The genomics of development rate variation in Cochliomyia macellaria (Diptera: Calliphoridae)

Abstract

Development rate is a quantitative trait that displays significant variation within many species, including Cochliomyia macellaria Meigen (Diptera: Calliphoridae). Calliphorids are a family of dipterans known as blow flies and are commonly used in forensic entomology to estimate the minimum postmortem interval (PMIMIN), given some assumptions are made. In order to dissect the genetic underpinnings of development rate variation in this species, artificial selection for fast and slow development with population-based resequencing was used. The objective of this study is to isolate and characterize genomic regions that are correlated to development rate variation in blow flies. The first approach used known regulatory development genes from Drosophila melanogaster Meigen (Diptera: Drosophilidae) and isolated variants that were associated with development time changes in artificially selected fast and slow development C. macellaria strains. Three variants located in Ras and Eip74EF were associated with fast or slow development in selection strains with a significant change in allele frequency. The second approach involved the comparison of pooled artificially selected fast and slow C. macellaria genomes to investigate the genetic basis of development rate variation. When comparing the fast and slow genomes, 699 sequences were identified that contained 7290 variants with consistent changes in allele frequency. The variants indicated that the genomic regions that are associated with development rate were associated with developmental processes, including regulation of RNA polymerase II activity, and transporter activity, such as protein dimerization. Of the 699 sequences, 69 sequences were related to Achaete-scute complex and 14 were related to Cyp12A7. The identification of the genomic regions that associated with development rates from this study provides an important resource for future studies in identifying potential genetic markers to increase the effectiveness of PMIMIN estimates. By using significantly associated variants as a priori candidates for future studies, the data increases the understanding of natural development variation in blow flies.