As an essential macroelement for all living cells, phosphorus is indispensable in agricultural production systems. Natural phosphorus reserves are limited, and it is therefore important to develop phosphorus-efficient crops. A major quantitative trait locus for phosphorus-deficiency tolerance, Pup1, was identified in the traditional aus-type rice variety Kasalath about a decade ago. However, its functional mechanism remained elusive until the locus was sequenced, showing the presence of a Pup1-specific protein kinase gene, which we have named phosphorus-starvation tolerance 1 (PSTOL1). This gene is absent from the rice reference genome and other phosphorus-starvation-intolerant modern varieties. Here we show that overexpression of PSTOL1 in such varieties significantly enhances grain yield in phosphorus-deficient soil. Further analyses show that PSTOL1 acts as an enhancer of early root growth, thereby enabling plants to acquire more phosphorus and other nutrients. The absence of PSTOL1 and other genes-for example, the submergence-tolerance gene SUB1A-from modern rice varieties underlines the importance of conserving and exploring traditional germplasm. Introgression of this quantitative trait locus into locally adapted rice varieties in Asia and Africa is expected to considerably enhance productivity under low phosphorus conditions.

The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency

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.

https://link.springer.com/content/pdf/10.1007%2Fs00122-013-2066-0.pdf

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

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

/pdf/genotyping-by-sequencing-gbs-an-ultimate-marker-assisted-2qv3na21mn.pdf

Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding.

Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. When combined with precise phenotyping methods, these technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population. Here we summarize current trends and future prospects for utilizing NGS-based technologies to develop crops with improved trait performance and increase the efficiency of modern plant breeding. It is our hope that the application of NGS technologies to plant breeding will help us to meet the challenge of feeding a growing world population.

/pdf/harvesting-the-promising-fruits-of-genomics-applying-genome-q0g3kjpl7j.pdf

Harvesting the promising fruits of genomics: Applying genome sequencing technologies to crop breeding

Recent advances in next-generation sequencing (NGS) and single nucleotide polymorphism (SNP) genotyping promise to greatly accelerate crop improvement if properly deployed. High-throughput SNP genotyping offers a number of advantages over previous marker systems, including an abundance of markers, rapid processing of large populations, a variety of genotyping systems to meet different needs, and straightforward allele calling and database storage due to the bi-allelic nature of SNP markers. NGS technologies have enabled rapid whole genome sequencing, providing extensive SNP discovery pools to select informative markers for different sets of germplasm. Highly multiplexed fixed array platforms have enabled powerful approaches such as genome-wide association studies. On the other hand, routine deployment of trait-specific SNP markers requires flexible, low-cost systems for genotyping smaller numbers of SNPs across large breeding populations, using platforms such as Fluidigm's Dynamic Arrays™, Douglas Scientific's Array Tape™, and LGC's automated systems for running KASP™ markers. At the same time, genotyping by sequencing (GBS) is rapidly becoming popular for low-cost high-density genome-wide scans through multiplexed sequencing. This review will discuss the range of options available to modern breeders for integrating SNP markers into their programs, whether by outsourcing to service providers or setting up in-house genotyping facilities, and will provide an example of SNP deployment for rice research and breeding as demonstrated by the Genotyping Services Lab at the International Rice Research Institute.

https://e-sc.org/upload/pbb/pdf/pbb-02-195.pdf

High-Throughput SNP Genotyping to Accelerate Crop Improvement

Multiplexed single nucleotide polymorphism (SNP) markers have the potential to increase the speed and cost-effectiveness of genotyping, provided that an optimal SNP density is used for each application. To test the efficiency of multiplexed SNP genotyping for diversity, mapping and breeding applications in rice (Oryza sativa L.), we designed seven GoldenGate VeraCode oligo pool assay (OPA) sets for the Illumina BeadXpress Reader. Validated markers from existing 1536 Illumina SNPs and 44 K Affymetrix SNP chips developed at Cornell University were used to select subsets of informative SNPs for different germplasm groups with even distribution across the genome. A 96-plex OPA was developed for quality control purposes and for assigning a sample into one of the five O. sativa population subgroups. Six 384-plex OPAs were designed for genetic diversity analysis, DNA fingerprinting, and to have evenly-spaced polymorphic markers for quantitative trait locus (QTL) mapping and background selection for crosses between different germplasm pools in rice: Indica/Indica, Indica/Japonica, Japonica/Japonica, Indica/O. rufipogon, and Japonica/O. rufipogon. After testing on a diverse set of rice varieties, two of the SNP sets were re-designed by replacing poor-performing SNPs. Pilot studies were successfully performed for diversity analysis, QTL mapping, marker-assisted backcrossing, and developing specialized genetic stocks, demonstrating that 384-plex SNP genotyping on the BeadXpress platform is a robust and efficient method for marker genotyping in rice.

Ma. Ymber Reveche

Papers

High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform