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Identification of SNPs in Goats (Capra hircus) using RNA-Seq Analysis

Keywords: Capra hircus , transcriptome , RNA-Seq , Single nucleotide polymorphism

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Single Nucleotide Polymorphisms (SNPs) have become the marker of choice for genome-wide association studies. In order to provide the best genome coverage for the analysis of performance and production traits, a large number of relatively evenly distributed SNPs are needed. Gene-associated SNPs may fulfill these requirements of large numbers and genome wide distribution. In addition, gene-associated SNPs could themselves be causative SNPs for traits. The main objective of our work was to identify large numbers of gene-associated SNPs using high-throughput next generation sequencing. Transcriptome sequencing was conducted on 2 tissues viz. liver and kidney for 5 breeds of goat (Kanniadu, Osmanabadi, Black Bengal, Changthangi and Sirohi) using Illumina next generation sequencing technology. Approximately 46.4 million reads for Black Bengal, 61.9 from Kanniadu, 58.2 from Changthangi, 47.3 from Osmanabadi, 73.2 from Sirohi were obtained by sequencing gene transcripts derived from kidney while 37, 27.2, 19.4, 56.9 and 80.7 million reads were obtained by gene transcripts derived from liver. The analysis of total number of SNPs in liver and kidney revealed that out of a total of 68597 SNPs in liver, the total number of transversions was 21300 and the number of transitions was 47297. A total of 1574 SNPs of liver were complex. Similarly for kidney the total number of 72047 SNPs were categorised into 22774 transversions and 49273 transitions. The total number of complex SNPs in kidney was 1597. The number of transitions is more than double the number of transversions in both the tissues. Further analysis of transversion revealed a preponderance of cytosine and guanine change compared to other nucleotides. 12863 and 11319 transversions out of 21300 and 22774 transversions respectively for liver and kidney revealed this bias. When multiple individuals with different genetic backgrounds were used, RNA-Seq was very effective for the identification of SNPs. The SNPs identified in this report provides a much needed resource for genetic studies in goat and shall contribute to the development of a highdensity SNP array. Validation and testing of these SNPs using SNP arrays will form the material basis for genome association studies and whole genome-based selection in goats.


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