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ANIMAL GENETICS |
* Estação Zootécnica Nacional–Instituto Nacional de Recursos Biológicos (INRB), 2005-048 Vale de Santarém, Portugal;
and
Escola Superior Agrária de Santarém, Apartado 310, 2001-904 Santarém, Portugal;
and
Instituto Superior de Agronomia, Tapada da Ajuda, 1349-017 Lisboa, Portugal;
and
Departamento de Genética, Universidad de Córdoba, Edifício Gregor Mendel, Campus de Rabanales s/n, 14071 Córboda, Spain;
and
# Laboratório de Genética Molecular, Servicio de Cria Caballar y Remonta, Apartado Oficial Sucursal 2, 14071 Córdoba, Spain; and
|| Faculdade de Medicina Veterinária–Universidade Técnica de Lisboa, 1300-477 Lisboa, Portugal
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: diversity • genetic variability • microsatellite • native breed • pig
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). The intensification of agriculture that took place after that time caused major changes to pig breeding, with traditional systems being replaced by intensive production based on a reduced number of exotic breeds, while native breeds were progressively abandoned and became virtually extinct (Gama, 2006). Currently, 3 native breeds of pigs are recognized in Portugal [i.e., Bísaro (BI), Alentejano (AL), and Malhado de Alcobaça (MA)], but the majority of the breeding stock used by the swine industry is based on exotic breeds, such as Large White (LW), Landrace (LR), Duroc (DU), and Pietrain (PI).
Detailed knowledge of population structure among and within breeds of livestock is essential for establishing conservation priorities and strategies (Caballero and Toro, 2002). Microsatellite markers have proved extremely useful for the analysis of population structure and relationships, and have been widely used for genetic characterization of several species and populations, including European pig breeds (Laval et al., 2000; Martinez et al., 2000; SanCristobal et al., 2006). Nevertheless, information on native breeds of pigs is still scarce, even though they possess unique characteristics in terms of adaptation, hardiness, and quality of products.
In this study we used microsatellite markers 1) to evaluate the degree and pattern of genetic variability in 3 native and 4 exotic breeds of pigs currently used in Portugal, differing in population size and selection history, and 2) to assess with different statistical tools the integrity and degree of admixture with exotic breeds that occurred after restoration of native swine breeds in recent years.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Animals, Sampling, and DNA Extraction
Individual blood samples were collected in 9-mL vials containing EDTA (Vacuette, Greiner Bio-one GmbH, Kremsmünster, Austria) from 249 representative animals registered in the herdbook of the 7 breeds under analysis, namely, the native breeds AL (n = 23), BI (n = 32), and MA (n = 50), and the exotic breeds DU (n = 31), LR (n = 40), LW (n = 33), and PI (n = 40). Samples were collected in 4 to 7 registered herds per breed (with the exception of MA, which was sampled in the only registered herd) from animals that were unrelated up to the third generation. Blood samples were collected and frozen immediately after collection until further analysis, when they were thawed and DNA was extracted with Chelex 100 (Bio-Rad, Amadora, Portugal) and proteinase-K (Qbiogen, Illkirch, France), as described by Walsh et al. (1991).
Amplification of Microsatellite Markers
A panel of 22 microsatellite markers was established, according to the recommendations of the Food and Agriculture Organization of the United Nations and the International Society for Animal Genetics (Food and Agriculture Organization of the United Nations, 2004). The markers used, chromosome location, and expected size of fragments are summarized in Table 1. In the initial stages of this work, 2 additional microsatellite markers (S0386 and CGA) were also considered, but because of difficulties in amplification and inconsistency of results, they were discarded from further analyses. Primers were labeled with fluorescent markers to distinguish between fragments of similar size, and microsatellite markers were grouped in 4 multiplex reactions, according to PCR conditions and expected fragment sizes (Table 1). For the PCR, extracted DNA was added to sterilized water, Qiagen Master Mix (containing Hotstart DNA Polymerase, buffer multiplex PCR with MgCl2, and deoxy nucleotide 5'-triphosphate mix, Qiagen, Madrid, Spain) and the primer mixture. In multiplexes 1 to 3, thermocyclers were programmed to start at 95°C (15 min) and then followed a series of 30 cycles, with denaturing at 94°C (30 s), annealing at 57°C (2 min), and extension at 72°C (1 min), followed by a final elongation period of 30 min at 64°C and ending at 4°C. In multiplex 4, the protocol followed was similar, with the only differences being that the temperature of annealing lasted for 3 min, and the final temperature of elongation was 60°C.
