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What you’ll learn to do: Discuss the role environment plays on phenotypes
In recent years, scientists have begun to research how our environment can impact our phenotypes: most common diseases are a result of both your genes and your environment. Examples of these single-gene disorders are Huntington disease and Tay Sachs.
It is becoming difficult to group diseases into either purely “genetic” or “environmental” because most diseases are a little bit of both. For example, emphysema can be the result of both smoking and a disorder called alpha-1-AT deficiency.
In this outcome, we’ll learn about a few different ways our environment can impact us.
- Describe polygenic inheritance and how to recognize it
- Describe continuous variation and how to recognize it
- Explain pleiotropy and its impact on traits in a population
- Identify gene-environment interaction and how this impacts trait expression
Polygenic Inheritance and Environmental Effects
How is Height Inherited?
Many heritable human characteristics don’t seem to follow Mendelian rules in their inheritance patterns. For example, consider human height. Unlike a simple Mendelian characteristic, human height displays:
- Continuous variation. Unlike Mendel’s pea plants, humans don’t come in two clear-cut “tall” and “short” varieties. In fact, they don’t even come in four heights, or eight, or sixteen. Instead, it’s possible to get humans of many different heights, and height can vary in increments of inches or fractions of inches. As an example, consider the bell curve-shaped graph in Figure 1, which shows the heights of a group of male high school seniors.
- A complex inheritance pattern. If you’ve paid attention to the heights of your friends and family, you may have noticed that many different patterns of inheritance are possible. Tall parents can have a short child, short parents can have a tall child, and two parents of different heights may or may not have a child of intermediate height. In addition, siblings with the same two parents may have a range of heights, ones that don’t fall into clear, distinct categories. Simple models involving one or two genes can’t accurately predict all of these inheritance patterns.
Some human characteristics, such as height, eye color, and hair color, don’t come in just a few distinct forms. Instead, they vary in small gradations, forming a spectrum or continuum of possible phenotypes.
How, then, is height inherited? Height and other similar features are controlled not just by one gene, but rather, by multiple (often many) genes that each make a small contribution to the overall outcome. This inheritance pattern is called polygenic inheritance (poly– = many). For instance, a recent study found over 400 genes linked to variation in height. When there are large numbers of genes involved, it becomes hard to distinguish the effect of each individual gene, and even harder to see that gene variants (alleles) are inherited according to Mendelian rules. In a further complication, height doesn’t just depend on genetics: it also depends a lot on environmental factors, such as a child’s overall health and the type of nutrition he or she receives while growing up.
We’ve learned about polygenic inheritance and continuous variation. Just what is the difference between these two types of inheritance?
[reveal-answer q=”646463″]Show Answer[/reveal-answer]
[hidden-answer a=”646463″]Polygenic traits are traits that rely on multiple genes. Continuous variation describes traits whose phenotypes occur on a continuum, rather than having a limited number of possible phenotypes. Traits with continuous variation are often also polygenic traits, but not always, and not all polygenic traits have continuous variation.[/hidden-answer]
Pleiotropy and Human Disorders
When we discussed Mendel’s experiments with purple-flowered and white-flowered plants, we didn’t mention any other phenotypes associated with the two flower colors. However, Mendel noticed that the flower colors were always correlated with two other features: the color of the seed coat (covering of the seed) and the color of the axils (junctions where the leaves met the main stem).
Genes like this, which affect multiple, seemingly unrelated aspects of an organism’s phenotype, are said to be pleiotropic (pleio– = many, –tropic = effects). The seemingly unrelated phenotypes can all be traced back to a defect in a single gene with several jobs.
Importantly, alleles of pleiotropic genes are transmitted in the same way as alleles of genes that affect single traits. Although the phenotype has multiple elements, these elements are specified as a package, and the dominant and recessive versions of the package would appear in the progeny of a monohybrid cross in a ratio of 3:1.
Pleiotropy in Human Genetic Disorders
Genes affected in human genetic disorders are often pleiotropic. For example, people with the hereditary disorder Marfan syndrome may have a constellation of seemingly unrelated symptoms:
- Unusually tall height
- Thin fingers and toes
- Dislocation of the lens of the eye
- Heart problems (in which the aorta, the large blood vessel carrying blood away from the heart, bulges or ruptures).
These symptoms don’t appear directly related to one another, but as it turns out, they can all be traced back to the mutation of a single gene.
Effect of the Environment
Characteristics that are influenced by environmental as well as genetic factors are called multifactorial. The idea of “nature versus nurture” — in other words, the relative influence of genetics versus environmental factors — has been and still is debated. Just looking at the genes of a given organism will not determine how that organism will develop and act. Even identical twins will show different characteristics, depending on the environment in which they live. Everyone is a product of their environment as well as their genetics.
Even when influenced by the environment, phenotypes have a normal range of expression. For instance, human height varies based on nutrition and genetics, but not many people are shorter than 4½ feet or taller than 7 feet. The range of phenotypic possibilities is called the norm of reaction. Hydrangeas, for example, may be blue, pink, or purple, but they are never naturally orange. Hydrangeas are blue in acidic soil with available aluminum, and they are pink in alkaline soil without available aluminum.
You may have heard about PKU, a pleiotropic disorder caused by defects in a single gene coding for an enzyme that converts the amino acid phenylalanine to tyrosine. Newborns are tested for this defect very early in life (Figure 3), so that if the results are positive, they can be given a diet limiting phenylalanine ingestion. That way, the toxic buildup is prevented and the children can develop normally. PKU is an example in which environmental factors can modify gene expression.
