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Small Ruminant Research 114 (2013) 64– 71

Contents lists available at SciVerse ScienceDirect

Small Ruminant Research

jou rna l h om epa ge: www.elsev ier .com/ locate /smal l rumres

omparative study of three simple molecular approaches inearch of mtDNA haplogroup identification of domestic sheep

ren Yüncüa,∗, Sevgin Demircia, Evren Koban Bas tanlarb, S ükrü Anıl Dogana,mut Tas demirc, Inci Togana

Department of Biological Sciences, Middle East Technical University, Ankara, TurkeyGenetic Engineering and Biotechnology Institute, TUBITAK Marmara Research Center, Kocaeli, TurkeyGeneral Directorate of Food and Control, Ministry of Food, Agriculture and Livestock, Ankara, Turkey

a r t i c l e i n f o

rticle history:eceived 7 November 2012eceived in revised form 21 February 2013ccepted 22 May 2013vailable online 26 June 2013

eywords:ative Turkish sheep breedsitochondrial DNA (mtDNA) haplogroups

HPGs)ontrol region (CR)T-ND2 regionT-ND4 region

a b s t r a c t

There are five (A–E) identified mitochondrial DNA (mtDNA) haplogroups (HPGs) in domes-tic sheep. In search of a quick and reliable HPG classification, three different molecularapproaches were tested: the restriction fragment length polymorphism (RFLP) methodapplied to mtDNA control region (CR), the single strand conformational polymorphism(SSCP) method applied to NADH dehydrogenase subunit 2 (MT-ND2) and to NADH dehydro-genase subunit 4 (MT-ND4) regions. The results were evaluated comparatively to elucidatetheir power in discriminating the sheep mtDNA HPGs. For this purpose, the HPGs of 622domestic sheep all from Turkey, hence from the genetic diversity hot spot of domesticsheep distribution were examined using these three approaches. The HPGs for some ofthe individuals (n = 240) were also obtained based on CR sequences. The SSCP analysis ofthe MT-ND2 region (by identifying HPGs as A, B, C/E and D) exhibited higher discrimina-tion power, sensitivity and specificity in HPG classification when compared with the SSCP

analysis of MT-ND4 region and RFLP analysis of CR.

In conclusion, to identify the mtDNA HPGs of sheep, and especially to identify the rareHPGs (D and E), first screening the individuals by MT-ND2 SSCP and then sequencing theCR of only those individuals revealing C/E HPGs (mixed strategy of genotyping) may bepreferred when sequencing is not easily available.

© 2013 Elsevier B.V. All rights reserved.

. Introduction

Mitochondrial DNA (mtDNA) is one of the most widelysed molecular markers to measure the genetic diversity

n livestock studies (Bruford et al., 2003; Toro et al., 2009).n every examined livestock species, more than one hap-ogroup (HPG) was observed (see Troy et al., 2001; Jia et al.,

007; Naderi et al., 2007; Meadows et al., 2007; Kobant al., 2011). Information that is obtained from the mtDNAPG distribution pattern over the geographic region of

∗ Corresponding author. Tel.: +90 312 210 51 79.E-mail address: eren26285@gmail.com (E. Yüncü).

921-4488/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.smallrumres.2013.05.014

a livestock species can give insight into the center(s) ofdomestication and migration patterns of maternal lin-eages of the livestock species (see Bruford and Townsend,2006; Lawson Handley et al., 2007; Naderi et al., 2008;Groeneveld et al., 2010; Koban et al., 2011). Mutationsin the livestock mtDNA are also associated with produc-tion traits; for example, meat quality in bovine (Mannenet al., 2003; Kim et al., 2009) and ewe prolificacy in sheep(Reicher et al., 2012).

In domestic sheep, many mtDNA HPG identification

studies have been carried out and they revealed the pres-ence of five mitochondrial HPGs: A and B (Wood and Phua,1996; Hiendleder et al., 1998), C (Guo et al., 2005; Pedrosaet al., 2005; Bruford and Townsend, 2006), D (Tapio et al.,

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2006) and E (Meadows et al., 2007, 2011). Among theseHPGs A, B and, to a certain extent, C were found to bethe most commonly observed HPGs (Guo et al., 2005;Bruford and Townsend, 2006; Tapio et al., 2006; Meadowset al., 2007; Reicher et al., 2012). In those studies, differ-ent approaches were used to identify HPGs where differentregions of mtDNA were examined by employing differ-ent methods. An analysis of the mtDNA control region(CR) by restriction fragment length polymorphism (RFLP)could differentiate individuals with respect to their HPGsas A, B, B/C (Bruford and Townsend, 2006). Single strandconformational polymorphism (SSCP) of partial MT-ND2and MT-ND4 regions has been reported to discriminateHPGs as A, B, C (Guo et al., 2005). The Cytochrome Bregion (Meadows et al., 2007) and mitogenome sequenc-ing (Meadows et al., 2011) identified all of the HPGswhile CR had the highest contribution to HPG determina-tion (Meadows et al., 2011). However, the power of someapproaches could have been underestimated since rarelyobserved HPGs D and E, even C, may not have been presentamong the analyzed samples as they were in earlier stud-ies (Wood and Phua, 1996; Hiendleder et al., 1998). Ina recent study, whole mtDNA sequencing revealed thatdifferent mtDNA regions were not equally informative inHPG determination and CR sequence was observed as themost informative region (Meadows et al., 2011). Thus, CRsequencing was accepted as the most efficient approach forthe mtDNA HPG determination in sheep.

The purpose of the present study was to evaluate theavailable approaches and identify an inexpensive approachthat could determine sheep mtDNA HPGs with accuracyas close to that of the CR sequencing as possible. In thisstudy, analyses of the MT-ND2 and the MT-ND4 regionsusing SSCP (Guo et al., 2005) and analysis of the CR usingRFLP (Bruford and Townsend, 2006) were carried out ona large number of samples (n = 622). Furthermore, HPGsof 240 selected individuals were also determined by theCR sequencing to test the accuracy of the results. All thesamples were from native Turkish sheep breeds. Therefore,they were expected to represent almost the full spectrumof sheep genetic diversity due to their proximity to thefirst domestication center (Zeder, 2008) and to the secondexpansion of sheep (Chessa et al., 2009). Consequently, themtDNA HPG determination approach based on the Turkishnative sheep may be applicable to the sheep from variousother geographic regions of the world.

2. Materials and methods

2.1. Sheep samples and DNA isolation

Sheep samples were composed of 622 individuals from 13 differentTurkish sheep breeds (40–50 sheep/breed) namely, Karayaka, Akkaraman,Gökceada, Daglıc , Morkaraman, Kıvırcık, Ivesi (Turkish Awassi) Herik,Karagül (Turkish Karakul), Hems in, C ine C aparı, Sakız (Turkish Chios)and Norduz. For further information about their collection sites, samplesizes and the number of sampled flocks, see supplementary document(Table S1). Three of the breeds were related to well-known transbound-ary breeds; Karakul, Awassi and Chios have been in Turkey since at least

the 1930s and have been kept isolated. Therefore, they were also con-sidered as Turkish native breeds. Blood samples were collected fromthe sheep and DNA was extracted by the phenol–chloroform method asdescribed in Sambrook et al. (1989). The concentration and quality of theDNA was measured by NanoDrop spectrophotometer. Samples, which had

search 114 (2013) 64– 71 65

approximately 1.80 and 2.00 for 260/280 and 260/230 nm ratios respec-tively, were considered for further molecular analyses.

2.2. PCR amplification

The complete CR was amplified using CR forward and reverse primers,which were designed in our laboratory. Sequences of these primers aregiven in the supplementary document (Table S2). The sequence lengthwas variable due to the different copy number (3–5) of the repeat regionof 75–76 base pair (bp). Most of the sequences were about 1190 bp longwith 4 repeats. The PCR conditions are given in supplementary document(Table S3).

The partial regions of MT-ND2 gene (206 bp) and MT-ND4 gene(280 bp) were amplified using the primers and the reaction conditionsas described by Guo et al. (2005). The only difference was in the annealingtemperature; 57 ◦C was set for both regions.

