Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

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Identification of Genome-wide Single Nucleotide Polymorphisms in Indigenous Cattle Breeds of Tamil Nadu

L. Mahalakshmi1, R. Thiagarajan1,*, S.M.K. Karthickeyan1, Sabiha Hayath Basha1
1Department of Animal Genetics and Breeding, Madras Veterinary College, Tamil Nadu Veterinary and Animal Sciences University, Chennai-600 007, Tamil Nadu, India.
Background: Indigenous cattle are well adapted to the hot tropical climate. Conservation and genetic improvement in the indigenous cattle breeds have become a necessity due to the changing climatic condition. Genome-based research in indigenous cattle for in-depth characterization of genetic variants will help in the improvement of economically important traits in these cattle breeds.

Methods: Blood samples were collected for five indigenous cattle breeds of Tamil Nadu from their breeding tracts. Pooled DNA samples were sequenced using the Illumina platform. Short sequences were aligned using BWA, variant calling and annotation were done using GATK and snpEff respectively.

Result: This study identified 29,366,340 variants, which include 25,944,935 SNPs and 3,421,405 indels with a variant rate of 91 bp across the genome. Around 215,820 SNPs were present in the coding region of 5,572 genes. This study will provide the framework for further genetic analysis of phenotypic variations in economically important traits in native cattle of Tamil Nadu.
In general, indigenous cattle are well-adapted to the tropical climate and resistant to most of the tropical diseases. They can survive well in a low input system by converting low-quality forage into high-quality milk and draught power Obeidat et al., (2002).

In India, the genetic improvement of native cattle has been implemented only in few breeds by selective breeding of proven bulls born to elite cows through artificial insemination. Selective breeding has contributed to significant genetic improvement in economically important traits, but the accuracy of selection has been low because of less availability of animals with records and the unavailability of deep pedigree information for selection. Genome-based research in indigenous animals has gained growing attention nowadays as evidenced by the release of the whole genome sequence of Nellore, Gir and Brahman cattle as well as the DNA chips such as Induschip, Indigau etc.

Over the last few years, a considerable number of genetic variants in the form of single nucleotide polymorphisms, indels and structural variations have been identified across the cattle genome as a result of a number of bovine whole-genome sequencing studies, HapMap and the 1000 bull genome projects, catalogued in dbSNP (https://www.ncbi.nlm.nih.gov/snp/). As of 2017, a total of 99.71 million SNPs have been deposited in dbSNP, among which, only 4.38 million SNPs are from indicine cattle. This indicates that more than 96 per cent of currently available SNPs are from taurine breeds. Consequently, the SNP arrays currently used for genotyping, genome-wide association studies and genomic selection are biased towards taurine cattle, impeding the accuracy of genomic selection in indicine cattle breeds (Iqbal et al., 2019). Hence, there is a need to discover a substantial number of SNPs across many indigenous breeds, belonging to various geographical regions, to build new high-density SNP arrays for unbiased genotyping and subsequent genomic selection for economically important traits in indigenous cattle.

In this study, we report for the first time the genetic characterization of five indigenous cattle breeds of Tamil Nadu, viz. Kangayam, Umblachery, Pulikulam, Bargur and Alambadi, which are known for their draught ability, through analyzing whole-genome sequence data. Moreover, this study will provide a valuable resource for genome-wide association studies and genomic selection, which will also help in further investigation of genetic mechanisms underlying the traits of interest in indigenous cattle.
A population of 304 animals, in agreement with the breed characteristics from all five indigenous cattle breeds of Tamil Nadu, were selected from their breeding tracts for this study. Among the total population, 225 animals were unrelated (38 Pulikulam, 63 Bargur, 48 Kangayam, 61 Umblachery and 15 Alambadi). Blood samples were collected from the jugular vein of the animals, under aseptic conditions, using vacutainers containing 0.5 per cent of Ethylene Diamine Tetra Acid (EDTA) anticoagulant. Collected samples were brought to the laboratory in a Thermo container and stored at -20°C until subjected to DNA extraction. Genomic DNA was extracted by using the standard Phenol-Chloroform method Sambrook and Russel. (2001). The quantity and quality of DNA were measured with NanodropTM 1000 spectrophotometer (Thermo Scientific, USA). Samples with an optical density ratio (260/280 nm) of 1.8 were considered for further processing. Agarose gel electrophoresis (0.8%) was used to check the integrity of genomic DNA.

DNA samples from each breed were classified into three groups based on sex and phenotype of the animals viz bulls, high milk yielders (more than two liters per day) and low milk yielders (less than one liter per day) for better SNP detection Liao et al., (2013). For unbiased and accurate estimation of allele frequencies, the concentration of DNA samples was standardized to 200 ng per microliter and samples were pooled by mixing equal amounts (10-20 μl) of DNA from each sample from a particular group Gautier et al., (2013).

The whole-genome sequencing library was prepared using QIAseq FX DNA Library Kit for Illumina (QIAGEN). The sequence data was generated using Illumina Hiseq 2500 and NovoSeq 6000 and quality control of sequenced data was carried out using FastQC (Andrews, 2017) and MultiQC (Ewels et al., 2016) programmes. After quality control, the reads were aligned to reference genome (Bos-indicus-1.0) using BWA algorithm Li and Durbin (2009) and duplicate reads were removed using PICARD. Variant calling was done using GATK (McKenna et al., 2010) and snpEff  (Cingolani et al., 2012) was used for annotation of variants.
Whole-genome sequencing and mapping of short reads
 
Sequencing was carried out using Illumina Hiseq 2500 and Novoseq 6000, which produced an average of 72.65±4.79 GBs of data ranging from 49.46 GB to 92.21 GB in each sample. Out of 424,750,051 raw paired-end reads produced for each sample, 391,021,066 reads (ranging from 267,321,089 to 512,695,893 reads) with a length of 151 bp, an average GC content of 44±0.25 per cent per sequence and Phred score of more than 30 were mapped to the reference genome (Bos-indicus-1.0) successfully. Variants identified using the Bos indicus reference genome will be more specific to indicine breeds Devadasan et al., (2020). Around 38,01,32,572±17,371,788.63 reads were uniquely mapped with an average depth of 19.35-fold ranging from 12.59 to 29.51 and coverage of 97.28 per cent (Table 1). Coverage was affected by GC content, sequencing technology, read length, library preparation, structural variants and novel sequences (Liao et al., 2013). An average of 15x depth coverage was sufficient to identify almost 75 per cent of heterozygous variants and the accuracy increases with the increase in depth (Bentley et al., 2008).

Table 1: Mapping statistics of sequence data.


 
Identification of SNPs and indels
 
The total length of the genome was 2,673,965,444 bp. The average depth achieved was 17.67-fold (ranging from 12.9 to 22.6). 29,366,340 variants per sample were identified across the genome, which included 25,944,935 SNPs and 3,421,405 indels. Multiallelic SNP sites were 1,042,672. Out of the total variants identified, 57 per cent (ranging from 52 to 62 per cent) were homozygous and 37 per cent (ranging from 37 to 47 per cent) were heterozygous. The mean ratio of transitions and transversions was 2.17±0.003 (2.14 to 2.19) in each sample (Table 2), which is in agreement with previous studies (Choi et al., 2015, Das et al., 2015 and Stafuzza et al., 2017), indicating accuracy of SNP identification. The variant rate of the genome was around 91bp (Table 3). A high variant rate was observed in chromosome 23 with one variant for every 74 bp, Y chromosome had the lowest variant rate with one variant per 1022 bp. The lower variant rate in Y chromosome may be due to its haploid state in males, which results in the reduction of sequencing depth and lower rate of variant identification. Also, selection process reduces the retention of mutation due to the effect of recessive allele in hemizygous condition Stafuzza et al., (2017). The number of variants is proportionate to the length of chromosome (Fig 1), which is in agreement with the previous studies by Kawahara-Miki et al., (2011) and Stafuzza et al., (2017).

Table 2: Per sample variant statistics.



Table 3: Variant rate across all chromosomes.



Fig 1: Variant distribution according to the length of chromosomes.



Among the total indels identified, 1,287,879 were insertions and 2,133,526 were deletions. The length of the indel varied from 1 to 49. Single nucleotide insertions and deletions (948,585 and 1,046,590 respectively) were more commonly present. These results are similar to the findings of Eck et al., (2009), Liao et al., (2013) and Iqbal et al., (2019). Number of indels reduced when the length increased (Fig 2).

Fig 2: Indel length distribution.



Around 25,237,418 variants out of the total were common for all the five breeds of cattle. Whereas, 1225844, 568,634, 528,093, 838,145 and 968,206 variants were specific to Alambadi, Bargur, Kangayam, Pulikulam and Umblachery cattle respectively.
 
Functional annotation of SNPs and indels in genome
 
Among the total SNPs, 61 per cent (17071364) of SNPs were present in intergenic region and 28.6 per cent (799258) were present in the intron region. SNPs found within 5kb upstream and downstream of a gene were 952518 (3.41 per cent) and 904544 (3.24 per cent) respectively. Around 191294 (0.7 per cent) SNPs were present in 3 prime and 5 prime UTR regions. Splice site SNPs included 761 splice acceptors, 911 splice donor and 20068 splice region SNPs (Table 4). These results are in accordance with the previous studies carried out by Kawahara-Miki et al., (2011), Liao et al., (2013), Das et al., (2015), Choi et al., (2015), Rosse et al., (2017) and Iqbal et al., (2019) in Kuchinoshima-Ushi cattle, Gir cattle, Danish Holstein cattle, Hanwoo, yanbian cattle, Guzera cattle and Native cattle breeds of Pakistan.

Table 4: Functional annotation of SNPs.



Functional annotation of indels showed that around 59.18 per cent (2,202,674) of indels were observed in the intergenic region and 29.9 per cent (1,112,682) were in the intronic region. Also, two indels lead to loss of exon, two cause 5 prime UTR truncation, 9235 leads to frameshift variation and 3624 affects splice sites (Table 5).

Table 5: Functional annotation of indels.

This is the first study to perform whole-genome sequencing to identify variants in Tamil Nadu native cattle breeds and identified 25,944,935 SNPs and 3,421,405 indels against the Nellore cattle genome. Alambadi cattle showed more specific variants than other breeds. Functional annotation revealed more of intergenic variants and intronic variants. Non-synonymous mutations are more in the coding region of 5572 genes. The variants identified in this study will serve as a useful genetic tool and as candidates in genomic selection and genome-wide association studies to improve economically important traits in indigenous cattle breeds of Tamil Nadu.
None

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