New Results

Low spontaneous mutation rate in a complex multicellular eukaryote with a haploid-diploid life cycle

Marc Krasovec, Agnieszka P. Lipinska, Susana M. Coelho
doi: https://doi.org/10.1101/2022.05.13.491831

Abstract

The spontaneous mutation rate µ is a crucial parameter driving evolution and biodiversity. Mutation rates are highly variable across species, suggesting that µ is susceptible to selection and drift and that species life cycle and life history may impact its evolution. In particular, asexual reproduction and haploid selection are expected to affect mutation rate, but very little empirical data is available to test this expectation. Here, we sequence 30 genomes of a parent-offspring pedigree in the brown algae Ectocarpus to test the impact of its life cycle on mutation rate. Ectocarpus alternates between a haploid and a diploid stage, both multicellular and free living, and utilizes both sexual and asexual reproduction. Ectocarpus is therefore a unique model to empirically test expectations of the effect of asexual reproduction and haploid selection on mutation rate evolution. We estimate that Ectocarpus has a nucleotide mutation rate of µbs=4.07×10−10 per site per generation, a surprisingly low number for a multicellular complex eukaryote. Effective population size (Ne) and genome size could not explain this low mutation rate. We propose that the haploid-diploid life cycle, combined with extensive asexual reproduction may be key drivers of mutation rate.

Introduction

The spontaneous mutation rate (µ) dictates the amount of new genetic diversity generated in a species, population, or genome each generation, and reflects the fidelity of genome transmission. The importance of mutations has been recognized for more than a century, and great efforts have been made to understand the mechanisms underlying mutations and mutation rate evolution (Morgan 1910; Muller 1928; Haldane 1937; Demerec 1937). In the 1960s, Mukai statistically estimated the rate of deleterious mutations in Drosophila (Mukai 1964; Keightley and Eyre-Walker 1999) using the decrease of fitness each generation during mutation accumulation experiments (MAE). MAEs, combined with genomic sequencing, remain an accurate and widely used method to directly measure spontaneous mutation rates, using the following of MA lines from a clone (or from an inbred couple in the case of sexual species) under minimal selection (Halligan and Keightley 2009; Lynch et al. 2016; Katju and Bergthorsson 2019). Selection is reduced during the experiment by imposing regular bottlenecks, generally at one cell or one couple, to decrease the population size of the MA lines. Such dramatic bottlenecks increase the drift (the random chance of allele frequency variation) to a level where selection is negligible. This allows the fixation of all mutations, including the deleterious and thereby gives access to the total spontaneous mutation rate. However, MAEs are sometimes not feasible because they require the following of dozens of lines during hundreds or thousands of generations. For species with long generation times, a pedigree with parent-offspring genome sequencing is more appropriate and this approach has been employed frequently notably in primates (Pfeifer 2017; Koch et al. 2019; Wang et al. 2020) and plants (Krasovec et al. 2018).

To date, spontaneous mutation rates have been estimated in more than seventy species, including several groups of eukaryotes, bacteria, and archaea (Table S1). We observe a variation of four orders of magnitude between ciliates, with mutation rates of 7.6×10−12 (Long et al. 2018b), and the great apes, including humans, with mutation rates higher than 1.0×10−8 mutations per site per generation (Besenbacher et al. 2016, Besenbacher 2019). The “drift barrier” hypothesis has been widely accepted to explain such wide variation (Sung et al. 2012a; Lynch et al. 2016). According to this hypothesis, the mutation rate per site is lower in species with large effective population size (Ne) because selection efficiently favours a small µ, which reduces the mutation load due to deleterious mutations. However, in species with a small effective population size, µ cannot be maintained at a low rate because of stronger drift which can counteract selection. Another possible contributor to mutation rate variation between species is the effect of genome size. Genome size may impact the cost of replication fidelity in species with large genomes, which is consistent with the positive correlation between genome size and mutation rate (Smeds et al. 2016; Krasovec et al. 2017). However, a potential confounding factor is that species with large genomes may have small effective population sizes (Lynch and Conery 2003), leading species with large genomes to have a high mutation rate following the drift barrier hypothesis. It is worthwhile to note, however, that the link between genome size and effective population size is debated (Charlesworth and Barton 2004; Whitney and Jr 2010; Lynch 2011; Whitney et al. 2011).

Current hypotheses are based on highly phylogenetically biased samples, particularly across eukaryotes. Indeed, the majority (>85%) of the estimates is based on only two eukaryotic groups, Opisthokonta and Archaeplastida (Table S1). Exceptions include one estimate for diatoms (Krasovec et al. 2019); four in alveolates (Sung et al. 2012b; Long et al. 2018b), one in haptophytes (Krasovec et al. 2020) and one in Amoebozoa (Saxer et al. 2012; Kucukyildirim et al. 2020). Interestingly, the mutation rates and spectra of these non-model species are strikingly different when compared to classical plant and animal models. Such a discrepancy may relate to organismal life cycle. For example, the low mutation rates in Paramecium (Sung et al. 2012b; Long et al. 2015) may be explained by the unusual life cycle of ciliates, in which a transcriptionally silent germline genome undergoes rounds of cell division between sexual cycles. Selection would favour a low mutation rate to limit the number of deleterious mutations accumulated in the germline genome before sexual reproduction. In the case of Dictyostelium discoideum (Amoebozoa), the short indel mutation rate is higher than that for single nucleotide mutations (Kucukyildirim et al. 2020), contrary to observations in model eukaryotes (Sung et al. 2016). In the haptophyte Emiliania huxleyi, the nucleotide mutation rate from GC to AT is lower than that from AT to GC (Krasovec et al. 2020), indicating that its mutational process tends to increase genome GC content, which is also the inverse of most studied eukaryotes.

These cases, as well as results from many other non-model organisms, highlight that the diversity of mutation rates in eukaryotes is underestimated. There is an enormous diversity of genome structures and life cycles across eukaryotes differing from classical biological models that may impact mutation rate evolution. Therefore, increasing the breadth of studied species across the tree of life is of critical importance to generate a more complete view of the causes, consequences and evolution of mutation rates.

Here, we test the effect of a haploid-diploid life cycle that combines both successive asexual generations and the presence of a persistent haploid stage on mutation rate evolution. Several theoretical predictions have suggested that such a life cycle may considerably reduce the mutation rate. First, asexual reproduction or reduced recombination are expected to increase the strength of selection for a low mutation rate because mutator alleles stay linked to a single lineage, increasing mutational load due to the accumulation of deleterious mutations over generations (Kimura 1967). This idea is supported by modelling approaches when drift has limited effect (Gervais and Roze 2017). A lower mutation rate would also be advantageous in this context as an extensive haploid phase is expected to increase susceptibility to deleterious mutations and thus increase the haploid purifying selection efficiency against mutator alleles, contrary to animals with a gamete-limited haploid phase.

