Segmenting the human genome based on states of neutral genetic divergence

Abstract

Many studies have demonstrated that divergence levels generated by different mutation types vary and covary across the human genome. To improve our still-incomplete understanding of the mechanistic basis of this phenomenon, we analyze several mutation types simultaneously, anchoring their variation to specific regions of the genome. Using hidden Markov models on insertion, deletion, nucleotide substitution, and microsatellite divergence estimates inferred from human-orangutan alignments of neutrally evolving genomic sequences, we segment the human genome into regions corresponding to different divergence states-each uniquely characterized by specific combinations of divergence levels. We then parsed the mutagenic contributions of various biochemical processes associating divergence states with a broad range of genomic landscape features. We find that high divergence states inhabit guanine-and cytosine (GC)-rich, highly recombining subtelomeric regions; low divergence states cover inner parts of autosomes; chromosome X forms its own state with lowest divergence; and a state of elevated microsatellite mutability is interspersed across the genome. These general trends are mirrored in human diversity data from the 1000 Genomes Project, and departures from them highlight the evolutionary history of primate chromosomes. We also find that genes and noncoding functional marks [annotations from the Encyclopedia of DNA Elements (ENCODE)] are concentrated in high divergence states. Our results provide a powerful tool for biomedical data analysis: segmentations can be used to screen personal genome variants-including those associated with cancer and other diseases-and to improve computational predictions of noncoding functional elements. W hole-genome sequencing studies have demonstrated that divergence estimates for several mutation types (e.g., nucleotide substitutions, insertions, and deletions) vary substantially across the human genome. This phenomenon has been studied at various genomic scales and evolutionary distances (reviewed in ref. 1), and-whereas initially of interest solely to evolutionary biologists-is now entering the purview of main biomedical research. Specifically, human population (e.g., ref.2) and cancer (3, 4) genome resequencing projects have revealed that incidences of single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and copy number variants (CNVs) vary across the genome. Divergence estimates for different mutation types also covary across the genome (5, 6)-e.g., substitution rates increase in regions with high indel rates (7)-suggesting that regional variation is an important and general characteristic of mutations.Variation in divergence is often linked to genomic landscape features such as base composition, replication timing, and recombination rates (1). For instance, nucleotide substitution rates are elevated in late-replicating regions because of an accumulation of single-stranded DNA susceptible to endogenous damage (8) and are affected by chromatin structure (9) and ...

The effects of chromatin organization on variation in mutation rates in the genome

“…In fact, elevated mutation rates were strongly positively associated with indicators of closed chromatin for diverse cancers (leukemia, melanoma, small cell lung cancer, and prostate cancer) and diverse substitution mutations (transitions and transversions, and CpG mutations and non-CpG mutations). This corresponds to the segmentation analysis DS+ state that was also associated with closed chromatin 61 . To explain a positive association of closed chromatin with elevated substitution rates, several hypotheses were proposed, including differing accessibility to DNA repair complexes, variation in the ability to signal repair, and increased exposure to mutagens at the nuclear periphery 15 .…”

“…Another way to infer the influence of chromatin organization on mutation rates is via an Hidden Markov Model (HMM) 62 segmentation of the genome based on rates of different mutation types 61 . HMMs have been used extensively in genomics to model stretches of DNA – the sequences of observations – in algorithms predicting genes (the hidden states) 63 and more recently to produce segmentations of the genome based on epigenomic signatures 64 .…”

“…Specifically, to apply HMMs in a study of mutation rates, divergence estimates were computed for four mutation types – base substitutions, insertions, deletions, and microsatellite repeat number alterations – in non-overlapping windows of neutrally evolving sequences present in human–orang-utan genomic alignments 61 . Modeling the resulting observations with HMMs revealed distinct divergence states characterized by biologically meaningful combinations of elevated, average, or depressed divergence levels for the four mutation types, e.g., a state where all four mutation rates are elevated; one where only one mutation type is elevated, whereas the other divergence types are average, etc.…”

“…Since the mildly elevated DS+ state makes up 18% of the genome while the highly elevated IDS++ state comprises only 8%, this suggests that pairwise results do not provide enough resolution to detect the less common but more nuanced associations. The insertion warm state, I+, was found in the open chromatin regions, while the state with low insertion, deletion, and substitution rates (IDS−) was located in closed chromatin 61 . The microsatellite state (M+) did not show any preference to a particular chromatin organization 61 .…”

“…The insertion warm state, I+, was found in the open chromatin regions, while the state with low insertion, deletion, and substitution rates (IDS−) was located in closed chromatin 61 . The microsatellite state (M+) did not show any preference to a particular chromatin organization 61 . The segmentation analysis also placed different states “geographically” in the genome, with the IDS++ state tending to occupy the tips of the chromosomes while other states were situated closer to the middle of the chromosomes; the M+ state was interspersed ( Figure 3) .…”