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The PCR products were submitted to analysis of fragments by capillary electrophoresis, with an ABI310 automated sequencer (Applied Biosystems, Applera Europe B.V., Darmstadt, Germany), using the ROX size standard according to the manufacturer’s specifications. Results of capillary electrophoresis were read directly and interpreted with GeneScan and Genotyper software (Applied Biosystems, Applera Europe B.V.), respectively.
Statistical Analyses
The total number of alleles per marker, allele frequencies, and observed and expected heterozygosities (Nei, 1973) were obtained with Genetix version 4.03 software (Belkhir et al., 1998), and the effective number of alleles per locus (ne) was calculated from the expected heterozygosity (Hartl and Clark, 1997). Polymorphic information content (PIC) of each locus (Botstein et al., 1980) and the probability of exclusion in parentage tests, per locus and cumulative (Jamieson and Taylor, 1997), were calculated with Cervus version 2.0 software (Marshall, 2001). Compliance with the Hardy-Weinberg equilibrium (HWE) by locus, breed, and global were investigated with Fisher’s exact test (Guo and Thompson, 1992), with the Genepop version 3.4 package (Raymond and Rousset, 2001). Wright’s F-statistics were obtained with Genetix version 4.03 software (Belkhir et al., 1998). Phylogenetic analyses were carried out with Populations version 1.2.28 software (available at www.cnrs-gif.fr/pge; last accessed May 8, 2007), to obtain estimates of genetic distances among breeds, including the standard genetic distance (DS) of Nei (1972), as well as the genetic distance (DR) of Reynolds (1983). The matrix of genetic distances was used to establish the dendrogram of breeds, using the neighbor-joining method (Saitou and Nei, 1987), with 10,000 replicates to obtain the corresponding bootstrapping values and assess the robustness of the dendrogram topology. A tree representing individual genetic distances was obtained with the Populations software, using the allele-sharing distance (DAS) of Chakraborty and Jin (1993). The genetic distance among populations was also assessed by a factorial analysis of correspondence, which condenses into a few synthetic variables the information contained in the loci analyzed, and allows the representation in space of the populations considered with respect to the defined axes (Cañón et al., 2001). This analysis was performed with Genetix version 4.03 software (Belkhir et al., 1998), and results were graphically visualized with Statistica version 6.0 (StatSoft Inc., Tulsa, OK). The proportion of mixed ancestry in the populations analyzed was further evaluated with the Bayesian clustering algorithm implemented by the Structure version 2.1. computer program (Pritchard et al., 2000). This approach assumes that an individual may have mixed ancestry from different underlying populations, and uses multilocus genotypes and a Monte Carlo Markov Chain simulation to infer population structure and to assign individuals to the assumed populations. In our case, different numbers of assumed populations (K) were evaluated (from K = 2 to K = 9) with the mixed-ancestry model, and the adequateness of the different alternatives was tested by Ln Pr(X|K) (i.e., the likelihood of the observed distribution of genotypes given the assumed number of "ancestral" populations). In preliminary analyses, different settings were tested for burn-in and run length until the Ln Pr(X|K) obtained after several restarts was similar. In the final analyses, for the different values of K considered, runs of 5 x 105 iterations were carried out, after a burn-in period of 1 x 105 iterations, and the results were graphically displayed with Distruct software (available at http://rosenberglab.bioinformatics.med.umich.edu/distruct.html; last accessed June 13, 2007). After assessing the most likely number of underlying populations, the contribution of each of the K populations to the analyzed breeds was calculated. In addition, the contributions of the populations to the genome of an individual were used to assign it to the population with the largest contribution, and from there to the breed in which the population had the major representation (Rosenberg et al., 2001).
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RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Allele frequencies are available from the corresponding author upon request. The total number of alleles found for the 22 microsatellite markers was 235, and polymorphisms in all loci were observed in all of the breeds, with the exception of S0101, which was monomorphic in DU. The observed amplitude in allele sizes slightly exceeded the expected range, indicating that some new alleles were present in the populations analyzed (Table 1
). The number of alleles per locus ranged between 6 (S0090) and 20 (S0005), with a global mean for the 22 markers of 10.68 alleles per locus. On the other hand, ne per locus ranged between 1.39 (S0227) and 9.63 (S0005), with a mean across loci of 4.33.
