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. 2020 Jun 16;10(7):309. doi: 10.1007/s13205-020-02297-z

Reduced representation approach for identification of genome-wide SNPs and their annotation for economically important traits in Indian Tharparkar cattle

M Joel Devadasan 1, D Ravi Kumar 1, M R Vineeth 1, Anjali Choudhary 1, T Surya 1, S K Niranjan 2, Archana Verma 1,✉, Jayakumar Sivalingam 2,✉
PMCID: PMC7297873  PMID: 32582506

Abstract

The present study was carried out in Tharparkar cattle for identification of genome-wide SNPs and microsatellites, and then annotate the identified high-quality SNPs to milk production, fertility, carcass, adaptability and immune response of economically important traits. A total of 146,011 SNPs were identified with respect to Bos taurus reference genome which are indicus specific, out of which 10,519 SNPs were found to be novel. Similarly, a total of 87,047 SNPs were identified with respect to Bos indicus reference genome. After final annotation of SNPs identified with respect to Bos indicus reference genome, 2871 SNPs were found to be associated in 383 candidate genes having to do with milk production, fertility, carcass, immune response and adaptability traits. Following that, 2571 microsatellites were identified. The information mined from the data might be of importance for the future breed improvement programs, conservation efforts and for enhancing the SNPs density of the existing bovine SNP chips.

Electronic supplementary material

The online version of this article (10.1007/s13205-020-02297-z) contains supplementary material, which is available to authorized users.

Keywords: ddRAD, SNPs, Indicine cattle, Tharparkar, Economic traits

Introduction

Cattle mainly reared for meat and milk have contributed to the survival of humans for many years and also have become an indicator of socioeconomic status of farmer and human nutrition. Domesticated cattle breeds consist of two subspecies referred to as taurine (Bos taurus) and indicine or zebu (Bos indicus). The most important characteristics that are considered in dairy and beef industries are less feed intake (Seif et al. 1979), higher growth rate with low input (Cartwright 1955), average milk yield (Johnson 1965) and optimum reproductive function (Skinner and Louw 1966; Rocha et al. 1998). These characteristics are mostly well performed by indicine cattle than taurine cattle in spite of extreme harsh environment. There are more than 75 indicine cattle breeds in which most of them are present in developing countries.

So far, genetic improvements of indicine cattle breeds are less progressive as compared to taurine breeds due to insufficient molecular genetic information. Though, advancement in NGS (Next Generation Sequencing), cattle genetics and genomic studies are now well established, available SNP databases are found to be biased totally toward taurine breeds, which hinder the success of genomics studies in indicine cattle breeds (Iqbal et al. 2019). Furthermore, existing bovine SNP array chips have always showed a significant number of monomorphic SNPs (single nucleotide polymorphism) in case of Indicine cattle breeds. With the invention of NGS technologies, the density of SNPs present across the genome of indicine cattle was comparatively higher (1 SNP in every 285 bp) than that of taurine cattle (1 SNP in every 714 bp) (Bovine HapMap Consortium 2009; Nayee et al. 2018).

SNP identification from whole genome sequence could also be a challenge due to the presence of uninformative and repetitive sequences that could interfere with SNP calling. There are, however, different economical approaches that offer high flexibility and reliability to overcome the limitation of SNP calling and provide necessary approaches to explore the SNPs present in indicine cattle genome (Nayee et al. 2018; Iqbal et al. 2019).

Evidently, genome-wide SNPs identification studies have been carried out using reduced representation methods in many organisms (Dacosta et al. 2016; Ba et al. 2017; Malik et al. 2018). RAD (restriction enzyme-based DNA sequencing) is one of the reduced representation approaches, which can unravel thousands of genetic markers across the genome in both the model and non-model organisms. Therefore, in this study we used the ddRAD method (double-digest restriction site-associated DNA sequencing) for identifying SNPs in indicine cattle, which is a more efficient method for identifying variants across the genome of any given species (Peterson et al. 2012).

