7.3: Linkage Reduces Recombination Frequency - Biology

7.3:  Linkage Reduces Recombination Frequency - Biology

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Having considered unlinked loci above, let us turn to the opposite situation, in which two loci are so close together on a chromosome that the parental combinations of alleles always segregate together (Figure (PageIndex{3})). This is complete (or absolute) linkage and is rare, as the loci must be so close together that crossovers are never detected between them.

7.3: Linkage Reduces Recombination Frequency - Biology

By the time the chemical nature of the gene was uncovered, genetics was already a mature science. In fact, Mendel's formulation of the basic principles of heredity was not even dependent on an understanding of the fact that genes existed within chromosomes. Rather, the existence of genes was inferred solely from the expression in offspring of visible traits at predicted frequencies based on the traits present in the parental and grandparental generations. Today, of course, the field of genetics encompasses a broad spectrum of inquiry from molecular studies on gene regulation to analyses of allele frequencies in natural populations, with many subfields in between. To distinguish the original version of genetics — that of Mendel and his followers — from the various related fields that developed later, several terms have been coined including "formal" genetics, "transmission" genetics, or "classical" genetics. Transmission genetics is the most informative term since it speaks directly to the feature that best characterizes the process by which Mendelian data are obtained — through an analysis of the transmission of genotypes and phenotypes from parents to offspring.

Mendel himself only formulated two of the three general features that underlie all studies in transmission genetics from sexually reproducing organisms. His formulations have been codified into two laws. The first law states, in modern terms, that each individual carries two copies of every gene and that only one of these two copies is transmitted to each child. At the other end of this equation, a child will receive one complete set of genes from each parent, leading to the restoration of a genotype that contains two copies of every gene. Individuals (and cells) that carry two copies of each gene are considered "diploid."

Mendel's first law comes into operation when diploid individuals produce "haploid" gametes — sperm or eggs — that each carry only a single complete set of genes. In animals, only a certain type of highly specialized cell — known as a "germ cell" — is capable of undergoing the transformation from the diploid to the haploid state through a process known as meiosis. At the cell division in which this transformation occurs, the two copies of each gene will separate or segregate from each other and move into different daughter (or brother) cells. This event provides the name for Mendel's first law: "the law of segregation." Segregation can only be observed from loci that are heterozygous with two distinguishable alleles. As a result of segregation, half of an individual's gametes will contain one of these alleles and half will contain the other. Thus, a child can receive either allele with equal probability. 43

While Mendel's first law is concerned with the transmission of individual genes in isolation from each other, his second law was formulated in an attempt to codify the manner in which different genes are transmitted relative to each other. In modern terms, Mendel's second law states that the segregation of alleles from any one locus will have no influence on the segregation of alleles from any other locus. In the language of probability, this means that each segregation event is independent of all others and this provides the name for Mendel's second law: "the law of independent assortment."

Independent assortment of alleles at two different loci — for example, A and B — can only be observed from an individual who is heterozygous at both with a genotype of the form A/a, B/b as illustrated in Figure 7.2. Each gamete produced by such an individual will carry only one allele from the A locus and only one allele from the B locus. Since the two alleles are acquired independently of each other, it is possible to calculate the probability of any particular allelic combination by simply multiplying together the probability of occurrence of each alone. For example, the probability that a gamete will receive the A allele is 0.5 (from the law of segregation) and the probability that this same gamete will receive the b allele is similarly 0.5. Thus, the probability that a gamete will have a combined A b genotype is 0.5 x 0.5 = 0.25. The same probabilities are obtained for all four possible allelic combinations (A B, a b, A b, a B). Since the number of gametes produced by an individual is very large, these probabilities translate directly into the frequencies at which each gamete type is actually present and, in turn, the frequency with which each will be transmitted to offspring (Figure 7.2).

As we all know today, Mendel's second law holds true only for genes that are not linked together on the same chromosome. 44 When genes A and B are linked, the numbers expected for each of the four allele sets becomes skewed from 25% (Figure 7.3). Two allele combinations will represent the linkage arrangements on the parental chromosomes (for example, A B and a b), and these combinations will each be transmitted at frequency of greater than 25%. The remaining two classes will represent recombinant arrangements that will be transmitted at a frequency below 25%. In the extreme case of absolute linkage, only the two parental classes will be transmitted, each at a frequency of 50%. At intermediate levels of linkage, transmission of the two parental classes together will be greater than 50% but less than 100%.

In 1905, when evidence for linkage was first encountered in the form of loci whose alleles did not assort independently, its significance was not appreciated (Bateson et al., 1905). The terms coupling and repulsion were coined to account for this unusual finding through some sort of underlying physical force. In a genetics book from 1911, Punnett imagined that alleles of different genes might "repel one another, refusing, as it were, to enter into the same zygote, or they may attract one another, and becoming linked, pass into the same gamete, as it were by preference" (Punnett, 1911). What this hypothesis failed to explain is why alleles found in repulsion to each other in one generation could become coupled to each other in the next generation. But even as Punnett's genetics text was published, an explanation was at hand. In 1912, Morgan and his colleagues proposed that coupling and repulsion were actually a consequence of co-localization of genes to the same chromosome: coupled alleles are those present on the same parental homolog, and alleles in repulsion are those present on alternative homologs (Morgan and Cattell, 1912 and Figure 7.3). Through the process of crossing over, alleles that are in repulsion in one generation (for example the A and b alleles in Figure 7.3) can be brought together on the same homolog — and thus become coupled — in the next generation. In 1913, Sturtevant used the rates at which crossing over occurred between different pairs of loci to develop the first linkage map with six genes on the Drosophila X chromosome (Sturtevant, 1913). Although the original rationale for the terms coupling and repulsion was eliminated with this new understanding, the terms themselves have been retained in the language of geneticists (especially human geneticists). Whether alleles at two linked loci are coupled or in repulsion is referred to as the phase of linkage.

The purpose of this chapter is to develop the concepts of transmission genetics as they are applied to contemporary studies of the mouse. This discussion is not meant to be comprehensive. Rather, it will focus on the specific protocols and problems that are most germane to investigators who seek to place genes onto the mouse linkage map and those who want to determine the genetic basis for various traits that are expressed differently by different animals or strains.

7.2.2 Linkage and recombination The backcross

Genetic linkage is a direct consequence of the physical linkage of two or more loci within the same pair of DNA molecules that define a particular set of chromosome homologs within the diploid genome. Genetic linkage is demonstrated in mice through breeding experiments in which one or both parents are detectably heterozygous at each of the loci under investigation. In the simplest form of linkage analysis — referred to as a backcross — only one parent is heterozygous at each of two or more loci, and the other parent is homozygous at these same loci. As a result, segregation of alternative alleles occurs only in the gametes that derive from one parent, and the genotypes of the offspring provide a direct determination of the allelic constitution of these gametes. The backcross greatly simplifies the interpretation of genetic data because it allows one to jump directly from the genotypes of offspring to the frequencies with which different meiotic products are formed by the heterozygous parent.

For each locus under investigation in the backcross, one must choose appropriate heterozygous and homozygous genotypes so that the segregation of alleles from the heterozygous parents can be followed in each of the offspring. For loci that have not been cloned, the genotype of the offspring can only be determined through a phenotypic analysis. In this case, if the two alleles present in the heterozygous parent show a complete dominant/recessive relationship, then the other parent must be homozygous for the recessive allele. For example, the A allele at the agouti locus causes a mouse to have a banded "agouti" coat color, whereas the a allele determines a solid "non-agouti" coat color. Since the A allele is dominant to a, the homozygous parent must be a/ a. In an A/ a x a/ a backcross, the occurrence of agouti offspring would indicate the transmission of the A allele from the heterozygous parent, and the occurrence of non-agouti offspring would indicate the transmission of the a allele.

In the case just described, the wild-type allele (A) is dominant and the mutant allele (a) is recessive. Thus, the homozygous parent must carry the mutant allele (a/ a) and express a non-agouti coat color. In other cases, however, the situation is reversed with mutations that are dominant and wild-type alleles that are recessive. For example, the T mutation at the T locus causes a dominant shortening of the tail. Thus, if the T locus were to be included in a backcross, the heterozygous genotype would be T/+ and the homozygous genotype would be wild-type (+/+) to allow one to distinguish the transmission of the T allele (within short-tailed offspring) from the + allele (within normal-tailed offspring).

As discussed in Chapter 8, most loci are now typed directly by DNA-based techniques. As long as both DNA alleles at a particular locus can be distinguished from each other, 45 it does not matter which is chosen for inclusion in the overall genotype of the homozygous parent. The same holds true for all phenotypically defined loci at which pairs of alleles act in a codominant or incompletely dominant manner. In all these cases, the heterozygote (A 1 /A 2 for example) can be distinguished from both homozygotes (A 1 /A 1 and A 2 /A 2 ). Map distances

In the example presented in Figure 7.3, an animal is heterozygous at both of two linked loci, which results in two complementary sets of coupled alleles — A B and a b. The genotype of this animal would be written as follows: AB/ab. 46 In the absence of crossing over between homologs during meiosis, one or the other coupled set — either A B or a b — will be transmitted to each gamete. However, if a crossover event does occur between the A and B loci, a non-parental combination of alleles will be transmitted to each gamete. In the example shown in Figure 7.3, the frequency of recombination between loci A and B can be calculated directly by determining the percentage of offspring formed from gametes that contain one of the two non-parental, or "recombinant," combinations of alleles. In this example, the recombination frequency is 10%.

To a first degree, crossing over occurs at random sites along all of the chromosomes in the genome. A direct consequence of this randomness is that the farther apart two linked loci are from each, the more likely it is that a crossover event will occur somewhere within the length of chromosome that lies between them. Thus, the frequency of recombination provides a relative estimate of genetic distance. Genetic distances are measured in centimorgans (cM) with one centimorgan defined as the distance between two loci that recombine with a frequency of 1%. Thus, as a further example, if two loci recombine with a frequency of 2.5%, this would represent an approximate genetic distance of 2.5 cM. In the mouse, correlations between genetic and physical distances have demonstrated that one centimorgan is, on average, equivalent to 2,000 kilobases. It is important to be aware, however, that the rate of equivalence can vary greatly due to numerous factors discussed in Section 7.2.3.

Although the frequency of recombination between two loci is roughly proportional to the length of DNA that separates them, when this length becomes too large, the frequency will approach 50%, which is indistinguishable from that expected with unlinked loci. The average size of a mouse chromosome is 75 cM. Thus, even when genes are located on the same chromosome, they are not necessarily linked to each other according to the formal definition of the term. However, a linkage group does include all genes that have been linked by association. Thus, if gene A is linked to gene B, and gene B is linked to gene C, the three genes together — A B C — form a linkage group even if the most distant members of the group do not exibit linkage to each other. Genetic interference

A priori, one might assume that all recombination events within the same meiotic cell should be independent of each other. A direct consequence of this assumption is that the linear relationship between recombination frequency and genetic distance — apparent in the single digit centimorgan range — should degenerate with increasing distances. The reason for this degeneration is that as the distance between two loci increases, so does the probability that multiple recombination events will occur between them. Unfortunately, if two, four, or any other even number of crossovers occur, the resulting gametes will still retain the parental combination of coupled alleles at the two loci under analysis as shown in Figure 7.4. Double (as well as quadruple) recombinants will not be detectably different from non-recombinants. As a consequence, the observed recombination frequency will be less than the actual recombination frequency.

Consider, for example, two loci that are separated by a real genetic distance of 20 cM. According to simple probability theory, the chance that two independent recombination events will occur in this interval is the product of the predicted frequencies with which each will occur alone which is 0.20 for a 20 cM distance. Thus, the probability of a double recombination event is 0.2 x 0.2 = 0.04. The failure to detect recombination in 4% of the gametes means that two loci separated by 20 cM will only show recombination at a frequency of 0.16. 47 A similar calculation indicates that at 30 cM, the observed frequency of recombinant products will be even further removed at 0.21. In 1919, Haldane simplified this type of calculation by developing a general equation that could provide values for recombination fractions at all map distances based on the formulation just described. This equation is known as the "Haldane mapping function" and it relates the expected fraction of offspring with detectable recombinant chromosomes (r) to the actual map distance in morgans (m) 48 that separates the two loci (Haldane, 1919):

After working through this hypothetical adjustment to recombination rates, it is now time to state that multiple events of recombination on the same chromosome are not independent of each other. In particular, a recombination event at one position on a chromosome will act to interfere with the initiation of other recombination events in its vicinity. This phenomenon is known, appropriately, as "interference." Interference was first observed within the context of significantly lower numbers of double crossovers than expected in the data obtained from some of the earliest linkage studies conducted on Drosophila (Muller, 1916). Since that time, interference has been demonstrated in every higher eukaryotic organism for which sufficient genetic data have been generated.

Significant interference has been found to extend over very long distances in mammals. The most extensive quantitative analysis of interference has been conducted on human chromosome 9 markers that were typed in the products of 17,316 meiotic events (Kwiatkowski et al., 1993). Within 10 cM intervals, only two double-crossover events were found this observed frequency of 0.0001 is 100-fold lower than expected in the absence of interference. Within 20 cM intervals, there were 10 double-crossover events (including the two above) this observed frequency of 0.0005 is still 80-fold lower than predicted without interference. As map distances increase beyond 20 cM, the strength of interference declines, but even at distances of up to 50 cM, its effects can still be observed (Povey et al., 1992). 49

If one assumes that human chromosome 9 is not unique in its recombinational properties, the implication of this analysis is that for experiments in which fewer than 1,000 human meiotic events are typed, multiple crossovers within 10 cM intervals will be extremely unlikely, and within 25 cM intervals, they will still be quite rare. Data evaluating double crossovers in the mouse are not as extensive, but they suggest a similar degree of interference (King et al., 1989). Thus, for all practical purposes, it is appropriate to convert recombination fractions of 0.25, or less, directly into centimorgan distances through a simple multiplication by 100.

When it is necessary to work with recombination fractions that are larger than 0.25, it is helpful to use a mapping function that incorporates interference into an estimate of map distance. Since the effects of interference can only be determined empirically, one cannot derive such a mapping function from first principles.

Instead, equations have been developed that fit the results observed in various species (Crow, 1990). The best-known and most widely-used mapping function is an early one developed by Kosambi (1944):

By solving Equation 7.2 for the observed recombination fraction, r, one obtains the "Kosambi estimate" of the map distance, m K , which is converted into centimorgans through multiplication by 100. Later, Carter and Falconer (1951) developed a mapping function that assumes even greater levels of interference based on the results obtained with linkage studies in the mouse: 50

Although it is clear that the Carter-Falconer mapping function is the most accurate for mouse data, the Kosambi equation was more easily solvable in the days before cheap, sophisticated hand-held calculators were available. Although the Carter-Falconer function is readily solvable today, it is not as well-known and not as widely used.

Interference works to the benefit of geneticists performing linkage studies for two reasons. First, the approximate linearity between recombination frequency and genetic distance is extended out much further than anticipated from strictly independent events. 51 Second, the very low probability of multiple recombination events can serve as a means for distinguishing the correct gene order in a three-locus cross, since any order that requires double recombinants among markers within a 20 cM interval is suspect. When all possible gene orders require a double or triple crossover event, it behooves the investigator to go back and re-analyze the sample or samples in which the event supposedly occurred. Finally, if the genotypings are shown to be correct, one must consider the possibility that an isolated gene conversion event has occurred at the single locus that differs from those flanking it.

7.2.3 Crossover sites are not randomly distributed Theoretical considerations in the ideal situation

Although genetic interference will restrict the randomness with which crossover events are distributed relative to each other within individual gametes, it will not affect the random distribution of crossover sites observed in large numbers of independent meiotic products. Thus, a priori, one would still expect the resolution of a linkage map to increase linearly with the number of offspring typed in a genetic cross. Assuming random sites of recombination, the average distance, in centimorgans, between crossover events observed among the offspring from a cross can be calculated according to the simple formula (100/ N ) where N is the number of meiotic events that are typed. For example, in an analysis of 200 meiotic events (200 backcross offspring or 100 intercross offspring), one will observe, on average, one recombination event every 0.5 cM. With 1,000 meiotic events, the average distance will be only 0.1 cM which is equivalent to approximately 200 kb of DNA. Going further according to this formula, with 10,000 offspring, one would obtain a genetic resolution that approached 20 kb. This would be sufficient to separate and map the majority of average-size genes in the genome relative to each other.

Once again, however, the results obtained in actual experiments do not match the theoretical predictions. In fact, the distribution of recombination sites can deviate significantly from randomness at several different levels. First, in general, the telomeric portions of all chromosomes are much more recombinogenic than are those regions closer to the centromere in both mice (de Boer and Groen, 1974) and humans (Laurie and Hulten, 1985). This effect is most pronounced in males and it leads to an effect like a rubber band when one tries to orient male and female linkage maps relative to each other (Donis-Keller et al., 1987). Second, different sites along the entire chromosome are more or less prone to undergo recombination. Third, even within the same genomic region, rates of recombination can vary greatly depending on the particular strains of mice used to produce the hybrid used for analysis (Seldin et al., 1989 Reeves et al., 1991 Watson et al., 1992). Finally, the sex of the hybrid can also have a dramatic effect on rates of recombination (Reeves et al., 1991). Gender-specific differences in rates of recombination

Gender-specific differences in recombination rates are well known. In general, it can be stated that recombination occurs less frequently during male meiosis than during females meiosis. An extreme example of this general rule is seen in Drosophila melanogaster where recombination is eliminated completely in the male. In the mouse, the situation is not as extreme with males showing a rate of recombination that is, on average, 50-85% of that observed in females (Davisson et al., 1989). However, the ratio of male to female rates of recombination can vary greatly among different regions of the mouse genome. In a few regions, the recombination rates are indistinguishable between sexes, and in even fewer regions yet, the male rates of recombination exceed female rates. Nevertheless, the general rule of higher recombination rates in females can be used to maximize data generation by choosing gender appropriately for a heterozygous F 1 animal in a backcross. For example, to maximize chances of finding initial evidence for linkage, one could choose males as the F 1 animals, but to maximize the resolution of a genetic map in a defined region, it would be better to use females. These considerations are discussed further in Section 9.4. Recombinational hotspots

The most serious blow to the unlimited power of linkage analysis has come from the results of crosses in which many thousands of offspring have been typed for recombination within small well-defined genomic regions. When the recombinant chromosomes generated in these crosses were examined at the DNA level, it was found that the distribution of crossover sites was far from random (Steinmetz et al., 1987). Instead, they tended to cluster in very small "recombinational hotspots" of a few kilobases or less in size (Zimmerer and Passmore, 1991 Bryda et al., 1992) The accumulated data suggest that these small hotspots may be distributed at average distances of several hundred kilobases apart from each other with 90% or more of all crossover events restricted to these sites.

The finding of recombinational hotspots in mice is surprising because it was not predicted from very high resolution mapping studies performed previously in Drosophila which showed an excellent correspondence between linkage and physical distances down to the kilobase level of analysis (Kidd et al., 1983). Thus, this genetic phenomenon — like genomic imprinting (Section 5.5) — might be unique to mammals. Unlike imprinting, however, the locations of particular recombinational hotspots do not appear to be conserved among different subspecies or even among different strains of laboratory mice.

Figure 7.5 illustrates the consequences of hotspot-preferential crossing over on the relationship between linkage and physical maps. In this example, 2,000 offspring from a backcross were analyzed for recombination events between the fictitious A and F loci. These loci are separated by a physical distance of 1,500 kb and, in our example, 17 crossover events (indicated by short vertical lines on the linkage map) were observed among the 2,000 offspring. A recombination frequency of 17/2,000 translates into a linkage distance of 0.85 cM. This linkage distance is very close to the 0.75 cM predicted from the empirically determined equivalence of 2,000 kb to 1 cM. However, when one looks further at loci between A and F, the situation changes dramatically. The B and C loci are only 20 kb apart from each on the physical map but are 0.4 cM apart from each other on the linkage map because a hotspot occurs in the region between them. With random sites of crossing over, the linkage value of 0.4 cM would have predicted a physical distance of 800 kb. The reciprocal situation occurs for the loci D and E which are separated by a physical distance of 400 kb but which show no recombination in 2000 offspring. In this case, random crossing over would have predicted a physical distance of less than 100 kb.

The existence and consequences of recombinational hotspots can be viewed in analogy to the quantized nature of matter. For experiments conducted at low levels of resolution — for example, in measurements of grams or centimorgans — the distribution of both matter and crossover sites will appear continuous. At very high levels of resolution, however, the discontinuous nature of both will become apparent. In practical terms, the negative consequences of hotspots on the resolution of a mouse linkage map will only begin to show up as one goes below the 0.2 cM level of analysis.

With the limited number of very large sample linkage studies performed to date, it is not possible to estimate the portion of the mouse genome that is dominated by hotspot-directed recombination. Furthermore, it is still possible that some genomic regions will allow unrestricted recombination as in Drosophila. Nevertheless, the available data suggest that for much of the genome, there will be an upper limit to the resolution that can be achieved in linkage studies based on a single cross. This limit will be reached at a point when the density of crossover sites passes the density of hotspots in the region under analysis. From the data currently available, it appears likely that this point will usually be crossed before one reaches 500 meiotic events corresponding to 0.2 cM or 400 kb. One strategy that can be used to overcome this limitation is to combine information obtained from several crosses with different unrelated inbred partners, each of which is likely be associated with different hotspot locations. This approach is discussed more fully in Section 9.4. Frequencies of recombination can vary greatly between different chromosomal regions.

As mentioned previously, the telomeric portions of chromosomes show higher rates of recombination per DNA length than more centrally located chromosomal regions. However, there is still great variation in recombination rates even among different non-telomeric regions. Some 1 mb regions produce recombinants at a rate equivalent to 2 cM or greater, whereas other regions of equivalent size only recombine with a rate equivalent to 0.5 cM or less in animals of the same gender. This variation could be due to differences in the number and density of recombination hotspots. In addition, the "strength" of individual hotspots, in terms of recombinogenicity, may differ from one site to another. Such differences could be specified by the DNA sequences at individual hotspots or by the structure of the chromatin that encompass multiple hotspots in a larger interval. A final variable may be generalized differences in the rates at which recombination can occur in regions between hotspots. Many more empirical studies will be required to sort through these various explanations.

7.2.4 A history of mouse mapping The classical era

Although its significance was not immediately recognized, the first demonstration of linkage in the mouse was published in 1915 by the great twentieth century geneticist J.B.S. Haldane (1915). What Haldane found was evidence for coupling between mutations at the albino (c) and pink-eyed dilution (p) loci, which we now know to lie 15 cM apart on Chr 7. Since that time, the linkage map of the mouse has expanded steadily at a near-exponential pace. During the first 65 years of work on the mouse map, this expansion took place one locus at a time. First, each new mutation had to be bred into a strain with other phenotypic markers. Then further breeding was pursued to determine whether the new mutation showed linkage to any of these other markers. This process had to be repeated with different groups of phenotypic markers until linkage to one other previously mapped marker was established. At this point, further breeding studies could be conducted with additional phenotypic markers from the same linkage group to establish a more refined map position.

In the first compendium of mouse genetic data published in the Biology of the Laboratory Mouse in 1941 (Snell, 1941), a total of 24 independent loci were listed, of which 15 could be placed into seven linkage groups containing either two or three loci each the remaining nine loci were found not to be linked to each other or to any of the seven confirmed linkage groups. By the time the second edition of the Biology of the Laboratory Mouse was published in 1966, the number of mapped loci had grown to 250, and the number of linkage groups had climbed to 19, although in four cases, these included only two or three loci (Green, 1966).

With the 1989 publication of the second edition of the Genetic Variants and Strains of the Laboratory Mouse (Lyon and Searle, 1989), 965 loci had been mapped on all 20 recombining chromosomes. However, even at the time that this map was actually prepared for publication (circa late 1987), it was still the case that the vast majority of mapped loci were defined by mutations that had been painstakingly incorporated into the whole genome map through extensive breeding studies. The middle ages: recombinant inbred strains

The first important conceptual breakthrough aimed at reducing the time, effort, and mice required to map single loci came with the conceptualization and establishment of recombinant inbred (abbreviated RI) strains by Donald Bailey and Benjamin Taylor at the Jackson Laboratory (Bailey, 1971 Taylor, 1978 Bailey, 1981). As discussed in detail in Section 9.2, a set of RI strains provides a collection of samples in which recombination events between homologs from two different inbred strains are preserved within the context of new inbred strains. The power of the RI approach is that loci can be mapped relative to each other within the same "cross" even though the analyses themselves may be performed many years apart. Since the RI strains are essentially preformed and immortal, typing a newly defined locus requires only as much time as the typing assay itself.

Although the RI mapping approach was extremely powerful in theory, during the first two decades after its appearance, its use was rather limited because of two major problems. First, analysis was only possible with loci present as alternative alleles in the two inbred parental strains used to form each RI set. This ruled out nearly all of the many loci that were defined by gross phenotypic effects. Only a handful of such loci — primarily those that affect coat color — were polymorphic among different inbred strains. In fact, in the prerecombinant DNA era, the only other loci that were amenable to RI analysis were those that encoded: (1) polymorphic enzymes (called allozymes or isozymes) that were observed as differentially migrating bands on starch gels processed for the specific enzyme activity under analysis (Womack, 1979) (2) immunological polymorphisms detected at minor histocompatibility loci (Graff, 1978) and (3) other polymorphic cell surface antigens (called alloantigens or isoantigens) that could be distinguished with specially developed "allo-antisera" (Boyse et al., 1968). In retrospect, it is now clear that RI strains were developed ahead of their time their power and utility in mouse genetics is only now — in the 1990s — being fully unleashed. DNA markers and the mapping panel era

Two events that occurred during the 1980s allowed the initial development of a whole genome mouse map that was entirely based on DNA marker loci. The first event was the globalization of the technology for obtaining DNA clones from the mouse genome and all other organisms. Although the techniques of DNA cloning had been developed during the 1970s, stringent regulations in the U.S. and other countries had prevented their widespread application to mammalian species like the mouse (Watson and Tooze, 1981). These regulations were greatly reduced in scope during the early years of the 1980s so that investigators at typical biological research facilities could begin to clone and characterize genes from mice. The globalization of the cloning technology was greatly hastened in 1982 by the publication of the first highly detailed cloning manual from Cold Spring Harbor Laboratory, officially entitled Molecular Cloning: A Laboratory Manual, but known unofficially as "The Bible" (Maniatis et al., 1982). 52

Although DNA clones were being recovered at a rapid rate during the 1980s, from loci across the mouse genome, their general utilization in linkage mapping was not straightforward. The only feasible technique available at the time for mapping cloned loci was the typing of restriction fragment length polymorphisms (RFLPs). Unfortunately, as discussed earlier in this book (Sections 2.3 and 3.2), the common ancestry of the traditional inbred strains made it difficult, if not impossible, to identify RFLPs between them at most cloned loci.

The logjam in mapping was broken not through the development of a new molecular technique, but rather, through the development of a new genetic approach. This was the second significant event in terms of mouse mapping during the 1980s — the introduction of the interspecific backcross. François Bonhomme and his French colleagues had discovered that two very distinct mouse species — M. musculus and M. spretus — could be bred together in the laboratory to form fertile F 1 female hybrids (Bonhomme et al., 1978). With the three million years that separate these two Mus species (Section 2.3), basepair substitutions have accumulated to the point where RFLPs can be rapidly identified for nearly every DNA probe that is tested. Thus, by backcrossing an interspecific super-heterozygous F 1 female to one of its parental strains, it becomes possible to follow the segregation of the great majority of loci that are identified by DNA clones through the use of RFLP analysis.

Although the "spretus backcross" could not be immortalized in the same manner as a set of RI strains, each of the backcross offspring could be converted into a quantity of DNA that was sufficient for RFLP analyses with hundreds of DNA probes. In essence, it became possible to move from a classical three-locus backcross to a several hundred locus backcross. Furthermore, the number of loci could continue to grow as new DNA probes were used to screen the members of the established "mapping panel" (until DNA samples were used up). The spretus backcross revolutionized the study of mouse genetics because it provided the first complete linkage map of the mouse genome based on DNA markers and because it provided mapping panels that could be used to rapidly map essentially any new locus that was defined at the DNA level. Microsatellites

The most recent major advance in genetic analysis has come not from the development of new types of crosses but from the discovery and utilization of PCR-based DNA markers that are extremely polymorphic and can be rapidly typed in large numbers of animals with minimal amounts of sample material. These powerful new markers — especially microsatellites — have greatly diminished the essential need for the spretus backcross and they have breathed new life into the usefulness of the venerable RI strains. Most importantly, it is now possible for individual investigators with limited resources to carry out independent, sophisticated mapping analyses of mutant genes or complex disease traits. As Philip Avner of the Institut Pasteur in Paris states: "If the 1980s were the decade of Mus spretus — whose use in conjunction with restriction fragment length polymorphisms revolutionized mouse linkage analysis, and made the mouse a formidably efficient system for genome mapping — the early 1990s look set to be the years of the microsatellite" (Avner, 1991). Microsatellites and other PCR-typable polymorphic loci are discussed at length in Section 8.3.

Linkage of Genetics: Features, Examples, Types and Significance

When two or more characters of parents are transmitted to the offsprings of few generations such as F1, F2, F3 etc. without any recombination, they are called as the linked characters and the phenomenon is called as linkage.

This is a deviation from the Mendelian principle of independent assortment.

Mendel’s law of independent assortment is applicable to the genes that are situated in separate chromosomes. When genes for different characters are located in the same chromosome, they are tied to one another and are said to be linked.

They are inherited together by the offspring and will not be assorted independently. Thus, the tendency of two or more genes of the same chromosome to remain together in the process of inheritance is called linkage. Bateson and Punnet (1906), while working with sweet pea (Lathyrus odoratus) observed that flower colour and pollen shape tend to remain together and do not assort independently as per Mendel’s law of independent assortment.

When two different varieties of sweet pea—one having red flowers and round pollen grain and other having blue flower and long pollen grain were crossed, the F1 plants were blue flowered with long pollen (blue long characters were respectively dominant over red and round characters). When these blue long (heterozygous) hybrids were crossed with double recessive red and round (homozygous) individuals (test cross), they failed to produce expected 1:1:1:1 ratio in F2 generation. These actually produced following four combinations in the ratio of 7 : 1 : 1 : 7 (7 blue long : 1 blue round : 1 red long : 7 red round) (Fig. 5.6).

The above result of the test cross clearly indicates that the parental combinations (blue, long and red, round) are seven times more numerous than the non-parental combinations. Bateson and Punnet suggested that the genes (such as B and L) coming from the same parent (BBLL × bbll) tend to enter the same gamete and to be inherited together (coupling). Similarly, the genes (B and 1) coming from two different parents (such as BBLL x bbll), tend to enter different gametes and to be inherited separately and independently (repulsion).

Morgan’s View of Linkage:

Morgan (1910), while working on Drosophila stated that coupling and repulsion are two aspects of linkage. He defined linkage as the tendency of genes, present in the same chromosome, to remain in their original combination and to enter together in the same gamete.’

The genes located on the same chromosome and are being inherited together are known as linked genes, and the characters controlled by these are known as linked characters. Their recombination frequency is always less than 50%. All those genes which are located in the single chromosome form one linkage group. The total number of linkage group in an organism corresponds to the number of chromosome pairs. For example, there are 23 linkage groups in man, 7 in sweet pea and 4 in Drosophila melanogaster.

Features of Theory of Linkage:

Morgan and Castle formulated ‘The Chromosome Theory of Linkage’.

It has the following salient features:

1. Genes that show linkage are situated in the same chromosome.

2. Genes are arranged in a linear fashion in the chromosome i.e., linkage of genes is linear.

3. The distance between the linked genes is inversely proportional to the strength of linkage. The genes which are closely located show strong linkage, whereas those, which are widely separated, have more chance to get separated by crossing over (weak linkage).

4. Linked genes remain in their original combination during course of inheritance.

5. The linked genes show two types of arrangement on the chromosome. If the dominant alleles of two or more pairs of linked genes are present on one chromosome and their recessive alleles of all of them on the other homologue (AB/ab), this arrangement is known as cis-arrangement. However, if the dominant allele of one pair and recessive allele of second pair are present on one chromosome and recessive and dominant alleles on the other chromosome of a homologous pair (Ab/aB), this arrangement is called trans arrangement (Fig. 5.7).

Examples of Linkage:

Maize provides a good example of linkage. Hutchinson crossed a variety of maize having coloured and full seed (CCSS) with a variety having colourless and shrunken seeds (ccss). The gene C for colour is dominant over its colourless allele c and the gene S for full seed is dominant over its shrunken allele s. All the F1 plants produced coloured and full seed. But in a test cross, when such F1 females (heterozygous) are cross pollinated with the pollen from a plant having colourless and shrunken seeds (double recessive), four types of seeds are produced (Fig. 5.8).

From the above stated result it is clear that the parental combinations are more numerous (96.4%) than the new combination (3.6%). This clearly indicates that the parental characters are linked together. Their genes are located in the same chromosome and only in 3.6% individuals these genes are separated by crossing over. This is an example of incomplete linkage.

Morgan (1911) crossed an ordinary wild type Drosophila with grey body and long wings (BB VV) with another Drosophila (mutant type) with black body and vestigial wings (bbvv). All the hybrids in F1 generation are with grey bodies and long wings (BbVv) i.e., phenotypically like the wild type of parents. If now a male of F, generation (Bb Vv) is back crossed with a double recessive female (test cross) having black body and vestigial wings (bbvv) only parental combinations are formed in F2 generation without the appearance of any new combinations. The results indicate that grey body character is inherited together with long wings.

It implies that these genes are linked together. Similarly, black body character is associated with vestigial wing. Since only parental combinations of character appear in the offspring of F2 generation and no new or non-parental combinations appear, this shows complete linkage. Complete linkage is seen in Drosophila males.

Types of Linkage:

Depending upon the presence or absence of new combinations or non-parental combinations, linkage can be of two types:

If two or more characters are inherited together and consistently appear in two or more generations in their original or parental combinations, it is called complete linkage. These genes do not produce non-parental combinations.

Genes showing complete linkage are closely located in the same chromosome. Genes for grey body and long wings in male Drosophila show complete linkage.

(ii) Incomplete Linkage:

Incomplete linkage is exhibited by those genes which produce some percentage of non-parental combinations. Such genes are located distantly on the chromosome. It is due to accidental or occasional breakage of chromosomal segments during crossing over.

Significance of Linkage:

(i) Linkage plays an important role in determining the nature of scope of hybridization and selection programmes.

(ii) Linkage reduces the chance of recombination of genes and thus helps to hold parental characteristics together. It thus helps organism to maintain its parental, racial and other characters. For this reason plant and animal breeders find it difficult to combine various characters.

Analysis of the recombination landscape of hexaploid bread wheat reveals genes controlling recombination and gene conversion frequency

Background: Sequence exchange between homologous chromosomes through crossing over and gene conversion is highly conserved among eukaryotes, contributing to genome stability and genetic diversity. A lack of recombination limits breeding efforts in crops therefore, increasing recombination rates can reduce linkage drag and generate new genetic combinations.

Results: We use computational analysis of 13 recombinant inbred mapping populations to assess crossover and gene conversion frequency in the hexaploid genome of wheat (Triticum aestivum). We observe that high-frequency crossover sites are shared between populations and that closely related parents lead to populations with more similar crossover patterns. We demonstrate that gene conversion is more prevalent and covers more of the genome in wheat than in other plants, making it a critical process in the generation of new haplotypes, particularly in centromeric regions where crossovers are rare. We identify quantitative trait loci for altered gene conversion and crossover frequency and confirm functionality for a novel RecQ helicase gene that belongs to an ancient clade that is missing in some plant lineages including Arabidopsis.

Conclusions: This is the first gene to be demonstrated to be involved in gene conversion in wheat. Harnessing the RecQ helicase has the potential to break linkage drag utilizing widespread gene conversions.

Keywords: Crossover Gene conversion QTL Recombination Wheat.

Conflict of interest statement

Ethics approval and consent to participate

All plants used in this study were grown in controlled growth chambers complying with Norwich Research Park guidelines. Plant material was supplied from the Germplasm Resources Unit at the John Innes Centre, Norwich, UK.

Consent for publication
Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Recombination landscape of wheat. a…

Recombination landscape of wheat. a The number of COs recorded for each RIL…

Fine-scale analysis of sequence exchange…

Fine-scale analysis of sequence exchange events. a The number of COs and/or GCs…

Output from QTL analysis from…

Output from QTL analysis from the Paragon × Chinese Spring population. QTL analysis…

Examination of candidate genes from…

Examination of candidate genes from QTL analysis RecQ-7 and RuvB . a Box…

Genetics Chapter 7

A. there is a large map distance between one of the outside genes in the heterozygous parent and the middle gene, while there is a short map distance between the middle gene and the other outside gene.

B. the physical distance between two genes is very short compared with the genetic map distance between these two genes.

C. crossing over has been enhanced for genes that are located near the centromere of chromosomes because there is less interference of one crossover on the occurrence of a second crossover event.

D. far fewer double-crossover recombinant progeny were recovered from a testcross than would be expected from the map distances of the genes involved.

A. one parent who is homozygous recessive for one gene pair and a second parent who is homozygous recessive for a second gene pair.

B. two parents who are both heterozygous for two or more genes.

C. one parent who shows the dominant phenotype for one or more genes and a second parent who is homozygous recessive for these genes.

D. one parent who shows the recessive phenotype for one or more genes and a second parent who is homozygous dominant for these genes.

A. the location of chromosomes in the nucleus when they line up at metaphase during mitosis

B. the distance in numbers of nucleotides between two genes

C. the linear order of genes on a chromosome

D. the location of double crossovers that occur between two genes

A. Association studies allow genes that have no obvious phenotype to be accurately mapped.

B. Genetic recombination of alleles is associated with physical exchange between chromosomes.

C. Crossing over does not occur in male Drosophila, so there is no genetic recombination.

D. Genes are located on chromosomes and the map distance between them could often be measured by the number of nucleotides in the DNA.

B. complete linkage and chromosome interference.

C. somatic-cell hybridization and chromosome interference.

D. chromosome interference and independent assortment.

A. double crossovers and other multiple-crossover events occur more often when genes are close to each other and can be readily detected, so these map distances are more accurate than those for genes that are far apart.

B. crossover interference will cause more double crossovers and other multiple-crossover events to occur than would be expected and thus result in a higher number of recombinant progeny than expected to occur with genes that are far apart.

C. when genes are far apart, single-crossover recombinant classes are more difficult to detect than when genes are close together.

D. with genes that are far apart, double crossovers and other multiple-crossover events often lead to lethal recombinants that reduce the number of recombinant progeny.


Variation in recombination rates in humans and other diploid organisms can be shaped by evolutionary and molecular processes [1], but these forces are only partially understood. High-resolution human recombination maps have been estimated using both parent–offspring transmission [2, 3] and patterns of linkage disequilibrium (LD) [4,5,6,7]. These have revealed localized regions with higher or lower recombination rates, known as recombination hotspots and coldspots, respectively [5]. Sequences analysis has shown that human recombination hotspots are associated with a number of sequence features such as PRDM9 binding motifs [8], CpG islands, and GC-rich repeats [4, 5, 9], and that recombination coldspots are associated with repetitive elements, transcribed regions, and telomeres [5, 6].

Outside recombination hotspots, differences in epigenomic signatures are associated with differences in recombination rate [10, 11]. In particular, the level of DNA methylation, primarily established at prophase I when recombination occurs [12], is reported to be positively correlated with recombination rate [11]. A causal effect of DNA methylation on recombination rate was established using a methylation-deficient strain of Arabidopsis, which showed reduction of recombination rate in euchromatic regions [13, 14].


Bernstein K, Gangloff S, Rothstein R. The RecQ DNA helicases in DNA repair. Annu Rev Genet. 201044:393–417.

Borrill P, Ramirez-Gonzalez R, Uauy C. expVIP: a customisable RNA-seq data analysis and visualisation platform. Plant Physiol. 2016170:2172–86.

Brachet E, Beneut C, Serrentino M, Borde V. The CAF-1 and Hir histone chaperones associate with sites of meiotic double-strand breaks in budding yeast. PLOSone. 201510:5.

Brenchley R, et al. Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature. 2012491:705–10.

Burridge A, Wilkinson P, Winfield M, Barker G, Allen A, Coghill J, Waterfall C, Edwards K. Conversion of array-based single nucleotide polymorphic markers for use in targeted genotyping by sequencing in hexaploid wheat (Tritium aestivum). Plant Biotechnol J. 201716(4):867–76.

Cesario J, McKim KS. RanGTP is required for meiotic spindle organization and the initiation of embryonic development in Drosophila. J Cell Sci. 2011124(22):3797–810.

Chen J, Cooper D, Chuzhanova N, Ferec C, Patrinos GP. Gene conversion: mechanisms, evolution and human disease. Nat Rev Genet. 20078:762–75.

Clavijo BJ, et al. An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations. Genome Res. 201727(5):885–96.

Darrier B, et al. High-resolution mapping of CO events in the hexaploid wheat genome suggests a universal recombination mechanism. Genetics. 2017206(3):1373–88.

Duroc Y, et al. Concerted action of the MutLβ heterodimer and Mer3 helicase regulates the global extent of meiotic gene conversion. eLife. 20176:e21900.

Esch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES. Using crossovers in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics. 2007177(3):1851–8.

Fernandes JB, Séguéla-Arnaud M, Larcheveque C, Lloyd AH, Mercier R. Unleashing meiotic crossovers in hybrid plants. PNAS. 2018115(10):2431–6.

Gardiner, L., Brabbs, T. and Hall, A. Recombination landscape of hexaploid bread wheat. Datasets. (2019).

Girard C, Chelysheva L, Choinard S, Froger N, Macaisne N, et al. Correction: AAA-ATPase FIDGETIN-LIKE 1 and helicase FANCM antagonize meiotic crossovers by distinct mechanisms. PLoS Genet. 201511(9):e1005448.

Griffiths S, Sharp R, Foote T, Bertin I, Wanous M, Reader S, Colas I, Moore G. Molecular characterization of Ph1 as a major chromosome pairing locus in polyploid wheat. Nature. 2006439:749–52.

Griffiths S, Wingen L, Edwards K. Populations axiom SNPs data - John Innes Centre, hdl:11529/10996, CIMMYT Research Data & Software Repository Network, V6 2017.

Halldorsson BV, et al. The rate of meiotic gene conversion varies by sex and age. Nat Genet. 201648(11):1377–84.

Hartung F, Puchta H. The RecQ gene family in plants. J Plant Physiol. 2006163(3):287–96.

Hartung F, Suer S, Puchta H. Two closely related RecQ helicases have antagonistic roles in homologous recombination and DNA repair in Arabidopsis thaliana. Proc Natl Acad Sci. 2007104(47):18836–41.

Higgins JD, Wright KM, Bomblies K, Franklin FCH. Cytological techniques to analyze meiosis in Arabidopsis arenosa for investigating adaptation to polyploidy. Front Plant Sci. 20134:546.

Hoek M, Myers M, Stillman B. An analysis of CAF-1-interacting proteins reveals dynamic and direct interactions with the KU complex and 140303 proteins. J Biol Chem. 2011286(12):10876–87.

Huang F, Mazina OM, Zentner IJ, Cocklin S, Mazin AV. Inhibition of homologous recombination in human cells by targeting RAD51 recombinase. J Med Chem. 201255(7):3011–20.

Jordan KW, et al. The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J. 2018.

Kalab P, Heald R. The RanGTP gradient-a GPS for the mitotic spindle. J Cell Sci. 2008121:1577–86.

Karow, J., Constantinou, A., Li, Ji-Liang, L., West, S. & Hickson, I. The Bloom’s syndrome gene product promotes branch migration of Holliday junctions. PNAS, 97(12): 6504–6508 (2000).

Krasileva KV, et al. Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci. 2017114(6):913–21.

Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 201644:W242–5.

Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 200925:1754–60.

Li H, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 200925:2078–9.

Li HQ, Terada R, Li MR, Lida S. RecQ helicase enhances homologous recombination in plants. FEBS Lett. 2004574(1–3):151–5.

Li X, Tyler J. Nucleosome disassembly during human non-homologous end joining followed by concerted HIRA- and CAF-1 dependent reassembly. eLife. 20165:e15129.

Löytynoja A, Goldman N. A model of evolution and structure for multiple sequence alignment. Philos Trans R Soc Lond Ser B Biol Sci. 2008363:3913–9.

Mascher M, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017544:427–33.

McKenna A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 201020:1297–303.

Mercier R, Mézard C, Jenczewski E, Macaisne N, Grelon M. The molecular biology of meiosis in plants. Annu Rev Plant Biol. 201566:297–327.

Pardo-Manuel De Villena F, Sapienza C. Recombination is proportional to the number of chromosome arms in mammals. Mamm Genome. 200112:318–22.

Qi J, Chen Y, Copenhaver G, Ma H. Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping. PNAS. 2014111(27):10007–12.

Schnable PS, Hsia A, Nikolau B. Genetic recombination in plants. Curr Opin Plant Biol. 19981:123–9.

Séguéla-Arnaud M, et al. Multiple mechanisms limit meiotic crossovers: TOP3α and two BLM homologs antagonize crossovers in parallel to FANCM. Proc Natl Acad Sci U S A. 2015112(15):4713–8.

Shalev G, Sitrit Y, Avivi-Ragolski N, Lichtenstein C, Levy A. Stimulation of homologous recombination in plants by expression of the bacterial resolvase RuvC. PNAS. 199996(13):7398–402.

Shi W, et al. The role of RPA2 phosphorylation in homologous recombination in response to replication arrest. Carcinogenesis. 201031(6):994–1002.

Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 201430:1312–3.

Sun Y, et al. Deep genome-wide measurement of meiotic gene conversion using tetrad analysis in Arabidopsis Thaliana. PLoS Genet. 20128(10):e1002968.

Szostak JW, Orr-Weaver TL, Rothstein RJ, Stahl FW. The double- strand break repair model for recombination. Cell. 198333:25–35.

Talbert PB, Henikoff S. Centromeres convert but don’t cross. PLoS Biol. 20108(3):e1000326.

The International Wheat Genome Sequencing Consortium (IWGSC) et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018361(6403):eaar7191.

Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. Jalview version 2 - a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009.

Wiedemann G, et al. RecQ helicases function in development, DNA repair, and gene targeting in Physcomitrella patens. Plant Cell. 201830:717–36.

Wilkinson PA, Winfield MO, Barker GLA, Allen AM, Burridge A, Coghill JA, Burridge A, Edwards KJ. CerealsDB 2.0: an integrated resource for plant breeders and scientists. BMC Bioinformatics. 201213:219.

Wingen LU, et al. Wheat landrace genome diversity. Genetics. 2017205(4):1657–76.

Wijnker E, et al. The genomic landscape of meiotic crossovers and gene conversions in Arabidopsis thaliana. eLife. 20132:e01426.

Yang S, et al. Great majority of recombination events in Arabidopsis are gene conversion events. Proc Natl Acad Sci U S A. 2012109(51):20992–7.

Zhao Q, Brkljacic J, Meier I. Two distinct interacting classes of nuclear envelope–associated coiled-coil proteins are required for the tissue-specific nuclear envelope targeting of Arabidopsis RanGAP. Plant Cell. 200820(6):1639–51.

Ziolkowski PA, Underwood CJ, Lambing C, Martinez-Garcia M, Lawrence EJ, et al. Natural variation and dosage of the HEI10 meiotic E3 ligase control Arabidopsis crossover recombination. Genes Dev. 201731:306–17.

Results and Discussion

Recombination Landscapes Are Highly Conserved between Wild and Domesticated Barley

The physical distribution and frequency of recombination events, that is, recombination landscape, play a role in plant adaptation as some genes are more likely to recombine than others. The barley genome is highly compartmentalized, with for example, disease resistance genes located in highly recombining distal regions of the chromosomes and genes involved in photosynthesis in low-recombining interstitial regions ( Mascher et al. 2017). Previous characterizations of the recombination landscape of barley focused on domesticated barley ( Künzel et al. 2000 Higgins et al. 2012 Phillips et al. 2012, , 2015 Dreissig et al. 2015, , 2017), except for cytological studies revealing a slightly higher number of chiasmata in domesticated barley than in wild barley ( Ross-Ibarra 2004). Here, we asked whether the fine-scale physical distribution of recombination events differs between domesticated and wild barleys. We hypothesized different recombination landscapes might be a consequence of adaptation to different environments, for example, natural habitats versus post-Neolithic farming.

In order to estimate recombination rates in wild barley (H. vulgare spp. spontaneum (K. Koch) Thell), we applied coalescent theory to a single nucleotide polymorphism (SNP) data set comprising 26,417 positions in a natural population of 74 geo-referenced accessions ( fig. 1) ( Milner et al. 2019). The Interval program from the LDhat package was used to estimate the population-scaled recombination rate (ρ) along the seven chromosomes of barley. We validated ancestral recombination rates estimated from population genetic data by comparing them to experimental measurements obtained through pollen nuclei sequencing of an F1 hybrid between two modern barley cultivars ( Dreissig et al. 2017). We observed a positive correlation of 0.81 between ancestral and experimental recombination landscape across relative chromosomal intervals of 0.01 (% of total chromosome length, P = 1.48 × 10 −24 ). These correlations are comparable to previous work in Arabidopsis ( Choi et al. 2013) and wheat ( Darrier et al. 2017), showing that coalescent-based methods provide reliable estimates of recombination landscapes. Next, we attempted to compare wild barley and domesticated landraces in order to test if the domestication process had an impact on their respective recombination landscapes. We estimated the population-scaled recombination rate in barley landraces using a large SNP data set comprising 26,334 SNPs and 100 randomly selected geo-referenced barley landraces ( fig. 1, Milner et al. 2019). For both wild barley and landraces, ρ was summed over relative chromosomal intervals and averaged across all seven chromosomes. Based on Spearman’s rank correlation, the two recombination landscapes are highly similar (r = 0.91, P = 2.08 × 10 −39 ). Elevated recombination rates are strictly confined to distal chromosomal regions, leaving about 80% of the chromosomes nearly devoid of recombination ( fig. 2). In both, the extent of the recombining region is smaller on the short arm and greater on the long arm of all chromosomes. At the fine-scale, however, differences became visible. On the long arm, elevated recombination rates are detected in more interstitial regions in wild barley ( fig. 2, 80–90% relative chromosome length), which is most pronounced on chromosome 2H, 3H, 5H, 6H, and 7H ( supplementary fig. 2 , Supplementary Material online). In domesticated landraces, elevated recombination rates are more distally confined on the long arm (90–100% relative chromosome length), with no striking difference between chromosomes except for 7H ( supplementary fig. 2 , Supplementary Material online). On the short arm, however, this does not seem to be the case, as elevated recombination rates are strictly confined to the first 5% of the chromosome in both groups.

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Origin of wild barley and landrace accessions. Collection sites of wild barley accessions (blue) and barley landraces (red) are shown. Colouring represents annual mean temperature under present conditions (°C). Within the inlet, which is zoomed in on the Fertile Crescent, the black arrow indicates the direction in which wild barley sub-populations were sampled.

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Origin of wild barley and landrace accessions. Collection sites of wild barley accessions (blue) and barley landraces (red) are shown. Colouring represents annual mean temperature under present conditions (°C). Within the inlet, which is zoomed in on the Fertile Crescent, the black arrow indicates the direction in which wild barley sub-populations were sampled.

Comparison of recombination landscapes between wild barley and domesticated landraces. Normalized recombination rate (0 = lowest value, 1 = highest value within a population) of wild barley (blue) and domesticated landraces (red) along relative chromosomal positions (0 = distal end of the short arm, 1 = distal end of the long arm) derived from the average of all seven chromosomes. On the short arm, highest values in both wild and domesticated barley overlap within the distal tip (5%) of the chromosome. On the long arm, highest values of wild barley reside within 80–90% of chromosome length, whereas highest values of domesticated barley reside within the distal tip (90–100% chromosome length). Enrichment of Gene Ontology (GO) terms in genomic compartments are adopted from Mascher et al. (2017). Coloured rectangles indicate −log10-transformed P-values from 1.3 (green) to 18.4 (red).

Comparison of recombination landscapes between wild barley and domesticated landraces. Normalized recombination rate (0 = lowest value, 1 = highest value within a population) of wild barley (blue) and domesticated landraces (red) along relative chromosomal positions (0 = distal end of the short arm, 1 = distal end of the long arm) derived from the average of all seven chromosomes. On the short arm, highest values in both wild and domesticated barley overlap within the distal tip (5%) of the chromosome. On the long arm, highest values of wild barley reside within 80–90% of chromosome length, whereas highest values of domesticated barley reside within the distal tip (90–100% chromosome length). Enrichment of Gene Ontology (GO) terms in genomic compartments are adopted from Mascher et al. (2017). Coloured rectangles indicate −log10-transformed P-values from 1.3 (green) to 18.4 (red).

Wild barley was estimated to have diverged from its most recent common ancestor approximately 4 Ma ( Brassac and Blattner 2015), and domestication began approximately 10,000 years ago ( Badr et al. 2000). By comparing the recombination landscape of wild barley with that of domesticated barley landraces, we show that recombination landscapes are highly conserved throughout domestication. Our data provide evidence for a strict separation between chromosomal regions permissive for recombination and chromosomal regions suppressive for recombination, even in long-term ancestral recombination data. Previous work demonstrated meiotic recombination is largely suppressed in heterochromatic regions enriched in CG, CHG, and CHH DNA methylation ( Melamed-Bessudo and Levy 2012 Mirouze et al. 2012 Yelina et al. 2012), and histone modifications such as H3K27me3, H3K9me3, H3K27me1, and H3K9me2 ( Aliyeva-Schnorr et al. 2015 Baker et al. 2015). A possible explanation for our observations could be that the chromatin environment suppressive for recombination is highly conserved across evolutionary time-scales. At the fine-scale, however, elevated recombination rates are shifted toward more distal regions on the long arm of the chromosomes in domesticated barley. Distal and interstitial regions show different gene contexts, with distal regions enriched for defense response genes and interstitial regions rather enriched for genes involved in basic cellular processes, such as nucleic acid metabolism, DNA repair, photosynthesis, and mRNA processing ( fig. 2). As a consequence of this compartmentalization, differences in recombination rate might be caused by selection for elevated recombination rates in regions harboring defense response genes throughout barley’s domestication, as recombination hotspots tend to be found near disease resistance genes ( Serra et al. 2018). Wild barley, which is not exposed to high pathogen pressure and does not show strong selection on resistance genes ( Stukenbrock and McDonald 2008 Ma et al. 2019), may therefore show a different ancestral recombination landscape.

Natural Variation in Recombination Rate

Recombination rates are highly variable at multiple scales, such as along chromosomes, sexes, individuals, populations, and species ( Stapley et al. 2017). In a strictly inbreeding species such as barley ( Brown et al. 1978), recombination may be under selection to counterbalance inbreeding depressions and maintain fitness ( Charlesworth et al. 1977). Previous studies have shown increased chiasmata frequency in inbreeding plants ( Stebbins 1950 Rees and Ahmad 1963 Zarchi et al. 1972 Gibbs et al. 1975). In this study, we asked whether recombination rates differ among natural populations of wild barley.

To analyze variation in recombination rate within a wild barley population, subpopulations needed to be defined for which recombination rates could be estimated. We first performed a principal component analysis (PCA) to test for population structure. The first two principal components explained 8.28% of the observed variance and revealed a continuous gradient along the Fertile Crescent, resembling its shape in the PCA space ( supplementary fig. 3 A, Supplementary Material online). We analyzed population admixture using sNMF ( Frichot et al. 2014) with the number of ancestral populations (K) ranging from 1 to 20. As K increased, the cross-entropy criterion decreased and no local minimum was reached ( supplementary fig. 3 B and C, Supplementary Material online). This suggested a continuous genetic gradient along the Fertile Crescent, which is supported by the absence of major geographic obstacles.

Since it was not feasible to define subpopulations based on ancestry coefficients, we instead defined overlapping subpopulations based on the geographical distribution of wild barley in agreement with their distribution in the PCA space. Subpopulations were defined following a sliding window approach comprising 20 accessions per window with a step size of 1 accession. Sliding windows were moved along the geographical distribution of wild barley in the Fertile Crescent ( fig. 1, Russell et al. 2014, 2016). In total, population-scaled recombination rates (ρ) were estimated in 55 subpopulations and averaged across all 7 chromosomes. Our analysis revealed substantial variation among subpopulations ( fig. 3A). Importantly, similar trends were observed between individual chromosomes ( supplementary fig. 4 , Supplementary Material online). For example, the lowest and highest genome-wide average ρ varied by a factor of 5.3. Since ρ is affected by effective population size (ρ = 4Ne× r), we estimated 4Ne based on nucleotide diversity (thetaW, Watterson’s theta) in each subpopulation and an assumed mutation rate (mu) of 3.5 × 10 −9 per bp per year ( Lin et al. 2002). Effective population size varied between subpopulations by a factor of 1.33 and was positively correlated with ρ ( fig. 3B, r = 0.79, P = 4.85 × 10 −13 ). We used the estimates of 4Ne to obtain the effective recombination rate per generation in each subpopulation (re = ρ/4Ne). After correcting for differences in effective population size, variation in effective recombination rate (re) remained largely unchanged, with the lowest and highest genome-wide average re varying by a factor of 4.54 ( fig. 3C, Kruskal–Wallis test, P < 2.2 × 10 −16 ). Therefore, variation in the population-scaled recombination rate seemed unlikely to be entirely caused by differences in effective population size. However, it cannot be excluded that different populations may experience different mutation rates. We also tested whether the observed pattern is explained by geographical distance within subpopulations, which may result in higher genetic diversity in subpopulations spanning wider geographical ranges ( Owuor et al. 1997 Hübner et al. 2009 Russell et al. 2014) affecting estimates of the population-scaled recombination rate. For each subpopulation, we calculated the longitudinal, latitudinal, and altitudinal range as a measure of geographical distance. For example, almost the whole range of recombination rate values was found twice over distinct geographical ranges (e.g., 50–70 and 300–400 km, supplementary fig. 5 , Supplementary Material online). A low number of haplotypes in narrow populations, and a lack of contact in extremely dispersed populations could cause low recombination rates in extremely narrow or dispersed populations. On the other hand, the absence of a linear association and our observation of the full range of recombination rate values over similar geographical ranges suggest that geographical distance does not primarily explain variation in the effective recombination rate. Finally, estimates of effective recombination rates may be influenced by selection. Based on previous work showing that wild barley is not subjected to strong selection and rather found in a state of random genetic drift ( Russell et al. 2016 Milner et al. 2019), we conclude the observed differences are unlikely to be caused by patterns of selection. Taken together, effective recombination rates are potentially affected by a multitude of population genetic factors, as well as actual differences in meiotic recombination rate.

Subpopulation analysis of ρ, 4Ne, and r in geo-referenced wild barley accessions. Seventy-four geo-referenced wild barley accessions were divided into 55 sub-populations of 20 accessions per sub-population according to a sliding window approach with a step size of 1 accession. Sliding windows are moved along the geographical distribution of wild barley across the Fertile Crescent. (A) Estimation of genome-wide mean population-scaled recombination rate (ρ). (B) Correlation between effective population size (4Ne), based on estimates of Watterson’s theta (thetaW) and an assumed mutation rate (mu) of 3.5 × 10 −9 , and population-scaled recombination rate (ρ). (C) Genome-wide mean effective recombination rate (re) corrected for differences in effective population size.

Subpopulation analysis of ρ, 4Ne, and r in geo-referenced wild barley accessions. Seventy-four geo-referenced wild barley accessions were divided into 55 sub-populations of 20 accessions per sub-population according to a sliding window approach with a step size of 1 accession. Sliding windows are moved along the geographical distribution of wild barley across the Fertile Crescent. (A) Estimation of genome-wide mean population-scaled recombination rate (ρ). (B) Correlation between effective population size (4Ne), based on estimates of Watterson’s theta (thetaW) and an assumed mutation rate (mu) of 3.5 × 10 −9 , and population-scaled recombination rate (ρ). (C) Genome-wide mean effective recombination rate (re) corrected for differences in effective population size.

Environmental Factors Shape Effective Recombination Rate in Natural Populations

There is a large body of experimental evidence showing correlations between recombination rates and environmental conditions. Particularly, the effect of temperature on meiotic recombination was studied in a number of experimental systems, such as Drosophila, Arabidopsis, barley, and other plants ( Dowrick 1957 Mange 1968 Zhuchenko et al. 1985 Jackson et al. 2015 Phillips et al. 2015 Lloyd et al. 2018 Modliszewski et al. 2018). However, as mentioned by Lloyd et al. (2018), an interesting question is whether these observations are reflective of what occurs in natural populations. Therefore, we sought to explore the relationship between effective recombination rate and environmental conditions in natural populations.

To address this question in natural populations of wild barley, we extracted annual mean temperature values for 74 geo-referenced wild barley accessions under present (1970–2000), Mid Holocene (MH, about 6,000 years BP), and Last Glacial Maximum (LGM, about 22,000 years BP) conditions. After the LGM and throughout the MH, wild barley showed a range expansion across the Fertile Crescent reflecting its current geographical distribution ( Russell et al. 2014). We therefore focused on environmental conditions during the MH. Plotting recombination rate against annual mean temperature revealed a nonlinear relationship between temperature and recombination rate ( fig. 4A). Across an annual mean temperature range from 15.6 to 19.5 °C, recombination rate was lower in subpopulations at either end of the scale and higher over the intermediate range, showing a reverse U-shaped curve. The same trend was observed by correlating recombination with different temperature conditions, that is, present conditions, MH conditions, and LGM conditions ( supplementary figs. 6 and 7 , Supplementary Material online). Considering the contribution of meiotic recombination to the effective recombination rate, it is important to consider the timing of meiosis. Meiosis usually takes place in spring under temperatures, which may be different to the annual mean. We therefore used present climate data to test if the trend observed for annual temperature is similar to that observed for approximate spring temperatures, which we considered the mean of March and April. A significant positive correlation indicated that annual mean temperature values reveal a similar trend as spring temperatures ranging from 12 to 16 °C (Pearson’s r = 0.978, P = 1.28 × 10 −37 ).

Correlation between recombination rate and environmental variables. Recombination rate estimated in 55 subpopulations of wild barley is plotted against environmental variables. The black line represents a smoothed curve over the data and the gray area represents the 95% confidence interval of the smoothed curve. (A) Relationship between recombination rate and annual mean temperature under Mid Holocene conditions. (B) Reverse U-shaped relationship between recombination rate and isothermality under Mid Holocene conditions. (C) Reverse U-shaped relationship between recombination rate and annual mean solar radiation under present conditions. (D) Correlation between recombination rate and annual precipitation under Mid Holocene conditions.

Correlation between recombination rate and environmental variables. Recombination rate estimated in 55 subpopulations of wild barley is plotted against environmental variables. The black line represents a smoothed curve over the data and the gray area represents the 95% confidence interval of the smoothed curve. (A) Relationship between recombination rate and annual mean temperature under Mid Holocene conditions. (B) Reverse U-shaped relationship between recombination rate and isothermality under Mid Holocene conditions. (C) Reverse U-shaped relationship between recombination rate and annual mean solar radiation under present conditions. (D) Correlation between recombination rate and annual precipitation under Mid Holocene conditions.

Interestingly, variation in recombination rate was best explained by isothermality ( fig. 4B), which describes temperature variability based on the day-to-night temperature range relative to the summer-to-winter temperature range (i.e., higher values indicating larger temperature variation and vice versa). We observed a reverse U-shaped curve, showing higher recombination rates across an intermediate isothermality range and lower recombination rates at either end of the scale. To test for a systematic bias in our sliding window approach, we performed the same analysis on a set of 55 randomized subpopulations, that is, randomly grouped accessions contrary to grouped according to geographical distribution, focusing on one representative chromosome. When subpopulations were chosen randomly, no correlation with temperature or isothermality was found (MH annual mean temperature: P = 0.11 MH isothermality: P = 0.11 supplementary fig. 8 , Supplementary Material online).

In addition to temperature, we also observed a nonlinear relationship between recombination rate and annual solar radiation ( fig. 4C). A positive linear relationship was observed with annual precipitation across the three different climate conditions ( fig. 4D, present: r = 0.298, P = 0.027 MH: r = 0.572, P = 5.1 × 10 −6 LGM: r = 0.765, P = 1.1 × 10 −11 ). Across time, from past (LGM) to present conditions, annual precipitation generally decreased in the Fertile Crescent. Interestingly, higher precipitation under LGM conditions appears to be better suited to explain differences in recombination rate. This is in agreement with a positive correlation between outcrossing rate and annual precipitation ( Abdel-Ghani et al. 2004), which also results in higher effective recombination rates ( Nordborg 2000). In barley, self-fertilization occurs while the spike is still enclosed in the flag leaf sheath ( Alqudah and Schnurbusch 2017), which results in a high inbreeding coefficient in our data (F = 0.978, CV = 0.02%). Therefore, although outcrossing does play a role, we conclude it is likely to have a minor effect in strictly inbreeding barley.

The differences in effective recombination rate between subpopulations were strictly confined to distal regions of the chromosome and no difference was observed in interstitial regions ( fig. 5). Considering the contribution of the meiotic recombination rate to the effective recombination rate, it is tempting to speculate on the molecular mechanisms leading to an increase in physically confined regions of the chromosomes. In plants, Drosophila, and yeast, temperature was shown to affect the frequency and distribution of class I crossover as well as meiotic chromosome axis and synaptonemal complex length ( Börner et al. 2004 Higgins et al. 2012 Aggarwal et al. 2015 Phillips et al. 2015 Lloyd et al. 2018 Modliszewski et al. 2018). This implies a mechanistic role of some proteins involved in meiotic chromosome axis, synaptonemal complex, and/or CO formation in mediating temperature-dependent plasticity of the recombination landscape which might in part be of biophysical origin as various proteins involved in these processes and their activity are temperature sensitive ( Morgan et al. 2017). In addition, UV-radiation and the nutritional state of the plant affect the formation of DNA double strand breaks and meiotic recombination ( Grant 1952 Griffing and Langridge 1963 Ries et al. 2000 Knoll et al. 2014 Aggarwal et al. 2015 Mercier et al. 2015 Si et al. 2015 Martín et al. 2017 Rey et al. 2018 Aggarwal DD, Rybnikov SR, Cohen I, Rashkovetsky E, Michalak P, Korol AB, unpublished data).

Recombination landscapes of different sub-populations. Recombination rates of different sub-populations (red = #27, blue = #4, green = #55) along the physical length of chromosome 5H. These three subpopulation were selected to represent the observed minima and maxima among all subpopulations. Variation in recombination rate is strictly confined to distal regions of the chromosome and no variation is observed in the low-recombining pericentromeric region.

Recombination landscapes of different sub-populations. Recombination rates of different sub-populations (red = #27, blue = #4, green = #55) along the physical length of chromosome 5H. These three subpopulation were selected to represent the observed minima and maxima among all subpopulations. Variation in recombination rate is strictly confined to distal regions of the chromosome and no variation is observed in the low-recombining pericentromeric region.

Our observations suggest that effective recombination rates are higher in populations subjected to intermediate annual temperature, isothermality, and solar radiation rather than extremes. This seems to be contradictory of what was observed in experimental studies in Arabidopsis and barley ( Phillips et al. 2015 Lloyd et al. 2018 Modliszewski et al. 2018), where temperature extremes were associated with increased recombination rate. However, key differences between our observations and experimental studies are the number of generations analyzed, the multitude of environmental conditions considered, and population genetic factors. Effective recombination rates estimated by patterns of linkage disequilibrium (LD) are the result of thousands of rounds of recombination and patterns of selection, outcrossing, and ancestral admixture. On the other hand, experimental studies are often limited to one generation owing to the time it takes to cultivate plants. We therefore think of our observations as long-term effective recombination rates, which may differ from short-term responses of the recombination machinery to environmental stress. These differences between long- and short-term effects might be influenced by different alleles of meiotic regulators, leading to differences in recombination rates ( Sidhu et al. 2017 Ziolkowski et al. 2017). However, since key meiotic regulators generally show high sequence conservation ( Villeneuve and Hillers 2001 Sidhu et al. 2017), our observations may be explained by meiotic plasticity toward the environment. Our data do not enable us to dissect the precise environmental conditions every population/individual faced during each meiotic cycle over time within the Fertile Crescent. However, they allow us to describe broad environmental effects on the recombination landscape of natural populations. For a strictly inbreeding species such as barley, the observed pattern can be interpreted as a measure to balance the loss of fitness caused by inbreeding. Interestingly, Morrell et al. (2005) observed surprisingly low levels of LD in wild barley, comparable to that of Zea mays, an outbreeding species. Our data provide evidence for high effective recombination rates under specific environmental conditions, which could explain the rapid LD decay observed by Morrell et al. (2005).

Taken together, our observations imply differences in how effective recombination rates are correlated with environmental conditions over long periods of time versus short-term responses of the recombination machinery to environmental stress. Under natural conditions, plant populations are subjected to varying environmental conditions, which are tightly linked, such as temperature, light, and precipitation. In controlled experiments, however, often only a single parameter of interest is changed in order to study its effects. Our observations suggest an interplay between temperature, light, and precipitation shaping variation in effective recombination rate in wild barley. A maximum effective recombination rate under intermediate temperature and light, as well as high precipitation may be interpreted as a means of generating genetic diversity upon which selection can act ( Presgraves 2005). In a strictly inbreeding species, this might be a mechanism to counteract the negative effects of inbreeding and maintain fitness.

13.1 Chromosomal Theory and Genetic Linkages

In this section, you will explore the following question:

Connection for AP ® Courses

Proposed independently by Sutton and Boveri in the early 1900s, the Chromosomal Theory of Inheritance states that chromosomes are vehicles of genetic heredity. As we have discovered, patterns of inheritance are more complex than Mendel could have imagined. Mendel was investigating the behavior of genes. He was fortunate in choosing traits coded by genes that happened to be on different chromosomes or far apart on the same chromosome. When genes are linked or near each other on the same chromosome, patterns of segregation and independent assortment change. In 1913, Sturtevant devised a method to assess recombination frequency and infer the relative positions and distances of linked genes on a chromosome based on the average number of crossovers between them during meiosis.

The content presented in this section supports the Learning Objectives outlined in Big Idea 3 of the AP ® Biology Curriculum Framework. The AP ® Learning Objectives merge essential knowledge content with one or more of the seven Science Practices. These objectives provide a transparent foundation for the AP ® Biology course, along with inquiry-based laboratory experiences, instructional activities, and AP ® exam questions.

Big Idea 3 Living systems store, retrieve, transmit and respond to information essential to life processes.
Enduring Understanding 3.A Heritable information provides for continuity of life.
Essential Knowledge 3.A.2 In eukaryotes, heritable information is passed to the next generation via processes that include the cell cycle and mitosis or meiosis plus fertilization.
Science Practice 7.1 The student can connect phenomena and models across spatial and temporal scales.
Learning Objective 3.10 The student is able to represent the connection between meiosis and increased genetic diversity necessary for evolution.
Essential Knowledge 3.A.3 The chromosomal basis of inheritance provides an understanding of the pattern of passage (transmission) of genes from parent to offspring.
Science Practice 1.1 The student can create representations and models of natural or man-made phenomena and systems in the domain.
Science Practice 7.2 The student can connect concepts in and across domain(s) to generalize or extrapolate in and/or across enduring understandings and/or big ideas.
Learning Objective 3.12 The student is able to construct a representation that connects the process of meiosis to the passage of traits from parent to offspring.

Teacher Support

Introduce genetic linkage using visuals such as this video.

Students can read about corn genetics in this review article.

Students can read about linked genes and Mendel’s work in this article.

Have students work through inheritance scenarios where genes are linked and where they are on different chromosomes using the following activity sheet.

Teacher preparation notes for this activity are available here.

The Science Practice Challenge Questions contain additional test questions for this section that will help you prepare for the AP exam. These questions address the following standards:
[APLO 3.2][APLO 3.11][APLO 3.14][APLO 3.15][APLO 3.28][APLO 3.26][APLO 3.17][APLO 4.22]

Long before chromosomes were visualized under a microscope, the father of modern genetics, Gregor Mendel, began studying heredity in 1843. With the improvement of microscopic techniques during the late 1800s, cell biologists could stain and visualize subcellular structures with dyes and observe their actions during cell division and meiosis. With each mitotic division, chromosomes replicated, condensed from an amorphous (no constant shape) nuclear mass into distinct X-shaped bodies (pairs of identical sister chromatids), and migrated to separate cellular poles.

Chromosomal Theory of Inheritance

The speculation that chromosomes might be the key to understanding heredity led several scientists to examine Mendel’s publications and re-evaluate his model in terms of the behavior of chromosomes during mitosis and meiosis. In 1902, Theodor Boveri observed that proper embryonic development of sea urchins does not occur unless chromosomes are present. That same year, Walter Sutton observed the separation of chromosomes into daughter cells during meiosis (Figure 13.2). Together, these observations led to the development of the Chromosomal Theory of Inheritance , which identified chromosomes as the genetic material responsible for Mendelian inheritance.

The Chromosomal Theory of Inheritance was consistent with Mendel’s laws and was supported by the following observations:

  • During meiosis, homologous chromosome pairs migrate as discrete structures that are independent of other chromosome pairs.
  • The sorting of chromosomes from each homologous pair into pre-gametes appears to be random.
  • Each parent synthesizes gametes that contain only half of their chromosomal complement.
  • Even though male and female gametes (sperm and egg) differ in size and morphology, they have the same number of chromosomes, suggesting equal genetic contributions from each parent.
  • The gametic chromosomes combine during fertilization to produce offspring with the same chromosome number as their parents.

Despite compelling correlations between the behavior of chromosomes during meiosis and Mendel’s abstract laws, the Chromosomal Theory of Inheritance was proposed long before there was any direct evidence that traits were carried on chromosomes. Critics pointed out that individuals had far more independently segregating traits than they had chromosomes. It was only after several years of carrying out crosses with the fruit fly, Drosophila melanogaster, that Thomas Hunt Morgan provided experimental evidence to support the Chromosomal Theory of Inheritance.

Genetic Linkage and Distances

Mendel’s work suggested that traits are inherited independently of each other. Morgan identified a 1:1 correspondence between a segregating trait and the X chromosome, suggesting that the random segregation of chromosomes was the physical basis of Mendel’s model. This also demonstrated that linked genes disrupt Mendel’s predicted outcomes. The fact that each chromosome can carry many linked genes explains how individuals can have many more traits than they have chromosomes. However, observations by researchers in Morgan’s laboratory suggested that alleles positioned on the same chromosome were not always inherited together. During meiosis, linked genes somehow became unlinked.

Homologous Recombination

In 1909, Frans Janssen observed chiasmata—the point at which chromatids are in contact with each other and may exchange segments—prior to the first division of meiosis. He suggested that alleles become unlinked and chromosomes physically exchange segments. As chromosomes condensed and paired with their homologs, they appeared to interact at distinct points. Janssen suggested that these points corresponded to regions in which chromosome segments were exchanged. It is now known that the pairing and interaction between homologous chromosomes, known as synapsis, does more than simply organize the homologs for migration to separate daughter cells. When synapsed, homologous chromosomes undergo reciprocal physical exchanges at their arms in a process called homologous recombination , or more simply, “crossing over.”

To better understand the type of experimental results that researchers were obtaining at this time, consider a heterozygous individual that inherited dominant maternal alleles for two genes on the same chromosome (such as AB) and two recessive paternal alleles for those same genes (such as ab). If the genes are linked, one would expect this individual to produce gametes that are either AB or ab with a 1:1 ratio. If the genes are unlinked, the individual should produce AB, Ab, aB, and ab gametes with equal frequencies, according to the Mendelian concept of independent assortment. Because they correspond to new allele combinations, the genotypes Ab and aB are nonparental types that result from homologous recombination during meiosis. Parental types are progeny that exhibit the same allelic combination as their parents. Morgan and his colleagues, however, found that when such heterozygous individuals were test crossed to a homozygous recessive parent (AaBb × aabb), both parental and nonparental cases occurred. For example, 950 offspring might be recovered that were either AaBb or aabb, but 50 offspring would also be obtained that were either Aabb or aaBb. These results suggested that linkage occurred most often, but a significant minority of offspring were the products of recombination.

Visual Connection

  1. Yes, the predicted offspring frequencies range from 0\% to 100\%
  2. No, the predicted offspring frequencies cannot be higher than 30\% .
  3. Yes, the predicted offspring frequencies range from 0\% to 60\% .
  4. No, the predicted offspring frequencies range from 0\% to 50\% .

Science Practice Connection for AP® Courses

Think About It

A test cross involving F1 dihybrid flies produces more parental-type offspring than recombinant-type offspring. How can you explain these observed results?

Teacher Support

The question is an application of Learning Objective 3.12 and Science Practices 1.1 and 7.2, and Learning Objective 3.10 and Science Practice 7.1 because students are explaining how meiosis can result in gametes with genetic variation in turn, these gametes can introduce variation in offspring.


More parental type offspring are produced because the genes that are being examined in the dihybrid cross are linked. Genes whose loci are nearer to each other are less likely to be separated onto different chromatids during meiosis as a result of chromosomal crossover. Therefore, there will be more offspring with the parental phenotype than the recombinant phenotype.

More information about linked genes can be found at the following resources:

Everyday Connection for AP® Courses

Genetic Markers for Cancers

Scientists have used genetic linkage to discover the location in the human genome of many genes that cause disease. They locate disease genes by tracking inheritance of traits through generations of families and creating linkage maps that measure recombination among groups of genetic “markers.” The two BRCA genes, mutations which can lead to breast and ovarian cancers, were some of the first genes discovered by genetic mapping. Women who have family histories of these cancers can now be screened to determine if one or both of these genes carry a mutation. If so, they can opt to have their breasts and ovaries surgically removed. This decreases their chances of getting cancer later in life. The actress Angelia Jolie brought this to the public’s attention when she opted for surgery in 2014 and again in 2015 after doctors found she carried a mutated BRCA1 gene.

  1. Genes responsible for temperament are on the same chromosome as genes responsible for certain facial features.
  2. A single gene codes for both temperament and certain facial features, such as jaw size.
  3. Genes responsible for mild temperament are only expressed when genes encoding a cute face are also present.
  4. The products of genes encoding temperament interact with the products of genes encoding facial features.

Genetic Maps

Janssen did not have the technology to demonstrate crossing over so it remained an abstract idea that was not widely accepted. Scientists thought chiasmata were a variation on synapsis and could not understand how chromosomes could break and rejoin. Yet, the data were clear that linkage did not always occur. Ultimately, it took a young undergraduate student and an “all-nighter” to mathematically elucidate the problem of linkage and recombination.

In 1913, Alfred Sturtevant, a student in Morgan’s laboratory, gathered results from researchers in the laboratory, and took them home one night to mull them over. By the next morning, he had created the first “chromosome map,” a linear representation of gene order and relative distance on a chromosome (Figure 13.4).

Visual Connection

Which of the following statements is true?
  1. Recombination of the red/brown eye and long/short aristae alleles will occur more frequently than recombination of the alleles for wing length and body color.
  2. Recombination of the body color and red/cinnabar eye alleles will occur more frequently than recombination of the alleles for wing length and aristae length.
  3. Recombination of the body color and aristae length alleles will occur more frequently than recombination of red/brown eye alleles and the aristae length alleles.
  4. Recombination of the gray/black body color and long/short aristae alleles will not occur.

As shown in Figure 13.4, by using recombination frequency to predict genetic distance, the relative order of genes on chromosome 2 could be inferred. The values shown represent map distances in centimorgans (cM), which correspond to recombination frequencies (in percent). Therefore, the genes for body color and wing size were 65.5 − 48.5 = 17 cM apart, indicating that the maternal and paternal alleles for these genes recombine in 17 percent of offspring, on average.

To construct a chromosome map, Sturtevant assumed that genes were ordered serially on threadlike chromosomes. He also assumed that the incidence of recombination between two homologous chromosomes could occur with equal likelihood anywhere along the length of the chromosome. Operating under these assumptions, Sturtevant postulated that alleles that were far apart on a chromosome were more likely to dissociate during meiosis simply because there was a larger region over which recombination could occur. Conversely, alleles that were close to each other on the chromosome were likely to be inherited together. The average number of crossovers between two alleles—that is, their recombination frequency —correlated with their genetic distance from each other, relative to the locations of other genes on that chromosome. Considering the example cross between AaBb and aabb above, the frequency of recombination could be calculated as 50/1000 = 0.05. That is, the likelihood of a crossover between genes A/a and B/b was 0.05, or 5 percent. Such a result would indicate that the genes were definitively linked, but that they were far enough apart for crossovers to occasionally occur. Sturtevant divided his genetic map into map units, or centimorgans (cM) , in which a recombination frequency of 0.01 corresponds to 1 cM.

By representing alleles in a linear map, Sturtevant suggested that genes can range from being perfectly linked (recombination frequency = 0) to being perfectly unlinked (recombination frequency = 0.5) when genes are on different chromosomes or genes are separated very far apart on the same chromosome. Perfectly unlinked genes correspond to the frequencies predicted by Mendel to assort independently in a dihybrid cross. A recombination frequency of 0.5 indicates that 50 percent of offspring are recombinants and the other 50 percent are parental types. That is, every type of allele combination is represented with equal frequency. This representation allowed Sturtevant to additively calculate distances between several genes on the same chromosome. However, as the genetic distances approached 0.50, his predictions became less accurate because it was not clear whether the genes were very far apart on the same chromosome or on different chromosomes.

In 1931, Barbara McClintock and Harriet Creighton demonstrated the crossover of homologous chromosomes in corn plants. Weeks later, homologous recombination in Drosophila was demonstrated microscopically by Curt Stern. Stern observed several X-linked phenotypes that were associated with a structurally unusual and dissimilar X chromosome pair in which one X was missing a small terminal segment, and the other X was fused to a piece of the Y chromosome. By crossing flies, observing their offspring, and then visualizing the offspring’s chromosomes, Stern demonstrated that every time the offspring allele combination deviated from either of the parental combinations, there was a corresponding exchange of an X chromosome segment. Using mutant flies with structurally distinct X chromosomes was the key to observing the products of recombination because DNA sequencing and other molecular tools were not yet available. It is now known that homologous chromosomes regularly exchange segments in meiosis by reciprocally breaking and rejoining their DNA at precise locations.

Link to Learning

Review Sturtevant’s process to create a genetic map on the basis of recombination frequencies here.

  1. Chromosomal crossover is a specific, non-random process during which chromosomes are linked together and exchange DNA, which contributes to the genetic diversity.
  2. Chromosomal crossover occurs during meiosis when chromosome pairs are linked and exchange DNA. Thus, crossover increases the variance of genetic combinations in the haploid gamete cell.
  3. Chromosomal crossover results in the inheritance of enhanced genetic material by offspring, and the subsequent recombination event is not variable in frequency or location.
  4. Chromosomal crossover occurs during the mitotic process when chromosomes are linked together and recombination takes place, increasing the variance of genetic combinations in the haploid mitotic cells formed from mitosis.

Mendel’s Mapped Traits

Homologous recombination is a common genetic process, yet Mendel never observed it. Had he investigated both linked and unlinked genes, it would have been much more difficult for him to create a unified model of his data on the basis of probabilistic calculations. Researchers who have since mapped the seven traits investigated by Mendel onto the seven chromosomes of the pea plant genome have confirmed that all of the genes he examined are either on separate chromosomes or are sufficiently far apart as to be statistically unlinked. Some have suggested that Mendel was enormously lucky to select only unlinked genes, whereas others question whether Mendel discarded any data suggesting linkage. In any case, Mendel consistently observed independent assortment because he examined genes that were effectively unlinked.

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    Crossing Over of Genes: Mechanism, Theories and Types

    The linkage is caused due to linked genes borne on the same chromosome. Morgan pointed out that the phenomenon of complete linkage occurs rarely because sometimes the linked genes show the tendency to separate during meiosis and new combinations are formed.

    This is due to interchange of parts between two homologous chromosomes for which the term “crossing over” is used.

    Thus, crossing over may be defined as a “mechanism of the recombination of the genes due to interchange of chromosomal segments at the time of pairing.”

    In the linkage experiment with maize, it is seen that the genes for seed colour C and full seed S remain associated in the parental combination in about 96 per cent but break apart in about 4 per cent (see Fig. 5.8). This recombination of linked genes to interchange parts between homologous chromosomes is termed as crossing over.

    Crossing over takes place in the segment of the chromosome between the loci of the genes C and S in some cells but not in others, so that about 96 per cent of the gametes contain the parental gene combination and 4 per cent contain recombination’s.

    Mechanism of Crossing Over:

    During the zygotene stage of the first prophase of meiosis, the homologous maternal and paternal chromosomes start pairing and lie closely side by side. This phenomenon is called synapsis. This pairing of homologous chromosomes is brought about by the mutual attraction between the allelic genes. The paired chromosomes are known as bivalent. A recent study reveals that synapsis and chiasma formation is facilitated by a highly organised structure of filaments called synaptonemal complex. Synapsis is followed by the duplication of chromosomes which change the bivalent nature of chromosome pair into tetravalent.

    During this each of the homologous chromosomes in a bivalent split longitudinally into two sister chromatids attached to the undivided centromere. Thus, four chromatids are formed which remain side by side as two pairs. Later, in pachytene stage crossing over takes place during which the non-sister chromatids of homologous pair twist over each other, the point of contact of cross over chromatids being called as chiasma (Fig. 5.9).

    In crossing over two or three chromatids are involved and accordingly two or more chiasmata are formed. At each chiasma the chromatid breaks and the broken segment rejoin a new chromatid (Fig. 5.10A & B). Thus exchange of parts of chromatids brings about alteration of original sequence of genes in the chromosome.

    After crossing over is completed, the non-sister chromatids repel each other due to lack of attraction between them. The repulsion or separation of chromatids starts from the centromere towards the end just like a zipper and this separation process is named as terminalization. The process of terminalization continues through diplotene, diakinesis and ends in metaphase I.

    At the end of terminalization the twisting chromatids separate so that the homologous chromosomes are separated completely and move to opposite poles in Anaphase I. The crossing over thus brings about alteration of the linear sequence of gene in chromosomes that produce gametes and thus add new combination of character in progeny.

    Theories of Crossing Over:

    (i) Contact First Theory (by Serebrovsky):

    According to this theory the inner two chromatids of the homologous chromosomes undergoing crossing over first touch each other and then cross over. At the point of contact breakage occurs. The broken segments again unite to form new combinations (Fig. 5.11).

    (ii) The Breakage-First Theory (By Muller):

    According to this theory the chromatids under-going crossing over first of all break into two without any crossing over and after that the broken segments reunite to form the new combinations (Fig. 5.11).

    (iii) Strain Theory (by Darlington):

    According to this theory the breakage in chromosomes or chromatids is due to strain caused by pairing and later the breakage parts again reunite.

    Types of Crossing Over:

    (i) Single Crossing Over:

    In this type of crossing over only one chiasma is formed all along the length of a chromosome pair. Gametes formed by this type of crossing over are called single cross over gametes (Fig. 5.10A and B).

    (ii) Double Crossing Over:

    In this type two chiasmata are formed along the entire length of the chromosome leading to breakage and rejoin of chromatids at two points. The gametes produced are called double cross over gametes (Fig. 5.14B).

    (iii) Multiple Crossing Over:

    In this type more than two chiasmata are formed and thus crossing over occurs at more than two points on the same chromosome pair. It is a rare phenomenon.

    Factors Influencing Crossing Over:

    In Drosophila, crossing over is completely suppressed in male but very high in female. Also there is a tendency of reduction of crossing over in male mammals.

    Gowen first discovered that mutation reduces crossing over in all the chromosomes of Drosophila.

    Inversion is an intersegmental change in the chromosome. In a given segment of chromosome crossing over is suppressed due to inversions.

    Plough has experimentally shown that when Drosophila is subjected to high and low temperature variations, the percentage of crossing over in certain parts of the chromosome is increased.

    Muller demonstrated that X-ray irradiations increase crossing over near centromere. Similarly Hanson has shown that radium increases crossing over.

    Bridges has demonstrated that the age also influences the rate of crossing over in Drosophila. When the female becomes older the rate of crossing over increases.

    High calcium diet in young Drosophila decreases crossing over rate where as diet deficient of metallic ions increases crossing over.

    8. The frequency of crossing over is less at the ends of the chromosome and also near the centromere in comparison to other parts.

    Significance of Crossing Over:

    1. Crossing over provides direct proof for the linear arrangement of genes.

    2. Through crossing over segments of homologous chromosomes are interchanged and hence provide origin of new characters and genetic variations.

    3. Crossing over has led to the construction of linkage map or genetic maps of chromosomes.

    4. Linkage group and linear order of the genes help to reveal the mechanism and nature of the genes.

    5. Crossing over plays a very important role in the field of breeding to improve the varieties of plants and animals.

    Watch the video: Genetic Recombination, Linked Genes, and Crossing Over (August 2022).