Quantitative Trait Loci Intervals Research Articles

An approach to detecting quantitative trait loci and candidate genes associated with salinity tolerance in faba bean (Vicia faba)

AbstractFaba bean is one of the most important high protein cool‐season grain legumes, but it is sensitive to salinity stress. Improved salt tolerance of faba bean is needed to increase its yield and area of production. In this study, a recombinant inbred line (RIL) population, derived from the bi‐parental cross of 'Icarus' × 'Ascot', was phenotyped under control and saline treatments. Quantitative trait locus (QTL) analysis of this population detected multiple QTL for leaf ionic concentration (Na+, K+ and Cl−) under both treatments. Of these, seven QTL were detected under salt treatment, including three for leaf K+:Na+ (qK:Na‐S‐1, qK:Na‐S‐2 and qK:Na‐S‐3) and one each for Na+ accumulation (qNa‐S), K+ accumulation (qK‐S), Cl− accumulation (qCl‐S) and Na+:K+ (qNa:K‐S). An important region was identified on linkage group I.A/III/V, which contained three co‐located QTL (qNa‐S, qCl‐S and qK:Na‐S‐3) for leaf Na+, Cl− and K+:Na+ accumulation. Potential candidate genes within these QTL intervals were shortlisted using the Medicago truncatula genome. These findings are expected to facilitate the breeding of new salt tolerant faba bean cultivars.

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.

Genetic control and phenotypic characterization of panicle architecture and grain yield-related traits in foxtail millet (Setaria italica).

Multi-environment QTL mapping identified 23 stable loci and 34 co-located QTL clusters for panicle architecture and grain yield-related traits, which provide a genetic basis for foxtail millet yield improvement. Panicle architecture and grain weight, both of which are influenced by genetic and environmental factors, have significant effects on grain yield potential. Here, we used a recombinant inbred line (RIL) population of 333 lines of foxtail millet, which were grown in 13 trials with varying environmental conditions, to identify quantitative trait loci (QTL) controlling nine agronomic traits related to panicle architecture and grain yield. We found that panicle weight, grain weight per panicle, panicle length, panicle diameter, and panicle exsertion length varied across different geographical locations. QTL mapping revealed 159 QTL for nine traits. Of the 159 QTL, 34 were identified in 2 to 12 environments, suggesting that the genetic control of panicle architecture in foxtail millet is sensitive to photoperiod and/or other environmental factors. Eighty-eight QTL controlling different traits formed 34 co-located QTL clusters, including the triple QTL cluster qPD9.2/qPL9.5/qPEL9.3, which was detected 23 times in 13 environments. Several candidate genes, including Seita.2G388700, Seita.3G136000, Seita.4G185300, Seita.5G241500, Seita.5G243100, Seita.9G281300, and Seita.9G342700, were identified in the genomic intervals of multi-environmental QTL or co-located QTL clusters. Using available phenotypic and genotype data, we conducted haplotype analysis for Seita.2G002300 and Seita.9G064000,which showed high correlations with panicle weight and panicle exsertion length, respectively. These results not only provided a basis for further fine mapping, functional studies and marker-assisted selection of traits related to panicle architecture in foxtail millet, but also provide information for comparative genomics analyses of cereal crops.

Genes Associated with Foliar Resistance to Septoria Nodorum Blotch of Hexaploid Wheat (Triticum aestivum L.).

