Pedigree validation is an essential step in breeding programs in most livestock breeds. The pedigree errors have a large impact on the efficiency of a selection response of the breeding program (Parlato and Van Vleck, 2012) and raise the question of trusting in the issued pedigree certificates (Leroy et al. 2011). Although pedigree information has an important role in the breeding programs, its recording is not an easy task, especially in breeds which are raised under an extensive production system. There are many factors contributing to the difficulty of recording of pedigree information, notably costs involved in mating system control and registration of offspring at birth. Therefore, cost-effective parentage control systems that can be implemented under common production conditions are very important for both conservation and improvement programs in livestock (Leroy et al. 2011). Pedigree errors in animal breeding could happen due to several factors. Errors are commonly observed in extensive breeding systems where natural mating of multiple sires is practiced and recording systems are poorly established. Parentage misidentification may also occur in a controlled system due to human errors in mothering up, recording and in the artificial insemination process, and also due to errors made by the animal itself for example animals jumping fences (Souza et al. 2012). DNA testing to identify the correct parentage is the best solution to overcome the effects of pedigree error on genetic evaluation (Parlato and Van Vleck, 2012; Souza et al. 2012). Microsatellites are extensively used in livestock parentage control because of their many advantages, including high variability due to high mutation rates, simple assays, high distribution in genome coverage and co-dominance, rapid detection (Goldstein et al. 1995). Mutations typically result in length variations, with the number of repeat units either increased or decreased in this sequence (Goldstein et al. 1995). Usha et al. (1994) and Heyen et al. (1997) suggested using at least five microsatellite markers in the same analysis to achieve a 0.99 probability of exclusion of an incorrect sire. The validity of the pedigree records is a major concern in many countries such as Iran, where DNA testing is not routinely done to ascertain the parentage of the animals before registration. In many countries and especially in Iran, a rural enterprise, the males and females graze together and the pedigree of the offspring can be largely unknown. The lack of information from relatives in many Iranian sheep populations has hindered the development of optimum breeding strategies. To design an efficient improvement program and genetic evaluation system for Iranian sheep breed, as well as a crossbreeding program, accurate estimates of the population genetic parameters is pre-required and ideally all pedigrees and relationships should be correctly recorded. Otherwise, it can produce biased evaluations when pedigrees contain errors and procedures utilize wrong information from the relatives (Weller et al. 2004; Van Eenennaam et al. 2007). The most common mating approach in extensive sheep production systems is the use of multiple sires. The combination of these males makes the paternal origin of the new born individual as a challenging question. On the maternal side there might be a natural cross fostering. The challenged task included pedigree verification and parental identification using molecular marker information. Applying capillary electrophoresis since it has advantages of high separation efficiency, short analysis time, low sample and solvent consumption, low cost of the running and lower effect of matrices comparing with the other separation techniques (Stewart et al. 2011; Wang et al. 2009). The aim of the work could be addressed to the parentage control in the crossbred sheep herd, in order to discuss the problems of applicability of this method for parentage verification.
MATERIALS AND METHODS
Animals and location of study
The twenty-four animals in the experiment were obtained from a Khalat Poshan research farm in Basmanj located approximately 10 km from Tabriz city in the North West of Iran. The pedigree information used in this study were collected from the Breeding Station of Tabriz University during 2012-2013 and the experimental pedigree included 4 rams, 24 cross-bred lambs (14 males, 14 females) from Ghazel × Baluchi, Ghazel × Baluchi × Merinos and Baluchi × Moghani × Merinos cross-bred lambs. The selection of the animals for our study was based on their physical condition, general health and age. The breeding design was three two-way crosses, with one ram to 7-8 ewes ratio. The breeds were mated equally and each mating type was carried out in triplicate and all rams and ewes were allocated to the mating groups randomly. The mating was natural and went for a period of 51 days (three estrous cycles).
Blood collection and DNA extraction
The twenty-four blood samples were collected from the animals in the experiment at the research station through the jugular vein in tubes containing 10 μL 0.5 M EDTA (pH=8). The tubes were kept on ice before being stored at -20 ˚C. The DNA was then extracted from the thawed whole blood samples using the commercially available QIAGEN®(USA) DNA extraction kit, according to the manufacturers’ instruction. An OD260 reading value of one corresponded to 50 mg double-stranded DNA/mL and OD260 1 indicated 33 mg/mL single stranded DNA. The ratio of readings taken at 260 nm and 280 nm (OD260/OD280) was used as an indicator of the purity of the nucleic acid.
