Genomic tools to reduce error in PMI estimates derived from entomological evidence

Forensic entomologists use predictable patterns in blow fly development to estimate a postmortem interval (PMI). While current methods are accurate when assumptions are satisfied, they can be imprecise for the later stages of development, which encompass approximately the last 75% of immature development. The research proposed here is designed to decrease error in PMI predictions derived from developmental data through the application of developmental and quantitative genetic approaches to the study of the development of a forensic indicator fly species, Cochliomyia macellaria. These experiments will identify genetic markers of blow fly developmental progress and genotypes with fast or slow development rates. Recent data in developmental genetics indicate that such genomic data can decrease error in predictions of age Lucilia sericata, another forensically informative blow fly. However, those studies did not use the best genes for this purpose, as genomic tools were unavailable. Also, quantitative genetic theory predicts that genotype and environment can influence variation in a continuously variable trait, including the development time and size phenotypes used by forensic entomologists. This means that genetic and environmental factors are potential sources of error in forensic entomology. Environmental influences (i.e., temperature) are known to affect blow fly development rates, with fluctuating temperatures exerting differential effects on development compared to constant temperatures. In order to characterize the effect of environmental factors, we propose to conduct transcriptomic profiling of C. macellaria raised in different thermal environments to identify genetic markers of developmental progress and thermal environments. Recent data indicate that different populations of the same blow fly species raised in the same environment can differ in development rate such that the assumption of an inappropriate developmental data set may result in >10% error in PMI predictions. This is an indication of a genotypic effect on the blow fly phenotype. Therefore, we propose to use selection experiments, de novo assembly, and re-sequencing in C. macellaria selected lines to find genes responsible for fast and slow development. The selection experiments will enable an evaluation of the genetic limits of development time in C. macellaria, which will define one component of error in a PMI estimate. A genomic assessment of the populations exposed to selection will enable the identification of genetic variation that can be used to predict whether a fly is a fast or slow developing member of its species. Upon the identification and validation of the proposed genetic markers, we propose to test their utility in a validation study by phenotyping and genotyping wild flies across different ecoregions in Texas. The combination of results from the proposed experiments will allow forensic entomologists to predict a range of developmental progress and define expectations of the appropriate data set to use. For instance, a forensic entomologist would be able to identify an evidentiary sample that has completed some proportion of its development and state that the most appropriate minimum development time estimate could be obtained by assuming a data set representative of a fast/slow developing strain raised at high/low temperatures. Such a genomics-based approach would decrease error associated with a PMI estimate by pinpointing more specific periods of immature blow fly development and by accounting for genetic and environmental effects on blow fly development. The blow fly targeted in this study is a common forensic indicator species in the Southern United States (one of two common species typically encountered in Texas casework), thus any knowledge gained will immediately be applicable to a number of large metropolitan areas.

ca/ncf