Principal component analysis of genetic structure in Kilakarsal and Vembur sheep breeds by using single nucleotide polymorphic (SNP) markers within TLR genes

R Selvam, N Murali, A K Thiruvenkadan, V Ramesh Saravanakumar, G Ponnudurai, K Thilak Pon Jawahar and P Kathiravan

Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tirunelveli – 627 358, Tamil Nadu, India.
selvam.r@tanuvas.ac.in

Abstract

The genetic structure among the Kilakarsal and Vembur sheep were studied by principal components analysis. A total of 25 SNP (each five SNP in TLR3, TLR5, TLR6, TLR9 and TLR10) were utilized for genotyping of Kilakarsal and Vembur sheep by using competitive allele specific PCR assay. The allele frequencies and allele sharing genetic distances (based on identical by state) among pairs of individuals within and across different sheep breeds was estimated by using PEAS program. Pair-wise inter individual allele sharing distance among both the sheep breeds were utilized to perform principal components analysis using SPSS. Among 25 SNPs analysed, 22 were found to be polymorphic in both Kilakarsal and Vembur sheep. The pairwise allele sharing distances indicated that certain individuals across populations were more related than individuals within populations. The top three principal components explained respectively 45.1, 20.2 and 11.9 % of the total variation and together explained 77.2 % of the variation in the dataset. The top eight principal components had eigen values greater than one and cumulatively explained 97.48 % of the total variation in the dataset. Principal components scattergam did not reveal the presence of cryptic genetic structure among the two sheep breeds under study

Key words: allele, KASP, PCR, PEAS

Introduction

Domestication of livestock species and a long history of migrations, selection and adaptation have created an enormous variety of breeds.India has 45 recognised breeds of sheep and Tamil Nadu is endowed with ten recognized sheep breeds, of which the Kilakarsal and Vembur are distributed inSouthern agro climatic zone of Tamil Nadu. These sheep breeds of Tamil Nadu are well known for their heat tolerance, mutton production and adaptability to the local agro-ecological conditions. Preliminary surveys in the native tract of these breeds revealed that the population of indigenous breeds of sheep has declined considerably due to indiscriminate crossbreeding with non-descript and exotic breeds. Hence, for effective and meaningful conservation, genetic characterisation of native breeds is the first step to safeguard our valuable germplasm (Tantia and Vij 2000).

In general, some of the breeds have similar phenotypic characters, but will be unrelated genetically. On the other hand, some breeds may look very different but may be genetically related. In general, the breeds, which share the same alleles at similar frequencies, are genetically close related, whereas those having the same alleles at different frequencies or carrying many different alleles are genetically distinct (Maleviciute et al 2002). A more reliable measure of differences among the breeds is genetic distance, which can be estimated from the differences in the frequencies of alleles at a number of marker loci. From the pattern of within-population genetic variation at marker loci, it is possible to deduce demographic factors important to the conservation of domestic animal diversity. Hence, the genetic diversity among the breeds of any livestock species can better be analysed using genetic markers (Hailu and Getu 2015).

The single-nucleotide polymorphisms (SNPs) are most modern genetic markersand are useful for rapid, large-scale, and cost-effective genotyping (Schlotterer 2004). SNPs plays an important role in livestock population structure, genetic variation, origin, and evolution research (Vignal et al 2002). The SNP are numerous and widely distributed throughout the entire genome, have high genetic stability, excellent repeatability, distribute in both coding and non-coding regions of genomes and beneficial for effectively distinguishing heterozygote from homozygote alleles because of its co-dominances (Syvanen et al 2001).

Toll like receptors are a class of pattern recognition receptors that play a critical role in the early innate immune response to invading pathogens (Medzhitov 1997). Many studies have reported that genetic variation in the TLR genes modify cellular immune response and alter susceptibility to disease. Recent studies have indicated that there were plenty of polymorphisms in the TLR genes in humans and livestock (Uenishi and Hiroki 2009). Tabeta et al (2004) stated that 13 TLR which recognize molecular patterns from all major classes of pathogens was identified in mammals. Mucha et al (2009) identified and mapped TLR1–TLR10genes in cattle chromosomes and sheep TLR genes had also been identified by the high similarity of the genomic structure between sheep and cattle.

The present study thus aimed at exploring SNPs within TLR 3, 5, 6, 9 and 10 genes, calculating allele sharing distance for all pair-wise combinations of individuals both within and across the Kilakarsal and Vembur sheep breeds and assessing the genetic relationship among the sheep breeds by principal components analysis.

Material and methods

A total of 100 sheep, 50 each of Kilakarsal (Instructional Livestock Farm Complex, Veterinary College and Research Institute, Tirunelveli and District Livestock Farm, Abishegappatti, Tirunelveli) and Vembur breed (Instructional Livestock Farm Complex, Veterinary College and Research Institute, Tirunelveli, and Government Sheep Farm, Sattur, Virudhunagar District) were randomly selected for this study. The animals were aged from 6 to 18 months at the beginning of the study and of both sexes. All the animals were maintained under normal management and grazing conditions during the course of study and identified with numbered ear tags.The blood samples were collected routinely from all the experimental animals. DNA was isolated from blood samples using a modified high salt method (Miller et al 1988). The purity and concentration of DNA samples were estimated by Nanodrop (Thermo Scientific).

A total of 25 SNP (each five SNP in TLR3, TLR5, TLR6, TLR9 and TLR10) were utilized for genotyping of Kilakarsal and Vembur sheep by using competitive allele specific PCR assay based on FRET chemistry. Details of SNP viz., SNP ID, name of the gene, gene symbol, and chromosome location, alleles at each locus, genic region, synonyms /non synonyms and amino acid change are presented in Table 1. Competitive allele specific polymerase chain reaction based end point genotyping was performed using real time PCR to type the SNPs. Allele discrimination module implemented in real time PCR (Illumina, USA) was utilized to call the genotypes based on fluorescence intensity recorded for each of the two alleles. The KBioscience competitive allele specific PCR genotyping system (KASP) is a homogeneous, fluorescent, endpoint genotyping technology. KASP genotyping assays are based on competitive allele-specific PCR and enable bi-allelic scoring of SNPs and insertions and deletions at a specific locus.

The allele frequencies and allele sharing genetic distances (based on identical by state) among pairs of individuals within and across different sheep breeds was estimated by using PEAS (Package for Elementary Analysis of SNP data) software programme (Xu et al 2010). The allele sharing distance was calculated for all pair-wise combinations of individuals both within and across Kilakarsal and Vembur sheep populations by subtracting average proportion of alleles shared from one (Bowcock et al 1994), resulted in a matrix of 100 X 100 genetic distance estimates which was utilized for deriving principal components.A scatter gram of the first three largest principal component scores was drawn to visualize the clustering of sheep in a three dimensional geometric space.

Results

Quantity and Quality of Genomic DNA

The isolated DNA had a mean concentration of 381.86 ± 70.69 (Range: 52.40 to 2236.80) and 641.70 ± 94.27 μg/ml (Range: 53.60 to 2986.60), respectively in Kilakarsal and Vembur breeds of sheep respectively. The OD _{260: 280 ratio} was found to be 1.88 ± 0.007 (Range: 1.70 to 1.97) and 1.91 ± 0.009 (Range: 1.73 to 1.99) in Kilakarsal and Vembur breeds of sheep, respectively.

Pairwise Allele Sharing Distances

The allele frequency at different Toll like receptor gene SNP loci in Kilakarsal and Vembur sheep are presented in the Table 2. To evaluate within and between population diversities, pairwise allele sharing distances were calculated for all possible pairs of individuals within and across populations using PEAS. Distance was plotted separately where pairs were drawn from within the same breed (blue bars) and from across the breeds (red bars) (Figures 1a and b). The distribution of inter-individual allele sharing distances from within and across populations appeared to be normal although with a slightly longer tail towards the lower extreme. A large overlap was observed in the distribution of inter-individual distances estimated from within and between breeds indicating that certain individuals across populations were more related than individuals within populations.

