Open Access Resources for Genome Wide Association Studies (GWAS) in rice (Oryza sativa) illustrate the power of population-specific mapping

Authors: MCCOUCH, SUSAN - Cornell University; WRIGHT, MARK - Cornell University; TUNG, CHIH-WEI - Cornell University; MARON, LYZA - Cornell University; MCNALLY, KENNETH - International Rice Research Institute; FITZGERALD, MELLISSA - International Rice Research Institute; SINGH, NAMRATA - Cornell University; DECLERCK, GENEVIEVE - Cornell University; AGOSOTO PEREZ, FRANCISCO - Cornell University; KORNILIEV, PAVEL - Cornell University; GREENBERG, ANTHONY - Cornell University; NAREDO, MARIA ELIZABETH - International Rice Research Institute; MERCADO, SHEILA - International Rice Research Institute; HARRINGTON, SANDRA - Cornell University; SHI, YUXIN - Cornell University; BRANCHINI, DARCY - Cornell University; KUSER-FALCAO, PAULA - Cornell University; LEUNG, HEI - International Rice Research Institute; EBANA, KOWARU - National Institute Of Agrobiological Sciences (NIAS); YANO, MASAHIRO - National Institute Of Agrobiological Sciences (NIAS); Eizenga, Georgia; McClung, Anna; MEZEY, JASON - Cornell University

Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2015
Publication Date: 2/4/2016
Citation: McCouch, S.R., Wright, M.H., Tung, C., Maron, L.G., McNally, K., Fitzgerald, M., Singh, N., DeClerck, G.A., Agosoto Perez, F., Korniliev, P., Greenberg, A., Naredo, M.B., Mercado, S.M., Harrington, S.E., Shi, Y., Branchini, D.A., Kuser-Falcao, P.R., Leung, H., Ebana, K., Yano, M., Eizenga, G.C., McClung, A.M., Mezey, J. 2016. Open access resources for genome wide association studies (GWAS) in rice (Oryza sativa) illustrate the power of population-specific mapping. Nature Communications 7:10532. doi: 10.1038/ncomms10532.

Interpretive Summary: Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. Development of new crop varieties that can be grown under sustainable production systems and which are resilient to climate change will be necessary to meet this increased demand. A large collection of 1,568 rice cultivars from around the world was assembled and a set of 700,000 DNA markers, called single nucleotide polymorphisms (SNPs), was used to characterize the collection at a genetic level. This information provides a new way of evaluating genetic variability among diverse rice cultivars that will help researchers to develop better utilization strategies for breeding new cultivars. In addition, these SNP markers will help to identify genes controlling economically important traits that can be efficiently combined in new breeding lines. The power of this methodology was demonstrated using grain length which is a major factor in determining market classes for rice. In addition, other studies are underway to investigate rice blast disease, tolerance to cool temperatures at the seedling stage, mineral element concentrations, root development, and a host of other traits of agronomic importance. With a better understanding of these agronomically important traits, it should be possible to identify new genetic variation to deal with the challenges of increasing production especially on marginal land, adapting to extremes in climate, and developing more sustainable agricultural systems.

Technical Abstract: Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. New sources of genetic variation are key to enhancing the productivity, sustainability and resilience of crop varieties and agricultural systems that can meet that demand. Here we launch a high resolution, open-access research platform to facilitate genome-wide association studies (GWAS) in rice, a staple food crop. The platform provides an immortal collection of 1,568 diverse germplasm accessions, a high-density genomic dataset containing 700,000 single nucleotide polymorphism (SNP) markers, tailored for gene discovery, well-documented analytical strategies, and a suite of bioinformatics resources to facilitate biological interpretation. The 700,000 SNPs were ascertained using next-generation sequencing of 145 rice accessions spanning all the major subpopulations of O. sativa, as well as, several related wild species. We illustrate the application of our research platform using grain length, an agronomically relevant phenotype, and demonstrate the power and resolution of our new high-density rice array (HDRA), the accompanying genotypic dataset, and an expanded diversity panel for detecting major and minor effect QTLs (quantitative trait loci) and subpopulation-specific alleles. The GWAS resources and datasets generated on this project are designed to facilitate the collection of phenotypic information on a wide range of traits and characteristics and to rapidly and efficiently convert that information into focused hypotheses about causal genes and QTL locations. They also provide a bridge between genomics and applied plant breeding. With increasing accuracy over time, datasets generated for different purposes and at differing levels of resolution can be leveraged against each other via shared haplotypes, making it feasible to undertake cross-population imputation of both genotype and phenotype, and to predict phenotypic outcomes from genotypic data. For plant breeders interested in genome-wide prediction strategies, GWAS results can be incorporated as fixed variables into genomic selection models to improve the accuracy of genomic estimated breeding values.