Two identical twins (female) live in different parts of the country. One is very committed to a healthy lifestyle: not smoking, exercising regularly, eating a diet rich in fresh produce, and avoiding red meats and processed foods. The other is not as careful: she smokes, is overweight, and often eats fast and processed foods. They are aware that several women in their family have had breast cancer, and decide to consult a doctor about their odds of developing the disease. Which of the following statements by the doctor sounds most correct?
- As identical twins, you are genetically the same, so your chances of developing breast cancer are identical.
- The twin with the healthy lifestyle should not be terribly concerned, while the one with the unhealthy lifestyle is at a higher risk.
- Breast cancer has a genetic component, and the twins have identical genes, so they have the same genetic risk. However, environmental factors such as smoking, obesity, and consumption of red meat have been shown to increase the risk of cancer. While both twins should monitor themselves closely, the twin who smokes and is overweight may want to consider a healthier lifestyle to decrease her risk of breast cancer.
[reveal-answer q=”41921″]Show Answer[/reveal-answer]
[hidden-answer a=”41921″]Option A is wrong; while it has been shown that certain genes may predispose people to cancer, there are many associations between environmental effects and cancer. Option B is also wrong; familial cancers have a genetic component which may or may not be balanced by a healthy lifestyle. Option C is the most correct answer; lifestyle choices are important, but genetic influences are to be taken seriously, especially if there is a family pattern associated with them.[/hidden-answer]
While genes and genetic causes play a large role in health and phenotypes, the environment also plays an important role. Understanding this can enable the treatment of some disorders, such as the case with PKU in which limiting the intake of phenylalanine can prevent toxic build up of this amino acid. Often the norm of reaction is set by genetic factors but ultimately determined by environmental exposures.
Check Your Understanding
Answer the question(s) below to see how well you understand the topics covered in the previous section. This short quiz does not count toward your grade in the class, and you can retake it an unlimited number of times.
Use this quiz to check your understanding and decide whether to (1) study the previous section further or (2) move on to the next section.
Genetics and Epigenetics of Addiction DrugFacts
Why do some people become addicted while others don't? Family studies that include identical twins, fraternal twins, adoptees, and siblings suggest that as much as half of a person's risk of becoming addicted to nicotine, alcohol, or other drugs depends on his or her genetic makeup. Finding the biological basis for this risk is an important avenue of research for scientists trying to solve the problem of drug addiction.
Genetics is the study of genes. Genes are functional units of DNA that make up the human genome. They provide the information that directs a body's basic cellular activities. Research on the human genome has shown that, on average, the DNA sequences of any two people are 99.9 percent the same. However, that 0.1 percent variation is profoundly important—it accounts for three million differences in the nearly three billion base pairs of DNA sequence! These differences contribute to visible variations, like height and hair color, and invisible traits, such as increased risk for or protection from certain diseases such as heart attack, stroke, diabetes, and addiction.
Some diseases, such as sickle cell anemia or cystic fibrosis, are caused by a change, known as a mutation, in a single gene. Some mutations, like the BRCA 1 and 2 mutations that are linked to a much higher risk of breast and ovarian cancer, have become critical medical tools in evaluating a patient's risk for serious diseases. Medical researchers have had striking success at unraveling the genetics of these single-gene disorders, though finding treatments or cures has not been as simple. Most diseases, including addiction, are complex, and variations in many different genes contribute to a person's overall level of risk or protection. The good news is that scientists are actively pursuing many more paths to treatment and prevention of these complex illnesses.
11.5: Genetics and the Environment - Biology
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Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.
The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Gene and Environment Interaction
Few diseases result from a change in a single gene or even multiple genes. Instead, most diseases are complex and stem from an interaction between your genes and your environment. Factors in your environment can range from chemicals in air or water pollution, mold, pesticides, diet choices, or grooming products.
Subtle differences in one person&rsquos genes can cause them to respond differently to the same environmental exposure as another person. As a result, some people may develop a disease after being exposed to something in the environment while others may not.
As scientists learn more about the connection between genes and the environment, they pursue new approaches for preventing and treating disease that consider individual genetic codes.
What is NIEHS Doing?
NIEHS studies a wide range of diseases and disorders with genetic and environmental components. In addition, new technologies and computational approaches are under development to tease out the gene and environment interactions that underpin disease.
- Autism - High levels of air pollution increase the risk for autism in children with a genetic variant called MET, which is involved in brain development. 1 This genetic variant did not increase the risk for the 75% of the population exposed to lower levels of air pollution, suggesting that autism may be caused by an interaction of genetic and environmental factors.
- DNA Repair - Molecules damaged by environmental exposures like ultraviolet light or certain chemicals are incorporated into DNA, triggering cell death that may lead to cancer, diabetes, hypertension, cardiovascular and lung disease, and Alzheimer&rsquos disease. 2
- Metabolism - Researchers in the NIEHS Metabolism, Genes, and Environment Group discovered that a protein called SIRT1, which plays a critical role in early development and metabolism, could provide the basis for therapeutic targets for metabolic diseases and aging at the genetic level. 3
- Parkinson&rsquos disease - The chance of developing Parkinson&rsquos disease after pesticide exposure was greater in people who had a genetic variation that affected the production of nitric oxide, a molecule that can damage neurons. 4 Lifestyle choices related to diet, exercise, and nicotine use also have been linked to the chance of developing Parkinson&rsquos disease.
