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Thursday, January 31, 2019

Public health genomics

From Wikipedia, the free encyclopedia

Public health genomics is the use of genomics information to benefit public health. This is visualized as more effective preventive care and disease treatments with better specificity, tailored to the genetic makeup of each patient. According to the Centers for Disease Control and Prevention (U.S.), Public Health genomics is an emerging field of study that assesses the impact of genes and their interaction with behavior, diet and the environment on the population’s health.

This field of public health genomics is less than a decade old. A number of think tanks, universities, and governments (including the U.S., UK, and Australia) have started public health genomics projects. Research on the human genome is generating new knowledge that is changing public health programs and policies. Advances in genomic sciences are increasingly being used to improve health, prevent disease, educate and train the public health workforce, other healthcare providers, and citizens.

Public policy

Public policy has protected people against genetic discrimination, defined in Taber's Cyclopedic Medical Dictionary (2001) as unequal treatment of persons with either known genetic abnormalities or the inherited propensity for disease; genetic discrimination may have a negative effect on employability, insurability and other socio-economic variables. Public policy in the U.S. that protect individuals and groups of people against genetic discrimination include the Americans with Disabilities Act of 1990, Executive Order 13145 (2000) that prohibits genetic discrimination in the workplace for federal employees, and the Genetic Information Nondiscrimination Act of 2008

Main public concerns regarding genomic information are that of confidentiality, misuse of information by health plans, employers, and medical practitioners, and the right of access to genetic information.

Ethical concerns

One of the many facets involved in public health genomics is that of bioethics. This has been highlighted in a study in 2005 by Cogent Research, that found when American citizens were asked what they thought the strongest drawback was in using genetic information, they listed "misuse of information/invasion of privacy" as the single most important problem. In 2003, the Nuffield Council on Bioethics published a report, Pharmacogenetics: Ethical Issues. Authors of the document explore four broad categories of ethical and policy issues related to pharmacogenetics: information, resource, equity and control. In the introduction to the report, the authors clearly state that the development and application of pharmacogenetics depend on scientific research, but that policy and administration must provide incentives and restraints to ensure the most productive and just use of this technology.

Genetic susceptibility to disease

Single nucleotide polymorphisms (SNPs) are single bases within a gene sequence that differ from that gene's consensus sequence, and are present in a subset of the population. SNPs may have no effect on gene expression, or they can change the function of a gene completely. Resulting gene expression changes can, in some cases, result in disease, or in susceptibility to disease (e.g., viral or bacterial infection). 

Some current tests for genetic diseases include: cystic fibrosis, Tay–Sachs disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, high cholesterol, some rare cancers and an inherited susceptibility to cancer. A select few are explored below.

Herpes virus and bacterial infections

Since the field of genomics takes into account the entire genome of an organism, and not simply its individual genes, the stud of latent viral infection falls into this realm. For example, the DNA of a latent herpes virus integrates into the host’s chromosome and propagates through cell replication, although it is not part of the organism's genome, and was not present at the birth of the individual.

An example of this is found in a study published in Nature, which showed that mice with a latent infection of a herpesvirus were less susceptible to bacterial infections. Murine mice were infected with murine gamma herpes virus 68 and then challenged with the Listeria monocytogenes bacterium. Mice that had a latent infection of the virus had an increased resistance to the bacteria, but those with a non-latent strain of virus had no change in susceptibility to the bacteria. The study went on to test mice with murine cytomegalovirus, a member of the betaherpesvirinae subfamily, which provided similar results. However, infection with human herpes simplex virus type-1 (HSV-1), a member of the alphaherpesvirinae subfamily, did not provide increased resistance to bacterial infection. They also used Yersinia pestis (the causative agent of the Black Death) to challenge mice with a latent infection of gammaherpesvirus 68, and they found the mice did have an increased resistance to the bacteria. The suspected reason for this is that peritoneal macrophages in the mouse are activated after latent infection of the herpesvirus, and since macrophages play an important role in immunity, this provides the mouse with a stronger, active immune system at the time of bacterial exposure. It was found that the latent herpesvirus caused an increase in interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), cytokines which both lead to activation of macrophages and resistance to bacterial infection.

Influenza and Mycobacterium tuberculosis

Variations within the human genome can be studied to determine susceptibility to infectious diseases. The study of variations within microbial genomes will also need to be evaluated to use genomics of infectious disease within public health. The ability to determine if a person has greater susceptibility to an infectious disease will be valuable to determine how to treat the disease if it is present or prevent the person from getting the disease. Several infectious diseases have shown a link between genetics and susceptibility in that families tend to have heritability traits of a disease.

During the course of the past influenza pandemics and the current influenza epizootic there has been evidence of family clusters of disease. Kandun, et al. found that family clusters in Indonesia in 2005 resulted in mild, severe and fatal cases among family members. The findings from this study raise questions about genetic or other predispositions and how they affect a persons susceptibility to and severity of disease. Continued research will be needed to determine the epidemiology of H5N1 infection and whether genetic, behavioral, immunologic, and environmental factors contribute to case clustering.

Host genetic factors play a major role in determining differential susceptibility to major infectious diseases of humans. Infectious diseases in humans appear highly polygenic with many loci implicated but only a minority of these convincingly replicated. Over the course of time, humans have been exposed to organisms like Mycobacterium tuberculosis. It is possible that the human genome has evolved in part from our exposure to M. tuberculosis. Animal model studies and whole genome screens can be used to identify potential regions on a gene that suggest evidence of tuberculosis susceptibility. In the case of M. tuberculosis, animal model studies were used to suggest evidence of a locus which was correlated with susceptibility, further studies were done to prove the link between the suggested locus and susceptibility. The genetic loci that have been identified as associated with susceptibility to tuberculosis are HLA-DR, INF-γ, SLC11A1, VDR, MAL/TIRAP, and CCL2. Further studies will be needed to determine genetic susceptibility to other infectious diseases and ways public health officials can prevent and test for these infections to enhance the concept of personalized medicine.

Type 1 Diabetes, immunomics, and public health

The term genomics, referring to the organism’s whole genome, is also used to refer to gene informatics, or the collection and storage of genetic data, including the functional information associated with the genes, and the analysis of the data as combinations, patterns and networks by computer algorithms. Systems biology and genomics are natural partners, since the development of genomic information and systems naturally facilitates analysis of systems biology questions involving relationships between genes, their variants (SNPs) and biological function. Such questions include the investigation of signaling pathways, evolutionary trees, or biological networks, such as immune networks and pathways. For this reason, genomics and these approaches are particularly suited to studies in immunology. The study of immunology using genomics, as well as proteomics and transcriptomics (including gene profiles, either genomic or expressed gene mRNA profiles), has been termed immunomics

Accurate and sensitive prediction of disease, or detection during early stages of disease, could allow the prevention or arrest of disease development as immunotherapy treatments become available. Type-1 diabetes markers associated with disease susceptibility have been identified, for example HLA class II gene variants, however possession of one or more of these genomic markers does not necessarily lead to disease. Lack of progression to disease is likely due to the absence of environmental triggers, absence of other susceptibility genes, presence of protective genes, or differences in the temporal expression or presence of these factors. Combinations of markers have also been associated with susceptibility to type-1 diabetes however again, their presence may not always predict disease development, and conversely, disease may be present without the marker group. Potential variant genes (SNPs) or markers that are linked to the disease include genes for cytokines, membrane-bound ligands, insulin and immune regulatory genes. 

Meta-analyses have been able to identify additional associated genes, by pooling a number of large gene datasets. This successful study illustrates the importance of compiling and sharing large genome databases. The inclusion of phenotypic data in these databases will enhance discovery of candidate genes, while the addition of environmental and temporal data should be able to advance the disease progression pathways knowledge. HUGENet, which was initiated by the Centers for Disease Control and Prevention (U.S.), is accomplishing the integration of this type of information with the genome data, in a form available for analysis. This project could be thought of as an example of ‘metagenomics’, the analysis of a community’s genome, but for a human rather than a microbial community. This project is intended to promote international data sharing and collaboration, in addition to creating a standard and framework for the collection of this data.

Nonsyndromic hearing loss

Variations within the human genome are being studied to determine susceptibility to chronic diseases, as well as infectious diseases. According to Aileen Kenneson and Coleen Boyle, about one sixth of the U.S. population has some degree of hearing loss. Recent research has linked variants in the gap junction beta 2 (GJB2) gene to nonsyndromic prelingual sensorineural hearing loss. GJB2 is a gene encoding for connexin, a protein found in the cochlea. Scientists have found over 90 variants in this gene and sequence variations may account for up to 50% of nonsyndromic hearing loss. Variants in GJB2 are being used to determine age of onset, as well as severity of hearing loss. 

It is clear that there are also environmental factors to consider. Infections such as rubella and meningitis and low birth weight and artificial ventilation, are known risk factors for hearing loss, but perhaps knowing this, as well as genetic information, will help with early intervention. 

Information gained from further research in the role of GJB2 variants in hearing loss may lead to newborn screening for them. As early intervention is crucial to prevent developmental delays in children with hearing loss, the ability to test for susceptibility in young children would be beneficial. Knowing genetic information may also help in the treatment of other diseases if a patient is already at risk.

Further testing is needed, especially in determining the role of GJB2 variants and environmental factors on a population level, however initial studies show promise when using genetic information along with newborn screening.

Genomics and health

Pharmacogenomics

The World Health Organization has defined pharmacogenomics as the study of DNA sequence variation as it relates to different drug responses in individuals, i.e., the use of genomics to determine an individual’s response. Pharmacogenomics refers to the use of DNA-based genotyping in order to target pharmaceutical agents to specific patient populations in the design of drugs.

Current estimates state that 2 million hospital patients are affected by adverse drug reactions every year and adverse drug events are the fourth leading cause of death. These adverse drug reactions result in an estimated economic cost of $136 billion per year. Polymorphisms (genetic variations) in individuals affect drug metabolism and therefore an individual's response to a medication. Examples of ways in which genetics may affect an individual’s response to drugs include: drug transporters, metabolism and drug interactions. Pharmacogenetics may be used in the near future by public health practitioners to determine the best candidates for certain drugs, thereby reducing much of the guesswork in prescribing drugs. Such actions have the potential to improve the effectiveness of treatments and reduce adverse drug events.

Nutrition and health

Nutrition is very important in determining various states of health. The field of nutrigenomics is based on the idea that everything ingested into a person’s body affects the genome of the individual. This may be through either upregulating or downregulating the expression of certain genes or by a number of other methods. While the field is quite young there are a number of companies that market directly to the public and promote the issue under the guise of public health. Yet many of these companies claim to benefit the consumer, the tests performed are either not applicable or often result in common sense recommendations. Such companies promote public distrust towards future medical tests that may test more appropriate and applicable agents. 

An example of the role of nutrition would be the methylation pathway involving methylene tetrahydrofolate reductase (MTHFR). An individual with the SNP may need increased supplementation of vitamin B12 and folate to override the effect of a variant SNP. Increased risk for neural tube defects and elevated homocysteine levels have been associated with the MTHFR C677T polymorphism. 

In 2002, researchers from the Johns Hopkins Bloomberg School of Public Health identified the blueprint of genes and enzymes in the body that enable sulforaphane, a compound found in broccoli and other vegetables, to prevent cancer and remove toxins from cells. The discovery was made using a “gene chip,” which allows researchers to monitor the complex interactions of thousands of proteins on a whole genome rather than one at time. This study was the first gene profiling analysis of a cancer-preventing agent using this approach. University of Minnesota researcher Sabrina Peterson, coauthored a study with Johanna Lampe of the Fred Hutchinson Cancer Research Center, Seattle, in October 2002 that investigated the chemoprotective effect of cruciferous vegetables (e.g., broccoli, brussels sprouts). Study results published in The Journal of Nutrition outline the metabolism and mechanisms of action of cruciferous vegetable constituents, discusses human studies testing effects of cruciferous vegetables on biotransformation systems and summarizes the epidemiologic and experimental evidence for an effect of genetic polymorphisms (genetic variations) in these enzymes in response to cruciferous vegetable intake.

Healthcare and genomics

Members of the public are continually asking how obtaining their genetic blueprint will benefit them, and why they find that they are more susceptible to diseases that have no cures

Researchers have found that almost all disorders and diseases that affect humans reflect the interplay between the environment and their genes; however we are still in the initial stages of understanding the specific role genes play on common disorders and diseases. For example, while news reports may give a different impression, most cancer is not inherited. It is therefore likely that the recent rise in the rates of cancer worldwide can be at least partially attributed to the rise in the number of synthetic and otherwise toxic compounds found in our society today. Thus, in the near future, public health genomics, and more specifically environmental health, will become an important part of the future healthcare-related issues.

Potential benefits of uncovering the human genome will be focused more on identifying causes of disease and less on treating disease, through: improved diagnostic methods, earlier detection of a predisposing genetic variation, pharmacogenomics and gene therapy.

For each individual, the experience of discovering and knowing their genetic make-up will be different. For some individuals, they will be given the assurance of not obtaining a disease, as a result of familial genes, in which their family has a strong history and some will be able to seek out better medicines or therapies for a disease they already have. Others will find they are more susceptible to a disease that has no cure. Though this information maybe painful, it will give them the opportunity to prevent or delay the on-set of that disease through: increased education of the disease, making lifestyle changes, finding preventive therapies or identifying environmental triggers of the disease. As we continue to have advances in the study of human genetics, we hope to one day incorporate it into the day-to-day practice of healthcare. Understanding one's own genetic blueprint can empower oneself to take an active role in promoting their own health.

Genomics and understanding of disease susceptibility can help validate family history tool for use by practitioners and the public. IOM is validating the family history tool for six common chronic diseases (breast, ovarian, colorectal cancer, diabetes, heart disease, stroke) (IOM Initiative). Validating cost effective tools can help restore importance of basic medical practices (e.g. family history) in comparission to technology intensive investigations.

The genomic face of immune responses

A critical set of phenomena that ties together various aspects of health interventions, such as drug sensitivity screening, cancer or autoimmune susceptibility screening, infectious disease prevalence and application of pharmacologic or nutrition therapies, is the systems biology of the immune response. For example, the influenza epidemic of 1918, as well as the recent cases of human fatality due to H5N1 (avian flu), both illustrate the potentially dangerous sequence of immune responses to this virus. Also well documented is the only case of spontaneous "immunity" to HIV in humans, shown to be due to a mutation in a surface protein on CD4 T cells, the primary targets of HIV. The immune system is truly a sentinel system of the body, with the result that health and disease are carefully balanced by the modulated response of each of its various parts, which then also act in concert as a whole. Especially in industrialized and rapidly developing economies, the high rate of allergic and reactive respiratory disease, autoimmune conditions and cancers are also in part linked to aberrant immune responses that are elicited as the communities' genomes encounter swiftly changing environments. The causes of perturbed immune responses run the gamut of genome-environment interactions due to diet, supplements, sun exposure, workplace exposures, etc. Public health genomics as a whole will absolutely require a rigorous understanding of the changing face of immune responses.

Newborn screening

The experience of newborn screening serves as the introduction to public health genomics for many people. If they did not undergo prenatal genetic testing, having their new baby undergo a heel stick in order to collect a small amount of blood may be the first time an individual or couple encounters genetic testing. Newborn genetic screening is a promising area in public health genomics that appears poised to capitalize on the public health goal of disease prevention as a primary form of treatment. 

Most of the diseases that are screened for are extremely rare, single-gene disorders that are often autosomal recessive conditions and are not readily identifiable in neonates without these types of tests. Therefore, often the treating physician has never seen a patient with the disease or condition and so an immediate referral to a specialty clinic is necessary for the family.

Most of the conditions identified in newborn screening are metabolic disorders that either involve i) lacking an enzyme or the ability to metabolize (or breakdown) a particular component of the diet, like phenylketonuria, ii) abnormality of some component of the blood, especially the hemoglobin protein, or iii) alteration of some component of the endocrine system, especially the thyroid gland. Many of these disorders, once identified, can be treated before more severe symptoms, such as mental retardation or stunted growth, set in.

Newborn genetic screening is an area of tremendous growth. In the early 1960s, the only test was for phenylketonuria. In 2000, roughly two-thirds of states in the US screened for 10 or fewer genetic diseases in newborns. Notably, in 2007, 95% of states in the US screen for more than 30 different genetic diseases in newborns. Especially as costs have come down, newborn genetic screening offers “an excellent return on the expenditure of public health dollars.”

Understanding traditional healing practices

Genomics will help develop an understanding of the practices that have evolved over centuries in old civilizations and which have been strengthened by observations (phenotype presentations) from generation to generation, but which lack documentation and scientific evidence. Traditional healers associated specific body types with resistance or susceptibility to particular diseases under specific conditions. Validation and standardization of this knowledge/ practices has not yet been done by modern science. Genomics, by associating genotypes with the phenotypes on which these practices were based, could provide key tools to advance the scientific understanding of some of these traditional healing practices.

Epistasis

From Wikipedia, the free encyclopedia

The gene for total baldness is epistatic to those for blond hair or red hair. The hair-color genes are hypostatic to the baldness gene. The baldness phenotype supersedes genes for hair colour and so the effects are non-additive.
 
Epistasis is the phenomenon where the effect of one gene (locus) is dependent on the presence of one or more 'modifier genes', i.e. the genetic background. Originally the term meant that the phenotypic effect of one gene is masked by a different gene (locus). Thus, epistatic mutations have different effects in combination than individually. It was originally a concept from genetics but is now used in biochemistry, computational biology and evolutionary biology. It arises due to interactions, either between genes, or within them, leading to non-linear effects. Epistasis has a large influence on the shape of evolutionary landscapes, which leads to profound consequences for evolution and evolvability of phenotypic traits.

History

Understanding of epistasis has changed considerably through the history of genetics and so too has the use of the term. In early models of natural selection devised in the early 20th century, each gene was considered to make its own characteristic contribution to fitness, against an average background of other genes. Some introductory courses still teach population genetics this way. Because of the way that the science of population genetics was developed, evolutionary geneticists have tended to think of epistasis as the exception. However, in general, the expression of any one allele depends in a complicated way on many other alleles. 

In classical genetics, if genes A and B are mutated, and each mutation by itself produces a unique phenotype but the two mutations together show the same phenotype as the gene A mutation, then gene A is epistatic and gene B is hypostatic. For example, the gene for total baldness is epistatic to the gene for brown hair. In this sense, epistasis can be contrasted with genetic dominance, which is an interaction between alleles at the same gene locus. As the study of genetics developed, and with the advent of molecular biology, epistasis started to be studied in relation to Quantitative Trait Loci (QTL) and polygenic inheritance

The effects of genes are now commonly quantifiable by assaying the magnitude of a phenotype (e.g. height, pigmentation or growth rate) or by biochemically assaying protein activity (e.g. binding or catalysis). Increasingly sophisticated computational and evolutionary biology models aim to describe the effects of epistasis on a genome-wide scale and the consequences of this for evolution.[3][4] Since identification of epistatic pairs is challenging both computationally and statistically, some studies try to prioritize epistatic pairs.

Classification

Quantitative trait values after two mutations either alone (Ab and aB) or in combination (AB). Bars contained in the grey box indicate the combined trait value under different circumstances of epistasis. Upper panel indicates epistasis between beneficial mutations (blue). Lower panel indicates epistasis between deleterious mutations (red).
 
Since, on average, mutations are deleterious, random mutations to an organism cause a decline in fitness. If all mutations are additive, fitness will fall proportionally to mutation number (black line). When deleterious mutations display negative (synergistic) epistasis, they are more deleterious in combination than individually and so fitness falls with the number of mutations at an increasing rate (upper, red line). When mutations display positive (antagonistic) epistasis, effects of mutations are less severe in combination than individually and so fitness falls at a decreasing rate (lower, blue line).
 
Terminology about epistasis can vary between scientific fields. Geneticists often refer to wild type and mutant alleles where the mutation is implicitly deleterious and may talk in terms of genetic enhancement, synthetic lethality and genetic suppressors. Conversely, a biochemist may more frequently focus on beneficial mutations and so explicitly state the effect of a mutation and use terms such as reciprocal sign epistasis and compensatory mutation. Additionally, there are differences when looking at epistasis within a single gene (biochemistry) and epistasis within a haploid or diploid genome (genetics). In general, epistasis is used to denote the departure from 'independence' of the effects of different genetic loci. Confusion often arises due to the varied interpretation of 'independence' among different branches of biology. The classifications below attempt to cover the various terms and how they relate to one another.

Additivity

Two mutations are considered to be purely additive if the effect of the double mutation is the sum of the effects of the single mutations. This occurs when genes do not interact with each other, for example by acting through different metabolic pathways. Simple, additive traits were studied early on in the history of genetics, however they are relatively rare, with most genes exhibiting at least some level of epistatic interaction.

Magnitude epistasis

When the double mutation has a fitter phenotype than expected from the effects of the two single mutations, it is referred to as positive epistasis. Positive epistasis between beneficial mutations generates greater improvements in function than expected. Positive epistasis between deleterious mutations protects against the negative effects to cause a less severe fitness drop.

Conversely, when two mutations together lead to a less fit phenotype than expected from their effects when alone, it is called negative epistasis. Negative epistasis between beneficial mutations causes smaller than expected fitness improvements, whereas negative epistasis between deleterious mutations causes greater-than-additive fitness drops.

Independently, when the effect on fitness of two mutations is more radical than expected from their effects when alone, it is referred to as synergistic epistasis. The opposite situation, when the fitness difference of the double mutant from the wild type is smaller than expected from the effects of the two single mutations, it is called antagonistic epistasis. Therefore, for deleterious mutations, negative epistasis is also synergistic, while positive epistasis is antagonistic; conversely, for advantageous mutations, positive epistasis is synergistic, while negative epistasis is antagonistic.

The term genetic enhancement is sometimes used when a double (deleterious) mutant has a more severe phenotype than the additive effects of the single mutants. Strong positive epistasis is sometimes referred to by creationists as irreducible complexity (although most examples are misidentified).

Sign epistasis

Sign epistasis occurs when one mutation has the opposite effect when in the presence of another mutation. This occurs when a mutation that is deleterious on its own can enhance the effect of a particular beneficial mutation. For example, a large and complex brain is a waste of energy without a range of sense organs, but sense organs are made more useful by a large and complex brain that can better process the information.

At its most extreme, reciprocal sign epistasis occurs when two deleterious genes are beneficial when together. For example, producing a toxin alone can kill a bacterium, and producing a toxin exporter alone can waste energy, but producing both can improve fitness by killing competing organisms.

Reciprocal sign epistasis also leads to genetic suppression whereby two deleterious mutations are less harmful together than either one on its own, i.e. one compensates for the other. This term can also apply sign epistasis where the double mutant has a phenotype intermediate between those of the single mutants, in which case the more severe single mutant phenotype is suppressed by the other mutation or genetic condition. For example, in a diploid organism, a hypomorphic (or partial loss-of-function) mutant phenotype can be suppressed by knocking out one copy of a gene that acts oppositely in the same pathway. In this case, the second gene is described as a "dominant suppressor" of the hypomorphic mutant; "dominant" because the effect is seen when one wild-type copy of the suppressor gene is present (i.e. even in a heterozygote). For most genes, the phenotype of the heterozygous suppressor mutation by itself would be wild type (because most genes are not haplo-insufficient), so that the double mutant (suppressed) phenotype is intermediate between those of the single mutants. 

In non reciprocal sign epistasis, fitness of the mutant lies in the middle of that of the extreme effects seen in reciprocal sign epistasis. 

When two mutations are viable alone but lethal in combination, it is called Synthetic lethality or unlinked non-complementation.

Haploid organisms

In a haploid organism with genotypes (at two loci) ab, Ab, aB or AB, we can think of different forms of epistasis as affecting the magnitude of a phenotype upon mutation individually (Ab and aB) or in combination (AB). 

Interaction type ab Ab aB AB
No epistasis (additive)  0 1 1 2 AB = Ab + aB + ab 
Positive (synergistic) epistasis 0 1 1 3 AB > Ab + aB + ab 
Negative (antagonistic) epistasis 0 1 1 1 AB < Ab + aB + ab 
Sign epistasis 0 1 -1 2 AB has opposite sign to Ab or aB
Reciprocal sign epistasis 0 -1 -1 2 AB has opposite sign to Ab and aB

Diploid organisms

Epistasis in diploid organisms is further complicated by the presence of two copies of each gene. Epistasis can occur between loci, but additionally, interactions can occur between the two copies of each locus in heterozygotes. For a two locus, two allele system, there are eight independent types of gene interaction.

Additive A locus Additive B locus Dominance A locus Dominance B locus

aa aA AA

aa aA AA

aa aA AA

aa aA AA
bb 1 0 –1
bb 1 1 1
bb –1 1 –1
bb –1 –1 –1
bB 1 0 –1
bB 0 0 0
bB –1 1 –1
bB 1 1 1
BB 1 0 –1
BB –1 –1 –1
BB –1 1 –1
BB –1 –1 –1



















Additive by Additive Epistasis Additive by Dominance Epistasis Dominance by Additive Epistasis Dominance by Dominance Epistasis

aa aA AA

aa aA AA

aa aA AA

aa aA AA
bb 1 0 –1
bb 1 0 –1
bb 1 –1 1
bb –1 1 –1
bB 0 0 0
bB –1 0 1
bB 0 0 0
bB 1 –1 1
BB –1 0 1
BB 1 0 –1
BB –1 1 –1
BB –1 1 –1

Genetic and molecular causes

Additivity

This can be the case when multiple genes act in parallel to achieve the same effect. For example, when an organism is in need of phosphorus, multiple enzymes that break down different phosphorylated components from the environment may act additively to increase the amount of phosphorus available to the organism. However, there inevitably comes a point where phosphorus is no longer the limiting factor for growth and reproduction and so further improvements in phosphorus metabolism have smaller or no effect (negative epistasis). Some sets of mutations within genes have also been specifically found to be additive. It is now considered that strict additivity is the exception, rather than the rule, since most genes interact with hundreds or thousands of other genes.

Epistasis between genes

Epistasis within the genomes of organisms occurs due to interactions between the genes within the genome. This interaction may be direct if the genes encode proteins that, for example, are separate components of a multi-component protein (such as the ribosome), inhibit each other's activity, or if the protein encoded by one gene modifies the other (such as by phosphorylation). Alternatively the interaction may be indirect, where the genes encode components of a metabolic pathway or network, developmental pathway, signalling pathway or transcription factor network. For example, the gene encoding the enzyme that synthesizes penicillin is of no use to a fungus without the enzymes that synthesize the necessary precursors in the metabolic pathway.

Epistasis within genes

Just as mutations in two separate genes can be non-additive if those genes interact, mutations in two codons within a gene can be non-additive. In genetics this is sometimes called intragenic complementation when one deleterious mutation can be compensated for by a second mutation within that gene. This occurs when the amino acids within a protein interact. Due to the complexity of protein folding and activity, additive mutations are rare.

Proteins are held in their tertiary structure by a distributed, internal network of cooperative interactions (hydrophobic, polar and covalent). Epistatic interactions occur whenever one mutation alters the local environment of another residue (either by directly contacting it, or by inducing changes in the protein structure). For example, in a disulphide bridge, a single cysteine has no effect on protein stability until a second is present at the correct location at which point the two cysteines form a chemical bond which enhances the stability of the protein. This would be observed as positive epistasis where the double-cysteine variant had a much higher stability than either of the single-cysteine variants. Conversely, when deleterious mutations are introduced, proteins often exhibit mutational robustness whereby as stabilizing interactions are destroyed the protein still functions until it reaches some stability threshold at which point further destabilizing mutations have large, detrimental effects as the protein can no longer fold. This leads to negative epistasis whereby mutations that have little effect alone have a large, deleterious effect together.

In enzymes, the protein structure orients a few, key amino acids into precise geometries to form an active site to perform chemistry. Since these active site networks frequently require the cooperation of multiple components, mutating any one of these components massively compromises activity, and so mutating a second component has a relatively minor effect on the already inactivated enzyme. For example, removing any member of the catalytic triad of many enzymes will reduce activity to levels low enough that the organism is no longer viable.

Heterozygotic epistasis

Diploid organisms contain two copies of each gene. If these are different (heterozygous / heteroallelic), the two different copies of the allele may interact with each other to cause epistasis. This is sometimes called allelic complementation, or interallelic complementation. It may be caused by several mechanisms, for example transvection, where an enhancer from one allele acts in trans to activate transcription from the promoter of the second allele. Alternately, trans-splicing of two non-functional RNA molecules may produce a single, functional RNA. Similarly, at the protein level, proteins that function as dimers may form a heterodimer composed of one protein from each alternate gene and may display different properties to the homodimer of one or both variants.

Evolutionary consequences

Fitness landscapes and evolvability

The top row indicates interactions between two genes that are either additive (a), show positive epistasis (b) or reciprocal sign epistasis (c). Below are fitness landscapes which display greater and greater levels of global epistasis between large numbers of genes. Purely additive interactions lead to a single smooth peak (d), as increasing numbers of genes exhibit epistasis, the landscape becomes more rugged (e) and when all genes interact epistatically the landscape becomes so rugged that mutations have seemingly random effects (f).

In evolutionary genetics, the sign of epistasis is usually more significant than the magnitude of epistasis. This is because magnitude epistasis (positive and negative) simply affects how beneficial mutations are together, however sign epistasis affects whether mutation combinations are beneficial or deleterious.

A fitness landscape is a representation of the fitness where all genotypes are arranged in 2D space and the fitness of each genotype is represented by height on a surface. It is frequently used as a visual metaphor for understanding evolution as the process of moving uphill from one genotype to the next, nearby, fitter genotype.

