Quantitative Trait Loci For Plant Height Research Articles

Finding Stable QTL for Plant Height in Super Hybrid Rice

Plant height (PH) is one of the most important agronomic traits determining plant architecture in rice. To investigate the genetic basis of plant height in the high-yielding hybrid rice variety Nei2You No.6, recombinant inbred sister lines (RISLs) were used to map quantitative trait loci (QTL) over four years. A total of 19 minor/medium-effect QTLs were mapped on eleven chromosomes except chromosome 10, totally explaining 44.61–51.15% phenotypic variance in four environments. Among these, qPH-1a, qPH-1b, qPH-2b, qPH-3b, qPH-6, and qPH-7b were repeatedly detected over four years. Among these, the qPH-6 was mapped to an interval of 22.11–29.41 Mb on chromosome 6L, which showed the highest phenotypic variation explained (PVE) of 10.22–14.05% and additive effect of 3.45–4.63. Subsequently, evaluation of near isogenic lines (NILs) showed that the qPH-6 allele from the restorer line (R8006) could positively regulate plant height, resulting in an 18.50% increase in grain yield. These results offered a basis for further mapping of qPH-6 and molecular breeding in improving plant architecture in rice.

Identification of Quantitative Trait Loci Associated With Partial Resistance to Fusarium Root Rot and Wilt Caused by Fusarium graminearum in Field Pea.

Fusarium root rot, caused by a complex of Fusarium spp., is a major disease of field pea (Pisum sativum). The development of genetic resistance is the most promising approach to manage the disease, but no pea germplasm has been identified that is completely resistant to root rot. The aim of this study was to detect quantitative trait loci (QTL) conferring partial resistance to root rot and wilting, caused by five fungal isolates representing Fusarium solani, F. avenaceum, F. acuminatum, F. proliferatum, and F. graminearum. Evaluation of the root rot-tolerant cultivar “00-2067” and susceptible cultivar “Reward” was carried out with the five species. There was a significant difference (p < 0.001) between the mean root rot values of the two cultivars inoculated with the F. avenaceum (F4A) and F. graminearum (FG2) isolates. Therefore, in the QTL study, the F8 recombinant inbred line (RIL) population derived from “Reward” × “00-2067” was inoculated in the greenhouse (4 ×) with only F4A and FG2. The parents and F8 population were genotyped using 13.2K single nucleotide polymorphisms (SNPs) and 222 simple sequence repeat (SSR) markers. A significant genotypic effect (p < 0.05) and high heritability (79% to 92.1%) were observed for disease severity, vigor, and plant height following inoculation with F4A and FG2. Significant correlation coefficients were detected among and within all traits. This suggested that a high proportion of the genetic variance was transmitted from the parents to the progeny. However, no significant QTL (LOD > 3) were detected for the RILs inoculated with F4A. In the case of the RILs inoculated with FG2, 5 QTL for root rot severity and 3 QTL each for vigor and plant height were detected. The most stable QTL for plant height (Hgt-Ps3.1) was detected on Chrom5/LGIII. The two most stable QTL for partial resistance to FG2, Fg-Ps4.1, and Fg-Ps4.2 were located in a 15.1-cM and 11.2-cM genomic region, respectively, on Chrom4/LGIV. The most stable QTL for vigor (Vig-Ps4.1) was found in the same region. Twenty-five major and moderate effect digenic epistatic interactions were detected. The identified region on chrom4/LGIV could be important for resistance breeding and marker development.

Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq).

Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array.

Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

QTL identified for stay-green in a multi-reference nested association mapping population of wheat exhibit context dependent expression and parent-specific alleles

A stay-green phenotype is useful for adaptation of wheat to end-of-season drought conditions. We identified quantitative trait loci (QTL) for stay-green traits, as well as for height, days to anthesis and yield, in a multi-reference nested association mapping (MR-NAM) population of wheat (Triticum aestivum L.) in two environments differing in degree of drought stress experienced post-anthesis. The MR-NAM population consisted of three inter-related nested association mapping populations developed by nesting 11 diverse adaptation donors within three common reference parents, adapted to the northern, southern and western cropping regions of Australia, respectively. The construction of the MR-NAM population enables the assessment of the effect on a trait of multiple alleles at any particular locus, in different genetic backgrounds, and facilitates concurrent QTL mapping and germplasm development. This approach enabled identification of parent-specific alleles and context dependent expression. Using a new statistical method specifically developed to identify QTL in MR-NAM populations, we identified 65 QTL for stay-green traits. Co-location was observed between (i) trait by loci associations for some of the different stay-green traits, (ii) for QTL between the two environments, and (iii) between QTL for stay-green traits, plant height and grain yield. Some QTL co-located with those identified in other studies however, others are likely novel. Genetic markers associated with QTL for stay-green can be applied in breeding to enrich populations for stay-green traits in early generations of selection, prior to field testing in yield plots, in particular for the development of wheat cultivars targeted to end-of-season drought-stressed environments. This information is important for breeders, because it facilitates identification of the sources of the most promising alleles at particular loci for specific genetic backgrounds and growing environments.

