Rex Bernardo: A CRISPR Breeding Method for Quantitative Traits in Plants
The application of CRISPR technology for genome editing is conceptually straightforward when the genes controlling a trait are known. As such, CRISPR technology holds much promise for qualitative traits with known major genes. But for quantitative traits, for which the underlying genes are largely unknown, the path for applying CRISPR technology is less clear. In this seminar, Dr. Bernardo will describe a possible way of using CRISPR technology in a non-genome-editing way to improve quantitative traits. Projected results for grain yield and other agronomic traits in the intermated B73 x Mo17 maize population suggest that CRISPR technology could possibly double the current genetic gains.
Gurdev Khush: What It Will Take to Feed 5 Billion Rice Consumers in 2035
Rice is the most important food crop. Worldwide 3.9 million people depend upon rice for more than 20% of their daily calories. Global rice demand is estimated to rise from 710 million tons in 2015 to 760 million tons in 2020 to 850 million tons in 2035. This is an overall increase of 140 million tons in next 20 years. To meet this challenge we must develop rice varieties with higher yield potential and narrow the yield gap. Strategies to increase the yield potential include; (1) Conventional hybridization and Selection, (2) Ideotype breeding, (3) Hybrid breeding, (4) Enhancement of Photosynthesis, (5) Physiological approaches, and (6) Genomic approaches. Yield gap can be narrowed by developing varieties with durable resistance to diseases and insects and by improving management practices.
Patrick Schnable: High-Throughput, Field-Based Phenomics of Maize
Our goal is to develop statistical models that will predict crop performance in diverse agronomic environments. Crop phenotypes such as yield and drought tolerance are controlled by genotype, environment (considered broadly) and their interaction (GxE). As a consequence of the next generation sequencing revolution genotyping data are now available for a wide diversity of accessions in each of the major crops. The necessary volumes of phenotypic data, however, remains limiting and our understanding of molecular basis of GxE is limited. To address this limitation, we are constructing new sensors and robots to automatically collect large volumes of phenotypic data. Two types of high-throughput, high-resolution, field-based phenotyping systems and new sensors will be described. These technologies will be introduced within the context of the Genomes to Fields Initiative.
Evans Lagudah: Wheat Rust Resistance Genes in Wheat: A Multifaceted Landscape
Currently, there are over 200 designated genes/loci in hexaploid wheat that confer resistance to at least one of the three rust pathogen species- Pucciniatriticina,Pucciniagraminis, and Puccinia striiformis. Some of these genes have been introgressed from wild relatives of wheat. A myriad of expression patterns characterize the resistance phenotypes, which includes a blurring of all developmental stages, others specific to adult plant stage and in some instances suppression of otherwise functional resistance genes. A wide spectrum of resistance gene durability is also evident, spanning short-lived (>3yrs) ones to those deployed over 100 years without increased pathogen virulence. Genes encoding the prevalent nucleotide binding leucine rich repeat (NLR) proteins found in plant immune receptors control the seedling/all stage resistance genes isolated so far and an adult plant rust resistance. Interestingly, a subset of the genes involved in adult plant resistance are unrelated to NLR proteins and represent unique classes of resistance genes. Furthermore, some of these APR genes confer partial resistance to multiple pathogen species and transgenic studies have shown that they function in other crop species beyond the Triticeae and provide resistance against pathogen species that are un-adapted to wheat. With current advances in mutational genomics coupled with the wheat genome/pan-genome assembly, it is envisaged that a flood of wheat resistance genes will be cloned which may facilitate gene deployment strategies aimed at achieving more durable resistance.
Geoffrey Morris: Improving Crop Adaptation in Developing Countries With Genomics-Enabled Breeding
Crop improvement programs in developing countries face many challenges that are not fully addressed by conventional breeding methods or existing genomics-enabled technologies. These challenges include limited resources, multipurpose yield targets, diverse environmental stressors, and weak kinship of germplasm. To address the needs of developing country sorghum breeders, we are developing genomic resources and methods tailored to their programs. We analyzed genome-wide polymorphism of over 15,000 landraces, breeding lines, and genetic stocks using genotyping-by-sequencing and characterized population structure across agroclimatic regions. Using genome-wide association studies (GWAS) and nested association mapping (NAM), we dissected several traits thought to underlie agroclimatic adaptation. We demonstrated that genome-environment associations in georeferenced global landraces could be used to predict stress tolerance (drought and aluminum) in diverse germplasm. Currently, with West African breeders and physiologists, we are using GWAS and population genomic scans to identify markers for pre- and post-flowering drought tolerance, local adaptation, and farmer preference. With a Haitian breeder, we are adapting genomic selection methods with low-cost genotyping and integrated genomic predictions to help jumpstart breeding programs that are at an early stage of development. By leveraging genomic advances in partnership with developing country programs, we aim to develop more climate-resilient sorghum varieties for smallholder farmers.
Waseem Hussain: Genotyping-by-sequencing derived high-density linkage map and its application to QTL mapping of flag leaf traits in bread wheat
Josh Clevenger: Haplotype-based genotyping accelerates translation of genomics to cultivar improvement in Allotetraploid peanut
The advent of the Arachis ipaënsis and Arachis duranensis reference genomes has led to a substantial increase in the efficiency of genotyping from Next-Generation Sequence (NGS) data. The next step is to translate these data into cultivar improvement. A novel pipeline, SWEEP, was developed that increased the precision of single nucleotide polymorphism (SNP) detection in cultivated A. hypogaea and was used to design the first large-scale SNP array for Arachis spp. In a short time, the Axiom Arachis array has been used to generate large amounts of genotyping information. To improve the efficiency of sequence-based genotyping and extend to all polyploid crops, SWEEP has been improved to polymorphic haplotypes. This haplotype-based approach phases homeologous sequence and correctly identifies polymorphisms between genotypes with higher precision. Additionally, mate pair information can be leveraged to assay long range haplotypes, gaining higher marker resolution and genomic information. In a peanut breeding program, this haplotype-based genotyping pipeline has been used to accelerate marker-assisted breeding tools; including leveraging QTL-seq to deploy markers strongly linked to late leaf spot and white mold resistance, and quickly identify more tightly linked markers to nematode resistance. This newly acquire efficiency in sequence-based genotyping has bridged the gap between genomics and breeding in Arachis species by accelerating the translation of sequence data into tools for cultivar improvement.