Statistical methods for the detection of single nucleotide polymorphisms (SNPs) using new generation genome sequencers

A single nucleotide polymorphism, or SNP, is a site of the genome where variation occurs within a population. Almost all SNPs have only two alleles (variants). Variations in the DNA sequences of humans can affect how humans develop diseases and respond to pathogens, chemicals, drugs, vaccines, and other agents. SNPs are also thought to be key enablers in realizing the concept of personalized medicine. The importance of SNPs also comes from their ability to in uence disease risk, drug efficacy and side-effects. Thus, there should be confidence in the methods for the detection of SNPs that a SNP found is not a sequencing error. In this work, a statistical method based on a likelihood ratio test has been considered to detect these SNPs. The effciency of this test has been compared with a threshold test using simulations. Results of the analysis of real genome sequence data are also presented.