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Background
Salinity is one of the most severe and widespread abiotic stresses that affect rice production. The identification of major-effect quantitative trait loci (QTLs) for traits related to salinity tolerance and understanding of QTL × environment interactions (QEIs) can help in more precise and faster development of salinity-tolerant rice varieties through marker-assisted breeding. Recombinant inbred lines (RILs) derived from IR29/Hasawi (a novel source of salinity) were screened for salinity tolerance in the IRRI phytotron in the Philippines (E1) and in two other diverse environments in Senegal (E2) and Tanzania (E3). QTLs were mapped for traits related to salinity tolerance at the seedling stage.Results
The RILs were genotyped using 194 polymorphic SNPs (single nucleotide polymorphisms). After removing segregation distortion markers (SDM), a total of 145 and 135 SNPs were used to construct a genetic linkage map with a length of 1655 and 1662 cM, with an average marker density of 11.4 cM in E1 and 12.3 cM in E2 and E3, respectively. A total of 34 QTLs were identified on 10 chromosomes for five traits using ICIM-ADD and segregation distortion locus (SDL) mapping (IM-ADD) under salinity stress across environments. Eight major genomic regions on chromosome 1 between 170 and 175 cM (qSES1.3, qSES1.4, qSL1.2, qSL1.3, qRL1.1, qRL1.2, qFWsht1.2, qDWsht1.2), chromosome 4 at 32 cM (qSES4.1, qFWsht4.2, qDWsht4.2), chromosome 6 at 115 cM (qFWsht6.1, qDWsht6.1), chromosome 8 at 105 cM (qFWsht8.1, qDWsht8.1), and chromosome 12 at 78 cM (qFWsht12.1, qDWsht12.1) have co-localized QTLs for the multiple traits that might be governing seedling stage salinity tolerance through multiple traits in different phenotyping environments, thus suggesting these as hot spots for tolerance of salinity. Forty-nine and 30 significant pair-wise epistatic interactions were detected between QTL-linked and QTL-unlinked regions using single-environment and multi-environment analyses.Conclusions
The identification of genomic regions for salinity tolerance in the RILs showed that Hasawi possesses alleles that are novel for salinity tolerance. The common regions for the multiple QTLs across environments as co-localized regions on chromosomes 1, 4, 6, 8, and 12 could be due to linkage or pleiotropic effect, which might be helpful for multiple QTL introgression for marker-assisted breeding programs to improve the salinity tolerance of adaptive and popular but otherwise salinity-sensitive rice varieties.Background
Fixed arrays of single nucleotide polymorphism (SNP) markers have advantages over reduced representation sequencing in their ease of data analysis, consistently higher call rates, and rapid turnaround times. A 6 K SNP array represents a cost-benefit “sweet spot” for routine genetics and breeding applications in rice. Selection of informative SNPs across species and subpopulations during chip design is essential to obtain useful polymorphism rates for target germplasm groups. This paper summarizes results from large-scale deployment of an Illumina 6 K SNP array for rice.Results
Design of the Illumina Infinium 6 K SNP chip for rice, referred to as the Cornell_6K_Array_Infinium_Rice (C6AIR), includes 4429 SNPs from re-sequencing data and 1571 SNP markers from previous BeadXpress 384-SNP sets, selected based on polymorphism rate and allele frequency within and between target germplasm groups. Of the 6000 attempted bead types, 5274 passed Illumina’s production quality control. The C6AIR was widely deployed at the International Rice Research Institute (IRRI) for genetic diversity analysis, QTL mapping, and tracking introgressions and was intensively used at Cornell University for QTL analysis and developing libraries of interspecific chromosome segment substitution lines (CSSLs) between O. sativa and diverse accessions of O. rufipogon or O. meridionalis. Collectively, the array was used to genotype over 40,000 rice samples. A set of 4606 SNP markers was used to provide high quality data for O. sativa germplasm, while a slightly expanded set of 4940 SNPs was used for O. sativa X O. rufipogon populations. Biparental polymorphism rates were generally between 1900 and 2500 well-distributed SNP markers for indica x japonica or interspecific populations and between 1300 and 1500 markers for crosses within indica, while polymorphism rates were lower for pairwise crosses within U.S. tropical japonica germplasm. Recently, a second-generation array containing ~7000 SNP markers, referred to as the C7AIR, was designed by removing poor-performing SNPs from the C6AIR and adding markers selected to increase the utility of the array for elite tropical japonica material.Conclusions
The C6AIR has been successfully used to generate rapid and high-quality genotype data for diverse genetics and breeding applications in rice, and provides the basis for an optimized design in the C7AIR.![点击此处可从《Plant Breeding》网站下载免费的PDF全文](/ch/ext_images/free.gif)
Accelerated erosion removes fertile top soil along with nutrients through runoff and sediments, eventually affecting crop productivity and land degradation. However, scanty information is available on soil and nutrient losses under different crop covers in a vertisol of Central India. Thus, a field experiment was conducted for 4 years (2010–2013) to study the effect of different crop cover combinations on soil and nutrient losses through runoff in a vertisol.
Materials and methodsVery limited information is available on runoff, soil, and nutrient losses under different vegetative covers in a rainfed vertisol. Thus, the hypothesis of the study was to evaluate if different crop cover combinations would have greater impact on reducing soil and nutrient losses compared to control plots in a vertisol.
This experiment consisted of seven treatment combinations of crop covers namely soybean (Glycine max) (CC1), maize (Zea mays) (CC2), pigeon pea (Cajanus cajan) (CC3), soybean (Glycine max)?+?maize (Zea mays) ??1:1 (CC4), soybean (Glycine ma x))?+?pigeon pea (Cajanus cajan) ?2:1 (CC5), maize (Zea mays)?+?pigeon pea (Cajanus cajan) ??1:1 (CC6), and cultivated fallow (CC7). The plot size was 10?×?5 m with 1% slope, and runoff and soil loss were measured using multi-slot devisor. All treatments were arranged in a randomized block design with three replications.
Results and discussionResults demonstrated that the runoff and soil loss were significantly (p?<?0.05) higher (289 mm and 3.92 Mg ha?1) under cultivated fallow than those in cropped plots. Among various crop covers, sole pigeon pea (CC3) recorded significantly higher runoff and soil loss (257 mm and 3.16 Mg ha?1) followed by that under sole maize (CC2) (235 mm and 2.85 Mg ha?1) and the intercrops were in the order of maize?+?pigeon pea (211 mm and 2.47 Mg ha?1) followed by soybean?+?maize (202 mm and 2.38 Mg ha?1), and soybean?+?pigeon pea (195 mm and 2.15 Mg ha?1). The lowest runoff and soil loss were recorded under soybean sole crop (194 mm and 2.27 Mg ha?1). The data on nutrient losses indicated that the highest losses of soil organic carbon (SOC) (25.83 kg ha?1), total nitrogen (N), phosphorus (P), and potassium (K) (7.76, 0.96, 32.5 kg ha?1) were recorded in cultivated fallow (CC7) as compared to those from sole and intercrop treatments. However, sole soybean and its intercrops recorded the minimum losses of SOC and total N, P, and K, whereas the maximum losses of nutrients were recorded under pigeon pea (CC3). The system productivity in terms of soybean grain equivalent yield (SGEY) was higher (p?<?0.05) from maize?+?pigeon pea (3358 kg ha?1) followed by that for soybean?+?pigeon pea (2191 kg ha?1) as compared to sole soybean. Therefore, maize?+?pigeon pea (1:1) intercropping is the promising option in reducing runoff, soil-nutrient losses, and enhancing crop productivity in the hot sub-humid eco-region.
ConclusionsStudy results highlight the need for maintenance of suitable vegetative cover as of great significance to diffusing the erosive energy of heavy rains and also safe guarding the soil resource from degradation by water erosion in vertisols.
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