The genetic dissection of quantitative traits in crops

Most traits of interest in plant breeding show quantitative inheritance, which complicate the breeding process since phenotypic performances only partially reflects the genetic values of individuals. The genetic variation of a quantitative trait is assumed to be controlled by the collective effects of quantitative trait loci (QTLs), epistasis (interaction between QTLs), the environment, and interaction between QTL and environment. Exploiting molecular markers in breeding involve finding a subset of markers associated with one or more QTLs that regulate the expression of complex traits. Many QTL mapping studies conducted in the last two decades identified QTLs that generally explained a significant proportion of the phenotypic variance, and therefore, gave rise to an optimistic assessment of the prospects of markers assisted selection. Linkage analysis and association mapping are the two most commonly used methods for QTL mapping. This review provides an overview of the two QTL mapping methods, including mapping population type and size, phenotypic evaluation of the population, molecular profiling of either the entire or a subset of the population, marker-trait association analysis using different statistical methods and software as well as the future prospects of using markers in crop improvement

The effects of resource availability and environmental conditions on genetic rankings for carbon isotope discrimination during growth in tomato and rice

Carbon isotope discrimination (Δ) is frequently used as an index of leaf intercellular CO2 concentration (ci) and variation in photosynthetic water use efficiency. In this study, the stability of Δ was evaluated in greenhouse grown tomato and rice with respect to variable growth conditions including temperature, nutrient availability, soil flooding (in rice), irradiance, and root constriction in small soil volumes. Δ exhibited several characteristics indicative of contrasting set-point behavior among genotypes of both crops. These included generally small main environmental effects and lower observed levels of G x E interaction across the diverse treatments than observed in associated measures of relative growth rate, photosynthetic rate, biomass allocation pattern, or specific leaf area. Growth irradiance stood out among environmental parameters tested as having consistently large main affects on Δ for all genotypes screened in both crops. We suggest that this may be related to contrasting mechanisms of stomatal aperture modulation associated with the different environmental variables. For temperature and nutrient availability, feedback processes directly linked to ci and/or metabolite pools associated with ci may have played the primary role in coordinating stomatal conductance and photosynthetic capacity. In contrast, light has a direct effect on stomatal aperture in addition to feedback mediated through ci

Post-Domestication Selection in the Maize Starch Pathway

Modern crops have usually experienced domestication selection and subsequent genetic improvement (post-domestication selection). Chinese waxy maize, which originated from non-glutinous domesticated maize (Zea mays ssp. mays), provides a unique model for investigating the post-domestication selection of maize. In this study, the genetic diversity of six key genes in the starch pathway was investigated in a glutinous population that included 55 Chinese waxy accessions, and a selective bottleneck that resulted in apparent reductions in diversity in Chinese waxy maize was observed. Significant positive selection in waxy (wx) but not amylose extender1 (ae1) was detected in the glutinous population, in complete contrast to the findings in non-glutinous maize, which indicated a shift in the selection target from ae1 to wx during the improvement of Chinese waxy maize. Our results suggest that an agronomic trait can be quickly improved into a target trait with changes in the selection target among genes in a crop pathway

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support

The molecularization of public sector crop breeding: progress, problems, and prospects

Molecular markers and genetic maps are available for most important food crops. Marker-trait associations have been established for a diverse array of traits in these crops, and research on marker/quantitative trait loci (QTL) validation and refinement is increasingly common. Researchers are now routinely using candidate gene-based mapping and genome-wide linkage disequilibrium and association analysis in addition to classical QTL mapping to identify markers broadly applicable to breeding programs. Marker-assisted selection (MAS) is practiced for enhancing various host plant resistances, several quality traits, and a number of abiotic stress tolerances in many well-researched crops. Markers are also increasingly used to transfer yield or quality- enhancing QTL alleles from wild relatives to elite cultivars. Large-scale MAS-based breeding programs for crops such as rice, maize, wheat, barley, pearl millet, and common bean have already been initiated worldwide. Advances in "omics" technologies are now assisting researchers to address complex biological issues of significant agricultural importance: modeling genotype-by-environment interaction; fine-mapping, cloning, and pyramiding of QTL; gene expression analysis and gene function elucidation; dissecting the genetic structure of germplasm collections to mine novel alleles and develop genetically structured trait-based core collections; and understanding the molecular basis of heterosis. The challenge now is to translate and integrate this knowledge into appropriate tools and methodologies for plant breeding programs. The role of computational tools in achieving this is becoming increasingly important. It is expected that harnessing the outputs of genomics research will be an important component in successfully addressing the challenge of doubling world food production by 2050