Tembrock, L. R. | bioRxiv Science

bioRxiv2022

A chromosome-scale genome assembly of a Bacillus thuringiensis Cry1Ac insecticidal protein resistant strain of Helicoverpa zea

Helicoverpa zea (Lepidoptera: Noctuidae) is an insect pest of major cultivated crops in North and South America. The species has adapted to different host plants and developed resistance to several insecticidal agents, including Bacillus thuringiensis (Bt) insecticidal proteins in transgenic cotton and maize. H. zea populations persist year-round in tropical and subtropical regions, but seasonal migrations into temperate zones increase the geographic range of associated crop damage. To better understand the genetic basis of these physiological and ecological characteristics, we generated a high-quality chromosome-level assembly for a single H. zea male from Bt resistant strain, HzStark_Cry1AcR. Hi-C data were used to scaffold an initial 375.2 Mb contig assembly into 30 autosomes and the Z sex chromosome (scaffold N50 = 12.8 Mb and L50 = 14). The scaffolded assembly was error-corrected with a novel pipeline, polishCLR. The mitochondrial genome was assembled through an improved pipeline and annotated. Assessment of this genome assembly indicated 98.8% of the Lepidopteran Benchmark Universal Single-Copy Ortholog set were complete (98.5% as complete single-copy). Repetitive elements comprised approximately 29.5% of the assembly with the plurality (11.2%) classified as retroelements. This chromosome-scale reference assembly for H. zea, ilHelZeax1.1, will facilitate future research to evaluate and enhance sustainable crop production practices. SignificanceWe established a chromosome-level reference assembly for Helicoverpa zea, an insect pest of multiple cultivated crops in the Americas. This assembly of a Bacillus thuringiensis insecticidal protein resistant strain, HzStark_Cry1AcR, will facilitate future research in areas such as population genomics and adaptations to agricultural control practices.

genomics↗

bioRxiv2016

Effect of error and missing data on population structure inference using microsatellite data

Missing data and genotyping errors are common in microsatellite data sets. We used simulated data to quantify the effect of these data aberrations on the accuracy of population structure inference. Data sets with complex, randomly-generated, population histories were simulated under the coalescent. Models describing the characteristic patterns of missing data and genotyping error in real microsatellite data sets were used to modify the simulated data sets. Accuracy of ordination, tree-based, and model-based methods of inference was evaluated before and after data set modifications. The ability to recover correct population clusters decreased as missing data increased. The rate of decrease was similar among analytical procedures, thus no single analytical approach was preferable. For every 1% of a data matrix that contained missing genotypes, 2-4% fewer correct clusters were found. For every 1% of a matrix that contained erroneous genotypes, 1-2% fewer correct clusters were found using ordination and tree-based methods. Model-based procedures that minimize the deviation from Hardy-Weinberg equilibrium in order to assign individuals to clusters performed better as genotyping error increased. We attribute this surprising result to the inbreeding-like nature of microsatellite genotyping error, wherein heterozygous genotypes are mischaracterized as homozygous. We show that genotyping error elevates estimates of the level of genetic admixture. Overall, missing data negatively impact population structure inference more than typical genotyping errors.

evolutionary biology↗