6.2.1 GEI analysis
Ideally, the rice growing environments should be free of severe disease to obtain maximum yield potential of the testing lines. In our experiments, some practices were taken to prevent pest or disease spread, such as, applying Furadan to control golden snail before transplanting and chemical spraying after transplanting. There may have been differences in low level diseases/pest that may contribute to the high GEI. However, we regard them as valid environmental components.
The combined analysis presented in Table 3.1 showed the relatively high magnitude of the GEI variance relative to the genotypic variance for GY. The GEI effect was nearly twice as much as the genotype effect. The GEI for GY was partitioned into principal component axis following the AMMI analysis (Figure 3.1). The first two principal components i.e. IPCA 1 and IPCA 2, which accounted for 68.4% of the total variation, were significant and sufficient to explain the GEI.
The genotype-by-season interaction was found to be the major source of GEI for GY. The DS and WS environments in IRRI were grouped into different groups and discriminated genotypes in different ways. These data (Figure 3.1) demonstrate it is possible to select genotypes with stable performance across seasons. Developing varieties adapted to both of
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DS and WS has been the goal of IRRI’s irrigated breeding program. However, with distinctive and highly repeatable seasonal pattern and different genotype responses to seasons much larger genetic progress could have been made by breeding for the two seasons separately. Thus, breeding for different seasons separately to exploit the repeatable GEI caused by seasonal changes (as demonstrated in Figure 3.1) was recommended, even with the current breeding gene pools.
It is worth summarizing the similarity of environments in China and at IRRI. Since the rice paddy was irrigated as required in all the testing environments water availability was not a limiting factor for rice growth. The weather conditions including rainfall, temperature and solar radiation were the main source of environmental difference. The maximum temperature of SC and three WS environments was 35 °C. The biplot analysis grouped SC and three WS environments together. Three environments in the DS formed one group with a maximum temperature of 38 °C. JX alone was the third group with a maximum temperature of 37 °C (Figure 3.1b). Thus, IRRI breeding lines with stable and good performance in the WS could be used in SC. Similarly, selection is better to be done in DS in IRRI for use in JX, China. Clearly, great attention should be paid to the relevance of performance at IRRI to their target production environments when IRRI breeding lines are introduced. On the other hand, with a global mandate IRRI’s irrigated rice breeding program should expand its testing and selection environments to allow exploiting specific adaption and providing critical and relevant performance information to the developing countries that largely depend on IRRI for new breeding lines.
The different N rates used had only a relatively small effect on the relative performance of genotypes, compared with the season. The contribution of genotype-by-N interaction could become more important if environment determined by location and season has been fixed. Overall, GEI exceeds genotype effects with season and location greater than N fertilizer.
To maximise the value of these results requires that the genes creating the genotype and GEI benefits reported on here need to be identified and tested and that structure and the genetic diversity of the breeding population needs to be assessed. It is necessary to identify new QTLs for GY, which could be used to enlarge the current gene pool. Thus the second and third series of experiments proceeded.
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6.2.2 Testing the usefulness of known genes/QTLs for GY and related traits
One of the objectives of the newly designed irrigated breeding program at IRRI is to increase the genetic diversity of its breeding populations by incorporating elite lines from other large breeding programs. The genetic diversity of the base population of 360 lines was studied using all the 46 markers for the 39 targeted genes/QTLs and 53 random SSR markers. It was found (Table 4.2) that the genetic diversity and PIC value of the current population were lower than those of the populations of 83 global indica lines (0.694 and 0.665) and 495 Chinese indica lines (0.623 and 0.595) assessed by Wang et al. (2014) but higher than the population of 299 inbred indica rice varieties mainly from one of the largest indica breeding program (Guangdong Academy of Agricultural Sciences) in China (Chapter 4). The base population assembled mainly based on breeder’s experiences and phenotypic performance in IRRI still has relatively lower genetic diversity and should be greatly increased by introducing breeding lines from programs with limited germplasm exchange. Based on the following considerations breeding programs in China are particularly interesting. Firstly, the majority of indica rice production environment in China is subtropical climates while all other indica breeding programs are in tropical regions. Secondly, Chinese indica varieties are well-known for high yield. Thirdly, The Green Super Rice program has proven that Chinese varieties performed well in many tropical regions in Asia and Africa, particularly in the dry season.
The usefulness of known genes/QTLs was tested using 39 well characterized genes/QTLs for yield and related traits in the population of indica breeding lines for irrigated ecosystem from IRRI and a few other breeding programs. Using association analysis, all the studied genes/QTLs were found to be associated with at least two of the 11 measured traits in one of the eight testing environments or the average environment (Table 4.3). The numbers of genes/QTLs associated with GY, DTF, PH, GN, SN, PN, TN, TGW, PB, SB and SR were 16, 25, 39, 16, 11, five, five, ten, 29, six and 11, respectively. However, all the genes/QTLs were associated with traits unreported previously thus further investigations on the effects of the target genes/QTLs on all important agronomic traits are needed. For all genes/QTLs environment showed large effects and significant gene-by-environment interaction was present. This implies that (1) the effects of the target genes in the TPEs need to be tested before MAS is implemented and (2) It will be difficult to use these genes in breeding for wide adaptation. These genes/QTLs were also associated with two or more traits. It is
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important for MAS to investigate the effects of the target genes on other important agronomic traits.