Tharparkar is one of the indicine cattle breed considered as the most important milch cattle breed of the western arid region of India; it has a distinct identity amongst all Indian cattle breeds which can withstand harsh desert climatic conditions and provide livelihood security to the most of the rural farmers. The breeding tract of the cattle includes Kachchh district of Gujarat and Barmer, Jaisalmer and Jodhpur districts of Rajasthan. The breed size is medium compact with white and light gray colored coat. The color of the cattle deepens during the winter season. As it is well known for its heat tolerance, disease and tick resistance (Gahlot 1999), it is considered as an indispensable component of agriculture livestock sector in arid region. These adaptability characteristics have also drawn the attention of dairy and beef cattle breeders in the country for using them extensively in cross-breeding programs with exotic as well as non-descript cattle for improving their production performances. Till date, very few reports are available on Tharparkar cattle, which are inadequate to initiate any genetic improvement of economically important traits as well as conservation studies. The aim of this study was to discover genome-wide SNPs and microsatellites using the ddRAD method in the Tharparkar cattle and annotation of SNPs to identify genes of economically important traits.

Materials and methods

Blood collection

The blood samples of the animals were collected in a sterile Vacutainer tube coated with 0.5% EDTA (ethylenediaminetetraacetic acid) with the relevant guidelines and regulations as approved (13-10-2018) by IAEC (Institutional Animal Ethics Committee) from seven adult Tharparkar cows, situated in Livestock Research Centre, National Dairy Research Institute, Karnal, Haryana, India (29.704° N Latitude, 76.982° E Longitude). Then the genomic DNA (deoxyribonucleic acid) was extracted using the phenol–chloroform–isoamyl alcohol method (Sambrook et al. 1989) for further downstream analysis.

Library preparation

After initial quality and quantity check of DNA, standard RAD protocol was followed for further sequencing workflow (Peterson et al. 2012). Double digestion of DNA with Sph I and MluC I restriction enzymes was carried out, followed by combinatorial barcoding including both Illumina index and an inline barcode for library preparation of size about 250–350 bp. Furthermore, all samples were pooled after adapter ligation and size selection was carried out. Finally, the samples were sequenced in SciGenom Labs Pvt. Ltd, Cochin, Kerala, using Illumina HiSeq 2000 platform which is based on reversible dye terminators that enable the identification of single bases as they are introduced into DNA strands by bridge amplification.

Bioinformatics analysis

SNPs identification

FastQC v0.11.8 (Andrews 2010) was used to screen sequenced raw reads, and then PRINSEQ v0.20.4 (Schmieder and Edwards 2011) was used to trim adapter sequences and barcodes attached over restriction enzymes cut sites in sequence reads. STACKS v2.2 (Catchen et al. 2011) was used to discard those sequences, which do not have the cut site of both the restriction enzymes and phred score value below 15. Quality control (QC) passed reads were aligned using Bowtie2 v2.3.4.1 (Langmead and Salzberg 2012) separately (-MQ 10, -D 20 -R 3 -N 0 -L 20 -i S,1,0.50) to both Bos indicus (Bos_indicus_1.0) and Bos taurus (Bos_taurus_UMD_3.1.1) reference genomes. SAM (Sequence Alignment Format) files obtained from alignment were then converted into BAM (Binary Alignment Format) files using Samtools v1.9 (Li 2011), followed by sorting, indexing, merging and mpileup to obtain a single BCF (Binary Calling Format) file. Then SNPs and InDels (Insertion and Deletion) were obtained at the minimum read depth (RD) of 2, 5, and 10 with quality score ≥ 30 using Vcftools v0.1.15 (Danecek et al 2011). Novel SNPs were identified from reads aligned with Bos taurus by comparing them with available cattle database v4.0 SNPs at NCBI (National Centre for Biological Information) using SnpSift v4.4 (Cingolani et al. 2012).

