Variations in Genome Sizes

Variations in Genome Sizes

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Why is there wide variation in genome size amongst groups of protists, insects, amphibians and plants, but less variation within groups of mammals and reptiles?

You're asking about the C-value enigma, in particular this kind of diagram:1

The quick answer is that there is no "why" in evolution; things happen and if they're beneficial they tend to stick around more than the deleterious things. The longer, (slightly) more satisfying answer is non-coding DNA. Thanks to non-coding DNA the size of a genome doesn't even correlate to the number of genes. The human genome, for example, has over 20,000 genes but is less than two percent protein-coding; the flower Utricularia gibba, however, has over 28,000 genes with about two orders of magnitude smaller of a genome, due to the fact that its genome is 97% protein-coding. In short, there is no real meaning to be found in genome size variation due to non-coding portions, which while they are sometimes important regulators can often be just transposons (40% of our genome). I will also point out that all of those groups you mention have species with significant polyploidy.

The are a number of other interesting ideas. In the process of researching this, I stumbled upon the Animal Genome Size Database, which is an absolute gem of a treasure trove. It's an unbelievable mine where I've spent a good half-hour just reading. In particular, the statistics pages list interesting correlations for certain groups. Mammalian genome size, for example, correlates:

  • Positively with red blood cell size, despite lack of nuclei
  • Negatively with metabolic rate
  • Not at all with developmental rate or longevity

For amphibians, however, there is:

  • Positive correlation with red blood cell and nucleus sizes in both frogs and salamanders
  • Negative correlation with developmental rate in many groups
  • Negative correlation with brain complexity in both frogs and salamanders
  • No strong relationship with metabolic rate in either frogs or salamanders
  • Some association with intensity and frequency of metamorphosis

If you have time, I highly recommend reading through T. Ryan Gregory's PhD thesis entitled The C-Value Enigma. It's around 350 pages, but each chapter is in separate pdfs and is the most in-depth treatment I've seen, despite being about a decade old. It's been moderately life-changing for me to read, some of the interpretations he presents challenged my thinking on the subject of genome evolution entirely. Should genomes be treated as individuals, and what would that mean? I digress. Chapters 3, 4, 6, and 7 are what you'll want, in particular the last twenty or so pages of chapter 7.

Dr. Gregory now has a faculty position and his lab's research page is a little easier to read. I love this image:

As genome size increases in eukaryotes, the percentage of the genome composed of genes (white circles) becomes smaller. Conversely, the percentage of the genome made up of transposable elements (black circles) increases with genome size.

It seems like the best way to look at genome size and find some meaning is within a specific group. There are correlations, for example, that are present in frogs and salamanders, but not in either group alone. Some of the groups are very unbalanced; in insects, only Orthoptera tend to have very large genomes. Interestingly, a number of those groups also undergo some form of metamorphosis, but the correlations aren't what you might have expected: insects that develop quickly and metamorphose have smaller genomes, but "[i]n a more general sense, small genome size seems linked to rapid diversification… Conversely, insects without metamorphosis may possess very large genomes… "

18.4F: Variations in Size and Number of Genes

  • Contributed by Boundless
  • General Microbiology at Boundless

The genome size does not always correlate with the complexity of the organism and, in fact, shows great variation in size and gene number.

  • Describe how variations in the size and number of genes can arise through evolutionary mechanisms

Within-population genome size variation is mediated by multiple genomic elements that segregate independently during meiosis

Within-species variation in genome size has been documented in many animals and plants. Despite its importance for understanding eukaryotic genome diversity, there is only sparse knowledge about the maintenance and evolution of such variation on a population level. Here we study a natural population of the rotifer Brachionus asplanchnoidis whose members differ up to 1.9-fold in genome size, but were still able to interbreed and produce viable offspring. We show that genome size is highly heritable and can be artificially selected up or down, but not beyond a minimum diploid genome size. Analyses of segregation patterns in haploid males reveal that large genomic elements (multiple Mega-Base pairs in size) provide the substrate of genome size variation. These elements explain the short-term evolutionary dynamics of genome size as well as unexpected patterns, like increased genome size variation among inbred lines. Overall, our study explains how intraspecific genome size variation can be maintained in populations and provides a framework for understanding the evolutionary forces that drive changes in genome size on a population level.

Materials and Methods

Study organisms

The three focal Calanus species differ distinctly in size (see Table  1 ). The smallest of them, C.ਏinmarchicus, is the most southern one, with main distribution in the North Atlantic, but partly extending into arctic waters (Blaxter etਊl. 1998 Hirche and Kosobokova 2007). In Oslofjorden, it is less abundant than the more southern relative C. helgolandicus (Claus). The latter species was only found in the southern sites, and therefore not a part of this study. The medium sized C. glacialis is mainly associated with arctic shelf seas, although occasionally being observed in the Norwegian sea (Mauchline 1998 Broms etਊl. 2009). It is, however, numerically dominant in Lurefjorden about 30 km north of Bergen. The much larger C. hyperboreus has its main distribution in the Arctic basins (Mauchline 1998). It is scarce in the Norwegian Sea, apparently without successful reproduction (Broms etਊl. 2009), but does exist as isolated populations in deeper parts of some of the southern Norwegian fjords such as Oslofjorden. The last and more distantly related species, P. norvegica, has about the same maximum body size as C. hyperboreus. It is common in southern Norway as well as in arctic waters. Several other Paraeuchaeta species larger than P. norvegica, occur in the Arctic, but with no known occurrence in southern Norway.

