Exhaustive prediction of disease susceptibility to coding base changes in the human genome.

ABSTRACT:

Background

Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Bases are subject to various levels of selection pressure, reflected in their inter-species conservation.

Results

We propose a method that is not dependant on transcription information to score each coding base in the human genome reflecting the disease probability associated with its mutation. Twelve factors likely to be associated with disease alleles were chosen as the input for a support vector machine prediction algorithm. The analysis yielded 83% sensitivity and 84% specificity in segregating disease like alleles as found in the Human Gene Mutation Database from non-disease like alleles as found

SUBMITTER: Kulkarni V 

PROVIDER: S-EPMC2537574 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature