Genetic Diversity in Katchaikatty Sheep based on Microsatellites Polymorphism

Livestock production constitutes a very important component of the agricultural economy of developing countries, with multipurpose uses, such as skins, fibre and fertilizer. Small ruminants play an important role in Indian economy and provide livelihood to two-third of rural community, especially in areas where crop and dairy farming are not economical. Furthermore, livestock are closely linked to the social and cultural lives of several million resource-poor farmers for whom animal ownership ensures varying degrees of sustainable farming and economic stability. Sheep is one of the important species of livestock in India contributing to meat production at almost nine per cent. India has a rich diversity of sheep genetic resources, with about 74.26 million sheep (Report, 2019) and 44 distinct breeds distributed in the different agro-climatic regions of the country.

Tamil Nadu ranks fifth among the Indian states in sheep population (Report, 2019) and has a rich repository of sheep genetic resources. Earlier the state had been reported to have eight descript sheep breeds (Ganesakale and Rathnasabapathy, 1973; Acharya, 1982) and a few non-listed sheep breeds namely Ramnad type (Pattanam sheep), Katchaikatty and Chevadu. Now, Katchaikatty and Chevadu have been recognized and registered.

The Katchaikatty sheep are distributed in the villages of Vadipatti block of Madurai district in Southern Tamil Nadu and are reared by Konar and Pallar communities. However, a few Katchaikatty flocks are also found in Melaneelithanallur block of Tirunelveli district (Ravimurugan et al., 2012). Like most of the sheep breeds of this region, Katchaikatty sheep also belong to the meat type as their wool is extremely coarse and hairy. The animals are adapted to their breeding tract and the flocks are stationary with the average size of 43. Katchaikatty sheep is black in color, moderate in size with long tail. The head is medium in length and breadth, ears are very short and stumpy. Rams have twisted horns and approximately five per cent of the ewes have thin small horns while the rest are polled. The neck is well set to thorax, thick and broad in males and slender in females. The legs are medium-sized, straight and squarely set under the body. Animals have typical meaty conformation and the mean body weight reported was 34.42±1.97 kg in rams and 28.14±0.7 kg in ewes (Report, 2006; Ravimurugan et al., 2012). The population of Katchaikatty sheep reported was 1506 (Report, 2022). Ram fighting (baiting) is an important event in and around the villages of Madurai district for which these rams are exclusively maintained and for this cultural importance, this breed fetches more market price than other sheep breeds of Tamil Nadu.

Molecular characterisation plays a major role in uncovering the history, estimating the level of diversity, distinctiveness and genetic structure of farm animal breeds as well as populations. It can serve as an aid in the genetic management of small populations for avoiding excessive inbreeding. The development of tools for the analyses of deoxyribonucleic acid (DNA) that had taken place in the last few decades increased enormously the capacity to characterise variation within and between breeds or populations. The restricted traditional characterisation by means of phenotypic attributes can now be complemented by an increasingly available number of molecular markers and the development of sophisticated statistical techniques for the analyses. The characterizations by molecular markers potentially indicate the genetic variability both within and between populations and also contribute for establishing conservation priorities. Molecular genetic markers are the inherited allelic variations at a locus that can be used to understand the genetic events. The molecular markers used for such objectives are generally microsatellites (simple tandem repeats, STR), amplified fragment length polymorphism (AFLP), variable number of tandem repeats (VNTR), random amplified polymorphic DNA (RAPD), single strand conformation polymorphisms (SNP) and restriction fragment length polymorphisms (RFLP). Amongst the different molecular markers, microsatellites, having very short sequence motifs (1-6 base pair in length), are the markers of choice as they are highly polymorphic, densely distributed in the genome, co-dominant and are inherited in Mendelian fashion as well as ease of genotyping, which make them valuable for molecular characterization (Queller et al., 1993; Fan et al., 2008). By characterization using specific microsatellite markers, the degree and pattern of genetic differences within and between populations or breeds in each livestock species could be determined, which would help in the genetic improvement and conservation programmes (Forbes et al., 1995; Nahas et al., 2008; Bozzi et al., 2009). Considering the population size, adaptation and the need for utility in the area in which it is distributed, this study describes the molecular characterization of Katchaikatty sheep, genetic variability and genetic structure of the population by microsatellite marker analysis. 