Genetic control of Fusarium head blight resistance in two Yangmai 158-derived recombinant inbred line populations.

Stably expressed type I and type II resistance QTL were identified using two Yangmai 158-derived RIL populations, and plant-height and flowering-time QTL intervals detected did not contribute to the FHB resistance variations. Yangmai 158 (Y158) is an elite wheat cultivar widely grown in China with stable Fusarium head blight (FHB) resistance. To enrich the genetic basis underlying FHB resistance, QTL mapping was conducted using two recombinant inbred line (RIL) populations derived from crosses of Y158 with susceptible lines Annong 8455 and Veery. Survey with makers linked to Fhb1, Fhb2, Fhb4 and Fhb5 in resistance cultivar Wangshuibai indicated that both Y158 and the susceptible lines do not contain these QTL. The RIL populations were surveyed with 65 PCR markers and 55K chip, which generated 23,159 valid marker data, to produce genetic maps for whole genome scanning of quantitative trait loci (QTL). A total of six QTL, all with the Y158 alleles for better resistance and including one stably expressed QTL for type I resistance (Qfhi.nau-2D) and one stably expressed QTL for type II resistance (Qfhs.nau-2A), were identified. Moreover, taking advantage of the great genetic variations in plant height and flowering time, QTL conditioning these two traits were determined. Of six plant-height QTL and three flowering-time QTL intervals detected, none were associated with FHB resistance. The FHB resistance QTL in Y158 were shown to be useful alternatives in FHB resistance breeding programs. The SNP markers flanking Qfhs.nau-2A and Qfhi.nau-2D have been converted to breeder-friendly PCR-based markers to facilitate their applications.

Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39–12.26% and 20.11–27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43–6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7–8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43–6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72–9.59%, 5.62–7.75%, and 6.68–10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

Genetic analysis of the \u201chead top shape\u201d quality trait of Chinese cabbage and its association with rosette leaf variation

The agricultural and consumer quality of Chinese cabbage is determined by its shape. The shape is defined by the folding of the heading leaves, which defines the head top shape (HTS). The overlapping HTS, in which the heading leaves curve inward and overlap at the top, is the shape preferred by consumers. To understand the genetic regulation of HTS, we generated a large segregating F2 population from a cross between pak choi and Chinese cabbage, with phenotypes ranging from nonheading to heading with either outward curving or inward curving overlapping heading leaves. HTS was correlated with plant height, outer/rosette leaf length, and petiole length. A high-density genetic map was constructed. Quantitative trait locus (QTL) analysis resulted in the identification of 22 QTLs for leafy head-related traits, which included five HTS QTLs. Bulked segregant analysis (BSA) was used to confirm HTS QTLs and identify candidate genes based on informative single-nucleotide polymorphisms. Interestingly, the HTS QTLs colocalized with QTLs for plant height, outer/rosette leaf, and petiole length, consistent with the observed phenotypic correlations. Combined QTL analysis and BSA laid a foundation for molecular marker-assisted breeding of Chinese cabbage HTS and directions for further research on the genetic regulation of this trait.

MP3RNA-seq: Massively parallel 3' end RNA sequencing for high-throughput gene expression profiling and genotyping.

Transcriptome deep sequencing (RNA-seq) has become a routine method for global gene expression profiling. However, its application to large-scale experiments remains limited by cost and labor constraints. Here we describe a massively parallel 3' end RNA-seq (MP3RNA-seq) method that introduces unique sample barcodes during reverse transcription to permit sample pooling immediately following this initial step. MP3RNA-seq allows for handling of hundreds of samples in a single experiment, at a cost of about $6 per sample for library construction and sequencing. MP3RNA-seq is effective for not only high-throughput gene expression profiling, but also genotyping. To demonstrate its utility, we applied MP3RNA-seq to 477 double haploid lines of maize. We identified 19,429 genes expressed in at least 50% of the lines and 35,836 high-quality single nucleotide polymorphisms for genotyping analysis. Armed with these data, we performed expression and agronomic trait quantitative trait locus (QTL) mapping and identified 25,797 expression QTLs for 15,335 genes and 21 QTLs for plant height, ear height, and relative ear height. We conclude that MP3RNA-seq is highly reproducible, accurate, and sensitive for high-throughput gene expression profiling and genotyping, and should be generally applicable to most eukaryotic species.

