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14.12: Pleiotropy and Human Disorders - Biology

14.12: Pleiotropy and Human Disorders - Biology


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Based on Mendel’s experiments, you might imagine that all genes control a single characteristic, are present in two copies, and affect some harmless aspect of an organism’s appearance (such as color, height, or shape). For instance, how can we explain observations like the following?

  • The genetic disorder Marfan syndrome is caused by a mutation in one gene, yet it affects many aspects of growth and development, including height, vision, and heart function.

To understand observations like these, we need to look more deeply at what genes are. Rather than abstract “heritable factors,” genes are stretches of DNA found on chromosomes, and most of them encode (specify the sequence of) proteins that do a certain job in the cell or body. In this article, we’ll look in more detail at genes affecting multiple characteristics (pleiotropy).

Pleiotropy

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)[1]. In plants with white flowers, the seed coats and axils were colorless, while in plants with purple flowers, the seed coats were brown-gray and the axils were reddish. Thus, rather than affecting just one characteristic, the flower color gene actually affected three.

Based on similar diagram by Ingrid Lobo

Genes like this, which affect multiple, seemingly unrelated aspects of an organism’s phenotype, are said to be pleiotropic (pleio– = many, –tropic = effects)[2]. We now know that Mendel’s flower color gene encodes a regulator protein that activates pigment biosynthesis, and that it works in several different parts of the pea plant (flowers, seed coat, and leaf axils). Thus, the seemingly unrelated phenotypes can all be traced back to a defect in a single gene with several jobs.

Alleles of pleiotropic genes are transmitted in the same way as alleles of genes that affect single traits. In these cases, the difference is that the phenotype contains multiple elements. These elements are specified as a package, and there would be both a dominant and recessive version of this package of traits.

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[3]:

  • 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. This gene encodes a protein that assembles into chains, making elastic fibrils that give strength and flexibility to the body’s connective tissues[4]. Disease-causing mutations in the Marfan syndrome reduce the amount of functional protein produced, resulting in fewer fibrils. The eye and the aorta normally contain many fibrils that help maintain structure, explaining why these two organs are strongly affected in Marfan syndrome[5]. In addition, the fibrils serve as “storage shelves” for growth factors. When there are fewer of them in Marfan syndrome, the growth factors cannot be shelved and thus cause excess growth (leading to the characteristic tall, thin Marfan build)[6].



Pleiotropy in eye disease and related traits

Abstract

Pleiotropy plays an important role in furthering our understanding of human biology and disease. Results from genome-wide association studies (GWASs) indicate that pleiotropy is widespread in the human genome. The characterization of these pleiotropic mechanisms not only helps explain the shared genetic architecture among different diseases and traits, but also contributes to novel insights in ophthalmic genetics. In this chapter, we focus on GWAS results to illustrate the interesting phenomenon of pleiotropy, provide examples in eye-related diseases and traits, and explain their implications for genomic medicine. A greater understanding of pleiotropy inevitably contributes to advances in precision medicine.


What is Pleiotropy?

By definition, pleiotropy is a situation in which one gene controls for the expression of multiple phenotypic traits. These traits don&rsquot have to be clearly linked, i.e., eye shape and eye color, but can instead be completely unrelated. In many cases, this multi-trait effect is because a gene codes for a certain product, whereas that protein/product serves multiple purposes in the body, catalyzes numerous reactions or interacts with various signal receptors. In this way, a single gene is able to have a measurable impact on different systems.

Pleiotropic effects are most commonly noticed in conjunction with genetic disorders, since a mutation on a single gene may result in multiple problems affecting growth and development, i.e., height, weight, skeletal development. If there hadn&rsquot been a mutation on that gene, the pleiotropic impact may have gone unnoticed. Some of the genetic disorders linked to pleiotropy include sickle cell anemia, Marfan Syndrome and Phenylketonuria, among others.

There are various types of pleiotropy, including developmental pleiotropy, gene pleiotropy, selectional pleiotropy and antagonistic pleiotropy, all of which we will discuss in more detail below.


Pleiotropy Examples

An example of pleiotropy that occurs in humans is sickle cell disease. Sickle cell disorder results from the development of abnormally shaped red blood cells. Normal red blood cells have a biconcave, disc-like shape and contain enormous amounts of a protein called hemoglobin.

Hemoglobin helps red blood cells bind to and transport oxygen to cells and tissues of the body. Sickle cell is a result of a mutation in the beta-globin gene. This mutation results in red blood cells that are sickle-shaped, which causes them to clump together and become stuck in blood vessels, blocking normal blood flow. The single mutation of the beta-globin gene results in various health complications and causes damage to multiple organs including the heart, brain, and lungs.

Phenylketonuria, or PKU, is another disease resulting from pleiotropy. PKU is caused by a mutation of the gene responsible for the production of an enzyme called phenylalanine hydroxylase. This enzyme breaks down the amino acid phenylalanine that we get from protein digestion. Without this enzyme, levels of the amino acid phenylalanine increase in the blood and damage the nervous system in infants. PKU disorder may result in several conditions in infants including intellectual disabilities, seizures, heart problems, and developmental delays.

Frizzled Feather Trait

The frizzled feather trait is an example of pleiotropy seen in chickens. Chickens with this particular mutated feather gene display feathers that curl outward as opposed to lying flat. In addition to curled feathers, other pleiotropic effects include a faster metabolism and enlarged organs. The curling of the feathers leads to a loss of body heat requiring a faster basal metabolism to maintain homeostasis. Other biological changes include higher food consumption, infertility, and sexual maturation delays.


Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index

Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D). We used a population of 10,487 individuals of European ancestry with genome-wide genotyping from the Electronic Medical Records and Genomics (eMERGE) Network and another population of 13,711 individuals of European ancestry from the BioVU DNA biobank at Vanderbilt genotyped using Illumina HumanExome BeadChip. A meta-analysis of the two study populations replicated the well-described associations between FTO variants and obesity (odds ratio [OR] = 1.25, 95% Confidence Interval = 1.11-1.24, p = 2.10 × 10(-9)) and FTO variants and T2D (OR = 1.14, 95% CI = 1.08-1.21, p = 2.34 × 10(-6)). The meta-analysis also demonstrated that FTO variant rs8050136 was significantly associated with sleep apnea (OR = 1.14, 95% CI = 1.07-1.22, p = 3.33 × 10(-5)) however, the association was attenuated after adjustment for body mass index (BMI). Novel phenotype associations with obesity-associated FTO variants included fibrocystic breast disease (rs9941349, OR = 0.81, 95% CI = 0.74-0.91, p = 5.41 × 10(-5)) and trends toward associations with non-alcoholic liver disease and gram-positive bacterial infections. FTO variants not associated with obesity demonstrated other potential disease associations including non-inflammatory disorders of the cervix and chronic periodontitis. These results suggest that genetic variants in FTO may have pleiotropic associations, some of which are not mediated by obesity.

Keywords: BMI Exome chip FTO PheWAS genetic association pleiotropy.


Unraveling the complex genetic model for cystic fibrosis: pleiotropic effects of modifier genes on early cystic fibrosis-related morbidities

The existence of pleiotropy in disorders with multi-organ involvement can suggest therapeutic targets that could ameliorate overall disease severity. Here we assessed pleiotropy of modifier genes in cystic fibrosis (CF). CF, caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, affects the lungs, liver, pancreas and intestines. However, modifier genes contribute to variable disease severity across affected organs, even in individuals with the same CFTR genotype. We sought to determine whether SLC26A9, SLC9A3 and SLC6A14, that contribute to meconium ileus in CF, are pleiotropic for other early-affecting CF co-morbidities. In the Canadian CF population, we assessed evidence for pleiotropic effects on (1) pediatric lung disease severity (n = 815), (2) age at first acquisition of Pseudomonas aeruginosa (P. aeruginosa) (n = 730), and (3) prenatal pancreatic damage measured by immunoreactive trypsinogen (n = 126). A multiple-phenotype analytic strategy assessed evidence for pleiotropy in the presence of phenotypic correlation. We required the same alleles to be associated with detrimental effects. SLC26A9 was pleiotropic for meconium ileus and pancreatic damage (p = 0.002 at rs7512462), SLC9A3 for meconium ileus and lung disease (p = 1.5 × 10(-6) at rs17563161), and SLC6A14 for meconium ileus and both lung disease and age at first P. aeruginosa infection (p = 0.0002 and p = 0.006 at rs3788766, respectively). The meconium ileus risk alleles in SLC26A9, SLC9A3 and SLC6A14 are pleiotropic, increasing risk for other early CF co-morbidities. Furthermore, co-morbidities affecting the same organ tended to associate with the same genes. The existence of pleiotropy within this single disorder suggests that complementary therapeutic strategies to augment solute transport will benefit multiple CF-associated tissues.


