Authors : Mukesh Kumari; Dr. Preveen Kumari

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2jxsddvw

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DOI : https://doi.org/10.38124/ijisrt/26mar1590

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Abstract : Wheat (Triticum aestivum L.) is one of the most important staple crops worldwide, but its productivity is severely affected by drought stress, a major abiotic constraint intensified by climate change. Identification of genetic variations associated with drought tolerance is essential for developing resilient wheat varieties. In this context, in silico approaches provide a rapid, cost-effective, and high-throughput method for detecting molecular markers such as single nucleotide polymorphisms (SNPs).The present study focuses on the in silico mining of SNPs in drought-responsive candidate genes of wheat using publicly available genomic databases and bioinformatics tools. Candidate genes associated with drought tolerance—such as those involved in osmotic regulation, stress signaling, and transcriptional control—were selected and analyzed. Sequence alignment and comparative genomics approaches were employed to identify SNP variations across different wheat genotypes. Functional annotation and predictive analysis were further conducted to evaluate the potential impact of identified SNPs on gene expression and protein function.The results revealed a significant number of SNPs distributed across coding and non-coding regions, with several non-synonymous SNPs potentially influencing drought tolerance traits. These SNPs can serve as valuable molecular markers for marker-assisted selection (MAS) and genomic breeding programs. The study highlights the effectiveness of in silico strategies in accelerating genetic improvement and supports the development of drought-resilient wheat cultivars.

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