Asset Details
Do you wish to reserve the book?
A method for calculating probabilities of fitness consequences for point mutations across the human genome
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A method for calculating probabilities of fitness consequences for point mutations across the human genome
Do you wish to request the book?
A method for calculating probabilities of fitness consequences for point mutations across the human genome
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A method for calculating probabilities of fitness consequences for point mutations across the human genome
2015
Overview
Adam Siepel and colleagues develop a statistical method, fitCons, which combines comparative and functional genomic data to estimate the probability that a point mutation will influence fitness. They generate fitCons scores for three human cell types from ENCODE data sets and demonstrate improved prediction power for
cis
regulatory elements in comparison to conventional conservation-based scores.
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.
Publisher
Nature Publishing Group US,Nature Publishing Group
