24.3: Linkage Reduces Recombination Frequency - Biology

24.3:  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.

Sequence-based ultra-dense genetic and physical maps reveal structural variations of allopolyploid cotton genomes

SNPs are the most abundant polymorphism type, and have been explored in many crop genomic studies, including rice and maize. SNP discovery in allotetraploid cotton genomes has lagged behind that of other crops due to their complexity and polyploidy. In this study, genome-wide SNPs are detected systematically using next-generation sequencing and efficient SNP genotyping methods, and used to construct a linkage map and characterize the structural variations in polyploid cotton genomes.


We construct an ultra-dense inter-specific genetic map comprising 4,999,048 SNP loci distributed unevenly in 26 allotetraploid cotton linkage groups and covering 4,042 cM. The map is used to order tetraploid cotton genome scaffolds for accurate assembly of G. hirsutum acc. TM-1. Recombination rates and hotspots are identified across the cotton genome by comparing the assembled draft sequence and the genetic map. Using this map, genome rearrangements and centromeric regions are identified in tetraploid cotton by combining information from the publicly-available G. raimondii genome with fluorescent in situ hybridization analysis.


We report the genotype-by-sequencing method used to identify millions of SNPs between G. hirsutum and G. barbadense. We construct and use an ultra-dense SNP map to correct sequence mis-assemblies, merge scaffolds into pseudomolecules corresponding to chromosomes, detect genome rearrangements, and identify centromeric regions in allotetraploid cottons. We find that the centromeric retro-element sequence of tetraploid cotton derived from the D subgenome progenitor might have invaded the A subgenome centromeres after allotetrapolyploid formation. This study serves as a valuable genomic resource for genetic research and breeding of cotton.

Morgan’s Experiment

Morgan picked Drosophila melanogaster as his subject for the following reasons:

  • He noticed a white-eyed male drosophila instead of the regular red eyes.
  • It was small in size
  • They have a short lifespan and so many generations can be studied in a short time frame.
  • They have a high rate of reproduction

He crossed a purebred white eyed male with purebred red-eyed female. As expected following Mendel’s laws, the F1 progeny were born with red eyes. When F1 generation was crossed among each other, the ratio of red-eyed to white eyed progeny were 3:1. However, he noticed that there was no white- eyed female in the F2 generation.

To understand further, he performed a cross between a heterozygous red-eyed female with a white-eyed male. This gave a ratio of 1:1:1:1 in the progeny(1 white eyed female, 1 red eyed female, 1 white eyed male and 1 red eyed male). This made Morgan think about the linkage between the traits and sex chromosomes. He performed many more crosses and determined that the gene responsible for the eye color was situated on the X chromosome.

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Recombination and linkage disequilibrium in Arabidopsis thaliana

Linkage disequilibrium (LD) is a major aspect of the organization of genetic variation in natural populations. Here we describe the genome-wide pattern of LD in a sample of 19 Arabidopsis thaliana accessions using 341,602 non-singleton SNPs. LD decays within 10 kb on average, considerably faster than previously estimated. Tag SNP selection algorithms and 'hide-the-SNP' simulations suggest that genome-wide association mapping will require only 40%–50% of the observed SNPs, a reduction similar to estimates in a sample of African Americans. An Affymetrix genotyping array containing 250,000 SNPs has been designed based on these results we demonstrate that it should have more than adequate coverage for genome-wide association mapping. The extent of LD is highly variable, and we find clear evidence of recombination hotspots, which seem to occur preferentially in intergenic regions. LD also reflects the action of selection, and it is more extensive between nonsynonymous polymorphisms than between synonymous polymorphisms.


Orangutan history

The best fitting demographic model (M6) suggests that the two Pongo species diverged 650–1000 kya and experienced a burst of admixture around 300 kya. Given the Pleistocene history of periodic sea-level changes in South East Asia [26], such a scenario of secondary contact seems biogeographically more plausible than continuous migration. Reassuringly, our estimates of the divergence time under M6 are consistent with previous estimates based on the SMC [8, 24] and agree well with species splits estimated for other island-endemic mammals in SE Asia [26].

Overall, our results are in general agreement with previous analyses regarding the absence of recent gene flow (< 250 kya) between Bornean and Sumatran orangutans [13]. Likewise, our inference of a larger Ne in Sumatran compared to Bornean orangutans agrees with relative measures of nucleotide diversity and previous analyses using various types of data [12, 19, 23, 25]. While we infer a contraction for the Bornean population under M3, in agreement with the simpler models explored by [25], sampling at finer spatial scales would be required to resolve substructure in both the Sumatran and Bornean populations.

Reassuringly, the time of secondary admixture under M6 agrees with the estimated split time between the two Pongo species for simpler models M1–M4 (Table 1) which are similar to those considered by Locke et. al. [23]. Using the joint SFS (δaδi, [3]), Locke et. al. [23] estimate a species divergence time of 400 kya, which is somewhat older than our estimate (250–300 kya) under M1–M4. However, a similar difference in estimates has already been noted by the Hidden Markov Model approach of Mailund et. al. [13] (see Supplemental Text S2 in [13]) which models a simplified demography of speciation with continuous gene flow and recombination using whole genome data.

Finally, the recent discovery of a new species (P. tapanuliensis, [33]) in Sumatra does not significantly affect our overall results as illustrated by the cbSFS analysis excluding the individual from that population (Additional file 2: Table S5). We do note that the newly inferred effective population sizes are lower than our previous estimates which is to be expected as the removal of the KB9258 individual (from the south of Lake Toba) will have significantly reduced (given its “outlier” status, [37]) the overall polymorphism contained in the cbSFS. In this analysis, which attempts to account for the new species, the genome-wide recombination rate was kept fixed (2×10 −8 /bp/generation) to offset the loss of information. This could explain the lower estimates of the divergence times obtained with the cbSFS from 500-bp blocks.

Absolute model fit and the effect of selection

Like most demographic inference methods, ABLE assumes selective neutrality. Furthermore, efficient calculation or approximation of the bSFS relies on the assumption that blocks are statistically exchangeable which ignores heterogeneity in mutation and recombination rates.

We can visualize the absolute fit of our demographic model to the data by comparing the observed distribution of bSFS configurations to that expected under M6 (obtained using 50 million simulated blockwise ARGs). If the data were generated entirely by the inferred demographic history, we would expect the most common bSFS configurations to fit this expectation most closely (see Additional file 1: Figure S11). In contrast, Fig. 7 shows that, irrespective of which demographic model we assume, some aspects of the data are poorly captured. In particular, bSFS configurations with few (or no) mutations (shown in blue) are common and overrepresented in the data. This mismatch is compatible with background selection [38] and/or positive selection reducing genetic diversity at a fraction of blocks.

Absolute model fit to the observed 2-kb bSFS for the most common configurations. Each point represents a unique mutational configuration making up the bSFS. The expected bSFS (x-axis) was generated with ABLE using 50 million ARGs at the MCLE for each model (Table 1) and plotted against the observed bSFS (y-axis) from the orangutan data. The diagonal black line indicates the perfect match between the expected and observed. The colors represent the total number of SNPs contained in each configuration

Linked selection may reduce estimates of ancestral Ne under neutral assumptions. Which could explain why we obtained a much smaller effective size for the ancestral population (Table 1 and Additional file 2: Table S1) than previous studies [12, 19, 23, 25], while our Ne estimates for the two current populations agree fairly well [13]. As expected, this signature of linked selection disappears when we consider a bSFS with shorter block size (Additional file 1: Figure S12). It will be interesting to explore the possibility of jointly inferring demography and various forms of selection using the bSFS [39].

Effect of block length and sample size

An interesting property of the bSFS is that it collapses to the SFS in both the limits of minimal block length (one base) and maximal block length (all data in a single block). At both extremes, all linkage information is lost and so the information contained in the distribution of bSFS types must be maximized at some intermediate block length. While ABLE relies on an arbitrary partitioning of the genome into blocks of a fixed length, recombination breakpoints in the ARG define real “blocks” of sequence that are identical by descent (IBD) with a length distribution that depends on the demographic history in a complex way. Because the distance of IBD blocks is a direct function of the length of genealogical branches, information about different demographic processes is maximized over different physical scales. For example, a burst of recent admixture generates an excess of long blocks that share descent via the admixture event but have different ancestry prior to admixture. The fact that one generally has little prior knowledge about the demography makes it challenging to decide on the most informative block length for a particular dataset.

However, given knowledge of the relative ratio of mutation over recombination events μ/ρ and assuming that information in the bSFS is maximized if blocks contain on average some small number x of IBD tracts, block length can be defined heuristically for a particular x. For example, assuming μ/ρ≈1 for Great Apes, our 2-kb blocks contain on average two to three recombination events within each Pongo species (given θW=2.19 and 2.91 in 2kb blocks for the Bornean and Sumatran populations respectively). A sensible upper (but equally heuristic) bound for the block length is the length at which the number of unique bSFS configurations is maximized which is around 5 kb for the history inferred for the two orangutan species (Additional file 1: Figure S13). However, attempts to partitioning the orangutan data into blocks much longer than 2 kb led to substantial loss of data (given the modest overall coverage), so we did not explore this further.

The fact that ABLE and multi-locus approaches in general rely on a fixed (and necessarily arbitrary) block length is a definite limitation. Thus, an interesting direction for future work would be to integrate CL estimates based on the bSFS over a range of block sizes which should improve the power to infer recent demographic events. A related inference scheme that integrates over a range of window sizes has recently been implemented [20].

Our finding of larger r estimates when using shorter blocks for the orangutan data was surprising. Given that our method ignores heterogeneity in both r and μ, both of which increase auto-correlation across short distances, we expected to find the opposite, i.e., a decrease in r estimates for shorter blocks. However, our simulation analysis showed that ABLE gives relatively unbiased estimates of r for short (500 bp) blocks when inference was performed with two or more diploid samples per population (Additional file 2: Table S3). A plausible explanation for the large r estimates for the orangutan data could be gene conversion because conversion events that span block boundaries are indistinguishable from cross-over events. Results from a simple simulation of a bSFS from 500-bp blocks with gene conversion do highlight this as a probable cause for obtaining higher recombination rate estimates (Additional file 2: Table S3). Furthermore, gene conversion must have a diminishing effect on the bSFS for blocks that are longer than the typical conversion tract length of several hundred bases (see Table 2 in [40]). In the future, it should be possible to use this dependence on block length to develop explicit estimators for gene conversion and cross-over rates.

Even under a complex demography such as M6, our simulation-based power analyses indicate that most demographic parameters can be reasonably recovered with only a single diploid genome per population (Additional file 2: Table S3). Increasing sample size to two diploid genomes more than halved the standard deviation in estimates for some parameters, most notably the recombination rate (Additional file 1: Figure S2). However, a further increase in sample size gave a negligible improvement, despite the considerable computational cost (Additional file 1: Figure S3) involved: the number of unique bSFS configurations increased more than threefold with three rather than two diploid genomes per population. This diminishing return with increasing sample size (in terms of sequences) is a fundamental property of the coalescent [41, 42]: going backwards in time, larger samples in each species are likely to have coalesced down to a small number of lineages (see Fig. 3 in [42]) before the admixture event and so are unlikely to contribute much additional information about older demographic processes.

The SFS, the bSFS, and the cbSFS

In this paper, we have explored the intuition that using linkage information contained in the bSFS should improve demographic inference compared to the SFS which is only a function of the expected length of genealogical branches [5, 6]. It has previously been shown that the bSFS for a small sample (n=5) contains significantly more information about past bottlenecks than the SFS for a large sample (n=20, see Fig. 3 in [22]). Likewise, our analysis comparing ABLE with the SFS-based ai [3] for progressively complex subdivided population scenarios (M1, M2, and M6) resulted in improved inferences (with the bSFS) of ancestral population sizes, divergence times, and admixture rates albeit with increased variance in the estimates (Additional file 1: Figure S8, Figure S9, and Figure S10).

However, we only make use of a subset of two diploid genomes for the ABLE analysis compared to the whole sample of five diploid genomes used by ai. This increase in performance can be explained by the fact that the bSFS is a higher dimensional and therefore much richer summary of sequence variation than the SFS [18, 22]. However, this increase in information comes at a computational cost (Additional file 1: Figure S3) and it may be fruitful in general to narrow down parameter space using SFS-based approaches such as ai [3] prior to an ABLE analysis. Finally, the cbSFS scheme provides for an alternative by considering all subsets of the original sample which enables the analysis of arbitrarily large samples at minimal computational cost.

Limits to inference

While our choice of models was guided by previous knowledge of the demographic history of orangutans [13, 23–25], it remains to be determined what the limits of model complexity and identifiability are with our approach and to what degree the distribution of bSFS patterns overcomes the non-identifiability of the SFS [7, 43, 44]. Unlike analytic likelihood calculations (e.g., [18]), there is no significant increase in computational cost with increasing model complexity when approximating the likelihood for a given point in parameter space. However, searching parameter space carries an obvious and rapidly increasing cost with greater model complexity. Like all approximate likelihood approaches, ABLE requires the user to make careful choices about the number of parameters, the number of genealogies to sample per point in parameter space, and the search bounds for the MCLE, all of which are crucial elements of the optimization strategy [4]. In this regard, we suggest that simple pilot analyses varying some or all of the factors mentioned above (see Additional file 1: Figure S14 and Figure S13) should help to inform the inference strategy.

It is also clear that, independent of the inference approach, the information in the data is finite, so there must be a hard limit on how realistic a history one can hope to infer. Thus, the fact that ABLE can, in principle, be used for fitting any demographic model puts the onus of constraining inference to scenarios that are both statistically identifiable and biologically interpretable on the user. Evaluating the relative fit of simpler nested models is an important sanity check on the limits of information in the data. For instance, our comparison of analyses based on 2-kb and 500-bp blocks (Fig. 5 and Additional file 1: Figure S15, respectively) highlights the limits of our inference scheme for short block lengths.

The inferential approach implemented in ABLE makes use of the coalescent simulator ms [45] for sampling blockwise genealogies or ARGs. In principle, ABLE can accommodate other simulators and is thus amenable to include additional processes such as linked selection [46, 47]. Another interesting avenue for further research is to apply approximate composite likelihoods based on the bSFS along the genome. Such an approach would not only help improve upon recombination maps for non-model organisms but could also provide a robust framework to identify outlier regions of the genome under positive selection and/or affected by introgression from another species.

Inferring the landscape of recombination using recurrent neural networks

Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here we describe ReLERNN, a deep learning method for accurately estimating a genome-wide recombination landscape using as few as four samples. Rather than use summaries of linkage disequilibrium as its input, ReLERNN considers columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, while largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.


Positive (antagonistic or diminishing-returns) epistasis

As a consequence of the input of deleterious mutations in the different genes the direction of epistasis is positive, meaning that mutations have a weaker effect on protocell fitness when combined (Section 3 in S1 Text, see S1 Fig). Under this condition, decreased recombination is always favored [17,29]. It is worth mentioning that antagonistic epistasis has been predicted from studies of the effect of mutations on RNA folding [30] and analyses of RNA viruses [31], as well as in E. coli and S. cerevisiae using flux balance analysis and in silico studies of metabolic networks [32].

Chromosomatisation and genome expansion

We first summarize the main findings and then focus on a particular scenario to understand the dynamics of the system. In all analyzed situations, chromosomes always spread despite strong within-protocell selection against them. Even if a long chromosome breaks, a diverse set of smaller chromosomes with different number of genes can be present at equilibrium. However, in all cases chromosomes with full set(s) of genes dominate the system. If the split size S is low (i.e., if the maximal number of genes at the time of protocell division is low), chromosomes with one full set of essential genes are present in relatively high concentration. With increasing split size the concentration of chromosomes with two (or more) full sets of genes increases that is, we observe a genome expansion of linked genes as a function of split size. Chromosome breakage produces solitary genes and shorter chromosomes that contain no full sets of genes, reducing the average length of chromosomes and protocell fitness. Nevertheless, in the transient period chromosome breakage introduces the necessary variation to reach an optimal composition of genes in the chromosome. Without chromosome breakage the system could freeze in a suboptimal state and, in equilibrium, only a few types of chromosomes remain in the system that excludes further optimization. Finally, we have found that within-cell recombination does not affect the results qualitatively.

We now focus on a particular case assuming D = 3 essential genes (A, B and C). Table 1 shows the number and ratio of different types of genes in chromosomes with different gene numbers in equilibrium. The most frequent chromosome (

50%) in the population was perfectly balanced with genes ABC, and the second most frequent (

21%) chromosome with genes ABCABC. Balanced ABCABCABC chromosomes (

1.5%) were also present. In other cases the gene composition was less balanced, but on the whole there is an almost perfect equilibrium in gene composition at the population level. Breaks produce solitary genes recurrently and because of the assortment load the ratio of solitary genes of different types is not well balanced.

An Introduction to Genetic Analysis. 7th edition.

If two breaks occur in one chromosome, sometimes the region between the breaks rotates 180 degrees before rejoining with the two end fragments. Such an event creates a chromosomal mutation called an inversion. Unlike deletions and duplications, inversions do not change the overall amount of the genetic material, so inversions are generally viable and show no particular abnormalities at the phenotypic level. In some cases, one of the chromosome breaks is within a gene of essential function, and then that breakpoint acts as a lethal gene mutation linked to the inversion. In such a case, the inversion could not be bred to homozygosity. However, many inversions can be made homozygous furthermore, inversions can be detected in haploid organisms. In these cases, the breakpoint is clearly not in an essential region. Some of the possible outcomes of inversion at the DNA level are shown in Figure 17-14.

Figure 17-14

Effects of inversions at the DNA level. Genes are represented by A, B, C, and D. Template strand is dark green nontemplate strand is light green jagged lines indicate break in DNA. The letter P stands for promoter thick arrow indicates the position (more. )

Most analyses of inversions use heterozygous inversions𠅍iploids in which one chromosome has the standard sequence and one carries the inversion. Microscopic observation of meioses in inversion heterozygotes reveals the location of the inverted segment because one chromosome twists once at the ends of the inversion to pair with the other, untwisted chromosome in this way the paired homologs form an inversion loop (Figure 17-15).

Figure 17-15

The chromosomes of inversion heterozygotes pair in a loop at meiosis. (a) Diagrammatic representation each chromosome is actually a pair of sister chromatids. (b) Electron micrographs of synaptonemal complexes at prophase I of meiosis in a mouse heterozygous (more. )

The location of the centromere relative to the inverted segment determines the genetic behavior of the chromosome. If the centromere is outside the inversion, then the inversion is said to be paracentric, whereas inversions spanning the centromere are pericentric:

How do inversions behave genetically? Crossing-over within the inversion loop of a paracentric inversion connects homologous centromeres in a dicentric bridge while also producing an acentric fragment𠅊 fragment without a centromere. Then, as the chromosomes separate in anaphase I, the centromeres remain linked by the bridge, which orients the centromeres so that the noncrossover chromatids lie farthest apart. The acentric fragment cannot align itself or move and is, consequently, lost. Tension eventually breaks the bridge, forming two chromosomes with terminal deletions (Figure 17-16). The gametes containing such deleted chromosomes may be inviable but, even if viable, the zygotes that they eventually form are inviable. Hence, a crossover event, which normally generates the recombinant class of meiotic products, instead produces lethal products. The overall result is a lower recombinant frequency. In fact, for genes within the inversion, the RF is zero. For genes flanking the inversion, the RF is reduced in proportion to the relative size of the inversion.

Figure 17-16

Meiotic products resulting from a single crossover within a paracentric inversion loop. Two nonsister chromatids cross over within the loop.

Inversions affect recombination in another way too. Inversion heterozygotes often have mechanical pairing problems in the region of the inversion these pairing problems reduce the frequency of crossing-over and hence the recombinant frequency in the region.

The net genetic effect of a pericentric inversion is the same as that of a paracentric one𠅌rossover products are not recovered𠅋ut for different reasons. In a pericentric inversion, because the centromeres are contained within the inverted region, the chromosomes that have crossed over disjoin in the normal fashion, without the creation of a bridge. However, the crossover produces chromatids that contain a duplication and a deficiency for different parts of the chromosome (Figure 17-17). In this case, if a nucleus carrying a crossover chromosome is fertilized, the zygote dies because of its genetic imbalance. Again, the result is the selective recovery of noncrossover chromosomes in viable progeny.

Figure 17-17

Meiotic products resulting from a meiosis with a single crossover within a pericentric inversion loop.


Two mechanisms reduce the number of recombinant products among the progeny of inversion heterozygotes: elimination of the products of crossovers in the inversion loop and inhibition of pairing in the region of the inversion.

It is worth adding a note about homozygous inversions. In such cases the homologous inverted chromosomes pair and cross over normally, there are no bridges, and the meiotic products are viable. However, an interesting effect is that the linkage map will show the inverted gene order.

Geneticists use inversions to create duplications of specific chromosome regions for various experimental purposes. For example, consider a heterozygous pericentric inversion with one breakpoint at the tip (T) of the chromosome, as shown in Figure 17-18. A crossover in the loop produces a chromatid type in which the entire left arm is duplicated if the tip is nonessential, a duplication stock is generated for investigation. Another way to make a duplication (and a deficiency) is to use two paracentric inversions with overlapping breakpoints (Figure 17-19). A complex loop is formed, and a crossover within the inversion produces the duplication and the deletion. These manipulations are possible only in organisms with thoroughly mapped chromosomes for which large sets of standard rearrangements are available.

Figure 17-18

Generation of a viable nontandem duplication from a pericentric inversion close to a dispensable chromosome tip.

Figure 17-19

Generation of a nontandem duplication by crossing-over between two overlapping inversions.

We have seen that genetic analysis and meiotic chromosome cytology are both good ways of detecting inversions. As with most rearrangements, there is also the possibility of detection through mitotic chromosome analysis. A key operational feature is to look for new arm ratios. Consider a chromosome that has mutated as follows:

Note that the ratio of the long to the short arm has been changed from about 4 to about 1 by the pericentric inversion. Paracentric inversions do not alter the arm ratio, but they may be detected microscopically if banding or other chromosome landmarks are available.


The main diagnostic features of inversions are inversion loops, reduction of recombinant frequency, and reduced fertility from unbalanced or deleted meiotic products, all observed in individuals heterozygous for inversions. Some inversions may be directly observed as an inverted arrangement of chromosomal landmarks.

Inversions are found in about 2 percent of humans. The heterozygous inversion carriers generally show no adverse phenotype but produce the expected array of abnormal meiotic products from crossing-over in the inversion loop. Let us consider pericentric inversions as an example. Persons heterozygous for pericentric inversions produce offspring with the duplication�letion chromosomes predicted these offspring show varying degrees of abnormalities depending on the lengths of the chromosome regions affected. Some phenotypes caused by duplication�letion chromosomes are so abnormal as to be incapable of survival to birth and are lost as spontaneous abortions. However, there is a way to study the abnormal meiotic products that does not depend on survival to term. Human sperm placed in contact with unfertilized eggs of the golden hamster penetrate the eggs but fail to fertilize them. The sperm nucleus does not fuse with the egg nucleus, and, if the cell is prepared for cytogenetic examination, the human chromosomes are easily visible as a distinct group (Figure 17-20). This technique makes it possible to study the chromosomal products of a male meiosis directly and is particularly useful in the study of meiotic products of men who have chromosome mutations.

Figure 17-20

Human sperm and hamster oocytes are fused to permit study of the chromosomes in the meiotic products of human males. (After original art by Renພ Martin.)

In one case, a man heterozygous for an inversion of chromosome 3 underwent sperm analysis. The inversion was a large one with a high potential for crossing-over in the loop. Four chromosome 3 types were found in the man’s sperm—normal, inversion, and two recombinant types (Figure 17-21). The sperm contained the four types in the following frequencies:

Figure 17-21

(a) Four different chromosomes 3 found in sperm of a man heterozygous for a large pericentric inversion. The duplication-deletion types result from a crossover in the inversion loop. (b) Two complete sperm chromosome sets containing the two duplication�letion (more. )

The duplication-q�letion-p recombinant chromosome had been observed previously in several abnormal children, but the duplication-p�letion-q type had never been seen, and probably zygotes receiving it are too abnormal to survive to term. Presumably, deletion of the larger q fragment has more severe consequences than deletion of the smaller p fragment.

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Gene Mapping

The chromosome mapping or gene mapping is based on two important assumptions:(i) that genes are arranged on a chromosome in a linear fashion, and (ii) that the percentage of crossing over (recombination frequencies) between the two genes is an index of their distance apart. A chromosome map is a line on which the genes are represented points that are separated by distance proportional to the amount of crossing over. The gene mapping is based on the percentage of crossing over between genes, it is sometimes known as a crossing over map. The relationship between the cross over frequency and the distance between loci was first suggested in 1913 by A. I. Sturtevant. Thus, the chromosome map is a condensed graphic representation of relative distances between the linked genes, expressed in percentage of recombination among the genes in one linkage group. Distances between genes can be expressed in map units, where one map unit is defined as 1 per cent recombination. So, The representation in figure of relative position of genes on the chromosome is known as chromosome map in the process of identifying gene loci is called gene mapping.

Explanation of Gene Mapping

The amount of crossing over, on which the gene mapping is prepared, has been drawn from Test crosses. There is no direct microscopic examination of the chromosome. The gene mapping is based on two assumptions which are very easy to understand, those are: i) The genes are arranged in a liner fashion on the chromosome. ii) The percentage of crossing over between the two genes is an index of their distance in the chromosome.

Recombination frequency or Cross over value = Total number of recombinants in Test Cross / Total number of progeny of the Test Cross. The value is generally represented as a per cent of the total population. The variation in recombination frequency is governed by the distance between the genes. Closer the distance between the two genes, the less are the chances of crossing over. As a result there is lower frequency of recombination. The greater is the distance between the genes, the higher is the percentage of crossing over between them.

Construction of gene mapping

To construct the gene mapping of an animal or plant, its chromosomes are first represented by straight lines and then the positions of genes are determined from the percentage of crossing over data. The percentage of crossing over is governed by the distance between the genes concerned. If the two genes are closer then the chance of crossing over will be less I which will be reflected in the recombination frequency.

Technique of gene mapping

The assumption of consistent gene order along the chromosome, coupled with the fact that crossing occurs, makes it almost certain that gene loci can be determined in relation to each other. If genes are located in a consistent linear order along a chromosome, then the distance between any two genes and the amount of (Tossing over that occurs between them should be in direct proportion. Homologous chromosomes cross over each other and such cross over occasionally lead to an exchange of chromosome segments between the pairs. When such an exchange occurs, the linkage between certain genes is broken and new gamete possibilities arise. Higher the number of such gametes, greater will be the portion of the offspring showing the cross over phenotype. This percentage, therefore, gives direct measure of the amount of chromosomal crossing over. In other word, it provides a direct measure of the distance between the genes involved.

Factors Affecting Gene Mapping

(i) Double Crossing Over : This phenomenon occurs between two genes which are situated by long distance on the same chromosome. It has been observed, though there is double crossing over yet the two genes are remaining on the same chromosome. There is no apparent sign of crossing over. So, calculation of crossing over percentage may cause mistakes in the chromosome map. (ii) Interference : One chiasma may interfere to form another chiasma formation in the vicinity. As a result, one crossing over may reduces the crossing over in the vicinity. (iii) Temperature : High and low temperatures increase the frequency of crossing over. Hence, the temperature causes fluctuations in the location of genes on chromosome. (iv) X-ray : This ray increases the frequency of crossing over and disturb the location of genes on chromosome mapping. (v) Age : Experiment of Bridges shows that crossing over is more frequent in older females of Drosophila. Thus age also affects the frequency of crossing over. Hence, ageing also cause fluctuations in loci of genes on chromosome. (vi) Location : Crossing over is less frequent near centromere and near the terminal ends of chromosome. (vii) Sex : The males of many organisms show less frequency of crossing over. In male Drosophila there is no crossing over. Thus, sex may also affect the frequency of crossing over.

Utility or Importance of Gene Mapping

(i) Chromosome mapping or gene mapping are very useful in the study of genetic engineering. (ii) Chromosome maps are very helpful to find out the exact location of new mutant gene in chromosome. (iii) Chromosome maps have established the validity that genes are arranged in a linrar fashion in chromosome. (iv) Gene Mapping have established the concept that the specific genes occupy the specific loci in the specific chromosome.

Watch the video: Gene conversion - Jim Haber Brandeis (September 2022).


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