Genevieve DeClerck

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.

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.

HopX1 in Erwinia amylovora functions as an avirulence protein in apple and is regulated by HrpL.

Fire blight is a devastating disease of rosaceous plants caused by the Gram-negative bacterium Erwinia amylovora. This pathogen delivers virulence proteins into host cells utilizing the type III secretion system (T3SS). Expression of the T3SS and of translocated and secreted substrates is activated by the alternative sigma factor HrpL, which recognizes hrp box promoters upstream of regulated genes. A collection of hidden Markov model (HMM) profiles was used to identify putative hrp boxes in the genome sequence of Ea273, a highly virulent strain of E. amylovora. Among potential virulence factors preceded by putative hrp boxes, two genes previously known as Eop3 and Eop2 were characterized. The presence of functionally active hrp boxes upstream of these two genes was confirmed by β-glucuronidase (GUS) assays. Deletion mutants of the latter candidate genes, renamed hopX1(Ea) and hopAK1(Ea), respectively, did not differ in virulence from the wild-type strain when assayed in pear fruit and apple shoots. The hopX1(Ea) deletion mutant of Ea273, complemented with a plasmid overexpressing hopX1(E)(a), suppressed the development of the hypersensitivity response (HR) when inoculated into Nicotiana benthamiana; however, it contributed to HR in Nicotiana tabacum and significantly reduced the progress of disease in apple shoots, suggesting that HopX1(Ea) may act as an avirulence protein in apple shoots.

Corrigendum: Open access resources for genome-wide association mapping in rice

Nature Communications 7: Article number: 10532 10.1038/ncomms10532 (2016); Published: April202016; Updated: April202016 The affiliation details for Paula R. Kuser-Falcao are incorrect in this Article. The correct affiliation details for this author are given below: Embrapa Agriculture Informatics, 13083-886 Campinas, Brazil.