Pyramiding of Low Chalkiness QTLs Is an Effective Way to Reduce Rice Chalkiness.

Rice chalkiness is a key limiting factor of high-quality rice. The breeding of low chalkiness varieties has always been a challenging task due to the complexity of chalkiness and its susceptibility to environmental factors. In previous studies, we identified six QTLs for the percentage of grain chalkiness (PGC), named qPGC5, qPGC6, qPGC8.1, qPGC8.2, qPGC9 and qPGC11, using single-segment substitution lines (SSSLs) with genetic background of Huajingxian 74 (HJX74). In this study, we utilized the six low chalkiness QTLs to develop 17 pyramiding lines with 2-4 QTLs. The results showed that the PGC decreased with the increase of QTLs in the pyramiding lines. The pyramiding lines with 4 QTLs significantly reduced the chalkiness of rice and reached the best quality level. Among the six QTLs, qPGC5 and qPGC6 showed greater additive effects and were classified as Group A, while the other four QTLs showed smaller additive effects and were classified as Group B. In pyramiding lines, although the presence of epistasis, additivity remained the main component of QTL effects. qPGC5 and qPGC6 showed stronger ability to reduce rice chalkiness, particularly in the environment of high temperature (HT) in the first cropping season (FCS). Our research demonstrates that by pyramiding low chalkiness QTLs, it is feasible to develop the high-quality rice varieties with low chalkiness at the best quality level even in the HT environment of FCS.

QTL epistasis plays a role of homeostasis on heading date in rice.

If there was no gene interaction, the gene aggregation effect would increase infinitely with the increase of gene number. Epistasis avoids the endless accumulation of gene effects, playing a role of homeostasis. To confirm the role, QTL epistases were analyzed by four single-segment substitution lines with heading date QTLs in this paper. We found that QTLs of three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the relationship "positive QTLs-negative one order interactions-positive two order interactions". In this way, the aggregation effect of QTLs was partially neutralized by the opposite epistatic effect sum. There also were two exceptions, QTL OsMADS50 and gene Hd3a-2 were always with consistent effect directions with their epistases, implying they could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

qGLF5 from Oryza rufipogon Griff. improves kernel shape, plant architecture, and yield in rice.

KEY MESSAGE: A novel QTL qGLF5 from Oryza rufipogon Griff. improves yield per plant and plant architecture in rice. Kernel size and plant architecture are critical agronomic traits that are key targets for improving crop yield. From the single-segment substitution lines of Oryza rufipogon Griff. in the indica cultivar Huajingxian74 (HJX74) background, we identified a novel quantitative trait locus (QTL), named qGLF5, which improves kernel shape, plant architecture, and yield per plant in rice. Compared with the control HJX74, the plant height, panicles per plant, panicle length, primary branches per panicle, secondary branches per panicle, and kernels per plant of the near-isogenic line-qGLF5 (NIL-qGLF5) are significantly increased. NIL-qGLF5 has long and narrow kernels by regulating cell number, cell length and width in the spikelet hulls. Yield per plant of NIL-qGLF5 is increased by 35.02% compared with that of HJX74. In addition, qGLF5 significantly improves yield per plant and plant architecture of NIL-gw5 and NIL-GW7. These results indicate that qGLF5 might be beneficial for improving plant architecture and kernel yield in rice breeding by molecular design.

GS6.1 controls kernel size and plant architecture in rice.

MAIN CONCLUSION: A novel QTL GS6.1 increases yield per plant by controlling kernel size, plant architecture, and kernel filling in rice. Kernel size and plant architecture are critical agronomic traits that greatly influence kernel yield in rice. Using the single-segment substitution lines (SSSLs) with an indica cultivar Huajingxian74 as a recipient parent and American Jasmine as a donor parent, we identified a novel quantitative trait locus (QTL), named GS6.1. Near isogenic line-GS6.1 (NIL-GS6.1) produces long and narrow kernels by regulating cell length and width in the spikelet hulls, thus increasing the 1000-kernel weight. Compared with the control, the plant height, panicles per plant, panicle length, kernels per plant, secondary branches per panicle, and yield per plant of NIL-GS6.1 are increased. In addition, GS6.1 regulates the kernel filling rate. GS6.1 controls kernel size by modulating the transcription levels of part of EXPANSINs, kernel filling-related genes, and kernel size-related genes. These results indicate that GS6.1 might be beneficial for improving kernel yield and plant architecture in rice breeding by molecular design.

Grain shape is a factor affecting the stigma exsertion rate in rice.

Stigma exsertion rate (SER) is an index of outcrossing ability in rice and is a key trait of male sterile lines (MSLs) in hybrid rice. In this study, it was found that the maintainer lines carrying gs3 and gs3/gw8 showed higher SER. Single-segment substitution lines (SSSLs) carrying gs3, gw5, GW7 or gw8 genes for grain shape and gene pyramiding lines were used to reveal the relationship between grain shape and SER. The results showed that the grain shape regulatory genes had pleiotropic effects on SER. The SERs were affected by grain shapes including grain length, grain width and the ratio of length to width (RLW) not only in low SER background, but also in high SER background. The coefficients of determination (R2) between grain length and SER, grain width and SER, and grain RLW and SER were 0.78, 0.72, and 0.91 respectively. The grain RLW was the most important parameter affecting SER, and a larger grain RLW was beneficial to stigma exsertion. The pyramiding line PL-gs3/GW7/gw8 showed the largest grain RLW and the highest SER, which will be a fine breeding resource. Further research showed that the grain shape regulatory genes had pleiotropic effects on stigma shape, although the R2 values between grain shape and stigma shape, and stigma shape and SER were lower. Our results demonstrate that grain shape is a factor affecting SER in rice, in part by affecting stigma shape. This finding will be helpful for breeding MSLs with high SER in hybrid rice.

Fine Mapping of Two Major Quantitative Trait Loci for Rice Chalkiness With High Temperature-Enhanced Additive Effects.

Chalkiness is a crucial determinant of rice quality. During seed filling period, high temperature usually increases grain chalkiness, resulting in poor grain quality. Rice chalkiness was controlled by quantitative trait loci (QTLs) and influenced by environmental conditions. In this study, we identified two single-segment substitution lines (SSSLs) 22-05 and 15-06 with significantly lower percentage of grain chalkiness (PGC) than recipient Huajingxian 74 (HJX74) over 6 cropping seasons. Two major QTLs for chalkiness, qPGC5 and qPGC6, were located by substitution mapping of SSSLs 22-05 and 15-06, respectively. qPGC5 was located in the 876.5 kb interval of chromosome 5 and qPGC6 was located in the 269.1 kb interval of chromosome 6. Interestingly, the PGC of HJX74 was significantly different between the two cropping seasons per year, with 25.8% in the first cropping season (FCS) and 16.6% in the second cropping season (SCS), while the PGC of SSSLs 22-05 and 15-06 did not significantly differ between FCS and SCS. The additive effects of qPGC5 and qPGC6 on chalkiness in the SSSLs were significantly greater in FCS than in SCS. These results showed that qPGC5 and qPGC6 had major effects on chalkiness and the SSSL alleles were more effective in reducing chalkiness under high temperature condition in FCS. The fine-mapping of the two QTLs will facilitate the cloning of genes for chalkiness and provide new genetic resources to develop new cultivars with low chalkiness even under high temperature condition.

Reconstruction of the High Stigma Exsertion Rate Trait in Rice by Pyramiding Multiple QTLs.

Asian cultivated rice is a self-pollinating crop, which has already lost some traits of natural outcrossing in the process of domestication. However, male sterility lines (MSLs) need to have a strong outcrossing ability to produce hybrid seeds by outcrossing with restorer lines of male parents in hybrid rice seed production. Stigma exsertion rate (SER) is a trait related to outcrossing ability. Reconstruction of the high-SER trait is essential in the MSL breeding of rice. In previous studies, we detected eighteen quantitative trait loci (QTLs) for SER from Oryza sativa, Oryza glaberrima, and Oryza glumaepatula using single-segment substitution lines (SSSLs) in the genetic background of Huajingxian 74 (HJX74). In this study, eleven of the QTLs were used to develop pyramiding lines. A total of 29 pyramiding lines with 2-6 QTLs were developed from 10 SSSLs carrying QTLs for SER in the HJX74 genetic background. The results showed that the SER increased with increasing QTLs in the pyramiding lines. The SER in the lines with 5-6 QTLs was as high as wild rice with strong outcrossing ability. The epistasis of additive by additive interaction between QTLs in the pyramiding lines was less-than-additive or negative effect. One QTL, qSER3a-sat, showed minor-effect epistasis and increased higher SER than other QTLs in pyramiding lines. The detection of epistasis of QTLs on SER uncovered the genetic architecture of SER, which provides a basis for using these QTLs to improve SER levels in MSL breeding. The reconstruction of the high-SER trait will help to develop the MSLs with strong outcrossing ability in rice.

Dynamic analysis of QTLs on plant height with single segment substitution lines in rice.

Dynamic regulation of QTLs remains mysterious. Single segment substitution lines (SSSLs) and conditional QTL mapping and functional QTL mappings are ideal materials and methods to explore dynamics of QTLs for complex traits. This paper analyzed the dynamics of QTLs on plant height with SSSLs in rice. Five SSSLs were verified with plant height QTLs first. All five QTLs had significant positive effects at one or more developmental stages except QTL1. They interacted each other, with negative effects before 49 d after transplanting and positive effects since then. The five QTLs selectively expressed in specific periods, mainly in the periods from 35 to 42 d and from 49 to 56 d after transplanting. Expressions of epistasis were dispersedly in various periods, negative effects appearing mainly before 35 d. The five QTLs brought the inflexion point ahead of schedule, accelerated growth and degradation, and changed the peak plant height, while their interactions had the opposite effects. The information will be helpful to understand the genetic mechanism for developmental traits.

Natural variations in grain length 10 (GL10) regulate rice grain size.

Grain size is an important determinant of grain weight and yield in rice. Although several genes related to grain size have been identified, natural variations in these genes that affect grain size are poorly characterized. Here, we describe the grain length QTL GL10, encoding MADS56, which positively regulates grain length and grain weight. A natural allelic variation of NIL-gl10, containing an ∼1.0-kb deletion in the first exon that abolishes its transcription, results in shorter grain length, lower grain weight and delayed flowering in gl10 plants. The knockout of GL10 in the HJX74 background leads to grain phenotypes similar to that of NIL-gl10, while overexpression of GL10 results in increased grain length and weight and earlier heading date. GL10 regulates grain length by promoting greater longitudinal cell growth in the grain glume. Additionally, GL10 participates in the regulation of gibberellic acid (GA) signaling pathway genes in young panicle tissues. Analysis of GL10 haplotypes shows obvious divergence between the japonica and indica lineages. Our findings reveal an allelic variation of GL10 that may explain differences in grain length among modern cultivars and could be used to breed rice varieties with optimized grain shape.

Substitution Mapping of Two Closely Linked QTLs on Chromosome 8 Controlling Grain Chalkiness in Rice.

Rice varieties are required to have high yield and good grain quality. Grain chalkiness and grain shape are two important traits of rice grain quality. Low chalkiness slender grains are preferred by most rice consumers. Here, we dissected two closely linked quantitative trait loci (QTLs) controlling grain chalkiness and grain shape on rice chromosome 8 by substitution mapping. Two closely linked QTLs controlling grain chalkiness and grain shape were identified using single-segment substitution lines (SSSLs). The two QTLs were then dissected on rice chromosome 8 by secondary substitution mapping. qPGC8.1 was located in an interval of 1382.6 kb and qPGC8.2 was mapped in a 2057.1 kb region. The maximum distance of the two QTLs was 4.37 Mb and the space distance of two QTL intervals was 0.72 Mb. qPGC8.1 controlled grain chalkiness and grain width. qPGC8.2 was responsible for grain chalkiness, grain length and width. The additive effects of qPGC8.1 and qPGC8.2 on grain chalkiness were not affected by higher temperature. Two closely linked QTLs qPGC8.1 and qPGC8.2 were dissected on rice chromosome 8. They controlled the phenotypes of grain chalkiness and grain shape. The two QTLs were insensitive to higher temperature.