The genetic control of host response to the fungal necrotrophic disease Septoria nodorum blotch (SNB) in bread wheat is complex, involving many minor genes. Quantitative trait loci (QTL) controlling SNB response were previously identified on chromosomes 1BS and 5BL. The aim of this study, therefore, was to align and compare the genetic map representing QTL interval on 1BS and 5BS with the reference sequence of wheat and identify resistance genes (R-genes) associated with SNB response. Alignment of QTL intervals identified significant genome rearrangements on 1BS between parents of the DH population EGA Blanco, Millewa and the reference sequence of Chinese Spring with subtle rearrangements on 5BL. Nevertheless, annotation of genomic intervals in the reference sequence were able to identify and map 13 and 12 R-genes on 1BS and 5BL, respectively. R-genes discriminated co-located QTL on 1BS into two distinct but linked loci. NRC1a and TFIID mapped in one QTL on 1BS whereas RGA and Snn1 mapped in the linked locus and all were associated with SNB resistance but in one environment only. Similarly, Tsn1 and WK35 were mapped in one QTL on 5BL with NETWORKED 1A and RGA genes mapped in the linked QTL interval. This study provided new insights on possible biochemical, cellular and molecular mechanisms responding to SNB infection in different environments and also addressed limitations of using the reference sequence to identify the full complement of functional R-genes in modern varieties.

Molecular dissection of maize seedling salt tolerance using a genome-wide association analysis method.

SummarySalt stress is a major devastating abiotic factor that affects the yield and quality of maize. However, knowledge of the molecular mechanisms of the responses to salt stress in maize is limited. To elucidate the genetic basis of salt tolerance traits, a genome‐wide association study was performed on 348 maize inbred lines under normal and salt stress conditions using 557 894 single nucleotide polymorphisms (SNPs). The phenotypic data for 27 traits revealed coefficients of variation of >25%. In total, 149 significant SNPs explaining 6.6%–11.2% of the phenotypic variation for each SNP were identified. Of the 104 identified quantitative trait loci (QTLs), 83 were related to salt tolerance and 21 to normal traits. Additionally, 13 QTLs were associated with two to five traits. Eleven and six QTLs controlling salt tolerance traits and normal root growth, respectively, co‐localized with QTL intervals reported previously. Based on functional annotations, 13 candidate genes were predicted. Expression levels analysis of 12 candidate genes revealed that they were all responsive to salt stress. The CRISPR/Cas9 technology targeting three sites was applied in maize, and its editing efficiency reached 70%. By comparing the biomass of three CRISPR/Cas9 mutants of ZmCLCg and one zmpmp3 EMS mutant with their wild‐type plants under salt stress, the salt tolerance function of candidate genes ZmCLCg and ZmPMP3 were confirmed. Chloride content analysis revealed that ZmCLCg regulated chloride transport under sodium chloride stress. These results help to explain genetic variations in salt tolerance and provide novel loci for generating salt‐tolerant maize lines.

Identification of Novel Loci and Candidate Genes for Resistance to Powdery Mildew in a Resequenced Cucumber Germplasm.

Powdery mildew (PM) is one of the most serious diseases in cucumber and causes huge yield loss. Multiple quantitative trait loci (QTLs) for PM resistance have been reported in previous studies using a limited number of cucumber accessions. In this study, a cucumber core germplasm (CG) consisting of 94 resequenced lines was evaluated for PM resistance in four trials across three years (2013, 2014, and 2016). These trials were performed on adult plants in the field with natural infection. Using genome-wide association study (GWAS), 13 loci (pmG1.1, pmG1.2, pmG2.1, pmG2.2, pmG3.1, pmG4.1, pmG4.2, pmG5.1, pmG5.2, pmG5.3, pmG5.4, pmG6.1, and pmG6.2) associated with PM resistance were detected on all chromosomes except for Chr.7. Among these loci, ten were mapped to chromosomal intervals where QTLs had been reported in previous studies, while, three (pmG2.1, pmG3.1, and pmG4.1) were novel. The loci of pmG2.1, pmG5.2, pmG5.3 showed stronger signal in four trials. Based on the annotation of homologous genes in Arabidopsis and pairwise LD correlation analysis, candidate genes located in the QTL intervals were predicted. SNPs in these candidate genes were analyzed between haplotypes of highly resistant (HR) and susceptible (HS) CG lines, which were defined based on combing disease index data of all trials. Furthermore, candidate genes (Csa5G622830 and CsGy5G015660) reported in previous studies for PM resistance and cucumber orthologues of several PM susceptibility (S) genes (PMR5, PMR-6, and MLO) that are colocalized with certain QTLs, were analyzed for their potential contribution to the QTL effect on both PM and DM in the CG population. This study shows that the CG germplasm is a very valuable resource carrying known and novel QTLs for both PM and DM resistance, which can be exploited in cucumber breeding.

Physical mapping and candidate gene prediction of branch number on the main stem in soybean [Glycine max (L.) Merr.

Branch number on the main stem (BNMS) is an important factor that affects crop plant architecture and yield in soybean [Glycine max (L.) Merr.]. With the aim of elucidating the genetic basis of BNMS, 10 consensus quantitative trait loci (QTLs) were identified, which were on 9 chromosomes and explained 0.3–33.3% of the phenotypic variance. One new QTL-qBN.N (R2 = 19.6%) was detected in two populations (F2:3-Taiyuan 2017 and F2:4-Taiyuan 2018), which occur only in Taiyuan. Thus, the interaction analysis of QTL × environment confirmed that QTL-qBN.N was greatly affected by the environment. Of these, QTL-qBN.C2 (R2 = 33.3%) was defined as a major QTL, and was also verified and fine-mapped in the recombinant inbred line population. The high-coverage re-sequencing of two parental lines and newly developed InDel PCR-based markers allowed the region of qBN.C2 to be narrowed down to 304.9 kb. According to the gene annotation of the QTL interval, a total of 24 genes were obtained. Three potential candidate genes were identified by quantitative real-time PCR, and only Glyma.06G188400 had the highest relative expression at the axillary meristem, and thus it may potentially participate in branching development. The successful and rapid fine-mapping and discovery of candidate genes for BNMS in soybean were achieved in this study.

SLAF-Based Linkage Map Construction and QTL Mapping of Nitrogen Use Efficiency in Rice (Oryza sativa L.)

Rice production relies on large amounts of synthetic nitrogen fertilizer to meet crop nutritional demands. However, the current fertilizer use efficiency is poor, with an average of 30–50% of total applied nitrogen (N) recovered in grains. Consequently, optimizing N availability and N use efficiency (NUE) has become a major target for rice yield improvement, and looking for quantitative trait loci (QTL) associated with NUE can provide useful information for developing nitrogen efficient rice genotypes. The present study aims to identify QTLs associated with NUE and its two components, agronomic NUE (agNUE) and grain nitrogen increase rate (GIR). A recombinant inbred line population (RIL) derived from a cross between Nipponbare and OM052, consisting of 159 F3 individuals was developed and cultivated in low-N (8 kg N/0.67 ha, N8) and high-N (18 kg N/0.67 ha, N18) conditions. Specific length amplified fragment sequencing (SLAF) was used to genotype the RILs and construct a high-density linkage map spanning 1587.70 cM, consisting 2707 SLAF markers with an average interval of 0.59 cM between adjacent marker loci. QTL analysis confirmed four significant QTLs distributed among chromosome 1, 6, and 11, with the proportion of phenotypic variance explained by each QTL ranging from 2.96 to 11.11% and LOD scores from 1.67 to 3.75. Functional annotation of genes located within the QTL intervals revealed NUE putative candidate genes. Overall, the QTLs identified in this study contribute information that could be useful for NUE improvement in rice.

Genome-wide association mapping identifies novel loci underlying fire blight resistance in apple.

