Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Gramene database in 2010: updates and extensions.

Now in its 10th year, the Gramene database (http://www.gramene.org) has grown from its primary focus on rice, the first fully-sequenced grass genome, to become a resource for major model and crop plants including Arabidopsis, Brachypodium, maize, sorghum, poplar and grape in addition to several species of rice. Gramene began with the addition of an Ensembl genome browser and has expanded in the last decade to become a robust resource for plant genomics hosting a wide array of data sets including quantitative trait loci (QTL), metabolic pathways, genetic diversity, genes, proteins, germplasm, literature, ontologies and a fully-structured markers and sequences database integrated with genome browsers and maps from various published studies (genetic, physical, bin, etc.). In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data.

A Coordinated Suite of Wild-Introgression Lines in Indica and Japonica Elite Backgrounds.

Rice, Oryza sativa L., is a cultivated, inbreeding species that serves as the staple food for the largest number of people on earth. It has two strongly diverged varietal groups, Indica and Japonica, which result from a combination of natural and human selection. The genetic divergence of these groups reflects the underlying population structure of their wild ancestors, and suggests that a pre-breeding strategy designed to take advantage of existing genetic, geographic and ecological substructure may provide a rational approach to the utilization of crop wild ancestors in plant improvement. Here we describe the coordinated development of six introgression libraries (n = 63 to 81 lines per library) in both Indica (cv. IR64) and Japonica (cv. Cybonnet) backgrounds using three bio-geographically diverse wild donors representing the Oryza rufipogon Species Complex from China, Laos and Indonesia. The final libraries were genotyped using an Infinium 7K rice SNP array (C7AIR) and analyzed under greenhouse conditions for several simply inherited (Mendelian) traits. These six interspecific populations can be used as individual Chromosome Segment Substitution Line libraries and, when considered together, serve as a powerful genetic resource for systematic genetic dissection of agronomic, physiological and developmental traits in rice.

Corrigendum: A Coordinated Suite of Wild-Introgression Lines in Indica and Japonica Elite Backgrounds.

[This corrects the article DOI: 10.3389/fpls.2020.564824.].

Large-scale deployment of a rice 6 K SNP array for genetics and breeding applications.

BACKGROUND: Fixed arrays of single nucleotide polymorphism (SNP) markers have advantages over reduced representation sequencing in their ease of data analysis, consistently higher call rates, and rapid turnaround times. A 6 K SNP array represents a cost-benefit "sweet spot" for routine genetics and breeding applications in rice. Selection of informative SNPs across species and subpopulations during chip design is essential to obtain useful polymorphism rates for target germplasm groups. This paper summarizes results from large-scale deployment of an Illumina 6 K SNP array for rice. RESULTS: Design of the Illumina Infinium 6 K SNP chip for rice, referred to as the Cornell_6K_Array_Infinium_Rice (C6AIR), includes 4429 SNPs from re-sequencing data and 1571 SNP markers from previous BeadXpress 384-SNP sets, selected based on polymorphism rate and allele frequency within and between target germplasm groups. Of the 6000 attempted bead types, 5274 passed Illumina's production quality control. The C6AIR was widely deployed at the International Rice Research Institute (IRRI) for genetic diversity analysis, QTL mapping, and tracking introgressions and was intensively used at Cornell University for QTL analysis and developing libraries of interspecific chromosome segment substitution lines (CSSLs) between O. sativa and diverse accessions of O. rufipogon or O. meridionalis. Collectively, the array was used to genotype over 40,000 rice samples. A set of 4606 SNP markers was used to provide high quality data for O. sativa germplasm, while a slightly expanded set of 4940 SNPs was used for O. sativa X O. rufipogon populations. Biparental polymorphism rates were generally between 1900 and 2500 well-distributed SNP markers for indica x japonica or interspecific populations and between 1300 and 1500 markers for crosses within indica, while polymorphism rates were lower for pairwise crosses within U.S. tropical japonica germplasm. Recently, a second-generation array containing ~7000 SNP markers, referred to as the C7AIR, was designed by removing poor-performing SNPs from the C6AIR and adding markers selected to increase the utility of the array for elite tropical japonica material. CONCLUSIONS: The C6AIR has been successfully used to generate rapid and high-quality genotype data for diverse genetics and breeding applications in rice, and provides the basis for an optimized design in the C7AIR.

Whole-genome expression profiling defines the HrpL regulon of Pseudomonas syringae pv. tomato DC3000, allows de novo reconstruction of the Hrp cis clement, and identifies novel coregulated genes.

Pseudomonas syringae pv. tomato DC3000 is a model pathogen of tomato and Arabidopsis that uses a hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS) to deliver virulence effector proteins into host cells. Expression of the Hrp system and many effector genes is activated by the HrpL alternative sigma factor. Here, an open reading frame-specific whole-genome microarray was constructed for DC3000 and used to comprehensively identify genes that are differentially expressed in wild-type and deltahrpL strains. Among the genes whose differential regulation was statistically significant, 119 were upregulated and 76 were downregulated in the wild-type compared with the deltahrpL strain. Hierarchical clustering revealed a subset of eight genes that were upregulated particularly rapidly. Gibbs sampling of regions upstream of HrpL-activated operons revealed the Hrp promoter as the only identifiable regulatory motif and supported an iterative refinement involving real-time polymerase chain reaction testing of additional HrpL-activated genes and refinements in a hidden Markov model that can be used to predict Hrp promoters in P. syringae strains. This iterative bioinformatic-experimental approach to a comprehensive analysis of the HrpL regulon revealed a mix of genes controlled by HrpL, including those encoding most type III effectors, twin-arginine transport (TAT) substrates, other regulatory proteins, and proteins involved in the synthesis or metabolism of phytohormones, phytotoxins, and myo-inositol. This analysis provides an extensively verified, robust method for predicting Hrp promoters in P. syringae genomes, and it supports subsequent identification of effectors and other factors that likely are important to the host-specific virulence of P. syringae.

Bioinformatics-enabled identification of the HrpL regulon and type III secretion system effector proteins of Pseudomonas syringae pv. phaseolicola 1448A.

The ability of Pseudomonas syringae pv. phaseolicola to cause halo blight of bean is dependent on its ability to translocate effector proteins into host cells via the hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS). To identify genes encoding type III effectors and other potential virulence factors that are regulated by the HrpL alternative sigma factor, we used a hidden Markov model, weight matrix model, and type III targeting-associated patterns to search the genome of P. syringae pv. phaseolicola 1448A, which recently was sequenced to completion. We identified 44 high-probability putative Hrp promoters upstream of genes encoding the core T3SS machinery, 27 candidate effectors and related T3SS substrates, and 10 factors unrelated to the Hrp system. The expression of 13 of these candidate HrpL regulon genes was analyzed by real-time polymerase chain reaction, and all were found to be upregulated by HrpL. Six of the candidate type III effectors were assayed for T3SS-dependent translocation into plant cells using the Bordetella pertussis calmodulin-dependent adenylate cyclase (Cya) translocation reporter, and all were translocated. PSPPH1855 (ApbE-family protein) and PSPPH3759 (alcohol dehydrogenase) have no apparent T3SS-related function; however, they do have homologs in the model strain P. syringae pv. tomato DC3000 (PSPTO2105 and PSPTO0834, respectively) that are similarly upregulated by HrpL. Mutations were constructed in the DC3000 homologs and found to reduce bacterial growth in host Arabidopsis leaves. These results establish the utility of the bioinformatic or candidate gene approach to identifying effectors and other genes relevant to pathogenesis in P. syringae genomes.

Open access resources for genome-wide association mapping in rice.

Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. To address the demand, plant breeders seek new sources of genetic variation to enhance the productivity, sustainability and resilience of crop varieties. Here we launch a high-resolution, open-access research platform to facilitate genome-wide association mapping in rice, a staple food crop. The platform provides an immortal collection of diverse germplasm, a high-density single-nucleotide polymorphism data set tailored for gene discovery, well-documented analytical strategies, and a suite of bioinformatics resources to facilitate biological interpretation. Using grain length, we demonstrate the power and resolution of our new high-density rice array, the accompanying genotypic data set, and an expanded diversity panel for detecting major and minor effect QTLs and subpopulation-specific alleles, with immediate implications for rice improvement.

Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.).

A total of 2414 new di-, tri- and tetra-nucleotide non-redundant SSR primer pairs, representing 2240 unique marker loci, have been developed and experimentally validated for rice (Oryza sativa L.). Duplicate primer pairs are reported for 7% (174) of the loci. The majority (92%) of primer pairs were developed in regions flanking perfect repeats > or = 24 bp in length. Using electronic PCR (e-PCR) to align primer pairs against 3284 publicly sequenced rice BAC and PAC clones (representing about 83% of the total rice genome), 65% of the SSR markers hit a BAC or PAC clone containing at least one genetically mapped marker and could be mapped by proxy. Additional information based on genetic mapping and "nearest marker" information provided the basis for locating a total of 1825 (81%) of the newly designed markers along rice chromosomes. Fifty-six SSR markers (2.8%) hit BAC clones on two or more different chromosomes and appeared to be multiple copy. The largest proportion of SSRs in this data set correspond to poly(GA) motifs (36%), followed by poly(AT) (15%) and poly(CCG) (8%) motifs. AT-rich microsatellites had the longest average repeat tracts, while GC-rich motifs were the shortest. In combination with the pool of 500 previously mapped SSR markers, this release makes available a total of 2740 experimentally confirmed SSR markers for rice, or approximately one SSR every 157 kb.

PICARA, an analytical pipeline providing probabilistic inference about a priori candidates genes underlying genome-wide association QTL in plants.

PICARA is an analytical pipeline designed to systematically summarize observed SNP/trait associations identified by genome wide association studies (GWAS) and to identify candidate genes involved in the regulation of complex trait variation. The pipeline provides probabilistic inference about a priori candidate genes using integrated information derived from genome-wide association signals, gene homology, and curated gene sets embedded in pathway descriptions. In this paper, we demonstrate the performance of PICARA using data for flowering time variation in maize - a key trait for geographical and seasonal adaption of plants. Among 406 curated flowering time-related genes from Arabidopsis, we identify 61 orthologs in maize that are significantly enriched for GWAS SNP signals, including key regulators such as FT (Flowering Locus T) and GI (GIGANTEA), and genes centered in the Arabidopsis circadian pathway, including TOC1 (Timing of CAB Expression 1) and LHY (Late Elongated Hypocotyl). In addition, we discover a regulatory feature that is characteristic of these a priori flowering time candidates in maize. This new probabilistic analytical pipeline helps researchers infer the functional significance of candidate genes associated with complex traits and helps guide future experiments by providing statistical support for gene candidates based on the integration of heterogeneous biological information.