To test the prediction that haploid selection during the haploid-diploid life cycle in combination with asexual reproduction decreases the mutation rate, we employed the model brown alga Ectocarpus. Ectocarpus is a multicellular complex organism representative of an important, but underexplored, eukaryotic group, the brown algae. Brown algae have been evolving independently from other eukaryotic multicellular groups (plants and animals) for more than a billion years (Coelho and Cock 2020). They represent the third most developmentally complex multicellular lineage on the planet. Ectocarpus has a haploid-diploid life cycle alternating between two independent, free living complex multicellular stages which are morphologically distinct: the gametophyte (haploid) and the sporophyte (diploid) (Bothwell et al. 2010; Coelho and Cock 2020). In the field, the two stages may inhabit different ecological niches and are often present during different seasons of the year (Couceiro et al. 2015), with a considerable portion of their life cycle spent in the haploid phase, potentially subject to haploid purifying selection (Immler and Otto 2018). Moreover, Ectocarpus can reproduce both sexually and asexually (through spores produced from diploid sporophytes or through parthenogenesis via non-fertilised gametes which regenerate as haploid individuals), and some populations reproduce almost exclusively asexuality (Couceiro et al. 2015). Consequently, Ectocarpus supplies an opportunity not only to study mutation rate evolution in a broad taxonomic context, but also to investigate the effect of haploid purifying selection and asexual reproduction.

In this study, we generated extensive genomic sequencing data from a pedigree of Ectocarpus (Coelho et al. 2012b, 2020; Coelho and Cock 2020) to directly estimate the mutation rate and effective population size. We show that the spontaneous mutation rate of this multicellular organism is remarkably small, on the order of mutation rates of bacteria or unicellular eukaryotes, while its effective population size is on the order of that of other multicellular organisms. We propose that the combination of haploid purifying selection and extensive asexual reproduction during the haploid-diploid life cycle may be the driving forces explaining the unusually low mutation rate of this organism.

Methods

The Ectocarpus sp7 inbred lineage was generated by genetic crosses between siblings over eight meiotic generations from a wild type field collected diploid sporophyte (Ec17; Figure S1). We then started a lineage from this cross with the sporophyte individual Ec372SP, producing successive generations of sporophytes and gametophytes. We sequenced the genome of Ec372SP (Generation 0), two parent individuals from a first asexual reproduction of Ec372SP (419f and 420m), and 30 gametophytes originating from the individual 467SP obtained after the production of 5 generations of gametophytes and 6 generations of sporophytes (Figure S1). DNA was extracted using the OmniPrep Plant kit (G-Biosciences) following the manufacturer’s instructions. The DNAseq libraries were prepared following a PCR-free protocol (Collibri PCR-free PS DNA library prep kit, ThermoFisher) and the genomic DNA of each individual was sequenced by Illumina NovaSeq with 150 bp paired end reads.

Raw reads were trimmed with fastp (Chen et al. 2018) and trimgalore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) to remove poly(G) tails and overrepresented sequences (Table S2). Cleaned reads were then mapped onto the reference Ectocarpus genome (EctsiV2 from ORCAE database: https://bioinformatics.psb.ugent.be/orcae, (Sterck et al. 2012)) assembled with bwa mem (Li and Durbin 2010), and bam files were treated with samtools (Li et al. 2009). SNP calling to detect nucleotide and short insertion-deletion mutations was done with HaplotypeCaller from GATK (McKenna et al. 2010). Structural mutations were detected with lumpy (Layer et al. 2014), delly (Rausch et al. 2012) and svaba (Wala et al. 2018). Sites were considered callable with a coverage >9x in all samples. Then, de novo mutation candidates were filtered as follows: (1) the mutation must be found in only one individual; (2) the alternative allele must be 100% of the coverage of the site; (3) none of the other individuals have any alternative reads even at low quality; (4) the site is haploid in all non-mutated individuals; (5) mutation candidates were manually checked in the pileup file of all individuals; and (6) for structural mutations, candidates at a single position in a single individual were removed if similar variants were found near the position in other individuals. Because we considered only mutations appearing in a single individual, shared mutations appearing from Ec372SP to Ec467SP are not counted in the study.

The mutation rate was calculated as follows: n/(G*x30xg) with n = the number of mutations, G* = the number of callable sites, 30 = the number of sequenced individuals after 467SP, and g = the generation number (g=1). Coverage analysis was done with bedtools (Quinlan and Hall 2010). Structural variants were not explored in the scaffold Chr_00, which represents a collection of all contigs that could not be placed on the chromosomes.

To test the putative dosage compensation of duplicated chromosomes in the individual L467_27, we performed transcriptome analysis using triplicate samples of L467_27 with L467_26 as a control. Total RNA was sequenced by Illumina HiSeq with 100 bp paired-end reads. Raw reads were mapped against the reference transcriptome with RSEM (Li and Dewey 2011) with Bowtie2 (Langmead and Salzberg 2012) to get TPM values for each genes. Only genes with TPM higher than 1 were selected for analysis.

Results

Spontaneous mutation rate We analysed genomic data from the individual at the origin of the lineage (the sporophyte Ec372SP), two parent individuals from the first generation and 30 progenies. Mutations were called on 163,675,306 callable sites corresponding to 83.46% of the genome (Table S3). No structural or short indel mutations were found, but we detected two nucleotide mutations, one from A to G on chromosome 16 at position 1,499,719 (intergenic, individual L467_25) and one from C to A on chromosome 11 at position 645,251 (intron of the gene Ec-11_000650, individual L467_27). This gives a nucleotide mutation rate µbs=4.07×10−10 mutations per site per generation (Table 1), making Ectocarpus one of the rare multicellular species with a nucleotide mutation rate below 1.0×10−09 (Table S1, Figure 1). The GC content of Ectocarpus is 53.59%. Based on the two identified nucleotide mutations, µbs GC->AT=4.38×10−10 and µbs AT->GC=3.81×10−10, giving a modest mutation spectrum bias toward GC to AT mutations of 1.15. However, this result is a preliminary estimate, as it is based on only two mutations. Although we identified no indels or structural mutations, if we assume the occurrence of one hypothetical indel and one hypothetical structural mutation in our pedigree, the total mutation rate would be below 8.15×10−10 mutations per site per generation (Table 1).

View this table:
Table 1.

Spontaneous mutation rates of Ectocarpus. µbs, nucleotide mutation rate per site; µid, short insertion-deletion mutation rate per site; µst, structural mutation rate per site; Ubs, nucleotide mutation rate per genome; Uid, short insertion-deletion mutation rate per genome; Ust, structural mutation rate per genome. µid, µst, Uid and Ust, are calculated assuming one hypothetical mutation.

Figure 1.
Figure 1.

A. Nucleotide mutation rates µbs of multicellular species (data in Table S1). B. Nucleotide mutation rates µbs of species with effective population size Ne between ∼1 to ∼ 5 million, on the same order as Ectocarpus (Ne∼2 million). Data from Table S1 of Lynch et al. 2016. C. Nucleotide mutation rates µbs of species with genome size between ∼100 to ∼ 300 Mb, on the same order as Ectocarpus (214 Mb). Ap: Acyrthosiphon pisum; Sp: Spirodela polyrhiza; Lm: Lemna minor; Ect: Ectocarpus.

High chromosome duplication rate in Ectocarpus

Assessment of raw genome coverage revealed a duplication of 4 chromosomes (C14, C16, C18, C19) in the individual L467_27 (Figure 2). Assuming four independent whole genome duplication (WCD) events, this finding suggests a chromosome duplication rate of 0.0048 duplications per chromosome per generation or 0.1333 chromosome duplications per cell per generation. However, assuming only one independent WCD event (because these duplications were identified in a single progeny), we calculated these rates to instead be 0.0012 duplications per chromosome per generation and 0.0333 chromosome duplications per cell per generation, respectively.

Figure 2.
Figure 2.

Whole genome raw coverage by 5kb windows showing the four chromosome duplications (in blue) of the L467_27 individual. Grey bars mark chromosome separations.

Chromosome duplications are usually highly deleterious (Sheltzer and Amon 2011), so we investigated if these chromosomal aberrations had an effect on individual fitness. Individual clone L467_27 was cultivated in standard culture conditions (Coelho et al. 2012a), and its development was closely followed by regular morphological measures (Table S4) and compared with a sibling without chromosome duplication (L467_26). After 3 weeks in culture, L467_27 exhibited an extensive decrease in fitness, with its growth being markedly slower (Table S4, Figure S2). When cultivated in fertility-inducing conditions, control strain L467_26 produced plurilocular sporangia after 18 days as expected, while L467_27 did not produce meiotic reproductive structures (unilocular sporangia) even after 25 days. Taken together, these results suggest that the identified chromosome duplications did have a negative effect on the growth and reproductive fitness of this clone. To explore the effect of the WCDs on transcription, we measured the level of genome-wide transcription of L467_27 and its sibling control (L467_26). Comparative transcriptomic analyses indicated a ∼1.71-fold higher mRNA level for genes located within the duplicated regions compared to non-duplicated regions (Figure S3), suggesting a general lack of dosage compensation in the line harbouring WCDs.

Effective population size

Our estimate of the spontaneous mutation rate allows us to calculate the effective population size (Ne), following πs=4.Ne.µ. The neutral diversity (πs) of Ectocarpus has been previously estimated based on 49 sporophytes from three European and one South American population and six gametophytes from two European and one South American population (Avia et al. 2018). The neutral diversity of autosomal genes is πs=0.00323, which leads to an estimated effective population size of 1,984,029. Ectocarpus has a haploid UV sex chromosome system (Ahmed et al. 2014; Coelho et al. 2018, 2019), making it possible to calculate the effective population size of the different parts of the sex chromosome: πs of the pseudo-autosomal region (PAR, the recombining part of the sex chromosome) was estimated to be 0.00439, giving an effective population size of 2,696,560; and πs of the sex-determining region (SDR, non-recombining) is 0.00221, leading to an effective population size of 1,357,494. The higher Ne of the PAR region may be caused by balancing selection between male and female alleles (Avia et al. 2018), whereas the smaller Ne of the SDR is likely caused by the absence of recombination (Ahmed et al. 2014). For these reasons, we will consider hereafter the effective population size of Ectocarpus as the effective population size calculated for autosomal genes. The time of suppression of recombination on the SDR is given following πs=2.t.µ, where t is the divergence time, calculated here as 2,714,988 generations. This result is consistent with an old UV sexual system, as has been previously suggested (Lipinska et al. 2017).

Discussion

Low level of spontaneous mutation in Ectocarpus

Although Ectocarpus is a complex multicellular eukaryotic species, our results using pedigree sequencing revealed an exceptionally low spontaneous mutation rate, on the same order of magnitude as that of unicellular organisms such as bacteria (Long et al. 2018a), yeast (Lynch et al. 2008; Zhu et al. 2014) and phytoplankton (Ness et al. 2012; Krasovec et al. 2017, 2019, 2020). To date, estimates of mutation rates in multicellular species such as insects (Schrider et al. 2013; Keightley et al. 2014a; b; Liu et al. 2017; Oppold and Pfenninger 2017; Krasovec 2021), small vertebrates (Uchimura et al. 2015; Smeds et al. 2016; Feng et al. 2017; Malinsky et al. 2018), and plants (Ossowski et al. 2010; Xie et al. 2016; Krasovec et al. 2018) are at least one order of magnitude higher. Ectocarpus is therefore one of the few cases of a multicellular species with a very low spontaneous mutation rate (Figure 2A). This observation suggests that the selection pressure for a low mutation rate in Ectocarpus may be stronger than for most multicellular species.

Ne and genome size do not explain low µbs in Ectocarpus

Following the drift barrier hypothesis (Sung et al. 2012a; Lynch et al. 2016), effective population size is key to understand mutation rate variation between species. The effective population size of Ectocarpus is about 2 million, a value unlikely to be sufficient to explain its low mutation rate when compared to the available literature (Lynch et al. 2016). Species with an effective population size on the same order of magnitude as Ectocarpus typically have a mutation rate an order of magnitude higher (Figure 2B). For example, the effective population sizes of Daphnia pulex, Drosophila melanogaster, Mesoplasma florum, Pristionchus pacificus, Neurospora crassa, Heliconius melpomene and Trypanosoma brucei are between 1 to 5 million, with spontaneous mutation rates from 1.4×10−9 to 9.8×10−9 mutations per site per generation. Species with a mutation rate similar to Ectocarpus instead have a much larger effective population size of 10 million or more (see Table S1, Lynch et al. 2016). In other words, the mutation rate of Ectocarpus does not fit classical species expectations.