Annotation

SNPs obtained at RD 10 with respect to Bos indicus mapped reads were annotated using SnpEff v4.4 (Cingolani et al. 2012). Then, genes annotated to the SNPs were screened on literature and in animal QTL (quantitative trait loci) database v 2019 (www.animalgenome.org) for identifying candidate genes trait-wise. Subsequently, candidate genes associated with milk production, fertility, immune response, adaptability and carcass traits were identified and annotated gene-wise separately using Vcftools v0.1.15 (Danecek et al 2011) and SnpSift v4.4 (Cingolani et al. 2012).

Microsatellite identification

After quality control of raw reads, processed reads were used to create the consensus sequences using uSTACKS v1.4 and were used as input files for QDD v3 (pipe1.pl script) (Meglécz et al. 2010) to detect microsatellite markers. Further, consensus sequences containing polymorphic microsatellites present in all samples were separated by QDD v3 (pipe2.pl script). Finally MISA (Beier et al. 2017) tool was used to find -di, -tri, -tetra, -penta and -hexa nucleotide repeats by setting different threshold levels for each motifs with interleaving distance of 100 bp.

Results

SNPs identification

A total of 12.33 million paired-end raw reads were obtained with mean base pair length of 151 bp. After quality control, 97.05% of good quality reads were retained (Table 1). Then these processed reads were mapped to the reference genome of Bos indicus (Bos_indicus_1.0) and Bos taurus (Bos_taurus_UMD_3.1.1) with the genome coverage of 4.54% and 4.61%, respectively. The overall alignment rates of those mapped reads were 92.14% and 99.82% with Bos indicus and Bos taurus reference genomes, respectively. In the end, the total numbers of SNPs and InDels were identified separately with respect to Bos indicus genome and Bos taurus in various read depths (Table 2). Apart from this, high quality SNPs obtained at RD10 mapped reads of Bos taurus genome were further used to identify 10,519 novel SNPs.

Table 1.

Number of raw, processed and aligned reads of Tharparkar cattle

Sample population Bos indicus Bos taurus
Sample number Number of raw reads Number of QC processed reads Total aligned reads Total aligned reads in % Total aligned reads Total aligned reads in %
Tharparkar Cattle T-1 2,507,656 2,427,116 2,242,897 92.41 2,424,446 99.89
T-2 1,889,184 1,830,976 1,718,370 93.85 1,828,229 99.85
T-3 708,670 689,248 631,213 91.58 687,249 99.71
T-4 1,721,710 1,673,978 1,548,429 92.50 1,669,458 99.73
T-5 1,617,244 1,569,414 1,440,094 91.76 1,567,373 99.87
T-6 1,406,220 1,360,058 1,231,668 90.56 1,229,943 99.86
T-7 2,481,048 2,417,386 2,233,181 92.38 2,230,054 99.86
Total 12,331,732 11,968,176 8,802,955 11,636,752
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Table 2.

Number of SNPs and INDELS identified at different filtration levels

Reference genome Bos indicus Bos taurus
Filtration level RD2 RD5 RD10 RD2 RD5 RD10
SNPs 106,797 98,383 87,047 183,910 167,049 146,011
INDELs 11,050 9850 8465 11,618 10,371 8912
Total variants 117,847 108,233 95,512 195,528 177,420 154,923
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Annotation

Annotation was carried out with the SNPs obtained from RD 10 (Bos indicus). The majority of SNPs were found in transcript regions followed by intronic regions and intergenic regions (Table 3) with transition and transversion ratio of 2.64 per sample on average. Subsequently, 1042 SNPs in 109 genes associated with milk production (Table S1), 496 SNPs in 69 genes associated with fertility (Table S2), 756 SNPs in 97 genes associated with carcass traits (Table S3), 243 SNPs in 61 genes associated with adaptability traits (Table S4) and 334 SNPs in 47 genes associated with immune response traits (Table S5) were annotated. Further, chromosome-wise SNPs were listed separately for Bos indicus and Bos taurus aligned reads (Table S6).

Table 3.

