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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.
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. [5]
The upper DNA molecule differs from the lower DNA molecule at a single base-pair location (a G/A polymorphism) In genetics and bioinformatics, a single-nucleotide polymorphism (SNP / s n ɪ p /; plural SNPs / s n ɪ p s /) is a germline substitution of a single nucleotide at a specific position in the genome.
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]
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 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.
Identifying variants is a popular aspect of sequence analysis as variants often contain information of biological significance, such as explaining the mechanism of drug resistance in an infectious disease. These variants could be single nucleotide variants (SNVs), small insertions/deletions (indels), and large structural variants.
A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk. In a GWAS, the polarity of analysis is from one or a few phenotypes to many possible DNA variants. [3]