A second characteristic expected to affect mutation rate is genome size, but similarly as with effective population size, multicellular species with similar genome sizes to Ectocarpus typically have spontaneous mutation rates one order higher (Table S1, Figure 2C). For example, Prunus persica (Xie et al. 2016), Drosophila pseudoobscura (Krasovec 2021), Drosophila melanogaster (Schrider et al. 2013; Keightley et al. 2014a), Chironomus riparius (Oppold and Pfenninger 2017), Daphnia pulex (Flynn et al. 2016) or Apis mellifera (Liu et al. 2017) all have genome sizes between 100 and 300 Mb but substantially higher mutation rates than Ectocarpus. The duckweed species Spirodela polyrhiza is uniquely similar to Ectocarpus in this regard, with a genome size of 140 Mb and a very low mutation rate (Sandler et al. 2020).

Asexuality and persistent haploid stage may explain small µbs in Ectocarpus

The low mutation rate of Ectocarpus is unlikely to be explained by effective population size and genome size. Instead, one possible explanation may be this species’ capacity for asexual reproduction. This hypothesis would be in line with the Kimura prediction (Kimura 1967), suggesting that the selection coefficient k for a low mutation rate increases when the recombination rate r decreases (r = 0 for asexual reproduction), given the general assumption of mutations as deleterious. Note that only two other examples of multicellular species with particularly low mutation rates have so far been described: the pea aphids (Acyrthosiphon pisum) (Fazalova and Nevado 2020) and the duck weeds (Lemna minor) (Xu et al. 2019; Sandler et al. 2020). These organisms can also reproduce asexually (by budding and parthenogenesis) for several generations before engaging in sexual reproduction, supporting the hypothesis that asexual reproduction indeed may drive the evolution of low mutation rates. However, it appears that although Ectocarpus is capable of asexual reproduction, it may be highly sexual in the field, and significant variation of asexual reproduction frequency between populations has been observed (Couceiro et al. 2015). Therefore, it is possible that asexual reproduction alone may not fully provide an explanation for the very low mutation rates we observe in Ectocarpus.

An additional, non-mutually exclusive explanation for the low mutation rate we observe is the haploid-diploid life cycle of Ectocarpus, which includes a persistent, complex, multicellular haploid stage. The gametophyte haploid stage of Ectocarpus has a relatively complex morphology, is free-living and macroscopic, persists for several months, and is associated with the expression of the majority of organismal genes (Lipinska et al. 2015). In contrast, the haploid stage of animals such as Drosophila is limited to the gametes, and in the more common diploid state, genetic dominance has the potential to mask the effects of mildly deleterious mutations. Thus, in Ectocarpus, haploid purifying selection may optimise selection against mutator alleles or any de novo mutations with effects on mutation rate (Kimura 1967).

Whole chromosome duplication rate

Whole chromosome duplications have been reported in several mutation accumulation studies in unicellular green algae (Krasovec et al. 2022) and yeast (Zhu et al. 2014; Liu and Zhang 2019), where the whole chromosome duplication rate per chromosome per generation was below 0.0001. The chromosome duplication rate we measured for Ectocarpus was about 100-fold higher than in these species, in striking contrast to its low nucleotide mutation rate. The mutational processes between WCD and point mutations are very different: point mutations result from replication errors, whereas WCDs result from mis-segregation of chromosomes. WCD may result from a mis-segregation of the chromosome set between daughter cells. Aneuploid karyotypes are broadly considered to be deleterious because they cause an imbalance in gene dosage and transcript production (Hou et al. 2018). Indeed, the deleterious effect of dosage imbalance can also apply to single gene duplications. In Caenorhabditis elegans, excess transcripts from duplicated genes have been shown to be highly deleterious, and selection acts more strongly against duplication of highly transcribed genes (Konrad et al. 2018). Mechanisms of dosage compensation, well known for sex chromosomes, may reduce the deleterious effects of such gene dosage imbalance by adjusting the transcription of genes on aneuploid chromosomes (Disteche 2012). However, the relevance of dosage compensation mechanisms for autosomal chromosomes, particularly immediately after duplication events is poorly understood, especially in emergent models such as Ectocarpus. In this study, the reduced fitness of individuals with duplicated chromosomes could reflect highly deleterious effects of WCD and lack of dosage compensation, which was further supported by our transcriptomic analysis.

Structural mutation rate µst is poorly understood

Understanding of the rate of structural mutations (sub-chromosomal copy number variations, inversions, large insertion-deletions and chromosomal rearrangements) is comparatively rare in the literature (Katju and Bergthorsson 2019). Spontaneous structural mutation rates have been calculated for only few species such as green algae (Leger-Pigout and Krasovec 2022), yeast (Lynch et al. 2008; Zhu et al. 2014; Liu and Zhang 2019), Daphnia (Keith et al. 2016; Ho and Schaack 2021) and Caenorhabditis elegans (Konrad et al. 2018). Structural mutations are evolutionarily important as they impact a large portion of the genome and may originate major adaptations. Notably, inversions can cause recombination arrest and lead to the creation not only of supergenes (Taylor and Campagna 2016) butmay also contribute to the emergence of the non-recombining regions of sex-chromosomes (Charlesworth 2016; Graves 2016). It is therefore crucial to estimate the structural mutation rate to have a complete view of genome dynamics, with the total mutation rate µtot=µbs+µid+µst. Almost all current estimates include only spontaneous nucleotide and short indel mutations, preventing us from fully understanding mutation spectrum evolution and the total amount of genetic diversity generated each generation.

Together, our work suggests that organismal life cycle plays a significant role in mutation rate evolution and corroborates the predicted effects of asexuality and a persistent complex haploid phase on mutation rate. More broadly, investigations of non-model or emergent model species will significantly improve our understanding of mutation rate evolution across the tree of life.

Author contributions

Original idea: MK, SMC; Data analysis: MK, Interpretation: MK, AL, SMC; First draft: MK; Final draft, editing and revisions: MK, SMC; Project management and funding: SMC.

Competing interests

The authors declare no conflicts of interest.

Data availability statement

Genomic raw reads are available under the bioproject xxx.

Supplemental Tables Legends

Table S1. Published nucleotide mutation rates from pedigree and mutation accumulation studies.

Table S2. Genomic coverage and fastq statistics.

Table S3. Number of callable sites with a minimum coverage threshold of >9× per line.

Table S4. Fitness measurements of wild type and lines showing chromosome duplications

Supplemental Figures

Figure S1.
Figure S1.

Ectocarpus pedigree used in this study. All individuals originate from 372SP. Successive generations led to the individuals 467SP for which we sequenced thirty progenies. Orange: sequenced individuals; GA: gametophyte, haploid; SP: sporophyte, diploid.

Figure S2.
Figure S2.

Representative microscopy images of lines L467_26 and L467_27 at day 18 after germination. Asterisks represent plurilocular sporangia (where spores are produced). Sale bar=100µm.

Figure S3.
Figure S3.

Transcription ratio of genes linked to non-duplicated (n) and duplicated (2n) chromosomes of E467_27. Student test, p-value= 0.0395.

Acknowledgements

We are grateful to Akira Peters for generating the inbred lines, Andrea Belkacemi and Min Zheng for help with DNA and RNA extractions and library preparations, Dorothee Koch for help with algal cultures, and Denis Roze for comments on the manuscript. This work was founded by the Max Planck Society and a European Research Council grant 864038 to SMC.

References

  1. ↵
    Ahmed S., J. M. Cock, E. Pessia, R. Luthringer, A. Cormier, et al., 2014 A haploid system of sex determination in the brown alga Ectocarpus sp. Curr Biol 24: 1945–1957. https://doi.org/10.1016/j.cub.2014.07.042
    OpenUrlCrossRefPubMed
  2. ↵
    Avia K., A. P. Lipinska, L. Mignerot, A. E. Montecinos, M. Jamy, et al., 2018 Genetic Diversity in the UV Sex Chromosomes of the Brown Alga Ectocarpus. Genes 9: 286. https://doi.org/10.3390/genes9060286
    OpenUrl
  3. ↵
    Besenbacher S., P. Sulem, A. Helgason, H. Helgason, H. Kristjansson, et al., 2016 Multi-nucleotide de novo Mutations in Humans. PLoS Genet. 12: e1006315. https://doi.org/10.1371/journal.pgen.1006315
    OpenUrlCrossRefPubMed
  4. ↵
    Besenbacher S., C. Hvilsom, T. Marques-Bonet, T. Mailund, and M. H. Schierup, 2019 Direct estimation of mutations in great apes reconciles phylogenetic dating. Nat Ecol Evol 3: 286–292. https://doi.org/10.1038/s41559-018-0778-x
    OpenUrl
  5. ↵
    Bothwell J. H., D. Marie, A. F. Peters, J. M. Cock, and S. M. Coelho, 2010 Role of endoreduplication and apomeiosis during parthenogenetic reproduction in the model brown alga Ectocarpus. New Phytol 188: 111–121. https://doi.org/10.1111/j.1469-8137.2010.03357.x
    OpenUrlCrossRefPubMedWeb of Science
  6. ↵
    Charlesworth B., and N. Barton, 2004 Genome size: does bigger mean worse? Curr Biol 14: R233–235. https://doi.org/10.1016/j.cub.2004.02.054
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    Charlesworth D., 2016 Plant Sex Chromosomes. Annual Review of Plant Biology 67: 397–420. https://doi.org/10.1146/annurev-arplant-043015-111911
    OpenUrlCrossRef
  8. ↵
    Chen S., Y. Zhou, Y. Chen, and J. Gu, 2018 fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34: i884–i890. https://doi.org/10.1093/bioinformatics/bty560
    OpenUrlCrossRefPubMed
  9. Cock J. M., L. Sterck, P. Rouzé, D. Scornet, A. E. Allen, et al., 2010 The Ectocarpus genome and the independent evolution of multicellularity in brown algae. Nature 465: 617–621. https://doi.org/10.1038/nature09016
    OpenUrlCrossRefPubMedWeb of Science
  10. ↵
    Coelho S. M., D. Scornet, S. Rousvoal, N. T. Peters, L. Dartevelle, et al., 2012a How to Cultivate Ectocarpus. Cold Spring Harb Protoc 2012: pdb.prot067934. https://doi.org/10.1101/pdb.prot067934
  11. ↵
    Coelho S. M., D. Scornet, S. Rousvoal, N. T. Peters, L. Dartevelle, et al., 2012b Ectocarpus: a model organism for the brown algae. Cold Spring Harb Protoc 2012: 193–198. https://doi.org/10.1101/pdb.emo065821
    OpenUrlCrossRef
  12. ↵
    Coelho S. M., J. Gueno, A. P. Lipinska, J. M. Cock, and J. G. Umen, 2018 UV Chromosomes and Haploid Sexual Systems. Trends Plant Sci 23: 794–807. https://doi.org/10.1016/j.tplants.2018.06.005
    OpenUrlCrossRef
  13. ↵
    Coelho S. M., L. Mignerot, and J. M. Cock, 2019 Origin and evolution of sex-determination systems in the brown algae. New Phytol 222: 1751–1756. https://doi.org/10.1111/nph.15694
    OpenUrl
  14. ↵
    Coelho S. M., A. F. Peters, D. Müller, and J. M. Cock, 2020 Ectocarpus: an evo-devo model for the brown algae. Evodevo 11: 19. https://doi.org/10.1186/s13227-020-00164-9
    OpenUrl
  15. ↵
    Coelho S. M., and J. M. Cock, 2020 Brown Algal Model Organisms. Annu Rev Genet 54: 71–92. https://doi.org/10.1146/annurev-genet-030620-093031
    OpenUrl
  16. ↵
    Couceiro L., M. Le Gac, H. M. Hunsperger, S. Mauger, C. Destombe, et al., 2015 Evolution and maintenance of haploid-diploid life cycles in natural populations: The case of the marine brown alga Ectocarpus. Evolution 69: 1808–1822. https://doi.org/10.1111/evo.12702
    OpenUrlCrossRefPubMed
  17. ↵
    Demerec M., 1937 Frequency of Spontaneous Mutations in Certain Stocks of Drosophila Melanogaster. Genetics 22: 469–478.
    OpenUrlFREE Full Text
  18. ↵
    Disteche C. M., 2012 Dosage Compensation of the Sex Chromosomes. Annu Rev Genet 46: 537–560. https://doi.org/10.1146/annurev-genet-110711-155454
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    Fazalova V., and B. Nevado, 2020 Low Spontaneous Mutation Rate and Pleistocene Radiation of Pea Aphids. Molecular Biology and Evolution 37: 2045–2051. https://doi.org/10.1093/molbev/msaa066
    OpenUrl
  20. ↵
    1. M. Przeworski
    Feng C., M. Pettersson, S. Lamichhaney, C.-J. Rubin, N. Rafati, et al., 2017 Moderate nucleotide diversity in the Atlantic herring is associated with a low mutation rate, ( M. Przeworski, Ed.). eLife 6: e23907. https://doi.org/10.7554/eLife.23907
    OpenUrlCrossRefPubMed
  21. ↵
    Flynn J. M., F. J. J. Chain, D. J. Schoen, and M. E. Cristescu, 2016 Spontaneous mutation accumulation in Daphnia pulex in selection-free versus competitive environments. Mol Biol Evol msw 234. https://doi.org/10.1093/molbev/msw234
  22. ↵