Region-wise distribution of SNPs

Region Count Percent
3 prime UTR variant 633 0.30%
5 prime UTR premature variant 38 0.01%
5 prime UTR variant 170 0.08%
Downstream gene variant 11,801 5.62%
Intergenic variant 55,312 26.37%
Intragenic variant 1145 0.54%
Intron variant 63,835 30.43%
Missense variant 337 0.16%
Noncoding transcript exon variant 321 0.15%
Noncoding transcript variant 63,466 30.26%
Splice acceptor variant 9 0.004%
Splice region variant 145 0.07%
Start lost 2 0.001%
Stop gained 3 0.001%
Synonymous variant 552 0.26%
Upstream gene variant 11,954 5.70%
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Microsatellites identification

A total of 1831 microsatellites were identified from 1666 ESTs (Expressed sequence tags) in cattle (Yan et al. 2008). However in the present study, 2571 microsatellites were identified from 4326 consensus sequences along with the frequency of repeat types (Table S7). Out of the total microsatellites identified in this study, dinucleotide, trinucleotide, tetranucleotide, pentanucleotide and hexanucleotide motifs were categorized specifically based on thresholds level 7, 5, 5, 5 and 3, respectively (Table 4).

Table 4.

Number of microsatellites based on motif types

Unit size Number of SSRs
Dinucleotide 1219
Trinucleotide 1058
Tetranucleotide 81
Pentanucleotide 196
Hexa-nucleotide 17
Total 2571
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Discussion

SNP identification and annotation

In cattle, phenotypic differences between breeds are much more pronounced than in other livestock animals. They are actually enhanced by strong artificial selection for different production goals, such as dairy or beef. Such phenotypic differences are driven by underlying changes in the genome structure, which emphasizes an importance of breed-specific genomic inferences, for which breed-wise genome-wide studies are the prerequisite (Czech et al. 2018). Hitherto, very few reports have been available on Tharparkar cattle, which is not sufficient for breed improvement programs. In the current era of genetic improvement programs, the preliminary information of genomic regions and the association with different production traits are very essential prerequisites for efficient association studies, genomic selection and fine mapping of genes associated with complex production traits (Gurgul et al. 2014). Therefore, the present investigation was carried out in Tharparkar cattle using the ddRAD method and identified 87,047 SNPs specific to Tharparkar cattle. In the study of Iqbal et al. (2019), the identified variants for indicine cattle breeds (Tharparkar cattle) were mapped and annotated to Bos taurus reference genome (Bos_taurus_UMD_3.1.1). However, Bos indicus cattle breeds are genetically distinctive from Bos taurus cattle breeds (Porto-Neto et al. 2013; Nayee et al. 2018), hence in this study mapping and final annotation of variants were done using Bos indicus (Bos_indicus_1.0) reference genome to obtain SNPs, which are more specific to indicine breed. Then high-quality SNPs for final annotation were selected with RD 10 and MQ (Mapping Quality) 30 as reported in the previous study (Altmann et al. 2012).

In this study, the average Ts/Tv ratio was found to be 2.6 4 per sample, a bit greater than normal ratio. But, the ratio is not universal for every species and varies even within individual from region to region (Keller et al. 2007), and also the ratio might be greater than the normal one while applying target-based methods on the genome (Patel et al. 2017). Still, a similar observation was made by many workers using reduced representation sequencing, which is in concordance with our observation (Kraus et al. 2011; Ba et al. 2017). Maximum numbers of SNPs were found to be in the transcript regions, followed by intron and intergenic regions in this study, which was in agreement with a previous report on SNPs annotation in riverine buffalo (Surya et al. 2018). SNPs available in the non-coding regions may also help in better understanding of intron usage in cis-acting regulatory genomic elements and in the acceptor sites which have functional effects on splicing (Li et al. 2015).

