Printer Friendly

Genetic diversity of Indonesian cattle breeds based on microsatellite markers.

INTRODUCTION

Indonesia has many native breeds of cattle, including Bali cattle, Pesisir, Sumba Ongole, Madura, Aceh, Grati, Ongole Grade, Katingan, Sumbawa, Pasundan, Jabres, and Galekan (the characteristics were described in Sutarno and Setyawan [1] and MARI [2]). Due to their vital role in the Indonesian socio-economy, conservation and breeding programs for Indonesian cattle breeds should be well-designed based on their potency and genetic information. However, several local breeds of cattle in Indonesia remain unrecorded, and there is a lack of scientific information regarding them as well. This condition leads to the in-situ conservation or breeding program becoming ineffective. The Pasundan cattle are an example of unrecorded local breed cattle in Indonesia [3].

Information about genetic diversity is needed to perform a conservation program especially regarding Indonesian local cattle resources and to provide an opportunity for farmers to develop animal breeding businesses. The development of molecular genetics analysis has made it possible to study the potency of certain cattle breeds at the deoxyribonucleic acid (DNA) level. Microsatellites are almost ideal genetic markers because they are abundant, codominant, highly polymorphic, and spread out across the entire euchromatic part of the genome [4].

The microsatellites markers can be used for estimating the genetic distance [5], the relationship among livestock breeds [6], paternity testing [7], and the genetic diversity [8]. This research was conducted to study the genetic diversity in several Indonesian cattle breeds using microsatellite markers as scientific evidence to classify the Indonesian cattle breeds that can be used to design breeding and conservation strategy for the Indonesian cattle breeds in future.

MATERIALS AND METHODS

Blood sample and DNA collection

This study was conducted following the guidelines of research implementation included in the Indonesian Institute of Sciences Regulation number 08/e/2013 about the ethical clearance of research and scientific publication. Ethical Clearance Committee of the Indonesian Institute of Sciences, Jakarta, Indonesia has approved all procedures related to the used of animal in this study (Register No. 9879/WK/HK/XI/2015). A total of 229 head of cattle including Simmental Purebred (n = 19), Simmental Crossbred (n = 27), Ongole Grade (n = 27), Bali (n = 20), Pesisir (n = 13), Holstein Friesian (n = 20), Sumba Ongole (n = 38), Madura (n = 20), Banteng (n = 20), and Pasundan cattle (n = 25) were used for blood sampling. Blood samples (3 to 5 mL) were taken from the coccygeal vein using Venoject and collected in Vaccutainer tubes containing an anticoagulant. The blood samples were used in the DNA extraction process using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following the producers method.

Primer and amplification

A total of 12 microsatellite labeled primers with high polymorphism information content (PIC) value (part of the 30 primers recommended by Food and agriculture organization of the United Nations [FAO]) were used in the polymerase chain reaction (PCR) process (primers sequence, annealing temperature, range of PCR product size, and label used were based on Agung [9]). The PCR reagent composition was as follows: KAPA2G Robust Hot Start Ready Mix PCR Kit (Kapa Biosystems, Cape Town, South Africa) (18 [micro]L), forward and reverse labeled primers (200 ng/[micro]L), nuclease free water, and DNA samples (5 to 30 ng/[micro]L). The program in the PCR machine (Eppendorf, Hamburg, Germany) was set at 94[degrees]C; 5 min (1 cycle), 35 cycles consisting of three stages: i) 94[degrees]C; 30 s, ii) 51[degrees]C to 59[degrees]C; 30 s (depending on primers), and iii) 72[degrees]C; 30 s, followed by one cycle at 72[degrees]C; 5 min. The PCR products were then visualized by electrophoresis using 2% agarose gel and followed by SyBr staining and captured in GBOX documentation System (Syngene, Cambridge, UK). Multiplex DNA fragment analysis was conducted afterwards for allele identification in 1st BASE Faboratory, Malaysia.