There are some common QTLs in rice, such as GS3 and GN1a. However, the two
QTLs were not identified in this study. The fact that those common QTLs couldn’t be
identified in a GWAS study might be attributed to a few reasons, such as having a fixed QTL, low frequency, low marker density and too small a population. In this study, the minor allele frequencies of the two QTLs were above 5% in the whole population. However, there was no polymorphism of the flanking markers of GS3 or GN1a within each of the subpopulations. So the common QTLs, GS3 and GN1a were not detectable since the model used for association analysis included population structure.
6.2.3 MTAs identification
The main drawback of AM is the high false positive rate caused by population structure and unequal familial relationships between genotypes in the panel. To reduce spurious associations these two factors need to be carefully considered. Using the model based Bayesian cluster method implemented in STRUCTURE, the 360 lines that were selected from 392 lines and genotyped with 53 random SSR markers were grouped into two subpopulations of 205 and 155 lines, respectively (Chapter 4). Three methods were used to detect population structure in the subset of 327 lines genotyped by GBS (Chapter 5). STRUCTURE analysis using 1,072 evenly distributed SNPs on 12 chromosomes grouped these lines into two subpopulations of 234 lines and 93 lines, respectively (Figure 5.1). The same result was obtained by PCA analysis using all the 76452 markers. Separate PCA conducted for the two subpopulations suggested that there was no sub-structure in either of them (Figure 5.2). Near neighbour joining (NJ) method also suggested that there are two subpopulations in the whole panel of 327 lines (Figure 5.3), although four of the lines grouped into the larger subpopulations by STRUCTURE and PCA were assigned to the small population.
The kinship coefficient among the 360 lines was calculated based on the 53 random SSR markers and it ranged from 0 to 1.80, with a mean of 0.49 (Figure 5.4). There were about 2.33% unrelated genotype pairs (kinship = 0), 6.96% distantly related genotype pairs with kinship being lower than 0.10 and 0.03% highly related genotype pairs with kinship being higher than 1.50. For the subset of 327 lines, the kinship coefficients for all the pairwise combinations ranged from 0 to 2, with a mean of 0.58. There were about 0.18%
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distantly related genotype pairs with kinship being lower than 0.10 and 0.46% highly related genotype pairs with kinship being higher than 1.50. The majority of the genotype pairs had kinship similar to half-sibs (kinship = 0.5). Generally, the relationship within a breeding population was greater than among the breeding populations.
Successful GWAS also depends on the extent of LD. LD analysis showed that LD decayed to half maximum (r2=0.25) within a physical distance of 200 kb in the whole population of 327 lines (Figure 5.5). The mean r2 among the individual chromosome ranged from 0.21 to 0.34. The LD decay was slower for the chromosomes 8, 12, 1 and 6, while it was faster for chromosomes 2, 9 and 10. The average r2 dropped to half of its maximum within 250kb for chromosomes 8 and 12, and 210 kb for chromosomes 1 and 6. The average r2 dropped to half of its maximum within 130 kb for chromosomes 2 and 9 and 150 kb for chromosome 10. In subpopulation 1 the average r2 for the whole genome also dropped to half of its maximum (0.36) within 190 kb while it dropped to half of its maximum value (0.65) within 350 Kb in the subpopulation 2 (Figure 5.5). Similar to the whole panel, the LD decay patterns differed among chromosomes within both of the two subpopulations. The maximum r2 for individual chromosome varied from 0.30 to 0.45 in the subpopulation 1 and 0.59 to 0.76 in the subpopulation 2. For subpopulation 1, the mean r2 dropped to half of its maximum within 260 kb for chromosome 12, 240 kb for chromosome 4, and 230 kb for chromosomes 4 and 6, respectively (Figure 5.5). LD decay was faster for chromosomes 9, 10, 11, 2 and 8 with the mean r2 dropping to its half maximum value within 100 kb, 130 kb, 130 kb, 140 kb and 140 kb, respectively. For the subpopulation 2, a slower LD decay was observed for chromosomes 1, 8, and 12, with r2 dropping to half its maximum value within 490 kb, 420 kb, and 400 kb, respectively (Figure 5.5). A faster LD decay was observed for chromosomes 9, 5, and 10. The mean r2 dropped to half its maximum within 170 kb, 180 kb and 260 kb, respectively.
GWAS using a MLM model controlling both population structure and relatedness identified 43 QTLs for all traits except PB, SB, and SR. Three QTLs on chromosome 6, 9 and 12 were identified for GY in DS2 (Table 5.3). The numbers of QTLs identified for the remaining traits varied from two to 26 (Tables 5.4-5.5). Most of the detected QTLs were found in only one environment. Eight of the identified QTLs were associated with more than one trait with five were associated with strongly correlated traits measured in the same or different environments (Figure 5.9). Four of the identified QTLs in the present study were located in the genomic regions where QTLs for yield or related traits have been reported previously. The DTF QTL on chromosome 3, qDTF-3-1, identified in four environments
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corresponds to Hd9, a major QTL for heading date in rice. Some QTLs were located in the regions containing QTLs previously identified for other related traits. One of the PN QTLs on chromosome 1, qPN-1-1 were in the regions of fine-mapped TGW QTLs, Gw1-1 and Gw1-2. The effects of identified QTLs were relatively small with the highest percentage of phenotypic variance explained by a single QTL being 9.6%.