Following the 2011 earthquake and tsunami that affected Japan, >20,000 ha of rice paddy field was inundated with seawater, resulting in salt contamination of the land. As local rice landraces are not tolerant of high salt concentrations, we set out to develop a salt-tolerant rice cultivar. We screened 6,000 ethyl methanesulfonate (EMS) mutant lines of a local elite cultivar, 'Hitomebore', and identified a salt-tolerant mutant that we name hitomebore salt tolerant 1 (hst1). In this Correspondence, we report how we used our MutMap method to rapidly identify a loss-of-function mutation responsible for the salt tolerance of hst1 rice. The salt-tolerant hst1 mutant was used to breed a salt-tolerant variety named 'Kaijin', which differs from Hitomebore by only 201 single-nucleotide polymorphisms (SNPs). Field trials showed that it has the same growth and yield performance as the parental line under normal growth conditions. Notably, production of this salt-tolerant mutant line ready for delivery to farmers took only two years using our approach.

Next-generation sequencing allows the identification of mutations responsible for mutant phenotypes by whole-genome resequencing and alignment to a reference genome. However, when the resequenced cultivar/line displays significant structural variation from the reference genome, mutations in the genome regions missing from the reference (gaps) cannot be identified by simple alignment.Here we report on a method called ‘MutMap-Gap’, which involves delineating a candidate region harboring a mutation of interest using the recently reported MutMap method, followed by de novo assembly, alignment, and identification of the mutation within genome gaps.We applied MutMap-Gap to isolate the blast resistant gene Pii from the rice cv Hitomebore using mutant lines that have lost Pii function.MutMap-Gap should prove useful for cloning genes that exhibit significant structural variations such as disease resistance genes of the nucleotide-binding site-leucine rich repeat (NBS-LRR) class.

From today's paper: Hardier and more productive strains of rice are being developed faster than ever.

 

Today, around 3.5 billion people will sit down to a meal of something and rice. The “something” will depend on whether it’s Jamaica, Shanghai or Kerala, but rice is one of the world’s constants. It is a staple food for nearly half the globe and provides a fifth of all the calories humanity consumes. It is hard to overstate its importance. And that importance is growing: the world’s population, which swept past seven billion last year, is projected to reach nine billion by 2050. How are we going to feed all those new mouths?

Advances in genome sequencing technologies have enabled researchers and breeders to rapidly associate phenotypic variation to genome sequence differences. We recently took advantage of next-generation sequencing technology to develop MutMap, a method that allows rapid identification of causal nucleotide changes of rice mutants by whole genome resequencing of pooled DNA of mutant F2 progeny derived from crosses made between candidate mutants and the parental line. Here we describe MutMap+, a versatile extension of MutMap, that identifies causal mutations by comparing SNP frequencies of bulked DNA of mutant and wild-type progeny of M3 generation derived from selfing of an M2 heterozygous individual. Notably, MutMap+ does not necessitate artificial crossing between mutants and the wild-type parental line. This method is therefore suitable for identifying mutations that cause early development lethality, sterility, or generally hamper crossing. Furthermore, MutMap+ is potentially useful for gene isolation in crops that are recalcitrant to artificial crosses.

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci: QTL). QTL mapping and isolation is important for efficient crop breeding by marker-assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. Since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been however time consuming and labor intensive. Here we report a rapid identification of plant QTL by whole genome resequencing of DNAs from two populations each composed of 20-50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL-seq as applied to plant species. We applied QTL-seq to rice recombinant inbred lines (RILs) and F2 populations and successfully identified QTL for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL-seq is able to detect QTL over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.