Fire blight, caused by epiphytotic gram-negative bacteria Erwinia amylovora, is the most destructive bacterial disease of apple (Malus spp.). Genetic mechanisms of fire blight resistance have mainly been studied using traditional biparental quantitative trait loci (QTL) mapping approaches. Here, we use large-scale historic shoot and blossom fire blight data collected over multiple years and genotyping-by-sequencing (GBS) markers to identify significant marker-trait associations in a diverse set of 566 apple [Malus domestica (Suckow) Borkh.] accessions. There was large variation in fire blight resistance and susceptibility in these accessions. We identified 23 and 38 QTL significantly (p < .001) associated with shoot and blossom blight resistance, respectively. The QTL are distributed across all 17 chromosomes of apple. Four shoot blight and 19 blossom blight QTL identified in this study colocalized with previously identified QTL associated with resistance to fire blight or apple scab. Using transcriptomics data of two apple cultivars with contrasting fire blight responses, we also identified candidate genes for fire blight resistance that are differentially expressed between resistant and susceptible cultivars and located within QTL intervals for fire blight resistance. However, further experiments are needed to confirm and validate these marker-trait associations and develop diagnostic markers before use in marker-assisted breeding to develop apple cultivars with decreased fire blight susceptibility.

Mapping Quantitative Trait Loci Associated With Graft (In)Compatibility in Apricot (Prunus armeniaca L.).

Graft incompatibility (GI) between the most popular Prunus rootstocks and apricot cultivars is one of the major problems for rootstock usage and improvement. Failure in producing long-leaving healthy grafts greatly affects the range of available Prunus rootstocks for apricot cultivation. Despite recent advances related to the molecular mechanisms of a graft-union formation between rootstock and scion, information on genetic control of this trait in woody plants is essentially missing because of a lack of hybrid crosses, segregating for the trait. In this study, we have employed the next-generation sequencing technology to generate the single-nucleotide polymorphism (SNP) markers and construct parental linkage maps for an apricot F1 population “Moniqui (Mo)” × “Paviot (Pa)” segregating for ability to form successful grafts with universal Prunus rootstock “Marianna 2624”. To localize genomic regions associated with this trait, we genotyped 138 individuals from the “Mo × Pa” cross and constructed medium-saturated genetic maps. The female “Mo” and male “Pa” maps were composed of 557 and 501 SNPs and organized in eight linkage groups that covered 780.2 and 690.4 cM of genetic distance, respectively. Parental maps were aligned to the Prunus persica v2.0 genome and revealed a high colinearity with the Prunus reference map. Two-year phenotypic data for characters associated with unsuccessful grafting such as necrotic line (NL), bark and wood discontinuities (BD and WD), and an overall estimate of graft (in)compatibility (GI) were collected for mapping quantitative trait loci (QTLs) on both parental maps. On the map of the graft-compatible parent “Pa”, two genomic regions on LG5 (44.9–60.8 cM) and LG8 (33.2–39.2 cM) were associated with graft (in)compatibility characters at different significance level, depending on phenotypic dataset. Of these, the LG8 QTL interval was most consistent between the years and supported by two significant and two putative QTLs. To our best knowledge, this is the first report on QTLs for graft (in)compatibility in woody plants. Results of this work will provide a valuable genomic resource for apricot breeding programs and facilitate future efforts focused on candidate genes discovery for graft (in)compatibility in apricot and other Prunus species.

An Integrated Approach of QTL Mapping and Genome-Wide Association Analysis Identifies Candidate Genes for Phytophthora Blight Resistance in Sesame (Sesamum indicum L.).

Phytophthora blight (PB) caused by Phytophthora nicotianae is a highly destructive disease in sesame (Sesamum indicum L.). In this study, we used linkage mapping and genome-wide association study (GWAS) to identify quantitative trait loci (QTL) and candidate genes associated with PB resistance. The QTL mapping in 90 RILs of the Goenbaek × Osan cross using genotyping-by-sequencing detected significant QTLs for PB resistance on chromosome 10, explaining 12.79%–13.34% of phenotypic variation. Association of this locus to PB resistance was also revealed through bulked segregant analysis in second RIL population (Goenbaek × Milsung cross) comprising 188 RILs. The GWAS of 87 sesame accessions evaluated against three P. nicotianae isolates identified 29 SNPs on chromosome 10 significantly associated with PB resistance. These SNPs were located within a 0.79 Mb region, which co-located with the QTL intervals identified in RIL populations, and hence scanned for identifying candidate genes. This region contained several defense-related candidate R genes, five of which were selected for quantitative expression analysis. One of these genes, SIN_1019016 was found to show significantly higher expression in the resistant parent compared to that in the susceptible parents and selected RILs. Paired-end sequencing of the gene SIN_1019016 in parental cultivars revealed two synonymous SNPs between Goenbaek and Osan in exon 2 of coding DNA sequence. These results suggested SIN_1019016 as one of the candidate gene conferring PB resistance in sesame. The findings from this study will be useful in the marker-assisted selection as well as the functional analysis of PB resistance candidate gene(s) in sesame.

Genomic Architecture of Phenotypic Plasticity in Response to Water Stress in Tetraploid Wheat.

Phenotypic plasticity is one of the main mechanisms of adaptation to abiotic stresses via changes in critical developmental stages. Altering flowering phenology is a key evolutionary strategy of plant adaptation to abiotic stresses, to achieve the maximum possible reproduction. The current study is the first to apply the linear regression residuals as drought plasticity scores while considering the variation in flowering phenology and traits under non-stress conditions. We characterized the genomic architecture of 17 complex traits and their drought plasticity scores for quantitative trait loci (QTL) mapping, using a mapping population derived from a cross between durum wheat (Triticum turgidum ssp. durum) and wild emmer wheat (T. turgidum ssp. dicoccoides). We identified 79 QTLs affected observed traits and their plasticity scores, of which 33 reflected plasticity in response to water stress and exhibited epistatic interactions and/or pleiotropy between the observed and plasticity traits. Vrn-B3 (TaTF1) residing within an interval of a major drought-escape QTL was proposed as a candidate gene. The favorable alleles for most of the plasticity QTLs were contributed by wild emmer wheat, demonstrating its high potential for wheat improvement. Our study presents a new approach for the quantification of plant adaptation to various stresses and provides new insights into the genetic basis of wheat complex traits under water-deficit stress.

The Genetic Architecture of Variation in the Sexually Selected Sword Ornament and Its Evolution in Hybrid Populations

Biologists since Darwin have been fascinated by the evolution of sexually selected ornaments, particularly those that reduce viability. Uncovering the genetic architecture of these traits is key to understanding how they evolve and are maintained. Here, we investigate the genetic architecture and evolutionary loss of a sexually selected ornament, the "sword" fin extension that characterizes many species of swordtail fish (Xiphophorus). Using sworded and swordless sister species of Xiphophorus, we generated a mapping population and show that the sword ornament is polygenic-with ancestry across the genome explaining substantial variation in the trait. After accounting for the impacts of genome-wide ancestry, we identify one major-effect quantitative trait locus (QTL) that explains ~5% of the overall variation in the trait. Using a series of approaches, we narrow this large QTL interval to several likely candidate genes, including genes involved in fin regeneration and growth. Furthermore, we find evidence of selection on ancestry at one of these candidates in four natural hybrid populations, consistent with selection against the sword in these populations.

Transcriptomic profiling of wheat near-isogenic lines reveals candidate genes on chromosome 3A for pre-harvest sprouting resistance

BackgroundPre-harvest sprouting (PHS) in wheat can cause severe damage to both grain yield and quality. Resistance to PHS is a quantitative trait controlled by many genes located across all 21 wheat chromosomes. The study targeted a large-effect quantitative trait locus (QTL) QPhs.ccsu-3A.1 for PHS resistance using several sets previously developed near-isogenic lines (NILs). Two pairs of NILs with highly significant phenotypic differences between the isolines were examined by RNA sequencing for their transcriptomic profiles on developing seeds at 15, 25 and 35 days after pollination (DAP) to identify candidate genes underlying the QTL and elucidate gene effects on PHS resistance. At each DAP, differentially expressed genes (DEGs) between the isolines were investigated.ResultsGene ontology and KEGG pathway enrichment analyses of key DEGs suggested that six candidate genes underlie QPhs.ccsu-3A.1 responsible for PHS resistance in wheat. Candidate gene expression was further validated by quantitative RT-PCR. Within the targeted QTL interval, 16 genetic variants including five single nucleotide polymorphisms (SNPs) and 11 indels showed consistent polymorphism between resistant and susceptible isolines.ConclusionsThe targeted QTL is confirmed to harbor core genes related to hormone signaling pathways that can be exploited as a key genomic region for marker-assisted selection. The candidate genes and SNP/indel markers detected in this study are valuable resources for understanding the mechanism of PHS resistance and for marker-assisted breeding of the trait in wheat.

Identification of major QTLs and candidate genes for pod and seed weight per plant in soybean

Pod and seed weight per plant (PSW) is one of the most important components of soybean yield. We analysed 147 recombinant inbred lines (RILs) of soybean. Using a combination of simple sequence repeat (SSR) and specific-length amplified fragment (SLAF) high-density maps, we applied composite interval mapping (CIM) and multiple interval mapping (MIM) to map quantitative trait loci (QTLs) for PSW across multiple years (2006–10 and 2013). We mapped 24 QTLs for PSW, with 10 QTLs identified by SSR genetic map and 14 by SLAF genetic map. Five consensus QTLs were integrated, and they were validated by a chromosome segment substitution line (CSSL) population. Furthermore, the functions of all genes located in consensus QTL intervals were predicted; nine candidate genes function directly or indirectly in regulating seed development as well as seed size and weight. Our results lay a foundation for the cloning of candidate genes related to PSW and marker-assisted breeding in soybean.

Intricate genetic variation networks control the adventitious root growth angle in apple

BackgroundThe root growth angle (RGA) typically determines plant rooting depth, which is significant for plant anchorage and abiotic stress tolerance. Several quantitative trait loci (QTLs) for RGA have been identified in crops. However, the underlying mechanisms of the RGA remain poorly understood, especially in apple rootstocks. The objective of this study was to identify QTLs, validate genetic variation networks, and develop molecular markers for the RGA in apple rootstock.ResultsBulked segregant analysis by sequencing (BSA-seq) identified 25 QTLs for RGA using 1955 hybrids of the apple rootstock cultivars ‘Baleng Crab’ (Malus robusta Rehd., large RGA) and ‘M9’ (M. pumila Mill., small RGA). With RNA sequencing (RNA-seq) and parental resequencing, six major functional genes were identified and constituted two genetic variation networks for the RGA. Two single nucleotide polymorphisms (SNPs) of the MdLAZY1 promoter damaged the binding sites of MdDREB2A and MdHSFB3, while one SNP of MdDREB2A and MdIAA1 affected the interactions of MdDREB2A/MdHSFB3 and MdIAA1/MdLAZY1, respectively. A SNP within the MdNPR5 promoter damaged the interaction between MdNPR5 and MdLBD41, while one SNP of MdLBD41 interrupted the MdLBD41/MdbHLH48 interaction that affected the binding ability of MdLBD41 on the MdNPR5 promoter. Twenty six SNP markers were designed on candidate genes in each QTL interval, and the marker effects varied from 0.22°-26.11°.ConclusionsSix diagnostic markers, SNP592, G122, b13, Z312, S1272, and S1288, were used to identify two intricate genetic variation networks that control the RGA and may provide new insights into the accuracy of the molecular markers. The QTLs and SNP markers can potentially be used to select deep-rooted apple rootstocks.

High-Resolution Linkage Map and QTL Analyses of Fruit Firmness in Autotetraploid Blueberry.

Blueberry (Vaccinium corymbosum and hybrids) is an autotetraploid crop whose commercial relevance has been growing steadily during the last 20 years. However, the ever-increasing cost of labor for hand-picking blueberry is one main constraint in competitive marketing of the fruit. Machine harvestability is, therefore, a key trait for the blueberry industry. Understanding the genetic architecture of traits related to machine harvestability through Quantitative Trait Loci (QTL) mapping is the first step toward implementation of molecular breeding for faster genetic gains. Despite recent advances in software development for autotetraploid genetic mapping, a high-resolution map is still not available for blueberry. In this study, we crafted a map for autotetraploid low-chill highbush blueberry containing 11,292 SNP markers and a total size of 1,953.97 cM (average density of 5.78 markers/cM). This map was subsequently used to perform QTL analyses in 2-year field trials for a trait crucial to machine harvesting: fruit firmness. Preliminary insights were also sought for single evaluations of firmness retention after cold storage, and fruit detachment force traits. Significant QTL peaks were identified for all the traits and overlapping QTL intervals were detected for firmness across the years. We found low-to-moderate QTL effects explaining the phenotypic variance, which suggest a quantitative nature of these traits. The QTL intervals were further speculated for putative gene repertoire. Altogether, our findings provide the basis for future fine-mapping and molecular breeding efforts for machine harvesting in blueberry.

Genetic analysis of oviposition deterrence to orange wheat blossom midge in spring wheat.

A major QTL for oviposition deterrence to orange wheat blossom midge was detected on chromosome 1A in the Canadian breeding line BW278 that was inherited from the Chinese variety Sumai-3. Orange wheat blossom midge (OWBM, Sitodiplosis mosellana Géhin, Diptera: Cecidomyiidae) is an important insect pest of wheat (Triticum aestivum L.) that reduces both grain yield and quality. Oviposition deterrence results in a reduction of eggs deposited on spikes relative to that observed on a wheat line preferred by OWBM. Quantification of oviposition deterrence is labor-intensive, so wheat breeders require efficient DNA markers for the selection of this trait. The objective of this study was to identify quantitative trait loci (QTL) for oviposition deterrence in a doubled haploid (DH) population developed from the spring wheat cross Superb/BW278. The DH population and check varieties were evaluated for OWBM kernel damage from five field nurseries over three growing seasons. QTL analysis identified major effect loci on chromosomes 1A (QSm.mrc-1A) and 5A (QSm.mrc-5A). Reduced kernel damage was contributed by BW278 at QSm.mrc-1A and Superb at QSm.mrc-5A. QSm.mrc-1A mapped to the approximate location of the oviposition deterrence QTL previously found in the American variety Reeder. However, haplotype analysis revealed that BW278 inherited this oviposition deterrence allele from the Chinese spring wheat variety Sumai-3. QSm.mrc-5A mapped to the location of awn inhibitor gene B1, suggesting that awns hinder OWBM oviposition. Single-nucleotide polymorphisms (SNPs) were identified for predicting the presence or absence of QSm.mrc-1A based upon haplotype. Functional annotation of candidate genes in 1A QTL intervals revealed eleven potential candidate genes, including a gene involved in terpenoid biosynthesis. SNPs for QSm.mrc-1A and fully awned spikes provide a basis for the selection of oviposition deterrence to OWBM.

A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.

QTL mapping for soybean (Glycine max L.) leaf chlorophyll-content traits in a genotyped RIL population by using RAD-seq based high-density linkage map

BackgroundDifferent soybean (Glycine max L.) leaf chlorophyll-content traits are considered to be significantly linked to soybean yield. To map the quantitative trait loci (QTLs) of soybean leaf chlorophyll-content traits, an advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3) population was adopted to phenotypic data acquisitions for the target traits across six distinct environments (seasons and soybean growth stages). Moreover, the restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage map of the RIL population was utilized for QTL mapping by carrying out the composite interval mapping (CIM) approach.ResultsCorrelation analyses showed that most traits were correlated with each other under specific chlorophyll assessing method and were regulated both by hereditary and environmental factors. In this study, 78 QTLs for soybean leaf chlorophyll-content traits were identified. