Analysis of genetic diversity among structural groups for 217 accessions in the USDA rice mini-core collection genotyped with DNA markers (Li et al. 2010).
Classical QTL mapping reveals only a slice of the genetic architecture for a trait because only two alleles that differ between the two parental lines segregate. A comprehensive analysis of genetic architecture requires consideration of a diverse population that represents genetic variation in a species. Association mapping provides an effective method to identify QTL that have effects across a broad spectrum of germplasm (Yu et al. 2006). Many studies have used association mapping for important traits since it was introduced from human genetics (Yu et al. 2006; Kim et al. 2006; Huang et al. 2010; Kang et al. 2008). Genome-wide association scans are expected to be effective when linkage disequilibrium (LD) and marker density are sufficiently high, so that the random markers have a greater chance of being in disequilibrium with QTL across diverse genetic materials (Kim et al. 2006). A substantial number of QTL at close to gene resolution for important traits have been identified by genome-wide association studies (GWAS) in rice (Zhao et al. 2007). Recently, the USDA Rice Mini-Core (URMC) collection was developed and serves as a genetically diversified panel for mining genes of interest (Li et al. 2010). The URMC was derived from 1,794 accessions in the USDA rice core collection using PowerCore software based on 26 phenotypic traits and 70 molecular markers (Agrama et al. 2009). The core collection represents over 18,000 accessions in the USDA global genebank of rice (Yan et al. 2007). The URMC contains 217 accessions originating from 76 countries and covering 14 geographic regions worldwide. The Objective of this review is to analyze the genetic diversity and differentiation of the URMC for genome-wide association mapping of harvest index, grain yield, sheath blight resistance and hull silica concentration.
2. Materials and methods
2.1. Rice association panel
Of 217 accessions in the URMC, 203 belong to
2.2. Location and field experiment
Evaluations were conducted for 14 traits in two field locations, USDA-ARS Dale Bumpers National Rice Research Center near Stuttgart, Arkansas and USDA-ARS Rice Research Unit near Beaumont, Texas during the growing season of 2009. The Stuttgart test site is located at N 34027’44” and W 91024’59”, representing a temperate climate with a 243 d frost free period and average temperature of 23.9 C during the growing season. The Beaumont test site is located at N 30003’47” and W 94017’45”, representing a subtropical climate with a 253 d frost free period and an average temperature of 26.1 C during the growing season. The experiments at both locations utilized a randomized complete block design having three replications with nine plants spaced 0.3×0.6 m in each plot. Li et al. (2012) had a detail description of experimental methods and field managements.
Data collection followed procedures described by Yan et al. (2005a; 2005b) with modifications. Fourteen characteristics were recorded using the methods described by Li et al. (2010; 2011; 2012), including heading days, plant height, plant weight, tiller per plant, grain yield per plant, harvest index, main panicle length, panicle branches, Grain per panicle, seed set percentage, 1000 grain weight, grains per cm panicle, grains per branch panicle, and grain weight per panicle.
Bulk tissue from five plants was collected from each accession as described by Brondani et al. (2006) and total genomic DNA was extracted using a rapid alkali extraction procedure (Xin et al. 2003) and a CTAB method as described in Hulbert and Bennetzen (1991). The bulked DNA allowed identification of the origin of heterogeneity, which can result from the presence of heterozygous individuals or from a mix of individuals with different homozygous alleles (Borba et al. 2005). A total of 155 molecular markers covering the entire rice genome, with approximately one marker per 10 cM on average, were used to genotype the URMC accessions. Among the markers, 149 SSRs were obtained from the Gramene database (http://www.gramene.org/), and five SSRs (AP5652-1, AP5652-2, AL606682-1, con673 and LJSSR1) were identified by Li et al(2011). The remaining marker was an
2.5. Statistical analysis marker and phenotype profile
Genetic distance was calculated from 155 molecular markers using Nei’s method (Nei and Takezaki 1983). Phylogenetic reconstruction was based on the UPGMA method implemented in
Each of the 14 phenotypic traits was modeled independently with the MIXED procedure in SASv.9.2, where genotype, location and interaction of location with genotype were defined as fixed effects while replication within a location (block effect) was a random effect. Broad-sense heritability was calculated using formula H2 = σg
2/n), where σg
2 as the genotypic variance, σe
2 as the environmental variance and n as the number of replications (Wang et al. 2007). Spearman rank correlation coefficients between each pair of the 14 traits were calculated using the mean of 9 plants, 3 in each of three replications for an accession, using the CORR procedure in SASv.9.2. Correlation coefficients were graphically displayed based on unweighted pair-group method using arithmetic average (UPGMA) by
2.6. Population structure
The model-based program
Fourteen phenotypic characteristics were used to calculate Mahalanobis distance as a measurement of genetic differentiation among the groups (Kouame and Quesenberry 1993). The Mahalanobis distance and Canonical discriminant analysis were performed by the procedures PROC CANDISC of the
2.7. Model comparisons and association analysis
The flexible mixed model (Yu et al. 2006) was used to control population structure. The methods for model comparisons and association mapping are referred to Li et al. (2012) for harvest index, Li et al. (2011) for grain yield, Jia et al. (2012) for sheath blight resistance and Bryant et al. (2011) for silica concentration in rice hulls.
3. Analysis of genetic structure and genetic diversity
3.1. Profile of DNA markers
Among 217 accessions in the URMC, the average number of alleles per locus was 13.5 ranging from 2 for RM338 to 57 for con673. PIC mean was 0.71 ranging from 0.30 for AP5625-1 to 0.97 for con673 among these markers. Since every accession was analyzed as a bulk of five plants, 54 (42.19%) loci showed heterozygosity and 38 (17.51%) accessions showed heterogeneity for at least one locus. Nei genetic distance (Nei and Takezaki 1983) was estimated for each pair of the 217 rice accessions which ranged from 0.021 to 1.000, with an average 0.752.
In previous studies, the average number of alleles per locus was 5.1 in Cho et al. (2000), 7.8 in Jain et al. (2004), 11.9 in Xu et al. (2004) and 11.8 in Garris et al. (2005). Recently, 13 alleles per locus were reported in the rice population studied by Thomson et al. (2007), 5.5 by Thomson et al. (2009) and 12.4 by Borba et al. (2009). The PIC in the URMC was 0.71, larger than it in the population studied by Cho et al. (2000) (0.56 PIC), Jain et al. (2004) (0.60), Xu et al. (2004) (0.66), Garris et al. (2005) (0.67), Thomson et al. (2007) (0.66), and Thomson et al. (2009) (0.45). The PIC was slightly less in our study than in the population studied by Borba et al. (2009) (0.75). Both the average allele number and PIC values are indicative of genetic diversity or gene richness in a germplasm collection. The higher the genetic diversity is in a collection, the greater the probability is for a gene of interest to be mined from the collection. Greater genetic diversity in the URMC is due to its global originations, multiple
3.2. Genetic structure and differentiation derived from DNA markersUPGMA tree showed that the accessions of
The ancestry of each accession was inferred from the Q value and classified into one of the six groups which corresponded to
The first-two axes in PCoA with 83.2% of total variation sufficiently discriminated the six main groups and two admixture groups (Fig. 1b). Each main group was distinguishable from another, but overlaps existed either among
Each accession with ancestry information was plotted on a world map using its latitude and longitude of geographic origin (Fig. 2). TEJ accessions were mainly distributed between latitudes 30 and 50 degrees north and south of the equator (i.e. temperate zone) while the other four groups scattered between latitude N 30 and S 30 degrees (i.e. tropical and subtropical zone).
In the URMC, the majority of accessions were IND (33%), followed by TRJ and AUS (18% each), TEJ (15%), WD (6%) and ARO with only six accessions (Fig. 3). All the marker loci were polymorphic in IND (Table 1). TRJ had 99% polymorphic loci, followed by WD, AUS, TEJ and ARO. IND had the most alleles per locus, TRJ and AUS the second most, TEJ and WD the third most and ARO had the fewest alleles. The largest number of private alleles per locus (alleles unique in one group and not found in another group) were found in WD (41.89%), followed by IND (23.78%) and AUS (17.66%). TRJ and TEJ had about equal private alleles, and the least was found in ARO. Gene diversity averaged 0.47 among the groups ranging from 0.37 in ARO to 0.52 in both IND and AUS. TRJ and WD had the same diversity (0.50), slightly greater than TEJ (0.43).