(a) All SNP loci	(b) FST outlier loci removed
Figure 1. Distribution of allele sharing distance (IBS) between pairs of individuals.

Genetic structure based on principal components analysis

In the present study, Pair-wise inter individual allele sharing distance among both the sheep breeds were utilized to perform principal components analysis using SPSS. A scatter gram of the first three largest principal component scores was drawn to visualize the clustering of sheep in a three dimensional geometric space. The top three principal components explained respectively 45.1, 20.2 and 11.9 % of the total variation and together explained 77.2 % of the variation in the dataset. The top eight principal components had eigen values greater than one and cumulatively explained 97.48 % of the total variation in the dataset. The first three largest principal components of individual sheep were plotted in a three dimensional scattergram and spikes were drawn from the centroid of each of the three parental populations. Although the centroids of Kilakarsal and Vembur breeds were placed slightly apart, most individual sheep from both breeds were found to be dispersed indicating complete admixture among them. Principal components scattergam did not reveal the presence of cryptic genetic structure among the two sheep breeds under study (Figure 2).

Figure 2. Three dimensional scatter gram of principal components derived from inter-individual pairwise allele sharing distance among Kilakarsal (KKL) and Vembur (VEM) sheep.

Discussion

In this study, the SNP markers within five toll like receptor genes (TLR 3, TLR 5, TLR 6, TLR 9 and TLR 10) were utilized (Table 1) for genotyping of Kilakarsal and Vembur sheep through KBiosciences competitive allele specific PCR based end point genotyping. Similarly, Lee et al (2012) described 30 single-nucleotide polymorphisms in sheep major histocompatibility complex class II and class III regions by using KASP PCR, Nikki et al. (2013) analysed the 69 SNPs of Bovine tuberculosis for genetic associations with BTB infection status in African buffalo by competitive allele-specific SNP genotyping. Kathiravan et al (2014) genotyped 713 sheep belonging to 22 breeds across Asia, Europe and South America and identified 41 SNPs across 38 candidate genes and analysed association of genotypes with host resistance characteristics against gastro-intestinal nematodes by using competitive allele specific PCR assay based on FRET chemistry.

Pairwise allele sharing distances

Pairwise allele sharing distances were estimated as the average fraction of alleles shared between two individuals over all loci in order to evaluate within and between population diversities (Watkins et al 2012). In the Kilakarsal and Vembur sheep population, a large overlap was observed in the distribution of inter-individual distances estimated from within and between breeds indicating that certain individuals across populations were more related than individuals within the same breed (Figure 2). This finding was comparable to the other studies in sheep breeds viz., Kathiravan et al (2014) in breeds from the same regions of Asia and South America; Sun et al (2010) and Liu et al (2014) in Chinese sheep population.

Genetic structure based on principal components analysis

Principal components analysis is widely used to evaluate patterns of population structure and for detecting and quantifying population structure for understanding the demography and evolutionary history of livestock breeds so as to elucidate the genetic basis for population admixture. It is an ordination technique used to display information contained in a distance matrix and to visualize relationship among variables within a dataset. This method helps to reduce the dimension of data and to detect structure among populations in a typical genetic diversity study. This uses orthogonal transformation to convert a set of observations of possibly correlated variables into a new set of values of linearly uncorrelated variables called as principal components. In conventional PCA, such synthetic principal components are derived from a dataset consisting of genotypes/allele frequency/genetic distance estimations. Sampled individuals are projected into a geometric space spanned by the top principal components (PCs) that explain maximum variation in the dataset. As the top PCs reflect variations due to population structure in the sample, individuals from the same population are found to form a cluster within the geometric space. Admixed individuals are dispersed along the line segment connecting the clusters of the two parental populations. The relative distances of an individual from the centroids of the clusters of the parental populations can also be used to estimate the admixture proportions of the individuals (Ma and Amos, 2012).