Genome-Wide Association Study of QTLs Conferring Resistance to Bacterial Leaf Streak in Rice.

Bacterial leaf streak (BLS) is a devastating rice disease caused by the bacterial pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), which can result in severe damage to rice production worldwide. Based on a total of 510 rice accessions, trialed in two seasons and using six different multi-locus GWAS methods (mrMLM, ISIS EM-BLASSO, pLARmEB, FASTmrMLM, FASTmrEMMA and pKWmEB), 79 quantitative trait nucleotides (QTNs) reflecting 69 QTLs for BLS resistance were identified (LOD > 3). The QTNs were distributed on all chromosomes, with the most distributed on chromosome 11, followed by chromosomes 1 and 5. Each QTN had an additive effect of 0.20 (cm) and explained, on average, 2.44% of the phenotypic variance, varying from 0.00-0.92 (cm) and from 0.00-9.86%, respectively. Twenty-five QTNs were detected by at least two methods. Among them, qnBLS11.17 was detected by as many as five methods. Most of the QTNs showed a significant interaction with their environment, but no QTNs were detected in both seasons. By defining the QTL range for each QTN according to the LD half-decay distance, a total of 848 candidate genes were found for nine top QTNs. Among them, more than 10% were annotated to be related to biotic stress resistance, and five showed a significant response to Xoc infection. Our results could facilitate the in-depth study and marker-assisted improvement of rice resistance to BLS.

Functional mapping of tillering QTLs using the Wang-Lan-Ding model and a SSSL population.

Understanding dynamic changes in the genetic architecture of quantitative traits is crucial in developmental genetics. Functional mapping is an appropriate method that passes a mathematical equation to describe a biological developmental process with the genetic mapping framework. Appropriate genetic model and applicable mapping population are indispensable condition for functional mapping of important agronomic traits in plants. Based on the Wang-Lan-Ding model, we ever applied a DH population to carry out functional mapping QTLs underlying growth trajectory on tiller number. However, inconsistent genetic background among the DH lines might disturb the mapping results. With the advent of innovative research materials, single segment substitution lines, allows us to do more precise genetic analyses. Thus functional mapping was again conducted on tiller number using the Wang-Lan-Ding model and a single segment substitution line population with the genetic background of Huajingxian 74 so as to explore QTL dynamic mechanism to regulate developmental traits. We detected that all five single segment substitution lines harbored tillering QTLs with additives and/or dominances to influence the four functional parameters, the optimum tillering time (t0), the maximum tiller number (K), the tillering increased rate (r) and the tillering decreased rate (c), which were estimated from the Wang-Lan-Ding model and with some biological meaning. They mainly brought the inflexion point (t0) delay, the peak increase (K) and the degradation (c) acceleration, while the growth (r) slow down. Moreover, epistatic interactions among these QTLs were confirmed to be prevalent. A total of 39 significant epistatic effects were detected to associate with the four parameters, occupying 34.8% of 112 pairs of epistatic interactions investigated. Contrary to the QTL effects, these epistatic effects largely decreased t0, K and c, while increased r. Our results indicated that the five QTL effects and their epistatic effects significantly changed the shape and trajectory of tiller number via influence of the four functional parameters. Rational use of these QTLs is expected to improve tillering number of rice by molecular design breeding.

GW10, a member of P450 subfamily regulates grain size and grain number in rice.