- Respiratory Syncytial Virus (RSV) - An international study that included NIEHS scientists discovered children with variations in a gene called TLR4 who were exposed to certain environmental factors developed severe cases of RSV bronchiolitis, a life-threatening respiratory disease. 5
New data analysis methods &ndash Many studies can only analyze one type of environmental exposure at a time, which does not account for combined effects of multiple exposures and genes acting together. But, a computational approach by NIEHS-funded researchers can simultaneously analyze data on multiple environmental exposures and their interactions with genes. 6 Researchers used data about atherosclerosis, a chronic heart condition, as a test case for the method&rsquos application in future studies.
Monitoring the state of the field &ndash NIEHS helped convene a workshop, &ldquoCurrent Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases,&rdquo to explore issues surrounding study of the interplay between genes and the environment. 7 Attendees found that along with research challenges many exciting opportunities exist for new studies.
Francesco Redi, the founder of biology, is recognized to be one of the greatest biologists of all time.  Robert Hooke, an English natural philosopher, coined the term cell, suggesting plant structure's resemblance to honeycomb cells. 
Charles Darwin and Alfred Wallace independently formulated the theory of evolution by natural selection, which was described in detail in Darwin's book The Origin of Species, which was published in 1859. In it, Darwin proposed that the features of all living things, including humans, were shaped by natural processes over long periods of time. The theory of evolution in its current form affects almost all areas of biology.  Separately, Gregor Mendel formulated in the principles of inheritance in 1866, which became the basis of modern genetics.
In 1953, James D. Watson and Francis Crick described the basic structure of DNA, the genetic material for expressing life in all its forms,  building on the work of Maurice Wilkins and Rosalind Franklin, suggested that the structure of DNA was a double helix.
Ian Wilmut led a research group that in 1996 first cloned a mammal from an adult somatic cell, a Finnish Dorset lamb named Dolly.    
Students who aspire to a research-oriented career usually pursue a graduate degree such as a master’s or a doctorate (e.g., PhD) whereby they would receive training from a research head based on an apprenticeship model that has been in existence since the 1800s.  Students in these graduate programs often receive specialized training in a particular subdiscipline of biology. 
Biologists who work in basic research formulate theories and devise experiments to advance human knowledge on life including topics such as evolution, biochemistry, molecular biology, neuroscience and cell biology.
Biologists typically conduct laboratory experiments involving animals, plants, microorganisms or biomolecules. However, a small part of biological research also occurs outside the laboratory and may involve natural observation rather than experimentation. For example, a botanist may investigate the plant species present in a particular environment, while an ecologist might study how a forest area recovers after a fire.
Biologists who work in applied research use instead the accomplishments gained by basic research to further knowledge in particular fields or applications. For example, this applied research may be used to develop new pharmaceutical drugs, treatments and medical diagnostic tests. Biological scientists conducting applied research and product development in private industry may be required to describe their research plans or results to non-scientists who are in a position to veto or approve their ideas. These scientists must consider the business effects of their work.
Swift advances in knowledge of genetics and organic molecules spurred growth in the field of biotechnology, transforming the industries in which biological scientists work. Biological scientists can now manipulate the genetic material of animals and plants, attempting to make organisms (including humans) more productive or resistant to disease. Basic and applied research on biotechnological processes, such as recombining DNA, has led to the production of important substances, including human insulin and growth hormone. Many other substances not previously available in large quantities are now produced by biotechnological means. Some of these substances are useful in treating diseases.
Those working on various genome (chromosomes with their associated genes) projects isolate genes and determine their function. This work continues to lead to the discovery of genes associated with specific diseases and inherited health risks, such as sickle cell anemia. Advances in biotechnology have created research opportunities in almost all areas of biology, with commercial applications in areas such as medicine, agriculture, and environmental remediation.
Most biological scientists specialize in the study of a certain type of organism or in a specific activity, although recent advances have blurred some traditional classifications. [ why? ]
- study genetics, the science of genes, heredity, and variation of organisms. study the nervous system. study the process of development and growth of organisms study the chemical composition of living things. They analyze the complex chemical combinations and reactions involved in metabolism, reproduction, and growth. study the biological activity between biomolecules. investigate the growth and characteristics of microscopic organisms such as bacteria, algae, or fungi. study life functions of plants and animals, in the whole organism and at the cellular or molecular level, under normal and abnormal conditions. Physiologists often specialize in functions such as growth, reproduction, photosynthesis, respiration, or movement, or in the physiology of a certain area or system of the organism. use experimental methods traditionally employed in physics to answer biological questions . apply the techniques of computer science, applied mathematics and statistics to address biological problems. The main focus lies on developing mathematical modeling and computational simulation techniques. By these means it addresses scientific research topics with their theoretical and experimental questions without a laboratory. and wildlife biologists study animals and wildlife—their origin, behavior, diseases, and life processes. Some experiment with live animals in controlled or natural surroundings, while others dissect dead animals to study their structure. Zoologists and wildlife biologists also may collect and analyze biological data to determine the environmental effects of current and potential uses of land and water areas. Zoologists usually are identified by the animal group they study. For example, ornithologists study birds, mammalogists study mammals, herpetologists study reptiles and amphibians, ichthyologists study fish, cnidariologists study jellyfishes and entomologists study insects. study plants and their environments. Some study all aspects of plant life, including algae, lichens, mosses, ferns, conifers, and flowering plants others specialize in areas such as identification and classification of plants, the structure and function of plant parts, the biochemistry of plant processes, the causes and cures of plant diseases, the interaction of plants with other organisms and the environment, the geological record of plants and their evolution. Mycologists study fungi, such as yeasts, mold and mushrooms, which are a separate kingdom from plants.
