Parentage verification of Valle del Belice dairy sheep using multiplex microsatellite panel

Small Ruminant Research 113 (2013) 62–65

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Technical note

Parentage verification of Valle del Belice dairy sheep using multiplex microsatellite panel A.J.M. Rosa a , M.T. Sardina b,∗ , S. Mastrangelo b , M. Tolone b , B. Portolano b a EMBRAPA Cerrados Brazilian Agricultural Research Corporation, Ministry of Agriculture, Livestock and Food Supply, Br 020, km 18 – Cx.p. 08223, 73310-970 Planaltina, DF, Brazil b Dipartimento Scienze Agrarie e Forestali, Università degli Studi di Palermo, Viale delle Scienze, Parco d’Orleans, 90128 Palermo, Italy

a r t i c l e

i n f o

Article history: Received 13 July 2012 Received in revised form 26 March 2013 Accepted 28 March 2013 Available online 1 May 2013 Keywords: Valle del Belice dairy sheep Microsatellites Multiplex Parentage test

a b s t r a c t The aim of this work was to develop and evaluate a PCR based microsatellite markers multiplex system for parentage verification of Sicilian Valle del Belice dairy sheep. A total of 85 samples of blood and hair were collected and genotyped for 24 microsatellite markers in multiplex electrophoresis runs. A total of 269 alleles were detected across the 24 loci investigated. The PIC considering all loci was equal to 0.736, showing that this microsatellite panel was very polymorphic and highly informative. A parentage test was performed on 64 families generated with multiple sires. Results indicated 20.3% and 29.7% misidentification rates for females and males, respectively. In 8 cases, out of 13 maternal exclusions, the real mother was identified among other females within the flock. The observed misidentification rates indicated the necessity of keeping more efficient collection of genealogical records, in order to properly control inbreeding or implement a breeding program. The parentage test presented here could be a helpful tool on verifying or even reconstructing the current pedigree data of Valle del Belice dairy sheep breed. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Animal breeding programs use mixed models to predict breeding values for economically important traits and select seedstock animals. The methodology of Henderson (1988) uses a relationship matrix of the animals to solve the mixed model generating additive genetic values with BLUP properties. Therefore, paternity errors have a detrimental effect on population genetic parameters estimation and breeding value prediction (Van Vleck, 1970; Lee and Pollak, 1997). Paternity errors can reach up to 20% of animals registered in various countries (Ron et al., 1996), drastically reducing the genetic gain, beside impacting inbreeding control. The Valle del Belice

∗ Corresponding author. Tel.: +39 091 23896069; fax: +39 091 23860814. E-mail address: [email protected] (M.T. Sardina). 0921-4488/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.smallrumres.2013.03.021

is the most appreciated sheep breed reared in Sicily for milk production, and nowadays about 215,000 animals are enrolled in the herd book belonging to approximately 1250 flocks (ASSONAPA, 2012). In the Sicilian farming system, natural mating is the most common practice. Moreover, the exchange of rams among flocks is quite unusual and the reproductive management is characterized by unrecorded mating with multiple sires. Therefore, there is a need to generate reliable pedigree for inbreeding control beside engendering a breeding program. For pedigree reconstruction, given the demand of heterozygosity, microsatellite is the most commonly used markers characterized by higher number of alleles than other neutral markers (e.g. Single Nucleotide Polymorphisms, SNPs). 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 (De Nise et al., 2004), cats (Lipinski et al., 2007), horses (Tozaki et al., 2001), cattle (Van Eenennaam et al., 2007), goats and sheep

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Table 1 Number of samples (N), number of alleles (k), observed heterozygosity (Ho) and expected heterozygosity (He), polymorphic information content (PIC), non-exclusion probabilities considering first (NE-1P), second (NE-2P) and pair parent (NE-PP); individual (NE-I) and sib identity (NE-SI); Hardy–Weinberg equilibrium (HWE) and null allele frequency (F(Null)). Locus

N

k

Ho

He

PIC

NE-1P

NE-2P

NE-PP

NE-I

NE-SI

HWE

F(Null)

BRN CSRD247 INRA063A McM527 OarFCB128 SPS115 ILSTS005 TCRBV6 INRA132 MAF209 IDVGA45 APPO10 DU194351 DU323541 OarCP49 DU223896 BM827B ILSTS011 DU206192 McM54B DU216028 DU502595 LSCV36B MNS25B

83 84 84 84 83 84 83 84 83 84 83 84 83 81 83 71 84 85 82 84 85 79 77 72

9 10 14 11 8 7 11 9 12 11 12 8 13 17 17 11 7 6 30 2 13 12 11 8

0.566 0.762 0.738 0.798 0.747 0.786 0.735 0.607 0.819 0.702 0.831 0.571 0.434 0.914 0.819 0.493 0.619 0.706 0.805 0.488 0.871 0.722 0.792 0.655

0.632 0.812 0.813 0.843 0.718 0.774 0.690 0.737 0.867 0.799 0.837 0.751 0.544 0.892 0.862 0.793 0.779 0.732 0.932 0.501 0.876 0.850 0.815 0.668

0.556 0.782 0.788 0.817 0.672 0.734 0.657 0.694 0.847 0.765 0.814 0.711 0.522 0.876 0.842 0.759 0.740 0.693 0.922 0.374 0.857 0.829 0.789 0.623

0.788 0.551 0.533 0.493 0.692 0.625 0.705 0.664 0.435 0.574 0.493 0.649 0.825 0.372 0.442 0.581 0.615 0.670 0.254 0.876 0.415 0.464 0.534 0.737

0.644 0.375 0.358 0.324 0.515 0.446 0.519 0.486 0.276 0.398 0.324 0.468 0.643 0.228 0.282 0.404 0.436 0.487 0.146 0.813 0.260 0.299 0.359 0.557

0.481 0.192 0.170 0.151 0.326 0.262 0.313 0.295 0.114 0.213 0.146 0.278 0.437 0.080 0.117 0.217 0.252 0.293 0.035 0.719 0.102 0.124 0.172 0.355

0.210 0.063 0.057 0.047 0.124 0.089 0.128 0.110 0.034 0.071 0.046 0.100 0.230 0.023 0.036 0.074 0.086 0.109 0.010 0.376 0.030 0.039 0.057 0.152

0.488 0.362 0.360 0.343 0.424 0.388 0.439 0.411 0.327 0.371 0.345 0.402 0.537 0.313 0.330 0.375 0.384 0.413 0.289 0.595 0.322 0.338 0.360 0.459

NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS

+0.0529 +0.0284 +0.0456 +0.0271 −0.0353 −0.0096 −0.0367 +0.0950 +0.0266 +0.0636 −0.0012 +0.1361 +0.1086 −0.0150 +0.0228 +0.2286 +0.1121 +0.0190 +0.0725 +0.0103 +0.0009 +0.0773 +0.0119 −0.0223

**

NS NS **

NS NS NS NS NS

NS: not significant. ** p < 0.01.

(Glowatzki-Mullis et al., 2007) aiming to reach the minimum recommended combined non-exclusion probability of 0.001 suggested as an acceptable level for correctly identifying the real sire (Luikart et al., 1999; Sherman et al., 2004; Van Eenennaam et al., 2007). The aim of this work was to develop and evaluate a PCR based microsatellite markers multiplex system for parentage verification of Sicilian Valle del Belice dairy sheep. Besides, we performed parentage tests on 64 families aiming to estimate misidentification rates and start pedigree reconstruction of 3 flocks participating in the breeding program. 2. Materials and methods 2.1. Sampling and DNA extraction A total of 85 samples of blood and hair were collected belonging to 12 flocks. The number of animals sampled per flock ranged from 5 to 15 individuals (Table S2). About 10 ml of blood was collected from jugular vein using vacutainers tube containing EDTA as anticoagulant. Genomic DNA was extracted from buffy coats of nucleated cells using a salting out method (Miller et al., 1988). Hair samples were picked from the dorsal area, collected in plastic bags and maintained at 4 ◦ C until processing. Bulbs were cut and placed on 1.5 ml tubes and mixed with alkaline lysis buffer according to Schmitteckert et al. (1999). 2.2. Microsatellites amplifications and analysis A total of 24 microsatellite markers (BRN, CSRD247, INRA063A, McM527, OarFCB128, SPS115, ILSTS005, TCRBV6, INRA132, MAF209, IDVGA45, APPO10, DU194351, DU323541, OarCP49, DU223896, BM827B, ILSTS011, DU206192, McM54B, DU216028, DU502595, LSCV36B, and MNS25B) were selected as suggested by ISAG (www.isag. org.uk; 2005 Panels Markers for Sheep and Goats.pdf) and FAO (New Microsatellite marker sets – Recommendation of joint ISAG/FAO Standing Committee.pdf) or obtained from the NCBI website (www.ncbi.nlm.nih.gov). In order to fit a larger number of markers

in a single electrophoresis run, primers for markers BM827, McM54, LSCV36, and MNS25A were redesigned to generate larger PCR fragments and renamed as BM827B, McM54B, LSCV36B, and MNS25B. Moreover, loci DU194351, DU323541, DU223896, DU206192, DU216028, and DU502595 were selected based on location and DNA sequence information available at NCBI website (http://www.ncbi.nlm.nih.gov). Within these loci, STRs (small tandem repeats) were present. Therefore, primer pairs were designed in order to generate fragments of 300–500 bp. These markers were chosen taking into account the level of polymorphism reported in ISAG/FAO panels of markers for parentage verification, location on different chromosomes when available, and ability to co-amplify in multiplex PCR reactions. Genotypes for all 24 microsatellite markers were determined by means of four multiplex fluorescent PCR reactions (Table S1), and fragment lengths determined in a semi-automated single combined multiplex electrophoresis run by using ABI 3130 Genetic Analyzer and GeneMapper version 4.0 with recommended protocols (Applied Biosystems). Each reaction was performed in a total volume of 20 ␮l containing 50 ng template DNA, 1X Qiagen Multiplex PCR Master Mix, 1X PCR Master Mix, primer mix, and nuclease-free water. The thermal cycling conditions were as follows: initial denaturation at 95 ◦ C for 15 min; 32 cycles of 95 ◦ C for 45 s, 58 ◦ C for 1 min 50 s, and 72 ◦ C for 1 min 20 s; and final extension at 60 ◦ C for 30 min. Table 2 Summary statistics for the 24 microsatellite markers panel obtained using Cervus v3.0.3. Number of individuals Number of loci Mean number of alleles per locus Mean proportion of individuals typed Mean observed heterozygosity Mean expected heterozygosity Mean polymorphic information content (PIC) Combined non-exclusion probability (first parent) Combined non-exclusion probability (second parent) Combined non-exclusion probability (parent pair) Combined non-exclusion probability (identity) Combined non-exclusion probability (sib identity)

85 24 11.21 0.9441 0.708 0.772 0.736 0.00000101 1.77E − 0010 2.35E − 0017 3.06E − 0028 1.11E − 0010

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Table 3 Mean number of alleles per locus (MNA), expected heterozygosity (He), polymorphic information content (PIC), non-exclusion probabilities considering first (NE-1P), second (NE-2P) and pair parent (NE-PP); individual (NE-I) and sib identity (NE-SI). Marker set

MNA

He

PIC

NE-1P

NE-2P

NE-PP

NE-I

NE-SI

8 12 16 24

15.63 14.25 12.75 11.29

0.8701 0.8499 0.8310 0.7718

0.8508 0.8275 0.8045 0.7362

0.00083588 0.00007516 0.0000109 0.00000101

0.00002075 0.0000004 0.00000001 1.77E−10

0.00000001 1.20E−11 4.80E−14 2.35E−17

6.91E−13 1.00E−17 5.67E−22 3.06E−28

0.0001257 0.00000219 0.00000005 1.11E−10

Supplementary material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.smallrumres.2013.03.021. 2.3. Statistical analysis and parentage assignment Number of alleles per locus (k), observed and expected heterozygosity (Ho and He, respectively), polymorphic information content (PIC), non-exclusion probabilities considering first (NE-1P), second (NE-2P) and pair parent (NE-PP), individual (NE-I) and sib identity (NE-SI), Hardy–Weinberg equilibrium (HWE), and null allele frequency (F(Null)), were estimated using Cervus version 3.0.3 (Kalinowski et al., 2007). In order to assess the presence of inbreeding within flock, the Fis value was calculated for each flock using the Genetix software package version 4.05 (bootstrap analysis on 1000 replicates) (Belkhir et al., 2004). Parentage testing was performed in a total of 64 triplets, accordingly to Mendelian inheritance, ISAG guidelines, and Cervus manufacturer protocols.

3. Results A total of 269 alleles were detected across the 24 loci investigated. The number of alleles per locus ranged from 2 for McM54B to 30 for DU206192 (Table 1) with an average of 11.21 (Table 2). The PIC considering all loci was equal to 0.736, showing that the microsatellites panel used was highly informative. McM54B was found to be the least informative marker (0.374), whereas DU206192 the most informative one (0.922) (Table 2). Ho varied from 0.434 (DU194351) to 0.914 (DU323541) and He from 0.501 (McM54B) to 0.932 (DU206192) (Table 1). Only two markers (DU223896 and DU206192) showed statistically significant deviations from HWE (p < 0.01). The combined non-exclusion probabilities were 1.01 × 10−6 , 1.77 × 10−10 , and 2.35 × 10−17 , for the first, second, and the parent pair, respectively (Table 2), whereas the combined identity and sib-identity non-exclusion probabilities were 3.06 × 10−28 and 1.11 × 10−10 , respectively, when all 24 microsatellite markers were used (Table 3). These values increased when a subset of 16, 12, and 8 markers, selected based on PIC and non-exclusion probability values, was used (Table 3). The estimated Fis for each flock ranged from 0.017 to 0.165, and this probably reflects a moderate level of inbreeding in this breed (Table S2). Supplementary material related to this article found, in the online version, at http://dx.doi.org/10.1016/j. smallrumres.2013.03.021. 4. Discussion In this study we developed and evaluated a PCR based microsatellite markers multiplex system for parentage verification of Sicilian Valle del Belice dairy sheep. The majority of the markers were highly polymorphic and generally in Hardy–Weinberg equilibrium, except for the marker

DU223896 that showed the largest difference between observed and expected heterozygosity, and DU206192 for the highest null alleles frequency. The average number of alleles per marker (11.21) was considerably higher than that found in sheep populations in Bhutan (Dorji et al., 2010) and in Iran (Saberivand et al., 2011), whereas similar results were reported by Souza et al. (2012) in Santa Inês sheep breed. Glowatzki-Mullis et al. (2007) analyzed 10 different breeds with a 19-microsatellite multiplex (of which 10 are in common with our study) and found an average maximum of 7.79 alleles for Red Engadin sheep. The different Fis values of the flocks reflect different levels of inbreeding. The high level of inbreeding could be affected by the farming system. Within Sicilian flocks, rams and ewes are reared together; therefore mating with close relatives could be quite frequent. Moreover, the reduced or absent exchange of rams between different flocks may have induced the development of a substructure within this population (Tolone et al., 2012). This could be more frequent where the assessment of parentage is not carried out (Pariset et al., 2003). The combined non-exclusion probabilities were estimated for different marker panels considering the full set of 24 markers, 16, 12, and 8 microsatellite markers. The results showed the best combined non-exclusion probabilities for the panel with 24 microsatellite markers. Therefore, these results indicated the panel of 24 markers as the most effective for parentage assignment in Valle del Belice sheep breed, respect to subsets of 16, 12, and 8 markers, although reasonable non-exclusion probabilities were obtained with only 16 highly polymorphic loci, i.e. mean PIC bigger than 0.8 (Table 3). The combined non-exclusion probabilities obtained for our microsatellite markers were smaller than other parentage tests published (Luikart et al., 1999; Arruga et al., 2001; Glowatzki-Mullis et al., 2007) and/or commercially available, and reached the minimum recommended combined non-exclusion probability of 0.001 suggested as an acceptable level for correctly identifying the real sire (Luikart et al., 1999; Sherman et al., 2004; Van Eenennaam et al., 2007). A parentage test was performed on 64 (possible sires, possible ewes, and progeny) families generated with multiple sires mating as indicated by the farmers. Exclusions due to genotype incompatibility for markers with high null allele frequency (>0.05) were considered only when offspring, or both parents, were heterozygous in order to avoid false exclusion (Dakin and Avise, 2004). Misidentification rate obtained for females was 20.3% (13 out of 64 cases). Paternity errors were identified in 19 out of 64 cases (29.7%). These results were expected,