A total of 2871 SNPs were annotated in 383 candidate genes responsible for milk production, fertility, carcass, adaptability and immune response traits. All 383 candidate genes annotated in Tharparkar cattle were found to be well studied and associated in the cattle. IGFBP2 and CAST genes identified have been reported to be associated with milk fat percentage, milk yield and milk protein percentage in Holstein Friesian cattle (Pimentel et al. 2011). GHR gene has been associated with milk composition traits in the Holstein population (Waters et al. 2011). In Nellore cattle, APP gene was reported to have an association with age at first calving (Regatieri et al. 2017). Likewise, GRAMD1B and ZNF521 genes in Nordic cattle were reported to have an association with fertility index, inseminations per conception and interval from calving to first insemination (Höglund et al. 2015). In Guzerat cattle, a QTL region age at puberty has been associated with the TG gene (Fernández et al. 2017). In Brazilian Nellore beef cattle, candidate genes STIM2, MERTK, CCDC85A, GARNL3, GPC6 and CCDC88c were recognized with genetic network of growth and meat quality traits (Mudadu et al. 2016). The previous studies discussed over here supports the SNPs identified in the Indian Tharparkar cattle.

Microsatellites identification

Yan et al. (2008) used ESTs for microsatellites identification in cattle. However, genomic microsatellite markers contain a higher level of polymorphic information than those derived from ESTs (Chabane et al. 2005). Thus in this study, microsatellites were identified from genome-wide sequences, which clearly indicates that genome-wide sequences are a more valuable resource for mining microsatellite than ESTs. In this study, dinucleotide repeats were found to be more abundant than trinucleotide, tetranucleotide, pentanucleotide and hexanucleotide repeats, which were in agreement with previous microsatellites identification reports on several animal species (Rohrer et al. 2002; Pérez et al. 2005).

Conclusion

This is the first report on genome-wide identification of genetic variants in the Indian Tharparkar cattle. In this scenario, information generated in this study may provide baseline data for further research in identifying potential genomic markers for milk production, fertility, carcass, immune response and adaptability of economically important traits that can be used in genetic improvement and conservation of this breed. Also, variants identified in this study might be included in the existing bovine SNP chips to reduce bias over taurine breeds of cattle. Microsatellites identified using genome-wide sequences might be a more valuable source for identifying than other resources. The approach and methods used in this study might be used in any model and non-model organisms to study basic molecular information in a cost-effective manner.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file 1 (DOCX 198 kb) (197.4KB, docx)

Acknowledgements

The authors thank the Director, ICAR-National Dairy Research Institute for the financial assistance, and Director, ICAR-National Bureau of Animal Genetic Resources, for the support to carry out this research work. The authors express their sincere thanks to Dr Parameswari B, Senior Scientist (Plant Pathology) and Dr. Neeraj Kulshreshtha, Principal Scientist (Plant Breeding) and Head, Regional Center, Sugarcane Breeding Institute Regional center, Karnal, Haryana, for providing computational facilities for this work.

Author contributions

AV and JS conceived and designed the study. MJD and AC collected blood samples. MJD and AC conducted experiments. MJD, DRK, MRV, AC and TS conducted bioinformatics and data analysis. MJD drafted the manuscript. AV, JS and SKN provided critical inputs during the data analysis and manuscript preparation.

Data availability

Data supporting this paper were generated by ICAR-NDRI and analyzed at ICAR-NDRI and ICAR-NBAGR. The datasets generated in the study has been deposited in the NCBI (PRJNA633222).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding authors state that there is no conflict of interest.

Ethical approval

The blood samples of the animals were collected in accordance with the relevant guidelines and regulations as approved by IAEC (Institutional Animal Ethics Committee) of ICAR-National Dairy Research Institute, Haryana, India.

Contributor Information

Archana Verma, Email: archana.ndri@gmail.com.

Jayakumar Sivalingam, Email: jeyvet@gmail.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file 1 (DOCX 198 kb) (197.4KB, docx)

Data Availability Statement

Data supporting this paper were generated by ICAR-NDRI and analyzed at ICAR-NDRI and ICAR-NBAGR. The datasets generated in the study has been deposited in the NCBI (PRJNA633222).

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