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Nat Genet. 2015 Mar;47(3):276-83. doi: 10.1038/ng.3196. Epub 2015 Jan 19.

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

Author information

1
Graduate Field of Computer Science, Cornell University, Ithaca, New York, USA.
2
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
3
1] Graduate Field of Computer Science, Cornell University, Ithaca, New York, USA. [2] Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.

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, fitCons scores indicate that 4.2-7.5% of nucleotides in the human genome have influenced fitness since the human-chimpanzee divergence, and they suggest that recent evolutionary turnover has had limited impact on the functional content of the genome.

PMID:
25599402
PMCID:
PMC4342276
DOI:
10.1038/ng.3196
[Indexed for MEDLINE]
Free PMC Article

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