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SNPs are the most common genetic variant found in all individual with one SNP every 100–300 bp in some species. [4] Since there is a massive number of SNPs on the genome , there is a clear need to prioritize SNPs according to their potential effect in order to expedite genotyping and analysis.
A tag SNP is a representative single-nucleotide polymorphism in a region of the genome with high linkage disequilibrium (the non-random association of alleles at two or more loci). Tag SNPs are useful in whole-genome SNP association studies, in which hundreds of thousands of SNPs across the entire genome are genotyped.
The next step is to identify SNPs from aligned tags and score all discovered SNPs for various coverage, depth and genotypic statistics. Once a large-scale, species-wide SNP production has been run, it is possible to quickly call known SNPs in newly sequenced samples. [8]
Pileup format is a text-based format for summarizing the base calls of aligned reads to a reference sequence. This format facilitates visual display of SNP/indel calling and alignment.
A SNP array can also be used to generate a virtual karyotype using software to determine the copy number of each SNP on the array and then align the SNPs in chromosomal order. [10] SNPs can also be used to study genetic abnormalities in cancer. For example, SNP arrays can be used to study loss of heterozygosity (LOH). LOH occurs when one allele ...
The use of SNPs is being extended in the HapMap project, which aims to provide the minimal set of SNPs needed to genotype the human genome. SNPs can also provide a genetic fingerprint for use in identity testing. [1] The increase of interest in SNPs has been reflected by the furious development of a diverse range of SNP genotyping methods.
Nucleotide sequence analyses identify functional elements like protein binding sites, uncover genetic variations like SNPs, study gene expression patterns, and understand the genetic basis of traits. It helps to understand mechanisms that contribute to processes like replication and transcription. Some of the tasks involved are outlined below.
The calculation of prior probabilities depends on available data from the genome being studied, and the type of analysis being performed. For studies where good reference data containing frequencies of known mutations is available (for example, in studying human genome data), these known frequencies of genotypes in the population can be used to estimate priors.