difficult to identify these key gene mutations in most common and complex human diseases given our advanced molecular tools? Do most genes really serve as independent informational units (Pigliucci, 2010), given the fact that the function of individual genes does not divulge the emergent properties of a genetic network (Chouard, 2008)?

Obviously, the time to rethink the laws of genetics and move the field forward (from Mendel's extreme selection to the real world of genetic and environmental complexity) is long overdue. Such a challenging transition will likely generate much confusion, as it did for Mendel.

It is thus interesting to know Mendel's thoughts about his hypotheses following his unsuccessful experiments on hawkweed and other species, which ultimately might question his observations in pea plants. This issue might also relate to the pity that he did not continue his research after he published his milestone publication. A common explanation was that he interrupted his research because of more duties and issues from the monastery. Knowing his increased confusion when dealing with different species, it is not totally unreasonable to speculate that this confusion also contributed to the discontinuation of his remarkable experiments.

Finally, the effort of discussing the key limitation of Mendel's laws is not simply to discredit research based on simplified systems, as initially building knowledge based on low-hanging fruit is a common practice in science. That is why both Newton's laws and Einstein's theory of relativity represent keystones in physics. However, there is a crucial difference between many physical/chemical laws and genetic laws. Physical/chemical laws are supported by the vast majority of experiments, with limited exceptions, whereas genetic laws are only correct in exceptional cases. For example, Newton's second law, F   =   MA (force   =   mass   ×   acceleration), is supported by nearly all experimental data, except when velocity is close to the speed of light (when the special theory of relativity is needed). In contrast, as we just discussed, Mendel's laws can only be supported by limited experimental data from very limited cases.

Why is there such a huge difference between physical laws and Mendel's laws of genetics in terms of application toward a majority of cases? This question is not only highly significant to rethink the future of genetics but also interesting in relation to the philosophy of science. In addition to the obvious feature of heterogeneity in biology, which could complicate the prediction power of genetic laws, we need to examine what Mendel had done to initially discover and then establish the laws of genetics.

On the surface, Mendel followed similar patterns that other giants of science used when searching for his scientific theory: his initial surprised observation was that the same characteristics kept appearing with unexpected regularity when he crossed certain varieties. He thus designed systematic cross experiments with highly selected traits. His observations included the disappearance of the recessive phenotype from F1, the recovery of the recessive phenotype from F2, and a dominant to recessive phenotype relationship that closely matched a 3:1 ratio. He then introduced a model to explain how the independent genetic factors (both dominant and recessive) can be separated and recombined during the cross without dilution by its counterparts. By scoring the number of offspring, the genetic factors and their defined phenotypes can be illustrated simply by the numbers! His analyses thus validated his models which lead to the laws of genetics.

What were the potential problems then?

First, the phenotypes were not correctly classified (the initial observations were not very solid). There was no clear cut between dominant and recessive phenotypes; rather, there were many in between phenotypes. For example, in between the green and yellow seeds, there are many nontypical greens and nontypical yellows. If a careful classification is used, the data distribution would be far from a 3:1 ratio. The same is true for the seed shapes, as well as other traits, challenging the most basic assumption that phenotypes should be divided into dominant and recessive classifications. From the initial observation to the validation of the model, the data presentation was problematic. Without a solid factual basis, any law will inevitably fail.

Second, it is possible that under specific conditions, some exceptional strong traits might display the pattern close to 3:1 ratio. However, we should not generalize these into the general law. A more realistic model should be established to explain most biological cases.

It should be pointed out that, in fact, Mendel did describe some inconsistencies in other species. However, these important discussions/confusions were ignored by other educators who were keen to tell the successful and easy-to-understand story of Mendel's law. Again, in James Peters' introduction for Mendel's classical paper, he wrote:

I have not included the last few pages of Mendel's original paper, which dealt with experiments on hybrids of other species of plants, and with remarks on certain other questions of heredity. These paragraphs have little bearing on the principles Mendel proposed in this paper, and I have found from experience with my students that these pages serve primarily to confuse rather than to clarify.

That is potentially problematic. Many scientific concepts, in a clear-cut and well-designed system, are simple, precise, and even beautiful. However, when put into a broad context, or through the lens of reality, it can become confusing, conflicting, and hard to understand. We do need to show the real picture of science to students. It is a crucial way to illustrate the limitations of some beautiful theories; we cannot just retell rosy stories.

That is the partial reason why most researchers nowadays firmly believe they can finally identify key common genetic factors for most complex traits despite how difficult the task actually is. They say, Mendel did it, why not us? It's just a bit more complicated than his single gene traits. Similar examples can be found in cancer research, evolutionary research, and many other fields of biology. Now, knowing the reality behind Mendel's data, it is up to us to change our attitudes toward genetic approaches.

1.3. Diminishing Power of Gene-Based Genomics

The past 30   years of genomic research has transformed biological research as well as increased interest and expectations of the general public toward science. This is particularly true once the sequencing of the human genome was successfully completed, an achievement that has been praised as the greatest scientific achievement of mankind, as the entire sequence represents the book of humanity and language with which God created life. During a joint announcement of the United States and United Kingdom on June 26, 2000, surrounded by two teams of scientists, US president Bill Clinton proudly announced that

It is now conceivable that our children and our children's children will know the term cancer only as a constellation of stars. According to him, Genome science […] will revolutionize the diagnosis, prevention and treatment of most, if not all, human diseases.

The White House Office of the Press Secretary, 2000a

Headlines appeared all over the world following this announcement. The New York Times' banner proclaimed, Genetic code of human life is cracked by scientists. Time Magazine made it their cover story. The Guardian called it, The breakthrough that changes everything. The Wall Street Journal opined, This is truly big stuff, and the Economist read, The results are a huge step toward a proper understanding of how humans work.

Such hyperbole was not created by politicians in concert with the media but came directly from the scientific genomics community, particularly from many of the leaders who functioned as scientific advisors to politicians as well as spokespersons to the general public. All media information came from these scientists' estimates of the impact that sequencing would have. The following are a few examples.

Francis Collins, then the head of the US Genome Agency at the National Institute of Health, said: "It is probably the most important scientific effort mankind has ever mounted, and that includes splitting the atom and going to the moon." He predicted the genetic diagnosis for cancer would be achieved in 10   years and in another 5   years, the development of treatments. Over the longer time, perhaps in another 15 or 20   years, you will see a complete transformation in therapeutic medicine (The White House Press Release, 2000b).

Roland Wolf of the Imperial Cancer Research Fund said: "The sequencing of the human genome represents one of the great achievements in human science. It really will be a landmark in the evolution of man."

Mike Stratton, head of the cancer genome project at the Sanger Center in Cambridge, stated: "Today is the day in which the scientific community hands over its gift of the human genome sequence to humanity. This is a gift that is very delicate, very fragile, very beautiful …."

The genome was picked by Science as Breakthrough of the Year in 2000. According to Science, compiling maps and sequences of genetic patterns might well be the breakthrough of the Decade, perhaps even the Century, for all its potential to alter our view of the world we live in.

Nearly two decades have elapsed since these pronouncements and exciting predictions were made. During these hopeful 19   years of hard work, with the genome sequencing information in hand, many other