When do i stop burning cavefish

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Still have questions? Find more answers. Previously Viewed. There are much more surface fish than cavefish and the impact of migration of cavefish on surface fish diversity is likely extremely low, and most likely null. The new time frame we propose for the evolution of the cavefish populations would not allow enough time for the fixation of many de novo mutations and most derived alleles that reached fixation in caves were probably already present in the ancestral population.

This was also suggested by a recent population genomic study [ 73 ]. This may imply that the cave phenotype evolved mainly by changes in the frequencies of alleles that were rare in the ancestral surface population.

In particular, some of these alleles would have been loss-of-function or deleterious mutations that could not reach high frequency in surface populations but they could reach high frequency or fixation quickly in a small cave population where they are neutral or even advantageous.

Although we estimated that all cavefish populations are probably recent, less than 20, years old, the number of independent and approximatively simultaneous adaptations to cave and evolution of cave phenotype is still an open question.

The evolution in a short period of time of the phenotype of individuals belonging to a population adapting to a new environment, is actually not that unexpected and has already been observed in other fish species such as the stickleback [ 74 ], dwarf whitefishes [ 75 ] and African cichlids [ 76 , 77 ]. Recently, the first European cavefish, with a well differentiated cave phenotype, has been described. The phylogenetic analysis of mtDNA haplotypes, the analysis of genetic differentiation using microsatellite loci and the recent glacial history of the region suggests that these fish population is highly isolated but for less than 20, years [ 78 ].

Mexican cavefish could thus be another and striking illustration that many phenotypic changes can accumulate in parallel and in a short period of time thanks to standing genetic variation [ 79 ]. The relative roles of selection and drift in allelic frequency changes is not yet understood, but if the recent origin of cavefish populations is confirmed, they would be an excellent model to analyze this issue using population genomics tools such as the quantification of selective sweep around candidate loci most likely involved in the adaptation to a cave environment.

With a multilocus microsatellite polymorphism dataset [ 33 ] and the program IMa2 [ 53 ] divergence times of pairs of populations were estimated. The program is based on an isolation-with-migration IM model and uses Metropolis-coupled Markov chain MCMC techniques to estimate the posterior densities of the time of divergence, population sizes and gene flow [ 58 ]. The model assumes random population samples, a stepwise mutation model, neutral mutation, freely recombining loci and constant population sizes and gene exchange rates.

Although modelling constant population sizes and gene exchange rates might not be ideal in the case of cavefish populations, modelling general patterns requires simplifications and this is presently the only option in programs such as IMa2 and Migrate which is the program previously used to estimate gene flow [ 33 , 63 ].

It was also not possible to take into account changes in generation time after the separation of the populations. It is thus more the order of magnitude of the parameter estimations that can be discussed rather than maximum likelihood values per se. As the upper limit of the prior distribution of each model parameter must be set, we ran short MCMC chains to test several sets of parameters that allowed the identification of suitable values of upper limits of mutation-scaled effective population sizes, gene flows and divergence time.

It allowed also to estimate the length of the burn-in period. Such a large sample of alleles for many loci was not available for many comparisons. The parameter sets used in the different analyses are indicated as a command line in the legend of the figures that summarize the output of IMa2. R Jeffery in Several studies showed that the differentiation of the surface populations in the El Abra region and Texas is very low [ 33 , 37 , 39 ], suggesting that the comparison of any of them with a cavefish population should give about the same result.

Both observations suggest that they have not been isolated for a very long time. The pooled embryo samples had been previously sequenced using the Sanger and methods [ 80 ] Additional file 1 : Figure S The Astyanax mexicanus transcriptome was assembled with Newbler ver. We also tried to generate a transcriptome assembly using the Illumina sequences, but whereas this resulted in more contigs 49, than the sequences, many of them were concatenations of different transcripts and in some cases the same transcript was found in more than one contig.

We therefore mapped the Illumina sequences onto the contigs to identify and annotate SNPs. Putative coding sequences in each contig were identified using the zebrafish Zv9 proteome available at EnsEMBL 73 as a reference [ 81 ].

We identified contigs containing domains that matched different zebrafish proteins and which were most likely chimeric contigs. These contigs were removed , i. In total, we analyzed 12, putative protein coding contigs. Illumina sequences were aligned to contigs with BWA [ 82 ] using the default parameters for paired-end reads. Because we filtered SNPs after detection using different parameter thresholds described below, we used the allowPotentiallyMisencodedQuals and —rf BadCigar options.

When a complete coding sequence was identified, i. The 55, SNPs in the coding sequences were annotated as synonymous or non-synonymous, according to which amino acid was coded for by the alternative codons resulting from the SNP. The ancestral allele and the derived allele were inferred according to the allele found in the outgroup Hyphessobrycon anisitsi Fig.

SNPs for which the ancestral allele and derived allele could not be identified, either because in Hyphessobrycon anisitsi no sequence could be identified or there was another allele present or the allele was polymorphic, were discarded.

The number of SNPs in the different classes depended on the thresholds used to consider a SNP as reliable and polymorphic in each population. The rationale for the set of thresholds selected is given below. We found only one SNPs in this class Table 3. It suggests that Illumina sequencing did not generate a number of sequencing errors that would significantly inflate the number of SNPs identified. We examined the effect of the thresholds applied to parameters used to discard SNPs before their classification and population genomics analyses.

First we looked at the effect of sequencing depth. Whereas the mean sequencing depth was , the standard deviation was very large When the minimal number of reads per population at a SNP site was set to or higher, the relative frequencies of the eight SNP classes were very stable, indicating that was a good compromise between the stability of the distribution of the SNPs into different classes and the number of SNPs discarded Additional file 1 : Figure S We then considered the effect of the e-value of the blast between the Astyanax contig and the zebrafish sequence used for annotation, in order to discard poorly conserved sequences that were misidentified as protein coding.

It appeared that the SNP classification was stable whichever the threshold was used, i. We also examined the effect of the interval between SNPs, because we would expect clusters of spurious SNPs in poorly sequenced regions. As expected, there was an excess of shared polymorphisms class 7 with a small window size.

All the above thresholds, apart from that for MAF, are trade-offs between quality and quantity of the data. The results were thus also robust according to this parameter, and the use of different sets of parameters led to similar distribution of SNP classes that led to the same conclusion. More detailed on the rationale of the method is given in Additional file 4.

The evolutionary model and its parameters are those of IMa2, i. The range of values tested were defined according observation in the field, estimations found in the literature and the results of the analysis with IMa2. There could be migrations from the surface to the cave. The smallest probability of migration was set to 0. We also took into account that genetic drift could have occurred in the laboratory stocks effective population size in the lab was set to 10 and the number of generation was set to All mutations were neutral frequency changes at each locus were driven by genetic drift only and each locus were evolving independently.

For a given set of parameters, each ten generations after the isolation of two populations, we estimated the frequency of SNPs in each category and we estimated a score of goodness of fit with the frequencies obtained with the real SNP data set.

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