2.3. RFLP and SSCP

The CR PCR products were incubated with 2 units of NSiI enzyme(Bruford and Townsend, 2006) at 37 ◦C and the resulting restriction pro-files of the overnight digestion were checked on 3% agarose gel using EtBrstaining (Bruford and Townsend, 2006).

The analysis of MT-ND2 and MT-ND4 regions based on the SSCPmethod were carried out as described by Guo et al. (2005). The PCR prod-ucts of partial MT-ND2 and MT-ND4 regions were run on non-denaturingpolyacrylamide gels at 100 V for 19–22 h and at 75 V for 14–16 h, respec-tively. The banding patterns were visualized by the silver staining method(Bassam et al., 1991).

2.4. Initial HPG A, B and B/C classification based on the CR RFLPbanding patterns of the samples

The banding patterns that were obtained by the CR RFLP analysiswere mostly parallel to those results reported by Bruford and Townsend’s(2006) study (A, B, A/C). It was noticed that Bruford and Townsend’s (2006)naming of HPGs A and B was the opposite of Hiendleder et al.’s (1998).Since Hiendleder et al.’s (1998) denomination was adopted by later stud-ies (Guo et al., 2005; Pedrosa et al., 2005; Tapio et al., 2006; Meadowset al., 2007), this denomination was used in the present study. Commongroupings resulted after MT-ND2 SSCP and MT-ND4 SSCP analyses werematched the groupings (A, B and B/C) by CR RFLP analysis. Therefore,the SSCP banding patterns were named in reference to the RFLP analysisresults. In addition, samples with uncommon MT-ND2- SCCP and MT-ND4SSCP banding patterns were sequenced to understand if they representrare HPGs.

2.5. Sequencing and HPG identification of individuals with respect totheir CR

Individuals to be sequenced for their CR were selected as follows:58 randomly selected individuals all presumably belonging to HPG A inCR RFLP; 70 randomly selected individuals presumably belonging to HPGB in CR RFLP; all the individuals of B/C grouping (n = 93) based on RFLPanalysis; all the individuals exhibiting rare and unknown banding pat-terns, i.e. those banding patterns that did not match the reported onesbased on RFLP analysis (n = 4); MT-ND2 SSCP analysis (n = 2), and MT-ND4SSCP analysis (n = 13). In total, the CR of 240 individuals was sequenced.Reference sequences for the HPGs were chosen from Meadows et al.’s(2011) study. The GenBank accession numbers for the reference sequencesare: HM236174–HM236175 (HPG A); HM236176–HM236177 (HPG B);HM236178–HM236179 (HPG C); HM236180–HM236181 (HPG D) andHM236182–HM236183 (HPG E).

All the individuals that revealed unknown banding patterns for MT-ND2 and MT-ND4 regions and 2 individuals from each of the 5 HPGs werealso sequenced for their MT-ND2 (n = 10) and MT-ND4 (n = 23) partialregions.

PCR primers were used for sequencing the MT-ND2 and MT-ND4partial regions. In addition to the same PCR primers used for amplifi-

cation, internal primers HC2 (Townsend, 2000) and SOAD (Bruford andTownsend, 2006) were also used in the cycle sequencing reactions ofmtDNA. The sequences of these primers and the experimental conditionsfor CR sequencing are given in the supplementary document (Tables S2and S3). All the PCR products were purified with GeneClean Turbo for

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CR kit (Q-BioGene). After purification, the PCR products were sent to theEFGEN Gene Research and Biotechnology Limited (Ankara, Turkey) forequencing. The sequences were assembled by ChromasPro (Technely-ium PTY, Australia), aligned and edited by BioEdit (Hall, 1999).