For the different markers, the mean number of alleles per locus-breed ranged between 3.17 (S0227) and 8.33 (S0068), with a pooled mean of 5.36 (Table 2). The ne ranged between 1.34 (S0227) and 4.37 (S0005), with an overall mean of 2.97. Overall, private alleles (i.e., where one allele at a given locus was found exclusively in one population) were detected in 9 of the loci studied, especially in the AL breed, which had private alleles in nearly one-third of the markers analyzed (Table 2). Private alleles were also found in BI (S0101, S0228), DU (S0068, S0005), LR (S0068), MA (S0226), and PI (SW632). The PIC per locus was greatest (0.888) for S0005 and least (0.268) for S0227. In agreement with these results, the probability of exclusion in parentage testing when both candidate parents had a known genotype was quite high for S0005 but low for S0227, and the combined results of the 22 markers for parentage testing indicated that they are extremely powerful, with a precision greater than 0.9999.
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). For the different microsatellites, expected heterozygosity exceeded 0.7 in S0068, SW857, S0002, S0005, and SW122, but was less than 0.3 for the S0227 and S0225 markers. The expected heterozygosity exceeded the observed heterozygosity in all loci except S0226, S0002, and SW857 (even though the differences were minor for these loci), with a mean difference of 0.046 between expected and observed heterozygosity when all loci were considered. When the agreement with HWE was tested by locus within breed at P < 0.05, only in 4 of the 22 loci studied were all breeds in equilibrium. For all loci combined, on average, nearly one-third of the breed-loci combinations did not comply with the HWE.
The divergence between expected and observed heterozygosity for all individuals, as reflected in the FIT parameter (Table 2), had a global mean of 0.239 for all loci, and ranged for the different markers between 0.096 (SW911) and 0.429 (S0225), with the exception of the S0215 locus, which had an extremely high deficit in heterozygosity (0.636).
Genetic differentiation among breeds, evaluated by FST, corresponded to nearly 0.18 of the genetic variability when all loci were considered, and for each locus individually it ranged between 0.081 (SW911) and 0.420 (S0225). The within-breed deficit in heterozygosity, as evaluated by the FIS parameter, resulted in a global mean of 0.067 for all loci, and ranged between –0.026 (S0002) and 0.181 (S0005), with the exception of the S0215 microsatellite marker, which had an extremely high FIS estimate (0.481).
Breed Diversity
The within-breed analyses (Table 3) indicated that the mean number of alleles per locus was greatest in LR (7.18) and least in DU (3.77), whereas the other breeds had mean values ranging between 5.18 and 6.27. The ne per locus was greatest in BI, LR, and AL (ranging between 3.19 and 3.70), and least in DU (2.26). The average expected heterozygosity was above 0.63 for BI, LR, and AL, and below 0.5 in DU, ranging between 0.56 and 0.59 for the other breeds. The proportion of loci that were not in HWE (P < 0.05) in each of the breeds analyzed ranged between 0.23 (AL) and 0.41 (BI, LW, and PI), mostly because of a less than expected number of heterozygotes in these loci. With the exception of MA, all breeds had a less than expected proportion of heterozygous individuals, with a deficit in heterozygosity (FIS) ranging between 0.038 in LR and 0.173 in BI. A slight, even though nonsignificant (P > 0.05), excess of heterozygous animals was observed in MA.
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Genetic distances among breeds (Table 4) indicated that the results were very consistent when estimated by either DR or DS, with a correlation of 0.98 between the 2 estimates. The closest distances were of LR with MA and BI, whereas DU was the more differentiated breed overall when compared with all others; the same was observed when pairwise comparisons of DU with other breeds were evaluated case by case.
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. The bootstrap values were modest, indicating that the pattern of phylogenetic relationships among the 7 breeds may not be very reliable. Nevertheless, the general pattern found indicated a branch including DU and AL, and another branch with MA and LR, with BI, PI, and LW radiating from the center. In the factorial analyses of correspondence, the first, second, and third axis accounted for 25.0, 19.4, and 17.6% of the total inertia, respectively. The results of these analyses (Figure 2) showed that the first and second components separated DU and AL, respectively, from the other populations, a cluster grouping LR, LW, and BI closer to each other, and MA and PI separated in different directions from those 3 breeds.
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The tree of individuals based on DAS, obtained through neighbor-joining (see figure in the online supplement), showed that, in nearly all cases, animals grouped very well together by breed, with the exception of BI, which had 2 distinct clusters of individuals. It is interesting to note that for LR, 2 clusters could also be identified, one closer to PI and another to MA.
The results of the analyses with Structure are summarized in Table 5. The Ln Pr(X|K) increased sharply between K = 2 and K = 7, and stabilized between K = 7 and K = 9, dropping afterward. These results would thus indicate that the appropriate value of K would be between 7 and 9, but given the small changes in Ln Pr(X|K) in this range, the smaller K was assumed to be the correct number of underlying populations, as suggested by Pritchard et al. (2007).
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, for values of K ranging between 2 and 7. When K = 2, DU and AL were separated from the other breeds (Table 5), and they clustered together with PI when K = 3, with MA separating from the other breeds. Progressively, as K increased, the contributions of the assumed populations resulted in the complete separation of the 7 breeds, which were essentially identified with each one of the ancestral populations when K = 7. For K = 8 and K = 9, the LR and BI breeds were subdivided into 2 populations each, whereas the other breeds remained with homogeneous contributions of the original populations, similar to those observed for K = 7.
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. Each one of the breeds was very closely identified with one of the "ancestral" populations, from which it received a contribution to its gene pool of at least 0.882 (contribution of population 1 to BI). Furthermore, the dominant contribution of a given base population was made only to one breed, with the contributions to other breeds being negligible (always less than 0.05). When the criterion of assigning an individual to the population with the major contribution to its genome was used, 99.2% of the animals were correctly classified in their original breed, with the only errors being for 1 MA individual classified as BI, and 1 BI classified as LR.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). In recent years, increased awareness of the value and importance of local genetic resources, as well as consumer demand for high-quality meat products, have turned attention back to traditional breeds that had almost disappeared, and efforts have been made toward recovering those nearly extinct genetic resources (Martyniuk, 2004).
In Portugal, 3 native breeds of pigs are currently recognized (i.e., BI, AL, and MA), with the number of registered females equaling approximately 1,500, 11,500, and 200, respectively. The BI is a native breed found in northern Portugal that belongs to the Celtic group (Porter and Tebbit, 1993); it is white spotted, characterized by slow growth and high prolificacy (Santos Silva et al., 2000). Some crossbreeding with LR, LW, and PI may have taken place before the BI breed was officially recognized in the late 1990s (Ramos et al., 2003). The AL breed belongs to the Iberian group (Martinez et al., 2000), and is traditionally raised in the "montado-de-hesa" system, with finishing taking place with acorns and pasture. Malhado de Alcobaça is a white spotted breed, established in the late 19th century from BI, Berkshire, and Yorkshire pigs, probably with some introduction of LR in the more recent past, and it was recognized as a distinct breed in the last few years. All of the native breeds of pigs in Portugal nearly became extinct in the 1970s, but interest in these breeds has expanded in recent years because of the high value of their processed products. They have been reestablished from the narrow base that still existed in the 1980s.
Currently, the vast majority of the breeding stock used by the swine industry in Portugal is composed of exotic breeds, with LW and LR representing the most frequently used maternal breeds and DU and PI being paternal lines often used in organized crossbreeding programs. These exotic breeds correspond to the concept of "transboundary breeds" (Food and Agriculture Organization of the United Nations, 2007) and have been included in different genetic diversity studies on pigs (e.g., Laval et al., 2000; SanCristobal et al., 2006). Information on native breeds is very scarce, even though they possess unique characteristics in terms of adaptation, hardiness, and quality of products, and could thus represent an interesting reservoir of genetic diversity (Cañón et al., 2006; Foulley et al., 2006; Peter et al., 2007), in addition to being a major source of between-breed diversity (Ollivier et al., 2005).
Knowledge of the structure of a livestock population in terms of sources of variability among and within breeds is essential for establishing conservation priorities and strategies (Caballero and Toro, 2002), with the long-term objective of maintaining genetic diversity for future generations (Notter, 1999). In the particular case of native breeds that have nearly become extinct, the possible existence of bottlenecks or admixture with exotic breeds in the recent past warrants special attention. Therefore, an assessment of their genetic diversity and possible relationships with other breeds represents a major step toward the development of conservation and improvement programs.
Microsatellite markers are particularly suitable for genetic diversity studies, because of their large number, distribution throughout the genome, high level of polymorphism, codominant inheritance, neutrality with respect to selection, and easy automation of analytical procedures (Cañón et al., 2001). These markers have proved extremely useful for the analysis of population structure and relationships, and have been widely used for genetic characterization of several species and populations, including European pig breeds (Laval et al., 2000; Martinez et al., 2000; SanCristobal et al., 2006). Baumung et al. (2004) reviewed the studies on genetic diversity carried out in different livestock species and concluded that there was a need for further research on additional breeds. They recommended the use of a common set of genetic markers to make information compatible among studies.
In our study, the set of microsatellites used was, in general, useful for the intended purposes (i.e., characterization of within- and between-breed genetic diversity). The means for number of alleles per locus (10.68) and alleles per locus-breed (5.36) indicate high levels of genetic variability in the populations studied and are within the range found in other European breeds of pigs (Laval et al., 2000; SanCristobal et al., 2006). Differences between breed-loci combinations were important, such that, for example, the S0101 locus was monomorphic in DU and showed little variation in MA and PI (with 2 and 3 alleles, respectively), whereas the maximum number of alleles per locus was 11 for the S0005 marker in AL and for S0355 in LR. The existence of private alleles in 9 of the 22 markers analyzed, mostly in the AL breed, but also in BI, DU, LR, MA, and PI, suggests that these loci could be useful in breed differentiation and assignment, especially because the private alleles were often at a high frequency.
The ne was greater than 5 and PIC was above 0.7 in 11 of the 22 markers analyzed, indicating the high degree of polymorphism for these loci in the breeds studied. On the other hand, PIC was below 0.5 and ne was less than 2.2 in loci S0215, S0225, and S0227, which makes these markers of limited usefulness for within-breed analyses. Accordingly, these were also the loci with the lowest power of exclusion in parentage tests. Nevertheless, when compared with the other loci, S0215 and S0225 had the greatest FST, indicating that, even though of limited use for within-breed analyses, these markers might be interesting for between-breed differentiation.
When all loci were considered, the expected heterozygosity was 0.667, indicating that high levels of genetic diversity exist in the breeds analyzed, and are greater than those found in other European breeds (Laval et al., 2000; SanCristobal et al., 2006) and similar to those reported for Chinese breeds (Li et al., 2004). Nevertheless, expected heterozygosity was under 0.3 for the S0227 and S0225 markers, as a result of these loci having the same highly predominant allele in nearly all breeds. Accordingly, these were also the loci with the lowest ne and PIC.
In 19 out of the 22 loci, the observed heterozygosity was less than expected under HWE. Several factors can contribute to less than expected heterozygosity in a population, including inbreeding, population subdivision (Wahlund effect), the presence of null alleles, and lack of neutrality relative to selection, with selection in favor of homozygotes (Maudet et al., 2002). The very high deficit in heterozygosity observed in locus S0215 (FIS = 0.481) likely indicates the presence of null alleles (SanCristobal et al., 2003), but, when considered for the other loci, the deficit in heterozygosity probably reflects the subdivision of the whole population into breeds. Of the 154 breed-locus combinations, 52 did not comply (P < 0.05) with HWE, and equilibrium was found for all breeds only in loci S0101, S0227, SW240, and SW857, and for none of the breeds in locus S0355. The noncompliance with HWE was mostly a result of a less than expected heterozygosity (39 breed-locus combinations), which would reflect the within-breed deficit in heterozygosity, most likely because of accumulated inbreeding or population subdivision. Therefore, with the exception of marker S0215, the disequilibrium found in most loci reflects both differences among breeds and the within-breed deficit in heterozygosity.
