bioRxivEvolutionary BiologyPreprint
Sequence conservation need not imply purifying selection: evidence from mammalian stop codon usage
Abstract
The assumption that conservation of sequence implies the action of purifying selection is central to diverse methodologies to infer functional importance. In mammals, however, GC-biased gene conversion (gBGC), a meiotic mismatch repair bias strongly favouring GC over AT, can in principle mimic the action of selection. As mutation is GC[->]AT biased, to demonstrate that gBGC does indeed cause false signals requires confidence that an AT-rich residue is selectively optimal compared to its more GC-rich allele, while showing also that the GC-rich alternative is conserved. We propose that mammalian stop codon evolution provides a robust test case. Although in most taxa TAA is the optimal stop codon, TGA is both abundant and conserved in mammalian genomes. We show that this mammalian exceptionalism is well explained by gBGC mimicking purifying selection and that TAA is the selectively optimal codon. Supportive of gBGC, we observe (i) TGA usage trends are consistent at the focal stop and elsewhere (in UTR sequences), (ii) that higher TGA usage and higher TAA[->]TGA substitution rates are predicted by high recombination rate and (iii) across species the difference in TAA TGA rates between GC rich and GC poor genes is largest in genomes that possess higher between-gene GC variation. TAA optimality is supported both by enrichment in highly expressed genes and trends associated with effective population size. High TGA usage and high TAA[->]TGA rates in mammals are thus consistent with gBGCs predicted ability to "drive" deleterious mutations and supports the hypothesis that sequence conservation need not be indicative of purifying selection. A general trend for GC-rich trinucleotides to reside at frequencies far above their mutational equilibrium in high recombining domains supports generality of these results.
- organism: mouse
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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