A neighbor joining tree was reconstructed to visualize within andetween HPG relationships based on the CR sequences using MEGA 5Tamura et al., 2011). For the tree reconstruction, the sequence data of 240urkish samples, the reference sequences (Meadows et al., 2011) for eachf the HPGs (GenBank accession numbers are given above), two referenceequences (Hiendleder et al., 2002) from Ovis vignei (GenBank accessionumbers: AY091490–AY091491) and two reference sequences from Ovismmon (GenBank accession numbers: AF242347–AF242348) were used.

irst, the nucleotide substitution model was determined using jModelTest.1.1 (Posada, 2008) as HKY+I+G model (Hasegawa et al., 1985) (gamma-istributed rate heterogeneity � = 0.429, proportion of invariable site.621) with 4 free parameters, according to Bayesian Information Criterion

ig. 1. Neighbor joining tree reconstructed using the CR sequence data from the

ection 2). On the tree, the positions of the references are indicated by arrows. Th–E were labeled in accordance with the reference sequences and the CR sequeepresent the outgroups.

search 114 (2013) 64– 71

(BIC) (Schwarz, 1978). Also, TPM1uf+I+G (Kimura, 1981) model (gamma-distributed rate heterogeneity � = 0.43, the proportion of invariable site0.621), with 5 free parameters was found to be appropriate according tothe Akaike Information Criterion (AIC) (Akaike, 1974). Because the HKYmodel was not included in MEGA 5, the Tamura-Nei model, which isthe nearest model of HKY (also covering the TPM1uf+I+G model), wasemployed.

The HPGs determined by the CR sequencing were used to estimate thesensitivity and specificity for band patterns. The sensitivity is determinedby the proportion of the number of individuals that are identified withthe method and confirmed with CR sequencing to the number of all indi-viduals in that band pattern. The specificity is the ratio of the number of

individuals that are similarly identified with the method and confirmedwith CR sequencing to addition of this number with the number of indi-viduals that are falsely identified as another HPG by the method yet aredetermined to be in the HPG with CR sequencing.

present study and the reference sequences retrieved from GenBank (seee numbers on the tree are bootstrap values for the main branches. HPGs

nces of wild sheep samples, Ovis vignei and Ovis ammon (see Section 2),

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3. Results

Totally 622 samples were screened by CR RFLP, MT-ND2SSCP and MT-ND4 SSCP approaches. In order to associatebanding patterns with HPGs as defined in the literature, aneighbor joining tree was reconstructed (Fig. 1) based onthe CR sequences of 240 selected Turkish samples togetherwith the reference sequences retrieved from GenBank (seeSection 2). Among the samples of the present study, all five

mtDNA HPGs were observed; 71 samples clustered withinHPG A, 88 within HPG B, 69 within HPG C, two within HPGD and 10 within HPG E. The HPG frequencies which wereestimated based on sequence data did not represent the

Fig. 2. Gel images of the observed banding patterns: (A) CR screened by RFLP

screened by SSCP. The labels (numbers) of the banding patterns are shown abosequencing analysis) were shown below the wells. Rarely observed unknown banunknown banding patterns based on sequencing, the HPG names were given belabeled as “M”.

search 114 (2013) 64– 71 67

relative abundance of the HPGs in Turkish sheep because allthe samples with uncommon banding patterns were delib-erately sequenced to find the rare HPGs described in theliterature or to find a new HPG.

CR RFLP analysis revealed seven distinct banding pat-terns. Furthermore, the SSCP analysis revealed four distinctbanding patterns for MT-ND2 region and nine distinctbanding patterns for MT-ND4 region as shown in Fig. 2.These banding patterns were labeled using Roman numer-

als for grouping. Table 1 presents the observed frequenciesfor the banding patterns (groups) shown in Fig. 2. Thesebanding patterns were evaluated in the light of thesequence data of some of the samples from each of these

(B) MT-ND2 partial region screened by SSCP (C) MT-ND4 partial regionve the wells and the HPG names of the banding patterns (identified byding patterns were indicated by a “?” symbol. Upon identification of thelow the “?” symbol. The DNA ladder (GeneRuler 100 bp DNA ladder) is

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roupings (Table 1). Details of calculations for Table 1 wereiven in supplementary document.

Among the rare unknown SSCP banding patterns of MT-D2 and MT-ND4 regions, pattern IV of MT-ND2 region

initially considered as unknown) seemed to be diagnosticor identifying HPG D and was named accordingly (Fig. 2nd Table 1).