The estimated FST, which corresponds to the proportion of genetic variability accounted for by differences among breeds, ranged between 0.081 and 0.420 for the different loci, with a global mean of 0.184. Therefore, even though the majority of genetic variability was observed within breeds, there was a high breed differentiation, suggesting reproductive isolation and low gene flow between breeds. The FST estimated here was in the range of values reported by several authors for analyses with microsatellites in swine breeds, with FST ranging from 0.11 to 0.27 in studies with European breeds (Laval et al., 2000; Martinez et al., 2000; SanCristobal et al., 2006) and from 0.18 to 0.26 with Chinese and Korean breeds (Fan et al., 2002; Li et al., 2004; Kim et al., 2005). These estimates in pigs are greater than those reported for other livestock species, with FST estimates ranging from 0.07 to 0.13 in cattle (MacHugh et al., 1998; Kantanen et al., 2000; Cañón et al., 2001; Mateus et al., 2004; Wiener et al., 2004), 0.03 to 0.11 in goats (Li et al., 2002; Iamartino et al., 2005; Cañón et al., 2006; Martinez et al., 2006), and 0.06 to 0.08 in sheep (Álvarez et al., 2004; Sodhi et al., 2006; Peter et al., 2007). Overall, these results indicate that the degree of breed differentiation is consistently greater in swine than in other livestock species, which does not lend support to the idea that the magnitude of genetic differentiation among breeds would be similar across species (Álvarez et al., 2004). Several factors could explain the greater degree of genetic distinctiveness among breeds of swine when compared with other livestock species, such as less intermingling of breeds in the past, a much shorter generation interval and thus greater rates of genetic drift, and the fact that selection has been more intense and with a longer history in swine than in other species, which could also contribute to greater breed differentiation if the marker genes are in some way linked to the selected traits.
Genetic diversity, assessed through both allelic richness and expected heterozygosity, was much less in DU than in the other breeds, as has also been reported by Li et al. (2004), Kim et al. (2005), and SanCristobal et al. (2006). The greatest levels of expected heterozygosity were found in BI, LR, and AL, and a similar pattern was found for the total and effective number of alleles per locus. On the other hand, the AL had the largest number of private alleles, even though it was represented by the smallest sample size in this experiment. Taken together, these results indicate that native breeds of pigs in Portugal still represent an important reservoir of genetic diversity, even though they have gone through genetic bottlenecks in the recent past because of disease outbreaks and poor competitiveness in intensive systems. Thus, it was anticipated that they would show reduced levels of genetic variability because of founder effects and genetic drift, which would be reflected mostly in a reduction in allelic diversity (Luikart and Cornuet, 1998).
Depending on the breed considered, between 0.23 and 0.41 of the loci were not in HWE, mostly because of a deficit in heterozygosity, with positive FIS for all breeds except MA. The within-breed deficit in heterozygosity followed an interesting pattern, such that in the exotic breeds DU, LR, LW, and PI, the FIS ranged between 0.038 and 0.065, whereas in the native AL and BI the FIS was 0.139 and 0.175, respectively. On the other hand, the native MA had a slight surplus of heterozygous individuals, which was somewhat unexpected given that all sampled animals came from the only registered herd. These results suggest that, in the exotic breeds considered here, the exchange of animals across herds and countries has kept inbreeding at lesser levels, whereas in AL and BI, the small number of breeding animals and herds, as well as the development of these breeds from a very narrow base in the 1980s, may have resulted in accumulated inbreeding. Another possibility is that, at least in BI, some subdivision of the population (Whalund effect) may exist, as suggested by the tree of individuals (see figure in the online supplement). On the other hand, the situation with MA probably reflects a particular concern in managing inbreeding on the only sampled farm, with the creation of family lines and crosses among them to minimize inbreeding.
The level of differentiation among breeds was confirmed by the good clustering in the neighbor-joining tree of individuals based on allele-sharing distances, in which all breeds grouped very well together. The only exception was BI and LR, with 2 distinct, but close, clusters for each one. This result is in agreement with the distributions observed for these 2 breeds in the analysis with Structure for K = 9, where BI and LR had 2 distinct base populations each. In the case of BI, the 2 populations may correspond to different geographical areas where sampling was carried out, or possibly to 2 foundation nuclei of the breed when it was reestablished in the mid-1980s. For LR, the 2 clusters probably represent different origins, because it is well known that LR strains from different countries show genetic differences among them (Laval et al., 2000; Foulley et al., 2006).
Genetic relationships among breeds were quite consistent when estimated by either DS or DR. Indeed, the DR were smaller than the corresponding DS, as would be anticipated from the relationship among expectations for the 2 estimates, which can be expressed as E[DS] = E[DR]H/(1 – H), where H represents founder heterozygosity (SanCristobal et al., 2003). Relationships among breeds, assessed by their genetic distances, indicate that DU was the more distanced breed from all of the others, whereas the closest relationships were of LR with BI and MA, which is a possible indicator of the influence that LR may have had in the development of these 2 breeds in the past.