- Aquatic biologists study micro-organisms, plants, and animals living in water. Marine biologists study salt water organisms, and limnologists study fresh water organisms. Much of the work of marine biology centers on molecular biology, the study of the biochemical processes that take place inside living cells. Marine biology is a branch of oceanography, which is the study of the biological, chemical, geological, and physical characteristics of oceans and the ocean floor. (See the Handbook statements on environmental scientists and hydrologists and on geoscientists.) investigate the relationships among organisms and between organisms and their environments, examining the effects of population size, pollutants, rainfall, temperature, and altitude. Using knowledge of various scientific disciplines, ecologists may collect, study, and report data on the quality of air, food, soil, and water. investigate the evolutionary processes that produced the diversity of life on Earth, starting from a single common ancestor. These processes include natural selection, common descent, and speciation.
Biologists typically work regular hours but longer hours are not uncommon. Researchers may be required to work odd hours in laboratories or other locations (especially while in the field), depending on the nature of their research.
Many biologists depend on grant money to fund their research. They may be under pressure to meet deadlines and to conform to rigid grant-writing specifications when preparing proposals to seek new or extended funding.
Marine biologists encounter a variety of working conditions. Some work in laboratories others work on research ships, and those who work underwater must practice safe diving while working around sharp coral reefs and hazardous marine life. Although some marine biologists obtain their specimens from the sea, many still spend a good deal of their time in laboratories and offices, conducting tests, running experiments, recording results, and compiling data.
Biologists are not usually exposed to unsafe or unhealthy conditions. Those who work with dangerous organisms or toxic substances in the laboratory must follow strict safety procedures to avoid contamination. Many biological scientists, such as botanists, ecologists, and zoologists, conduct field studies that involve strenuous physical activity and primitive living conditions. Biological scientists in the field may work in warm or cold climates, in all kinds of weather.
The highest honor awarded to biologists is the Nobel Prize in Physiology or Medicine, awarded since 1901, by the Royal Swedish Academy of Sciences. Another significant award is the Crafoord Prize in Biosciences established in 1980.
Family studies clearly demonstrate that tongue rolling is not a simple genetic character, and twin studies demonstrate that it is influenced by both genetics and the environment. Despite this, tongue rolling is probably the most commonly used classroom example of a simple genetic trait in humans. Sturtevant (1965) said he was "embarrassed to see it listed in some current works as an established Mendelian case." You should not use tongue rolling to demonstrate basic genetics.
Behavior, environment, and genetic factors all have a role in causing people to be overweight and obese
Obesity results from the energy imbalance that occurs when a person consumes more calories than their body burns. Obesity is a serious public health problem because it is associated with some of the leading causes of death in the U.S. and worldwide, including diabetes, heart disease, stroke, and some types of cancer.
Do Genes Have a Role in Obesity?
In recent decades, obesity has reached epidemic proportions in populations whose environments promote physical inactivity and increased consumption of high-calorie foods. However, not all people living in such environments will become obese, nor will all obese people have the same body fat distribution or suffer the same health problems. These differences can be seen in groups of people with the same racial or ethnic background and even within families. Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the same environment suggests that genes do play a role in the development of obesity.
How Could Genes Influence Obesity?
Genes give the body instructions for responding to changes in its environment. Studies of resemblances and differences among family members, twins, and adoptees offer indirect scientific evidence that a sizable portion of the variation in weight among adults is due to genetic factors. Other studies have compared obese and non-obese people for variation in genes that could influence behaviors (such as a drive to overeat, or a tendency to be sedentary) or metabolism (such as a diminished capacity to use dietary fats as fuel, or an increased tendency to store body fat). These studies have identified variants in several genes that may contribute to obesity by increasing hunger and food intake.
Rarely, a clear pattern of inherited obesity within a family is caused by a specific variant of a single gene (monogenic obesity). Most obesity, however, probably results from complex interactions among multiple genes and environmental factors that remain poorly understood (multifactorial obesity).
Any explanation of the obesity epidemic has to consider both genetics and the environment. One explanation that is often cited is the mismatch between today&rsquos environment and &ldquoenergy-thrifty genes&rdquo that multiplied in the distant past, when food sources were unpredictable. In other words, according to the &ldquothrifty genotype&rdquo hypothesis, the same genes that helped our ancestors survive occasional famines are now being challenged by environments in which food is plentiful year round. Other hypotheses have been proposed including a role for the gut microbiome as well as early life exposures associated with epigenetic changes.
Can Public Health Genomics Help?
With the exception of rare genetic conditions associated with extreme obesity, currently, genetic tests are not useful for guiding personal diet or physical activity plans. Research on genetic variation that affects response to changes in diet and physical activity is still at an early stage. Doing a better job of explaining obesity in terms of genes and environment factors could help encourage people who are trying to reach and maintain a healthy weight.
What about Family History?
Health care practitioners routinely collect family health history to help identify people at high risk of obesity-related diseases such as diabetes, cardiovascular diseases, and some forms of cancer. Family health history reflects the effects of shared genetics and environment among close relatives. Families can&rsquot change their genes but they can change the family environment to encourage healthy eating habits and physical activity. Those changes can improve the health of family members&mdashand improve the family health history of the next generation.
How Can You Tell If You or Your Family Members Are Overweight?
Most health care practitioners use the Body Mass Index (BMI) to determine whether a person is overweight. Check your Body Mass Index with a BMI calculator.
Gender identity: Biology or environment?
There is strong evidence that sexual orientation is largely tied to biology and that initial gender assignment is the strongest predictor of gender identity in the case of intersex children. Researchers have yet to precisely pinpoint the etiology of transsexualism, however. Various studies suggest that both biological and environmental variables may play a role in transgender development, says Eric Vilain, MD, PhD, chief of the division of medical genetics and professor of human genetics, pediatrics and urology at the University of California, Los Angeles.
In 1999, scientists identified anatomic brain differences between transsexuals and nontranssexuals (Journal of Psychosomatic Research). More recently, Vilain and his colleagues determined that genetics may have a mild to moderate effect on transgender development (Biological Psychiatry, 2009).
The biological evidence to date is not that strong, though, says Vilain. He points to another study in the April 2010 issue of the International Journal of Andrology showing that fetal exposure to a particular chemical appeared to have an effect on brain development that is linked to gender role behavior. It's quite possible that being transgender stems from a combination of genetic and environmental factors, Vilain concludes.
In this essay, we call to attention what every medical researcher knows about the etiology of cardiovascular disease but most deny, or choose to ignore, when designing, carrying out, and reporting genetic studies. Medical research is entering an era of synthesis that will take advantage of the successes of reductionism over the past decade in defining and describing human genome variations. Meaningful insights into the role of such variation requires a biological model of genome-phenotype relationships that incorporates interactions between subsets of possible genetic and environmental agents as causations in particular contexts indexed by time and space. We make recommendations for what needs to be done to cope with these complexities.
The triumph of the reductionism of the Greeks is a pyrrhic victory: We have succeeded in reducing all of ordinary physical behavior to a simple, correct Theory of Everything only to discover that it has revealed exactly nothing about many things of great importance. R.B. Laughlin and D. Pines
Common chronic multifactorial diseases are responsible for the greatest demand on medical services. 1–3 They also make the largest contribution to loss of human life and productivity in westernized societies (eg, see Murray and Lopez 4 and the American Heart Association 5–7 ). Deviations from health attributable to these diseases, which include cardiovascular disease (CVD), cancer, diabetes, and the psychiatric disorders, typically aggregate in families but they do not segregate as Mendelian single-gene disorders. The distribution of disease among individuals, families, and populations is a direct consequence of the distribution of interactions between the effects of many susceptibility genes and many environmental exposures, that, through dynamic, epigenetic, regulatory mechanisms, ultimately become integrated to generate the disease phenotype. 8–18 The genetic analysis of a multifactorial disease presents the most difficult research challenge facing human geneticists today. We consider CVD in this presentation to illustrate the issues encountered in using genetic information in research to understand the etiology of most common chronic diseases as well as in the identification and treatment of individuals who are at increased risk. We call to attention what every cardiovascular researcher knows about the etiology of CVD but most deny, or choose to ignore, when designing, carrying out, and reporting genetic studies of CVD. We close by suggesting steps that should be taken to cope with this inconsistency.
All cases of CVD have a complex multifactorial etiology. Neither genetic nor environmental agents acting independently cause disease. Full knowledge about an individual’s genetic makeup or exposures to adverse environments cannot predict with certainty the onset, progression, or severity of disease. Disease develops as a consequence of interactions between the “initial” conditions, coded in the genotype, and exposures to environmental agents indexed by time and space 19–21 that are integrated by dynamic, regulatory networks at levels above the genome. 22 The interaction of an individual’s environmental experiences with her/his genotype determines the history of her/his multidimensional phenotype, beginning at conception and continuing through adulthood. At a particular point in time, each genotype has a range of possible phenotypes determined by the range of possible environmental histories. To illustrate this relationship, by collapsing an individual’s phenotype into a single dimension, two of the many possible phenotype histories for a genotype are given in Figure 1. The phenotype of an individual in a particular environmental niche, at a particular point in time, is influenced by the phenotype produced by previous genotype-environment interactions and the potential of the genotype-phenotype combination to react to contemporary environments. The potential to react is constantly changing throughout life from conception to death. 18,23,24 The consequence of these interactions with exposures to environmental agents indexed by time and space is that many individuals who have a genotype that predicts an increased risk of disease will remain healthy because of exposures to compensatory environments. The converse will also be true. Individuals who have a genotype that has a low risk of disease might develop disease because of an adverse environmental history. The important role of the individualized history of exposures to environmental agents makes the average reaction of a group of individuals with the same genotype to the wide range of potential environmental agents over a lifetime a poor predictor of the risk of disease for most individuals with that genotype. CVD research has revealed tens of high-risk environmental agents and hundreds of genes, each with many variations, that influence disease risk. As the number of interacting agents that are involved increases, a smaller number of cases of disease will have the same etiology and be associated with a particular multigene genotype.
Figure 1. Two possible environmental histories in the time-space continuum encountered by a genotype.
The important role that biochemistry and physiology play in the connections between the genome and disease phenotypes brings into question the utility of the overused, simplistic view that the genome produces an independent, isolated, and fixed one-way flow of information from genome to phenotype. Figure 2 shows how a particular multigene genotype is connected to the domain of potential coronary artery phenotypes through the primary biochemical and physiological subsystems. The phenotypic measures of health are constantly being shaped, changed, and transposed as a consequence of epigenetic networks of cellular and organismal dimensions that evolve over the lifetime of the individual. At the level of the cell, these networks influence DNA methylation and repair they also serve to organize coordinated responses to heat-shock, oxygen deprivation, and other environmental changes. 25 The relationships between subsystems influence the trajectory of an individual’s phenotype across the potential reaction surface associated with a particular genotype. The phenotype produced by these subsystems continuously feeds back information to influence the expression of the participating genes and the relationships between the intermediate agents that make up the connecting subsystems. Predicting multifactorial disease outcomes without consideration of epigenetic networks is increasingly seen as naive. 13,16–18 Few genetic studies of the CVDs recognize the realities of the dynamic relationships between an individual’s genotype, her/his history of exposures to environmental agents, such as smoking, a high-fat diet, or a statin drug, and the contemporary phenotype in predicting phenotypic outcomes for a future point in time and a particular environmental niche.
Figure 2. A model for an individual’s propensity to develop coronary artery disease.
It is relevant to recognize that disease prevalence is a consequence of the intersection of the genetic variation that represents a population with the possible histories of environmental exposures that each member of that population might have experienced. Each population is expected to have a different distribution of relative genotype frequencies and a different constellation of possible environmental histories. The distribution of the relative frequencies of genotypes involved in determining the distribution of individual susceptibilities to disease in a particular population is defined by the number of segregating susceptibility genes, the number of alleles of each gene and their relative frequencies, and the correlation between alleles of each gene and between alleles of different genes. There are hundreds of genes known to have functional allelic variations that might contribute to determining an individual’s susceptibility to CVD. All functional variations in a particular gene are not expected to be present in all populations. 26–30 Because new DNA variations arise in isolation and because chance, selection, and migration work as “filters” in each population to modify the relative frequencies of genetic variations in evolutionary time, different populations will have different combinations of DNA variations and hence, a different array of alleles and genotypes, for any particular susceptibility gene. This is a major fact that is most often ignored when developing strategies for understanding and predicting an individual’s disease risk, as well as the development of therapies, by using knowledge about the phenotype(s) associated with a single-gene variation derived from studies of only a few populations.
Different subsets of genes will be influencing phenotypic variation in different subgroups of the same population. Because multigene genotypes will have a multinomial distribution, different combinations of susceptibility genes will be involved in determining disease risk in different individuals in different families. Therefore, every individual who has, or will develop, disease, even if they are drawn from the same population, is not expected to have the same genotype for all of the susceptibility genes. It follows that because the incidence of disease is a consequence of interactions of a population-specific distribution of susceptibility genotypes with the population-specific combinations of exposures to environmental agents over time, the cardinal genetic question must be this: which variations, in which genes, and in which populations are useful for understanding disease and predicting which individuals will develop disease in which strata of environmental histories? Answering this question will establish the population-specific genetic architecture of disease and provide the basis for including genetic information into the practice of individualized medicine. Few genetic studies of common multifactorial diseases recognize the importance of this question.
Two Different Research Strategies for Studying Genetic Architecture
It is acknowledged by most researchers that information about genetic variation can be useful in the identification of presymptomatic individuals at increased risk of developing a common multifactorial disease and also in predicting progression and severity for those with disease. There is not universal accord, however, on the extent to which information about the complexity of the etiologies of a common multifactorial disease, such as CVD (illustrated in Figures 1 and 2 ), should be included in developing a strategy to use genetic information to diagnose and predict clinical end points in medicine and public health. The a priori importance assigned by researchers to the role that knowledge about the complexity of the etiologies of disease can play in developing genetic predictors is reflected by the research strategies that they employ. We next review two of these alternative strategies.
Disease Is a ‘Simple’ Consequence of Variations in Independent Causal Agents
The widely held belief that each case of disease is caused by a variation in a single agent follows from the medical successes that have been achieved for the infectious diseases. 31 The industrialization of medicine has encouraged a search for particular agents that “control” the health of individuals and populations. It is taken to be axiomatic that knowledge about the nature of each causal agent can provide the power to prevent, or alter the course of, disease. The success of this reductionist research paradigm 32 depends critically on three major simplifying assumptions: (1) it is possible to isolate each causal agent (genetic or environmental) without altering its role in producing the phenotype ie, the role of an agent is not changed as a consequence of the process of measurement (2) manipulation of an agent does not alter the behaviors of the other agents that influence the phenotype and (3) the relationships between causal agents and outcomes are invariant ie, they are static, not dynamic.
The validity of these assumptions is particularly critical for genotype-phenotype association studies. If they are false, then applications of single-gene analyses, one gene at a time, to unravel the genetic architecture of CVD are suspect and likely to be misleading. An analysis of studies reported in the leading cardiovascular research publications documents that the reductionist paradigm is accepted by most CVD genetic researchers without question, and its application is the rule rather than the exception. Much is being reported on the nature of the bits and parts of the genetic etiology of CVD, but too little attention is being paid to researching their integration into a model that predicts the emergent phenotypes that are measures of health.
It is apparent to many, but voiced by few, that focusing on the single genes with large marginal (independent) effects on disease risk might not produce the promised medical successes. 3,12,15,33,34 As Morton 35 has emphasized, the genetic architecture of the continuously distributed phenotypes of health will not be revealed by the reductionist paradigm embraced by molecular biology. Currently, only a small fraction of the risk of CVD is attributable to the influences of variations in single genes with large phenotypic effects. 36,37 Furthermore, if we do not accept and study the possibility that single genes, which have small, average genotypic effects in the population at large, can make a major contribution to understanding and predicting disease in particular individuals in particular strata of the population because of their interactions with other genes and environments, it will not be possible to adequately evaluate the utility of genetic information. As Morin 38 implores in a call for a paradigm shift, the very way we think about the problem prevents us from knowing. Cohen and Rice 39 call to attention the conundrum we all face: “The problem that afflicts all sciences is the fact that once you have defined the kind of answers that you expect to get, it is very difficult to know what you are missing.” Also, Popper, a prominent 20th-century philosopher of science, pointed out that the ability to formulate new questions is fundamental to initiating a paradigm shift. 40 This is clearly an issue faced by geneticists in their search for an understanding of the distribution of the common multifactorial diseases, such as CVD, among individuals, families, and populations in the postgenomic era. A plan for executing the prevailing research paradigm to identify independent, causal, genetic variations is laid out by Botstein and Risch. 41 Morowitz 42 presents chemical and biologic arguments that make clear the necessity for an alternative research paradigm for studying the genetics of human disease.
Disease Is a Consequence of the ‘Complex’ Organization of Interacting Agents
This view of the genetic analysis problem takes into account the networks of intermediate biochemical and physiological subsystems that connect genome variation with phenotypic variation illustrated in Figures 1 and 2 . It embraces four fundamental aspects of disease etiology. First, the same network of interacting, quantitatively varying, intermediate biochemical and physiological agents that influences the so-called normal range of interindividual variability among the healthy also influences the development of disease. Individuals with disease are just in a different part of the multidimensional genotype-environment–intermediate biochemical and physiological state space, defined by variation and covariation of the agents, than are individuals who are healthy. Inferences about the role of molecular variation from studies that focus primarily on individuals who have CVD cannot provide unbiased information for prediction of disease risk among individuals in the population at large. The sampling issue is not widely appreciated among laboratory-based researchers. The probability of observing a particular genotype in a sample ascertained because they have a particular disease phenotype cannot be equal to the probability of encountering the disease phenotype in individuals with a particular genotype. For instance, the cumulative risk of CVD death by age 65 years is 0.7 for individuals with familial hypercholesterolemia, a well-known lipid disorder associated with a defect in the LDL receptor gene. 43 However, only a small fraction of those with CVD have this gene defect. 36 It follows that genetic predictors that provide valuable information about selected patient groups seen in hospitals will be much less valuable in general practice, in which patients are drawn from the population at large.
Second, the biologic relationships between the network of interacting agents and cardiovascular health are nonlinear. 44,45 Disease is a consequence of an individual’s homeodynamic mechanisms that do not compensate for disturbances in levels of, and the relationships among, the agents involved in causation. 16,46 Changes in the level of one agent might influence disease risk by altering the relationship(s) among other agents. In most cases, the size of the marginal (independent) effect of a variation in an agent is inversely related to the dependence of its effect on the context defined by other interacting agents. 15 Geneticists, in particular, must be aware that the context in which a molecular process takes place deserves as much study as the biochemical content of the process. 24,39,47 We should be asking how do genetically influenced changes in relationships between agents influence risk of disease? The current disconnect between our knowledge about genomic variation and coordinated variation in intermediate traits that influence disease risk presents the challenge of reformulation of the questions being asked and new analytical skills yet to be developed that are necessary to address them.
Third, the genetic architecture of cardiovascular health is expected to be population-specific. Few populations will have the same relative frequency distributions of genetic variations. 26–30,46,48 Differences among populations in the relative frequency of a susceptibility genotype or an environmental exposure will contribute to differences in the utility of a susceptibility genotype for prediction of disease within a particular population. Even for those rare instances when the relative genotype frequencies are the same across populations, the contribution of genetic variation to prediction will still be different, because its influence on variation in disease susceptibility depends on a particular combination of environmental exposures whose relative frequencies vary among populations.
Last, studies of genetic architecture can be guided by an understanding of the complexity of the etiologies of cardiovascular health. Studies to document the complexity of etiology and the influences of context defined by gender, age, and other measures of environmental effects should be a priority. 49–51 We concur with Anderson 52 that we must turn to nature to inform us about the type of model that should be used to describe nature. Even though it might never be possible to know everything about etiologies, 52–55 the study of genetic architecture can be guided by our current knowledge, albeit incomplete. Recognition of the complexity of the organization of interacting agents can foster synergy between efforts to predict disease and efforts to understand the etiology of disease. For example, a particular gene might be a candidate for prediction because its product is involved in the metabolism of intermediate biochemical and/or physiological agents that define disease. Also, etiologic relationships between agents involved in causation might suggest genetic studies of trait relationships and their role in prediction of disease. A research strategy to extract the full utility of genetic information that recognizes these considerations must begin by documenting the nature and extent of the complexities rather than seeking universal invariant, context-independent effects of single genes or genotypes. So, what steps should be taken to cope with these complexities? It seems imperative that geneticists consider the following.
Admit That Etiology Is Complex
We should ask questions and carry out research that reflect the reality of the problem. Pretending that the etiology of a common human disease like CVD is caused by the independent actions of multiple agents is deterring progress. Accepting the complexity of etiology provides a framework for organizing an immense amount of observations. 56 The result will bring clarity to the formulation of appropriate questions and selection of research methods.
Test Commonly Held Assumptions
The goal of CVD research must be to formulate a mathematical/statistical/computer model that summarizes the complexity of the etiology in manageable dimensions. What one is willing to assume guides the process and determines the validity of the applications of the model. The first step toward building such a model must be to test the validity of the assumptions that are being made in its formulation.
Ask Relevant Questions
Distinguishing between interesting questions and important questions is a function of social, economic, and cultural preoccupations of the community at large. The definition of an important question will suggest the appropriate model, measures, sampling design, and analytic tools necessary to interpret the data collected. A model counts as an explanation only if it meets the needs of an individual (in clinical medicine) or a community (in public health programs). Physicians and public health workers will ultimately decide whether a model has explanatory power or provides understanding.
Refocus on Measurement of the Environment
In the pregenome era, environmental factors were considered to be the major predictors of diseases. In the postgenomic era, genetic factors have supplanted rather than complemented the environmental approach. We must return to placing equal emphasis on the role of environment and the interactions with the new-found genetic factors. This will require new technologies to measure the environments, both internal and external to the individual, with the same precision as measurements of DNA.