In PCA, the variables are treated equally as opposed to being divided into dependent and independent variables, as is done in regression analysis. The original variables are transformed into new uncorrelated variables that are called principal components. Each PC is a linear combination of the original variables. The initial variates are replaced with a smaller number of latent variates allowing the data to be summarized more concisely with minimal loss of information. Thus, instead of analysing a large number of original variables with complex interrelationships, the investigator can analyse a smaller number of uncorrelated PCs (Morrison, 1976). One of the measures used to determine the amount of information conveyed by each PC is its variance known as Eigen value. For this reason, the PCs are arranged in order of decreasing variance. Thus, the most informative PC is the first and the least informative is the last while a variable with zero variance does not distinguish between the members of the population. To reduce the dimensionality of a problem, only the first few PCs are analysed. The PCs not analysed convey only a small amount of information since their variances are small. A high coefficient of a PC on a given variable is an indication of high correlation between that variable and the PC. PC scatter graphs are drawn by plotting the PC coefficients. Two- and three-dimensional scatter graphs are used. Related breeds are clustered together (Cavalli-Sforza et al 1994). In animal genetic diversity studies, PCs are used to determine relationships among populations, supplementing relationships determined using phylogenetic analyses. PCs can be more convenient than phylogenetic trees, if clusters of populations are more visible. They are also more flexible than trees since they can use a greater number of parameters. It is usually easier to compare PC maps than it is to compare trees (Okomo, 1997).

In the present study, the top three principal components explained respectively 45.1, 20.2 and 11.9 % of the total variation and together explained 77.2 % of the variation in the dataset in the Kilakarsal and Vembur sheep populations. The top eight principal components had Eigen values greater than one and cumulatively explained 97.48 % of the total variation in the dataset. Most of individual sheep from Kilakarsal and Vembur breeds were found to be dispersed, indicating complete admixture among them. Principal components scattergam did not reveal the presence of cryptic genetic structure among the two sheep breeds under study (Figure 2).

Similar findings were observed by Kathiravan et al (2014) in which the clustering of sheep breeds followed their geographical origin and broadly differentiated into European and Asian groups and the South Indian sheep breeds viz. Madras Red, Mecheri, Pattanam and Nellore were clustering together closely in three dimensional scattergram of first three principal components derived from pairwise F_ST across 27 SNP loci studied. They also reported the first, second and third principal components explained 42.01, 38.12 and 7.15 % of total genetic variation respectively which is comparable to the values reported in Kilakarsal and Vembur sheep breeds in this study. Rudolf et al (2016) also reported that the South Asian (Indian) breeds including Madras Red, Mecheri, Pattanam and Nellore were clustered together in the Principal components analysis based on Pair-wise allele sharing distances between individual animals while it is distinct from Punjab Urial sheep population being distributed close to the cluster of West Asian and East European sheep (Hamdani-Karakachanska-West Palminska) and concluded that Punjab Urial sheep being genetically closer to West Asian and East European sheep breeds as compared to South Asian sheep breeds.

Conclusion

The pairwise allele sharing distances indicated that certain individuals across populations were more related than individuals within populations and the principal components analysis did not reveal the presence of cryptic genetic structure among the two sheep breeds under study. It may be due both the breeds are belongs to same geographical location, only few SNPs analysed and smaller sample size. Although the present analysis is preliminary in nature, the SNP loci on TLR 3, 5, 6, 9 and10 genes indicated their potential for future large scale genetic diversity studies. Future studies may be carried out with more number of SNPs, increased sample size and breed sampling may have been from non breeds.

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Received 15 December 2016; Accepted 29 December 2016; Published 1 February 2017

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