An inferred fitness consequence map of the rice genome.

The extent to which sequence variation impacts plant fitness is poorly understood. High-resolution maps detailing the constraint acting on the genome, especially in regulatory sites, would be beneficial as functional annotation of noncoding sequences remains sparse. Here, we present a fitness consequence (fitCons) map for rice (Oryza sativa). We inferred fitCons scores (ρ) for 246 inferred genome classes derived from nine functional genomic and epigenomic datasets, including chromatin accessibility, messenger RNA/small RNA transcription, DNA methylation, histone modifications and engaged RNA polymerase activity. These were integrated with genome-wide polymorphism and divergence data from 1,477 rice accessions and 11 reference genome sequences in the Oryzeae. We found ρ to be multimodal, with ~9% of the rice genome falling into classes where more than half of the bases would probably have a fitness consequence if mutated. Around 2% of the rice genome showed evidence of weak negative selection, frequently at candidate regulatory sites, including a novel set of 1,000 potentially active enhancer elements. This fitCons map provides perspective on the evolutionary forces associated with genome diversity, aids in genome annotation and can guide crop breeding programs.

Publisher Correction: An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences.

In the version of this article initially published, in the Methods section 'Statistics and data analysis', subsection 'Measuring entropy with INSIGHT', the equation for the maximized log likelihood incorrectly duplicated the equation for entropy; the equation read but should have read. The error has been corrected in the HTML and PDF versions of the article.

An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences.

Here we ask the question "How much information do epigenomic datasets provide about human genomic function?" We consider nine epigenomic features across 115 cell types and measure information about function as a reduction in entropy under a probabilistic evolutionary model fitted to human and nonhuman primate genomes. Several epigenomic features yield more information in combination than they do individually. We find that the entropy in human genetic variation predominantly reflects a balance between mutation and neutral drift. Our cell-type-specific FitCons scores reveal relationships among cell types and suggest that around 8% of nucleotide sites are constrained by natural selection.

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

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

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.

Genome-wide inference of natural selection on human transcription factor binding sites.

For decades, it has been hypothesized that gene regulation has had a central role in human evolution, yet much remains unknown about the genome-wide impact of regulatory mutations. Here we use whole-genome sequences and genome-wide chromatin immunoprecipitation and sequencing data to demonstrate that natural selection has profoundly influenced human transcription factor binding sites since the divergence of humans from chimpanzees 4-6 million years ago. Our analysis uses a new probabilistic method, called INSIGHT, for measuring the influence of selection on collections of short, interspersed noncoding elements. We find that, on average, transcription factor binding sites have experienced somewhat weaker selection than protein-coding genes. However, the binding sites of several transcription factors show clear evidence of adaptation. Several measures of selection are strongly correlated with predicted binding affinity. Overall, regulatory elements seem to contribute substantially to both adaptive substitutions and deleterious polymorphisms with key implications for human evolution and disease.

Bayesian inference of ancient human demography from individual genome sequences.

Whole-genome sequences provide a rich source of information about human evolution. Here we describe an effort to estimate key evolutionary parameters based on the whole-genome sequences of six individuals from diverse human populations. We used a Bayesian, coalescent-based approach to obtain information about ancestral population sizes, divergence times and migration rates from inferred genealogies at many neutrally evolving loci across the genome. We introduce new methods for accommodating gene flow between populations and integrating over possible phasings of diploid genotypes. We also describe a custom pipeline for genotype inference to mitigate biases from heterogeneous sequencing technologies and coverage levels. Our analysis indicates that the San population of southern Africa diverged from other human populations approximately 108-157 thousand years ago, that Eurasians diverged from an ancestral African population 38-64 thousand years ago, and that the effective population size of the ancestors of all modern humans was ∼9,000.