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especially for males, because these flocks are managed using multiple sires mating. However, not all sires were available for sampling and consequently were not analyzed. The misidentification rates are high compared to other livestock species (Crawford et al., 1993; Laughlin et al., 2003; Weller et al., 2004; Siwek and Knol, 2010), indicating the need to improve the identification system and genealogical data collection. The obtained non-exclusion probabilities and paternity test results indicated that this microsatellite panel is suitable for parentage testing animals from moderately inbred and structured populations, even when subjects are related (De Ungria et al., 2002; von Wurmb-Schwark et al., 2006; Wenk et al., 2006; Scarpetta et al., 2007), and could be used for pedigree reconstruction of Valle del Belice breed. A first pedigree reconstruction attempt allowed identifying the real mother in 8 cases, out of 13 maternal exclusions, reducing the maternal errors to 7.8% (5 out of 64 cases) and demonstrating the feasibility of performing large scale pedigree reconstruction by means of parentage testing. Parentage test should be performed in flocks belonging to the breeding program to confirm or reconstruct the pedigree of progeny generated through multiple sires technique, due to the detrimental effect of pedigree errors on inbreeding control, population parameters estimation, and breeding values prediction. DNA-based parentage, therefore, has the potential to assist the breeders to improve pedigree recording and selection accuracy, resulting in an increase in the rate of genetic improvement. All young sires and a subset of young ewes should also be tested every year to evaluate the consistency of pedigree data supplied by the farmers and control the pedigree errors. References Arruga, M.V., Monteagudo, L.V., Tejedor, M.T., Barrao, R., Ponz, R., 2001. Analysis of microsatellites and paternity testing in Rasa Aragonesa sheep. Res. Vet. Sci. 70, 271–273. ASSONAPA, 2012. http://www.assonapa.it/Consistenze/ Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N., Bonhomme, F., 2004. GENETIX 4.05, Logiciel sous Windows TM pour la Génétique des Populations. Laboratoire Génome, Populations, Interactions. Université de Montpellier II, Montpellier, France, CNRS UMR 5171. Crawford, A.M., Tate, M.L., McEwan, J.C., Kumaramanickavel, G., McEwan, K.M., Dodds, K.G., Swarbrick, P.A., Thompson, P., 1993. How reliable are sheep pedigrees? Proc. N. Z. Soc. Anim. Prod. 53, 363–366. Dakin, E.E., Avise, J.C., 2004. Microsatellite null alleles in parentage analysis. Heredity 93, 504–509. De Ungria, M.C., Frani, A.M., Magno, M.M., Tabbada, K.A., Calacal, G.C., Delfin, F.C., Halos, S.C., 2002. Evaluating DNA tests of motherless cases using a Philippine genetic database. Transfusion 427, 954–957. De Nise, S., Johnston, E., Halverson, J., Marshall, K., Rosenfeld, D., McKenna, S., Sharp, T., Edwards, J., 2004. Power of exclusion for parentage verification and probability of match for identity in American Kennel Club breeds using 17 canine microsatellite markers. Anim. Genet. 35, 14–17. Dorji, T., Jianlin, H., Wafula, P., Yamamoto, Y., Sasazaki, S., Oyama, K., Hanotte, O., Lin, B.Z., Mannen, H., 2010. Sheep genetic diversity in Bhutan using microsatellite markers. Anim. Sci. 81, 145–151. Glowatzki-Mullis, M.L., Muntwyler, J., Gaillard, C., 2007. Cost-effective parentage verification with 17-plex PCR for goats and 19-plex PCR for sheep. Anim. Genet. 38, 86–88. Henderson, C.R., 1988. Use of an average numerator relationship matrix for multiple-sire joining. J. Anim. Sci. 66, 1614–1621.

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