A Macaque's-Eye View of Human Insertions and Deletions: Differences in Mechanisms

Insertions and deletions (indels) cause numerous genetic diseases and lead to pronounced evolutionary differences among genomes. The macaque sequences provide an opportunity to gain insights into the mechanisms generating these mutations on a genome-wide scale by establishing the polarity of indels occurring in the human lineage since its divergence from the chimpanzee. Here we apply novel regression techniques and multiscale analyses to demonstrate an extensive regional indel rate variation stemming from local fluctuations in divergence, GC content, male and female recombination rates, proximity to telomeres, and other genomic factors. We find that both replication and, surprisingly, recombination are significantly associated with the occurrence of small indels. Intriguingly, the relative inputs of replication versus recombination differ between insertions and deletions, thus the two types of mutations are likely guided in part by distinct mechanisms. Namely, insertions are more strongly associated with factors linked to recombination, while deletions are mostly associated with replication-related features. Indel as a term misleadingly groups the two types of mutations together by their effect on a sequence alignment. However, here we establish that the correct identification of a small gap as an insertion or a deletion (by use of an outgroup) is crucial to determining its mechanism of origin. In addition to providing novel insights into insertion and deletion mutagenesis, these results will assist in gap penalty modeling and eventually lead to more reliable genomic alignments

A Map of Recent Positive Selection in the Human Genome

The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP) data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest ∼250 signals of recent selection in each population

Mutation Rate Distribution Inferred from Coincident SNPs and Coincident Substitutions

Mutation rate variation has the potential to bias evolutionary inference, particularly when rates become much higher than the mean. We first confirm prior work that inferred the existence of cryptic, site-specific rate variation on the basis of coincident polymorphisms—sites that are segregating in both humans and chimpanzees. Then we extend this observation to a longer evolutionary timescale by identifying sites of coincident substitutions using four species. From these data, we develop analytic theory to infer the variance and skewness of the distribution of mutation rates. Even excluding CpG dinucleotides, we find a relatively large coefficient of variation and positive skew, which suggests that, although most sites in the genome have mutation rates near the mean, the distribution contains a long right-hand tail with a small number of sites having high mutation rates. At least for primates, these quickly mutating sites are few enough that the infinite sites model in population genetics remains appropriate

How repetitive are genomes?

BACKGROUND: Genome sequences vary strongly in their repetitiveness and the causes for this are still debated. Here we propose a novel measure of genome repetitiveness, the index of repetitiveness, I(r), which can be computed in time proportional to the length of the sequences analyzed. We apply it to 336 genomes from all three domains of life. RESULTS: The expected value of I(r )is zero for random sequences of any G/C content and greater than zero for sequences with excess repeats. We find that the I(r )of archaea is significantly smaller than that of eubacteria, which in turn is smaller than that of eukaryotes. Mouse chromosomes have a significantly higher I(r )than human chromosomes and within each genome the Y chromosome is most repetitive. A sliding window analysis reveals that the human HOXA cluster and two surrounding genes are characterized by local minima in I(r). A program for calculating the I(r )is freely available at . CONCLUSION: The general measure of DNA repetitiveness proposed in this paper can be efficiently computed on a genomic scale. This reveals a broad spectrum of repetitiveness among diverse genomes which agrees qualitatively with previous studies of repeat content. A sliding window analysis helps to analyze the intragenomic distribution of repeats

The Diploid Genome Sequence of an Individual Human

Presented here is a genome sequence of an individual human. It was produced from ∼32 million random DNA fragments, sequenced by Sanger dideoxy technology and assembled into 4,528 scaffolds, comprising 2,810 million bases (Mb) of contiguous sequence with approximately 7.5-fold coverage for any given region. We developed a modified version of the Celera assembler to facilitate the identification and comparison of alternate alleles within this individual diploid genome. Comparison of this genome and the National Center for Biotechnology Information human reference assembly revealed more than 4.1 million DNA variants, encompassing 12.3 Mb. These variants (of which 1,288,319 were novel) included 3,213,401 single nucleotide polymorphisms (SNPs), 53,823 block substitutions (2–206 bp), 292,102 heterozygous insertion/deletion events (indels)(1–571 bp), 559,473 homozygous indels (1–82,711 bp), 90 inversions, as well as numerous segmental duplications and copy number variation regions. Non-SNP DNA variation accounts for 22% of all events identified in the donor, however they involve 74% of all variant bases. This suggests an important role for non-SNP genetic alterations in defining the diploid genome structure. Moreover, 44% of genes were heterozygous for one or more variants. Using a novel haplotype assembly strategy, we were able to span 1.5 Gb of genome sequence in segments >200 kb, providing further precision to the diploid nature of the genome. These data depict a definitive molecular portrait of a diploid human genome that provides a starting point for future genome comparisons and enables an era of individualized genomic information

RISCI - Repeat Induced Sequence Changes Identifier: a comprehensive, comparative genomics-based, in silico subtractive hybridization pipeline to identify repeat induced sequence changes in closely related genomes

<p>Abstract</p> <p>Background -</p> <p>The availability of multiple whole genome sequences has facilitated <it>in silico </it>identification of fixed and polymorphic transposable elements (TE). Whereas polymorphic loci serve as makers for phylogenetic and forensic analysis, fixed species-specific transposon insertions, when compared to orthologous loci in other closely related species, may give insights into their evolutionary significance. Besides, TE insertions are not isolated events and are frequently associated with subtle sequence changes concurrent with insertion or post insertion. These include duplication of target site, 3' and 5' flank transduction, deletion of the target locus, 5' truncation or partial deletion and inversion of the transposon, and post insertion changes like inter or intra element recombination, disruption etc. Although such changes have been studied independently, no automated platform to identify differential transposon insertions and the associated array of sequence changes in genomes of the same or closely related species is available till date. To this end, we have designed RISCI - 'Repeat Induced Sequence Changes Identifier' - a comprehensive, comparative genomics-based, <it>in silico </it>subtractive hybridization pipeline to identify differential transposon insertions and associated sequence changes using specific alignment signatures, which may then be examined for their downstream effects.</p> <p>Results -</p> <p>We showcase the utility of RISCI by comparing full length and truncated L1HS and AluYa5 retrotransposons in the reference human genome with the chimpanzee genome and the alternate human assemblies (Celera and HuRef). Comparison of the reference human genome with alternate human assemblies using RISCI predicts 14 novel polymorphisms in full length L1HS, 24 in truncated L1HS and 140 novel polymorphisms in AluYa5 insertions, besides several insertion and post insertion changes. We present comparison with two previous studies to show that RISCI predictions are broadly in agreement with earlier reports. We also demonstrate its versatility by comparing various strains of <it>Mycobacterium tuberculosis </it>for IS 6100 insertion polymorphism.</p> <p>Conclusions -</p> <p>RISCI combines comparative genomics with subtractive hybridization, inferring changes only when exclusive to one of the two genomes being compared. The pipeline is generic and may be applied to most transposons and to any two or more genomes sharing high sequence similarity. Such comparisons, when performed on a larger scale, may pull out a few critical events, which may have seeded the divergence between the two species under comparison.</p

Detection of lineage-specific evolutionary changes among primate species

<p>Abstract</p> <p>Background</p> <p>Comparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection.</p> <p>Results</p> <p>We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection.</p> <p>Conclusions</p> <p>DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.</p

Both Noncoding and Protein-Coding RNAs Contribute to Gene Expression Evolution in the Primate Brain

Despite striking differences in cognition and behavior between humans and our closest primate relatives, several studies have found little evidence for adaptive change in protein-coding regions of genes expressed primarily in the brain. Instead, changes in gene expression may underlie many cognitive and behavioral differences. Here, we used digital gene expression: tag profiling (here called Tag-Seq, also called DGE:tag profiling) to assess changes in global transcript abundance in the frontal cortex of the brains of 3 humans, 3 chimpanzees, and 3 rhesus macaques. A substantial fraction of transcripts we identified as differentially transcribed among species were not assayed in previous studies based on microarrays. Differentially expressed tags within coding regions are enriched for gene functions involved in synaptic transmission, transport, oxidative phosphorylation, and lipid metabolism. Importantly, because Tag-Seq technology provides strand-specific information about all polyadenlyated transcripts, we were able to assay expression in noncoding intragenic regions, including both sense and antisense noncoding transcripts (relative to nearby genes). We find that many noncoding transcripts are conserved in both location and expression level between species, suggesting a possible functional role. Lastly, we examined the overlap between differential gene expression and signatures of positive selection within putative promoter regions, a sign that these differences represent adaptations during human evolution. Comparative approaches may provide important insights into genes responsible for differences in cognitive functions between humans and nonhuman primates, as well as highlighting new candidate genes for studies investigating neurological disorders

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p