Deep sequencing of natural and experimental populations of Drosophila melanogaster reveals biases in the spectrum of new mutations

Abstract

Mutations provide the raw material of evolution, and thus our ability to study evolution fundamentally depends on having precise measurements of mutational rates and patterns. We generate a dataset for this purpose using i) de novo mutations from mutation accumulation experiments and ii) extremely rare polymorphisms from natural populations. The first, mutation accumulation (MA) lines, are the product of maintaining flies in tiny populations for many generations, therefore rendering natural selection ineffective and allowing new mutations to accrue in the genome. The second, rare genetic variation from natural populations, allows the study of mutation because very rare polymorphisms are relatively unaffected by the filter of natural selection. We use both methods in Drosophila melanogaster, first generating our own novel dataset of sequenced MA lines as well as performing a meta-analysis of all published MA mutations to-date (∼2,000 events), and then second we identify a high quality set of extremely rare (<=0.1%) polymorphisms that are fully validated with resequencing (∼70,000 events). We use these datasets to precisely measure mutational rates and patterns. Highlights of our results include: a high rate of multi-nucleotide mutation events at both short (∼5bp) and long (∼1kb) genomic distances, finding that mutation drives GC content lower in already GC-poor regions, and using our precise context-dependent mutation rates to predict long-term evolutionary patterns at synonymous sites. We also show that de novo mutations from independent MA experiments display similar patterns of single nucleotide mutation, and well match the patterns of mutation found in natural populations.