The 14 microsatellite markers were selected from the panels recommended by FAO (2004), as well as markers used in previous parentage verification studies on sheep and goats. Ten markers were selected from the ISAG panels. Four alternative markers were selected based on their usage in other studies and performance in the sheep population. The parameters for inclusion in the current test panel included previously reported (ISAG/FAO) levels of polymorphism, heterozygosity, null allele frequencies and fragment sizes. The 14 markers were grouped into different genotyping sets according to range and fluorescent labeling for cost-efficient genotyping (Table 1).
PCR mixture and program
The PCR for initial screening of the DNA samples from the twenty-four lambs for the 14 microsatellite loci was carried out in 25 µL volumes, comprising of 1.5 mM MgCl2, 0.2 mM dNTP, 0.01 mM of each primer (NEB®, UK), 50 ng of genomic DNA, and 0.2 U Taq DNA polymerase (Promega®, USA).
Table 1 Characteristics of microsatellite loci used for the present study
Unlabeled primers were used for this purpose. The touch-down PCR cycle was performed as follows: an initial denaturation for 3 min at 94 ˚C, followed by 10 cycles of denaturation for 45 s at 94 ˚C, annealing at temperatures from 65 ˚C with 1 degree reduction per cycle to 55 ˚C, and extension at 72 ˚C for 45 s, followed by 25 cycles of denaturation for 45 s at 94 ˚C, annealing at temperatures 55 ˚C, and extension at 72 ˚C for 45 s, and the final extension at 72 ˚C for 10 min.
The amplified products were separated by electrophoresis in 4% metaphor gels at 65 V for 2 to 3 h, depending on the expected allele size. The allele sizes were estimated by comparison to a 25 bp ladder of molecular weight markers. This step was carried out to confirm the success of the PCR amplification, estimate the amplicon sizes, and identify the heterozygous loci for each ram. The microsatellite loci that did not show a satisfactory pattern of amplification or which were uninformative, were excluded from the analyses. The remaining 14 microsatellite loci were used for the subsequent analysis.
Multiplex PCR and capillary electrophoresis
Multiplex PCR allows more than one target sequence site or loci to be amplified simultaneously by using more than one pair of primers in the reaction. This has the potential to save time and reduce the lab works. In the present study, PCR was performed for each locus individually, the PCR products of the loci for each designed multiplex system were mixed, and the fragment sizes analyzed as a multiplex system. The fragment analysis or genotyping of the PCR amplicon were done using the genetic analysis system (CEQ, Beckman Coulter). The labeled PCR products can be deteriorated quickly on exposure to light, and so fragment analysis was performed on the same day as PCR. The PCR products, however, may be stored at 4 ˚C up to a week by wrapping the PCR tubes/plates with aluminum foil. The PCR products (0.4-1 µL of each loci) were transferred to the CEQ sample plate with a 20 µL multi-channel pipette. The size Standard (0.5 µL) and the sample loading solution (SLS) buffer were mixed together and 10 µL was transferred to each of the eight tubes of the eppendorf PCR strip, and subsequently aliquot to the wells in the sample plate. For identification of the fragment sizes, the CEQ DNA size Standard Kit-400 was used. The DNA marker fragments were also labeled with well RED fluorescent dye and were from 60 to 100 bp in 10 bp increments and from 100 to 420 bp in 20 bp increments. The 14 microsatellite loci were grouped into PCR multiplex systems (3- to 5-plex systems) based on the above criteria.
The estimation of genotype and allele frequencies was performed using POP GENE software (Yeh et al. 1999). Allelic frequencies and number of alleles per locus were estimated by direct counting from observed genotypes. Heterozygosities, polymorphic information contents and exclusion probabilities were computed using the CERVUS (Ver. 2.0) software (Marshall et al. 1998). The theoretical powers for identity and parentage exclusion of these markers were calculated based on allelic frequency data. The combined powers of parentage exclusion (PE) were calculated in different scenarios; considering the situations where both parents are genotyped but only one parent is evaluated for exclusion (PE1) or both parents are evaluated for exclusion (PE2) and when only parent is genotyped and evaluated for exclusion (PE3). The parentage analysis was conducted applying the levels of confidence of 95% and 80%. The simulation was run to estimate the resolving power of a series of loci given their allele frequencies, and to estimate critical values of the log-likelihood statistics logarithm of the odds (LOD) or Delta, so that the confidence of parentage assignments made using the parentage analysis module could be evaluated statistically. In the simulation analysis, we considered: 1000 offspring, 100 mothers and 10 fathers, 95% of sampled typed, 92% of loci typed, and 1% of genotyping error. The simulation was performed for either paternal or maternal side, and for both parents with known sexes, each time for two levels of confidence. The assumption was that one of the parents was known. At first, the dam was assigned as known, then in a second run the sire was assigned as known. The wrongly recorded dams and sires in the pedigree file were put to missing. Parentage analysis was carried out in three steps: first assuming known dams and unknown sires; second, assuming known sires and unknown dams; and thirdly assuming that both of the parents were unknown. During this analysis to test each offspring, parentage was either assigned to the most-likely candidate parent (or parent pair) with a pre-determined level of confidence or was left unassigned.
RESULTS AND DISCUSSION
Genotypes were scored from all individuals based on the co-dominant nature of microsatellite loci (Figure 1).
Figure 1 Genotype pattern and range of allele size of ILSTS0049 locus (100-120 bp) in upper gel and OarFCB5 (70-100 bp) in lower gel
P1 candidate's father for k1 and k2 carry similar allele size
Observed allele sizes at each locus were within the reported ranges of earlier studies (Table 2). Highest and the reproducible multiplex group was included as follows: ILSTS0049 (100-120 bp), MCM512 (150-180), BMC1009 (200-210 bp), BM148 (250-270 bp) and CSSM032 (270-290 bp) loci (Figure 2). The average heterozygosities, polymorphism index content (PIC) and number of alleles per loci were 0.60, 0.58 and 4.93, respectively (Table 2). The total exclusion probability of 14 microsatellite loci was 0.9999 in the population by compatibility according to the Mendelian inheritance (Figure 3). The exclusion probabilities, as shown in Table 3, are values based on the allele frequencies of each of the markers alone, and can thus be computed in any family structure. The alleles of the offspring were found to be compatible with those of the parents in all cases. The pedigree was considered incorrect in 1 out of all the evaluated progeny, as the genotype of that progeny did not match to any of its parents (Table 3). Accurate pedigree information is required for a successful breeding program and improvement of productivity in the animal industry. Misidentification of parentage can lead to breeding inaccuracy, causing great financial losses in herd management and this is of more interest in beef industries (Cervini et al. 2006).
Table 2 Molecular diversity descriptive statistics of loci used in the present study
Na: number of alleles; N: overall number of genotype; Ne: effective number of allele; MFA: minor frequent allele; He: expected heterozygosity; Ho: observed heterozygosity; PIC: polymorphism information content and SI: shanon index.
Figure 2 Capillary electrophoresis pattern of 5-plex microsatellite assay
(1): ILSTS0049 locus (100-120 bp); (2): MCM512(150-180); (3): BMC1009(200-210 bp); (4): BM148(250-270 bp) and (5): CSSM032(270-290 bp)
A small rate of percentage misidentification can excessively endangers genetic patterns estimation. The paternity misidentification rate of 11% would result in a decrease of 11-15% in the genetic trend for milk traits (Banos et al. 2001). Moreover, the pedigree errors may reflect on the structure of selection indexes (Přibyl et al. 2004). Inaccurate pedigree information is a common problem in livestock production and paternity pedigree errors have a substantial negative impact on the national genetic evaluation and estimates of inbreeding (Pollak, 2005; Dodds et al. 2007). The proportion of misidentified progeny varies between 2.9 and 23% of cattle worldwide (Weller et al. 2004; Van Eenennaam et al. 2007). The parentage verification is crucial as accurate selection and genetic improvement rate has relied on valid pedigree information (Dodds et al. 2005) and it becomes more critical after widespread use of artificial insemination, embryo transfer and multiple sire breeding schemes in animal breeding. The genetic relationships among individuals in the population can be determined based on either pedigree or marker information. In the pedigree approach, founder animals are assumed to be unrelated to each other. Also, there is a possible occurrence of recording errors in the pedigree file. Therefore, marker based inference to genetic relationships seems to be an alternative approach. Since related individuals share more alleles than unrelated ones, relationships can be estimated from marker genotypes in every pair of individuals, including the founders. However, determination of the relationship which only based on the marker information is not an easy exercise (Weller et al. 2004; Van Eenennaam et al. 2007). In a classical analysis of genetic relationship, one of the useful parameters is the power of exclusion (PE), the power of a genetic marker in excluding a non-related individual chosen by chance in a specific population, as an alleged father in a paternity investigation. The PE is a parameter to solve problems of some genetic markers in a population and is most commonly used as molecular markers in pedigree verification. The paternity PE is the expected average probability that a polymorphic locus shows the exclusion of a male without kinship with the biological sire (Marshall et al. 1998). This index depends on the informative content of a locus, which depends on its number of alleles and its respective frequencies. Since the probabilities of exclusion of several loci, it is possible to calculate the combined PE (PEC), and the value of PEC is a function of the examined locus number, as well as of the informative content of each locus. The PE and CEP for each loci can be used in an analysis of genetic relationship. The genetic paternity testing can provide sire identity data for offspring when females have been exposed to multiple males. However, correct paternity assignment can be influenced by factors determined in the laboratory and by size and genetic composition of breeding groups. When parents are assigned on the basis of LOD scores, the most likely candidate parent is the candidate parent with the highest LOD score. The likelihood ratio is the likelihood that the candidate parent is the true parent divided by the likelihood that the candidate parent is not the true parent. A negative LOD score shows that the candidate parent is less likely to be the true parent than an arbitrary randomly chosen individual. A LOD score of zero, indicates that the candidate parent is equally likely to be the true parent as an arbitrary randomly chosen individual. A positive LOD score means that the candidate parent is most likely to be the true parent than arbitrary randomly chosen individual (Marshall et al. 1998). Jakabova et al. (2002) have also revealed that at least, five microsatellites with the highest individual PE values that have a 97% total exclusion probability should be used to obtain a high degree of incorrect parentage exclusion. Usha et al. (1994) reported for cattle parentage control, a total PE of 0.88 for two microsatellite loci can be used. Marklund et al. (1994) analyzed eight microsatellite loci in paternity testing to reach at total exclusion probability of 0.96 to 0.99 in different sheep breeds. The pedigree errors were shown to occur in 1% of the cases tested in this study. This is out of the pedigree errors ranges recorded by Barnett et al. (1999) in sheep (8.7 to 15.5%) and by Visscher et al. (2002) in cattle (2 to 22%) using both the microsatellite markers. The 7 microsatellites evaluated in this study provide sufficient power (75.3%) to be useful in a parentage determination panel to confirm parentage of offspring which are going to be selected for future breeding animals. The possibility of multiple paternities from sheep may require a greater power of exclusion, which would be provided by using more loci. The 1% mismatch rate found among pedigree in this study was relatively low and comparable to pedigree errors reported for four sheep breeds in France (1 to 10%, Leroy et al. 2011) and for New Zealand sheep flocks (0.5 to 9.4%, Crawford and Buchanan, 1993). Studies carried out on cattle (Geldermann et al. 1986; Banos et al. 2001; Baron et al. 2002; Visscher et al. 2002) have recorded higher mismatch rates (4-36%) based on microsatellite markers. These results indicated that more accurate ways of animal identification and parentage information are required. Several studies in cattle with simulation and real data showed that mismatch rate up to 5% have minimal effect on the estimation of genetic values, but the effect increased by increasing error rates (Van Vleck, 1970b).
Figure 3 Combined probability of exclusion (CEP) in parentage testing with increasing number of microsatellite markers
Table 3 Combined powers of exclusion and probability of identity for a panel with different numbers of markers*
* The combined powers of parentage exclusion (PE) were calculated, considering the situations where both parents are genotyped but only one parent is evaluated for exclusion (PE1) or both parents are evaluated for exclusion (PE2) and when only parent is genotyped and evaluated for exclusion (PE3).
Table 4 A summary of some reported results for parentage control in different livestock species
n: number of animals; Na: number of alleles; Ng: overall number of genotype; Ne: effective number of alleles; He: expected heterozygosity; Ho: observed heterozygosity; PIC: polymorphism information content and CEP: combined probability of exclusion.
In this context, Sherman et al. (2004) and Van Eenennaam et al. (2007) recommended a non-exclusion probability value of 0.001 as an acceptable level for correctly identifying the real sire. In our present study the pedigree error rate observed was 4.9%, which is expected to have minimal effect on the genetic evaluation of the population. The microsatellite based parentage tests for relationship verification or assignment in case of unrecorded mating or multiple sires have been developed for many species, including dogs (DeNise et al. 2004), cats (Lipinski et al. 1999), horses (Tozaki,2001a), cattle (Van Eenennaam et al. 2007), goats and sheep (Glowatzki-Mullis et al. 1995). Aiming to reach the minimum recommended combined non-exclusion probability of 0.001 suggested as an acceptable level for correctly identifying the real sire (Sherman et al. 2004; Van Eenennaam et al. 2007). Many factors related to animal species, breed and geographic area could explain the differences between our present results and those reported in the literature. Likewise, it is supposed that genetic tools used such as electrophoresis methods, DNA markers and even the difference in candidate SSR locus as well as human error of technical staff and genoytyping errors should be taken into consideration in the analysis (Table 4).
These results show that the developed microsatellite marker panel has sufficient power for either paternity exclusion or assignment and for individual identification in crossbred lambs. The microsatellites multiplex ILSTS0049, MCM512, BMC1009, BM148 and CSSM032 microsatellites showed the highest and reproducible results. Therefore, the results of this study can be used in the parentage testing in practice to provide parentage verification and is expected to contribute to the quality control in the breeding system.
The authors would like to express their gratitude to the university of Tabriz academic staff and Khalat Poshan research farm, Basmanj staff for collaboration during blood sampling and providing the pedigree information. This work was funded by Tabriz University Research Grant.