Toward an open-source 3D-printable laboratory.

Premise: Low-cost, repairable lab equipment is rare within the biological sciences. By lowering the costs of entry using 3D printing and open-source hardware, our goal is to empower both amateur and professional scientists to conduct research. Methods: We developed a modular system of 3D-printable designs called COBLE (Collection of Bespoke Laboratory Equipment), including novel and remixed 3D-printable lab equipment that can be inexpensively printed, assembled, and repaired for a fraction of the cost of retail equivalents. Results: Here we present novel tools that utilize 3D printing to enable a wide range of scientific experiments. We include additional resources for scientists and labs that are interested in utilizing 3D printing for their research. Discussion: By describing the broad potential that 3D-printed designs can have in the biological sciences, we hope to inspire others to implement and improve upon these designs, improving accessibility and enabling science for all.

A Simplified Microscopy Technique to Rapidly Characterize Individual Fiber Traits in Cotton.

Recent advances in phenotyping techniques have substantially improved the ability to mitigate type-II errors typically associated with high variance in phenotyping data sets. In particular, the implementation of automated techniques such as the High-Volume Instrument (HVI) and the Advanced Fiber Information System (AFIS) have significantly enhanced the reproducibility and standardization of various fiber quality measurements in cotton. However, micronaire is not a direct measure of either maturity or fineness, lending to limitations. AFIS only provides a calculated form of fiber diameter, not a direct measure, justifying the need for a visual-based reference method. Obtaining direct measurements of individual fibers through cross-sectional analysis and electron microscopy is a widely accepted standard but is time-consuming and requires the use of hazardous chemicals and specialized equipment. In this study, we present a simplified fiber histology and image acquisition technique that is both rapid and reproducible. We also introduce an automated image analysis program that utilizes machine learning to differentiate good fibers from bad and to subsequently collect critical phenotypic measurements. These methods have the potential to improve the efficiency of cotton fiber phenotyping, allowing for greater precision in unravelling the genetic architecture of critical traits such as fiber diameter, shape, areas of the secondary cell wall/lumen, and others, ultimately leading to larger genetic gains in fiber quality and improvements in cotton.

Genomic Analysis and Prediction within a US Public Collaborative Winter Wheat Regional Testing Nursery.

The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly unbalanced datasets that are common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat ( L.) varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be re-entered only once. The trial is grown at >30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing (GBS) for 939 SPRN entries dating back to 1992 to explore the potential use of the nursery as a genomic selection (GS) training population (TP). The GS prediction models across years (average = 0.33) outperformed year-to-year phenotypic correlation for yield ( = 0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set TPs. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.

QTL mapping of pre-harvest sprouting resistance in a white wheat cultivar Danby.

KEY MESSAGE: One major and three minor QTLs for resistance to pre-harvest sprouting (PHS) were identified from a white wheat variety "Danby." The major QTL on chromosome 3A is TaPHS1, and the sequence variation in its promoter region was responsible for the PHS resistance. Additive × additive effects were detected between two minor QTLs on chromosomes 3B and 5A, which can greatly enhance the PHS resistance. Pre-harvest sprouting (PHS) causes significant losses in yield and quality in wheat. White wheat is usually more susceptible to PHS than red wheat. Therefore, the use of none grain color-related PHS resistance quantitative trait loci (QTLs) is essential for the improvement in PHS resistance in white wheat. To identify PHS resistance QTLs in the white wheat cultivar "Danby" and determine their effects, a doubled haploid population derived from a cross of Danby × "Tiger" was genotyped using genotyping-by-sequencing markers and phenotyped for PHS resistance in two greenhouse and one field experiments. One major QTL corresponding to a previously cloned gene, TaPHS1, was consistently detected on the chromosome arm 3AS in all three experiments and explained 21.6-41.0% of the phenotypic variations. A SNP (SNP-222) in the promoter of TaPHS1 co-segregated with PHS in this mapping population and was also significantly associated with PHS in an association panel. Gene sequence comparison and gene expression analysis further confirmed that SNP-222 is most likely the causal mutation in TaPHS1 for PHS resistance in Danby in this study. In addition, two stable minor QTLs on chromosome arms 3BS and 5AL were detected in two experiments with allele effects consistently contributed by Danby, while one minor QTL on 2AS was detected in two environments with contradicted allelic effects. The two stable minor QTLs showed significant additive × additive effects. The results demonstrated that pyramiding those three QTLs using breeder-friendly KASP markers developed in this study could greatly improve PHS resistance in white wheat.

Quantifying variety-specific heat resistance and the potential for adaptation to climate change.

The impact of climate change on crop yields has become widely measured; however, the linkages for winter wheat are less studied due to dramatic weather changes during the long growing season that are difficult to model. Recent research suggests significant reductions under warming. A potential adaptation strategy involves the development of heat resistant varieties by breeders, combined with alternative variety selection by producers. However, the impact of heat on specific wheat varieties remains relatively unstudied due to limited data and the complex genetic basis of heat tolerance. Here, we provide a novel econometric approach that combines field-trial data with a genetic cluster mapping to group wheat varieties and estimate a separate extreme heat impact (temperatures over 34 °C) across 24 clusters spanning 197 varieties. We find a wide range of heterogeneous heat resistance and a trade-off between average yield and resistance. Results suggest that recently released varieties are less heat resistant than older varieties, a pattern that also holds for on-farm varieties. Currently released - but not yet adopted - varieties do not offer improved resistance relative to varieties currently grown on farm. Our findings suggest that warming impacts could be significantly reduced through advances in wheat breeding and/or adoption decisions by producers. However, current adaptation-through-adoption potential is limited under a 1 °C warming scenario as increased heat resistance cannot be achieved without a reduction in average yields.

Spiked GBS: a unified, open platform for single marker genotyping and whole-genome profiling.

BACKGROUND: In plant breeding, there are two primary applications for DNA markers in selection: 1) selection of known genes using a single marker assay (marker-assisted selection; MAS); and 2) whole-genome profiling and prediction (genomic selection; GS). Typically, marker platforms have addressed only one of these objectives. RESULTS: We have developed spiked genotyping-by-sequencing (sGBS), which combines targeted amplicon sequencing with reduced representation genotyping-by-sequencing. To minimize the cost of targeted assays, we utilize a small percent of sequencing capacity available in runs of GBS libraries to "spike" amplified targets of a priori alleles tagged with a different set of unique barcodes. This open platform allows multiple, single-target loci to be assayed while simultaneously generating a whole-genome profile. This dual-genotyping approach allows different sets of samples to be evaluated for single markers or whole genome-profiling. Here, we report the application of sGBS on a winter wheat panel that was screened for converted KASP markers and newly-designed markers targeting known polymorphisms in the leaf rust resistance gene Lr34. CONCLUSIONS: The flexibility and low-cost of sGBS will enable a range of applications across genetics research. Specifically in breeding applications, the sGBS approach will allow breeders to obtain a whole-genome profile of important individuals while simultaneously targeting specific genes for a range of selection strategies across the breeding program.