Analysis of sequence conservation at nucleotide resolution

Local DNA topography correlates with functional noncoding regions of the human genome

The three-dimensional molecular structure of DNA, specifically the shape of the backbone and grooves of genomic DNA, can be dramatically affected by nucleotide changes, which can cause differences in protein-binding affinity and phenotype. We developed an algorithm to measure constraint on the basis of similarity of DNA topography among multiple species, using hydroxyl radical cleavage patterns to interrogate the solvent-accessible surface area of DNA. This algorithm found that 12% of bases in the human genome are evolutionarily constrained-double the number detected by nucleotide sequence-based algorithms. Topography-informed constrained regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history.

Introduction to molecular combing: genomics, DNA replication, and cancer

The sequencing of the human genome inaugurated a new era in both fundamental and applied genetics. At the same time, the emergence of new technologies for probing the genome has transformed the field of pharmaco-genetics and made personalized genomic profiling and high-throughput screening of new therapeutic agents all but a matter of routine. One of these technologies, molecular combing, has served to bridge the technical gap between the examination of gross chromosomal abnormalities and sequence-specific alterations. Molecular combing provides a new perspective on the structure and dynamics of the human genome at the whole genome and sub-chromosomal levels with a resolution ranging from a few kilobases up to a megabase and more. Originally developed to study genetic rearrangements and to map genes for positional cloning, recent advances have extended the spectrum of its applications to studying the real-time dynamics of the replication of the genome. Understanding how the genome is replicated is essential for elucidating the mechanisms that both maintain genome integrity and result in the instabilities leading to human genetic disease and cancer. In the following, we will examine recent discoveries and advances due to the application of molecular combing to new areas of research in the fields of molecular cytogenetics and cancer genomics.

A catalog of neutral and deleterious polymorphism in yeast

The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences.

DNA sequence variation makes an important contribution to most traits that vary in natural populations. However, mapping mutations that underlie a trait of interest is a significant challenge. Genome sequencing of multiple organisms provides a complete list of DNA sequence differences responsible for any trait that differs among the organisms. Yet, distinguishing those DNA sequence variants that contribute to a trait from all other variants is not easy. Here, we sequence the genomes of two strains of yeast and, through comparisons with a reference genome, we catalog multiple types of DNA sequence variation among the three strains. Using a variety of comparative genomics methods, we show that a substantial fraction of DNA sequence variations has deleterious effects on fitness. Finally, we show that a subset of deleterious mutations is associated with changes in gene expression levels. Our results imply that comparative genomics methods will be a valuable approach to identifying DNA sequence changes underlying numerous traits of interest.

Local conservation scores without a priori assumptions on neutral substitution rates

Background

Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics. All methods we are aware of rely on a comparison with the neutral rate and conservation is estimated by measuring the deviation of a particular genomic region from this rate. Consequently, it is a reasonable assumption that inaccurate neutral rate estimates may lead to biased conservation and constraint estimates.

Results

We propose a conservation signal that is produced by local Maximum Likelihood estimation of evolutionary parameters using an optimized sliding window and present a Kullback-Leibler projection that allows multiple different estimated parameters to be transformed into a conservation measure. This conservation measure does not rely on assumptions about neutral evolutionary substitution rates and little a priori assumptions on the properties of the conserved regions are imposed. We show the accuracy of our approach (KuLCons) on synthetic data and compare it to the scores generated by state-of-the-art methods (phastCons, GERP, SCONE) in an ENCODE region. We find that KuLCons is most often in agreement with the conservation/constraint signatures detected by GERP and SCONE while qualitatively very different patterns from phastCons are observed. Opposed to standard methods KuLCons can be extended to more complex evolutionary models, e.g. taking insertion and deletion events into account and corresponding results show that scores obtained under this model can diverge significantly from scores using the simpler model.

Conclusion

Our results suggest that discriminating among the different degrees of conservation is possible without making assumptions about neutral rates. We find, however, that it cannot be expected to discover considerably different constraint regions than GERP and SCONE. Consequently, we conclude that the reported discrepancies between experimentally verified functional and computationally identified constraint elements are likely not to be explained by biased neutral rate estimates.