Blood samples (10 ml) from 50 unrelated rams and ewes of Katchaikatty sheep across the breeding tract were collected from the jugular vein of animal using vacutainers containing ethylenediaminetetraacetic acid (EDTA) as anticoagulant. The genomic DNA was isolated from the blood samples by Phenol-Chloroform method (Sambrook et al., 1989) and the DNA samples were diluted to reach a final concentration of 25-50 ng/µl. Then the genomic DNA was amplified by polymerase chain reaction (PCR) with 25 fluorescently tagged ovine-specific microsatellite markers (Table 1), which adhere to the guidelines of the International Society for Animal Genetics (ISAG), Food and Agriculture Organisation (FAO, 2004; FAO, 2011). Only 5’ end of forward primers of each primer was labeled with FAM, HEX, ROX and TAMRA fluorescent compounds to enable multiplexing and analysis on automated sequencer. The PCR amplifications were carried out in 15 µl reactions containing 3 µl of template DNA (20-50 ng/µl), 1.5 µl of PCR assay buffer (10X), 1.2 µl of MgCl₂ (2 mM), 1 µl of dNTPs (each at 200 µM), 0.5 µl of (10 picomoles) each of forward and reverse primers and 0.2 µl of Taq DNA Polymerase in Eppendorf thermocycler. The PCR reaction cycle was accomplished by initial denaturation at 95°C for 5 min, then denaturation at 95°C for 45 sec, primer annealing temperature (54-62°C) for 45 sec and an extension for 45 sec at 72°C, repeating the cycle for 30-40 times (varies according to the locus) and a final extension for 10 minutes at 72°C. Genotyping was carried out on an automated ABI PISM 3730XL Genetic Analyzer (Applied Biosystems, USA). Microsatellite fragment sizing was performed by the GeneMapperTM software version 4.0 which provided the size of the allele, genotype (based on peak height) and observed number of alleles (no). In each locus, microsatellite allele frequencies, effective number of alleles (ne), observed (Ho) and expected (He) heterozygosity and heterozygosity deficit estimate (FIS) and Chi-square (X2) test for Hardy-Weinberg equilibrium (HWE) were assessed using the POPGENE software version 1.31 (Yeh et al., 1999). The polymorphism information content (PIC) was calculated based on the frequencies in which the alleles occurred at each locus (Nei, 1978). Mode-shift analyses were performed by using the BOTTLENECK software version 1.2.02 (Cornuet and Luikart, 1996). Three mode shift tests viz. Sign-rank test, Standardised differences test and Wilcoxon test were utilised in each of the three models of mutation, Infinite Allele Model (IAM), Two Phase Model (TPM) and Stepwise Mutation Model (SMM) with the null hypothesis of mutation-drift equilibrium.

Many studies conducted in recent years on the genetic variability and diversity of the native sheep breeds utilize the microsatellites endorsed in 2011 for use in the genetic studies of sheep diversity analysis and suggested by the ISAG, FAO. The allele size observed and type of repeat for the recommended microsatellite markers studied in Katchaikatty sheep are presented in Table 1.

In Katchaikatty sheep, a total of 144 microsatellite alleles were observed in the present study. The number of alleles (no) at each locus varied up to 10 (CSSM31) with a mean of 5.76 alleles across all loci while one locus (OarHH64) was found to be monomorphic. The effective number of alleles (ne) ranged from one (OarHH64) to 6.43 (OarFCB48) with a mean of 3.40 across all loci, which showed high genetic polymorphism (Table 2). Less number of microsatellite alleles were reported in earlier studies as 125 in Nilagiri (Girish et al., 2007), 126 in Muzzafarnagari (Arora and Bhatia, 2004) and 131 in Kheri (Bhatia and Arora, 2008) while more number of alleles were also recorded as 148 in Jalauni (Arora et al., 2008), 165 in Patanwadi, 160 in Dumba, 181 in Marwari (Jyotsna et al., 2010) and 196 in Kilakarsal (Radha et al., 2011). A comparable number of alleles was reported as 143 in Coimbatore (Kumarasamy et al., 2009) and 147 in Vembur (Pramod et al., 2009). The number of alleles reported in exotic sheep was considerably low as five to 20 in Swiss sheep breeds (Saitbekova et al., 2001), seven to 22 in Turkish sheep breeds (Guiterrez-Gil et al., 2006), 10 to 23 in European sheep breeds (Handley et al., 2007), 11 to 33 in Alpine sheep breeds (Dalvit et al., 2008), seven to 25 in Greek sheep breeds (Ligda et al., 2009) and eight to 21 in Italian sheep breeds (Bozzi et al., 2009).

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Genetic diversity can be measured as the amount of actual or potential heterozygosity (Ho). Expected heterozygosity (He) is considered to be a better estimator of the genetic variability in a population. The observed heterozygosity ranged from 0.03 (CSSM47) to 0.91 (OarVH72) with a mean value of 0.50 (excluding the monomorphic locus), which could be due the selective outbreeding practice in Katchaikatty flock. The expected heterozygosity ranged from 0.40 (OarAE129) to 0.85 (OarFCB48) with a mean value of 0.65 (Table 2). Mean observed and expected heterozygosity observed in the Katchaikatty were comparable respectively with the values reported in Muzzafarnagari, 0.65 and 0.69 (Arora and Bhatia, 2004); Nilagiri sheep, 0.76 and 0.72 (Girish et al., 2007); Jalauni sheep, 0.58 and 0.69 (Arora et al., 2008); Coimbatore sheep, 0.74 and 0.81 (Kumarasamy et al., 2009); in Vembur sheep, 0.52 and 0.73 (Pramod et al., 2009); in Kilakarsal sheep, 0.60 and 0.72 (Radha et al., 2011).

The polymorphism information content (PIC) was described (Botstein et al., 1980) as a statical assessment of the informativeness of a marker. It depends upon the number of alleles and their relative population frequencies. The PIC values observed in the present study ranged from 0.34 (OarAE129) to 0.83 (OarFCB48) with a mean of 0.59 for all the 25 loci. Based on the PIC values, it was found that all markers except six (OarFCB128, BM827, OarHH41, OarAE129, OarCP20 and MAF214) used in the study showed values of more than 0.5 (Table 2), indicating that these microsatellite markers can effectively be used for molecular characterisation and genetic variability studies in sheep. Similar PIC values were found as 0.60 in Chokla, 0.60 in Nali (Mukesh et al., 2006) and 0.60 in Kheri sheep (Arora and Bhatia, 2008) while higher values were also observed in Jalauni, 0.64 (Arora et al., 2008); Chottanagpuri, 0.63 (Bhatia et al., 2008) and Kilakarsal sheep, 0.83 (Radha et al., 2011). Majority of the loci under investigation (18 out of 25) showed significant departure from Hardy-Weinberg Equilibrium (HWE) while the microsatellite loci namely BM757, OarFCB128, OarJMP29, CSRD247, HSC and CSSM31 were observed to be in HWE. The deviation from HWE is due to the effect of systematic and dispersive forces on the genetic constitution. In previous studies on Nilagiri sheep, 17 of 25 loci were reported to be in HWE (Girish et al., 2007) whereas 19 out of 27 loci in Coimbatore sheep (Kumarasamy et al., 2009), 19 out of 25 loci in Vembur sheep (Pramod et al., 2009) and 17 out of 23 loci in Kilakarsal sheep (Radha et al., 2011) were reported to have significant deviation from HWE.

The within population heterozygote deficit estimate (FIS) measures heterozygotes deficiency within population. The higher the values of FIS indicates closer relationship between the individuals. The FIS computed in the present study ranged from -0.38 (OarVH72) to 0.94 (CSSM47) with a mean of 0.21 across all the loci (Table 2). The positive FIS values were observed at 19 loci and varied from 0.0619 (BM757) to 0.9421 (CSSM47). Five loci revealed negative FIS values (FIS<0) indicating the absence of heterozygote deficit in these loci (Table 2). The mean FIS estimates reported as 0.12 in  Jalauni sheep (Arora et al., 2008); 0.07 in Coimbatore sheep (Kumarasamy et al., 2009) and 0.16 in Kilakarsal sheep (Radha et al., 2011) were less than that observed in the present study, which might be due to the closed breeding in the flocks of Katchaikatty sheep.

Identifying populations that have experienced a severe reduction in size (i.e., bottleneck) is important because bottlenecks can increase the rate of inbreeding, loss of genetic variation, fixation of deleterious alleles and increase the probability of population extinction. It is especially important to identify recently bottlenecked population (within few dozen generations), because such populations may not have had time to adapt to the problems caused by the small population size and might have a high risk of extinction. Recently bottlenecked populations are likely to have lost rare alleles, but still contain substantial heterozygosity and genetic variation which are lost slowly (Luikart et al., 1998). It is often very difficult to identify recently bottlenecked populations because historical population sizes and level of genetic variation are seldom known. Allele frequency distribution (Mode shift indicator) discriminated the many bottlenecked populations from stable populations. In the present study, allele sizes obtained from Katchaikatty sheep was subjected to bottleneck analysis using the program, bottleneck applying three tests viz. Sign-rank test, Standardised differences test and Wilcoxon test in each of the three models of mutation, IAM, TPM and SMM. In a population at mutation shift equilibrium (i.e., the effective size of which has remained constant in the past), there is approximately an equal probability that a locus shows a heterozygote excess or deficit. The results are summraised in Table 3.

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No mode-shift was detected in the frequency distribution of alleles and a normal L-shaped form was observed (Fig 1), which suggested that the Katchaikatty population had not experienced a genetic bottleneck, i.e., it has not undergone any recent reduction in the effective population size and remained mutation-drift equilibrium. Similarly, the sheep populations of Muzzafarnagri (Arora and Bhatia, 2004), Bellary (Kumar et al., 2007), Jalauni (Arora et al., 2008), Vembur (Pramod et al., 2009), Coimbatore (Kumarasamy et al., 2010) and Kilakarsal (Radha et al., 2011) breeds were also not reported for bottleneck. Nilagiri sheep population was the only breed of Tamil Nadu which experienced the genetic bottleneck (Girish et al., 2009).

The molecular characterization estimates of the present study supported the usefulness of FAO-recommended ovine-specific microsatellite markers to assess the genetic variability of Katchaikatty sheep and to characterise the genetic structure of the population. The results also revealed that the Katchaikatty sheep population did not exhibit a decrease in genetic variation or effective population size. The information generated could further contribute to the designing of genetic management, in such that the genetic variation in the population should not be reduced and formulation of conservation programmes for the Katchikatty sheep.

The authors are thankful to the Vice-Chancellor, TANUVAS for financial assistance and the Professor and Head, Department of Animal Genetics and Breeding and the Principal Investigator, NBAGR (ICAR) funded Core Laboratory, Madras Veterinary College for the credible and valuable suggestions during the study.

On behalf of all authors, I certify that there is no conflict of interest with respect to the manuscript of the research article submitted for publication.