Gupta Manish Kumar, Nutan Prakash, Chaturvedi Pragya and Misra Krishna
As a result of human genome project a large burst of genomic data comes. Researchers are trying to correlate this sequenced data to find out variations, which will help to study the effect of variations on disease progression. Single nucleotide polymorphism is one of the genetic markers which are most widely used in genetic association studies of a population. SNPs are DNA sequence variations that occur when a single nucleotide (A, T, C, or G) in the genome sequence is altered. SNPs found within a coding sequence are of particular interest to researchers because they are more likely to alter the biological function of a protein. Occasionally, SNPs can cause disease and can be used to search and isolate diseased gene. The SNPs found in this region and its linkage disequilibrium analysis to find out the effect of SNPs found and there correlation. However it is much easier, cheaper and faster than in vitro analysis, computational analysis will provide an insight to probable disease causing SNPs having some functional value which can be assayed in vitro. Present computational analysis is to find out SNPs in the chromosome 1.
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