KEY MESSAGE: A quantitative trait locus GW10 is located on Chromosome 10 by map-based cloning, which encodes a P450 Subfamily protein. The GW10 regulates grain size and grain number in rice involved in the BR pathway. Grain size and grain number play extremely important roles in rice grain yield. Here, we identify GW10, which encodes a P450 subfamily protein and controls grain size and grain number by using Lemont (tropical japonica) as donor parent and HJX74 (indica) as recipient parent. The GW10 locus was mapped into a 14.6 kb region in HJX74 genomic on the long arm of chromosome 10. Lower expression of the gw10 in panicle is contributed to the shorter and narrower rice grain, and the increased number of grains per panicle. In contrast, overexpression of GW10 is contributed to longer and wider rice grain. Furthermore, the higher expression levels of some of the brassinosteroid (BR) biosynthesis and response genes are associated with the NIL-GW10. The sensitivity of the leaf angle to exogenous BR in NIL-GW10 is lower than that in NIL-gw10 and in the KO-GW10, which implied that the GW10 should involve in the brassinosteroid-mediated regulation of rice grain size and grain number.

Fine mapping of two grain chalkiness QTLs sensitive to high temperature in rice.

BACKGROUND: Grain chalkiness is one of important factors affected rice grain quality. It is known that chalkiness is affected by the high temperature during the seed filling period. Although a larger of QTLs for chalkiness were reported across all 12 chromosomes, only a few of the QTLs were fine mapped or cloned up to now. Here, we fine map two QTLs for chalkiness in two single-segment substitution lines (SSSLs), 11-09 with substitution segment from O. sativa and HP67-11 with substitution segment from O. glaberrima. RESULTS: The grain chalkiness of SSSLs 11-09 and HP67-11 was significantly lower than that in the recipient Huajingxian 74 (HJX74) in consecutive 8 cropping seasons. The regression correlation analysis showed that percentage of chalky grain (PCG) and percentage of chalky area (PCA) were significantly and positively correlated with percentage of grain chalkiness (PGC). Two QTLs for grain chalkiness were located on two chromosomes by substitution mapping. qPGC9 was mapped on chromosome 9 with an estimated interval of 345.6 kb. qPGC11 was located on chromosome 11 and delimited to a 432.1 kb interval in the O. sativa genome and a 332.9 kb interval in the O. glaberrima genome. qPGC11 is a QTL for grain chalkiness from O. glaberrima and was mapped in a new region of chromosome 11. The effect of two QTLs was incomplete dominance. The additive effects of two QTLs on chalkiness in second cropping season (SCS) were significantly greater than that in first cropping season (FCS). CONCLUSIONS: qPGC11 is a new QTL for grain chalkiness. The two QTLs were fine mapped. The donor alleles of qPGC9 and qPGC11 were sensitive to the high temperature of FCS.

A Multi-Locus Association Model Framework for Nested Association Mapping With Discriminating QTL Effects in Various Subpopulations.

Nested association mapping (NAM) has been an invaluable approach for plant genetics community and can dissect the genetic architecture of complex traits. As the most popular NAM analysis strategy, joint multifamily mapping can combine all information from diverse genetic backgrounds and increase population size. However, it is influenced by the genetic heterogeneity of quantitative trait locus (QTL) across various subpopulations. Multi-locus association mapping has been proven to be powerful in many cases of QTL mapping and genome-wide association studies. Therefore, we developed a multi-locus association model of multiple families in the NAM population, which could discriminate the effects of QTLs in all subpopulations. A series of simulations with a real maize NAM genomic data were implemented. The results demonstrated that the new method improves the statistical power in QTL detection and the accuracy in QTL effect estimation. The new approach, along with single-family linkage mapping, was used to identify QTLs for three flowering time traits in the maize NAM population. As a result, most QTLs detected in single family linkage mapping were identified by the new method. In addition, the new method also mapped some new QTLs with small effects, although their functions need to be identified in the future.

BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing.

MOTIVATION: Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved. RESULTS: In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated. AVAILABILITY AND IMPLEMENTATION: The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Determination of absorption dose in chemical mutagenesis in plants.

Chemical mutagenesis is a useful tool for inducing mutations in plants. Seeds are often used as the material for chemical mutagenesis. The biological effect of a chemical mutagen on seeds is determined by absorption dose (the product of mutagen concentration and acting time, which starts after the mutagen is absorbed by the seeds). In practice, however, the concept of exposure dose (the product of mutagen concentration and treating time) is usually used instead because the time for absorbing mutagen is unknown. In this study, we conducted an experiment using ethyl methane sulphonate (EMS) to treat cauliflower seeds, in which five EMS concentrations (0%, 0.5%, 1.0%, 1.5% and 2.0%), three treating time lengths (4 h, 6 h and 8 h) and two pretreatments (non-presoaking and presoaking of seeds for 2 h) were set. We obtained a well-fitted nonlinear regression model for the relationship between seedling survival rate and the EMS treatment, and its marginal models for the two pretreatments. Based on the models, we determined the EMS absorption doses under the two different pretreatments and identified their 50% lethality dose (LD50). We found that presoaking could delay EMS absorption and therefore reduce the injury caused by EMS within a given treating time, but could hardly change the biological effect of EMS after it is absorbed. The conclusions about absorption dose and presoaking effect obtained in this study might be generally applicable to plant chemical mutagenesis in principle.

Estimation of QTL heritability based on pooled sequencing data.

Motivation: Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem. Results: In this paper, we propose a method called PQHE to estimate QTL heritability using pooled sequencing data obtained under different experimental designs. Simulation studies indicated that our method is correct and feasible. Four practical examples from rice and yeast are demonstrated, each representing a different situation. Availability and implementation: The R scripts of our method are open source under GPLv3 license at http://genetics.fafu.edu.cn/PQHE or https://github.com/biotangweiqi/PQHE. The R scripts require the R package rootSolve. Contact: wuwr@fafu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

EcoTILLING revealed SNPs in GhSus genes that are associated with fiber- and seed-related traits in upland cotton.

Cotton is the most important textile crop in the world due to its cellulose-enriched fibers. Sucrose synthase genes (Sus) play pivotal roles in cotton fiber and seed development. To mine and pyramid more favorable alleles for cotton molecular breeding, single nucleotide polymorphisms (SNPs) of GhSus family genes were investigated across 277 upland cotton accessions by EcoTILLING. As a result, a total of 24 SNPs in the amplified regions of eight GhSus genes were identified. These SNPs were significantly associated with at least one fiber- or seed-related trait measured in Nanjing, Anyang and Kuche in 2007-2009. Four main-effect quantitative trait nucleotides (QTNs) and five epistatic QTNs, with 0.76-3.56% of phenotypic variances explained by each QTN (PVE), were found to be associated with yield-related traits; six epistatic QTNs, with the 0.43-3.48% PVE, were found to be associated with fiber quality-related traits; and one main-effect QTN and one epistatic QTN, with the PVE of 1.96% and 2.53%, were found to be associated with seed oil content and protein content, respectively. Therefore, this study provides new information for molecular breeding in cotton.

Genetic dissection of heterosis using epistatic association mapping in a partial NCII mating design.

Heterosis refers to the phenomenon in which an F1 hybrid exhibits enhanced growth or agronomic performance. However, previous theoretical studies on heterosis have been based on bi-parental segregating populations instead of F1 hybrids. To understand the genetic basis of heterosis, here we used a subset of F1 hybrids, named a partial North Carolina II design, to perform association mapping for dependent variables: original trait value, general combining ability (GCA), specific combining ability (SCA) and mid-parental heterosis (MPH). Our models jointly fitted all the additive, dominance and epistatic effects. The analyses resulted in several important findings: 1) Main components are additive and additive-by-additive effects for GCA and dominance-related effects for SCA and MPH, and additive-by-dominant effect for MPH was partly identified as additive effect; 2) the ranking of factors affecting heterosis was dominance > dominance-by-dominance > over-dominance > complete dominance; and 3) increasing the proportion of F1 hybrids in the population could significantly increase the power to detect dominance-related effects, and slightly reduce the power to detect additive and additive-by-additive effects. Analyses of cotton and rapeseed datasets showed that more additive-by-additive QTL were detected from GCA than from trait phenotype, and fewer QTL were from MPH than from other dependent variables.