Develop Nontraditional Analytic Methods
We need to explore new mathematical and statistical methods that consider the biological and genetic facts that have accumulated over the past 100 years and incorporate them into the analysis of the exponentially growing databases generated by contemporary molecular technologies. Traditional statistical approaches to modeling biologic data are inadequate and inappropriate for addressing questions of genotype–intermediate trait–disease phenotype relationships when hundreds to thousands of measurements are considered simultaneously. New methods for information handling, model pruning, and biological interpretation of research results are required. Identification of the context in which a genetic model is useful should be a high priority. Meta-analyses will be of less value in sorting out the genetic architecture. A meaningful strategy will integrate bottom-up, top-down, and hierarchical approaches to identify the subset of key variables for predicting and understanding gene-environment-disease end-point relationships.
Replicate to Sort Out Invariant and Context-Dependent Genetic Effects From Type I Errors
We expect that the effects of few genes will be invariant across populations and environmental strata most will be context-dependent. A fraction of the context-dependent gene effects will be invariant across time within a particular stratum defined by age, gender, smoking habit, or other measures of exposures to environmental factors, both internal and external to the individual, which could help to distinguish them from Type I statistical errors (false-positives). A major research challenge will be to design and execute appropriate studies to distinguish between context-dependent effects and Type I errors. Inferences about genetic effects will be dependent on the coarseness of the graining of the interacting genetic and environmental agents that are involved in the etiology of the phenotype of interest. 57
Train Scientists for a Biocomplex Future
Connecting data with questions involves analytical skills that are in short supply. We have a plethora of data collectors and a dearth of qualified data analyzers. Turning the analytical step over to physicists, statisticians, and computer scientists who do not have basic training in biological research is likely to increase, rather than decrease, the disconnect between question and inferences in biology and medicine. The success of mining strategies being developed for very large data sets critically depends on the formulation of appropriate a priori hypotheses that follow from an understanding of the relevant questions.
Place Value on a Synthetic Mind Set in Promoting Young Scientists
The organization of the contemporary academic community is driven by the desire to deliver products and services according to management strategies adopted from industry. The genetic research community currently places highest value on finding and describing the bits and pieces of human health in promoting and rewarding research projects and individual scientists. Singularity of purpose, and a reductionist approach that has no interest in complexity, discourages imaginative solutions. We are in need of an academic environment that puts greater value on research projects and scientists committed to studying how the parts are put together. Such an environment should encourage a synthetic mind set among scientists espousing different disciplinary assumptions, foster communication that widens the context of scientific research, and place a premium on collaboration in promoting and rewarding young investigators.
The Human Genome Project has revealed thousands of genes and millions of gene variations that might influence human health. We are now faced with reconciling a high-dimensional causal-state space of molecular networks that connect DNA variation and the well-established role of exposures to high-risk environmental agents with the emergent, discrete, clinical outcomes that are relevant to medicine and public health. We are entering an era of synthesis that will take advantage of the successes of reductionism in defining participating agents. Meaningful insights will depend on a fundamental change in how we use this information to model, measure, and analyze genotype-phenotype relationships. The fact that epigenetic feedback mechanisms and interactions of many agents from the genome through intermediate biochemical and physiological subsystems with exposures to environmental agents contribute to the emergence of an individual’s clinical phenotype suggests that we will find heterogeneity in causation and predictability of agents among subsets of prevalent cases of disease. Embracing a more realistic biological model that incorporates the complexity of the interactions between agents as causations in a particular context indexed by time and space will be necessary to answer the three cardinal genetic questions about disease 14 : (1) where are the susceptibility genes located? (2) what are the functional DNA sequence variations in these genes? and (3) what are the statistical (for prediction) and biologic (for etiology) relationships between genotype variation and variation in onset, progression, and severity in which subsets of individuals? An unwillingness to adopt a realistic biological model for health when designing and analyzing studies of disease might be the greatest deterrent to answering these questions that are most relevant to the practice of medicine without prejudice.
This work was supported in part by National Institutes of Health (Bethesda, Md) grants HL39107, HL51021, and GM65509. We wish to thank our many friends for their criticisms and encouragement that helped to shape our perspective on common disease research.
If dialect stems from genetics , these outsiders should still sound like outsiders.
Addressing disparities related to living conditions, locations, and genetics has always been a factor of disease spread and mortality, but it has never been tracked, measured, and analyzed on such a scale.
In 2015, for example, two dozen of the world’s leading sports genetics researchers published a consensus statement in the British Journal of Sports Medicine affirming that “genetic tests have no role of play in talent identification.”
Factors of metabolic rateMetabolic rate and calorie requirements vary from person to person depending on factors such as genetics , gender, age, body composition and amount of exercise you do.
This seems remarkable on the face of it because there is no viable scientific opposition to evolution and it is widely accepted by biologists and other life scientists as being fundamental to understanding biology—from genetics to medicine.
Genetics alone does not an eating disorder make, generally speaking, and Bulik points out that environment still plays a role.
The at-home genetics testing company 23andme, established in 2006, helps people learn more about their “DNA relatives.”
Nature and nurture, genetics and family background all come into play.
“Keep in mind that our body shape is often determined by genetics ,” says Dr. Ball.
Recent research has shown perfectionism to be an issue of genetics .
This behavior, as the study of Genetics shows, may be determined in lesser organisms by experiment.
Genetics contain medicines which control the uterine and sexual systems, which may all be reckoned among Neurotics.
Scientific stock-breeding supplies valuable practical examples of applied Genetics , or the Science of Heredity.
Tuly, who knows more of psychology and genetics than I, convinced me of three things.
The civilization of illiteracy is one of sampling, a concept originating in genetics .