A method for calculating probabilities of fitness consequences for point mutations across the human genome.

ABSTRACT: We describe a new computational method for estimating the probability that a point mutation at each position in a genome will influence fitness. These 'fitness consequence' (fitCons) scores serve as evolution-based measures of potential genomic function. Our approach is to cluster genomic positions into groups exhibiting distinct 'fingerprints' on the basis of high-throughput functional genomic data, then to estimate a probability of fitness consequences for each group from associated patterns of genetic polymorphism and divergence. We have generated fitCons scores for three human cell types on the basis of public data from ENCODE. In comparison with conventional conservation scores, fitCons scores show considerably improved prediction power for cis regulatory elements. In addition, fitCon

SUBMITTER: Gulko B 

PROVIDER: S-EPMC4342276 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature