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Electronic Journal of Biotechnology

versión On-line ISSN 0717-3458

Electron. J. Biotechnol. vol.16 no.4 Valparaíso jul. 2013 

Animal Biotechnology
  Marine Biotechnology
Electronic Journal of Biotechnology ISSN: 0717-3458 Vol. 16 No. 4, Issue of July 15, 2013
© 2013 by Pontificia Universidad Católica de Valparaíso -- Chile Received November 29, 2012 / Accepted June 10, 2013
DOI: 10.2225/vol16-issue4-fulltext-11  

Decline of genetic variability in a captive population of Pacific white shrimp Penaeus (Litopenaeus) vannamei using microsatellite and pedigree information

Sergio Vela-Avitúa1 · Hugo H. Montaldo*1 · Laura Márquez-Valdelamar2 · Gabriel R. Campos-Montes3,4 · Héctor Castillo-Juárez5

1Universidad Nacional Autónoma de México, Facultad de Medicina Veterinaria y Zootecnia, Departamento de Genética y Bioestadística, Coyoacán, Mexico
2Universidad Nacional Autónoma de México, Instituto de Biología, Laboratorio de Biología Molecular, Coyoacán, Mexico
3Universidad Autónoma Metropolitana, Departamento de El Hombre y su Ambiente, Unidad Xochimilco, Coyoacán, Mexico
4Maricultura del Pacífico S.A. de C.V., Mazatlán, Sinaloa, Mexico
5Universidad Autónoma Metropolitana, Departamento de Producción Agrícola y Animal, Unidad Xochimilco, Coyoacán, Mexico

*Corresponding author:

Financial support: This research was supported by CONACyT Mexico Scholarship No 204598, and by Grant CONACyT-SAGARPA-2005 (No 12100).

Keywords: effective population size, genetic diversity, heterozygosity, microsatellites, selection, shrimp.


Background: The objective of this study was to estimate the decline of genetic variability and the changes in effective population size in three shrimp populations. One was a wild population collected at several points in the Mexican Pacific Ocean. The other two populations were different generations (7 and 9) from a captive population selected for growth and survival. Microsatellite markers and pedigree were both used to assess genetic variability and effective population size.

Results: Using 26 loci, both captive populations showed a decline in the expected heterozygosity (20%) and allelic diversity indices (48 to 91%) compared to the wild population (P < 0.05). The studied captive populations did not differ significantly from each other regarding their expected heterozygosity or allelic diversity indices (P > 0.05). Effective population size estimates based on microsatellites declined from 48.2 to 64.0% in cultured populations (P < 0.05) compared to the wild population.

Conclusions: An important decline of genetic variability in the cultured selected population due to domestication, and evidence of a further smaller decline in effective population size across generations in the selected population were observed when analyzing pedigree (41%) and microsatellite data (37%). Pedigree keeping is required to prevent the decline of effective population size and maintain genetic variability in shrimp breeding programs, while microsatellites are useful to assess effective population size changes at the population level.


Penaeus (Litopenaeus) vannamei has become the shrimp species most commonly used for culture worldwide (Benzie, 2009). Nowadays, breeding programs play a key role in the increase of shrimp production in these cultured populations.

The decline in genetic variability, when compared to wild reference populations, of advanced generations of cultured breeding populations has been estimated for Atlantic salmon (Salmo salar) (Norris et al. 1999), black tiger shrimp (Penaeus monodon) (Xu et al. 2001; Dixon et al. 2008), silver-lipped pearl oysters (Pinctada maxima) (Lind et al. 2009), and gilthead sea bream, (Sparus aurata) (Loukovitis et al. 2012), using different variability indices based on microsatellite data. On the other hand, studies involving the analysis of several generations of selected populations of P. vannamei have revealed non-significant declines in genetic variability (Cruz et al. 2004; Luvesuto et al. 2007; Pérez-Enríquez et al. 2009).

The effective population size (Ne) is a crucial parameter for determining the extent of genetic variability that can be maintained in a population (Lande and Barrowclough, 1987). Changes in Ne need to be evaluated during the domestication and artificial selection processes in order to predict the accumulation of inbreeding, which may cause inbreeding depression. Evaluation of Ne also allows determining the potential for further genetic improvement in the population (Hill, 2000).

There are well established methods for assessing Ne based on pedigree analysis (Meuwissen, 2009), but comparisons of those methods with estimates based on genetic markers are not available for aquaculture captive populations and, to our knowledge, are very scarce for any domestic animal population.

The objective of this study was to evaluate, using microsatellite loci, a possible loss in genetic variability and a decrease in the effective population size between two generations (7 and 9) of a captive population of P. vannamei, compared with a wild shrimp population. An additional objective was to compare estimates of effective population size for the same population, using microsatellite and pedigree information.

Materials and Methods

Populations and sampling procedures

P. vannamei samples were collected from a closed breeding population in a large commercial hatchery in Mexico. This population was started in 1998, incorporating wild shrimps from Sinaloa, Mexico, and domesticated shrimps from Venezuela, Colombia, Florida and Ecuador. Mass, family and within-family selections for growth rate were performed, in the absence of an organized breeding program, from 1998 to 2003. In 2004, a breeding program was initiated based on family estimated breeding values (EBVs) obtained through a mixed model methodology, and on within-family phenotypic selection for body weight. Since 2005, a two-stage selection program has been used to select for: (1) body weight at 28 days of age, and (2) body weight and survival at 130 days of age, based on family EBVs; harvest body weight (130 days of age) was also selected for using within-family data (Castillo-Juárez et al. 2007; Campos-Montes et al. 2013). The generation interval is one year, and the generations are discrete (i.e., they are produced annually). Full pedigree was available for the parents of animals that were born in 2003 and thereafter.

A restriction on mating between selected broodstock males and females was used, based on the expected inbreeding of future progeny. The expected inbreeding values were obtained from the pedigree-based numerator relationship matrix. The threshold was gradually relaxed over time as the average relationship in the population increased. This procedure is similar in several aspects to that described for tilapia (Oreochromis niloticus) by Ponzoni et al. (2010).

Samples were obtained as follows. Generation 7 (GEN07): One individual was sampled from each of 77 full-sib families (progeny from one pair of parents), which were randomly chosen from a total of 208 families of the breeding nucleus of the 2005 cycle. Generation 9 (GEN09): One individual was sampled from each of the 73 full-sib families randomly chosen from a total of 203 families of the breeding nucleus of the 2007 cycle. Wild samples (WILD): Samples were collected from 12 different locations (n = 55) along the northern Pacific coast of Mexico on an official fishing monitoring tour (Figure 1). Adult abdomens were used for WILD samples, while in the case of GEN07 and GEN09, 28-day old post-larvae abdomens obtained during the regular genetic evaluations of the breeding nucleus were used. All samples were stored in 70% ethanol at 4ºC until required for genetic analysis.


The QIAGEN DNeasy Blood and Tissue kit® was used for genomic DNA extraction. Each of the extractions was quantified by spectrophotometry and adjusted to 20 ng/µl. A set of 31 microsatellite loci was optimized for PCR amplification from a preliminary list of 72 polymorphic microsatellites for P. vannamei available in the literature and in Genbank. From this list, 6 multiplex groups were obtained. Table 1 shows the final composition of each of the multiplex groups and the annealing temperature (Ta) for each PCR reaction performed using the Multiplex QIAGEN PCR kit (Foster City, CA) according to the manufacturer recommendations. The amplified products were separated using capillary electrophoresis in a 3100 ABI PRISM Genetic Analyzer, and the resulting electropherograms were analyzed using ABI’s GeneMapper V. 3.7. Five loci were eliminated from further analysis due to technical problems regarding their amplification. Used and discarded microsatellites are shown in Table 1.

Population variability indices

Expected (He) and observed heterozygosity (Ho) for all the loci and populations were calculated according to Nei (1987) using the GENEPOP software, version 3.1 (Raymond and Rousset, 1995). The analyses of deviations from Hardy-Weinberg equilibrium and of population differentiation (FIS estimates) were performed with GENEPOP, version 3.1. The number of alleles (Ac) and the unique number of alleles per locus (Au) were obtained with HP-RARE 1.1, applying a correction for sample size through a rarefaction method (Kalinowski, 2005). In addition, values of the effective number of alleles were obtained (Ae) (Hedrick, 2005). The Fstat program, version 2, was used (Goudet, 1995) to obtain FST values for all populations (Weir and Cockerham, 1984) and between pairs of populations (Slatkin, 1995). The values of RST (Slatkin, 1995) were calculated using the RstCalc software (Goodman, 1997). Nei’s genetic unbiased distance (NeiD) was estimated for all pairs of populations (Nei, 1978) using the GENALEX 6 software (Peakall and Smouse, 2006).

Estimation of effective population size from microsatellite data

Ne was estimated from the microsatellite data using three different methods for single population data. The first was according to Waples (2006) using the software LDNe (Waples and Do, 2008), an approach based on linkage disequilibrium. Minimum allele frequency was set at 0.006. The estimate of Ne obtained with this method was denoted as Nem(1). The second method used was according to Tallmon et al. (2008), using the ONeSAMP software. This software uses summary statistics from simulated populations, approximate Bayesian computation and a Wright-Fisher model to estimate Ne from a single sample of microsatellite data. The estimate of Ne obtained with this method was denoted as Nem(2). The third method used was according to Hill (1981), using NeEstimator V1.3 (Peel et al. 2004), which is based on linkage disequilibrium (Nem(3)).

Estimation of effective population size from pedigree data

Effective population size was estimated in the captive populations by pedigree analysis (Nef) using data from 2002 to 2007 and the software PopReport (Groeneveld et al. 2009). Nef was estimated from the rate of co-ancestry between all selected males and all selected females that produced offspring at generation t (ft), as: Nef = 1/(2 Δf); where Δf = (ft-ft-1)/(1-ft-1) (Ponzoni et al. 2010). 


Genetic variability of populations

All analyzed loci were polymorphic, with a range of 2 to 26 alleles per locus. Across all populations, 23 out of the 26 loci used were not in Hardy Weinberg equilibrium (P < 0.05). The same analysis within populations showed that the WILD, GEN07 and GEN09 populations were not in Hardy Weinberg equilibrium in 22, 16 and 20 loci, respectively (P < 0.05). Most of this disequilibrium is attributable to a heterozygosity deficit indicated by positive FIS values. A total of 22, 15 and 17 loci showed a significant deficit in heterozygosity in the WILD, GEN07 and GEN09 populations, respectively.

Using results averaged over all loci, the captive populations (GEN07 and GEN09) did not differ significantly from each other with regard to their expected heterozygosity and allelic diversity (Table 2); however, WILD had a greater expected heterozygosity and greater allelic diversity than GEN07 and GEN09. Using He, the decline in estimated genetic variability from WILD to either GEN07 or GEN09 was 20%. Declines were observed when using genetic variability estimated from Ac, Ae, and Au (45, 47, and 90%, respectively).

Effective population size

Effective population size was calculated for GEN07 and GEN09 using pedigree data, and for all populations using microsatellite data (Table 3). No estimate was obtained for WILD with Nem(1), because the run failed to reach convergence. Nor was it possible to obtain an estimate of Nem(3), for GEN09, also due to convergence problems. Hence, an approach in which the data from GEN07 and GEN09 were analyzed together was used instead for obtaining an estimate of Nem(3) for GEN09.

