empirical-metagenomic-computational-techniques-illuminate-mechanisms

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered ...

in-vitro-rearing-solitary-bees-tool-for-assessing-larval-risk

<em>In Vitro</em> Rearing of Solitary Bees: A Tool for Assessing Larval Risk Factors

Although solitary bees provide crucial pollination services for wild and managed crops, this species-rich group has been largely overlooked in pesticide ...

University of Wisconsin-Madison

Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias

Instituto Nacional de Investigaciones Forestales Agricolas y Pecuarias

Traditional methods for trait phenotyping have been a bottleneck for research in many crop species due to their intensive labor, high cost, complex implementation, lack of reproducibility and propensity to subjective bias. Recently, multiple high-throughput phenotyping platforms have been developed, but most of them are expensive, species-dependent, complex to use, and available only for major crops. To overcome such limitations, we present the open-source software GiNA, which is a simple and free tool for measuring horticultural traits such as shape- and color-related parameters of fruits, vegetables, and seeds. GiNA is multiplatform software available in both R and MATLAB® programming languages and uses conventional images from digital cameras with minimal requirements. It can process up to 11 different horticultural morphological traits such as length, width, two-dimensional area, volume, projected skin, surface area, RGB color, among other parameters. Different validation tests produced highly consistent results under different lighting conditions and camera setups making GiNA a very reliable platform for high-throughput phenotyping. In addition, five-fold cross validation between manually generated and GiNA measurements for length and width in cranberry fruits were 0.97 and 0.92. In addition, the same strategy yielded prediction accuracies above 0.83 for color estimates produced from images of cranberries analyzed with GiNA compared to total anthocyanin content (TAcy) of the same fruits measured with the standard methodology of the industry. Our platform provides a scalable, easy-to-use and affordable tool for massive acquisition of phenotypic data of fruits, seeds, and vegetables.

GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping.

Determination of microsatellite lengths or other DNA fragment types is an important initial component of many genetic studies such as mutation detection, linkage and quantitative trait loci (QTL) mapping, genetic diversity, pedigree analysis, and detection of heterozygosity. A handful of commercial and freely available software programs exist for fragment analysis; however, most of them are platform dependent and lack high-throughput applicability.

Fragman: an R package for fragment analysis.

The application of genotyping by sequencing (GBS) approaches, combined with data imputation methodologies, is narrowing the genetic knowledge gap between major and understudied, minor crops. GBS is an excellent tool to characterize the genomic structure of recently domesticated (~200 years) and understudied species, such as cranberry (Vaccinium macrocarpon Ait.), by generating large numbers of markers for genomic studies such as genetic mapping.

Exploiting genotyping by sequencing to characterize the genomic structure of the American cranberry through high-density linkage mapping.

The American cranberry (Vaccinium macrocarpon Ait.) is a recently domesticated, economically important, fruit crop with limited molecular resources. New genetic resources could accelerate genetic gain in cranberry through characterization of its genomic structure and by enabling molecular-assisted breeding strategies. To increase the availability of cranberry genomic resources, genotyping-by-sequencing (GBS) was used to discover and genotype thousands of single nucleotide polymorphisms (SNPs) within three interrelated cranberry full-sib populations. Additional simple sequence repeat (SSR) loci were added to the SNP datasets and used to construct bin maps for the parents of the populations, which were then merged to create the first high-density cranberry composite map containing 6073 markers (5437 SNPs and 636 SSRs) on 12 linkage groups (LGs) spanning 1124 cM. Interestingly, higher rates of recombination were observed in maternal than paternal gametes. The large number of markers in common (mean of 57.3) and the high degree of observed collinearity (mean Pair-wise Spearman rank correlations >0.99) between the LGs of the parental maps demonstrates the utility of GBS in cranberry for identifying polymorphic SNP loci that are transferable between pedigrees and populations in future trait-association studies. Furthermore, the high-density of markers anchored within the component maps allowed identification of segregation distortion regions, placement of centromeres on each of the 12 LGs, and anchoring of genomic scaffolds. Collectively, the results represent an important contribution to the current understanding of cranberry genomic structure and to the availability of molecular tools for future genetic research and breeding efforts in cranberry.

Construction of a High-Density American Cranberry (Vaccinium macrocarpon Ait.) Composite Map Using Genotyping-by-Sequencing for Multi-pedigree Linkage Mapping.

Luis Diaz-Garcia

Department of Horticulture

University of Wisconsin-Madison

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

In Vitro Rearing of Solitary Bees: A Tool for Assessing Larval Risk Factors