If all mutations are additive, they can be acquired in any order and still give a continuous uphill trajectory. The landscape is perfectly smooth, with only one peak (global maximum) and all sequences can evolve uphill to it by the accumulation of beneficial mutations in any order. Conversely, if mutations interact with one another by epistasis, the fitness landscape becomes rugged as the effect of a mutation depends on the genetic background of other mutations. At its most extreme, interactions are so complex that the fitness is ‘uncorrelated’ with gene sequence and the topology of the landscape is random. This is referred to as a rugged fitness landscape and has profound implications for the evolutionary optimization of organisms. If mutations are deleterious in one combination but beneficial in another, the fittest genotypes can only be accessed by accumulating mutations in one specific order. This makes it more likely that organisms will get stuck at local maxima in the fitness landscape having acquired mutations in the 'wrong' order. For example, a variant of TEM1 β-lactamase with 5 mutations is able to cleave cefotaxime (a third generation antibiotic). However, of the 120 possible pathways to this 5-mutant variant, only 7% are accessible to evolution as the remainder passed through fitness valleys where the combination of mutations reduces activity. In contrast, changes in environment (and therefore the shape of the fitness landscape) have been shown to provide escape from local maxima. In this example, selection in changing antibiotic environments resulted in a "gateway mutation" which epistatically interacted in a positive manner with other mutations along an evolutionary pathway, effectively crossing a fitness valley. This gateway mutation alleviated the negative epistatic interactions of other individually beneficial mutations, allowing them to better function in concert. Complex environments or selections may therefore bypass local maxima found in models assuming simple positive selection.

High epistasis is usually considered a constraining factor on evolution, and improvements in a highly epistatic trait are considered to have lower evolvability. This is because, in any given genetic background, very few mutations will be beneficial, even though many mutations may need to occur to eventually improve the trait. The lack of a smooth landscape makes it harder for evolution to access fitness peaks. In highly rugged landscapes, fitness valleys block access to some genes, and even if ridges exist that allow access, these may be rare or prohibitively long. Moreover, adaptation can move proteins into more precarious or rugged regions of the fitness landscape. These shifting "fitness territories" may act to decelerate evolution and could represent tradeoffs for adaptive traits.

Rugged, epistatic fitness landscapes also affect the trajectories of evolution. When a mutation has a large number of epistatic effects, each accumulated mutation drastically changes the set of available beneficial mutations. Therefore, the evolutionary trajectory followed depends highly on which early mutations were accepted. Thus, repeats of evolution from the same starting point tend to diverge to different local maxima rather than converge on a single global maximum as they would in a smooth, additive landscape.

Evolution of sex

Negative epistasis and sex are thought to be intimately correlated. Experimentally, this idea has been tested in using digital simulations of asexual and sexual populations. Over time, sexual populations move towards more negative epistasis, or the lowering of fitness by two interacting alleles. It is thought that negative epistasis allows individuals carrying the interacting deleterious mutations to be removed from the populations efficiently. This removes those alleles from the population, resulting in an overall more fit population. This hypothesis was proposed by Alexey Kondrashov, and is sometimes known as the deterministic mutation hypothesis and has also been tested using artificial gene networks.

However, the evidence for this hypothesis has not always been straightforward and the model proposed by Kondrashov has been criticized for assuming mutation parameters far from real world observations. In addition, in those tests which used artificial gene networks, negative epistasis is only found in more densely connected networks, whereas empirical evidence indicates that natural gene networks are sparsely connected, and theory shows that selection for robustness will favor more sparsely connected and minimally complex networks.

Methods and model systems

Regression analysis

Quantitative genetics focuses on genetic variance due to genetic interactions. Any two locus interactions at a particular gene frequency can be decomposed into eight independent genetic effects using a weighted regression. In this regression, the observed two locus genetic effects are treated as dependent variables and the "pure" genetic effects are used as the independent variables. Because the regression is weighted, the partitioning among the variance components will change as a function of gene frequency. By analogy it is possible to expand this system to three or more loci, or to cytonuclear interactions.

Double mutant cycles

When assaying epistasis within a gene, site-directed mutagenesis can be used to generate the different genes, and their protein products can be assayed (e.g. for stability or catalytic activity). This is sometimes called a double mutant cycle and involves producing and assaying the wild type protein, the two single mutants and the double mutant. Epistasis is measured as the difference between the effects of the mutations together versus the sum of their individual effects. This can be expressed as a free energy of interaction. The same methodology can be used to investigate the interactions between larger sets of mutations but all combinations have to be produced and assayed. For example, there are 120 different combinations of 5 mutations, some or all of which may show epistasis...

Statistical coupling analysis

Computational prediction

Numerous computational methods have been developed for the detection and characterization of epistasis. Many of these rely on machine learning to detect non-additive effects that might be missed by statistical approaches such as linear regression. For example, multifactor dimensionality reduction (MDR) was designed specifically for nonparametric and model-free detection of combinations of genetic variants that are predictive of a phenotype such as disease status in human populations. Some of these approaches have been recently reviewed.

Inequality (mathematics)

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