The specificity and sensitivity for patterns I, II and IIIf all three approaches are given in Table 1. The termsspecificity” and “sensitivity” were inapplicable to the rareanding patterns (other than pattern IV of MT-ND2 SSCP)ince they were rare manifestations of the major HPGs. Theensitivities of banding pattern III of the RFLP and MT-ND4SCP analysis was 100% only because all of the HPGs wereound in them.

It can be seen in Table 1 that the MT-ND2 SSCP approachas able to differentiate HPGs A and B with the highest

ensitivity (97.18% and 97.65% respectively). The MT-D2 SSCP approach also has a high specificity for thesePGs (97.18% and 94.32% for HPGs A and B, respectively).oreover, for MT-ND2 SSCP, the HPG D could be differen-

iated with 100% specificity and sensitivity based on twoamples.

The relative frequencies of observed A and B HPGs byhe CR RFLP approach are 0.204 (127/622) for HPG A and.65 for HPG B. For the same total population, the corre-

ponding frequencies are 0.22 and 0.64 for MT-ND2 SSCP;.21 and 0.62 for MT-ND4 SCCP approaches. Thus observedrequencies for the two major HPGs are quite compatibleetween the three approaches.

able 1he results from three approaches: (A) CR RFLP (B) MT-ND2 SSCP (C) MT-ND4 SSCequenced samples, the number of HPGs that were identified for each banding pased on the sequencing results, rare-unknown patterns denoted by (–), the percenPGs for (A) CR RFLP (B) MT-ND2 SSCP (C) MT-ND4 SSCP. In IIIrd banding pattern

Bandingpattern

Observedfrequency outof 622

# of sequencedsamples (CR)

Identified HPGsby CRsequences

(A) CR RFLPI 392 77 HPG A(1), B(74),

II 127 60 HPG A(54), B(4),

III 99 99 HPG A(13), B(9),

D(2), E(10)IV 1 1 HPG A(1)

V 1 1 HPG A(1)

VI 1 1 HPG A(1)

VII 1 1 HPG B(1)

(B) MT-ND2 SSCPI 138 71 HPG A(69), B(1),

II 400 85 HPG A(1), B(83),

III 82 82 HPG A(1), B(4), CE(10)

IV 2 2 HPG D(2)

(C) MT-ND4 SSCPI 129 62 HPG A(56), B(4),

II 386 71 HPG A(1), B(68),

III 94 94 HPG A(12), B(9),

D(2), E(8)IV 1 1 HPG B(1)

V 3 3 HPG A(1), C(2)

VI 1 1 HPG B(1)

VII 2 2 HPG E(2)

VIII 4 4 HPG B(4)

IX 2 2 HPG A(1), B(1)

search 114 (2013) 64– 71

To examine the sequence divergences between thebanding patterns observed by a SSCP analysis of the partialMT-ND2 and MT-ND4 regions, two samples from eachof the five HPGs (based on mtDNA CR sequencing) weresequenced for both of these regions. In addition, sampleswith unknown banding patterns (shown as “–” in the HPGname column of Table 1) were also sequenced. The resultsare presented in Fig. 3. The samples with different bandingpatterns were differentiated from each other by at leastone mutation. For instance, based on the MT-ND2 SSCPapproach (Fig. 3A), HPG A of banding pattern I was differentfrom HPG B of pattern II at the 4182th position (T→C).Moreover, based on the MT-ND4 SSCP approach (Fig. 3B),HPG A of pattern I was different from HPG B of patternII at both the 10852th and the 11023th positions (A→G).However, based on both MT-ND2 and MT-ND4 SSCPapproaches, HPGs C and E had exactly the same sequences(Fig. 3). Therefore, these partial sequences were unableto discriminate between HPGs C and E. Both MT-ND2 andMT-ND4 seemed to have unique discriminating muta-tions for HPG D at the 4230th position (T→A) and at the11056th position (A→G) of their sequences respectively.Although the mutation present in MT-ND2 region revealeda distinct pattern, the one in MT-ND4 did not exhibit adistinct pattern for HPG D. Instead, individuals with D HPGsequences exhibited banding pattern III by the MT-ND4

SSCP approach along with some of the samples of differentHPGs (Table 1). Likewise, when the sequences of all foursamples exhibiting banding pattern VIII in the MT-ND4SSCP approach were examined, it was observed that there

P. The observed frequencies for each banding pattern, the number of CRattern by sequencing, the HPG names assigned to the banding patternstage of the sensitivity and specificity of HPGs with respect to the assigneds C indicates that C is the most common HPG.

HPG name Sensitivity ofthe HPG (%)

Specificity ofthe HPG (%)

C(2) HPG B 96.10% 93.67%C(2) HPG A 90.00% 93.10%C(65), HPG C/A/E/B/D 100.00% 87.61%

––––

C(1) HPG A 97.18% 97.18%C(1) HPG B 97.65% 94.32%(67), HPG C/E 93.90% 97.47%

HPG D 100.00% 100.00%

C(2) HPG A 90.32% 94.92%C(2) HPG B 95.77% 86.08%C(63), HPG C/A/B/E/D 100.00% 81.03%

––––––

E. Yüncü et al. / Small Ruminant Research 114 (2013) 64– 71 69

A) MT-ND2

Pattern Haplogroup defined Nucleotide Number ofby CR sequencing positions sequences

on mtDNA

4444122280232840

I A TTAT 2II B C... 2III C/E CCG. 4IV D C..A 2

(B) MT-ND4

Pattern Haplogroup defined Nucleotide Number ofby CR sequencing positions sequences

on mtDNA

111111111111000000000011888888999900455669112925328177274036

I A TACGTTTTGCAA 2 II B .G........G. 2 III C/E ........A.G. 4 III D ..........GG 2 IV B .G..C.....G. 1 V C ......C.A.G. 2V A .......C.... 1 VI B CG........G. 1VII E .....C..A.G. 2VIII B .T........G. 2VIII B .G.......TG. 2IX A ..T......... 1 IX B .G.A......G. 1

observefined w

CR sequ

Fig. 3. The polymorphic sites giving rise to each of the banding patternsregion. Nucleotide positions of the mutation sites on the mtDNA were deet al., 1998). The HPGs of these banding patterns were defined by mtDNA

were two different sequences revealing the same pattern.The unknown banding patterns (IV–IX) observed in theMT-ND4 SSCP approach were distinct from each other dueto a difference of at least one of the base pairs observed inthat region (Fig. 3B).

The partial MT-ND4 region revealed a higher numberof unknown banding patterns, and HPG variability basedon the SSCP analysis. Therefore, the MT-ND4 region seemsto be more variable or more prone to mutations than theMT-ND2 region.

4. Discussion

Present study is comprised of a large number of nativesheep samples collected from all over Turkey and consider-able amount of mtDNA variation is expected to be captured

d by the SSCP analysis of (A) partial MT-ND2 region (B) partial MT-ND4ith comparison to the reference mtDNA sequence AF010406 (Hiendlederencing.

by this sampling. In conformity with the expectation, in thepresent study, all mtDNA HPGs of domestic sheep were rep-resented by at least two samples. Thus, the discriminativepower of three simple approaches could be tested com-paratively. In all three approaches, three major bandingpatterns were detected where two of them were largelyassociated with HPG A and HPG B. The third pattern wasmainly associated with HPG C but also with HPG A, HPGB, HPG E (in all three approaches) and HPG D (in MT-ND4SSCP and CR RFLP approaches). There were banding pat-terns with low observation frequencies that were potentialcandidates for discriminating the rare HPGs. However, after

the sequencing analysis, only one such banding pattern; IVof MT-ND2 SSCP analysis, was found to be associated withthe least frequently observed sheep HPG; i.e. HPG D. Thus,the MT-ND2 SSCP approach, by identifying the rare HPG D,

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ad a higher power of discrimination compared to the otherwo approaches. However, the result for HPG D must beonsidered cautiously since it is based on two observationsnly.

There are pairwise nucleotide differences foundetween the HPGs C/E and D at their partial MT-ND4 regionfter sequencing. Therefore, it might also be possible toistinguish HPG D based on MT-ND4 SSCP analysis by opti-izing the experimental conditions further. Even if we

ould observe a distinct pattern for HPG D by MT-ND4 SSCP,till MT-ND2 SSCP would serve better as a HPG discrimi-ation approach because it revealed no unknown bandingatterns (after identifying banding pattern of D).

The present study contributed to the relatively rare col-ection of MT-ND2 and MT-ND4 region partial sequences ofheep mitochondria (Hiendleder, 1998; Hiendleder et al.,998; Guo et al., 2005; Burgstaller et al., 2007; Meadowst al., 2011). The mutation on the 4208th position of MT-D2 sequences of HPGs C and E differing from those ofPGs A, B and D (Fig. 3A), which was reported as one of theon-synonymous mutations responsible for a moderatelyonservative amino acid substitution between the HPGsReicher et al., 2012), was confirmed in the present study.his point mutation in the HPG C is one of the candidateon-synonymous mutations related to prolificacy in sheepReicher et al., 2012).

Sequencing the control region has been preferred fornalyzing mitochondrial DNA in many studies (Guo et al.,005; Pedrosa et al., 2005; Bruford and Townsend, 2006;apio et al., 2006; Meadows et al., 2007, 2011; Reicher et al.,012). Moreover, a shorter region of mtDNA sequence haseen proposed recently (Cai et al., 2011) as the study unit oftDNA HPGs, where all five HPGs can be identified. How-

ver, sequencing may not always be readily available forvery laboratory or only HPG identification instead of fullequences may be sufficient for some studies.

For instance, if the individuals with rare HPGs (D and) need to be identified for further mtDNA CR sequencingn order to subject the sequence data to demographic testsuch as a neutrality test (Fu, 1997), it seems that MT-ND2SCP can be used to identify individuals with HPG D. Outf 622 samples of the present study, only 82 individualsxhibiting a C/E banding pattern (pattern III of the MT-ND2SCP) and the 2 samples exhibiting D band pattern (pat-ern IV of the MT-ND2 SSCP) would have been sequenced.owever, the number of samples to be sequenced wouldave been 99 and 94, when using CR RFLP (banding pat-ern III) and MT-ND4 SSCP (banding pattern III) approaches,espectively. Moreover, there were 4 and 13 samples withnknown banding patterns detected by CR RFLP and MT-D4 SSCP approaches respectively. As these patterns might

epresent some rare HPGs (e.g. banding pattern VII asbserved by MT-ND4 SSCP analysis), the total number to beequenced would have been 103 and 107 for CR RFLP andT-ND4 SSCP respectively in order to identify individuals

arrying E and D HPGs.In another scenario, when the case was to identify the

PG composition of the samples analyzed in the presenttudy, then screening the samples using the SSCP analysisf the MT-ND2 region approach would require sequenc-ng only 82 of the 622 samples in total. This approach

search 114 (2013) 64– 71

has sensitivity of 97.18% and 97.65% for A and B, respec-tively, meaning only one type A, one type B and two typeC samples would have been falsely identified by a mixedgenotyping strategy. CR RFLP analysis has sensitivity of90.00%, 96.10% and MT-ND4 analysis has sensitivity of90.30%, 95.77% for A and B, respectively. These approacheswould have falsely identified one HPG A, four HPG B, andfour HPG C samples with mixed genotyping strategy. Itshould be kept in mind that these estimates were drawnfrom the present data set.

As a conclusion, given the higher discrimination power,the absence of any unknown banding patterns, higher sen-sitivity and specificity for the common HPGs A and B,we recommend using the MT-ND2 SSCP approach for fastscreening the HPGs of the samples before choosing thesamples to be sequenced further if sequencing is not easilyavailable.

Acknowledgements

This research was funded by Scientific and TechnicalResearch Council of Turkey (TUBITAK) as a part of theproject “In Vitro Conservation and Preliminary MolecularIdentification of Some Turkish Domestic Animal GeneticResources-I (TURKHAYGEN-I)” under the grant number106G115 and “Ancient DNA (aDNA) isolation and mtDNAhaplogroup determination from sheep samples unearthedfrom different archeological sites in Anatolia” under grantnumber 111T464. The authors thank to the two anony-mous referees for their contribution on manuscript, Dr.Anita Akkas for editing the manuscript and Dr. Ceren CanerBerkman for helping with the artwork.

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.smallrumres.2013.05.014.

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