Results from the AMOVA showed that 37.92% of total variation was due to differences among groups, 61.21% within groups and 0.88% within individuals. Pair-wise estimates of
3.3. Phenotypic analysis
Statistical analysis using a mixed model demonstrated that the differences due to genotypes and genotype × location interactions were highly significant at the 0.001 level of probability for all of the 14 traits (Table 2). The differences due to location were also significant for all traits except for panicle branches and seed set. Heritability was very high for all 14 traits. Heading had the highest heritability which was close to 100%. Although seed set had the lowest heritability, it was still above 70%. Heritability ranged from 77 to 97% among the other 12 traits. Harvest index had a heritability of 83% at Stuttgart and 90% at Beaumont. Correlation coefficients for each pair of the 14 traits were calculated using Spearman rank in each location for visualizing their relationships using PCA where the first two axes accounted for more than 50% phenotypic variation (Fig. 4a, b). At Stuttgart, 47 out of 91 correlations among the 14 traits were significant (<0.0001) (Fig. 4a), and 40 correlations were significant at Beaumont (Fig. 4b). Thirty four correlations were uniformly significant across two locations and their correlation directions (positive or negative) were also same across two locations.
3.4. Genetic structure and differentiation derived from phenotypic traits
Canonical discriminant analysis of 14 phenotypic traits for the mini-core accessions clearly separated the six plus two admixture model-based genetic groups derived from molecular data (Fig. 5). The first four significant (
All 14 traits were significantly different among the eight (six plus two admixtures) model-based genetic groups. However, only three traits, plant weight (biomass), tillers and grain yield per plant, had larger variation among groups than within groups. Therefore, they are considered the main discriminatory characters (
The most tillers were observed in AUS accessions PI 385697 (93) and 352687 (86), while the lowest were in TRJ PI 584567 (9) and PI 154464 (10). WD had the most tillers (60), followed by AUS (46), ADA (44), AUS (46), IND (38), ARO (27), TEJ (24), ADJ (21) and TRJ (18). The greatest plant weight was 731 g for PI 549215 (IND), and the lowest was 37g for PI 281630 (TEJ). Again, WD had the most plant weight (442g) and TEJ had the lowest (127g). PI 373335 (IND) had the highest grain yield per plant at 175g and PI 389933 (IND) had the lowest at 11g. ADA had the most grain yield per plant (127 g per plant), while TEJ had the lowest (55g).
3.5. Relationship between genetic and phenotypic differentiation
Both the dendrograms based on the Mahalanobis distance (D2) using the 14 phenotypic traits (Fig. 5) and based on the
Analysis developed by Mantel (1967) is widely used to describe the genetic relationship between genotypic and phenotypic measurements (Gaudeul et al. 2000, Gizaw et al. 2007). In our study, genetic distance derived from the DNA markers among the six plus two admixture model-based groups was highly and significantly correlated with the distance derived from 14 phenotypic traits (
In rice ancestry, structure and genetic diversity of germplasm collections has been studied using a variety of molecular markers such as SNP (Zhao et al. 2011), SSR (Cho et al. 2000; Jain et al. 2004; Xu et al. 2004; Garris et al. 2005; Thomson et al. 2007; 2009; Borba et al. 2009), RAPD (Mackill 1995) and isozyme (Glaszmann 1987) markers. Phenotypic characteristics are rarely used to analyze genetic diversity or structure in rice germplasm collections. Zeng et al. (2003) collected samples from each of six genetic groups for a diversity analysis using 31 phenotypic traits, but failed to reveal their genetic differentiations.
However, assessment of genetic diversity and structure using both genotypic and phenotypic characterization and relationship or accuracy between the genotypic and phenotypic assessments has long been attractive to the scientific community. Elias et al. (2001a) reported a significant positive association between genotypic and phenotypic distances (
We use both genotypic and phenotypic characterizations to analyze genetic differentiation in a plant germplasm collection. The present study in rice has a much greater association (
4. Association mapping of harvest index and components
Harvest index is a ratio of grain yield to total biomass, which measures farming success in partitioning assimilated photosynthate to harvestable product (Hay 1995; Sinclair 1998). In cereal crops, dramatic improvements of harvest index during domestication have made commercial cultivars dramatically different from their wild ancestors (Gepts 2004). Rice (
In rice, previous studies on harvest index have identified numerous QTL all using a classic linkage-mapping strategy with two parents. Mao et al. (2003) reported four main QTL on chromosome (Chr) 1, 4, 8 and 11 and an epistatic interaction between two QTL respectively on Chr 1 and Chr 5. Sabouri et al. (1999) identified three QTL each on Chr 2, 3 and 5, and two QTL close to each other on Chr 4. Lanceras et al. (2004) described harvest index QTL on Chr 1 and 3. However, mapping populations developed from different parental combinations and/or experiments conducted in different environments often result in partly or wholly non-overlapping sets of QTL (Hao et al. 2010).
4.1. Traits correlated with harvest index in our study
Six traits were significantly correlated with harvest index and these correlation directions were the same across the two locations. The correlations with harvest index were negative for heading (-0.46 at Stuttgart and -0.61 at Beaumont), plant height (-0.50 and -0.50), plant weight (-0.36 and -0.30), panicle length (-0.45 and -0.32), while positive for seed set (0.52 and 0.61) and grain weight/panicle (0.32 and 0.40) (Fig. 4a, b). In the PCA based on phenotypic traits of 203 mini-core accessions, four traits negatively correlated with harvest index were plotted on opposing axis from harvest index (Fig. 4a, b). Conversely, two traits positively correlated with harvest index were plotted in the same axis relatively close to harvest index.
4.2. Marker-trait associations
At Stuttgart, a total of 36 markers were significantly associated with harvest index traits at the 6.45×10-3 level of probability (the Bonferroni corrected significance level). Among 36 markers, seven were associated with harvest index, five with heading, three with plant height, six with plant weight, five with panicle length, nine with seed set and one with grain weight/panicle. Eight trait-marker associations have been reported by previous linkage mappings. Additionally, seven markers were associated with two or more harvest index traits, named “consistent” markers (Pinto et al. 2010). Out of the seven consistent markers, RM600, RM5 and RM302 were co-associated with harvest index and seed set, RM431 with heading and seed set, RM341 with plant height and panicle length, RM471 with heading and plant weight, and RM510 with three traits, plant height, harvest index and seed set.
At Beaumont, we identified 28 markers significantly associated with harvest index’s traits. Among 28 markers, two were associated with harvest index, three with heading, nine with plant height, six with plant weight, four with panicle length, three with seed set and one with grain weight/panicle. Similarly with Stuttgart, 11 trait-marker associations have been identified in previous QTL studies. Two consistent markers were RM208 co-associated with harvest index and seed set, and RM55 co-associated with plant height and plant weight.
Associations of RM431 with plant height, Rid12 and RM471 with plant weight and RM24011 with panicle length were found in both locations. The four markers that associated with the same trait across both locations are called “constitutive QTL” markers, while others that associated with a certain trait only at one location are called “adaptive QTL” markers (Mao et al. 2003).
4.3. Allelic effects
The allelic effects of the constitutive markers associated with their traits were estimated with the least square mean (LSMEAN) of phenotypic value and presented in Fig. 6. Meanwhile, an algorithm was employed to generate a letter-based representation of all-pairwise comparisons for allelic effect. For RM431, allele 253bp had a significantly larger effect than all other 6 alleles at Beaumont and than 4 others at Stuttgart to reduce plant height. For RM24011, allele 390bp had the greatest effect on decreasing panicle length while allele 411bp had the largest effect on increasing panicle length at both locations. However, for Rid12, the allelic effects were opposite between two locations. Allele 151bp of Rid12 had a decreasing effect on plant weight at Stuttgart, but an increasing effect at Beaumont instead. The 165 allele of Rid12 had an opposite effect to 151bp on plant weight. For RM471, the allelic effects on plant weight were not consistent from one location to another. The 109bp allele had the largest effect on decreasing plant weight at Stuttgart, but a fairly larger effect on increasing plant weight at Beaumont.
4.4. Genetic dissection of harvest index
Harvest index is an integrative trait including the net effect of all physiological processes during the crop cycle and its phenotypic expression is generally affected by genes responsible for non-target traits, such as heading (Lanceras et al. 2004; Hemamalini et al. 2000), plant height (Lanceras et al. 2004) and panicle architecture (Ando et al. 2008). The magnitude and direction of these gene functions on different phenotypes would bear heavily on the utility of such genes for improvement of these traits. In the current study, the traits like heading, plant height, plant weight and panicle length had a strong negative correlation with harvest index, while seed set and grain weight/panicle were positively correlated with harvest index. These phenotypic correlations were consistently reflected in the identification of molecular markers associated with harvest index and related traits. For example, four consistent markers at Stuttgart, RM600, RM302, RM25, and RM431, were associated with not only harvest index itself, but also for one or more additional traits correlated with harvest index. Another consistent marker, Rid12, associated with both heading and plant weight was close to a reported QTL “
Other markers were associated with the traits correlated with harvest index, but not with harvest index directly in this study. These markers have been reported either nearby or flanking the QTL for harvest index. RM5, which was associated with plant height in the Stuttgart study, was close to a reported QTL for harvest index on Chr 1 (Marri et al. 2005). RM471 associated with plant weight was close to the reported
5. Association mapping of grain yield and components
Yield is one of the most important and complex traits in crops that does not evolve independently but shows correlations with other traits. Thus, breeders have to consider correlated traits in breeding programs. Yield and its related traits are quantitatively inherited and controlled by many genes with small effects subject to environmental effects (Inostroza et al. 2009; Shi et al. 2009). Many studies have focused on the improvement and inheritance of agronomically important yield-related traits for achieving greater yield (Gravois and McNew 1993; Samonte et al. 1998). Other traits such as biomass, plant architecture, adaptation, and resistance to biotic and abiotic constraints may also indirectly affect yield through yield components or other physical and physiological mechanisms. Hence, estimation of the positions and effects of quantitative trait loci (QTL) for traits related to yield is of central importance for marker-assisted selection for yield improvement. In rice genetics, most QTLs related to yield have been identified through classic linkage mapping approaches (Moncada et al. 2001; Brondani et al. 2002; Thomson et al. 2003; Jiang et al. 2004; Suh et al. 2005). With a few notable exceptions, most of these QTLs have not been successfully validated or consistently used in crop improvement (Bernardo 2008). The classic approaches are too simplistic to effectively model most of the genetic variation for complex traits because they are unable to reflect the genetic realities of these traits (Cooper et al. 2005; Holland 2007).
|RM7003||12||41.0||0.0011||#||*||GY_QTL Marker (Thomson et al. 2003; Fu et al. 2010)|
|(3 markers)||RM431||1||154.6||0.0001||#||*||GY_QTL Marker (Fu et al. 2010 )|
|Rid12||7||41.0||0.0006||#||*||GY_QTL Marker (Xue et al. 2008)|
|RM7003||12||41.0||5.87×10-6||#||GY_QTL Marker (Thomson et al. 2003; Fu et al. 2010)|
|Tillers||RM431||1||154.6||0.0033||#||GY_QTL Marker (Fu et al 2010)|
|Rid12||7||41.0||0.0001||#||GY_QTL Marker (Xue et al. 2008)|
|RM125||7||41.0||0.0012||GY_QTL Marker (Jiang et al. 2004; Fu et al. 2010; Borba et al. 2010)|
|RM287||11||68.0||0.0050||GY_QTL Marker (Moncada et al. 2001)|
|RM245||9||91.8||0.0040||GY_QTL Marker (Suh et al. 2005)|
5.1. Traits correlated with grain yield per plant in our study
The traits significantly correlated with grain yield were plant height (0.43), plant weight (0.81), tillers (0.77), panicle length (0.30) and kernels/branch (0.40). All these traits were clustered into one branch except kernels/branch. This exploratory assessment showed that grain yield and the set of five correlated traits would serve as an appropriate base population for an association mapping application.
5.2. Marker-yield trait associations
Using the selected PCA model, a total of 30 marker loci were identified to have significant marker-trait associations at the 6.45×10-3 level of probability (the Bonferroni corrected significance level) for yield and its correlated traits (Table 4). Out of the 30 markers, four were associated with grain yield, three with plant height, six with plant weight, nine with tillers, five with panicle length and three with kernels/branch. Six markers were co-localized with previous identified QTL (Thomson et al. 2003; Jiang et al. 2004; Xue et al. 2008; Fu et al. 2010; Borba et al. 2010; Moncada et al. 2001) (Table 4).
Most importantly, eight of the 30 markers were synchronously associated with two or more traits (Table 4). RM471 was co-associated with three traits, grain yield, plant weight and kernels/branch. Three markers Rid12, RM224 and RM279 were co-associated with plant weight and tillers. RM431 was co-associated with plant height and tillers; RM509 with plant height and panicle length; RM7003 with grain yield and plant weight; and OSR13 with grain yield and kernels/branch. Three markers, OSR13, RM471 and RM7003 were included for the allelic analysis because they were not only associated with grain yield directly, but also co-associated with other yield correlated traits (Fig. 7). The allelic effect of each loci associated with the traits was estimated with mean of phenotypic value for each allele. For marker locus RM471, allele 126bp had the highest effect on all three traits (93.48 for grain yield, 266.23 for plant weight and 25.36 for kernels/branch), while two other alleles 109bp and 113bp had the lowest effect on grain yield with 48.19 and 49.90, and plant weight with 17.54 and 19.82, respectively (Fig. 7a and b). For OSR13, allele 123bp had a large effect on both grain yield and kernels/branch with 66.37 and 19.91, respectively while allele 115 had the highest effect on kernels/branch and the lowest on grain yield (Fig. 7c). For RM7003, allele 108bp had the highest effect on both traits (66.37 for grain yield and of 228.05 for plant weight) while the allele 106bp had the lowest effect on both traits (43.19 for grain yield and with 154.48 for plant weight) (Fig. 7d).
5.3. Trait-trait and marker-trait associations
Correlation among phenotypic traits is a common phenomenon in biology. Plant breeders need to consider trait correlations for either improving numerous correlated traits simultaneously or reducing undesirable side effects when their goal is only one of the correlated traits (Chen and Lubberstedt 2010). In this study, 34 of 91 pairs (37.36%) of 14 traits were observed to have significant correlation, and five traits were correlated with grain yield among 203 mini-core accessions. The correlations exhibited a complex network among these traits. Numerous researchers have concluded that rice yield is highly dependent on the number of productive tillers or panicles (Sharma and Choubey 1985; Dhanraj and Jagadish 1987), which is recently verified with a high correlation between tillers and yield (r=0.88; p < 0.01) by Borba et al. (2010). Panicle characters including panicle length, number of primary branches, secondary branches per primary branch, total kernels and seed set in a panicle, are reported to be tightly related to yield performance (Thomson et al. 2003; Ando et al. 2008; Terao et al. 2010). Although seed set and kernel weight per panicle were not directly correlated with yield in this study, they may be correlated in other panels of germplasm or may be indirectly contributable to yield. For example, seed weight per panicle, seed set and 1000 kernel weight are identified to be highly correlated with yield in wild rice (Oryza
Morphological correlations could be explained by either pleiotropy or linkage disequilibrium. The former describes the impact of a single gene on multiple phenotypic traits. The latter deals with influence of two or more genes on multiple traits, where the genes are physically located so close to each other, that they cannot be practically separated (Chen and Lubberstedt 2010). Co-association of a single gene (or two linked genes) with multiple traits that are phenotypically correlated has occurred in numerous studies. Yan et al. (2009) reported five SSRs that were co-associated with two correlated traits affecting stigma exertion, another five SSRs with two traits correlated to spikelets, and one SSR with three correlated traits to spikelets in rice. Similarly, Terao et al. (2010) identified the gene of
Several genes for grain yield and its related traits have been recently cloned, and each of these genes has a clearly distinct biological function (Li et al. 2003; Ashikari et al. 2005; Fan et al. 2006; Song et al. 2007). Molecular cloning and functional analyses of several genes have shown that these genes are mostly related to the synthesis and regulation of the phytohormone gibberellin (Peng et al. 1999; Ashikari et al. 1999; Spielmeyer et al. 2002; Itoh et al. 2004). For example, a semidwarf QTL “
Comparison of the allelic effect among different alleles at the same locus could determine which specific alleles would be most informative for marker assisted selection. For example, allele 126bp of RM471 and 108bp of RM7003 were considered major alleles with a positive effecton increasing yield among all the alleles in the loci (Fig. 7). Howeve, the allele 106bp of RM7003 would be less desirable because it had a negetaive effect which is associated with a decrease of both grain yield and plant weight among accessions containing the allele. Results of the present study demonstrated that genome-wide association mapping in the USDA rice mini-core collection could complement and enhance the information from linkage-based QTL studies, and help increase yield through improvement of these related traits by marker-assisted selection either directly or indirectly.
6. Association mapping of resistance to Sheath Blight disease
Rice sheath blight (ShB), caused by the soil-borne fungal pathogen
The necrotrophic ShB pathogen has a broad host range and no complete resistance has been identified in either commercial rice cultivars or wild related species (Mew et al. 2004; Eizenga et al. 2002). However, substantial differences in susceptibility to ShB among rice cultivars have been observed under field conditions (Jia et al. 2007). Differential levels of resistance and the associated resistance genes have been studied among rice germplasm accessions (Manosalva et al. 2009). Rice ShB resistance is believed to be controlled by multiple genes or quantitative trait loci (QTLs) (Pinson et al. 2005). Since Li et al. (1995) first identified ShB QTLs using restricted fragment length polymorphism (RFLP) markers under field conditions, over 30 resistant ShB QTLs have been reported using various mapping populations, such as F2s (Sharma et al. 2009; Che et al. 2003), double haploid (DH) lines (Kunihiro et al. 2002), recombinant inbred lines (RILs) (Liu et al. 2009; Jia et al. 2007; Prasad and Eizenga 2008), near-isogenic introgression lines (NIL) (Loan et al. 2004) and backcross populations (Zuo et al. 2007; Sato et al. 2004). ‘Teqing’ and ‘Jasmine 85’ have been repeatedly involved in these studies as the ShB resistant parents. We are the first to map rice ShB QTLs using an association mapping strategy in a global germplasm collection (Jia et al. 2012).
6.1. Phenotypic evaluation of Sheath Blight resistance
The isolate RR0140-1 of
Plant response to the sheath blight pathogen was measured using the ratio between the height of the pathogen growing up the plant and the height of the leaf collar on the last emerged leaf. Because mature plant height varied from 70 to 202 cm in this collection (Yan et al. 2007), the ratio excluded possible interference of plant height in scoring disease response. Therefore, the smaller the ratio, the greater the resistance was for an entry. Measurements were taken when the ratio reached 1.0 for 75% of the susceptible check plants, Lemont, so that maximum susceptibility was scored as 1.0.
ShB rating data were analyzed using the GLIMMIX procedure in SAS version 9.1.3. The experimental design of randomized incomplete block formed the basis of the statistical model, where the accession is a fixed effect and block is treated as random effect. The LSMEANS option was used to calculate the least-square means (LSMs) from 18 plant scores in 6 replicates of each entry and the LSMs were used for the association mapping. The statistical differences of the accession to each check (Jasmine 85 and Lemont) were determined by a Dunnett’s multiple comparison test, using the diff=control option.
6.2. Phenotypic variation of Sheath Blight severity ratings
The ShB severity ratings among the 217 entries were distributed normally, ranging from 0.256 ± 0.111 to 0.909 ± 0.096 with an average of 0.521 ± 0.008 (Fig. 8). The resistant check Jasmine 85 was rated 0.472 ± 0.021 and susceptible check Lemont was rated 0.946 ± 0.080. Twenty-four entries (11.1 %) were significantly more resistant to ShB than Jasmine 85 at the 5% level of probability while 54 others (24.9%) had similar resistance.
6.3. Marker loci and their alleles associated with Sheath Blight resistance
Ten marker loci were identified to be significantly associated with ShB resistance at the probability level of 5% or lower, three on chromosome (Chr) 11, two on Chr1, and one each on Chr2, 4, 5, 6 and 8 (Table 5). RM237 on Chr1 at 27.1 Mb had the highest significance rating for ShB at the 0.002 level of probability. RM11229 on the long arm of Chr1 explained the most phenotypic variation (9.5%) with significance at the 0.044 level of probability. RM11229 and 1233 each had six alleles, the most among the 217 mini-core entries, followed by RM341 and 254 (five alleles), RM237, 8217,146 and 408 (four), RM133 (three) and RM7203 (two) (Table 5).
Among the six alleles of RM11229, allele 158 was present in 18 entries that had the lowest average ShB rating (0.414), and thus, it was designated as the ‘putative resistant allele’ of this marker locus. Accordingly, ten alleles, one each from the ten associated marker loci, were noted as the putative resistant allele in Table 5 because they had the greatest effect to decrease ShB among all the alleles for their respective loci (Table 5). ShB rating was the smallest for putative resistant allele 158 of RM11229 among the ten putative resistant alleles. Of the other five putative resistant alleles, 139 of RM341 (present in 17 entries), 340 of RM146 (28 entries), 88 of RM7203 (120 entries), 169 of RM254 (12 entries) and 177 of RM1233 (35 entries), had lower ShB means ranging 0.447 - 0.470 than the resistant check Jasmine 85 (0.472), suggesting a stronger effect for resistance to ShB than Jasmine 85. The remaining four putative resistant alleles had similar ShB ratings with Jasmine 85, suggesting a similar effect for the level of ShB control.
|Resistant check ‘Jasmine 85’||0.472|
Among the ten putative resistant alleles, allele 88 of RM7203 was the most prevalent and existed in 120 (55%) of 217 entries in the mapping panel, followed by allele 230 of RM133 and 119 of RM408 (48% of the lines), allele 186 of RM8217 (23%), allele 340 of RM146, 128 of RM237 and 177 of RM1233 (13-16%), allele 139 of RM341 and 158 of RM1229 (8%), and allele 169 of RM254 (6%).