Linkage disequilibrium estimates for Nem(1) and Nem(3) were 65.9-103.5% larger than of Nef for GEN07 and 118-150% larger than for GEN09 respectively. Conversely, Nem(2), based on the Bayesian approach of Tallmon et al. (2008), was 50.6 and 38% smaller for GEN07 and GEN09 respectively (Table 3). According to the overlapping of the CL95 values, only the decline between GEN07 and GEN09 for Nem(1) (37%) was significant. This estimate was close to the one for Nef (41.2%). Nem(2) and Nem(3) estimates showed significant declines with respect to WILD in GEN07 (51.2 and 48.9% respectively) and GEN09 (64 and 54% respectively).

Genetic divergence and distance

Results for FST, RST and NeiD are shown in Table 4. FST values indicate that all the populations were significantly different from each other (P < 0.05). The divergences between GEN07 or GEN09 and WILD were 4.4 to 4.7 times those found for GEN07 and GEN09 with FST; 2.7 to 4.0 times in the case of RST, and 6 times in the case of NeiD.


An average deficit of heterozygotes, indicated by positive FIS values, was found in all populations. Surprisingly, FIS was higher in the WILD population, which may be taken as an indication of a higher ‘inbreeding coefficient’ estimate for that population; however, other authors have also found inconsistent results using this statistic in P. vannamei (De Lima et al. 2008) and in different species of invertebrates (e.g. Bierne et al. 2000; Xu et al. 2001; Goyard et al. 2003; Dixon et al. 2008; Lind et al. 2009; Meng et al. 2009), including P. vannamei (Cruz et al. 2004; Pérez-Enríquez et al. 2009). The presence of null alleles could be a better explanation of these values (Xu et al. 2001; Ball and Chapman, 2003; Goyard et al. 2003). Attempts to infer inbreeding levels of shrimp populations using FIS statistics may be therefore misleading, particularly when used with small numbers of loci and individuals. On the other hand, other studies showed that even when using as many as 200 markers loci, estimates of inbreeding coefficients have shown to be biased (Alves et al. 2008).

The FIS value in the WILD population (0.36) was smaller than the one reported by Valles-Jiménez et al. (2004), who obtained an FIS value of 0.53 for a sample of wild P. vannamei in different locations of the Pacific Ocean from Panama to Mexico. These differences may be the result of sampling errors, or may be caused by differences in the population structure (i.e. Wahlund effect) expected in samples from wild populations of several groups of P. vannamei.

Previous studies in P. vannamei suggested there was no loss of genetic diversity when they weren’t able to find differences in He between wild and cultured populations when using small numbers of microsatellite loci (Cruz et al. 2004); however, in other species, such as Atlantic salmon, Norris et al. (1999) found reductions in He when comparing cultured with wild populations using 15 microsatellite loci. We attribute the differences in the results to the number of loci evaluated. Several authors have proved that the number of loci is important when comparing populations; preliminary analysis (data not shown) indicate that at least 15 microsatellites and 50 individuals are required to obtain a standard deviation of He ≤ 0.03 using microsatellite data typical for P. vannamei. Koskinen et al. (2004), using simulated data, showed that the standard deviation of the Nei distance (NeiD) decreased as the number of loci increased, changing from 0.25 when only 6 loci were used, to less than 0.05 when 17 loci were used, for a NeiD value of 0.3. In accordance with these findings, some authors such as Xu et al. (2001) suggest that allelic diversity indices are more sensitive in detecting changes on the population’s structure than He values. Luikart et al. (2010) showed that He is less sensitive than allelic diversity when assessed soon after a bottleneck. This should be taken into account when drawing conclusions from this kind of studies, as the number of loci influences the kind of indices that should be used and the interpretation of the results.

Effective population size estimates based both on microsatellites and pedigree showed a decline between populations GEN07 and GEN09 (Table 3), even when non-significant statistical differences were found, but this trend was not observed for any of the other genetic variability indices (Table 2). This suggests that Ne estimates obtained from a relatively large number of polymorphic DNA markers may be more sensitive to relatively small genetic changes in the true genetic variability of the population than the other genetic variability indices used in this study (Table 2).

Absolute Ne values using microsatellite information were different to pedigree estimates (Table 3). Nevertheless, the relative change in the pedigree estimate of Ne between populations followed the same general trend for all methods. Cervantes et al. (2011) also found differences in absolute estimated values of Ne, but similar trends between methods based on microsatellites. Therefore, microsatellite estimates of Ne may be more useful for monitoring the evolution of the genetic variability of the populations than for predicting actual effective population sizes. This is what can be expected from the properties of the methods (Wang, 2005). Similar to what was found in this study, Ne estimated from microsatellite data with different methods in a study with sheep were different (Álvarez et al. 2008).

Population subdivision statistics revealed differences between the three studied groups when using FST and RST (Table 4). Similar findings were reported by Xu et al. (2001), Dixon et al. (2008), Zhang et al. (2010) for FST in breeding populations of Penaeus, which indicated a decline in genetic variability over time, although only 6 microsatellite loci were used in the latter study. Results for NeiD were similar to those for FST and RST. This suggests that these statistics may distinguish smaller genetic differences between populations better than either allelic indices or He when a limited number of loci is available; however, in this study the divergence values found between WILD and GEN07 were slightly larger than the divergence values between WILD and GEN09, which again seems to indicate a better sensitivity of Ne

In our study, we were able to detect important reductions of genetic variability due to domestication in the cultured selected population, and some evidence of a further decline in genetic variability over time in the breeding population, although a slower one. The estimated size of the decline in genetic variability depended on the statistics used.

Our results also suggest that the initial reduction of population size while the population was being established and during initial selection process has more importance for the reduction of the genetic variability of the population than the selection between generations 7 and 9. This implies that most of the reduction in the genetic variability occurred during domestication, as happened in cattle (The Bovine HapMap Consortium, 2009), therefore, maintaining an Ne as large as possible during the initial generations of domestication has important implications for the long-term viability of a selective breeding program. It is likely that Ho and FIS indices based on microsatellites are not useful to monitor changes in the genetic variability of shrimp populations due to the presence of null alleles.

The theory indicates that keeping a minimum effective population size of about 50 is a requirement for the maintenance of genetic variability in a closed breeding population (Meuwissen, 2009). The loss of genetic variability can be monitored using effective population sizes estimated from pedigree or from microsatellites. The methods for estimating effective population size based on linkage disequilibrium tended to give inflated values compared to estimates of coancestry obtained from pedigree, while the Talmon Bayesian method tended to slightly underestimate effective population sizes. DNA markers and pedigree information may be combined (e.g. Fernández et al. 2005) for the appropriate management of genetic variability on selected shrimp populations.

Concluding Remarks

We conclude that using microsatellites to assess the decline of genetic variability in breeding populations of P. vannamei is possible at the population level, but data from a single generation may not be informative enough, and absolute effective population sizes values are likely to be under or overestimated, even with the use of relatively large number of individuals and loci. Estimation of changes in effective population sizes was an efficient way to assess the decline in genetic variability in shrimp populations, superior to either expected heterozygosity, allelic diversity indices or genetic divergence measurements. Practical management of genetic variability and inbreeding in aquaculture nucleus populations at the individual level may still require the use of relationships estimated from pedigree; otherwise, these relationships should be calculated using SNP chips containing thousands of markers (Vignal et al. 2002).


The authors are grateful to the staff from Maricultura del Pacífico, Sinaloa, Mexico, for their assistance. We are grateful to Eduardo Casas (National Animal Disease Center, ARS, USDA, Ames, IA) for their comments to a previous version of this study. The second, fourth, and fifth authors belong to the National Research System of Mexico.


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