Cloning Wild Life: Zoos, Captivity, and the Future of Endangered Animals

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B The commercial Pietrain population. The zoo population of S. To understand their low diversity compared to other closely related species see also Bosse et al. Both methods indicated two population expansions, followed by a bottleneck Fig. All five individuals displayed similar demographic patterns Fig. Demographic history of Sus cebifrons.

Two methods were used to infer the demographic history of the wild Sus cebifrons population based on the genome sequence of one male. A The method by MacLeod et al. The original estimated Ne is visualized in black, and the upper and lower limits of the confidence interval are indicated.

The original estimated Ne values for the same individual are indicated in black, and the gray lines represent 10 bootstrap estimates for this particular individual and the other four are individuals from the same population. For both methods, the generation time was set at 5 yr, and the mutation rate at 1.

The x -axis displays the time before present in years, and the y -axis displays the estimated effective population size.

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Because demographic history largely determines the inbreeding load in a population, we looked at putative damaging mutations in the five resequenced S. Two classes of predicted deleterious variants were observed: high-frequency alleles close to fixation and rare alleles. The Sus cebifrons population contained much more nearly fixed predicted deleterious homozygous variants Supplemental Text; Supplemental Table S1; Supplemental Fig. For the purpose of this study, however, we focused on the low-frequency putative deleterious variants for the prediction of fitness.

Cloning Wild Life: Zoos, Captivity, and the Future of Endangered Animals

The total number of predicted low-frequency deleterious variants within the S. Gene ontology GO enrichment analysis showed generally an underrepresentation of genes coding for nucleotide binding proteins and an overrepresentation of genes coding for cell adhesion molecules Supplemental Table S2. The number of low-frequency deleterious sites within the 11 resequenced Pietrain pigs was , with on average sites per individual. Interestingly, an obvious overlap in GO terms was found in the enrichment analysis in genes containing low-frequency deleterious sites in the Pietrain and S.

Genes involved in transcription, RNA metabolic processes, and nucleic acid binding had significantly less deleterious mutations than expected in both groups. Most predicted deleterious sites were found in the heterozygous state in only one individual. This line of the Pietrain breed has been under strong artificial selection pressure for at least 30 generations, which corresponds to roughly 50 yr. This selection is thought to result in long haplotypes in some regions in the genome with reduced variation Groenen et al.

The length distribution of these ROHs ranged from 3. Chromosome 10 contained the least ROH coverage, and Chromosome 8 contained the most. Some ROHs were clustered within a particular region of the genome, whereas others appear more randomly distributed. Chromosomes 8 and 15 contained many ROHs at the same locus, indicating selection for a specific haplotype close to fixation.

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Based on the integrated haplotype score iHS , we found the strongest signatures of ongoing selection on Chromosomes 13 and 15 Fig. Signatures of selection in the Pietrain population. Extended haplotype homozygosity per chromosome for the Pietrain population. Values are based on the raw iHS signal before management over all chromosomes in the Pietrain population.

The x -axis displays the location on all 18 autosomes, and the y -axis shows the P -value of the iHS signal before management for each marker. Values greater than 2 are considered to be significant according to Voight et al.

Cloning Wild Life: Zoos, Captivity and the Future of Endangered Animals

We recorded the observed heterozygosity and genetic diversity in both populations during 10 generations of simulated management of both species. Management based on molecular coancestry maintained the highest level of genetic diversity in both populations Fig. Management based on IBD segments performed better in terms of diversity than management based on genealogical coancestry, with management based on longer segments maintaining less variation than management based on shorter segments.

go here The loss of genetic variation was stronger in the S. After 10 generations of management based on genealogical coancestry, the observed heterozygosity in the S. After the first generation of molecular-based management, the Pietrain population contained slightly more genetic variation than the base population in terms of heterozygosity, but this variation dropped from the second generation onward Fig.

Variation in both pig populations during management. Evolution of genetic diversity GD and observed heterozygosity OH during 10 generations under five different management strategies. A Variation in the S.


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The population size is kept constant at 10 individuals with a male to female ratio of B Variation in the Pietrain population. The population size is kept constant at 50 individuals with a male to female ratio of Fitness of individuals in the S. We recorded the total number of deleterious sites in the population and also the proportion of homozygous and heterozygous deleterious variants. The proportion of deleterious variants within individuals remained relatively stable over time, whatever the management strategy Fig.

However, the proportion of homozygous deleterious variants gradually increased, whereas the proportion of heterozygous ones conversely decreased. The slopes were slightly steeper under the genealogy-based management strategy than under marker-assisted management and were steepest with random management. The effect on fitness of management depended on the mean selection s and dominance h coefficients of the deleterious variants Fig. Based on predictions made on simulated data de Cara et al.

However, and maybe partly due to the small population size here managed, genealogical management resulted both in maintaining less fitness and diversity than any marker-based strategy Fig. Management based on shared segments and marker-by-marker performed relatively similarly in terms of fitness for the S. Concerning fitness, the loss of heterozygous deleterious variants was slowest in the marker-by-marker coancestry, and the increase in homozygous deleterious variants was slowest in the segment-based management for S.

These factors result in a slightly higher fitness in the segment-based managed population, especially for lower dominance Fig. Fitness and diversity during management of the S. The change in fitness and observed heterozygosity OH during 10 generations of management is displayed for five different management strategies. Deleterious sites are split into homozygous sites filled circles , heterozygous sites filled squares , and the total number of deleterious sites open circles. B Fitness change over 10 generations of management when a dominance coefficient of 0.


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C Fitness change over 10 generations of management when a dominance coefficient of 0. D Observed heterozygosity during 10 generations of management. After 10 generations of management, the fitness in the Pietrain population was lowest with the genealogical-based management under both scenarios Supplemental Fig. S5 , and it performed even worse than managing at random under scenario 1.

Although the marker-by-marker-based management outperformed the segment-based management in terms of observed heterozygosity Supplemental Fig. S5 , more fitness was maintained with the intermediate segment-based management under scenario 1 and roughly the same with the intermediate and long segments in scenario 2. We used the iHS statistic Voight et al. This statistic is designed to identify selection by searching for a long haplotype at high frequency in the population.

Some haplotypes were fixed in the population, resulting in regions without a signal, similar to the pericentral gaps observed on Chromosome 8 Supplemental Figs. S2, S6A.

As shown in Figure 6 and Supplemental Figure S6A, regions that were identified before management as having the highest P -value for a selective sweep have after management a larger difference in iHS signal before and after management than regions without selective sweeps.

For example, the sweep on Chromosome 13 was no longer present in the population after 10 generations of management Fig. This indicates that the signature left by a selective sweep is counteracted when the management strategy aims at optimizing variation in the genome. Indeed, we observed a positive correlation of 0.

The tails of the distribution of this difference in iHS signal before and after management were fatter when using the segment-based management strategy up to segments of 5 Mb compared to those that result when managing with the genealogy-based strategy Supplemental Fig.

This means that the segment-based strategy was more efficient in reducing the presence of long, similar haplotypes in the next generation. Therefore, in previously identified regions under selection, the reduction of the selection signature was stronger for the segment-based management strategies than for the genealogy-based method. Change in selective sweeps in the Pietrain population. Signatures of selection are measured as extended haplotype homozygosity iHS signal in the Pietrain population before and after management.

A Example of the effect of management on the selective sweep on Chromosome B Genome-wide correlation between the significance level of the iHS signal before management x -axis and the strength in iHS signal per marker before and after management y -axis. Negative values on the y -axis indicate a stronger signal in the population after management for the associated marker. Simulated data have predicted that using genetic marker information in the management of populations can maintain more variation than without this information de Cara et al.

Here, we have performed an empirical study to address the impact of whole-genome marker-assisted management strategies on 1 maintaining diversity; 2 the effect of the demographic history on the management strategy; 3 the impact on linked deleterious variants in the viability of the population; and 4 the maintenance of signatures of selection.

Our first objective was to determine whether the use of marker-by-marker based coancestry in management strategies outperforms genealogical coancestry and the recently postulated IBD-based coancestry de Cara et al. By managing the two pig populations through in silico management, with their actual genotypes, we have shown that the best management strategy in terms of maintaining diversity was using molecular coancestry.


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