Genome-Wide Association Study for Plant Height and Grain Yield in Rice under Contrasting Moisture Regimes

Abstract

Drought is one of the vitally critical environmental stresses affecting both growth and yield potential in rice. Drought resistance is a complicated quantitative trait that is regulated by numerous small effect loci and hundreds of genes controlling various morphological and physiological responses to drought. For this study, 270 rice landraces and cultivars were analyzed for their drought resistance. This was done via determination of changes in plant height and grain yield under contrasting water regimes, followed by detailed identification of the underlying genetic architecture via genome-wide association study (GWAS). We controlled population structure by setting top two eigenvectors and combining kinship matrix for GWAS in this study. Eighteen, five, and six associated loci were identified for plant height, grain yield per plant, and drought resistant coefficient, respectively. Nine known functional genes were identified, including five for plant height (OsGA2ox3, OsGH3-2, sd-1, OsGNA1, and OsSAP11/OsDOG), two for grain yield per plant (OsCYP51G3 and OsRRMh) and two for drought resistant coefficient (OsPYL2 and OsGA2ox9), implying very reliable results. A previous study reported OsGNA1 to regulate root development, but this study reports additional controlling of both plant height and root length. Moreover, OsRLK5 is a new drought resistant candidate gene discovered in this study. OsRLK5 mutants showed faster water loss rates in detached leaves. This gene plays an important role in the positive regulation of yield-related traits under drought conditions. We furthermore discovered several new loci contributing to the three investigated traits (plant height, grain yield, and drought resistance). These associated loci and candidate genes significantly improve our knowledge of the genetic control of these traits in rice. In addition, many drought resistant cultivars screened in this study can be used as parental genotypes to improve drought resistance of rice by molecular breeding.

Genomic analysis of maize lines introduced in the early stages of a breeding programme

“…In this sense, the marker associated with this region of the genome may be useful for the production of hybrids resistant to water stress. In fact, this notion is the hypothesis of several GWAS for grain yield (Ain et al., ; Ma et al., ; Pantalião et al., ; Sukumaran et al., ; Zhang et al., ). These authors claim the importance of applying GWAS for grain yield and its related traits to incorporate genes tolerant to environmental stress.…”

“…(), the use of GWAS for yield traits and its components has been limited in maize. In other grasses, such as rice and wheat, this paradigm has been overcome, and several studies have applied GWAS to search for major genes associated with grain yield; for instance, those responsible for tolerance to environmental stress (Ain et al., ; Ma et al., ; Pantalião et al., ; Sukumaran, Dreisigacker, Lopes, Chavez, & Reynolds, ). In the present context, where several lines have arisen from introduced germplasm, that is, temperate sources, a GWAS applied to grain yield may serve as a basis for the discovery of favourable alleles introduced from a tropical background.…”

Identification of QTLs for high grain yield and component traits in New Plant Types of rice

“…qTL-11.1, qTL-4.1 and qTL -6.1 , respectively. For panicle length(PL), previous researchers have reported the association of RM5709 with qPL-4.1 ; qSPP-4.1 , RM204 with qPL-6.1 [3], RM234 with qPPL-7.2 [30]; RM447 with qPL-8 [21, 88] and RM17 with qPL-12b . Four out of 13 SSRs viz., RM297, RM5575, RM5711, and RM22899 were found to be associated with four novel QTLs, qPL-1.1, qPL-5.1 and qPL-8.1 , respectively for panicle length (PL).…”

“…It utilizes natural variation [12], hence supposed to have great potential to evaluate and characterize a wide range of alleles. Several researchers have been reported the utility of association analysis in the identification of QTLs for different traits in rice, viz., Grain yield [13], grain yield under water deficit [14], deep root mass and the number of deep roots [15], grain quality traits [16], agronomic traits [17, 18], grain yield under reproductive drought stress [19], panicle architecture and spikelet’s/ panicle [20], plant height and grain yield [21].…”

Reducing the interval of a growth QTL on chromosome 4 in laying hens

“…The confidence interval was defined as the region around the most significant marker containing all markers that were in high linkage disequilibrium (LD, r 2 > 0.6) with the most significant marker (Ma et al . ).…”