Background

6.1.1 General background

The extreme importance of rice (particularly irrigated rice) to the global economy and food supply for more than half the world’s population has been demonstrated in Chapter 1. Therefore increasing rice production is still a big challenge today. Breeding has been a major contributor to rice yield increases in the past (Guimarães, 2009) and enhanced breeding processes will be a key part of that process into the future.

Although rice yield has increased dramatically since the Green Revolution, the increased yield potential of modern rice cultivars has remained stagnant for the past several decades due to the narrowed genetic diversity and various biotic and abiotic stresses ) (Caicedo et al., 2007; Nguyen and Ferrero, 2006; Zhu et al., 2007). The commonly used breeding methods, such as pedigree, bulk and backcross, have some disadvantages (Fujimaki, 1980), including limited use of the full range of available genetic resources, restrictions of the potential for genetic recombination, and difficulty of continuing to obtain improvements in successive breeding cycles.

6.1.2 Study scientific environment

To break the rice yield barrier new strategies are being formulated and implemented. This includes but is not limited to targeting varieties to the target population of environments (TPEs) by characterizing genotype-by-environment interaction (GEI), increasing genetic diversity by introducing elite lines from independent breeding programs, wide hybridization to discover and utilize unadapted germplasm, shortening breeding cycles using rapid generation advance methods and implementing MAS or genomic selection (GS). The International Rice Research Institute (IRRI) has proposed an integrated strategy to increase breeding efficiency by utilizing well proven conventional breeding methods, new techniques and methods enabled by modern molecular biology and genomics and advanced methods in experimental design and data analysis (GRiSP, 2010; Ye et al., 2013). This strategy utilizes (marker-assisted) recurrent selection to quickly pyramid the major genes/QTLs in the first few selection cycles and maintain genetic variation contributed by many minor genes to be explored in later cycles, explores GS to reduce the number of breeding cycles and the costs of

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phenotyping and adopts advanced experimental design and data analysis methods to improve heritability (Ye et al, 2012). This strategy is being implemented and refined in the “recurrent selection for population improvement program” initiated by IRRI in 2011 (GRiSP, 2010). The studies reported in this thesis fall within this program.

The overall objective of the studies reported by this thesis is to obtain essential information for designing more efficient mating and selection schemes for use in this general breeding strategy.

6.1.3 Study research background

Rice yield is collectively determined by genotype, environments and GEI. The stability of yield performance is one of the most desirable characters of a genotype to be released as a variety, which allows the developed varieties to be adopted across a large area (exploring the general adaption). On the other hand, to achieve maximum productivity requires targeting varieties to their best growing environments (utilizing the specific adaptation). However, GEI for GY of rice grown in irrigated lowland has not received adequate attention comparable to its importance. Data from the “Irrigated-Lowland Rice National Cooperative Testing Program” of the Philippines indicated that that season-by-location interaction (SLI) was highly significant in the combined analyses of variance over seasons and locations, while locations and seasons were not significant (Samonte and Hernandez, 1990 and 1991). Genotype-by-season interaction (GSI) and genotype-by-location interaction (GLI) were significant only in 10 and seven out of the 48 combined analyses, respectively, while genotype by-season-by-location interaction (GSLI) was significant in almost all cases. Datasets from the international irrigated rice yield nursery (IIRYN) conducted in 1993, 1994 and 1995 showed that the GEI sum of squares was 3-7 times the genotype sum of squares variance (INGER, 1993a; 1993b; 1994a; 1994b, 1995a, and 1995b). However, the large GEI could be at least partially caused by the fact that the soil and weather conditions in some of the testing sites were atypical. Therefore, there was only limited information on the magnitude of GEI for GY of rice applicable to a more diverse set of genotypes that has not undergone intense selection for yield stability across diverse environments.

As a model crop species for plant molecular biology and genomics, rice has been sequenced (Eckardt, 2000; IRGSP, 2005) and researchers have accumulated more molecular and genomic information on rice than on most other crops. Thousands of genes/QTLs for grain yield and yield related traits have been identified (Ashikari and Matsuoka, 2006; Huang

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et al., 2013; Miura et al., 2011; Xing and Zhang, 2010; Zuo and Li, 2014). Utilizing this genetic information offers the rice breeding community a range of modern tools and methods for addressing the continuing challenge of increasing rice yield. The potential benefits of using molecular markers linked to the genes of interest in breeding programs, which changed from phenotype-based toward a combination of phenotype- and genotype-based selection, have attracted much attention for more than two decades (Bernardo, 2008; Tester and Langridge, 2010). The efficiency and usefulness of marker-assisted selection (MAS) for traits of simple inheritance (i.e. qualitative traits controlled by one or a few genes) have been well proven in many crops, including rice (Collard et al., 2008; Ye and Smith, 2010; Ye et al., 2009). The success of MAS has motivated rice breeders to search for QTLs for complex traits including yield, especially those accounting for a large proportion of phenotypic variation (major QTLs). Many yield-related genes/QTLs have been identified and some of them are fine-mapped or cloned (Xing and Zhang, 2010, Guo and Ye, 2014). The use of these well- characterized genes/QTLs in improving yield has started. However, significant improvement of GY in farm environments has not been reported (Guo and Ye, 2014). For these fine mapped QTLs or cloned genes to make an impact in practical breeding, it is necessary to test their effects in different genetic backgrounds, since the effects of these well characterized genes/QTLs were usually tested using specific populations under specific environments.

The detection of the associations between traits of interest and molecular markers is the prerequisite for MAS. Two main approaches have been used to identify the associations between traits and markers, linkage mapping and association mapping (AM) or linkage disequilibrium (LD) mapping (Darvasi and Shifman, 2005). Although fundamentally different, both approaches share a common strategy that exploits recombination’s ability to break up the genome into fragments that can be correlated with phenotypic variation (Myles et al., 2009). Linkage mapping is mainly used to identify those genes segregating in the biparental crosses with contrasting genotypes. AM detects correlations between genotypes and phenotypes in a collection of germplasm based on LD. AM takes advantage of events that created association in a relatively distant past, which has removed association between a QTL and any marker not tightly linked to it due to recombination (Jannick and Walsh, 2002). It has two advantages: broader genetic variation and higher mapping resolution. Therefore, AM is more breeder-friendly and can be used in breeding populations.

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6.1.4 Study structure

In this study, a collection of 392 cultivars or advanced indica breeding lines were evaluated for GY and 10 other related traits in two target locations in China and two distinct seasons. The cultivars/lines were grown under three different rates of nitrogen fertilizer application at IRRI and the entire data set was used to identify patterns of genotype, environment, and GEI for GY under irrigated ecosystem. Forty-six markers closely linked to 39 cloned or fine-mapped genes/QTLs and 50 random SSR markers which were evenly distributed among the 12 chromosomes were used to genotype the current population. Association analysis was carried out between the 46 markers and the GY and 10 related traits to test the usefulness of the well-known genes/QTLs in this breeding population. The population was also genotyped with genotyping-by-sequencing (GBS) and produced 76K high quality SNPs. Genome-wide association analysis GWAS) was carried out for the 11 tested traits to identify new marker-trait associations (MTAs).