6.4. Number of putative resistant alleles and Sheath Blight resistance
The number of putative resistant alleles increased along with an increase of sheath blight resistance in an accession of rice germplasm. GSOR 310389 from Korea contained the most putative resistant alleles, eight out of ten, and had a ShB rating of 0.351 which was significantly more resistant than the resistant check Jasmine 85 which contained three putative resistant alleles and had a ShB rating of 0.472. Among seven entries containing six putative resistant alleles with a mean of 0.386 ShB, GSOR 310475 and 311475 were more resistant than Jasmine 85 and had ShB ratings of 0.324 and 0.336, respectively. Among 28 entries having five putative resistant alleles with a mean ShB rating of 0.444, seven were significantly more resistant than Jasmine 85. Seven, out of 35 entries which carried four putative resistant alleles and had a mean ShB of 0.466, were identified to be significantly more resistant than Jasmine 85. The mean ShB ratings for entries containing three, two, one and zero putative resistant alleles were 0.483, 0.535, 0.582 and 0.598, respectively. There was a strong and negative correlation between the ShB severity rating and number of putative resistant alleles (
Our mapping results showed that most entries containing a large number of putative resistant alleles were IND (Fig. 9). All entries with six or more putative resistant alleles were IND with only one exception of AUS. Among 28 entries with five putative resistant alleles, 25 were IND and the remaining three were AUS. There were 35 entries with four putative resistant alleles, nine were AUS, one was admix of TRJ, AUS and IND, and the remaining 25 were IND. Among 35 entries with three putative resistant alleles, 18 were IND, eight AUS, seven TRJ and two admixes of IND. However, among 51 entries without a single putative resistant allele, 26 were TEJ, 18 TRJ, four ARO and two admixes of TRJ-TEJ-ARO, and one IND. Among 72 entries that carried four or more putative resistant alleles, 58 (81%) were IND and 13 AUS (18%) plus admix of TRJ-AUS-IND.
6.5. Putative resistant alleles and ancestry background for Sheath Blight resistance
Jia et al. (2011) reported 52 entries that are significantly more resistant to ShB than Jasmine 85. The resistant entries were identified from 1,794 entries of the USDA rice core collection that has 35%
Our study demonstrated that: 1) a majority of the ShB putative resistant alleles existed in
7. Association mapping of silica concentration in rice hulls
7.1. Chemical analysis of silica concentration in rice hulls
The rough rice samples from test plots were dehulled with a Satake Rice Machine (Satake Engineering Co., LTD, Ueno Taito-Ku, Tokyo). After drying at 80oC for 2 hr, the hulls (~3g) were stored in 50 ml polypropylene tubes (Cat. # 05-539-5, Fisher Scientific, Houston, TX) at room temp. (22oC) until analyzed. Silica was determined using the molybdenum yellow method described by Saito et al. (2005) and Bryant et al. (2011).
7.2. Variation of silica concentration in the USDA rice mini-core collection
Si content averaged 200 mg g-1 and ranged from 118 mg g-1 for ACNO 430909, an Admixture of
7.3. Marker loci associated with silica concentration
We identified four associated markers in AR, and they were different from the four identified in TX (Table 6). Three out of four AR markers were among seven associated markers mapped in the combined location, whereas none of the TX markers were in. The 19.5% of the total silica content variation due to the difference of AR from TX might be responsible for the mapping results. It is known that the amount of silica present in the soil, the presence of other elements and/or nutrients, the amount of light, and temperature are all factors that affect silica concentrations in the plant (Ma and Takahashi 2002; Ma et al. 2002). RM263 from AR, RM6544 from both AR and Combined location and RM5371 from TX are all within a 1.5 Mb region where additive by additive QTL effects were previously identified by Dai et al. (2005). In summary, five of the marker-trait associations found in this study are within 1.5 Mb of the reported QTLs for silica concentrations from linkage mapping studies, and one marker-trait association (RM5371 on chromosome 6 at 25.83 Mb) overlaps with a QTL involved in grain arsenic concentration as well as silica concentration (Dai et al. 2005). The present study demonstrates that association mapping of the diverse germplasm in the USDA rice Mini-Core collection is an effective method for identifying new genetic markers and validating previously reported marker regions associated with silica concentration.
The authors thank Tiffany Sookaserm, Tony Beaty, Yao Zhou, Biaolin Hu, LaDuska Simpson, Curtis Kerns, Sarah Hendrix, Bill Luebke, Jodie Cammack, Kip Landry, Carl Henry, Jason Bonnette, and Piper Roberts for technical assistance; Xiaobai Li, Limeng Jia, Chengsong Zhu, Robert Fjellstrom and Anna McClung for professional assistance.
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