Probabilities of Fitness Consequences for Point Mutations Across the Human Genome

We describe a novel 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” based on 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 based on public data from ENCODE. Compared with conventional conservation scores, fitCons scores show considerably improved prediction power for cisregulatory 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. During the past decade, two major developments—the emergence of massively parallel, ultra-cheap DNA sequencing technologies and the use of these technologies as digital readouts for functional genomic assays—have led to a profusion of data describing various features of genomes, epigenomes, and transcriptomes1,2. However, investigators still have Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms Correspondence and requests for materials should be addressed to A.S. ([email protected]) and I.G. ([email protected]). 3Present addresses: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA (A.S.); The Efi Arazi School for Computer Science, The Interdisciplinary Center, Herzliya, Israel. (A.G.) URLs Cold Spring Harbor Laboratory Mirror of UCSC Genome Browser: http://genome-mirror.cshl.edu/ UCSC Genome Browser: http://genome.ucsc.edu INSIGHT: http://compgen.cshl.edu/INSIGHT/ GENCODE v15: ftp://ftp.sanger.ac.uk/pub/gencode/release15/ GERP: http://mendel.stanford.edu/SidowLab/downloads/gerp/ CADD: http://cadd.gs.washington.edu/download/ RegulomeDB: http://regulome.stanford.edu/downloads/ Gerstein Lab ENCODE-nets: http://encodenets.gersteinlab.org/ European Bioinformatics Institute’s E-GEUV-1 data set: http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/analysis_results/ Competing Interests The authors declare that they have no competing financial interests. Author Contributions I.G and A.S. conceived the study framework. B.G. and I.G performed the experiments. All authors analyzed the data. B.G., M.J.H. and I.G. developed analysis tools. B.G., I.G. and A.S. wrote the paper. I.G. and A.S. supervised the research. HHS Public Access Author manuscript Nat Genet. Author manuscript; available in PMC 2015 September 01. Published in final edited form as: Nat Genet. 2015 March ; 47(3): 276–283. doi:10.1038/ng.3196. A uhor M anscript