Quantitative trait loci for stem strength properties and lodging in two pea biparental mapping populations

AbstractLodging is the major factor that determines whether pea (Pisum sativum L.) can be mechanically harvested. The aim of this experiment was to investigate the relationship between stem mechanical parameters (diameter, stem wall thickness, stiffness [rigidity], and maximum self‐weight moment [stem strength]) and lodging in pea. Three years of data on stem mechanical parameters and 2 yr of data on lodging resistance were collected on the ‘Carneval’ × ‘MP1401’ pea cultivars and Wt10245 × Wt11238 lines. Mechanical parameters were measured separately for three stem sections: lower, middle, and upper. Nine quantitative trait loci (QTL) associated with stem mechanical parameters were identified in the Carneval × MP1401 genetic map. Eight QTL associated with stem mechanical parameters were identified in the Wt10245 × Wt11238 genetic map. These results confirmed QTL for lodging resistance found previously, especially in regards to located in chr5LG3 close to the A004 marker. Quantitative trait loci for stem diameter and stem strength of the upper and lower stem colocated with the Rfs marker in chr5LG3. A plant height QTL was detected close to AB141 marker, 2 cM (3.3 Mbp) from the Le gene. These results enable identification of pea genomic regions associated with agriculturally important traits and provide a foundation for further research to improve lodging resistance in pea.

Genetic Dissection of Adult Plant Resistance to Sharp Eyespot Using an Updated Genetic Map of Niavt14 × Xuzhou25 Winter Wheat Recombinant Inbred Line Population.

Wheat sharp eyespot, a disease mainly caused by soilborne fungus Rhizoctonia cerealis, is a threat to world wheat production. Wheat's genetic resistance to sharp eyespot is a potential approach to reducing the application of fungicides and farming practice inputs. To identify the genetic basis of sharp eyespot resistance in Niavt14, a recombinant inbred line population comprising 215 F8 lines from Niavt14 × Xuzhou25, was developed. An earlier linkage map (148 simple sequence repeat markers) was updated with 5,792 polymorphic Affymetrix Axiom 55K single-nucleotide polymorphisms to a new map of 5,684.2 centimorgans with 1,406 nonredundant markers. The new linkage map covered all 21 chromosomes of common wheat and showed a good collinearity with the IWGSC RefSeq v1.0 genome. We conducted quantitative trait locus (QTL) mapping for sharp eyespot resistance using the adult plant response data from the field of five consecutive growing seasons and one greenhouse test. Two stable QTL on chromosomes 2B and 7D that were identified in the previous study were confirmed, and three novel, stable QTL, explaining 4.0 to 17.5% phenotypic variation, were mapped on 1D, 6D, and 7A, which were independent of QTL for phenology and plant height. The QTL on 1D, 2B, 6D, and 7A showed low frequencies in 384 landraces (0 to 10%) and 269 elite cultivars (5 to 23%) from the southern winter wheat region and the Yellow and Huai River Valley facultative wheat region in China, respectively. These identified QTL could be used in wheat breeding programs for improving sharp eyespot resistance through marker-assisted selection.

Genetic Mapping by Integration of 55K SNP Array and KASP Markers Reveals Candidate Genes for Important Agronomic Traits in Hexaploid Wheat.

Agronomic traits such as heading date (HD), plant height (PH), thousand grain weight (TGW), and spike length (SL) are important factors affecting wheat yield. In this study, we constructed a high-density genetic linkage map using the Wheat55K SNP Array to map quantitative trait loci (QTLs) for these traits in 207 recombinant inbred lines (RILs). A total of 37 QTLs were identified, including 9 QTLs for HD, 7 QTLs for PH, 12 QTLs for TGW, and 9 QTLs for SL, which explained 3.0–48.8% of the phenotypic variation. Kompetitive Allele Specific PCR (KASP) markers were developed based on sequencing data and used for validation of the stably detected QTLs on chromosomes 3A, 4B and 6A using 400 RILs. A QTL cluster on chromosome 4B for PH and TGW was delimited to a 0.8 Mb physical interval explaining 12.2–22.8% of the phenotypic variation. Gene annotations and analyses of SNP effects suggested that a gene encoding protein Photosynthesis Affected Mutant 68, which is essential for photosystem II assembly, is a candidate gene affecting PH and TGW. In addition, the QTL for HD on chromosome 3A was narrowed down to a 2.5 Mb interval, and a gene encoding an R3H domain-containing protein was speculated to be the causal gene influencing HD. The linked KASP markers developed in this study will be useful for marker-assisted selection in wheat breeding, and the candidate genes provide new insight into genetic study for those traits in wheat.

BSA-seq-based identification of a major additive plant height QTL with an effect equivalent to that of Semi-dwarf 1 in a large rice F2 population

Bulked-segregant analysis is a time- and cost-saving strategy for identifying major quantitative trait loci (QTL) in a mapping population. Bulked-segregant analysis combined with whole-genome sequencing (BSA-seq) was performed to rapidly identify QTL for heading date, plant height, and panicle length in a large F2 population derived from two landraces: Chuan 7 (C7) and Haoboka (HBK). Twenty plants with extremely low or high phenotypic values for the target traits were selected from an F2 population of 940 plants to construct low- and high-value bulks. Three pairs of bulks for the three traits were constructed, resulting in six DNA pools. BSA-seq revealed nine QTL: four for heading date, three for plant height, and two for panicle length. These QTL were validated in a random F2 population or BC4F2 populations. The major novel plant height QTL, qPH8, acting additively with an effect equivalent to that of semi-dwarf 1 (sd1), is potentially valuable for hybrid rice breeding. qPH8 controls mainly the elongation of basal internodes. The C7 allele of qPH8 reduces plant height and increases lodging resistance without yield penalty, suggesting a potential role for qPH8 in improving rice plant architecture.

Environmental and genetic regulation of plant height in soybean

BackgroundShoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.ResultsIn this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80–26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19–1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.ConclusionsOverall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

Genetic Characterization of Multiple Components Contributing to Fusarium Head Blight Resistance of FL62R1, a Canadian Bread Wheat Developed Using Systemic Breeding.

Fusarium head blight (FHB) is a devastating fungal disease of small-grain cereals that results in severe yield and quality losses. FHB resistance is controlled by resistance components including incidence, field severity, visual rating index, Fusarium damaged kernels (FDKs), and the accumulation of the mycotoxin deoxynivalenol (DON). Resistance conferred by each of these components is partial and must be combined to achieve resistance sufficient to protect wheat from yield losses. In this study, two biparental mapping populations were analyzed in Canadian FHB nurseries and quantitative trait loci (QTL) mapped for the traits listed above. Nine genomic loci, on 2AS, 2BS, 3BS, 4AS, 4AL, 4BS, 5AS, 5AL, and 5BL, were enriched for the majority of the QTL controlling FHB resistance. The previously validated FHB resistance QTL on 3BS and 5AS affected resistance to severity, FDK, and DON in these populations. The remaining seven genomic loci colocalize with flowering time and/or plant height QTL. The QTL on 4B was a major contributor to all field resistance traits and plant height in the field. QTL on 4AL showed contrasting effects for FHB resistance between Eastern and Western Canada, indicating a local adapted resistance to FHB. In addition, we also found that the 2AS QTL contributed a major effect for DON, and the 2BS for FDK, while the 5AL conferred mainly effect for both FDK/DON. Results presented here provide insight into the genetic architecture underlying these resistant components and insight into how FHB resistance in wheat is controlled by a complex network of interactions between genes controlling flowering time, plant height, local adaption, and FHB resistance components.

Identification and Validation of a Novel Major Quantitative Trait Locus for Plant Height in Common Wheat (Triticum aestivum L.).

Plant height (PH) plays a pivotal role in plant morphological architecture and is associated with yield potential in wheat. For the quantitative trait locus (QTL) analysis, a recombinant inbred line population was developed between varieties differing significantly in PH. Two major QTL were identified on chromosomes 4B (QPh.sicau-4B) and 6D (QPh.sicau-6D) in multiple environments, which were then validated in two different backgrounds by using closely linked markers. QPh.sicau-4B explained 10.1–21.3% of the phenotypic variance, and the location corresponded to the dwarfing gene Rht-B1. QPh.sicau-6D might be a novel QTL for PH, explaining 6.6–13.6% of the phenotypic variance and affecting spike length, thousand-kernel weight, and spikelet compactness. Three candidate genes associated with plant growth and development were identified in the physical interval of QPh.sicau-6D. Collectively, we identified a novel stable and major PH QTL, QPh.sicau-6D, which could aid in the development of closely linked markers for marker-assisted breeding and cloning genes underlying this QTL.

GWAS reveals consistent QTL for drought and salt tolerance in a MAGIC population of 550 lines derived from intermating of 11 Upland cotton (Gossypium hirsutum) parents.

Cotton is grown in arid and semi-arid regions where abiotic stresses such as drought and salt are prevalent. There is a lack of studies that simultaneously address the genetic and genomic basis of tolerance to drought and salt stress. In this study, a multi-parent advanced generation inter-cross (MAGIC) population of 550 recombinant inbred lines (RILs) together with their 11 Upland cotton parents with a total of 473,516 polymorphic SNP markers was used to identify quantitative trait loci (QTL) for drought tolerance (DT) and salt tolerance (ST) at the seedling stage based on two replicated greenhouse tests. Transgressive segregation occurred in the MAGIC-RILs, indicating that tolerant and sensitive alleles recombined for tolerance to the abiotic stress during the intermating process for the population development. A total of 20 QTL were detected for DT including 13 and 7 QTL based on plant height (PH) and dry shoot weight (DSW), respectively; and 23 QTL were detected for ST including 12 and 11 QTL for PH and DSW, respectively. There were several chromosomes with QTL clusters for abiotic stress tolerance including four QTL on chromosome A13 and three QTL on A01 for DT, and four QTL on D08 and three QTL on A11 for ST. Nine QTL (21% of the 43 QTL) detected were in common between DT and ST, indicating a common genetic basis for DT and ST. The narrow chromosomal regions for most of the QTL detected in this study allowed identification of 53 candidate genes associated with responses to salt and drought stress and abiotic stimulus. The QTL identified for both DT and ST have significantly augmented the repertoire of QTL for abiotic stress tolerance that can be used for marker-assisted selection to develop cultivars with resilience to drought and/or salt and further genomic studies towards the identification of drought and salt tolerance genes in cotton.

SlGID1a Is a Putative Candidate Gene for qtph1.1, a Major-Effect Quantitative Trait Locus Controlling Tomato Plant Height

Plant height is an important agronomic trait in crops. Several genes underlying tomato (Solanum lycopersicum) plant height mutants have been cloned. However, few quantitative trait genes for plant height have been identified in tomato. In this study, seven quantitative trait loci (QTLs) controlling plant height were identified in tomato. Of which, qtph1.1 (QTL for tomato plant height 1.1), qtph3.1 and qtph12.1 were major QTLs and explained 15, 16, and 12% of phenotypic variation (R2), respectively. The qtph1.1 was further mapped to an 18.9-kb interval on chromosome 1. Based on the annotated tomato genome (version SL2.50, annotation ITAG2.40), Solyc01g098390 encoding GA receptor SlGID1a was the putative candidate gene. The SlGID1a gene underlying the qtph1.1 locus contained a single nucleotide polymorphism (SNP) that resulted in an amino acid alteration in protein sequence. The near-isogenic line containing the qtph1.1 locus (NIL-qtph1.1) exhibited shorter internode length and cell length than the wild type (NIL-WT). The dwarf phenotype of NIL-qtph1.1 could not be rescued by exogenous GA3 treatment. Transcriptome analysis and real-time quantitative reverse transcription PCR (qPCR) showed that several genes related to biosynthesis and signaling of GA and auxin were differentially expressed in stems between NIL-qtph1.1 and NIL-WT. These findings might pave the road for understanding the molecular regulation mechanism of tomato plant height.

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

Quantitative trait locus mapping of yield and plant height in autotetraploid alfalfa (Medicago sativa L.)

Alfalfa (Medicago sativa L.) is the most widely grown forage legume crop worldwide. Yield and plant height are important agronomic traits influenced by genetic and environmental factors. The objective of this study was to identify quantitative trait loci (QTL) and molecular markers associated with alfalfa yield and plant height. To understand the genetic basis of these traits, a full-sib F1 population composed of 392 individuals was developed by crossing a low-yielding precocious alfalfa genotype (male parent) with a high-yielding late-maturing alfalfa cultivar (female parent). The linkage maps were constructed with 3818 single-nucleotide polymorphism (SNP) markers on 64 linkage groups. QTL for yield and plant height were mapped using phenotypic data for three years. Sixteen QTL associated with yield and plant height were identified on chromosomes 1 to 8. Six QTL explained more than 10% of phenotypic variation, representing major loci controlling yield and plant height. One locus on chromosome 1 controlling yield traits had not been identified in previous studies. Three QTL co-located with other QTL (qyield-1 and qheight-7, qheight-5 and qyield-4, qheight-6, and qyield-6). With further validation, the markers closely linked with these QTL may be used for marker-assisted selection in breeding new alfalfa varieties with high yield.