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Hardy-Weinberg for triploids

Hardy-Weinberg for triploids


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Problem:

A certain species has somatic cells with ploidy 3n (the organism inherits three sets of homologous chromosomes from each of three parents). At a certain locus, there are three possible alleles A 1 , A 2 , A 3 , which are completely dominant in the order A 1 > A 2 > A 3 . The proportion of organisms exhibiting traits A 1 , A 2 , and A 3 are respectively 0.614, 0.306, and 0.08. In addition, the proportion of organisms that are completely heterozygous (genotype A 1 A 2 A 3 ) is 0.18. Which of the following are allele frequencies of A 1 , A 2 , and A 3 , respectively?

Answer Choices:

A. f(A 1 ) = 0.5, f(A 2 ) = 0.3, f(A 3 ) = 0.2

B. f(A 1 ) = 0.3, f(A 2 ) = 0.5, f(A 3 ) = 0.2

C. f(A 1 ) = 0.3, f(A 2 ) = 0.4, f(A 3 ) = 0.3

D. f(A 1 ) = 0.6, f(A 2 ) = 0.2, f(A 3 ) = 0.2

E. f(A 1 ) = 0.7, f(A 2 ) = 0.2, f(A 3 ) = 0.1

Correct Answer:

A

Solution:

No clue. I'd guess that the solution follows a hardy-weinberg model for polyploids. For simplicity, I will refer to A1, A2, and A3 as x, y, and z, respectively.

(x + y + z)^3 = x^3 + 3x^2y + 3xy^2 + y^3 + 3x^2z + 6xyz + 3y^2z + 3xz^2 + 3yz^2 + z^3

Given that z^3 = 0.08, shouldn't z (A3) equal the cube root of 0.08 = 0.43?

That doesn't match any of the answer choices, so I will assume that my steps were incorrect.

Any suggestions?


First, although it only speaks about cases of diploidy, you might want to have a look at Solving Hardy Weinberg problems.

Information given to us

The question gives the same names to the phenotypes than to the alleles which si rather confusing. Let's call the three possible phenotype $P_1$, $P_2$ and $P_3$. We know that the frequencies of these phenotypes are

$$f(P_1) = 0.614$$ $$f(P_2) = 0.306$$ $$f(P_3) = 0.08$$

Compute phenotype frequencies in terms of allele frequencies

Let, $x$, $y$ and $z$ be the frequencies of the three alleles, $A_1$, $A_2$ and $A_3$ (I chose those names to fit with the equations you wrote). The whole difficulty now is to express $f(P_1)$, $f(P_2)$ and $f(P_3)$ in terms of $x$, $y$ and $z$. You should try yourself before looking at the result. Start with $f(P_3)$, it is the easiest.

Here is the solution for $f(P_3)$

$f(P_3) = z^3$

Here is the solution for $f(P_2)$

$f(P_2) = y^3 + yz^2$

Here is the solution for $f(P_1)$

$f(P_1) = x^3 + 3x^2 (y+z) + xyz + xy^2 + xz^2$

Once, you have done that, you have three equations with three unknowns and you just have to solve them. Make sure at the end that $x+y+z = f(P_1) + f(P_2) + f(P_3) = 1$.


6| Rules of Inheritance

Although a background in genetics is certainly not essential for appreciation of many populational and ecological phenomena, it is a useful aid for application to some such phenomena and is required for a full understanding of others. Precise rules of inheritance were actually unknown when Darwin (1859) developed the theory of natural selection, but they were formulated a short time afterward (Mendel 1865). Darwin accepted the hypothesis of inheritance in vogue at the time: blending inheritance. Under the blending inheritance hypothesis, genetic makeups of both parents are imagined to be blended in their progeny, and all offspring produced by sexual reproduction should be genetically intermediate between their parents genetic variability is thus lost rapidly unless new variation is continually being produced. (Under blending inheritance and random mating, genetic variability is halved each generation.) Darwin was forced to postulate extremely high mutation rates to maintain the genetic variability observed in most organisms, and he was painfully aware of the inadequacy of knowledge on inheritance. Mendel's discovery of particulate inheritance represents one of the major empirical breakthroughs in biology.

Mendel performed breeding experiments with different varieties of peas, paying close attention to a single trait at a time. He had two types that bred "true" for yellow and green peas, respectively. When a purebred green pea plant was crossed with a purebred yellow pea plant, all progeny, or individuals of the first filial generation (F1), had yellow peas. However, when these F1 plants were crossed with each other or self-fertilized, about one out of every four offspring in the second filial generation (F2) had green peas. Furthermore, only about one-third of the yellow F2 pea plants bred true the other two-thirds, when self-fertilized, produced some offspring with green peas. All green pea plants bred true. Mendel proposed a very simple quantitative hypothesis to explain his results and performed many other breeding experiments on a variety of other traits that corroborated and confirmed his interpretations. Subsequent work has strengthened Mendel's hypothesis, although it has also led to certain modifications and improvements.

Modern terminology for various aspects of Mendelian inheritance is as follows: (1) the “character” or “dose” controlling a particular trait is termed an allele (2) its position on a chromosome (defined later) is termed its locus (3) a single dose is the haploid condition, designated by n, whereas the double-dosed condition, designated by 2n, is diploid (polyploids, such as triploids and tetraploids, are designated with still higher numbers) (4) the set of alternative alleles that may occur at a given locus (there can be only two alleles in an individual, but there may be more than two in any given population) is termed a gene (5) purebred diploid individuals with identical alleles are homozygotes, homozygous for the trait concerned (6) individuals with two different alleles, such as the preceding F2 plants, are heterozygotes, heterozygous at that locus (7) an allele that masks the expression of another allele is said to be dominant, whereas the one that is masked is recessive (8) unlinked alleles separate, or segregate, from each other in the formation of gametes (9) whenever heterozygotes or two individuals that are homozygous for different alleles mate, new combinations of alleles arise in the following generation by reassortment of the genetic material (10) observable traits of an individual (e.g., yellow or green in the previous example) are aspects of its phenotype, which includes all observable characteristics of an organism and (11) whether or not an organism breeds true is determined by its genotype, which is the sum total of all its genes.

Occasionally, some organisms have pairs of alleles with incomplete dominance. In such cases, the phenotype of the heterozygote is intermediate between that of the two homozygotes that is, phenotype accurately reflects genotype and vice versa. Presumably, alleles conferring advantages upon their bearers usually evolve dominance over time because such dominance ensures that a maximal number of the organism's progeny and descendants will benefit from possession of that allele. The apparent rarity of incomplete dominance is further evidence that dominance has evolved. So-called wild-type alleles, that is, those most prevalent in natural wild populations, are nearly always dominant over other alleles occurring at the same locus. Geneticists have developed numerous theories of the evolution of dominance, but the exact details of the process have not yet been completely resolved.

Mendel postulated that each pea plant had a double dose of the "character" controlling pea color but that only a single dose was transmitted into each of its sexual cells, or gametes (pollen and ovules or sperm and eggs). Purebred plants, with identical doses, produced genetically identical single-dosed gametes the above-mentioned F1 plants, on the other hand, with two different doses, produced equal numbers of the two kinds of gametes, half bearing the character for green and half that for yellow. In addition, Mendel proposed that yellow masked green whenever the two occurred together in double dose hence all F1 plants had yellow peas, but when self-fertilized, produced some F2 progeny with green peas. All green pea plants, which had a double dose of green, always bred true.

Cytological observations of appropriately prepared cell nuclei confirm Mendel's hypothesis beautifully (Figure 6.1). Microscopic examination of such cells reveals elongated dense bodies in cell nuclei these are chromosomes, which contain actual genetic material, DNA. Nuclei of diploid cells, including the zygote (the fertilized ovule or egg) and the somatic (body) cells of most organisms, always contain an even number of chromosomes. (The exact number varies widely from species to species, with as few as two in certain arthropods to hundreds in some plants.) Pairs of distinctly similar homologous chromosomes are always present and often easily detected visually. However, gametes contain only half the number of chromosomes found in diploid cells, and, except in polyploids, none of them are homologous. Thus, haploid cells contain only one full set of different chromosomes and alleles, or one genome, whereas all diploid cells contain two. During the reduction division ( meiosis) in which diploid gonadal cells give rise to haploid gametes, homologous chromosomes separate (Figure 6.1). Later, when male and female gametes fuse to form a diploid zygote that will develop into a new diploid organism, homologous chromosomes come together again. Hence, one genome in every diploid organism is of paternal origin while the other is of maternal ancestry. Because each member of a pair of homologous chromosomes separates from its homologue independently of other chromosome pairs, the previous generation's chromosomes are reassorted with each reduction division. Thus, the genetic material is regularly rearranged and mixed up by the dual processes of meiosis and the actual fusion of gametes (this has been termed the " Mendelian lottery").

  • Figure 6.1. Diagrammatic representation of the cytological events in cell nuclei showing how the two parental genomes are sorted and recombined in the next generation, or the F2. For simplicity, only one pair of chromosomes is shown and the complex events of the reduction division ( meiosis) are omitted.

Numerous different loci and allelic systems occur on each chromosome. Two different traits controlled by different alleles located on the same chromosome do not segregate truly independently but are statistically associated with or dissociated from one another. This is the phenomenon of linkage. During meiosis, homologous chromosomes can effectively exchange portions by means of crossovers this process is referred to as recombination. Because the frequency of occurrence of crossovers between two loci increases with the distance between them on the chromosome, geneticists use crossover frequencies to map the effective distance between loci, as well as their positions relative to one another on chromosomes. By means of close linkage, whole blocks of statistically associated alleles can be passed on to progeny as a functionally integrated unit of coadapted alleles.

Certain kinds of chromosomal rearrangements, such as inversions, may suppress crossovers. Indeed, a major advantage of chromosomes is that they enhance the degree to which clusters of genes can occur together. In many organisms a single pair of chromosomes, termed sex chromosomes, determine the sex of their bearer (the remaining chromosomes, which are not involved in sex determination, are autosomes). Typically one homologue of the sex chromosome pair is smaller. In the diploid state, an individual heterozygous for the sex chromosomes is heterogametic. In mammals, males are the heterogametic sex with an XY pair of sex chromosomes, whereas females are the homogametic sex with an XX pair. Because male-male matings are impossible, the homozygous genotype YY can never occur. In birds and some other organisms, the female is the heterogametic sex. In many reptiles, sex chromosomes do not exist and sex is determined by the environmental temperature at which eggs are incubated.

Although natural selection actually operates on phenotypes of individuals
(i.e., an organism's immediate fitness is determined by its total phenotype), the effectiveness of selection in changing the composition of a population depends on the heritability of phenotypic characteristics or the percentage of phenotypic variability attributable to genotype.

Traits that are under strong selection usually display low heritability because the genetic component of phenotypic variability has been reduced by selection. Because nongenetic traits are not inherited, differential reproduction by different phenotypes stemming from such nontransmittable traits obviously cannot alter a population's gene pool. Different genotypes may often have fairly similar phenotypes and thus similar fitnesses. Selection may even favor alleles that are "good mixers" and work well with a wide variety of other genes to increase their bearer's fitness in various genetic backgrounds (Mayr 1959). Conversely, of course, identical genotypes can develop into rather different phenotypes under different environmental conditions.

Genes that act to control the expression of other genes at different loci are called modifier or regulatory genes, whereas those that code for specific cell products are termed structural genes (some genes do not fit this dichotomy but may serve in both capacities). Although relatively little is known, geneticists imagine that an intricate hierarchy must exist leading from regulator genes to structural genes to proteins to other non-proteinaceous metabolic products to specific phenotypes. Moreover, complex interactions must occur among regulators as they do among proteins and other metabolites such as neurotransmitters and hormones.

Humans and the great apes share many genes, including the familiar ABO blood groups. A detailed molecular comparison of human proteins with those of chimpanzees by three different techniques (sequencing, immunological distance, and electrophoresis) revealed nearly identical amino acid sequences in the vast majority of proteins (99 percent similarity, with concordant results at 44 loci), presumably the products of structural genes (King and Wilson 1975). Apparently, a relatively few genetic changes in major regulatory genes can have profound phenotypic effects without much difference at the level of proteins. Thus, relatively trivial genetic differences can lead to major phenotypic differences. The genetic similarity between humans and chimps is comparable to that of sibling species in insects and other mammals. Recent DNA hybridization studies have demonstrated 98.4 percent similarity between humans and chimpanzees (Sibley et al. 1990). Whereas humans are placed in the family Hominidae, chimps are placed with the great apes in the family Pongidae. However, phylogenies based on molecular similarities show that humans are embedded within the apes (Figure 6.2). Clearly, it is time to consider reclassification!

  • Figure 6.2. Phylogeny of primates based on DNA hybridization. [Adapted from Diamond (1991) after Sibley and Ahlquist.]

A widespread misconception is that any phenotypic trait can always be assigned to either one of two mutually exclusive categories: genetic or environmental. However, this dichotomy is not only oversimplified but can be rather misleading. Because natural selection acts only on heritable phenotypic traits, even environmentally flexible traits must usually have an underlying genetic basis. For example, when grown on dry plant foods, the Texas grasshoppers Syrbula and Chortophaga become brown, but when fed on moist grasses, these same insects develop green phenotypes -- this classic "environmentally induced" polymorphism is presumably highly adaptive since it produces background color-matching cryptic green grasshoppers when environments are green but brown ones in brown environments (Otte and Williams 1972). The capacity for developmental plasticity itself has almost surely evolved in response to the unpredictable environment these grasshoppers must face. If enough were known, much environmentally determined phenotypic variation would presumably have a somewhat comparable basis in natural selection. Thus, truly nongenetic traits are unimportant and uninteresting simply because they cannot evolve and do not affect fitness. Indeed, for purposes of evolutionary ecology, virtually all traits can be considered as being subject to natural selection (those that are not cannot easily persist and have little or no evolutionary significance). The complete set of different phenotypes that can be produced by a given genotype across a range of environments is called its reaction norm.

Certain alleles do not obey the Mendelian lottery of meiosis and recombination: instead these "outlaw genes" obtain disproportionate representation in a carrier's gametes at the expense of alternate alleles on homologous chromosomes. An example of such a selfish gene is the " segregation distortion" allele in the fruit fly Drosophila. Males heterozygous for this sex-linked trait produce sperm of which some 95 percent carry the allele (rather than the expected 50 percent). This process is known as meiotic drive. Why don't more genes behave in this manner? Very probably the intense contest for representation in the gametes has itself ended in a stalemate, yielding the traditional Mendelian ratios (viewed in this way, segregation itself is clearly a product of natural selection).

Advocates of the "selfish gene" hypothesis (Dawkins 1982, 1989) argue somewhat as follows. Barring mutations, genes are perfect replicators, always making exact copies of themselves. However, phenotypes of individual organisms are transmitted to their offspring only imperfectly, at least in sexually reproducing species. Individuals can thus be viewed as mere "vehicles" for the genes that they carry.

Except for viruses, genes usually do not exist in isolation but must occur together in large clusters. A sort of "packaging problem" arises, with the number of copies left by any given allele depending upon the particular combination of other genes, or " genetic background," in which the allele concerned actually occurs. (Actually, of course, selection "sees" the phenotypic expression resulting from interactions among all alleles in a particular genetic constellation in a given environment.) Also, genes clearly cannot replicate themselves except by means of successful reproduction of the entire organism. Hence selection must usually favor genes that work together to enhance an individual's ability to perpetuate them, thus increasing its fitness. A "parliment of genes" acts to govern the phenotype. A mutant gene that prevented its bearer from reproducing would be short-lived indeed! Likewise, mutations that reduce the reproductive success of individuals will normally 1 be disfavored by natural selection. Viruses are indeed selfish genes, as their interests need not coincide with those of their hosts.

Each generation, sexually reproducing organisms mix their genetic materials. Such shared genetic material is called a gene pool, and all the organisms contributing to a gene pool are collectively termed a Mendelian population. Gene pools have continuity through time, even as individuals are added and removed by births and deaths. One of the most fundamental concepts of population genetics is the notion of gene frequency. An allele's frequency in the haploid gene pool, or its proportional representation, is traditionally represented by the symbol p or q. Changes in allele frequencies in a gene pool in time constitute evolution. An individual's ability to contribute its own genes to the gene pool represents that individual's fitness.

Equilibrium frequencies of various diploid genotypes that emerge in a given gene pool, given random mating and no evolution, can be calculated from the haploidgene frequencies using the binomial 2 expansion (also known as the Hardy-Weinberg equilibrium):

1 = (p + q)(p + q) = p 2 + 2pq + q 2

If p is the frequency of allele A1 and q the frequency of allele A2, the expected equilibrium frequencies of the three diploid genotypes A1/A1, A1/A2, and A2/A2 are given by the three terms at the right: p 2 , 2pq, and q 2 , respectively.

With random mating and no evolution, allele frequencies remain unchanged from generation to generation. The equilibrium frequency of heterozygotes reaches its maximum of 50 percent when the two alleles are equally represented (p = q = 0.5
Figure 6.3).

  • Figure 6.3.Frequencies of the three diploid genotypes for various gene frequencies in a two-allele system in Hardy-Weinberg equilibrium.

Population geneticists have relaxed these limiting assumptions and elaborated and extended these equations to model various phenomena such as random genetic drift, gene flow, nonrandom mating, and frequency-dependent selection. Genotypes can also be assigned fixed relative fitnesses when the heterozygote is fitter than either homozygote, both alleles are maintained indefinitely, even if one or both of the homozygotes is actually lethal! This widespread and important phenomenon is known as heterozygote advantage or heterosis. Such a reduction in fitness due to genetic segregation is termed genetic load. If one homozygote enjoys the highest fitness, it is favored by natural selection until that locus becomes "fixed" with an allele frequency of 1.0. Ultimately, maintenance of genetic variation depends on the precise rules coupling allele frequencies to genotype frequencies.

The fundamental source of variation between individuals is sexual reproduction reassortment and recombination of genes in each generation ensures that new genotypes will arise regularly in any population with genetic variability. In most higher organisms, no two individuals are genetically identical (except identical twins and progeny produced asexually). Population biologists are interested in understanding factors that create and maintain genetic variability in natural populations. When a population is reduced to a very small size, it must go through a genetic bottleneck which can greatly reduce genetic variability. Numerous genetic mechanisms, including linkage and heterosis, produce genetic variability both within and between populations.

At the outset, we must distinguish phenotypic from genotypic variation. The phenotypic component of variability is the total observable variability the genotypic component is that with a genetic basis. It is usually difficult to distinguish genetically induced variation from environmentally induced variation. However, by growing clones of genetically identical individuals (i.e., with the same genotype) under differing environmental conditions, biologists have been able to determine how much interindividual variation is due to the developmental plasticity of a particular genotype in different environments. Pedigree studies show that approximately half the phenotypic variation in height observed in human populations has a genetic basis and the remaining variation is environmentally induced. The proportion of phenotypic variation that has a genetic basis is known as heritability. Because natural selection can act only on heritable traits, many phenotypic variants may have little direct selective value. The degree of developmental flexibility of a given phenotypic trait strongly influences an organism's fitness such a trait is said to be canalized when the same phenotypic character is produced in a wide range of genetic and environmental backgrounds. Presumably, some genes are rather strongly canalized, such as those that produce " wild-type" individuals, whereas others are less determinant, allowing individuals to adapt and regulate via developmental plasticity. Such environmentally induced phenotypic varieties are common in plants, but they are less common among animals, probably because mobile organisms can easily select an appropriate environment. Presumably, it is selectively advantageous for certain genetically induced traits to be under tight control, whereas others increase individual fitness by allowing some flexibility of response to differing environmental influences.

Genotypic and phenotypic variation between individuals, in itself, is probably seldom selected for directly. But it may often arise and be maintained in a number of more or less indirect ways. Especially important are changing environments in a temporally varying environment, selective pressures vary from time to time and the phenotype of highest fitness is always changing. There is inevitably some lag in response to selection, and organisms adapted to tolerate a wide range of conditions are frequently at an advantage. (Heterozygotes may often be better able to perform under a wider range of conditions than homozygotes.) Indeed, in unpredictably changing environments, reproductive success may usually be maximized by the production of offspring with a broad spectrum of phenotypes (which may well be the major advantage of sexual reproduction).

Similar considerations apply to spatially varying environments because phenotypes best able to exploit various "patches" usually differ. On a broader geographic level, differences from one habitat to the next presumably often result in different selective milieus and therefore in different gene pools adapted to local conditions. Gene flow between and among such divergent populations can result in substantial amounts of genetic variability, even at a single spot.

Competition among members of a population for preferred resources may often confer a relative advantage on variant individuals that are better able to exploit marginal resources thus, competition within a population can directly favor an increase in its variability. By virtue of such variation between individuals, the population exploits a broader spectrum of resources more effectively and has a larger populational " niche breadth" the "between phenotype" component of niche breadth is great (Roughgarden 1972).

Because such increased phenotypic variability between individuals promotes a broader populational niche, this has been termed the "niche-variation hypothesis" (Soulé and Stewart 1970). Similarly, environments with low availability of resources usually require that individuals exploiting them make use of a wide variety of available resources in this case, however, because each individual must possess a broad niche, variation between individuals is not great (i.e., the "between-phenotype" component of niche breadth is slight, whereas the "within-phenotype" component is great).

One further way in which variability can be advantageous involves coevolutionary interactions between individuals belonging to different species, especially interspecific competition and predation. Fisher (l958b) likened such interspecific interactions and coevolution to a giant evolutionary game in which moves alternate with countermoves. It may well be more difficult to evolve against an unpredictable and variable polymorphic species than against a better standardized and more predictable monomorphic species. A possible example may be foraging birds developing a " search image" for prey items commonly encountered, often bypassing other less abundant kinds of suitable prey.

Classical Darwinian natural selection acts only on heritable phenotypic traits of individuals. As discussed earlier, selfish genes are also known to exist. Can selection operate on entire groups of individuals such as families, colonies, populations, species, communities, and ecosystems? To what extent is the individual a natural "unit" of selection? How are conflicts between suborganismal, organismal, and superorganismal levels of selection resolved? These questions are often discussed both by geneticists and by ecologists, but there is no clear consensus as to correct answers.

Many behavioral and ecological attributes can be interpreted as having evolved for the benefit of the group rather than the individual. As an example of such group selection, consider the assertion that "mockingbirds lay fewer eggs during a drought year because competition for limited food supplies would be detrimental to the species." Such statements have a fatal flaw: "Cheaters" that laid as many eggs as possible would reap a higher reproductive success than individuals that voluntarily decreased their clutch size for the "benefit of the species." The same phenomenon can be interpreted more plausibly in terms of classical Darwinian selection at the level of the individual. During droughts, parental birds cannot bring as many insects to their nest and therefore cannot feed and fledge as many chicks as they can when food supplies are more ample. Birds can actually leave more surviving offspring to breed in the next generation by laying fewer eggs. Most evolutionary biologists now dismiss the preceding sort of "naive" group selection as untenable.

In the last two decades, thinking about group selection has achieved considerably greater sophistication, although it remains speculative. Two distinct types of selection at the level of groups emerge from these mathematical arguments. For " extinction" group selection to oppose natural selection at the level of the individual, isolated selfish subgroups must go extinct faster than selfishness arises within altruistic subgroups and most newly founded isolates must be altruistic. "G raded" group selection requires that distinct subpopulations contribute differentially to reproduction in a bigger population at large. In essence, entire groups must possess differential rates of survivorship or reproduction (i.e., differential fitness).

A major consideration is the extent to which an individual's own best interests are in conflict with those of the group to which it belongs. Ultimately, the frequency of occurrence of socially advantageous behaviors depends largely on the precise form of the trade-offs between group benefit(s) versus individual cost(s). Any individual sacrificing its own reproductive success for the benefit of a group is obviously at a selective disadvantage (within that group) to any other individual not making such a sacrifice. Classical Darwinian selection will always favor individuals that maximize their own reproductive success. Clearly, the course of selection acting within groups cannot be altered by selection operating between groups. Group selection requires very restricted conditions.

Williams (1966a) reemphasized, restated, and expanded the argument against naive group selection, pointing out that classical Darwinian selection at the level of the individual is adequate to explain most putatively "group-selected" attributes of populations and species, such as those suggested by Wynne-Edwards (1962) and Dunbar (1960, 1968, 1972). Williams reminds us that group selection has more conditions and is therefore a more onerous process than classical natural selection furthermore, he urges that it be invoked only after the simpler explanation has clearly failed. Although group selection is certainly possible, it probably would not actually oppose natural selection at the individual level except under most unusual circumstances. A special form of selection at the level of the individual, kin selection, may frequently be the mechanism behind many phenomena interpreted as evidence for group selection. We will return to this issue from time to time in later chapters.

Modern molecular biotechnological tools, 3 such as restriction enzymes and gene splicing, now enable geneticists to transfer particular genes from one organism to another. For example, the firefly gene for luciferase has been successfully transferred to tobacco, resulting in transgenic bioluminescent plants. Human insulin and growth hormones are now routinely produced in chemostats of E. coli bacteria that have had human genes spliced into their genomes. Some researchers have even proposed using such transgenic bacteria as live vaccines (the genetically altered bacteria would live within humans and would confer resistance to particular diseases such as hepatitis). Such recombinant DNA technology has also enabled us to produce useful new life forms such as pollutant-eating bacteria that can help us to clean up what's left of our environment. Research is in progress to transfer nitrogen-fixing genes into crop plants.

There are legitimate concerns, however, about the safety of research on such man-made transgenic organisms, particularly the possibility of accidental release of virulent strains that might attack humans. Such concerns have been addressed by implementation of strict containment procedures for recombinant DNA products, as well as by selecting and creating host organisms for foreign DNA that are incapable of surviving outside the laboratory.

Obviously, genetically engineered organisms must eventually be designed for release into nature (indeed, genetically engineered tomatoes are now being grown commercially). Another concern is that genetically engineered organisms could have adverse effects on other species in natural ecosystems. We already have enough natural pests and certainly don't want to make any new ones!

Unfortunately, we still know far too little to engineer ecological systems intelligently (obviously genetic engineers should work hand in hand with ecological engineers). Still another problem is the human tendency to allow short-term financial returns to override long-term prospects.

Basic Mendelian Genetics

Darlington and Mather (1949) Darwin (1859) Ehrlich and Holm (1963) Fisher (1930) Ford (1931, 1964) King and Wilson (1975) Maynard Smith (1958) Mayr (1959) Mendel (1865) Mettler and Gregg (1969) Sheppard (1959).

Nature versus Nurture

Bradshaw (1965) Clausen, Keck, and Hiesey (1948) Greene (1989) Otte and Williams (1972) Quinn (1987) Via et al. (1995)

Selfish Genes

Alexander and Borgia (1978) Dawkins (1982, 1989) Hamilton (1967) Leigh (1977) Orgel and Crick (1980).

Population Genetics

Crow (1986) Crow and Kimura (1970) Falconer (1981) Fisher (1930, 1958a) Ginzburg and Golenberg (1985) Haldane (1932, 1941, 1964) Hedrick (1983) Mayr (1959) Mettler and Gregg (1969) Wright (1931, 1968, 1969, 1977, 1978).

Maintenance of Variability

Ehrlich and Raven (1969) Fisher (l958b) Mettler and Gregg (1969) Roughgarden (1972) Somero (1969) Soulé and Stewart (1970) Van Valen (1965) Wilson and Bossert (1971).

Units of Selection

Alexander and Borgia (1978) Boorman and Levitt (1972, 1973) Brown (1966) Cole (1954b) Darlington (1971) Darnell (1970) Dawkins (1976, 1982) Dunbar (1960, 1968, 1972) Emerson (1960) Emlen (1973) Eshel (1972) Fisher (l958a) Gilpin (1975a) Leigh (1977) Levins (1970, 1975) Lewontin (1970) Maynard Smith (1964) Sober and Wilson (1998) Uyenoyama (1979) Van Valen (1971) Wade (1976, 1977, 1978) Wiens (1966) Williams (1966a, 1971) D. S. Wilson (1975, 1980, 1983) E. O. Wilson (1973, 1976) Wright (1931) Wynne-Edwards (1962, 1964, 1965a, b).

Genetic Engineering

Baba et al. (1992) Cockburn (1991) Tiedje et al (1989) Simonsen and Levin (1988).

1. The qualifiers "usually" and "normally" in the above sentences are necessary because other copies of the genes concerned, identical by descent, occur in the bodies of related individuals (the important phenomenon of kin selection is considered later).

2. With only two alleles, q = 1 - p. For three alleles, the appropriate equation is a trinomial, with p + q + r = 1, and so on.

3. Other techniques, such as polymerase chain reaction (PCR) amplification of DNA segments, DNA sequencing, and DNA fingerprinting hold great promise as tools that will allow informative evolutionary studies.


Genetics of Polyploids | Vegetable Breeding

Polyploids are defined as organisms with other than 2 basic sets of chromosomes, that is, monoploids, triploids, tetraploids, and various aneuploids. Segregation in polyploids differs from that in diploids due to the many different ways in which the chromosomes can pair in a polyploid and due to larger number of alleles segregating at each locus.

Judging by the frequency with which different kinds of polyploids appear among crop plants, autotetraploidy appears to be more important to agriculture than the others. Four of the major crop species, namely, alfalfa, potato, coffee and peanut may be autotetraploids. In addition, several forage grasses and many ornamentals appear to be autotetraploids.

In respect of one locus with 2 alleles, A and a, 5 genotypes are possible in an autotetraploid. These are AAAA, AAAa, AAaa, Aaaa and aaaa. According to the number of dominant alleles, these 5 genotypes are referred to as quadruplex, triplex, duplex, simplex, and nulliplex respectively.

The meiosis in autotetraploids involves the partitioning of the 8 chromatids during meiosis I into 4 pairs, each pair corresponding to one of the 4 gametes produced by each sporocyte.

This process is much more complicated than that of diploids where meiosis involves the partitioning of only 4 chromatids, one to each of the 4 gametes. Depending upon the regularity of quadrivalent formation and the variable chiasma number between the kinetochore and the locus in question, 2 kinds of segregations are possible in the autotetraploids.

Random Chromosome Segregation:

The type of partitioning of chromatids where sister chromatids i.e., the chromatids derived from the same chromosome never end in the same gamete is called as random chromosome segregation. This happens only when bivalents are formed. For example, in a simplex (Aaaa) the segregation of chromosomes during meiosis is as follows. Here 8 chromatids are shown as (A1 A2), (a3 a4), (a5 a6) and (a7 a8).

The gametic output from any autotetraploid can be easily derived by making all possible pairs of chromosomes out of 4 chromosomes as given below:

A checkerboard method of deriving the gametes under random chromosome assortment for a duplex is shown in Fig. 5.5:

The Fig. 5.5 shows that two sister chromatids i.e., the chromatids derived from the same chromosome, for example, A1 and A2 and similarly other ones are not included in the same gamete as required under random chromosome assortment. The gametic output from the checkerboard is 4 AA : 16 Aa : 4 aa i.e. 1 A A : 4 Aa : 1 aa.

The zygotic frequencies can be easily worked out as given below:

Likewise various other gametic and phenotypic ratios can be determined.

Random Chromatid Segregation:

When quadrivalent formation is complete and there is occurrence of 50% crossing over between centromere and locus, the partitioning of 8 chromatids to the gametes is at random i.e., all the possible pairs of 8 chromatids of an autotetraploid have an equal chance of being included in the same gamete following meiosis. The gametic ratio under this system for a triplex (AAAa) as an example is given in Fig. 5.6. The gametic ratio is 15 AA : 12 Aa : 1 aa.

Given random pairing and 50% crossing over and tracing the relationship of 8 chromatids to the 4 centromeres in every spore mother cell and then through meiosis I and meiosis II, the gametic outputs are slightly different than under random chromatid segregation.

For example, a triplex yields gametes in a ratio of 15 AA : 12 Aa : 1 aa under chromatid segregation, but a ratio of 13 AA : 10 Aa : 1 aa under 50% crossing over situation.

It must be kept in mind that random chromosome assortment and random chromatic assortment are two theoretical extremes and real gametic output is intermediate between these extremes depending upon whether quadrivalents form or do not form and when centromere and the locus are partly linked.

The net effect of these 2 events is reflected in the value of alpha which itself is related to gametic output, for example, for a simplex, the gametic expectations in terms of alpha are:

For random chromosome assortment, α = 0 and for random chromatid assortment α = 0.1429. In an actual situation it is in between these two extreme values. Table 5.5 shows that the frequency of recessive phenotypes is much lower than in the equivalent diploids. Thus, auto-polyploids are genetically more stable than diploids and release their allelic variability at a much slower rate.


Biotechnology and Genetics in Fisheries and Aquaculture

Commencing with chapters covering genetic variation and how it can be measured, the authors then look at genetic structure in natural populations, followed by a new chapter covering genetics in relation to population size and conservation issues. Genetic variation of traits and triploids and the manipulation of ploidy are fully covered, and another new chapter is included, entitled 'From Genetics to Genomics'. The book concludes with a chapter covering the impact of genetic engineering in aquaculture.

With the inclusion of a wealth of up-to-date information, new text and figures and the inclusion of a third author, Pierre Boudry, the second edition of Biotechnology and Genetics in Fisheries and Aquaculture provides an excellent text and reference of great value and use to upper level students and professionals working across fish biology, aquatic sciences, fisheries, aquaculture, genetics and biotechnology. Libraries in all universities and research establishments where biological sciences, fisheries and aquaculture are studied and taught should have several copies of this excellent new edition on their shelves.


Results

Calling genotypes with mclust and APT

Without filtering SNPs, the numbers of SNPs for diploid offspring predicted to have one, two, or three clusters were 155, 3970 and 48,333, respectively, when using APT and 2008, 7534 and 42,916, respectively, when using mclust. For each SNP called by mclust, there were two possible models: heterogenous and homogenous cluster variance. The heterogenous cluster variance model was chosen for 68% of the SNPs for diploids and for 49% of the SNPs for triploids. Using mclust, the numbers of SNPs for triploid offspring predicted to have a single, two, three, or four clusters were 3149, 3528, 10,708, and 38,792, respectively. Of the 9542 SNPs with less than three clusters called by mclust for diploids and that were used in downstream analyses, 724 were given 100% no-calls, due to insufficient separation of clusters (see subsection “New genotype calling method” in “Methods”). For triploids, there were 3620 such markers. Figure 2 shows indicator statistics of mclust marker calling quality in diploids for different thresholds of (Delta ICL) . Increasing (Delta ICL) resulted in a lower ratio of SNPs with one or two clusters for diploids, while the ratio of SNPs with three clusters increased. Furthermore, the ratio of Mendelian errors (ER) decreased as (Delta ICL) increased, indicating that increasing the threshold for (Delta ICL) improves calling quality. This was supported by the decreasing ratio of missing genotypes (‘NoCalls’), which indicates that higher thresholds for (Delta ICL) results in retaining SNPs that have good separation of genotype clusters, i.e. SNPs with a low call uncertainty. The red horizontal lines in Fig. 2 show the values achieved by using the genotype calls from APT with all SNPs included in the analyses. APT achieved fewer Mendelian errors (ER) and fewer missing genotypes (‘NoCalls’) than our mclust implementation when all SNPs were used. Mclust needs a (Delta ICL) threshold of

110 to obtain similar levels of ER and no-calls as APT, which resulted in the use of

Effect of varying the (Delta ICL) threshold for marker selection. Statistics for diploid offspring/parent trios when varying the (Delta ICL) threshold for marker selection when genotypes are called by the mclust algorithm. ‘1 cluster’, ‘2 clusters’ and ‘3 clusters’ show the percentage of markers predicted to have one, two, and three clusters. ‘ER’ is the exclusion ratio shown in percent for trios. ‘NoCalls’ is the percentage of missing genotypes and ‘Removed markers’ shows the number of markers which are removed. The horizontal red lines show the values found for ‘ER’ and ‘NoCalls’ when using genotype calls from APT

Figure 3 shows the same statistics as Fig. 2 after removing SNPs below an (ICL) threshold (note: not (Delta ICL) ), i.e. a threshold on the (ICL) of the most likely model. All investigated (ICL) thresholds result in higher ER and NoCalls than was achieved by APT without SNP quality filtering. Because the variability of ER and no-calls seemed erratic, we decided not to use (ICL) as a marker quality filtering statistic in downstream analyses.

Effect of varying the (ICL) threshold for marker selection. Statistics for diploid offspring/parent trios when varying the (ICL) threshold for marker selection when genotypes are called by the mclust algorithm. ‘1 cluster’, ‘2 clusters’ and ‘3 clusters’ show the percentage of markers predicted to have one, two and three clusters. ‘ER’ is the exclusion ratio shown in percent for trios. ‘NoCalls’ is the percentage of missing genotypes and ‘Removed markers’ shows the number of markers which are removed. The horizontal red lines show the values found for ‘ER’ and ‘NoCalls’ when using genotype calls from APT

The histograms in Fig. 4 show that the (Delta ICL) achieved for one, two, and three clusters were roughly the same in triploids, while higher (Delta ICL) could be achieved for four clusters.

(Delta ICL) distributions for markers genotypes in triploids. Distribution of (Delta ICL) values for markers predicted to have one (top panel) to four (bottom panel) genotype clusters in triploids. Markers with red, green, cyan and purple indicate 1, 2, 3 and 4 predicted genotype clusters, respectively, while the gray bars shows the total number of markers for each value of (Delta < ext>)

Parentage assignment of diploid parents to triploid offspring

The QC filtering of SNPs for parentage assignment based on triploid (Delta ICL > 150) and parent call rates > 95% resulted in retaining 35, 375, 238, and 13,258 SNPs with one, two, three, and four genotype clusters, respectively, which resulted in the use of 13,906 SNPs for this parentage assignment. The ER between offspring and their first, second, third, and less likely candidate parents are shown in the top panel of Fig. 5, while the bottom panel zooms in on the best fitting parent candidates. The lowest ER between all triploid offspring and their third most likely candidate parent (i.e. the closest-fitting non-parent) was

0.003. Consequently, a 0.002 assignment threshold for offspring–candidate duos was applied, which also fitted well, based on visual inspection of Fig. 5. In other words, the candidate parent in any duo with an ER < 0.002 was assigned and assumed to be a true parent. At least one parent was assigned to all 379 triploids, and 304 were assigned both parents. Lacking assignments were likely due to genotypes of some parents being absent in the dataset.

Triploid exclusion ratios. Exclusion ratios (ER) for duos of triploid offspring and diploid parents are shown for the duos with lowest ER (red), second-lowest ER (green), third-lowest ER (turquoise) and 2000 randomly sampled ER from fourth lowest and above (purple)

Parent sex prediction for triploid offspring

A similar procedure as for parentage assignment was used for assignment of mothers of triploid offspring, using the mother exclusion ratios (‘mother.ER’). Figure 6 shows the ‘mother.ER’ between assigned, unassigned, and random pairs of offspring–parent candidates (note that ‘Assigned’ in Fig. 6 is for parentage assignment, not mother assignment). The minimum ‘mother.ER’ of the third-best parental candidates was

0.022, thus we set the ‘mother.ER’ assignment threshold at 0.02. Any assigned parent with a ‘mother.ER’ < 0.02 was assigned as mother, and any assigned parent with ‘mother.ER’ ≥ 0.02 was assumed to be the true father. This resulted in 58 assigned mothers and 65 assigned fathers. No mothers were assigned as fathers, or vice versa. In total, 304 offspring were assigned both their mother and father (the same as the two parents assigned above), 14 were assigned a mother only, and 61 were assigned a father only (i.e. as above, all individuals were assigned a father, a mother, or both). No offspring were assigned two apparent mothers or two apparent fathers.

Triploid maternal exclusion ratios. Maternal exclusion ratios (mother.ER) for duos of triploid offspring and diploid candidate mothers are shown for the assigned (green) and unassigned (maroon) duos and 2000 generated ‘mother.ER’ values from randomly sampled parent candidates, i.e. both sampling males and females which can possibly be true mothers (grey)

Investigating different thresholds for (Delta ICL) and marker call rate

In addition to the (Delta ICL) threshold statistic explored above, marker call rate is another marker quality statistic that is often employed when analyzing genotype datasets. Marker call rate is related to ICL, as both call rate and ICL use call uncertainty as a measure of marker quality. (Delta ICL) provides a probabilistic penalization of the mixture model likelihood [8], whereas marker call rate is the fraction of genotypes that fall below a pre-defined uncertainty threshold. We chose the uncertainty threshold of 0.15, i.e. all genotype calls above this threshold were defined as no-calls (missing genotypes) for both triploid offspring and diploid parents. Table 3 shows that increasing either the marker call rate threshold or the (Delta ICL) threshold tended to decrease Mendelian errors, i.e. decrease the ER, but also increased the number of removed markers. Note that, for the parents, we always used a marker call rate threshold of 95%.

Maternal recombination rates

Figure 7 shows the estimated maternal recombination fraction along each of the 29 chromosomes in the Atlantic salmon genome by looking at where the triploid offspring inherited the homozygous (AA/BB) or heterozygous (AB) allele from the mother (see “Methods”). The region with the lowest maternal recombination fraction on each chromosome was at the centromere, where recombination is known to be suppressed. In [13], chromosome 8 was reported to be metacentric, but in Fig. 7 it appears as acrocentric or telocentric. However, the p-arm of chromosome 8 contains highly repetitive regions and, therefore, few or no markers from this region may be represented on the SNP chip (personal communication with S Lien, see also [14]).

Triploid maternal recombination events. The fraction of heterozygous (A and B) alleles inherited from mothers for informative loci along each of the 29 chromosomes in Atlantic salmon. The x-axis is marker position on each chromosome and is scaled by chromosome size. All markers are required to have at least 50 trios with informative genotypes


RESULTS

All 68 isolates were found to be prototrophic and sporulated on SPO media. After tetrad dissection of representative strains the spores either were successfully mated with haploid testers MATa or MATα or underwent additional sporulation that showed the polyploid nature of the parental strain. A very low sporulation rate was obtained in the second sporulation test (see below).

Microsatellite variation:

Nineteen primer pairs designed for SSR analysis on the basis of the S. cerevisiae genome sequence proved efficient in amplifying fragments in the expected length range (see Table 2 ). To validate the relevance of these PCR products, parts of the amplimeres were sequenced. The resulting sequences were in agreement with the published genome and the predicted number of repeats from sizing analysis (data not shown). The scored microsatellite loci showed high polymorphism of allele size, with 2–9 alleles per locus and an average of 5.1 alleles per locus (Table 1). No significant differences were found in the level of SSR variation in any locus between the three major localities (NFS, SFS, and VB): the maximum of among the loci in the likelihood test for microsite heterogeneity was 7.3 (found for locus X-188.70), P = 0.05. Likewise, between the three microniches (habitats) tested (sunny, shady, and leaf), the maximum of among the loci in the likelihood test was 8.9 (found for locus XIII-388.60), P = 0.03. Keeping in mind the multiple-comparison nature of our analysis, these values cannot be considered as significant because the probability to reach a minimal P-value = 0.03 in 19 tests when H0 (no real difference between the locations) is true is ∼0.6 (B enjamini and H ochberg 1995).

PCR primers for amplification of the selected SSR loci

Ploidy variation:

High heterozygosity at the scored SSR loci indicated a variation in ploidy level, with one to four alleles per locus per strain. Flow cytometry analysis (FACS) was conducted for a sample of strains, representing the four putative levels of ploidy revealed from SSR analysis, and for a part of their progeny obtained by tetrad dissection. Our diploid isolates showed the same level of fluorescence per cell as the laboratory diploid control strain, whereas presumably tetraploid strains (according to SSR analysis) showed a doubled level of fluorescence per cell. High consistence was found between these sources of ploidy assessment (see Figure 1 ).

Analysis of DNA content by flow cytometry. Yeast cells were stained with propidium iodide for cell cycle analysis. Cell counts and relative DNA content are shown. (a) Diploid, triploid, and tetraploid natural isolates. (b) A tetraploid natural isolate and one of its diploid offspring.

Consequently, the numbers of di-, tri-, and tetraploid strains in our panel were found to be 21, 7, and 40 (31, 10, and 59%), correspondingly. No significant differences were found between the three microsite subpopulations (NFS, SFS, and VB) or the three microniches (sunny, shady, and leaf) with respect to variation in ploidy level ( and , correspondingly, P > 0.1).

At all three ploidy levels (di-, tri-, and tetraploid) the sporulation test was positive for all strains. As expected, our trials for tetrad analysis of triploid strains gave an extremely low level of viable spores. An interesting result was obtained by tetrad analysis of tetraploid strains: they showed high sporulation level and high viability of spores. Unlike the tetraploids themselves, a part of the offspring derived from the tetraploids displayed low sporulation level (<1%), with no viable spores, and therefore were considered a/α-diploid. Other diploid offspring, derived from meiosis of tetraploid strains, did not sporulate at all. These strains were successfully mated with an α-tester strain and the resulting triploids were able to undergo sporulation. The mating type deduced from these tests was confirmed by PCR. Almost all haploids derived from diploids as well as diploids derived from tetraploids displayed stability of mating type (being therefore heterothallic). It is noteworthy that homothallism is believed to be characteristic of most yeast in nature (e.g., M ortimer 2000). In light of the foregoing results obtained in our mating experiments, it was desirable to check the sequence of the HO locus that controls mating-type switch (R ussell et al. 1986). Such tests were conducted on a few haploids derived from diploids. Sequence analysis followed by alignment to the standard HO sequence revealed several point mutations, including deletions and SNPs. The first substitution, causing a nonsense mutation, appeared at position 235 (after the ATG codon). This mutation causes the formation of a short mutant polypeptide of only 79 amino acids, while the wild-type protein is 586 amino acids long (R ussell et al. 1986).

SSR allele diversity varied across ploidy levels (Table 3 ). Two tests shown below were conducted to estimate the significance of this variation.

Variation in allele diversity across ploidy levels and in entire population of S. cerevisiae isolates from Evolution Canyon

Likelihood test for equivalence of allele diversity for different ploidy levels:

This test employs maximum-likelihood estimation of allele frequencies, under two conditions for allele diversity equivalence. We compared likelihood function for observed data under hypotheses H1 and H0: “allele distribution was different (H1) vs. the same (H0) for different ploidy levels.” Supposing multinomial distribution of the allele numbers in the sampled strains, we calculated log-likelihood as Here and are the observed numbers of alleles for two ploidy levels I and II and are expected allele frequencies (they are positive and fit the trivial condition ). The symbol “∝” denotes that, in the equality, the term independent of and is omitted. For the compared hypotheses H1 and H0, and

If H0 is true then we expect that the statistic is asymptotically distributed as with d.f. = 1.

Rank test of diversity values comparison:

For diversity scores at ploidy levels I and II, the expected number of signs that “level I is greater than level II” in 19 pairs (corresponding to 19 marker loci) of diversity comparison is 9.5. In the case of diversity equivalence, the value has -distribution with d.f. = 1. Allele diversity was significantly higher in diploids than in tetraploids (P-value 0.005) and in tetraploids than in triploids (P-value 0.015). Allele-combination diversity significantly differed from the expected values in tetraploids (Table 4 ).

Allele combination diversity for the three ploidy levels of S. cerevisiae isolates from Evolution Canyon

Sexual vs. clonal reproduction:

An exceptional feature of the inspected population is the seemingly clonal structure of its higher ploidy parts. Namely, at both tri- and tetraploid levels, we observed a very limited number of different multilocus genotypes (clones), representing the majority of strains (Figure 2, Table 4 ).

Bootstrap consensus phylogenetic tree, demonstrating the relationship between S. cerevisiae isolates from Evolution Canyon and their relationship with laboratory strains. The tree was constructed on the basis of 19 SSR loci with distance matrices calculated as described in Statistical analysis in materials and methods . The numbers above the branches are the percentages of bootstrap trees containing each bipartition (only values >50% are given).

We think that the tight clustering shown in Figure 2 reflects clonality. Even with recent origin, one would expect that just one to two cycles of sexual reproduction of tetraploids would generate various segregants per each heterozygous locus (see also Table 4) and recombinant genotypes, which we do see in diploids, but not in tri- and tetraploids.

Low similarity in allele content was found between strains of different ploidy levels, except diploid strain nos. 03, 05, 07, and 23 that share their alleles with the cluster of tetraploids and might be either offspring or parental strains of the tetraploid strains. As indicated above, an interesting effect was found in tetrad analysis: all tested tetraploid strains produced diploid progeny, but all efforts to obtain haploids from this progeny failed. By contrast, haploid progeny was easily obtained from diploid strains.

In light of the foregoing findings, it is interesting to test whether the proportion of heterozygotes for SSR markers will show any pattern compatible with the assumed tendency of clonality. A simple test for deviation from Hardy–Weinberg (HW) proportions was, therefore conducted on diploids, the least clonal part of the population (Table 5 ). The observed number of homozygotes was found to exceed significantly the HW expectations, albeit the discrepancy varied among chromosomes and even within chromosomes. The majority of chromosomes displayed an excess of homozygotes despite the high polymorphism of the diploid part of the population. An exception was chromosome X with only one marker of seven that showed deviation from HW proportions. Presumably, this may reflect selection against heterozygotes for loci of this chromosome or complex genetic architecture of modifiers of mating preferences (hence proportions of homozygotes). The fact that significant paucity of heterozygotes compared to HW expectation is found for loci distantly located from the centromere and near-centromeric loci indicates that mitotic recombination alone cannot be the only cause, and selfing should be a playing factor here. The results of the HO sequence test show that at least some of the strains are strictly heterothallic, whereas the HW test indicates that at least part should be or should have been homothallic.

Discrepancy between the observed and expected (assuming panmixia) proportions of homozygotes among the diploid S. cerevisiae isolates from Evolution Canyon

Segregation analysis:

We considered 27 segregation cases to test whether segregations of the tetraploid strain are random. Analysis was made only for 9 loci from the 19, because the others could produce only a k = 1 possible set of four diploid genotypes. It was found that only two loci CMP2 (XIII-159.9) and ORF4 (XIII-209.8) demonstrate significant deviation from random segregation (P-values 0.07 and 0.005 correspondingly, see Table 6 ). It can be noted that both of them are situated on chromosome XIII and the locus that demonstrated high significance is closer to the centromere.

Results of test on random segregation

Taxonomic consideration:

Very high amplification efficiency of primer pairs designed for SSR loci on the basis of the published genome sequence of S. cerevisiae was achieved, while for S. paradoxus, a close relative of S. cerevisiae, it was reported recently that only 3 of 20 primer pairs, designed on the basis of S. cerevisiae genome sequence, resulted in amplimeres (J ohnson et al. 2004). This is a strong indication that the collected isolates are indeed S. cerevisiae. However, no simple explanation seems to fit the observed complicated pattern of ploidy variation, SSR distribution among and within the stains, and the outcomes of tetrad analysis of diploid isolates compared to the diploid progeny of tetraploid strains. This motivated us to conduct in-depth specific taxonomic tests on the basis of sequence comparison at two selected loci: ITS1-5.8S-ITS2 rDNA and translation EF-1 αA. Multiple sequence alignment for the representative isolates from Evolution Canyon with all available Saccharomyces sensu stricto sequences from GeneBank (http://www.ncbi.nlm.nih.gov) were conducted. For the translation EF-1 αA locus, the tested isolates showed high sequence similarity to S. cerevisiae. In fact, we found only one SNP out of 1127 bp.

For the ITS1-5.8S-ITS2 locus, sequencing of tetraploids, diploids, and their offspring yielded overlap of different sequences that could not be obviously merged to a single sequence. Only triploid strains tested formed a single sequence that could be analyzed. Most of the variation found between these triploid isolates and the members of the Saccharomyces sensu stricto group was due to mononucleotide repeats (MNRs). Our natural isolates tend to show a larger number of repeats compared to those in published sequences of three MNR tracts [all poly(T/A)] in the locus. Still, these results indicate that these isolates have closest homology to S. cerevisiae.

Microscale (microclimatic) differentiation:

The differences in number of alleles, allele diversity, and allele-combination diversity were tested for association with microclimatic contrasts: (a) major localities SFS, NFS, and VB and (b) variation among niches/habitats sun vs. shade vs. leaf surface. Only tetraploid strains displayed significant interslope differentiation in their allele combinations that involved two loci, IX-107.70 and XI-126.10 (χ 2 = 11.1, P < 0.001). No intraslope (i.e., interstation) variation was detected. A slight tendency toward between-niche variation was found for diploids (for shade-derived strains vs. remainder strains χ 2 = 2.7, P < 0.1 and for leaf vs. remainder χ 2 = 3.9, P < 0.05). Additional tests, especially using candidate stress-related genes (for tolerance to high temperature, desiccation, UV, etc.), are needed to check whether the foregoing diversity reflects ecological adaptation to microclimatic variation.


Biotechnology and Genetics in Fisheries and Aquaculture

The authors of this important book, Andy Beaumont and Kate Hoare, have written a text of great clarity, which carefully explains the science and application of molecular and genetic techniques to fisheries and aquaculture situations and what these new technologies have to offer. Contents include a full explanation of genetic variation and its measurement, genetic structure in natural populations, genetics and artificial selection in the hatchery, ploidy manipulation and the use of genetic engineering in aquaculture.

Biotechnology and Genetics in Fisheries and Aquaculture is of great use to biological sciences students, particularly those studying marine, freshwater and aquatic biology, fish biology, fisheries, aquaculture, population biology and genetics. The book is also extremely useful as a reference to personnel such as fish farmers and fisheries scientists and all those working in fisheries and aquaculture management and research. Libraries in all universities and research establishments where biological sciences, fisheries and aquaculture are studied and taught should have copies of this book on their shelves.


The gynogenetic reproduction of diploid and triploid hybrid spined loaches (Cobitis: Teleostei), and their ability to establish successful clonal lineages—on the evolution of polyploidy in asexual vertebrates

Polyploidisation is assumed to have played a significant role in the evolution of hybrid asexual lineages. The virtual absence of natural asexual systems in which more than a single ploidy level successfully establishes successful independent clonal lineages is generally explained by the strong effects of polyploidisation on fitness. Experimental crosses were made between diploid and triploid asexual Cobitis elongatoides × C. taenia hybrids (female) and both parental spined loach species (male). Genotyping of the progeny using allozymes and multilocus DNA fingerprinting, along with flow cytometric measurement of ploidy level, demonstrated the occurrence of gynogenetic reproduction in both female biotypes. The incorporation of the sperm genome occurred in some progeny, giving rise to a higher ploidy level, but the rate of polyploidisation differed significantly between the diploid and triploid females. These outcomes are consistent with the existence of developmental constraints on tetraploidy, which determine the rarity of tetraploids in natural populations. No cases of ploidy level reduction were observed. Since diploid and triploid hybrid populations occur where the lack of potential progenitor excludes the possibility of de novo origin, it is probable that both diploid and triploid females can establish successful clonal lineages. Spined loaches represent a unique example, among asexual vertebrates, where more than one ploidy level can establish persistent clonal lineages, which are reproductively independent of one another.

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Cell Biology Genetics and Plant Breeding May 2005 MGU MSc Botany

1. Explain mitochondriopathy.
2. Explain the structure and function of spindle apparatus.
3. Explain a translocation heterozygote.
4. Explain satellite DNA.
5. Give an account on chromosome library.
6. Write notes on Philadelphia chromosomes.
7. Comment on plastomes.
8. Give an account on the role of extrinsic and intrinsic proteins.
9. Explain breakage-fusion-bridge-cycle

II. Answer any two of the following: (2 X 4 = 8 Marks)

10. What is karyotype concept? Explain the recent trends in karyotype.
11. Give an account of the numerical variables of chromosomes. Explain their genetic consequences.
12. Write an account on the effects of high energy radiations on chromosomes.

III. Answer any one of the following: (1 X 8 = 8 Marks)

13. Discuss the structural details of various types of supernumerary chromosomes and giant chromosomes. Mention their significance.
14. Describe the various numerical and structural variations of chromosomes. Explain their meiotic behavior

Section B (Genetics)

IV. Answer any five of the following: (5 X 1 = 5 Marks)

15. What do you mean by cot value?
16. Explain apoptosis.
17. What is gene erosion?
18. Explain modifiers and suppressors.
19. Write notes on genetic polymorphism.
20. Explain microsatellites.

V. Answer any three of the following: (3 X 2 = 6 Marks)

21. Describe the applications of variability analysis in quantitative inheritance.
22. Distinguish between pseudo-genes and crypto-genes.
23. Distinguish between proto-oncogenes and oncogenes.
24. Write notes on site directed mutagenesis.

VI. Answer any three of the following: (3 X 4 = 12 Marks)

25. Mention the principles, methods and applications of DNA fingerprinting.
26. Distinguish between Southern, Western and Northern blotting.
27. Categorize the different forms of DNA. How do they differ from each other?
28. Describe Polymerase Chain Reaction. What are its applications?

VII. Answer any two of the following: (2 X 7 = 14 Marks)

29. Give a detailed account of the regulations of gene expression in prokaryotes and eukaryotes.
30. Explain Hardy-Weinberg law and its applications. How is it useful in predicting gene frequencies in a population?
31. “Transgenesis involves genetic transformation by non-sexual means” – Evaluate.

Section C (Plant Breeding)

VIII. Answer any four of the following: (4 X 2 = 8 Marks)

32. How are allopolyploids formed? Discuss their role in the evolution of Wheat and Brassica.
33. What are the necessities of plant introduction? Cite important achievements of plant introduction as a breeding method.
34. “Genetic diversity is always essential for crop improvement” – Explain
35. “Triploids are more preferred among grapes, bananas and watermelons than diploids” – Discuss.
36. Describe the techniques and applications of resistance breeding in plants.

IX. Answer any one of the following: (1 X 7 = 7 Marks)

37. Describe in detail the recent molecular approaches for crop improvement.
38. Give an account of the various methods and achievements of mutation breeding in crop plants.


Abstract

The scarcity of parthenogenetic vertebrates is often attributed to their ‘inferior’ mode of clonal reproduction, which restricts them to self-reproduce their own genotype lineage and leaves little evolutionary potential with regard to speciation and evolution of sexual reproduction. Here, we show that for some taxa, such uniformity does not hold. Using hybridogenetic water frogs (Pelophylax esculentus) as a model system, we demonstrate that triploid hybrid males from two geographic regions exhibit very different reproductive modes. With an integrative data set combining field studies, crossing experiments, flow cytometry and microsatellite analyses, we found that triploid hybrids from Central Europe are rare, occur in male sex only and form diploid gametes of a single clonal lineage. In contrast, triploid hybrids from north-western Europe are widespread, occur in both sexes and produce recombined haploid gametes. These differences translate into contrasting reproductive roles between regions. In Central Europe, triploid hybrid males sexually parasitize diploid hybrids and just perpetuate their own genotype – which is the usual pattern in parthenogens. In north-western Europe, on the other hand, the triploid males are gamete donors for diploid hybrids, thereby stabilizing the mixed 2n-3n hybrid populations. By demonstrating these contrasting roles in male reproduction, we draw attention to a new significant evolutionary potential for animals with nonsexual reproduction, namely reproductive plasticity.


Mythical and legendary hybrids

In ancient folktales many fictional hybrids have become part of the mythological narrative. Many mythological creatures are simple composites of known animals:

  • Basilisk and Cockatrice - both a combination of a cockerel and lizard or snake.
  • Bonnacon - a mixture between a horse and a bull.
  • Chimera - a fire breathing mixture between a goat, a snake, and a lion.
  • Griffin - beast with the body of a lion and the head and wings of an eagle.
  • Manticore - the face of a man, the body of a lion and the tail of a scorpion.
  • Mermaid and Merman - half fish, half human.
  • Satyr - the torso of a man, the legs and feet of a goat.

Some mythological hybrids were said to be the result of two species mixing.

  • Centaur - the offspring of Centaurus and the mares of Thessaly. Has the body of a horse with its neck and head replaced by the torso and head of a man.
  • Harpy - the torso of a woman with the wings and feet of a bird.
  • Hippogriff - the offspring of a griffin and a horse, typically a male griffin and a mare.
  • Minotaur - the offspring of Pasiphaë and a white bull. Has the body of a man and the head of a bull.
  • Nephilim - the offspring of a fallen angel and human woman.