Table 1

Body length (cephalothorax) variations between temperate and high arctic con‐specific populations of four calanoid copepods

PopulationAreaLocationSampling month nMeanSD95% C.I.
C. helgolandicus
OslofjordenSouthern fjord59뀙′N 10뀵𠌮Apr�192.350.092.31𠄲.39
OslofjordenSouthern fjord59뀙′N 10뀵𠌮Apr�142.540.092.49𠄲.59
Fram StraitHigh Arctic79뀅′N 08뀁𠌮Jul�52.800.102.74𠄲.85
KongsfjordenHigh Arctic78끘′N 11끑𠌮Jan�252.710.122.66𠄲.76
C. glacialis
LurefjordenSouthern fjord60끁′N 5ଈ𠌮Aug�142.560.102.50𠄲.62
BillefjordenHigh Arctic78뀹′N 16끄𠌮Dec�113.400.203.26𠄳.53
BillefjordenHigh Arctic78뀹′N 16끄𠌮Oct�113.370.123.29𠄳.44
BillefjordenHigh Arctic78뀹′N 16끄𠌮Mar�213.380.173.30𠄳.46
RijpfjordenHigh Arctic80뀰′N 22뀥𠌮Jan�133.400.163.30𠄳.49
C. hyperboreus
OslofjordenSouthern fjord59뀙′N 10뀵𠌮Feb�245.040.604.78𠄵.29
HinlopenHigh Arctic79뀸′N 18끑𠌮Sep�196.850.776.50𠄷.22
Fram StraitHigh Arctic79뀈′N 8뀂′WApr�116.610.196.48𠄶.74
P. norvegica
OslofjordenSouthern fjord59뀙′N 10뀵𠌮Feb�135.350.225.22𠄵.48
LurefjordenSouthern fjord60끁′N 5ଈ𠌮Jun�65.580.135.44𠄵.71
Fram StraitHigh Arctic79뀅′N 08뀁𠌮Jul�126.130.605.77𠄶.49


The complete list of locations and time of sampling of each species as well as sample size are given in Table  1 , and the location of the sites are shown in Figure  1 . The southern sites consisted of Oslofjorden and Lurefjorden, which differ fundamentally in predation regime. Lurefjorden is completely dominated by nonvisual invertebrate predators, such as the jellyfish Periphylla periphylla, and only a few visual predators (Bagøien etਊl. 2001 Sørnes etਊl. 2007), while visual predators, such as sprat, are common in Oslofjorden (Solberg etਊl. 2015). The arctic samples were taken from altogether six sites of the Svalbard archipelago (Fig.  1 ), depending on from where we were able to sample arctic material of the different species. To evaluate possible temporal variation within a population, we sampled C. glacialis at one of the arctic site (Billefjorden) once each year from December 2011 to March 2013.

Locations of the two southern and six northern sites where calanoid copepods were sampled for this study.

Samples were taken at all locations by use of WP2 nets with 0.5 m diameter and 200 μm mesh size (Gabrielsen etਊl. 2012), or with WP3 nets with 1 m2 opening and 1000 μm mesh size. From most sites samples had to be shipped for several days to our laboratory (Oslo). For the flow cytometer analyses, we needed living animals, and to improve survival, densities had to be kept low during transport and thereby reducing the possible sample size. After picking out the adults from each sample three to six animals were selected for genome size analyzes (see below), and body length and species identification were performed on the rest of the sample.

Species identification

In field studies, separation of the Calanus species is often based on size, although some overlap in body size may occur (Parent etਊl. 2011 Gabrielsen etਊl. 2012). To verify species identification, the samples were identified genetically, and the few samples where genetic analysis showed a mixture of our focal Calanus species were excluded from the study, as we then could not be sure that genome size had been analyzed on a homogenous one‐species sample.

The Calanus species were identified based on their restriction patterns after digesting amplicons of a partial sequence of their mitochondrial 16S rDNA with DdeI and VspI according to Lindeque etਊl. (1999). DNA was extracted using the E.Z.N.A. Tissue DNA kit (Omega Biotek, Norcross, GA, USA Inc) and PCR, restrictions and separation of digested amplicons were performed according to Gabrielsen etਊl. (2012). Representative individuals from all taxa were sequenced to ensure the correct interpretation of the digestion patterns, and unique sequences among these were submitted to Gen Bank (accession numbers <"type":"entrez-nucleotide","attrs":<"text":"KX371797","term_id":"1059484556","term_text":"KX371797">> KX371797‐ <"type":"entrez-nucleotide","attrs":<"text":"KX371807","term_id":"1059484566","term_text":"KX371807">> KX371807).

Body size analysis

Body size was measured as the length of cephalothorax of living adults laying on their sides, in a small plastic well (Ø =ꀖ mm) with seawater just covering their bodies. All measurements were carried out using a digital Leica DFC 425 camera on a Leica M205C microscope. The images were analyzed by Leica application suite (LAS version 3.7.0 Leica Microsystems, Wetxlar, Germany). We focused on adult size, as this is a life history trait generally assumed to be closely linked to fitness (e.g., Stearns 1992). This choice reduced the amount of animals available for the analyses compared with the common use of the much more abundant last copepodit stage (CV). The only exception to this procedure was arctic C.ਏinmarchicus, of which we were unable to get adult individuals in altogether four field cruises. Because of its key role in the North Atlantic, we nevertheless included arctic samples of this species by using CV, in order to exploit a possible increase in body size in arctic waters also in this species. This would not lead to erroneous arguments about a relative increase in size in northern populations, only a moderate underestimation of the increase (see e.g., Madsen etਊl. 2001). Similarly, a comparison of body length of 24 adults and 58 CV of arctic C. glacialis from the same two samples showed only a relatively small difference in size between these two last instars (mean ± SE: adults =ਃ.4 mm ±਀.07 CV =ਃ.2 mm ±਀.04) (F 1,80 =ꀐ.97, P =਀.001).

Significant size differences between samples were tested by general linear models, comparing mean and 95% C.I. of each sample. This and box plot of body length of arctic versus temperate animals of each Calanus species were performed in JMP 9 (SAS Institute, Cary, NC). To estimate relative difference in body length between animals from the southern and northern sites in each species, all con‐specific data from each climate zones were pooled and fitted to log‐linear models with a Gaussian likelihood. Relative differences with confidence intervals were then computed directly as the exponent of linear contrasts from the models in R3.1.2 (R Development Core Team 2014).

Genome size analysis

Genome size was analyzed on whole individuals by FCM, basically following the protocol of Jalal etਊl. (2013). In brief, animals were ground in grinding buffer (Korpelainen etਊl. 1997), followed by incubation with 1 mg RNase A (Invitrogen Life Science, Waltham, MA) and 50 μg propidium iodide (PI Invitrogen Life Science, Waltham, Mass. USA). Chicken fresh blood cells (CRBC) of Gallus gallus domesticus (5.0 ×ꀐ 5 ꃎlls/mL) were used as internal standard. The standard DNA𠄌ontent of CRBC was set to 2.5 pg DNA (Vergilino etਊl. 2009). In addition to CRBC nuclei, 2.5 μm alignment beads (Invitrogen Life Science) were used to keep instrument settings (amplification and sample rate) constant throughout the experiment and to confirm low coefficient of variation alignment. Depending on size, three to six adult copepods were analyzed from each sample, which was sufficient to obtain reliable and highly repeatable counts in succeeding analyses. Animals from all populations were analyzed for genome size except for C.ਏinmarchicus from Oslofjorden where the analysis failed to stain properly for unknown reasons. The genome size (DNA𠄌ontent) was gated and measured with high precision by FCM, as illustrated by the distinct peaks in Figure  2 . Stained samples were analyzed with a FACS Calibur flow cytometer equipped with a 488 nm laser (Becton, Dickinson, NJ). The PI fluorescence emission signal was measured in a FL2 detector (585/42 bandpass filter setup). Doublets and cell aggregates were discriminated from the analysis by gating around the singlet population in fluorescence pulse width (FL2‐W) versus pulse area (FL2𠄊) dual parameter cytogram (Shapiro 2003). Paraeuchaeta was analyzed with a different FL2𠄊 voltage than the other copepods because of its large genome, but we then also used the same chicken blood cells as internal standard, so this will not bias the results. We did however also run a comparative analysis with the same voltage for C. hyperboreus and Paraeuchaeta. The FCM results were analyzed using FCS express 3 (De Novo, Los Angeles, CA) and Modfit LT software (Verity, Topsham, ME). Calculation of nuclear DNA𠄌ontent was made according to Galbraith etਊl. (2001).

Representative flow cytometer DNA histograms of a selected population of each copepod species, compared with the standard chicken red blood cells ( CRBC ) Calanus finmarchicus (red), Calanus glacialis (green),and Calanus hyperboreus (purple). Paraeuchaeta norvegica ( PN ) is represented by a separate histogram because a different FL 2𠄊 voltage was applied than for the other copepods because of its large genome. FSC voltage was same for all. Note also that in these selected populations there was no overlap in genome size between C. glacialis and C. hyperboreus. Other populations showed considerable overlap, as shown in Table  2 .

Gene Duplications and Divergence

Gene duplications create genetic redudancy and can have various effects, including detrimental mutations or divergent evolution.

Learning Objectives

Explain the mechanisms of gene duplication and divergence

Key Takeaways

Key Points

  • Ectopic recombination occurs when there is an unequal crossing-over and the product of this recombination are a duplication at the site of the exchange and a reciprocal deletion.
  • Gene duplications do not always result in detrimental mutations they can contribute to divergent evolution, which causes genetic differences between groups to develop and eventually form new species.
  • Replication slippage can occur when there is an error during DNA replication and duplications of short genetic sequences are produced.
  • Retrotranspositions occur when a retrovirus copies their genome by reverse transcribing RNA to DNA and aberrantly attach to cellular mRNA and reverse transcribe copies of genes to create retrogenes.
  • Aneuploidy can occur when there is a nondisjunction even at a single chromosome thus, the result is an abnormal number of chromosomes.
  • Genetic divergence can occur by mechanisms such as genetic drift which contibute to the accumulation of independent genetic changes of two or more populations derived from a common ancestor.

Key Terms

  • paralogous: having a similar structure indicating divergence from a common ancestral gene
  • nondisjunction: the failure of chromosome pairs to separate properly during meiosis
  • retrogene: a DNA gene copied back from RNA by reverse transcription
  • genetic drift: an overall shift of allele distribution in an isolated population, due to random fluctuations in the frequencies of individual alleles of the genes

Gene Duplication

Gene duplication is the process by which a region of DNA coding for a gene is copied. Gene duplication can occur as the result of an error in recombination or through a retrotransposition event. Duplicate genes are often immune to the selective pressure under which genes normally exist. This can result in a large number of mutations accumulating in the duplicate gene code. This may render the gene non-functional or in some cases confer some benefit to the organism. There are multiple mechanisms by which gene duplication can occur.

Ectopic Recombination

Duplications can arise from unequal crossing-over that occurs during meiosis between misaligned homologous chromosomes. The product of this recombination is a duplication at the site of the exchange and a reciprocal deletion. Ectopic recombination is typically mediated by sequence similarity at the duplicate breakpoints, which form direct repeats. Repetitive genetic elements, such as transposable elements, offer one source of repetitive DNA that can facilitate recombination, and they are often found at duplication breakpoints in plants and mammals.

Gene Duplication: This figure indicates a schematic of a region of a chromosome before and after a duplication event. Ectopic recombination is typically mediated by sequence similarity at the duplicate breakpoints, which form direct repeats.

Replication Slippage

Replication slippage is an error in DNA replication, which can produce duplications of short genetic sequences. During replication, DNA polymerase begins to copy the DNA, and at some point during the replication process, the polymerase dissociates from the DNA and replication stalls. When the polymerase reattaches to the DNA strand, it aligns the replicating strand to an incorrect position and incidentally copies the same section more than once. Replication slippage is also often facilitated by repetitive sequence but requires only a few bases of similarity.


During cellular invasion by a replicating retroelement or retrovirus, viral proteins copy their genome by reverse transcribing RNA to DNA. If viral proteins attach irregularly to cellular mRNA, they can reverse-transcribe copies of genes to create retrogenes. Retrogenes usually lack intronic sequence and often contain poly A sequences that are also integrated into the genome. Many retrogenes display changes in gene regulation in comparison to their parental gene sequences, which sometimes results in novel functions.


Aneuploidy occurs when nondisjunction at a single chromosome results in an abnormal number of chromosomes. Aneuploidy is often harmful and in mammals regularly leads to spontaneous abortions. Some aneuploid individuals are viable. For example, trisomy 21 in humans leads to Down syndrome, but it is not fatal. Aneuploidy often alters gene dosage in ways that are detrimental to the organism and therefore, will not likely spread through populations.

Gene duplication as an evolutionary event

Gene duplications are an essential source of genetic novelty that can lead to evolutionary innovation. Duplication creates genetic redundancy and if one copy of a gene experiences a mutation that affects its original function, the second copy can serve as a ‘spare part’ and continue to function correctly. Thus, duplicate genes accumulate mutations faster than a functional single-copy gene, over generations of organisms, and it is possible for one of the two copies to develop a new and different function. This is an examples of neofunctionalization.

Gene duplication is believed to play a major role in evolution this stance has been held by members of the scientific community for over 100 years. It has been argued that gene duplication is the most important evolutionary force since the emergence of the universal common ancestor.

Another possible fate for duplicate genes is that both copies are equally free to accumulate degenerative mutations, so long as any defects are complemented by the other copy. This leads to a neutral “subfunctionalization” model, in which the functionality of the original gene is distributed among the two copies. Neither gene can be lost, as both now perform important non-redundant functions, but ultimately neither is able to achieve novel functionality. Subfunctionalization can occur through neutral processes in which mutations accumulate with no detrimental or beneficial effects. However, in some cases subfunctionalization can occur with clear adaptive benefits. If an ancestral gene is pleiotropic and performs two functions, often times neither one of these two functions can be changed without affecting the other function. In this way, partitioning the ancestral functions into two separate genes can allow for adaptive specialization of subfunctions, thereby providing an adaptive benefit.


Genetic divergence is the process in which two or more populations of an ancestral species accumulate independent genetic changes through time, often after the populations have become reproductively isolated for some period of time. In some cases, subpopulations living in ecologically distinct peripheral environments can exhibit genetic divergence from the remainder of a population, especially where the range of a population is very large. The genetic differences among divergent populations can involve silent mutations (that have no effect on the phenotype) or give rise to significant morphological and/or physiological changes. Genetic divergence will always accompany reproductive isolation, either due to novel adaptations via selection and/or due to genetic drift, and is the principal mechanism underlying speciation.

Genetic drift or allelic drift is the change in the frequency of a gene variant ( allele ) in a population due to random sampling. The alleles in the offspring are a sample of those in the parents, and chance has a role in determining whether a given individual survives and reproduces. A population’s allele frequency is the fraction of the copies of one gene that share a particular form. Genetic drift may cause gene variants to disappear completely and thereby reduce genetic variation. When there are few copies of an allele, the effect of genetic drift is larger, and when there are many copies the effect is smaller. These changes in gene frequency can contribute to divergence.

Divergent evolution is usually a result of diffusion of the same species to different and isolated environments, which blocks the gene flow among the distinct populations allowing differentiated fixation of characteristics through genetic drift and natural selection.Divergent evolution can also be applied to molecular biology characteristics. This could apply to a pathway in two or more organisms or cell types. This can apply to genes and proteins, such as nucleotide sequences or protein sequences that are derived from two or more homologous genes. Both orthologous genes (resulting from a speciation event) and paralogous genes (resulting from gene duplication within a population) can be said to display divergent evolution.

Phylogeny, rate variation, and genome size evolution of Pelargonium (Geraniaceae)

The phylogeny of 58 Pelargonium species was estimated using five plastid markers (rbcL, matK, ndhF, rpoC1, trnL-F) and one mitochondrial gene (nad5). The results confirmed the monophyly of three major clades and four subclades within Pelargonium but also indicate the need to revise some sectional classifications. This phylogeny was used to examine karyotype evolution in the genus: plotting chromosome sizes, numbers and 2C-values indicates that genome size is significantly correlated with chromosome size but not number. Accelerated rates of nucleotide substitution have been previously detected in both plastid and mitochondrial genes in Pelargonium, but sparse taxon sampling did not enable identification of the phylogenetic distribution of these elevated rates. Using the multigene phylogeny as a constraint, we investigated lineage- and locus-specific heterogeneity of substitution rates in Pelargonium for an expanded number of taxa and demonstrated that both plastid and mitochondrial genes have had accelerated substitution rates but with markedly disparate patterns. In the plastid, the exons of rpoC1 have significantly accelerated substitution rates compared to its intron and the acceleration was mainly due to nonsynonymous substitutions. In contrast, the mitochondrial gene, nad5, experienced substantial acceleration of synonymous substitution rates in three internal branches of Pelargonium, but this acceleration ceased in all terminal branches. Several lineages also have dN/dS ratios significantly greater than one for rpoC1, indicating that positive selection is acting on this gene, whereas the accelerated synonymous substitutions in the mitochondrial gene are the result of elevated mutation rates.


Figure 1 shows the estimated genome sizes of the nine D. melanogaster subgroup species represented according to their phylogeny ( Lachaise and Silvain 2004), plus D. pseudoobscura, considered as the outgroup. As the second estimate was always greater than the first estimate, the values should be compared within each experiment but not between them. However, the ranking of the genome sizes were very similar for both experiments (Spearman's rank correlation coefficient ρ = 0.95, P < 0.001). Drosophila orena genome size estimates appear to be about 1.6-fold greater than the next biggest genome size estimate in both experiments. The differences between the genome size values were less pronounced for the other species, although the variations in genome size among these species are significant. Indeed a two-way analysis of variance detected the effects of the experiment (F = 30.8, P < 0.001) and that of the species (F = 119.4, P < 0.001). Among these species, two groups can be distinguished with regard to genome size: a first group, with a genome size ratio between 0.45 and 0.47 for the first estimate and between 0.46 and 0.48 for the second estimate, includes D. melanogaster, D. sechellia, D. yakuba, and D. santomea a second group, with a genome size ratio ranging from 0.40 to 0.42 for the first estimate and from 0.41 to 0.43 for the second estimate, includes D. simulans, D. mauritiana, D. erecta, and D. teissieiri. The estimate for D. pseudoobscura lies within the second group.

The low variability in genome size values observed among the 16 population samples of D. melanogaster and among the 17 population samples of D. simulans ( fig. 2) suggests that the flow cytometry technique worked very reliably and indicates that the estimated genome size of the different species can be viewed as being highly trustworthy, even though some species were characterized by only one population sample.

Distribution of the relative genome size in 16 and 17 populations of Drosophila melanogaster and Drosophila simulans, respectively. Circles: first estimate triangles: second estimate.

Distribution of the relative genome size in 16 and 17 populations of Drosophila melanogaster and Drosophila simulans, respectively. Circles: first estimate triangles: second estimate.

Figure 3 shows the relationship between genome sizes expressed as the mean of the values from the two experiments and the RT-oligonucleotide/RP49 ratios for the two membranes analyzed. As the RT-oligonucleotide/RP49 ratio depends on several factors (the probe-specific activity, the duration of exposure, etc), the absolute values of the ratio should not be compared between the different membranes. However, the ranking of the ratio values were very similar in both membranes (Spearman's rank correlation coefficient ρ = 0.85, P < 0.01, see fig. 3), suggesting that the data were very consistent. The position of D. orena in this diagram is striking, as its dot blot ratio value lies within the range of values found for the other species, which contrasts to what was expected on the basis of its high estimated genome size. Excluding D. orena from the analysis leads to a statistically significant positive correlation between the dot blot ratio values of the other species and their genome size estimates (r = 0.76, P < 0.01 and r = 0.64, P < 0.05 for membranes 1 and 2, respectively). This suggests that the differences between the genome size estimates can be explained in part by differences in the proportions of RTRS amount, except for D. orena.

Relative genome size values versus dot blot ratios. (a) Membrane 1, (b) Membrane 2. Der: Drosophila erecta Dma: Drosophila mauritiana Dme C: Drosophila melanogaster Canton population Dme S: D. melanogaster Senegal population Dor: Drosophila orena Dsa: Drosophila santomea Dse: Drosophila sechellia Dsi C: Drosophila simulans Canberra population Dsi M: D. simulans MK2 population and Dte: Drosophila teissieri. See text for the values of the correlation coefficients.

Relative genome size values versus dot blot ratios. (a) Membrane 1, (b) Membrane 2. Der: Drosophila erecta Dma: Drosophila mauritiana Dme C: Drosophila melanogaster Canton population Dme S: D. melanogaster Senegal population Dor: Drosophila orena Dsa: Drosophila santomea Dse: Drosophila sechellia Dsi C: Drosophila simulans Canberra population Dsi M: D. simulans MK2 population and Dte: Drosophila teissieri. See text for the values of the correlation coefficients.


The term "genome size" is often erroneously attributed to a 1976 paper by Ralph Hinegardner, [2] even in discussions dealing specifically with terminology in this area of research (e.g., Greilhuber 2005 [3] ). Notably, Hinegardner [2] used the term only once: in the title. The term actually seems to have first appeared in 1968, when Hinegardner wondered, in the last paragraph of another article, whether "cellular DNA content does, in fact, reflect genome size". [4] In this context, "genome size" was being used in the sense of genotype to mean the number of genes.

In a paper submitted only two months later, Wolf et al. (1969) [5] used the term "genome size" throughout and in its present usage therefore these authors should probably be credited with originating the term in its modern sense. By the early 1970s, "genome size" was in common usage with its present definition, probably as a result of its inclusion in Susumu Ohno's influential book Evolution by Gene Duplication, published in 1970. [6]

With the emergence of various molecular techniques in the past 50 years, the genome sizes of thousands of eukaryotes have been analyzed, and these data are available in online databases for animals, plants, and fungi (see external links). Nuclear genome size is typically measured in eukaryotes using either densitometric measurements of Feulgen-stained nuclei (previously using specialized densitometers, now more commonly using computerized image analysis [7] ) or flow cytometry. In prokaryotes, pulsed field gel electrophoresis and complete genome sequencing are the predominant methods of genome size determination.

Nuclear genome sizes are well known to vary enormously among eukaryotic species. In animals they range more than 3,300-fold, and in land plants they differ by a factor of about 1,000. [8] [9] Protist genomes have been reported to vary more than 300,000-fold in size, but the high end of this range (Amoeba) has been called into question. [ by whom? ] In eukaryotes (but not prokaryotes), genome size is not proportional to the number of genes present in the genome, an observation that was deemed wholly counter-intuitive before the discovery of non-coding DNA and which became known as the "C-value paradox" as a result. However, although there is no longer any paradoxical aspect to the discrepancy between genome size and gene number, the term remains in common usage. For reasons of conceptual clarification, the various puzzles that remain with regard to genome size variation instead have been suggested by one author to more accurately comprise a puzzle or an enigma (the so-called "C-value enigma").

Genome size correlates with a range of measurable characteristics at the cell and organism levels, including cell size, cell division rate, and, depending on the taxon, body size, metabolic rate, developmental rate, organ complexity, geographical distribution, or extinction risk. [8] [9] Based on currently available completely sequenced genome data (as of April 2009), log-transformed gene number forms a linear correlation with log-transformed genome size in bacteria, archaea, viruses, and organelles combined, whereas a nonlinear (semi-natural logarithm) correlation is seen for eukaryotes. [10] Although the latter contrasts with the previous view that no correlation exists for the eukaryotes, the observed nonlinear correlation for eukaryotes may reflect disproportionately fast-increasing non-coding DNA in increasingly large eukaryotic genomes. Although sequenced genome data are practically biased toward small genomes, which may compromise the accuracy of the empirically derived correlation, and ultimate proof of the correlation remains to be obtained by sequencing some of the largest eukaryotic genomes, current data do not seem to rule out a possible correlation.

Genome reduction, also known as genome degradation, is the process by which an organism's genome shrinks relative to that of its ancestors. Genomes fluctuate in size regularly, and genome size reduction is most significant in bacteria.

The most evolutionarily significant cases of genome reduction may be observed in the eukaryotic organelles known to be derived from bacteria: mitochondria and plastids. These organelles are descended from primordial endosymbionts, which were capable of surviving within the host cell and which the host cell likewise needed for survival. Many present-day mitochondria have less than 20 genes in their entire genome, whereas a modern free-living bacterium generally has at least 1,000 genes. Many genes have apparently been transferred to the host nucleus, while others have simply been lost and their function replaced by host processes.

Other bacteria have become endosymbionts or obligate intracellular pathogens and experienced extensive genome reduction as a result. This process seems to be dominated by genetic drift resulting from small population size, low recombination rates, and high mutation rates, as opposed to selection for smaller genomes. [ citation needed ] Some free-living marine bacterioplanktons also shows signs of genome reduction, which are hypothesized to be driven by natural selection. [11] [12] [13]

In obligate endosymbiotic species Edit

Obligate endosymbiotic species are characterized by a complete inability to survive external to their host environment. These species have become a considerable threat to human health, as they are often capable of evading human immune systems and manipulating the host environment to acquire nutrients. A common explanation for these manipulative abilities is their consistently compact and efficient genomic structure. These small genomes are the result of massive losses of extraneous DNA, an occurrence that is exclusively associated with the loss of a free-living stage. As much as 90% of the genetic material can be lost when a species makes the evolutionary transition from a free-living to an obligate intracellular lifestyle. During this process the future parasite subjected to an environment rich of metabolite where somehow needs to hide within the host cell, those factors reduce the retention and increase the genetic drift leading to an acceleration of the loss of non-essential genes. [14] [15] [16] Common examples of species with reduced genomes include Buchnera aphidicola, Rickettsia prowazekii, and Mycobacterium leprae. One obligate endosymbiont of leafhoppers, Nasuia deltocephalinicola, has the smallest genome currently known among cellular organisms at 112 kb. [17] Despite the pathogenicity of most endosymbionts, some obligate intracellular species have positive fitness effects on their hosts.

The reductive evolution model has been proposed as an effort to define the genomic commonalities seen in all obligate endosymbionts. [18] This model illustrates four general features of reduced genomes and obligate intracellular species:

  1. "genome streamlining" resulting from relaxed selection on genes that are superfluous in the intracellular environment
  2. a bias towards deletions (rather than insertions), which heavily affects genes that have been disrupted by accumulation of mutations (pseudogenes) [19]
  3. very little or no capability for acquiring new DNA and
  4. considerable reduction of effective population size in endosymbiotic populations, particularly in species that rely on vertical transmission of genetic material.

Based on this model, it is clear that endosymbionts face different adaptive challenges than free-living species and, as emerged from the analysis between different parasites, their genes inventories are extremely different, leading us to the conclusion that the genome miniaturization follows a different pattern for the different symbionts. [20] [21] [22]

In 1991, John W. Drake proposed a general rule: that the mutation rate within a genome and its size are inversely correlated. [23] This rule has been found to be approximately correct for simple genomes such as those in DNA viruses and unicellular organisms. Its basis is unknown.

It has been proposed that the small size of RNA viruses is locked into a three-part relation between replication fidelity, genome size, and genetic complexity. The majority of RNA viruses lack an RNA proofreading facility, which limits their replication fidelity and hence their genome size. This has also been described as the "Eigen paradox". [24] An exception to the rule of small genome sizes in RNA viruses is found in the Nidoviruses. These viruses appear to have acquired a 3′-to-5′ exoribonuclease (ExoN) which has allowed for an increase in genome size. [25]

In 1972 Michael David Bennett [26] hypothesized that there was a correlation with the DNA content and the nuclear volume while Commoner and van’t Hof and Sparrow before him postulated that even cell size and cell-cycle length were controlled by the amount of DNA. [27] [28] More recent theories have brought us to discuss about the possibility of the presence of a mechanism that constrains physically the development of the genome to an optimal size. [29]

Those explanations have been disputed by Cavalier-Smith’s article [30] where the author pointed that the way to understand the relation between genome size and cell volume was related to the skeletal theory. The nucleus of this theory is related to the cell volume, determined by an adaptation balance between advantages and disadvantages of bigger cell size, the optimization of the ratio nucleus:cytoplasm (karyoplasmatic ratio) [31] [32] and the concept that larger genomes provides are more prone to the accumulation of duplicative transposons as consequences of higher content of non-coding skeletal DNA. [30] Cavalier-Smith also proposed that, as consequent reaction of a cell reduction, the nucleus will be more prone to a selection in favor for the deletion compared to the duplication. [30]

From the economic way of thinking, since phosphorus and energy are scarce, a reduction in the DNA should be always the focus of the evolution, unless a benefit is acquired. The random deletion will be then mainly deleterious and not selected due to the reduction of the gained fitness but occasionally the elimination will be advantageous as well. This trade-off between economy and accumulation of non-coding DNA is the key to the maintenance of the karyoplasmatic ratio.

The base question behind the process of genome miniaturization is whether it occurs through large steps or due to a constant erosion of the gene content. In order to assess the evolution of this process is necessary to compare an ancestral genome with the one where the shrinkage is supposed to be occurred. Thanks to the similarity among the gene content of Buchnera aphidicola and the enteric bacteria Escherichia coli, 89% identity for the 16S rDNA and 62% for orthologous genes was possible to shed light on the mechanism of genome miniaturization. [33] The genome of the endosymbiont B. aphidicola is characterized by a genome size that is seven times smaller than E. coli (643 kb compared to 4.6 Mb) [34] [35] and can be view as a subset of the enteric bacteria gene inventory. [35] From the confrontation of the two genomes emerged that some genes persist as partially degraded. [35] indicating that the function was lost during the process and that consequent events of erosion shortened the length as documented in Rickettsia. [36] [37] [38] This hypothesis is confirmed by the analysis of the pseudogenes of Buchnera where the number of deletions was more than ten times higher compared to the insertion. [38]

In Rickettsia prowazekii, as with other small genome bacteria, this mutualistic endosymbiont has experienced a vast reduction of functional activity with a major exception compared to other parasites still retain the bio-synthetic ability of production of amino acid needed by its host. [39] [40] [35] The common effects of the genome shrinking between this endosymbiont and the other parasites are the reduction of the ability to produce phospholipids, repair and recombination and an overall conversion of the composition of the gene to a richer A-T [41] content due to mutation and substitutions. [14] [39] Evidence of the deletion of the function of repair and recombination is the loss of the gene recA, gene involved in the recombinase pathway. This event happened during the removal of a larger region containing ten genes for a total of almost 10 kb. [35] [39] Same faith occurred uvrA, uvrB and uvrC, genes encoding for excision enzymes involved in the repair damaged DNA due to UV exposure. [33]

One of the most plausible mechanisms for the explanation of the genome shrinking is the chromosomal rearrangement because insertion/deletion of larger portion of sequence are more easily to be seen in during homologous recombination compared to the illegitimate, therefore the spread of the transposable elements will positively affect the rate of deletion. [30] The loss of those genes in the early stages of miniaturization not only this function but must played a role in the evolution of the consequent deletions. Evidences of the fact that larger event of removal occurred before smaller deletion emerged from the comparison of the genome of Bucknera and a reconstructed ancestor, where the gene that have been lost are in fact not randomly dispersed in the ancestor gene but aggregated and the negative relation between number of lost genes and length of the spacers. [33] The event of small local indels plays a marginal role on the genome reduction [42] especially in the early stages where a larger number of genes became superfluous. [43] [33]

Single events instead occurred due to the lack of selection pressure for the retention of genes especially if part of a pathway that lost its function during a previous deletion. An example for this is the deletion of recF, gene required for the function of recA, and its flanking genes. [44] One of the consequences of the elimination of such amount of sequences affected even the regulation of the remaining genes. The loss of large section of genomes could in fact lead to a loss in promotor sequences. This could in fact pushed the selection for the evolution of polycistronic regions with a positive effect for both size reduction [45] and transcription efficiency. [46]

One example of the miniaturization of the genome occurred in the microsporidia, an anaerobic intracellular parasite of arthropods evolved from aerobic fungi.

During this process the mitosomes [47] was formed consequent to the reduction of the mitochondria to a relic voided of genomes and metabolic activity except to the production of iron sulfur centers and the capacity to enter into the host cells. [48] [49] Except for the ribosomes, miniaturized as well, many other organelles have been almost lost during the process of the formation of the smallest genome found in the eukaryotes. [30] From their possible ancestor, a zygomycotine fungi, the microsporidia shrunk its genome eliminating almost 1000 genes and reduced even the size of protein and protein-coding genes. [50] This extreme process was possible thanks to the advantageous selection for a smaller cell size imposed by the parasitism.

Another example of miniaturization is represented by the presence of nucleomorphs, enslaved nuclei, inside of the cell of two different algae, cryptophytes and chlorarachneans. [51]

Nucleomorphs are characterized by one of the smallest genomes known (551 and 380 kb) and as noticed for microsporidia, some genomes are noticeable reduced in length compared to other eukaryotes due to a virtual lack of non-coding DNA. [30] The most interesting factor is represented by the coexistence of those small nuclei inside of a cell that contains another nucleus that never experienced such genome reduction. Moreover, even if the host cells have different volumes from species to species and a consequent variability in genome size, the nucleomorph remain invariant denoting a double effect of selection within the same cell.

The size of the genome and the complexity of living beings

Figure 2. Range of genome size in organisms of the three domains of life.


According to the data published so far, the size varies from 0.58 megabases (1 megabase (Mb) is one million base pairs (bp)) in the intracellular pathogen Mycoplasma genitalium, to more than 10 Mb in several species of cyanobacteria, with the exception of Bacillus megaterium, which has a genome of 30 Mb. The second smallest genome ever published is that of Buchnera sp. APS, endosymbiont of the cereal aphid Acyrthosiphon pisum, with a size of 641 kb. Recently, our research group has characterized six genomes smaller than even those of Mycoplasma, the smallest of all being that of Buchnera sp. CCE, endosymbiont of the aphid Cinara cedri, with a size of 0.45 Mb. In general, most genomes are less than 5 Mb in size, as shown in Table 1.

Is there a relationship between genome size and number of genes? The size of the prokaryotic gene is uniform, about 900 to 1000 bp. Therefore, one can estimate the gene density at each sequenced genome. As seen in Table 1, gene density is more or less constant, both in bacteria and archaea.

We can conclude that, at least in prokaryotes, genomes have a larger number of genes and are also more complex. That is, the number of genes reflects the lifestyle. Thus, smaller bacteria are specialists, such as obligate parasites and endosymbionts, and larger bacteria are generalists, and may even have a certain degree of development, such as sporulation in Bacillus.


Genome size in eukaryotes is defined as the C-value or amount of DNA per haploid genome, such as that which exists in the nucleus of a spermatozoon. It is called C, for constant or characteristic, to indicate the fact that size is practically constant within a species.

Referring back to Figure 2, we see that, in general, eukaryotes have larger genomes than prokaryotes, except for some endosymbiont or parasitic green algae, which have very small genomes. Specifically, the smallest eukaryotic genome ever sequenced is that of Guillardia theta, a symbiont red algae, of only 0.55 Mb. We can also see in the figure that there is a wide range of sizes, much greater than that of prokaryotes, more than 80,000-fold larger, from organisms such as yeast (1.2 Mb) to the amoeba (686,000 Mb). But is there, as in bacteria, a relationship between genome size and complexity of the organism?

In Figure 2 we have represented the range of C-value in several representative groups of eukaryotic organisms. As we can observe, unicellular protists such as amoebae show the greatest variation in C-values (23.5 Mb to 686.000 Mb, with a ratio of 29,191 between the largest and the smallest), while mammals, birds and reptiles show less variation in the size of their genome (a ratio of only 4, 1 and 4, respectively). Furthermore, the large variation in genome sizes between eukaryotic species does not seem to have a relationship with either the complexity of the organism or the number of genes they contain. For example, amoebae, which have the largest genomes, have 200 times more DNA than humans (3,400 Mb) and it is clear that an amoeba cannot be more complex than a human. Moreover, it would be expected that mammals, more complex organisms, present larger genomes. However, many other organisms, such as fish, amphibians or plants, have much larger genomes. Even when we compare the sizes between organisms that appear similar in terms of complexity, there are also wide differences in their C-values. To give some examples, flies and locusts, onions and lilies, etc. have considerable variations in the sizes of their genomes. Amphibians as a group have variations of up to 91 times and it is hard to believe that this may reflect variations of nearly 100 times the number of genes necessary to give rise to the corresponding amphibians, or that onions need 200 times more DNA than rice. Figure 3 shows some living beings with size proportional to the size of their genome and needs no further explanation.

Figure 3. Genome size in some living beings. The height of the drawings is proportional to the size of their genome. The specimes are amoebae, onions, grasshoppers, toads, humans, hens, Drosophila and Caenorhabditis, a nematode worm.

The mismatch between the C-values and the presumed amount of genetic information contained within the genomes was called C-value paradox. Since we cannot assume that a species possesses less DNA than the quantity required to specify its vital functions, we have to explain why many species contain this amount of excess DNA.


The first question that has to be clarified is whether there is a correlation between genome size and the number of genes. That is, are the differences in genome sizes due to gene or non-gene DNA?

We have known since the late 60s that the eukaryotic genome is composed of a large amount of repetitive DNA. Moreover, since the late 70s we have known that genes are interrupted by non-coding sequences, introns, which must be removed before the ribosome synthesizes protein. We are talking in both cases about a seemingly superfluous DNA, which contributes to the wide variation in C-values and therefore explains the apparent paradox.

The size and number of introns vary widely along the evolutionary scale, mammals being the ones with the highest number and larger size. Repetitive DNA also varies between organisms. Traditionally this DNA is classified as: highly repetitive, with sequences such as microsatellites and minisatellites and moderately repetitive, where transposable elements, the sequences that constitute the clearest example of selfish DNA, are found.

Number of genes and complexity of the organism

As sequences of whole genomes are completed, we will know with more or less accuracy the number of genes derived from these sequences, since what we had so far were indirect estimates. However, some data is proving to be surprising because, in some cases, there appears to be a clear correlation between the number of genes and the complexity of the organism. The nematode worm C. elegans has 18,000 genes (Table 1), about 5,000 more than Drosophila, a more complex organism. Man has only twice as many genes as C. elegans (estimates indicated about 100,000). We are also beginning to understand these data. There are mechanisms in higher eukaryotes that are able to «expand the proteome». That is, from the same DNA sequence, they can obtain more than one protein. Large introns found in mammals can, in many cases, «hide» information that cannot be inferred only with the DNA sequence. It will be some time before we can determine the number of proteins that an organism is able to synthesize. But this would be the subject of another paper. In any case, we can say that «more complex organisms have more gene functions.»

Common name or class Scientific name Genome size
Number of genes Gene density
EukaryotesSaccharomyces cerevisiae 12 6,241 480
Baker’s yeast
NematodeCaenorhabditis elegans9718,424190
CruciferousArabidopsis thaliana12525,498204
Fruit flyDrosophila melanogaster18013,60175
PufferfishFugu rubripes40035,000100
RiceOryza sativa450
Sea urchinStrongylocentrotus purpuratus90027,35030
MaizeZea mays2,400
HumanHomo sapiens3,40035,00010
OnionAllium cep18,000
AmoebaAmoeba dubia686,000
Archaea Aeropyrum pernix 1.55 1,522 981
EuryarchaeotaMethanococcus jannaschii1.661,7151033
Bacteria Buchnera sp. CCE0.45
Gram positiveMycoplama genitalium0.58479831
ProteobacteriaBuchnera sp. APS0.64564881
Gram negativeHaemophilus influenzae1.81,727959
CyanobacteriaSynechocystis sp.3.63,168880
Gram positiveBacillus subtilis4.24,100976
ProteobacteriaEscherichia coli4.64,288932

Table 1. Genome size, gene number and gene density.


There are many mutational mechanisms that can produce changes in genome size. Some of them occur on a large scale (whole genome duplication), while others occur on a very small scale (loss or gain of a few nucleotides). However, we must note that these mutations affect, in principle, a single cell, and if it is a gamete, the mutation may be transmitted to an offspring. This individual will have to live with others in a population and only in future generations will we know if the mutation it carries will extend to all individuals in the population (fixation) or, on the contrary, will disappear. The probability of one or the other occurring depends on evolutionary mechanisms such as natural selection (whether it provides an advantage or disadvantage to the individual) or genetic drift (random).

Chromosomal mechanisms often produce drastic changes with a single mutation. We can highlight the whole genome duplication, duplication affecting a single chromosome, or part of it. Equivalently, mutations that result in the loss of some chromosome fragment are also known. Such changes, though also frequent in plants and animals, appear to have significantly contributed to shape the evolution of the genome of the former.

«We can say that “more complex organisms have more gene functions”»

The mobile genetic elements or transposable elements, are other causes of large variations in genome size. These elements, of a few thousand nucleotides, are duplicated and the duplicate copies are inserted in other parts of the genome, causing rapid increases in its size, unless the mechanisms that control their proliferation intervene (as authentic selfish elements, they develop mechanisms to regulate their movement in the genome, so as not to produce irreparable damage, since that would imply their own disappearance). It has been estimated that the maize genome has been duplicated because of the transposable elements in the last three million years of its evolution.

It is considered that the spontaneous insertions or deletions (called indels) of a few nucleotides are one of the most important causes of the development of the size of the genome on the long term. In several species of insects, for example, a strong correlation has been shown between the overall rate of DNA loss of intergenic and non-coding regions and genome size. The fixation of these mutations is very unlikely if the indel affects a gene, but it is more likely if it affects pseudogenes (nonfunctional genes, recently inactivated) or other nonfunctional DNA sequences. The disintegration of the genes, or the disappearance of a gene in the genome, usually occur in a first step with its inactivation by a point mutation (formation of a pseudogene). Subsequently, the DNA that forms the pseudogenes is progressively removed until it disappears completely from the genome.

The variation in the length of the DNA of the minisatellites and microsatellites is another mechanism which can alter the size of the genome. These sequences are formed by a unit of few nucleotides, repeated contiguously from less than ten to several thousand times. The number of repeated units varies greatly, even in individuals of the same species. This is due to two mechanisms that cause both an increase and a decrease in the number of repetitions. These mechanisms are the recombination, called unequal, and the errors during DNA replication, due to the phenomenon known as slippage of the DNA polymerase. Indeed, these are the sequences that are being used for the identification of human remains, for example, due to their high variability.

One of the most important processes in the increase of genome size in unicellular organisms, especially prokaryotes, is horizontal gene transfer. This process involves the introduction into the genome of a DNA fragment from another species, containing one or more genes. The DNA is introduced into the cell by different mechanisms and must then recombine with the genome. If the genes introduced confer some advantage to the organism they remain like that, otherwise they can mutate and become inactive. An example of the importance of these processes can be seen in the intestinal bacteria Escherichia coli, some strains of which have become pathogenic for the transfer of virulence genes. The significance of this phenomenon in recent periods of evolution of higher organisms is much more limited, but there are many reported cases of intracellular DNA gene transfer from the genome of the mitochondria and chloroplasts to the nuclear genome.

All these mutations arise periodically in many species, including humans. Many of them are disadvantageous and selective pressure progressively removes them from the population, others are individually neutral but may be collectively beneficial or harmful, by increasing or decreasing the size of the genome. In bacteria, having a small genome can be positive to optimize the time and cost of DNA replication. In eukaryotic organisms, several advantages have been proposed for having a large genome size, though this is not always associated with a higher number of genes.

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