Aphids are major agricultural pests that cause significant yield losses of crop plants each year. Excessive dependence on insecticides for long-term aphid control is undesirable because of the development of insecticide resistance, the potential negative effects on non-target organisms and environmental pollution. Transgenic crops engineered for resistance to aphids via a non-toxic mode of action could be an efficient alternative strategy. (E)-β-Farnesene (EβF) synthases catalyze the formation of EβF, which for many pest aphids is the main component of the alarm pheromone involved in the chemical communication within these species. EβF can also be synthesized by certain plants but is then normally contaminated with inhibitory compounds. Engineering of crop plants capable of synthesizing and emitting EβF could cause repulsion of aphids and also the attraction of natural enemies that use EβF as a foraging cue, thus minimizing aphid infestation. In this review, the effects of aphids on host plants, plants’ defenses against aphid herbivory and the recruitment of natural enemies for aphid control in an agricultural setting are briefly introduced. Furthermore, the plant-derived EβF synthase genes cloned to date along with their potential roles in generating novel aphid resistance via genetically modified approaches are discussed.

Yu XD, Pickett J, Ma YZ, Bruce T, Napier J, Jones HD, Xia LQ (2012) Metabolic engineering of plant-derived (E)-β-farnesene synthase genes for a novel type of aphid-resistant genetically modified crop plants. J. Integr. Plant Biol. 54(5), 282–299.

Grain yield is one of the most important indexes in rice breeding, which is governed by quantitative trait loci (QTLs). Different mapping populations have been used to explore the QTLs controlling yield related traits. Primary populations such as F₂ and recombinant inbred line populations have been widely used to discover QTLs in rice genome-wide, with hundreds of yield-related QTLs detected. Advanced populations such as near isogenic lines (NILs) are efficient to further fine-map and clone target QTLs. NILs for primarily identified QTLs have been proposed and confirmed to be the ideal population for map-based cloning. To date, 20 QTLs directly affecting rice grain yield and its components have been cloned with NIL-F₂ populations, and 14 new grain yield QTLs have been validated in the NILs. The molecular mechanisms of a continuously increasing number of genes are being unveiled, which aids in the understanding of the formation of grain yield. Favorable alleles for rice breeding have been ‘mined’ from natural cultivars and wild rice by association analysis of known functional genes with target trait performance. Reasonable combination of favorable alleles has the potential to increase grain yield via use of functional marker assisted selection.

Bai X, Wu B, Xing Y (2012) Yield-related QTLs and their applications in rice genetic improvement. J. Integr. Plant Biol. 54(5), 300–311.

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Araus JL (2012) High-throughput phenotyping and genomic selection: The frontiers of crop breeding converge. J. Integr. Plant Biol. 54(5), 312–320.

rhm1 is a major recessive disease resistance locus for Southern corn leaf blight (SCLB). To further narrow down its genetic position, F₂ population and BC₁F₁ population derived from the cross between resistant (H95_rhm) and susceptible parents (H95) of maize (Zea mays) were constructed. Using newly developed markers, rhm1 was initially delimited within an interval of 2.5 Mb, and then finally mapped to a 8.56 kb interval between InDel marker IDP961–503 and simple sequence repeat (SSR) marker A194149–1. Three polymorphic markers IDP961–504, IDP B2–3 and A194149–2 were shown to be co-segregated with the rhm1 locus. Sequence analysis of the 8.56 kb DNA fragment revealed that it contained only one putative gene with a predicted amino acid sequence identical to lysine histidine transporter 1 (LHT1). Comparative sequence analysis indicated that the LHT1 in H95_rhm harbors a 354 bp insertion in its third exon as compared with that of susceptible alleles in B73, H95 and Mo17. The 354 bp insertion resulted in a truncation of the predicted protein of candidate resistance allele (LHT1-H95_rhm). Our results strongly suggest LHT1 as the candidate gene for rhm1 against SCLB. The tightly linked molecular markers developed in this study can be directly used for molecular breeding of resistance to Southern corn leaf blight in maize.

Zhao Y, Lu X, Liu C, Guan H, Zhang M, Li Z, Cai H, Lai J (2012) Identification and fine mapping of rhm1 locus for resistance to southern corn leaf blight in maize. J. Integr. Plant Biol. 54(5), 321–329.

Stripe (yellow) rust, caused by Puccinia striiformis Westend. f. sp. tritici Eriks (Pst), is one of the most important wheat (Triticum aestivum L.) diseases and causes significant yield losses. A recombinant inbred (RI) population derived from a cross between Yanzhan 1 and Xichang 76-9 cultivars was evaluated for resistance to wheat stripe rust strain CYR32 at both the seedling and adult plant stages. Four resistance quantitative trait loci (QTLs) were detected in this population, in which the major one, designated as Yrq1, was mapped on chromosome 2DS. The strategy of using the Brachypodium distachyon genome, wheat expressed sequence tags and a draft DNA sequences (scaffolds) of the D-genome (Aegilops tauschii Coss.) for the development of simple sequence repeat (SSR) markers was successfully used to identify 147 SSRs in hexaploid wheat. Of the 19 polymorphic SSRs in the RI population, 17 SSRs were mapped in the homeologous group 2 chromosomes near Yrq1 region and eight SSRs were genetically mapped in the 2.7 cM region of Yrq1, providing abundant DNA markers for fine-mapping of Yrq1 and marker-assisted selection in wheat breeding program. The effectiveness of Yrq1 was validated in an independent population, indicating that this resistance QTL can be successfully transferred into a susceptible cultivar for improvement of stripe rust resistance.

Cao X, Zhou J, Gong X, Zhao G, Jia J, Qi X (2012) Identification and validation of a major quantitative trait locus for slow-rusting resistance to stripe rust in wheat. J. Integr. Plant Biol. 54(5), 330–344.

Associating phenotypic traits and quantitative trait loci (QTL) to causative regions of the underlying genome is a key goal in agricultural research. InterStoreDB is a suite of integrated databases designed to assist in this process. The individual databases are species independent and generic in design, providing access to curated datasets relating to plant populations, phenotypic traits, genetic maps, marker loci and QTL, with links to functional gene annotation and genomic sequence data. Each component database provides access to associated metadata, including data provenance and parameters used in analyses, thus providing users with information to evaluate the relative worth of any associations identified. The databases include CropStoreDB, for management of population, genetic map, QTL and trait measurement data, SeqStoreDB for sequence-related data and AlignStoreDB, which stores sequence alignment information, and allows navigation between genetic and genomic datasets. Genetic maps are visualized and compared using the CMAP tool, and functional annotation from sequenced genomes is provided via an EnsEMBL-based genome browser. This framework facilitates navigation of the multiple biological domains involved in genetics and genomics research in a transparent manner within a single portal. We demonstrate the value of InterStoreDB as a tool for Brassica research. InterStoreDB is available from: http://www.interstoredb.org

Love CG, Andongabo AE, Wang J, Carion PWC, Rawlings CJ, King GJ (2012) InterStoreDB: A generic integration resource for genetic and genomic data. J. Integr. Plant Biol. 54(5), 345–355.