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Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM–WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM–WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM–WGP model for heading and physiological maturity dates. However, the CGM–WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM–WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM–WGP modelling and suggest future efforts should focus on calibrating CGM–WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect. [ABSTRACT FROM AUTHOR]

Abstract Background The Hessian fly (Mayetiola destructor) is an important insect pest of wheat. It has tractable genetics, polytene chromosomes, and a small genome (158 Mb). Investigation of the Hessian fly presents excellent opportunities to study plant-insect interactions and the molecular mechanisms underlying genome imprinting and chromosome elimination. A physical map is needed to improve the ability to perform both positional cloning and comparative genomic analyses with the fully sequenced genomes of other dipteran species. Results An FPC-based genome wide physical map of the Hessian fly was constructed and anchored to the insect's polytene chromosomes. Bacterial artificial chromosome (BAC) clones corresponding to 12-fold coverage of the Hessian fly genome were fingerprinted, using high information content fingerprinting (HIFC) methodology, and end-sequenced. Fluorescence in situ hybridization (FISH) co-localized two BAC clones from each of the 196 longest contigs on the polytene chromosomes. An additional 70 contigs were positioned using a single FISH probe. The 266 FISH mapped contigs were evenly distributed and covered 60% of the genome (95,668 kb). The ends of the fingerprinted BACs were then sequenced to develop the capacity to create sequenced tagged site (STS) markers on the BACs in the map. Only 3.64% of the BAC-end sequence was composed of transposable elements, helicases, ribosomal repeats, simple sequence repeats, and sequences of low complexity. A relatively large fraction (14.27%) of the BES was comprised of multi-copy gene sequences. Nearly 1% of the end sequence was composed of simple sequence repeats (SSRs). Conclusion This physical map provides the foundation for high-resolution genetic mapping, map-based cloning, and assembly of complete genome sequencing data. The results indicate that restriction fragment length heterogeneity in BAC libraries used to construct physical maps lower the length and the depth of the contigs, but is not an absolute barrier to the successful application of the technology. This map will serve as a genomic resource for accelerating gene discovery, genome sequencing, and the assembly of BAC sequences. The Hessian fly BAC-clone assembly, and the names and positions of the BAC clones used in the FISH experiments are publically available at http://genome.purdue.edu/WebAGCoL/Hfly/WebFPC/.

Abstract Background To have an insight into the Mayetiola destructor (Hessian fly) genome, we performed an in silico comparative genomic analysis utilizing genetic mapping, genomic sequence and EST sequence data along with data available from public databases. Results Chromosome walking and FISH were utilized to identify a contig of 50 BAC clones near the telomere of the short arm of Hessian fly chromosome X2 and near the avirulence gene vH13. These clones enabled us to correlate physical and genetic distance in this region of the Hessian fly genome. Sequence data from these BAC ends encompassing a 760 kb region, and a fully sequenced and assembled 42.6 kb BAC clone, was utilized to perform a comparative genomic study. In silico gene prediction combined with BLAST analyses was used to determine putative orthology to the sequenced dipteran genomes of the fruit fly, Drosophila melanogaster, and the malaria mosquito, Anopheles gambiae, and to infer evolutionary relationships. Conclusion This initial effort enables us to advance our understanding of the structure, composition and evolution of the genome of this important agricultural pest and is an invaluable tool for a whole genome sequencing effort.

Highly specialized obligate plant-parasites exist within several groups of arthropods (insects and mites). Many of these are important pests, but the molecular basis of their parasitism and its evolution are poorly understood. One hypothesis is that plant parasitic arthropods use effector proteins to defeat basal plant immunity and modulate plant growth. Because avirulence (Avr) gene discovery is a reliable method of effector identification, we tested this hypothesis using high-resolution molecular genetic mapping of an Avr gene (vH13) in the Hessian fly (HF, Mayetiola destructor), an important gall midge pest of wheat (Triticum spp.). Chromosome walking resolved the position of vH13, and revealed alleles that determine whether HF larvae are virulent (survive) or avirulent (die) on wheat seedlings carrying the wheat H13 resistance gene. Association mapping found three independent insertions in vH13 that appear to be responsible for H13-virulence in field populations. We observed vH13 transcription in H13-avirulent larvae and the salivary glands of H13-avirulent larvae, but not in H13-virulent larvae. RNA-interference-knockdown of vH13 transcripts allowed some H13-avirulent larvae to escape H13-directed resistance. vH13 is the first Avr gene identified in an arthropod. It encodes a small modular protein with no sequence similarities to other proteins in GenBank. These data clearly support the hypothesis that an effector-based strategy has evolved in multiple lineages of plant parasites, including arthropods. [ABSTRACT FROM AUTHOR]

Highly specialized obligate plant-parasites exist within several groups of arthropods (insects and mites). Many of these are important pests, but the molecular basis of their parasitism and its evolution are poorly understood. One hypothesis is that plant parasitic arthropods use effector proteins to defeat basal plant immunity and modulate plant growth. Because avirulence (Avr) gene discovery is a reliable method of effector identification, we tested this hypothesis using high-resolution molecular genetic mapping of an Avr gene (vH13) in the Hessian fly (HF, Mayetiola destructor), an important gall midge pest of wheat (Triticum spp.). Chromosome walking resolved the position of vH13, and revealed alleles that determine whether HF larvae are virulent (survive) or avirulent (die) on wheat seedlings carrying the wheat H13 resistance gene. Association mapping found three independent insertions in vH13 that appear to be responsible for H13-virulence in field populations. We observed vH13 transcription in H13-avirulent larvae and the salivary glands of H13-avirulent larvae, but not in H13-virulent larvae. RNA-interference-knockdown of vH13 transcripts allowed some H13-avirulent larvae to escape H13-directed resistance. vH13 is the first Avr gene identified in an arthropod. It encodes a small modular protein with no sequence similarities to other proteins in GenBank. These data clearly support the hypothesis that an effector-based strategy has evolved in multiple lineages of plant parasites, including arthropods. [ABSTRACT FROM AUTHOR]