Contents

Pleiotropic traits had been previously recognized in the scientific community but had not been experimented on until Gregor Mendel's 1866 pea plant experiment. Mendel recognized that certain pea plant traits (seed coat color, flower color, and axial spots) seemed to be inherited together however, their correlation to a single gene has never been proven. The term "pleiotropie" was first coined by Ludwig Plate in his Festschrift, which was published in 1910. [3] He originally defined pleiotropy as occurring when "several characteristics are dependent upon . [inheritance] these characteristics will then always appear together and may thus appear correlated". [4] This definition is still used today.

After Plate's definition, Hans Gruneberg was the first to study the mechanisms of pleiotropy. [3] In 1938 Gruneberg published an article dividing pleiotropy into two distinct types: "genuine" and "spurious" pleiotropy. "Genuine" pleiotropy is when two distinct primary products arise from one locus. "Spurious" pleiotropy, on the other hand, is either when one primary product is utilized in different ways or when one primary product initiates a cascade of events with different phenotypic consequences. Gruneberg came to these distinctions after experimenting on rats with skeletal mutations. He recognized that "spurious" pleiotropy was present in the mutation, while "genuine" pleiotropy was not, thus partially invalidating his own original theory. [5] Through subsequent research, it has been established that Gruneberg's definition of "spurious" pleiotropy is what we now identify simply as "pleiotropy". [3]

In 1941 American geneticists George Beadle and Edward Tatum further invalidated Gruneberg's definition of "genuine" pleiotropy, advocating instead for the "one gene-one enzyme" hypothesis that was originally introduced by French biologist Lucien Cuénot in 1903. [3] [6] This hypothesis shifted future research regarding pleiotropy towards how a single gene can produce various phenotypes.

In the mid-1950s Richard Goldschmidt and Ernst Hadorn, through separate individual research, reinforced the faultiness of "genuine" pleiotropy. A few years later, Hadorn partitioned pleiotropy into a "mosaic" model (which states that one locus directly affects two phenotypic traits) and a "relational" model (which is analogous to "spurious" pleiotropy). These terms are no longer in use but have contributed to the current understanding of pleiotropy. [3]

By accepting the one gene-one enzyme hypothesis, scientists instead focused on how uncoupled phenotypic traits can be affected by genetic recombination and mutations, applying it to populations and evolution. [3] This view of pleiotropy, "universal pleiotropy", defined as locus mutations being capable of affecting essentially all traits, was first implied by Ronald Fisher's Geometric Model in 1930. This mathematical model illustrates how evolutionary fitness depends on the independence of phenotypic variation from random changes (that is, mutations). It theorizes that an increasing phenotypic independence corresponds to a decrease in the likelihood that a given mutation will result in an increase in fitness. [7] Expanding on Fisher's work, Sewall Wright provided more evidence in his 1968 book Evolution and the Genetics of Populations: Genetic and Biometric Foundations by using molecular genetics to support the idea of "universal pleiotropy". The concepts of these various studies on evolution have seeded numerous other research projects relating to individual fitness. [1]

In 1957 evolutionary biologist George C. Williams theorized that antagonistic effects will be exhibited during an organism's life cycle if it is closely linked and pleiotropic. Natural selection favors genes that are more beneficial prior to reproduction than after (leading to an increase in reproductive success). Knowing this, Williams argued that if only close linkage was present, then beneficial traits will occur both before and after reproduction due to natural selection. This, however, is not observed in nature, and thus antagonistic pleiotropy contributes to the slow deterioration with age (senescence). [8]

Pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. The underlying mechanism is genes that code for a product that is either used by various cells or has a cascade-like signaling function that affects various targets.

One basic model of pleiotropy's origin describes a single gene locus to the expression of a certain trait. The locus affects the expressed trait only through changing the expression of other loci. Over time, that locus would affect two traits by interacting with a second locus. Directional selection for both traits during the same time period would increase the positive correlation between the traits, while selection on only one trait would decrease the positive correlation between the two traits. Eventually, traits that underwent directional selection simultaneously were linked by a single gene, resulting in pleiotropy.

Other more complex models compensate for some of the basic model's oversights, such as multiple traits or assumptions about how the loci affect the traits. They also propose the idea that pleiotropy increases the phenotypic variation of both traits since a single mutation on a gene would have twice the effect. [9]

Pleiotropy can have an effect on the evolutionary rate of genes and allele frequencies. Traditionally, models of pleiotropy have predicted that evolutionary rate of genes is related negatively with pleiotropy – as the number of traits of an organism increases, the evolutionary rates of genes in the organism's population decrease. [10] However, this relationship has not been clearly found in empirical studies. [11] [12]

In mating, for many animals the signals and receptors of sexual communication may have evolved simultaneously as the expression of a single gene, instead of the result of selection on two independent genes, one that affects the signaling trait and one that affects the receptor trait. [13] In such a case, pleiotropy would facilitate mating and survival. However, pleiotropy can act negatively as well. A study on seed beetles found that intralocus sexual conflict arises when selection for certain alleles of a gene that are beneficial for one sex causes expression of potentially harmful traits by the same gene in the other sex, especially if the gene is located on an autosomal chromosome. [14]

Pleiotropic genes act as an arbitrating force in speciation. William R. Rice and Ellen E. Hostert (1993) conclude that the observed prezygotic isolation in their studies is a product of pleiotropy's balancing role in indirect selection. By imitating the traits of all-infertile hybridized species, they noticed that the fertilization of eggs was prevented in all eight of their separate studies, a likely effect of pleiotropic genes on speciation. [15] Likewise, pleiotropic gene's stabilizing selection allows for the allele frequency to be altered. [16]

Studies on fungal evolutionary genomics have shown pleiotropic traits that simultaneously affect adaptation and reproductive isolation, converting adaptations directly to speciation. A particularly telling case of this effect is host specificity in pathogenic ascomycetes and specifically, in venturia, the fungus responsible for apple scab. These parasitic fungi each adapts to a host, and are only able to mate within a shared host after obtaining resources. [17] Since a single toxin gene or virulence allele can grant the ability to colonize the host, adaptation and reproductive isolation are instantly facilitated, and in turn, pleiotropically causes adaptive speciation. The studies on fungal evolutionary genomics will further elucidate the earliest stages of divergence as a result of gene flow, and provide insight into pleiotropically induced adaptive divergence in other eukaryotes. [17]

Antagonistic pleiotropy Edit

Sometimes, a pleiotropic gene may be both harmful and beneficial to an organism, which is referred to as antagonistic pleiotropy. This may occur when the trait is beneficial for the organism's early life, but not its late life. Such "trade-offs" are possible since natural selection affects traits expressed earlier in life, when most organisms are most fertile, more than traits expressed later in life. [18]

This idea is central to the antagonistic pleiotropy hypothesis, which was first developed by G. C. Williams in 1957. Williams suggested that some genes responsible for increased fitness in the younger, fertile organism contribute to decreased fitness later in life, which may give an evolutionary explanation for senescence. An example is the p53 gene, which suppresses cancer but also suppresses stem cells, which replenish worn-out tissue. [13]

Unfortunately, the process of antagonistic pleiotropy may result in an altered evolutionary path with delayed adaptation, in addition to effectively cutting the overall benefit of any alleles by roughly half. However, antagonistic pleiotropy also lends greater evolutionary "staying power" to genes controlling beneficial traits, since an organism with a mutation to those genes would have a decreased chance of successfully reproducing, as multiple traits would be affected, potentially for the worse. [19]

Sickle cell anemia is a classic example of the mixed benefit given by the staying power of pleiotropic genes, as the mutation to Hb-S provides the fitness benefit of malaria resistance to heterozygotes, while homozygotes have significantly lowered life expectancy. Since both of these states are linked to the same mutated gene, large populations today are susceptible to sickle cell despite it being a fitness-impairing genetic disorder. [20]


What is Polygenic Inheritance

In polygenic inheritance, a particular trait is determined by more than one gene. Thus, the effect of one gene on the trait is small. Here, the contributing genes exhibit incomplete dominance. Thus, the trait in the offspring is a mixture of parental traits. The external environmental factors also have an effect on polygenic inheritance. Most of the metric and meristic traits are under the influence of polygenic inheritance. The polygenic traits exhibit a continuous distribution in a population. Thus, the distribution curve of the polygenic inheritance is bell-shaped. A great variability of genotypes can be observed within a population in polygenic traits. The organisms at the middle of the distribution curve consist of a combination of both dominant and recessive alleles. The individuals with many of the dominant alleles or recessive alleles may appear at the end of the curve. The distribution curve of the polygenic inheritance of height in humans is shown in figure 3.

Figure 3: Polygenic inheritance of height

The color of the human eye is controlled by 16 different genes. The eye color is determined by the amount of melanin produced in front of the iris. The color can be either black, brown, green, hazel or blue. The skin color of humans is another example of polygenic inheritance. The color of the skin is determined by the amount of melanin produced in the skin. When the number of dark alleles present in the skin is high, the color of the skin becomes darker.


Genetic trade-offs

Decades earlier, the evolutionary theorist George C. Williams explored perhaps the most perplexing aspect of human biology: our inconvenient tendency to age and die. He suggested in 1957 that some of the genes that cause ageing evolved because they enhanced fitness early in life (G. C. Williams Evolution 11, 398–411 1957). Such ‘antagonistic pleiotropy’ — in which a single gene controls at least one beneficial and one detrimental trait — suggests that the design of biological structures is a complex optimization problem involving multiple trade-offs. Emotions and other aspects of mental function are not like machine components, each with a set function instead, they are embedded in complex overlapping biochemical pathways.

In 1994, Nesse teamed up with Williams for Why We Get Sick, a manifesto for “Darwinian medicine”. Their insights opened up new perspectives on the origins of diseases, arguing for ‘proximate’ causes (driven by anatomy, biochemistry and physiology) and higher-level ‘ultimate’ (evolutionary) causes. They noted that evolution selects for reproductive success rather than for health and happiness hence, the existence of human diseases and disorders. They also detailed the contingent and sometimes ‘irrational’ nature of biological legacies, such as the nerves and blood vessels that run across the human eye’s retinal surface. Cephalopod eyes don’t have this ‘flaw’.

Good Reasons for Bad Feelings builds on these insights. Adopting an “engineers’ point of view” on mental illnesses, Nesse suggests that anxiety, although apparently undesirable, is a design component with utility in certain situations — for instance, as a “smoke detector” for potentially life-threatening events. Depression might also perform adaptive functions. The psychiatrist Aubrey Lewis argued that by signalling distress, depression could prompt others into providing assistance through foraging and other activities. It has even been suggested that depressive behaviour in vervet monkeys (Chlorocebus pygerythrus) evolved to signal loss of status, deflecting attacks from dominant males.

Yet, however functional its components when appropriately regulated, mental illnesses cause suffering, and evidence-based treatments are sparse. Indeed, the field has seen no significant pharmaceutical breakthroughs for many years. Biological causes remain elusive, and biomarkers non-existent.

Psychiatry as a field, meanwhile, quivers with theoretical uncertainty. It has not become a sub-speciality of neurology, as one might have expected if mental illness mapped directly to neural behaviour. And common genetic variations with large effects on mental disorders are elusive. The various incarnations of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM) have enabled diagnostic consistency and the objectification of mental illnesses. But the DSM has resulted in overlapping diagnoses, and contrived symptom-cluster checklists. At times, it impinges on the territory of healthy mental function. Allen Frances, chair of the task force that wrote the manual’s fourth edition in 1994, revolted against out-of-control mental diagnosis in his 2013 book DSM: Saving Normal.


Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree

Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual's risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10 -4 ). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10 -4 ), diabetes (p = 1 × 10 -3 ), and lipid traits such as triglyceride levels (p = 3 × 10 -4 ) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1. Profile of co-occurring Mendelian diseases…

Fig. 1. Profile of co-occurring Mendelian diseases varies between affected and unaffected individuals.

Fig. 2. Association of polygenic risk scores…

Fig. 2. Association of polygenic risk scores for multiple traits with bipolar disorder (broad extended…

Fig. 3. Percentage of individuals with affected…

Fig. 3. Percentage of individuals with affected status for each decile of polygenic risk score.

Fig. 4. Locations of genomic regions that…

Fig. 4. Locations of genomic regions that contribute to the genome-wide risk score differences between…


Watch the video: Chapter 14 Part 5 - Human Autosomal Disorders (January 2023).