No abstract

In rice many QTL have been reported for yield and related traits using various bi-parental mapping populations. However, few of the well characterized genes/QTL have been successfully used in breeding for improved trait performance. One of the reasons is that the mapping populations used are irrelevant to breeding. Association mapping has also been used in identifying marker-trait associations that are effective in more complex background. However, the results from association mapping using diversity panels are difficult to be exploited in breeding, since most of the accessions had poor performance in many important agronomic traits. In this study, a genome-wide association study (GWAS) was performed using three multiparent advanced generation inter-cross (MAGIC) populations derived from elite indica lines (DC1, DC2 and 8-way) to identify QTL for 14 traits including yield, yield components and other related traits. The three MAGIC populations were phenotyped in the dry season (DS) and wet season (WS) of 2014 at the headquarters of the International Rice Research Institute (IRRI) and genotyped with a Rice SNP chip containing 4,500 markers. A total of 26 QTL on all chromosomes except 7, 9 and 11 were identified for 10 traits in the DS or WS. Six, two, 12, 10 and 20 out of the 26 QTL were identified in the DC1, DC2, 8-way, DC12 (DC1+DC2) and RMPRIL (DC1+DC2+8-way) populations, respectively. Nine of the QTL corresponded to known QTL/genes, including qFLW4 for FLW, qTGW3 and qTGW5 for TGW, qFGN4 for FGN, qSBN4 for SBN, qPH1.1 and qPH1.2 for PH, qHD3 and qHD6 for HD. All these nine QTL were identifiable using the RMPRIL population while only eight, seven, six and one could be identified using the 8-way, DC12, DC1 and DC2 populations, respectively. The 8-way population was more powerful than the DC1, DC2 and DC12 populations. The joint analysis, which combines different populations (e.g. DC12 and RMPRIL), increased the number of QTL identified and mapping resolution. The study showed that MAGIC populations derived from diverse elite parental lines can be used to detect QTL, which are ideal for linking gene identification and practical breeding. GWAS using such MAGIC populations had higher detection power (compared to assembled populations) and higher resolution (compared to biparental populations). The identified QTL are directly applicable, since the populations are good breeding populations as well.

Excessive amounts of metal are toxic and severely affect plant growth and development. Understanding the genetic control of metal tolerance is crucial to improve rice resistance to Fe, Zn, and Al toxicity. The multi-parent advanced generation inter-cross (MAGIC) populations were genotyped using a 55 K rice SNP array and screened at the seedling stage for Fe, Zn, and Al toxicity using a hydroponics system. Association analysis was conducted by implementing a mixed linear model (MLM) for each of the five MAGIC populations double cross DC1 (founders were SAGC-08, HHZ5-SAL9-Y3-Y1, BP1976B-2-3-7-TB-1-1, PR33282-B-8-1-1-1-1-1), double cross DC2 (founders of double cross were FFZ1, CT 16658-5-2-2SR-2-3-6MP, IR 68, IR 02A127), eight parents population 8way (founders were SAGC-08, HHZ5-SAL9-Y3-Y1, BP1976B-2-3-7-TB-1-1, PR33282-B-8-1-1-1-1-1, FFZ1, CT 16658-5-2-2SR-2-3-6MP, IR 68, IR 02A127), DC12 (DC1+DC2) and rice multi-parent recombinant inbred line population RMPRIL (DC1+DC2+8way). A total of 21, 30, and 21 QTL were identified for Fe, Zn, and Al toxicity tolerance, respectively. For multi tolerance (MT) as Fe, Zn, and Al tolerance-related traits, three genomic regions, MT1.1 (chr.1: 35.4–36.3 Mb), MT1.2 (chr.1: 35.4–36.3 Mb), and MT3.2 (chr.3: 35.4-36.2 Mb) harbored QTL. The chromosomal regions MT2.1 (chr.2: 2.4–2.8 Mb), MT2.2 (chr.2: 24.5–25.8 Mb), MT4 (chr.4: 1.2 Mb Mb), MT8.1 (chr.8: 0.7–0.9 Mb), and MT8.2 (chr.8: 2.2–2.4 Mb) harbored QTL for Fe and Zn tolerance, while MT2.3 (chr.2: 30.5–31.6 Mb), MT3.1 (chr.3: 12.5–12.8 Mb), and MT6 (chr.6: 2.0–3.0 Mb) possessed QTL for Al and Zn tolerance. The chromosomal region MT9.1 (chr.9: 14.2–14.7 Mb) possessed QTL for Fe and Al tolerance. A total of 11 QTL were detected across different MAGIC populations and 12 clustered regions were detected under different metal conditions, suggesting that these genomic regions might constitute valuable regions for further marker-assisted selection (MAS) in breeding programs.

BackgroundA number of studies reported major genes/QTLs for rice grain shapes, chalkiness and starch physicochemical properties. For these finely mapped QTLs or cloned genes to make an impact in practical breeding, it is necessary to test their effects in different genetic backgrounds. In this study, two hundred nineteen markers for 20 starch synthesis genes, 41 fine mapped grain shape and related traits QTLs/genes, and 54 chalkiness QTLs/genes plus 15 additional markers and a large indica population of 375 advanced lines were used to identify marker-trait associations under 6 environments that can be used directly in breeding for grain quality traits.ResultsThe significant associations detected by the QK model were used to declare the usefulness of the targeted genes/QTLs. A total of 65 markers were detected associations with grain quality trait at least in one environment. More phenotypic variations could be explained by haplotype than single marker, as exemplified by the starch biosynthesising genes. GBSSI was the major gene for AC and explained up to 55 % of the phenotypic variation, which also affected GC and accounted up to 11.31 % of the phenotypic variation. SSIIa was the major gene for chalkiness and explained up to 17 and 21 % of variation of DEC and PGWC, respectively. In addition, RMw513 and RM18068 were associated with DEC in 6 environments as well. Four markers (RGS1, RM15206, RMw513 and Indel1) tightly linked to GS3, gw5, and qGL7-2 were the most important ones for grain shapes. Allelic combinations between SSIIa and RMw513 revealed more variations in DEC.ConclusionsThe validated markers for genes/QTLs with major effects could be directly used in breeding for grain quality via marker-assisted selection. Creating desirable allelic combinations by gene pyramiding might be an effective approach for the development of high quality breeding lines in rice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-015-0064-3) contains supplementary material, which is available to authorized users.

Soil salinity is a serious menace in rice production threatening global food security. Rice responses to salt stress involve a series of biological processes, including antioxidation, osmoregulation or osmoprotection, and ion homeostasis, which are regulated by different genes. Understanding these adaptive mechanisms and the key genes involved are crucial in developing highly salt-tolerant cultivars. In this review, we discuss the molecular mechanisms of salt tolerance in rice—from sensing to transcriptional regulation of key genes—based on the current knowledge. Furthermore, we highlight the functionally validated salt-responsive genes in rice.