In the last few years, we have developed a series of pipeline computational tools for analyzing genomic data in complex autopolyploid species: tools like VCF2SM and SuperMASSA for processing raw DNA sequences to call genetic dosage markers (marker identification); MAPpoly for constructing a genetic linkage map from the dosage markers (linkage map construction and haplotype inference); QTLpoly for locating genes that are important to trait phenotypes by using the linkage map (QTL mapping) and also for performance prediction. MAPpoly and QTLpoly are currently implemented for full-sib families and are being extended to multiple families. In this project we propose to further extend MAPpoly and QTLpoly for general multiple-generation pedigree breeding populations that are typical in practical polyploid breeding programs. Moreover, we propose to develop a new downstream computational tool, called DecisionPoly, that is user-friendly and offers clearly illustrated actionable information to assist breeders to make the short and long-term breeding decisions based on the collected and learned information about their breeding populations for different breeding objectives.

The implementation of genomics-assisted breeding techniques in polyploid specialty crops is significantly delayed compared to diploid species. The development of new tools, user friendly interfaces and training materials are needed by polyploid crop breeders to accelerate genetic gain for key traits of importance and meet the needs of growers and consumers. Polyploid specialty crops contribute significantly to food production in the US and throughout the world. The list of polyploid specialty crops used for food includes roots and tubers (potato, sweet potato), fruit (strawberry, blackberry, blueberry, European plum, tart cherry, kiwi, persimmon, banana), vegetables (leek, watermelon), and other uses (coffee, basil, hops). The annual value of these crops in the US is about $9.5 billion and many times greater on a global scale. The production and use of polyploid food crops contributes substantially to the nutritional welfare and employment of millions of people. In addition to food crops, polyploid species are used as ornamentals (rose, chrysanthemum, lily, orchids, lantana) and for turfgrass (ryegrass, bentgrass, Kentucky bluegrass, tall fescue, bermudagrass, zoysia). The turfgrass and ornamental production sectors produce about 1/3 the value of all specialty crop production and 15% of agricultural production in the USA. This $16.7 billion industry employs about two million people and delivers an economic impact of at least $136 billion. The turfgrass and ornamentals used in home, private and public landscapes significantly impact human health and urban ecology. These plants enhance air and water quality, sequester carbon, reduce runoff and erosion, provide energy savings in heating and cooling, facilitate rain capture and storm water management, reduce noise and dust pollution, and promote wildlife habitat. In addition, they increase property values and psychological wellbeing. The production of food crops and the production and maintenance of turfgrass and ornamentals requires substantial resources (agricultural chemicals, fertilizers, and water). Given the increased scarcity of water and concern over the environmental contamination of agrochemicals, it is essential to move towards more sustainable production and landscape systems. A major component of these future more sustainable systems will be new cultivars with improved yield, quality and environmental resilience. Objective 1. The software developed will meet the five needs identified during the planning grant: (a) multi-SNP haplotype discovery and population genotyping using next-generation sequencing; (b) linkage mapping with multi-allelic markers and genotype quality scores; (c) GWAS and genomic selection in mixed ploidy populations and with multi-allelic markers; (d) QTL mapping in interconnected F1 populations; (e) fine mapping, haplotype visualization, and efficient assembly of QTL alleles across multiple loci. Objective 2. Software will be developed so the user can explore different designs for genetic mapping projects or breeding programs. Simulation options will include the mating design, genome size, meiotic properties, population size, and costs for genotyping and phenotyping. Objective 3. Complete documentation of the syntax and options for each software will be created, as well as example datasets and corresponding workflows. These training materials will be publicly available through a Polyploid Community Resource web page that will be developed and hosted by Washington State University. Graphical user interfaces will be developed for the command-line software developed in Objectives 1 and 2 and made available through the website. Hands-on workshops will be created to showcase the new software and train the polyploid breeding community about polyploid genetics and the use of the analytical toolset. Objective 4. Research projects involving the new computational tools are planned for six polyploid crops representing a range of ploidy levels, preferential pairing propensity, interspecific diversity among breeding germplasm, and genomic data/r

Banana, cassava, potato, sweetpotato and yam (collectively referred to as roots, tubers, and bananas or RTB crops hereafter) are major contributors to poverty alleviation and food and nutrition security in sub-Saharan Africa (SSA). RTB crops provide nearly 50% of total caloric intake in D.R. Congo, Ghana, Tanzania and Rwanda, 30% in Uganda, and 25% in Africa's most populated country, Nigeria. Moreover, given their role to buffer local food systems against external shocks such as conflicts disrupting global commodity supply chains, climate change, and the forecasted population growth, unprecedent domestic production and value of production growth is forecasted for these crops. To deliver nutritious, affordable RTB foods, and supplies for processors in SSA, this two-year project initiation proposal represents the first phase towards establishing a longer-term plan for an 11-year-long, multi-donor driven portfolio of investments in the genetic improvement of RTB crops. Our overarching purpose is to contribute, through the development of market-preferred, gender-sensitive and climate-resilient varieties, to poverty alleviation, food and nutrition security and overall quality of life of smallholder farmers, processors, and consumers in rural and urban areas. This project will contribute to all the One CGIAR���s Genetic Innovation impact areas, namely: nutrition, health, and food security; poverty reduction, livelihoods, and jobs; gender equality, youth, and social inclusion; climate adaptation and mitigation; and environmental health and biodiversity. We aim to achieve this by implementing state-of-the-art, streamlined breeding approaches, and the market-preferred varieties to be developed are expected to command increased adoption rates and to quickly replace the older varieties and landraces that are currently in use. NC State partner with the One CGIAR to build upon capacities in African countries as well as those within One CGIAR that were developed through extensive prior BMGF breeding investments such as Breeding Better Bananas, GT4SP (NCSU led), NextGen Cassava, RTBFoods, SASHA (NCSU partner), SweetGAINS (NCSU partner), Africa Yam and Excellence in Breeding. Moreover, we will build upon assets, infrastructure and human talent posted at several One CGIAR centers and national and international programs in SSA countries, research, development and extension programs, and advanced research institutions.

Forthcoming

This joint proposal from RTI and NCSU seeks to create a multi-faceted three-year Program in Genetic Discovery and Prediction (PGDP), initially organized around a demonstration and feasibility pilot for a highly ambitious effort the team calls the ����������������1000 GWAS Project.��������������� The Project will compile an unprecedented number of publicly available genome-wide association studies (GWAS, representing hundreds of thousands of patients). These studies have been used to identify genetic variants that predispose humans to disease and can be used to predict patient outcomes. The Project will re-analyze the combined data using the latest methods for genetic analysis and quality control, combined with new linkages to standard measures for phenotypes, as well as data on clinical covariates and exposures. In addition, the team will make progress on a GWAS Connector tool to support exploration and prioritization of dbGaP phenotypes for enriched secondary analysis. Finally, the Project will feed back into public repositories, providing an open-source analysis pipeline and community resource for ongoing research. The unprecedented data compilation and comprehensive analysis will reveal subtle and more complex interactions between genes, environmental exposures and resulting disease and treatment outcomes.

This project will develop modern genomic, genetic, and bioinformatics tools to facilitate crop improvement and improve genetic gains in sweetpotato, an important food security and cash crop with highly recognized potential to alleviate hunger, vitamin A deficiency, and poverty in Sub-Saharan Africa (SSA), and predominantly grown in small plot holdings by poor women farmers.

Genomic revolution has changed the ways of plant breeding from the traditional phenotype-based selection to marker-assisted selection that uses the whole-genome molecular marker information and the estimated marker and phenotype relationship. The marker-assisted selection and breeding has become the norm in plant breeding. The main challenge for marker-assisted predictive breeding is the genetic complexity of many quantitative traits that are important for breeding. There are usually many genetic loci that are segregating in the breeding population and important for breeding. The loci can have complex linkage structure (e.g., repulsion linkage) and interaction patterns. The key to improve the predictive power of selection is to take these complexities into account in building predictive functions. In this project, we propose to develop a new computational method that can effectively take into account these complexities for marker-assisted predictive breeding. This predictive method is based on a newly-improved multiple interval mapping (MIM) method that can map multiple quantitative trait loci (QTL) with epistasis in a breeding population. Based on the mapping result, the method can provide a predictive estimate of breeding value for any pair of breeding individuals. This new predictive breeding method should be much more powerful than many currently available methods as the new MIM method uses not only the information from estimated main effects of multiple loci, but also the epistatic effects, thus taking into account the likely compositions of genetic combination for a pair of breeding individuals or lines. We also propose to develop the predictive method suitable for the breeding populations of Syngenta Biotechnology, Inc. The end-result is a set of statistical and computational methods and computer programs that are applicable to certain type of breeding populations of Syngenta Biotechnology, Inc. for marker-assisted predictive breeding.

Genomic revolution has changed the ways of plant breeding from the traditional phenotype-based selection to marker-assisted selection that uses the whole-genome molecular marker information and the estimated marker and phenotype relationship to perform selection. The marker-assisted selection and breeding has become the norm in plant breeding. The main challenge for marker-assisted predictive breeding is the genetic complexity of many quantitative traits that are important for breeding. There are usually many genetic loci that are segregating in the breeding population and important for breeding. The loci can have complex linkage structure (e.g., repulsion linkage) and interaction patterns. The key to improve the efficiency of selection is to take these complexities into account in building a selection model. In this project, we propose to develop a new method that can effectively take into account these complexities for marker-assisted selection. This selection method is based on a newly-improved multiple interval mapping method that can map multiple quantitative trait loci (QTL) with epistasis in a breeding population. Based on the mapping result, the method can provide a predictive estimate of breeding value for any pair of breeding individuals. This new predictive breeding method should be much more powerful than many currently available methods as the new method uses not only the information from estimated main effects of multiple loci, but also the dominant and epistatic effects, thus taking into account the likely compositions of genetic combination for a pair of breeding individuals or lines. We also propose to develop the selection method suitable for the breeding populations of Syngenta Biotechnology, Inc. The end-result is a set of statistical and computational methods and computer programs that are applicable to certain type of breeding populations of Syngenta Biotechnology, Inc. for marker-assisted selection and predictive breeding.

One graduate student from the Bioinformatics Graduate Program will be placed as a graduate industrial trainee with Duke University. Under the supervision of Rima Kaddurah-Daouk, Maragatha Kuchibhatla and Steve Rozen, Duke University, and Zhao-Bang Zeng, Bioinformatics Research Center, the trainee will engage in consulting, collaboration, research and training related to the problems encountered at the metabolomics project. The trainee selected for this is Zheng Yan. The time the trainee will spend is 20 hours a week from June 1, 2008 to August 15, 2008. Total amount Requested includes the stipend, tuition and health insurance for the trainee and general support for the graduate programs. The general support for the graduate programs includes, for example, travel expenses of graduate students in the department and the faculty advisor, and recruiting expenses. The contract should be set up as "Fixed Price" payable to the University in 1 payment of $5,769 commencing on June 15, 2008. In the event that the trainee is not able to complete the estimated level of effort, the parties agree to negotiate an equitable adjustment to the salary and fringe benefits portion of the months not completed.

One graduate student from the Bioinformatics Graduate Program will be placed as a graduate industrial trainee with Duke University. Under the supervision of Rima Kaddurah-Daouk and Maragatha Kuchibhatla, Duke University, and Zhao-Bang Zeng, Bioinformatics Research Center, the trainee will engage in consulting, collaboration, research and training related to the problems encountered at the metabolomics project. The trainee selected for this is Hongjie Zhu. The time the trainee will spend is 10 hours a week from May 15, 2008 to May 14, 2009. Total amount Requested includes the stipend, tuition and health insurance for the trainee and general support for the graduate programs. The general support for the graduate programs includes, for example, travel expenses of graduate students in the department and the faculty advisor, and recruiting expenses. The contract should be set up as ?Fixed Price? payable to the University in 1 payment of $19,235 commencing on May 15, 2008. In the event that the trainee is not able to complete the estimated level of effort, the parties agree to negotiate an equitable adjustment to the salary and fringe benefits portion of the months not completed.