Data analysis

Data of alleles size (unit in base pairs) were generated using the multiplex DNA fragment analysis. The data was processed using CONVERT version 1.3.1 [10] to convert the size of alleles observed for each individual sample to assure suitability for further data analysis. The converted data was processed using POPGEN version 1:32 program [11] to generate the observed number of alleles ([n.sub.a]), effective number of alleles ([n.sub.e]), observed heterozygosity value ([H.sub.o]), expected heterozygosity value ([H.sub.e]), genetic differentiation ([F.sub.ST]), the global heterozygote deficit among breeds ([F.sub.IT]), and the heterozygote deficit within the breed ([F.sub.IS]), gene flow (Nm), Hardy-Weinberg equilibrium (HW), and allele frequency. The converted data was also processed using CERVUS version 3.0.7 program [12] to obtain the PIC value. The genetic distance value was used to make a dendrogram to illustrate the relationship among cattle populations using MEGA version 6.0 [13]. Bayesian clustering assignments were analyzed using STRUCTURE version 2.2 [14]. Ten independent runs were performed for each K between 2 and 10, with a burn-in period of 1,000,000 iterations followed by 1,000,000 iterations of the Markov Chain Monte Carlo algorithm. The STRUCTURE HARVESTER [15], which implements the Evanno method [16] was used to identify the optimal groups (K). The correspondence factorial analysis (CFA), principal coordinates analysis (PCoA), and principal component analysis (PCA) were conducted to determine the relationship among breeds. The GENETIX program [17] was used to perform the CFA and the GENALEX version 6.1 program [18] was used to perform the PCoA and analysis of molecular variance (AMOVA). The PCA were performed using adegenet package [19] of R version 3.2.0 (2015.4.16) [20], The F-statistics were estimated based on Weir and Cockerham [21] and the genetic distance value in ten breeds populations were estimated based on Nei's genetic identity and genetic distance [22].

RESULTS

Microsatellite polymorphism

Twelve microsatellite loci revealed high polymorphism to evaluate the genetic diversity. The 12 microsatellite loci from the entire population were analyzed and 862 alleles consisting of 86 alleles detected in the Simmental Purebred, 103 alleles in the Simmental Crossbred, 118 alleles in the Ongole Grade, 60 alleles in the Bali, 70 alleles in the Pesisir, 78 alleles in the Holstein Friesian, 98 alleles in the Sumba Ongole, 100 alleles in the Madura, 56 alleles in the Banteng, and 93 alleles in the Pasundan cattle.

The observed heterozygosity value ranged from 0.417 [+ or -] 0.321 (Banteng) to 0.719 [+ or -] 0.158 (Simmental Crossbred) and the expected heterozygosity value ranged from 0.550 [+ or -] 0.248 (Banteng) to 0.796 [+ or -] 0.151 (Madura). The expected heterozygosity was higher than the observed heterozygosity in all populations studied (Table 1). F-statistics were estimated in a fixation index as genetic differentiation ([F.sub.ST]), the global heterozygote deficit among ten cattle breeds ([F.sub.IT]), and the heterozygote deficit within the breed ([F.sub.IS]) among the twelve microsatellite markers (Table 2).

Among the 12 microsatellite markers, the estimation of the fixation index has been determined for [F.sub.ST], [F.sub.IT] and [F.sub.IS] with values ranging from 0.140 to 0.538,0.055 to 0.522, and -0.099 to 0.377, respectively. The estimated mean values of the total inbreeding ([F.sub.IT]), within line inbreeding ([F.sub.IS]) and genetic distance were 0.367,0.160, and 0.243 respectively. The PIC values ranged from 0.715 to 0.935. Therefore, the microsatellite markers in this study were very informative.

The result of genetic distance analysis shows that the Simmental Purebred and the Simmental Crossbred subpopulation have the lowest genetic distance value. In contrast, the Simmental Purebred and the Banteng subpopulation have the highest genetic distance value (Table 3). The locus INRA23 allele 205 is a specific allele candidate for the Sumba Ongole cattle, while the allele 219 is a specific allele candidate for Ongole Grade. Others specific breed allele candidates in this study are shown in Table 4, however, the specific allele candidates still need to be validated by further studies.

Genetic distance and structure of breeds

All identified alleles in this study could be used to classify the cattle population into clusters. There are subpopulations that are closely related, and they form their own respective cluster. The dendrogram of unweighted pair-group method with arithmetic mean (UPGMA) revealed three main clusters, with Simmental Purebred, Simmental Crossbred, and Holstein Friesian breeds in the first cluster (Bos taurus cluster); Ongole Grade, Sumba Ongole, Madura, Pasundan, and Pesisir breeds in the second cluster (Bos indicus cluster); Bali and Banteng breeds in the third cluster (Bos javanicus cluster) (Figure 1). The clustering of breeds into three groups by UPGMA analysis highlights the presence of clear genetic separation between breeds in different groups.

Using STRUCTURE HARVESTER, K optimal was obtained at K = 5. At K = 5, Madura and Pasundan cattle have genetic similarities. Furthermore, Simmental Purebred and Simmental Crossbred cattle also have genetic similarities. In addition, the Holstein Friesian cattle were identified as a separated cluster. The STRUCTURE analysis generated quite similar interpretation (Figure 2) to the dendrogram of UPGMA.

The results of the CFA (Figure 3), PCoA (Figure 4) and PCA (Figure 5) analysis also revealed three clusters: Bos taurus, Bos indicus, and Bos javanicus. Ongole Grade, Sumba Ongole, Madura, Pasundan, and Pesisir cattle were included in the Bos indicus cluster. Meanwhile, Simmental Purebred, Simmental Crossbred, and Holstein Friesian were clustered in Bos taurus cluster. Bali cattle and Banteng were separated in Bos javanicus cluster. The AM OVA result revealed that variation among individuals more varied (69%) than the variation in the inter-population (31%) (Table 5).

DISCUSSION

Microsatellite markers polymorphism

Utilization of five microsatellite markers (TGLA227, ETH225, BM1824, INRA005, and MM 12) to evaluate the genetic diversity of Indonesian cattle breeds (Ongole Grade, Aceh, Bali, and Madura cattle) was reported and provides a phylogenetic relationships between the breeds [23]. However, the genetic diversity study based on microsatellite marker analysis will generate more reliable data when the number of microsatellite markers are increased [24]. This study is the first to report the genetic diversity in Indonesian cattle breeds based on 12 of the 30 microsatellite primers recommended by FAO. The expected heterozygosity value was higher than the observed heterozygosity value in all populations studied. This condition can be explained by several factors, including null alleles, assortative mating, the Wahlund effect, selection against heterozygotes, inbreeding, or a combination of all these factors [25]. In addition, the low value of heterozygosity indicates that certain breeds are relatively well-conserved [26]. The informative locus in genetic application should have PIC value more than 0.5 and [H.sub.e]>0.6 [27]. In addition, the estimation of genotypic diversity in heterozygosity and PIC value informativeness of microsatellite markers were used to determine animal breed selection [28]. In this study, 12 microsatellite markers selected were highly informative for the ten cattle breeds and are appropriate for discrimination as well. The fixation indices (FIS, [F.sub.IT] and [F.sub.ST]) among ten cattle breeds in this study were positive (0.160, 0.367, and 0.243) and indicates that there has been a selection process in the population.

Based on Agung et al [29], the morphological characteristics between the Simmental Purebred and the Simmental Crossbred were significantly different. The results of this study were in accordance with previous study [30] that used the 12 microsatellite markers to separate the Simmental Purebred from the Simmental Crossbred populations and reported that the TGLA53 (allele 168) was the candidate for specific breed allele in Simmental Purebred while the SPS115 (allele 250) and TGLA122 (allele 181) were candidates for specific breed alleles in Simmental Crossbred. While for the Simmental population in this study, only SPS115 allele 250 was observed. The INRA23 allele 205 is a specific allele candidate for the Sumba Ongole cattle, while the allele 219 is a specific allele candidate for Ongole Grade although the allele frequencies (0.0143 and 0.0185 respectively) of these alleles were low. However, the INRA23 (allele 205 and 219) can be proposed as specific allele to separate these two Ongole breeds.

Diversity of Indonesian cattle breeds

In general, the Ongole Grade cattle are a crossbred of uncontrolled mating of Java cattle and Sumba Ongole cattle [31]. This study revealed a very close genetic relationship between the Ongole Grade and Sumba Ongole cattle, fience, the scientific evidence of the origin of Ongole Grade cattle in Indonesia based on microsatellite markers in this study can be presented as well.

Genetic distances based on PCA can illustrate the relative effect of intra and interspecies variation [32]. In addition, the PCA analysis results cluster individuals only based on their genotypes; hence, no assumptions can be made regarding the ffW or linkage equilibrium as well [33]. The PCA analysis indicated a close relationship between the Simmental Crossbred population and the Simmental Purebred population. The Madura, Pasundan, and Pesisir cattle population are closely related as well. The value of the genetic distance can be influenced by many factors, including the number of the population used in the study and the objectives of the breeding [34], as well as the massive introgression of possible breeds of cattle because of their geographical relationship [35].

High genetic similarities between Banteng and Bali cattle in this study are reasonable because the Bali cattle are the result of a direct domestication of wild Banteng in the Bali Island or in Blambangan (East Java Province) and proposed as the most suitable cattle breed for sustainable small farming in Indonesia [36]. The results from the CFA, PCoA and also PCA analysis in this study confirmed the previous report regarding the genetic relationship between Banteng and Bali cattle. Based on mitochondrial DNA and 16 micro satellite markers analysis, Bali cattle are closely related to Banteng. In addition, Bali, Madura and Pesisir cattle were distinct from Sahiwal, Red Sindhi, Tharparkar, Hariana and Nellore cattle [37]. Since 1967, the Indonesian Government appointed the Sapudi Island as the center of Madura cattle pure breeding program. Hence, the cross-breeding program for Madura cattle is prohibited under any circumstances on Sapudi Island. However, the great majority of the conventional Madura cattle will be crossbreds [38]. Based on Sutarno et al [23], the Madura cattle were clearly distinct from PO cattle, Aceh cattle, and Bali cattle. In contrast, based on CFA analysis in this study, the Madura cattle were clustered in the Bos indicus cluster (Sumba Ongole, Ongole Grade, Pesisir, and Pasundan cattle) (Figure 3). This condition can be caused by introgression of the zebu cattle was more intensive in the Madura cattle breeding program.

Pasundan cattle have been classified as newest Indonesian local cattle based on the Decree of the Minister of Agriculture number 1051/Kpts/SR.120/10/2014. These cattle are often called kacang (bean) cattle because of their relatively small body size [39], and they are produced from the crossing program between Bos javanicus and Bos indicus. The result of the genetic distance analysis shows that the Madura and the Pasundan cattle have low genetic distance value; thus, it can be interpreted that these cattle breeds have a very close genetic relationship, and this can serve as scientific evidence on the origin of the Pasundan cattle.

Pesisir cattle have been classified as one of Indonesian cattle breeds based on the Decree of the Minister of Agriculture Number 2908/Kpts/OT.140/6/2011. The Pesisir cattle have a unique performance due to their small body (having the smallest size among other Indonesian cattle breeds), and their natural habitat was only in West Sumatera, Indonesia [1]. According to the genetic relationship, the Pesisir cattle are clustered together with Ongole Grade, Sumba Ongole, Madura, and Pasundan cattle in Bos indicus cluster. This result can also be scientific evidence that the Pesisir cattle in West Sumatera, Indonesia are a type of Bos indicus cattle breeds.

The AMOVA is an essential element of the molecular ecologists toolkit. Coupled with hierarchical permutation tests, the approach facilitates rigorous statistical inference about the distribution of genetic variation in natural populations [40]. The AMOVA result in this study revealed that variation among individuals more varied than the variation in the inter-population. This condition can be caused by the sample populations used in this study (based on microsatellite markers of 10 population), which are mostly native or local Indonesian cattle breeds. In addition, designing breeding programs of Indonesian cattle breeds is very important to prevent genetic diversity loses.

CONCLUSION

The 12 microsatellite markers in this study are highly polymorphic and highly informative in detecting the level of genetic diversity among the Indonesian cattle breeds. All identified alleles in this study were able to classify the cattle population into three clusters i.e. Bos taurus cluster (Simmental Purebred, Simmental Crossbred, and Holstein Friesian cattle); Bos indicus cluster (Sumba Ongole, Ongole Grade, Madura, Pasundan, and Pesisir cattle); and Bos javanicus cluster (Banteng and Bali cattle).

https://doi.org/10.5713/ajas.18.0283

CONFLICT OF INTEREST

We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

ACKNOWLEDGMENTS

This research was funded by Research Center for Biotechnology-Indonesian Institute of Sciences through DIPA UNGGUTAN TIPI 2015-2017 scheme (#934/F/2014). Authors would like to thank all the breeding staff in PT. KAR farm, Bogor, Head of East Sumba Tivestock Department, East Nusa Tenggara Province, and also Mr Martono A.P. for technical supports. The authors are grateful to Herdian Saputra for the excellent assistance.

REFERENCES

[1.] Sutarno, Setyawan AD. Review: Genetic diversity of local and exotic cattle and their crossbreeding impact on the quality of Indonesian cattle. Biodiversitas 2015;16:327-54.

[2.] MARI (Ministry of Agriculture Republic Indonesia). Directorate of animal breeding and production [cited 2018 June 12], Available from: http://bibit.ditjenpkh.pertanian.go.id/jenisrumpun/sapi

[3.] Said S, Putra WPB, Anwar S, Agung PP, Yuhani H. Phenotypic, morphometric characterization and population structure of Pasundan cattle at West Java, Indonesia. Biodiversitas 2017;18: 1638-45.

[4.] Bennett P. Microsatellites. J Clin Pathol Mol Pathol 2000;53: 177-83.

[5.] Rehman MS, Khan MS. Genetic diversity of Hariana and Hissar cattle from Pakistan using microsatellite analysis. Pakistan Vet J 2009;29:67-71.

[6.] Maretto F, Ramljak J, Sbarra F, et al. Genetic relationships among Italian and Croatian Podolian cattle breeds assessed by microsatellite markers. Livest Sci 2012;150:256-64.

[7.] Stevanovic J, Stanimirovic Z, Dimitrijevic V, Maletic M. Evaluation of 11 microsatellite loci for their use in paternity testing in Yugoslav Pied cattle (YU Simmental cattle). Czech J Anim Sci 2010;55:221-6.

[8.] Seo JH, Lee JH, Kong HS. Assessment of genetic diversity and phylogenetic relationships of Korean native chicken breeds using microsatellite markers. Asian-Australas J Anim Sci 2017; 30:1365-71.

[9.] Agung PP, Anwar S, Wulandari AS, et aL The potency of Sumba Ongole (SO) cattle: a study of genetic characterization and carcass productivity. J Indonesian Trop Anim Agric 2015;40: 71-8.

[10.] Glaubitz JC. Convert: a user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes 2004;4:309-10.

[11.] Yeh FC, Boyle TJB. Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belg J Bot 1997; 129:157-63.

[12.] Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol 2007; 16:1099-106.

[13.] Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 2013;30:2725-9.

[14.] Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000;155: 945-59.

[15.] Earl DA, vonHoldt BM. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour 2012; 4:359-61.

[16.] Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 2005;14:2611-20.

[17.] Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. Montpellier, France: Universite de Montpellier II [cited 2017 Oct 14]. GENETIX 4.05, Logiciel sous Windows TM pour la genetique des populations. Laboratoire Genome, Populations, Interactions, CNRS UMR 5171. Available from: http://kimura.univmontp2.fr/genetix

[18.] Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28:2537-9.

[19.] Jombart T. Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 2008;24:1403-5.

[20.] R Development Core Team. R: A language and environment for statistical computing [cited 2017 Aug 28], Available from: http://www.R-project.org

[21.] Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution 1984;38:1358-70.

[22.] Nei M, Kumar S. Molecular evolution and phylogenetics. New York, USA: Oxford University Press Inc.; 2000.

[23.] Sutarno, Setyawan AD, Lymbery AJ. Genetic diversity of five indonesian native cattle breeds at microsatellite loci. Asian J Anim Sci 2015;9:57-64.

[24.] Selkoe KA, Toonen RJ. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett 2006;9:615-29.

[25.] Cervini M, Henrique-Silva F, Mortari N, Matheucd Jr. E. Genetic variability of 10 microsatellite markers in the characterization of Brazilian Nellore cattle (Bos indicus). Genet Mol Biol 2006; 29:486-90.

[26.] Czernekova V, Kott T, Dudkova G, Sztankoova Z, Soldat J. Genetic diversity between seven central European cattle breeds as revealed by microsatellite analysis. Czech J Anim Sci 2006;51: 1-7.

[27.] Botstein D, White RL, Skolnick M, Davis RW Construction of genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 1980;32:314-31.

[28.] Berthouly C, Bed'Hom B, Tixier-Boichard M, et al. Using molecular markers and multivariate methods to study the genetic diversity of local European and Asian chicken breeds. Anim Genet 2008;39:121-9.

[29.] Agung PP, Ridwan M, Handrie, et al. Morphological profile and estimation of genetic distance of Simmental crossbred. Jurnal Ilmu Ternak dan Veteriner 2014;19:112-22 (in Indonesian with English abstract).

[30.] Agung PP, Saputra F, Septian WA, et al. Study of genetic diversity among Simmental cross cattle in West Sumatra based on microsatellite markers. Asian-Australas J Anim Sci 2016;29:176-83.

[31.] Suyadi S, Hakim L, Wahjuningsih S, Nugroho H. Reproductive performance of Peranakan Ongole (PO)-and Limousin x PO Crossbred (Limpo) cattle at different altitude areas in East Java, Indonesia. J Appl Sci Agric 2014;9:81-5.

[32.] Nijman IJ, Otsen M, Verkaar ELC, et al. Hybridization of banteng (Bos javanicus) and zebu (Bos indicus) revealed by mitochondrial DNA, satellite DNA, AFLP and microsatellites. Heredity 2003;90:10-6.

[33.] Rutledge LY, Garroway CJ, Loveless KM, Patterson BR. Genetic differentiation of eastern wolves in Algonquin Park despite bridging gene flow between coyotes and grey wolves. Heredity 2010;105:520-31.

[34.] Radko A, Zyga A, Zabek T, Slota E. Genetic variability among Polish Red, Hereford and Holstein-Friesian cattle raised in Poland based on analysis of microsatellite DNA sequences. J Appl Genet 2005;46:89-91.

[35.] Zhou GL, Jin HG, Zhu Q, Guo SL, Wu YH. Genetic diversity analysis of five cattle breeds native to China using microsatellites. J Genet 2005;84:77-80.

[36.] Martojo H. Indigenous Bali cattle is most suitable for sustainable small farming in Indonesia. Reprod Domest Anim 2012;47:104.

[37.] Mohamad K, Olsson M, van Tol HTA, et al. On the origin of Indonesian cattle. PLoS ONE 2009;4:e5490.

[38.] Widi TSM, Udo HMJ, Oldenbroek K, et al. Unique cultural values of Madura cattle: is cross-breeding a threat? Anim Genet Resour 2014;54:141-52.

[39.] Hilmia N, Noor RR, Sumantri C, Gurnadi RE, Priyanto R. Pro ductivity and genetic diversity of local cattle in Ciamis-West Java. J Indonesian Trop Anim Agric 2013;38:10-9.

[40.] Fitzpatrick BM. Power and sample size for nested analysis of molecular variance. Mol Ecol 2009;18:3961-6.

Paskah Partogi Agung (1), *, Ferdy Saputra (2), Moch Syamsul Arifin Zein (3), Ari Sulistyo Wulandari (1), Widya Pintaka Bayu Putra (1), Syahruddin Said (1), and Jakaria Jakaria (4)

* Corresponding Author: Paskah Partogi Agung Tel: +62-21-875-4587, Fax: +62-21-875-4588, E-mail: paskah_partogi@yahoo.com

(1) Research Center for Biotechnology-indonesian Institute of Sciences, Cibinong 16911, West Java, Indonesia

(2) Laboratory of Genetics Indonesia, Cikarang Technopark, Bekasi, West Java 17550, Indonesia

(3) Research Center for Biology-lndonesian Institute of Sciences, Cibinong 16911, West Java, Indonesia

(4) Faculty of Animal Science, Bogor Agricultural University, Darmaga Campus, Bogor 16680, Indonesia

ORCID

Paskah Partogi Agung

https://orcid.org/0000-0003-2554-1893

Ferdy Saputra

https://orcid.org/0000-0002-7949-9777

Moch Syamsul Arifin Zein

https://orcid.org/0000-0002-3601-993X

Ari Sulistyo Wulandari

https://orcid.org/0000-0001-5559-7252

Widya Pintaka Bayu Putra

https://orcid.org/0000-0002-1102-6447

Syahruddin Said

https://orcid.org/0000-0001-8681-4985

Jakaria Jakaria

https://orcid.org/0000-0001-7921-8528

Submitted Apr 11, 2018; Revised May 30, 2018; Accepted Jul 31, 2018

Caption: Figure 1. Dendrogram of the Indonesian cattle breeds population using UPGMA method based on Nei [22],

Caption: Figure 2. Genetic structures of the Indonesian cattle breeds. Black lines separate individual populations whose names were indicated.

Caption: Figure 3. Correspondence factor analysis (CFA) of the Indonesian cattle breeds.

Caption: Figure 4. Principal coordinates analysis (PCoA) of the Indonesian cattle breeds.

Caption: Figure 5. First and second components of a principle components analysis of 12 microsatellite loci genotypes from Simmental Purebred (1), Simmental Crossbred (2), Ongole Grade (3), Bali (4), Pesisir (5), Holstein Friesian (6), Sumba Ongole (7), Madura (8), Banteng (9), and Pasundan (10) population.
Table 1. Summary statistic of the mean number of observed
allele ([N.sub.a]), mean number of effective alleles
([N.sub.e]), observed ([H.sub.o]), and expected ([H.sub.e])
heterozygosities, and PIC observed in the ten breeds

Breed (1))     N     [N.sub.a] [+ or -] SD    [N.sub.e] [+ or -] SD

A              19    7.167 [+ or -] 2.250     4.005 [+ or -] 1.392
B              27    8.583 [+ or -] 2.875     4.915 [+ or -] 2.006
C              27    9.833 [+ or -] 2.406     5.067 [+ or -] 2.075
D              20    5.455 [+ or -] 2.423     3.361 [+ or -] 1.320
E              13    6.364 [+ or -] 1.859     3.590 [+ or -] 1.384
F              20    7.091 [+ or -] 2.212     3.767 [+ or -] 1.294
G              38    8.167 [+ or -] 2.038     3.355 [+ or -] 1.353
H              20    8.417 [+ or -] 2.843     5.283 [+ or -] 2.114
I              20    4.750 [+ or -] 1.815     2.633 [+ or -] 1.185
J              25    7.750 [+ or -] 2.927     4.705 [+ or -] 2.059

Breed (1))    [H.sub.o] [+ or -] SD    [H.sub.e] [+ or -] SD     PIC

A              0.615 [+ or -] 0.171     0.745 [+ or -] 0.092    0.687
B              0.719 [+ or -] 0.158     0.761 [+ or -] 0.158    0.722
C              0.651 [+ or -] 0.177     0.788 [+ or -] 0.085    0.749
D              0.632 [+ or -] 0.261     0.648 [+ or -] 0.236    0.542
E              0.566 [+ or -] 0.271     0.704 [+ or -] 0.147    0.589
F              0.581 [+ or -] 0.144     0.727 [+ or -] 0.094    0.611
G              0.553 [+ or -] 0.258     0.662 [+ or -] 0.148    0.619
H              0.587 [+ or -] 0.232     0.796 [+ or -] 0.151    0.738
I              0.417 [+ or -] 0.321     0.550 [+ or -] 0.248    0.495
J              0.501 [+ or -] 0.240     0.740 [+ or -] 0.212    0.692
PIC, polymorphism information content; SD, standard
deviation.

(1)) A, Simmental Purebred; B, Simmental Crossbred; C,
Ongole Grade; D, Bali; E, Pesisir; F, Holstein Friesian; G,
Sumba Ongole; H, Madura; I, Banteng; J, Pasundan.

Table 2. Fixation indices ([F.sub.IS], [F.sub.IT], and
[F.sub.ST]) among ten cattle breeds

Loci        [N.sub.a]     [H.sub.o]     [H.sub.e]     PIC

BM1824         23           0.464         0.862      0.847
ILST6          25           0.442         0.881      0.869
TGLA126        23           0.626         0.934      0.928
TGLA53         32           0.633         0.893      0.885
TGLA227        32           0.461         0.917      0.909
TGLA122        30           0.698         0.927      0.920
ETH225         31           0.731         0.940      0.935
INRA23         24           0.632         0.911      0.903
SPS113         27           0.890         0.921      0.914
SPS115         19           0.550         0.825      0.810
BM1818         14           0.404         0.793      0.768
CSSM66         26           0.591         0.727      0.715
Mean
SD

Loci       [F.sub.IS]    [F.sub.IT]    [F.sub.ST]      Nm      HW

BM1824        0.324         0.474         0.222      0.874    ***
ILST6         0.377         0.516         0.223      0.869    ***
TGLA126       0.186         0.342         0.191      1.057     NS
TGLA53        0.158         0.302         0.172      1.207    ***
TGLA227       0.214         0.502         0.367      0.431    ***
TGLA122       0.121         0.267         0.166      1.256     NS
ETH225        0.035         0.217         0.188      1.077     NS
INRA23        0.119         0.341         0.252      0.743    ***
SPS113       -0.099         0.055         0.140      1.536     NS
SPS115        0.207         0.395         0.237      0.806    ***
BM1818        0.316         0.467         0.220      0.886    ***
CSSM66       -0.034         0.522         0.538      0.215    ***
Mean          0.160         0.367         0.243      0.913
SD            0.042         0.041         0.032      0.103

[N.sub.a], number of allele; [H.sub.o], observed
heterozygosity; [H.sub.e], expected heterozygosity; PIC,
polymorphism information content; Nm, gene flow; HW, Hardy-
Weinberg equilibrium; NS, not significant; SD, standard
deviation.

*** p<0.001.

Table 3. Genetic distance value in ten breeds populations
based on Nei's genetic identity (above diagonal) and genetic
distance (below diagonal)


Population       A         B         C         D         E         F

A               --      0.9240    0.3817    0.1262    0.1200    0.3478
B             0.0790      --      0.4428    0.1350    0.1412    0.3957
C             0.9632    0.8146      --      0.2012    0.5449    0.4143
D             2.0698    2.0027    1.6032      --      0.1990    0.2204
E             2.1204    1.9575    0.6072    1.6147      --      0.3094
F             1.0561    0.9272    0.8811    1.5125    1.1732      --
G             1.1047    0.9107    0.2922    2.1379    0.8710    0.9382
H             1.4766    1.3928    0.7344    2.0018    1.1991    1.7564
I             4.3995    3.8366    1.9575    0.9921    2.0597    3.1032
J             1.3869    1.3029    0.3480    2.1141    0.8195    1.7449

Population       G         H         I         J

A             0.3313    0.2284    0.0123    0.2498
B             0.4023    0.2484    0.0216    0.2717
C             0.7467    0.4798    0.1412    0.7061
D             0.1179    0.1351    0.3708    0.1207
E             0.4185    0.3015    0.1275    0.4406
F             0.3913    0.1727    0.0449    0.1747
G               --      0.5551    0.1290    0.5857
H             0.5885      --      0.4126    0.7649
I             2.0477    0.8852      --      0.2657
J             0.5349    0.2680    1.3253      --

A, Simmental Purebred; B, Simmental Crossbred; C, Ongole
Grade; D, Bali; E, Pesisir; F, Holstein Friesian; G, Sumba
Ongole; H, Madura; I, Banteng; J, Pasundan.

Table 4. Summary of specific alleles candidate in the cattle
breeds studied

Markers      A       B       C       D       E       F       G

BM1824      --      --      --      190     --      --      197
            --      --      --      194     --      --      199
            --      --      --      196     --      --      --
ILST6       --      --      276     282     301     --      300
            --      --      284     --      303     --      --
TGLA126     --      --      103     113     --      --      --
            --      --      129     --      --      --      --
TGLA53      164     132     159     159     151     139     --
            168     150     165     --      --      141     --
            --      154     --      --      --      145     --
            --      156     --      --      --      --      --
            --      162     --      --      --      --      --
TGLA227     95      --      87      --      --      69      84
            --      --      91      --      --      87      86
            --      --      --      --      --      103     88
            --      --      --      --      --      105     94
            --      --      --      --      --      --      96
            --      --      --      --      --      --      100
TGLA122     157     135     134     166     --      106     --
            171     165     165     --      --      186     --
            185     --      --      --      --      --      --
ETH225      --      --      128     --      163     --      --
            --      --      --      --      --      --      --
            --      --      --      --      --      --      --
            --      --      --      --      --      --      --
INRA23      --      --      219     --      --      --      205
SPS113      --      153     --      140     --      --      --
            --      --      --      146     --      --      --
            --      --      --      --      --      --      --
            --      --      --      --      --      --      --
SPS115      264     --      --      --      --      --      258
            --      --      --      --      --      --      262
            --      --      --      --      --      --      264
            --      --      --      --      --      --      --
BM1818      --      --      248     --      280     --      --
CSSM66      197     177     192     --      --      --      194
            --      --      --      --      --      --      --
            --      --      --      --      --      --      --

Markers      H       I       J

BM1824      161     216     193
            167     --      --
            187     --      --
ILST6       267     285     289
            307     --      --
TGLA126     --      --      --
            --      --      --
TGLA53      --      --      --
            --      --      --
            --      --      --
            --      --      --
            --      --      --
TGLA227     68      --      75
            72      --      103
            74      --      --
            98      --      --
            --      --      --
            --      --      --
TGLA122     --      --      156
            --      --      --
            --      --      --
ETH225      133     130     148
            137     160     --
            151     --      --
            157     --      --
INRA23      --      --      --
SPS113      123     122     --
            125     128     --
            127     136     --
            159     --      --
SPS115      --      --      241
            --      --      249
            --      --      253
            --      --      255
BM1818      272     278     282
CSSM66      170     210     --
            224     216     --
            228     220     --

A, Simmental Purebred; B, Simmental Crossbred; C, Ongole
Grade; D, Bali; E, Pesisir; F, Holstein Friesian; G, Sumba
Ongole; H, Madura; I, Banteng; J, Pasundan.

Table 5. Analysis of molecular variance among ten cattle breeds

Source               df        SS          MS      Est. Var      %

Among population     9     1,056.921    117.436      4.707      31
Among individual    219    2,318.900     10.589     10.589      69
Total               228    3,375.821       --       15.296      100

df, degrees of freedom; SS, sum of squared; MS, mean of
squared; Est.Var., estimated  variance.
COPYRIGHT 2019 Asian - Australasian Association of Animal Production Societies
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Open Access
Author:Agung, Paskah Partogi; Saputra, Ferdy; Zein, Moch Syamsul Arifin; Wulandari, Ari Sulistyo; Putra, Wi
Publication:Asian - Australasian Journal of Animal Sciences
Article Type:Report
Geographic Code:9INDO
Date:Apr 1, 2019
Words:5369
Previous Article:Evaluation of genotype by environment interactions on milk production traits of Holstein cows in southern Brazil.
Next Article:Hair follicle development and related gene and protein expression of skins in Rex rabbits during the first 8 weeks of life.
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |