Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding

BackgroundThis article describes the development of Multi-parent Advanced Generation Inter-Cross populations (MAGIC) in rice and discusses potential applications for mapping quantitative trait loci (QTLs) and for rice varietal development. We have developed 4 multi-parent populations: indica MAGIC (8 indica parents); MAGIC plus (8 indica parents with two additional rounds of 8-way F1 inter-crossing); japonica MAGIC (8 japonica parents); and Global MAGIC (16 parents – 8 indica and 8 japonica). The parents used in creating these populations are improved varieties with desirable traits for biotic and abiotic stress tolerance, yield, and grain quality. The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs. These MAGIC populations provide a useful germplasm resource with diverse allelic combinations to be exploited by the rice community.ResultsThe indica MAGIC population is the most advanced of the MAGIC populations developed thus far and comprises 1328 lines produced by single seed descent (SSD). At the S4 stage of SSD a subset (200 lines) of this population was genotyped using a genotyping-by-sequencing (GBS) approach and was phenotyped for multiple traits, including: blast and bacterial blight resistance, salinity and submergence tolerance, and grain quality. Genome-wide association mapping identified several known major genes and QTLs including Sub1 associated with submergence tolerance and Xa4 and xa5 associated with resistance to bacterial blight. Moreover, the genome-wide association study (GWAS) results also identified potentially novel loci associated with essential traits for rice improvement.ConclusionThe MAGIC populations serve a dual purpose: permanent mapping populations for precise QTL mapping and for direct and indirect use in variety development. Unlike a set of naturally diverse germplasm, this population is tailor-made for breeders with a combination of useful traits derived from multiple elite breeding lines. The MAGIC populations also present opportunities for studying the interactions of genome introgressions and chromosomal recombination.

[1]  Zhiwu Zhang,et al.  Mixed linear model approach adapted for genome-wide association studies , 2010, Nature Genetics.

[2]  C. Shi,et al.  The QTL analysis on maternal and endosperm genome and their environmental interactions for characters of cooking quality in rice (Oryza sativa L.) , 2008, Theoretical and Applied Genetics.

[3]  M. Soller,et al.  Advanced intercross lines, an experimental population for fine genetic mapping. , 1995, Genetics.

[4]  R. Mott,et al.  A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana , 2009, PLoS genetics.

[5]  D. Balding A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.

[6]  Brigitte Courtois,et al.  A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance. , 2008, Molecular plant-microbe interactions : MPMI.

[7]  THE AMERICAN ASSOCIATION OF CEREAL CHEMISTS. , 1942, Science.

[8]  F. V. van Eeuwijk,et al.  Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population , 2011, Proceedings of the National Academy of Sciences.

[9]  D. Mackill,et al.  Quantitative trait locus analysis for rice panicle and grain characteristics , 1998, Theoretical and Applied Genetics.

[10]  Peter J. Bradbury,et al.  Trait Analysis by Association, Evolution and Linkage (TASSEL) Version 2.1 , 2009 .

[11]  John D. Storey The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .

[12]  Mallikarjuna Rao Kovi,et al.  Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7 , 2010, BMC Genetics.

[13]  J. Pospíšilová Pessarakli, M. (ed.): Handbook of plant and crop stress , 1994, Biologia Plantarum.

[14]  Xuehui Huang,et al.  High-throughput genotyping by whole-genome resequencing. , 2009, Genome research.

[15]  W. Powell,et al.  Methods for linkage disequilibrium mapping in crops. , 2007, Trends in plant science.

[16]  S. Mccouch,et al.  The rice bacterial blight resistance gene xa5 encodes a novel form of disease resistance. , 2004, Molecular plant-microbe interactions : MPMI.

[17]  Robert J. Elshire,et al.  A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species , 2011, PloS one.

[18]  N. Jensen A Diallel Selective Mating System for Cereal Breeding 1 , 1970 .

[19]  Qifa Zhang,et al.  Genome-wide association studies of 14 agronomic traits in rice landraces , 2010, Nature Genetics.

[20]  M. Fitzgerald,et al.  Gelatinization temperature of rice explained by polymorphisms in starch synthase. , 2006, Plant biotechnology journal.

[21]  A. Bogdanove,et al.  Xanthomonas oryzae pathovars: model pathogens of a model crop. , 2006, Molecular plant pathology.

[22]  K. Broman The Genomes of Recombinant Inbred Lines , 2004, Genetics.

[23]  E. Septiningsih,et al.  A marker-assisted backcross approach for developing submergence-tolerant rice cultivars , 2007, Theoretical and Applied Genetics.

[24]  H. E. Kauffman,et al.  An improved technique for evaluating resistance of rice varieties to Xanthomonas oryzae , 1973 .

[25]  Qian Qian,et al.  Control of grain size, shape and quality by OsSPL16 in rice , 2012, Nature Genetics.

[26]  Zhijun Cheng,et al.  Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight , 2008, Cell Research.

[27]  R. M. Goodman Encyclopedia of Plant and Crop Science , 2004 .

[28]  Bin Han,et al.  GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein , 2006, Theoretical and Applied Genetics.

[29]  H. Leung,et al.  Dissecting quantitative resistance against blast disease using heterogeneous inbred family lines in rice , 2011, Theoretical and Applied Genetics.

[30]  Li-hong Xie,et al.  Allelic variation for a candidate gene for GS7, responsible for grain shape in rice , 2012, Theoretical and Applied Genetics.

[31]  Bevan Emma Huang,et al.  R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines , 2011, Bioinform..

[32]  Md. Mizanur Rahman,et al.  Characterizing the Saltol Quantitative Trait Locus for Salinity Tolerance in Rice , 2010, Rice.

[33]  M. Fitzgerald,et al.  Melting the secrets of gelatinisation temperature in rice , 2010 .

[34]  Edward S. Buckler,et al.  TASSEL: software for association mapping of complex traits in diverse samples , 2007, Bioinform..

[35]  W. Powell,et al.  From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. , 2008, Current opinion in plant biology.

[36]  Andrew W George,et al.  A multiparent advanced generation inter-cross population for genetic analysis in wheat. , 2012, Plant biotechnology journal.

[37]  R. Wing,et al.  Chromosome landing at the bacterial blight resistance gene Xa4 locus using a deep coverage rice BAC library , 2001, Molecular Genetics and Genomics.

[38]  M. Pessarakli Handbook of plant and crop stress , 1999 .

[39]  Ivana V. Yang,et al.  Genetic analysis of complex traits in the emerging Collaborative Cross. , 2011, Genome research.

[40]  Robert W. Williams,et al.  Genome Reshuffling for Advanced Intercross Permutation (GRAIP): Simulation and Permutation for Advanced Intercross Population Analysis , 2008, PloS one.

[41]  L. Kruglyak,et al.  Breeding Designs for Recombinant Inbred Advanced Intercross Lines , 2008, Genetics.

[42]  Centro Internacional de Agricultura Tropical,et al.  A standard evaluation system for rice. , 1983 .

[43]  T. Mew,et al.  Bacterial Blight of Rice , 2005 .

[44]  G. Gregorio,et al.  Investigation of seedling-stage salinity tolerance QTLs using backcross lines derived from Oryza sativa L. Pokkali , 2011 .

[45]  M. Pessarakli Physiology and Molecular Biology of the Effects of Salinity on Rice , 2010 .