    Gervais C., and D. Roze, 2017 Mutation Rate Evolution in Partially Selfing and Partially Asexual Organisms. Genetics 207: 1561–1575. https://doi.org/10.1534/genetics.117.300346
    OpenUrlAbstract/FREE Full Text
  23. ↵
    Graves J. A. M., 2016 Evolution of vertebrate sex chromosomes and dosage compensation. Nat Rev Genet 17: 33–46. https://doi.org/10.1038/nrg.2015.2
    OpenUrlCrossRefPubMed
  24. ↵
    Haldane J. B. S., 1937 The Effect of Variation of Fitness. The American Naturalist 71: 337–349.
    OpenUrlCrossRefWeb of Science
  25. ↵
    Halligan D. L., and P. D. Keightley, 2009 Spontaneous Mutation Accumulation Studies in Evolutionary Genetics. Annual Review of Ecology, Evolution, and Systematics 40: 151–172. https://doi.org/10.1146/annurev.ecolsys.39.110707.173437
    OpenUrlCrossRef
  26. ↵
    Ho E. K. H., and S. Schaack, 2021 Intraspecific variation in the rates of mutations causing structural variation in Daphnia magna. Genome Biology and Evolution evab241. https://doi.org/10.1093/gbe/evab241
  27. ↵
    Hou J., X. Shi, C. Chen, M. S. Islam, A. F. Johnson, et al., 2018 Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa. PNAS 115: E11321–E11330. https://doi.org/10.1073/pnas.1807796115
    OpenUrlAbstract/FREE Full Text
  28. ↵
    Immler S., and S. P. Otto, 2018 The Evolutionary Consequences of Selection at the Haploid Gametic Stage. Am Nat 192: 241–249. https://doi.org/10.1086/698483
    OpenUrlCrossRef
  29. ↵
    Katju V., and U. Bergthorsson, 2019 Old Trade, New Tricks: Insights into the Spontaneous Mutation Process from the Partnering of Classical Mutation Accumulation Experiments with High-Throughput Genomic Approaches. Genome Biol Evol 11: 136–165. https://doi.org/10.1093/gbe/evy252
    OpenUrlCrossRef
  30. ↵
    Keightley P. D., and A. Eyre-Walker, 1999 Terumi Mukai and the Riddle of Deleterious Mutation Rates. Genetics 153: 515–523.
    OpenUrlAbstract/FREE Full Text
  31. ↵
    Keightley P. D., R. W. Ness, D. L. Halligan, and P. R. Haddrill, 2014a Estimation of the spontaneous mutation rate per nucleotide site in a Drosophila melanogaster full-sib family. Genetics 196: 313–320. https://doi.org/10.1534/genetics.113.158758
    OpenUrlAbstract/FREE Full Text
  32. ↵
    Keightley P. D., A. Pinharanda, R. W. Ness, F. Simpson, K. K. Dasmahapatra, et al., 2014b Estimation of the spontaneous mutation rate in Heliconius melpomene. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msu302
  33. ↵
    Keith N., A. E. Tucker, C. E. Jackson, W. Sung, J. I. Lucas Lledó, et al., 2016 High mutational rates of largescale duplication and deletion in Daphnia pulex. Genome Res 26: 60–69. https://doi.org/10.1101/gr.191338.115
    OpenUrlAbstract/FREE Full Text
  34. ↵
    Kimura M., 1967 On the evolutionary adjustment of spontaneous mutation rates*. Genetics Research 9: 23–34. https://doi.org/10.1017/S0016672300010284
    OpenUrl
  35. ↵
    Koch E., R. M. Schweizer, T. M. Schweizer, D. R. Stahler, D. W. Smith, et al., 2019 De novo mutation rate estimation in wolves of known pedigree. Mol Biol Evol. https://doi.org/10.1093/molbev/msz159
  36. ↵
    Konrad A., S. Flibotte, J. Taylor, R. H. Waterston, D. G. Moerman, et al., 2018 Mutational and transcriptional landscape of spontaneous gene duplications and deletions in Caenorhabditis elegans. PNAS 115: 7386–7391. https://doi.org/10.1073/pnas.1801930115
    OpenUrlAbstract/FREE Full Text
  37. ↵
    Krasovec M., A. Eyre-Walker, S. Sanchez-Ferandin, and G. Piganeau, 2017 Spontaneous Mutation Rate in the Smallest Photosynthetic Eukaryotes. Mol. Biol. Evol. 34: 1770–1779. https://doi.org/10.1093/molbev/msx119
    OpenUrlCrossRef
  38. ↵
    Krasovec M., M. Chester, K. Ridout, and D. A. Filatov, 2018 The Mutation Rate and the Age of the Sex Chromosomes in Silene latifolia. Current Biology 28: 1832-1838.e4. https://doi.org/10.1016/j.cub.2018.04.069
    OpenUrlCrossRef
  39. ↵
    Krasovec M., S. Sanchez-Brosseau, and G. Piganeau, 2019 First Estimation of the Spontaneous Mutation Rate in Diatoms. Genome Biol Evol 11: 1829–1837. https://doi.org/10.1093/gbe/evz130
    OpenUrlCrossRef
  40. ↵
    Krasovec M., R. E. M. Rickaby, and D. A. Filatov, 2020 Evolution of Mutation Rate in Astronomically Large Phytoplankton Populations. Genome Biol Evol 12: 1051–1059. https://doi.org/10.1093/gbe/evaa131
    OpenUrlCrossRef
  41. ↵
    Krasovec M., 2021 The spontaneous mutation rate of Drosophila pseudoobscura. G3 (Bethesda) jkab151. https://doi.org/10.1093/g3journal/jkab151
  42. ↵
    Krasovec M., R. Merret, F. Sanchez, S. Sanchez-Brosseau, and G. Piganeau, 2022 High rates of spontaneous chromosomal duplications are compensated by translational regulation in a photosynthetic unicellular eukaryote. 2022.02.03.478961.
  43. ↵
    Kucukyildirim S., M. Behringer, W. Sung, D. A. Brock, T. G. Doak, et al., 2020 Low Base-Substitution Mutation Rate but High Rate of Slippage Mutations in the Sequence Repeat-Rich Genome of Dictyostelium discoideum. G3 (Bethesda) 10: 3445–3452. https://doi.org/10.1534/g3.120.401578
    OpenUrlAbstract/FREE Full Text
  44. ↵
    Langmead B., and S. L. Salzberg, 2012 Fast gapped-read alignment with Bowtie 2. Nat. Methods 9: 357–359. https://doi.org/10.1038/nmeth.1923
    OpenUrlCrossRefPubMedWeb of Science
  45. ↵
    Layer R. M., C. Chiang, A. R. Quinlan, and I. M. Hall, 2014 LUMPY: a probabilistic framework for structural variant discovery. Genome Biology 15: R84. https://doi.org/10.1186/gb-2014-15-6-r84
    OpenUrlCrossRefPubMed
  46. ↵
    Leger-Pigout M., and M. Krasovec, 2022 Very small spontaneous structural mutation rate in green algae. 2022.02.09.479524.
  47. ↵
    Li H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, et al., 2009 The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079. https://doi.org/10.1093/bioinformatics/btp352
    OpenUrlCrossRefPubMedWeb of Science
  48. ↵
    Li H., and R. Durbin, 2010 Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26: 589–595. https://doi.org/10.1093/bioinformatics/btp698
    OpenUrlCrossRefPubMedWeb of Science
  49. ↵
    Li B., and C. N. Dewey, 2011 RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12: 323. https://doi.org/10.1186/1471-2105-12-323
    OpenUrlCrossRefPubMed
  50. ↵
    Lipinska A., A. Cormier, R. Luthringer, A. F. Peters, E. Corre, et al., 2015 Sexual dimorphism and the evolution of sex-biased gene expression in the brown alga ectocarpus. Mol Biol Evol 32: 1581–1597. https://doi.org/10.1093/molbev/msv049
    OpenUrlCrossRefPubMed
  51. ↵
    Lipinska A. P., N. R. T. Toda, S. Heesch, A. F. Peters, J. M. Cock, et al., 2017 Multiple gene movements into and out of haploid sex chromosomes. Genome Biology 18: 104. https://doi.org/10.1186/s13059-017-1201-7
    OpenUrlCrossRef
  52. ↵
    Liu H., Y. Jia, X. Sun, D. Tian, L. D. Hurst, et al., 2017 Direct Determination of the Mutation Rate in the Bumblebee Reveals Evidence for Weak Recombination-Associated Mutation and an Approximate Rate Constancy in Insects. Mol Biol Evol 34: 119–130. https://doi.org/10.1093/molbev/msw226
    OpenUrlCrossRefPubMed
  53. ↵
    Liu H., and J. Zhang, 2019 Yeast Spontaneous Mutation Rate and Spectrum Vary with Environment. Curr Biol 29: 1584-1591.e3. https://doi.org/10.1016/j.cub.2019.03.054
    OpenUrlCrossRef
  54. ↵
    Long H., D. J. Winter, A. Y.-C. Chang, W. Sun, S. H. Whu, et al., 2015 Low base-substitution mutation rate in the ciliate Tetrahymena thermophila. bioRxiv 025536. https://doi.org/10.1101/025536
  55. ↵
    Long H., W. Sung, S. Kucukyildirim, E. Williams, S. F. Miller, et al., 2018a Evolutionary determinants of genome-wide nucleotide composition. Nature Ecology & Evolution 2: 237–240. https://doi.org/10.1038/s41559-017-0425-y
    OpenUrl
  56. ↵
    Long H., T. G. Doak, and M. Lynch, 2018b Limited Mutation-Rate Variation Within the Paramecium aurelia Species Complex. G3 (Bethesda) 8: 2523–2526. https://doi.org/10.1534/g3.118.200420
    OpenUrlAbstract/FREE Full Text
  57. ↵
    Lynch M., and J. S. Conery, 2003 The origins of genome complexity. Science 302: 1401–1404. https://doi.org/10.1126/science.1089370
    OpenUrlAbstract/FREE Full Text
  58. ↵
    Lynch M., W. Sung, K. Morris, N. Coffey, C. R. Landry, et al., 2008 A genome-wide view of the spectrum of spontaneous mutations in yeast. PNAS 105: 9272–9277. https://doi.org/10.1073/pnas.0803466105
    OpenUrlAbstract/FREE Full Text
  59. ↵
    Lynch M., 2011 Statistical Inference on the Mechanisms of Genome Evolution. PLOS Genetics 7: e1001389. https://doi.org/10.1371/journal.pgen.1001389
    OpenUrl
  60. ↵
    Lynch M., M. S. Ackerman, J.-F. Gout, H. Long, W. Sung, et al., 2016 Genetic drift, selection and the evolution of the mutation rate. Nat Rev Genet 17: 704–714. https://doi.org/10.1038/nrg.2016.104
    OpenUrlCrossRefPubMed
  61. ↵
    Malinsky M., H. Svardal, A. M. Tyers, E. A. Miska, M. J. Genner, et al., 2018 Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow. Nat Ecol Evol 2: 1940–1955. https://doi.org/10.1038/s41559-018-0717-x
    OpenUrl
  62. ↵
    McKenna A., M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, et al., 2010 The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20: 1297–1303. https://doi.org/10.1101/gr.107524.110
    OpenUrlAbstract/FREE Full Text
  63. ↵
    Morgan T. H., 1910 Sex Limited Inheritance in Drosophila. Science 32: 120–122. https://doi.org/10.1126/science.32.812.120
    OpenUrlFREE Full Text
  64. ↵
    Mukai T., 1964 The Genetic Structure of Natural Populations of Drosophila Melanogaster. I. Spontaneous Mutation Rate of Polygenes Controlling Viability. Genetics 50: 1–19.
    OpenUrlFREE Full Text
  65. ↵
    Muller H. J., 1928 The Measurement of Gene Mutation Rate in Drosophila, Its High Variability, and Its Dependence upon Temperature. Genetics 13: 279–357.
    OpenUrlFREE Full Text
  66. ↵
    Ness R. W., A. D. Morgan, N. Colegrave, P. D. Keightley, R. W. Ness, et al., 2012 Estimate of the Spontaneous Mutation Rate in Chlamydomonas reinhardtii. Genetics 192: 1447–1454. https://doi.org/10.1534/genetics.112.145078
    OpenUrlAbstract/FREE Full Text
  67. ↵
    Oppold A.-M., and M. Pfenninger, 2017 Direct estimation of the spontaneous mutation rate by shortterm mutation accumulation lines in Chironomus riparius. Evolution Letters 1: 86–92. https://doi.org/10.1002/evl3.8
    OpenUrl
  68. ↵
    Ossowski S., K. Schneeberger, J. I. Lucas-Lledó, N. Warthmann, R. M. Clark, et al., 2010 The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science 327: 92–94. https://doi.org/10.1126/science.1180677
    OpenUrlAbstract/FREE Full Text
  69. ↵
    Pfeifer S. P., 2017 Direct estimate of the spontaneous germ line mutation rate in African green monkeys. Evolution 71: 2858–2870. https://doi.org/10.1111/evo.13383
    OpenUrl
  70. ↵
    Quinlan A. R., and I. M. Hall, 2010 BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841–842. https://doi.org/10.1093/bioinformatics/btq033
    OpenUrlCrossRefPubMedWeb of Science
  71. ↵
    Rausch T., T. Zichner, A. Schlattl, A. M. Stütz, V. Benes, et al., 2012 DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28: i333–i339. https://doi.org/10.1093/bioinformatics/bts378
    OpenUrlCrossRefPubMedWeb of Science
  72. ↵
    Sandler G., M. Bartkowska, A. F. Agrawal, and S. I. Wright, 2020 Estimation of the SNP Mutation Rate in Two Vegetatively Propagating Species of Duckweed. G3 (Bethesda) 10: 4191–4200. https://doi.org/10.1534/g3.120.401704
    OpenUrlAbstract/FREE Full Text
  73. ↵
    Saxer G., P. Havlak, S. A. Fox, M. A. Quance, S. Gupta, et al., 2012 Whole genome sequencing of mutation accumulation lines reveals a low mutation rate in the social amoeba Dictyostelium discoideum. PLoS ONE 7: e46759. https://doi.org/10.1371/journal.pone.0046759
    OpenUrlCrossRefPubMed
  74. ↵
    Schrider D. R., D. Houle, M. Lynch, and M. W. Hahn, 2013 Rates and genomic consequences of spontaneous mutational events in Drosophila melanogaster. Genetics 194: 937–954. https://doi.org/10.1534/genetics.113.151670
    OpenUrlAbstract/FREE Full Text
  75. ↵
    Sheltzer J. M., and A. Amon, 2011 The Aneuploidy Paradox: Costs and Benefits of an Incorrect Karyotype. Trends Genet 27: 446–453. https://doi.org/10.1016/j.tig.2011.07.003
    OpenUrlCrossRefPubMedWeb of Science
  76. ↵
    Smeds L., A. Qvarnstrom, and H. Ellegren, 2016 Direct estimate of the rate of germline mutation in a bird. Genome Res. https://doi.org/10.1101/gr.204669.116
  77. ↵
    Sterck L., K. Billiau, T. Abeel, P. Rouzé, and Y. Van de Peer, 2012 ORCAE: online resource for community annotation of eukaryotes. Nat. Methods 9: 1041. https://doi.org/10.1038/nmeth.2242
    OpenUrlCrossRefPubMedWeb of Science
  78. ↵
    Sung W., M. S. Ackerman, S. F. Miller, T. G. Doak, and M. Lynch, 2012a Drift-barrier hypothesis and mutation-rate evolution. Proc. Natl. Acad. Sci. U.S.A. 109: 18488–18492. https://doi.org/10.1073/pnas.1216223109
    OpenUrlAbstract/FREE Full Text
  79. ↵
    Sung W., A. E. Tucker, T. G. Doak, E. Choi, W. K. Thomas, et al., 2012b Extraordinary genome stability in the ciliate Paramecium tetraurelia. Proc. Natl. Acad. Sci. U.S.A. 109: 19339–19344. https://doi.org/10.1073/pnas.1210663109
    OpenUrlAbstract/FREE Full Text
  80. ↵
    Sung W., M. S. Ackerman, M. M. Dillon, T. G. Platt, C. Fuqua, et al., 2016 Evolution of the Insertion-Deletion Mutation Rate Across the Tree of Life. G3 (Bethesda) 6: 2583–2591. https://doi.org/10.1534/g3.116.030890
    OpenUrl
  81. ↵
    Taylor S., and L. Campagna, 2016 Avian supergenes. Science 351: 446–447. https://doi.org/10.1126/science.aae0389
    OpenUrlAbstract/FREE Full Text
  82. ↵
    Uchimura A., M. Higuchi, Y. Minakuchi, M. Ohno, A. Toyoda, et al., 2015 Germline mutation rates and the long-term phenotypic effects of mutation accumulation in wild-type laboratory mice and mutator mice. Genome Res. https://doi.org/10.1101/gr.186148.114
  83. ↵
    Wala J. A., P. Bandopadhayay, N. F. Greenwald, R. O’Rourke, T. Sharpe, et al., 2018 SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res 28: 581–591. https://doi.org/10.1101/gr.221028.117
    OpenUrlAbstract/FREE Full Text
  84. ↵
    Wang R. J., G. W. C. Thomas, M. Raveendran, R. A. Harris, H. Doddapaneni, et al., 2020 Paternal age in rhesus macaques is positively associated with germline mutation accumulation but not with measures of offspring sociability. Genome Res 30: 826–834. https://doi.org/10.1101/gr.255174.119
    OpenUrlAbstract/FREE Full Text
  85. ↵
    Whitney K. D., and T. G. Jr, 2010 Did Genetic Drift Drive Increases in Genome Complexity? PLOS Genetics 6: e1001080. https://doi.org/10.1371/journal.pgen.1001080
    OpenUrl
  86. ↵
    Whitney K. D., B. Boussau, E. J. Baack, and T. G. Jr, 2011 Drift and Genome Complexity Revisited. PLOS Genetics 7: e1002092. https://doi.org/10.1371/journal.pgen.1002092
    OpenUrl
  87. ↵
    Xie Z., L. Wang, L. Wang, Z. Wang, Z. Lu, et al., 2016 Mutation rate analysis via parent–progeny sequencing of the perennial peach. I. A low rate in woody perennials and a higher mutagenicity in hybrids. Proc Biol Sci 283. https://doi.org/10.1098/rspb.2016.1016
  88. ↵
    Xu S., J. Stapley, S. Gablenz, J. Boyer, K. J. Appenroth, et al., 2019 Low genetic variation is associated with low mutation rate in the giant duckweed. Nature Communications 10: 1243. https://doi.org/10.1038/s41467-019-09235-5
    OpenUrl
  89. ↵
    Zhu Y. O., M. L. Siegal, D. W. Hall, and D. A. Petrov, 2014 Precise estimates of mutation rate and spectrum in yeast. Proc. Natl. Acad. Sci. U.S.A. 111: E2310–2318. https://doi.org/10.1073/pnas.1323011111
    OpenUrlAbstract/FREE Full Text
Back to top
Posted May 13, 2022.
Download PDF

Supplementary Material

Email
Low spontaneous mutation rate in a complex multicellular eukaryote with a haploid-diploid life cycle
Marc Krasovec, Agnieszka P. Lipinska, Susana M. Coelho
bioRxiv 2022.05.13.491831; doi: https://doi.org/10.1101/2022.05.13.491831
Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area