Even though robustness of the phylogenetic tree was not high, the resulting dendrogram shows results that are supported by the analysis of correspondence, and are in agreement with the history of the breeds. The phylogenetic analysis indicates that DU and AL on one side, and MA and LR on the other, diverge from the other breeds. This is, to some extent, confirmed by the analysis of correspondence, which highlights the separation of DU and AL from the other breeds. These results for DU and AL are in agreement with the history of the DU breed, which is thought to have resulted from the contributions of several breeds, including Portuguese pigs (Jones, 1998). On the other hand, the large branch length observed for DU may result from the fact that it has low within-breed genetic variability (SanCristobal et al., 2006).
The analysis with Structure confirms that each of the breeds analyzed is closely identified with a single ancestral population, and that there was very little admixture between the 7 breeds studied, which are now quite distinct from each other. This result is in line with our FST estimate and confirms the distinctiveness of and low gene flow between the swine breeds analyzed, contrasting with results obtained when Structure was applied, for example, to horse (Vega-Pla et al., 2006) and sheep (Álvarez et al., 2004) populations, where admixture of breeds is very common.
Overall, the results reported here indicate high levels of genetic variability and clear breed differentiation, with the native breeds AL and BI, together with LR, having the greatest levels of heterozygosity and ne indicating that the native breeds represent an interesting reservoir of genetic diversity and deserve appropriate conservation efforts. However, the greatest deficit in heterozygosity was observed in AL and BI, suggesting that inbreeding is likely to be high in these breeds.
Taken together, our results can be useful in outlining conservation strategies, even though the optimal approach for defining priorities in conservation programs is not a resolved issue. Weitzman (1993) proposed the use of a diversity function based on genetic distances among breeds, estimated from the loss in genetic diversity resulting from extinction of a given breed (i.e., its marginal diversity). This methodology has been evaluated in the context of livestock conservation by Thaon d’Arnoldi et al. (1998) for cattle and Laval et al. (2000) for swine breeds. Nevertheless, this approach ignores the possible contribution of within-breed diversity to overall genetic differences, and Caballero and Toro (2002) proposed an alternative approach in which each breed is evaluated based on its contribution to global coancestry of the population, considering its contributions to both between- and within-breed diversity. Fabuel et al. (2004) applied the method of Caballero and Toro (2002) to assess conservation priorities for different Iberian pig strains and concluded that the results would be completely different when compared with those obtained with Weitzman’s approach. Nevertheless, it remains a subject of discussion what optimal weights are to be given to the between- and within-breed components of genetic diversity in conservation programs (Ollivier et al., 2005; Toro et al., 2006).
In addition to the information provided by neutral genetic markers, which can be used to maximize retention of genetic diversity for the future, many other factors should be taken into account when defining conservation priorities. These include aspects such as the economic, demographic, social, ecological, and cultural roles associated with a given breed (Ruane, 2000), as well as its specific production and adaptation features, which may be necessary to cope with future changes and challenges in production-marketing systems, as well as in environmental constraints (Smith, 1986).
In conclusion, assessing genetic diversity should be the first step in establishing appropriate conservation programs in any livestock species. The set of microsatellite markers used in this work was generally suitable in evaluating diversity in the swine populations analyzed, revealing high levels of genetic variability, assessed by both the number of alleles and heterozygosity. The breeds sampled had a high level of differentiation, and most of them showed signs of accumulated inbreeding, especially the native breeds, which have gone through genetic bottlenecks in the recent past. The results reported here may serve as useful indicators in setting conservation priorities, taking into consideration both among-population diversity and within-population variability, in addition to information on traits of current or potential economic importance, including adaptation.
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Footnotes |
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2 Corresponding author: genetica.ezn{at}mail.telepac.pt
Received for publication October 29, 2007. Accepted for publication June 3, 2008.
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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 C. Dalvit, M. De Marchi, E. Zanetti, and M. Cassandro Genetic variation and population structure of Italian native sheep breeds undergoing in situ conservation J Anim Sci, December 1, 2009; 87(12): 3837 - 3844. [Abstract] [Full Text] [PDF]
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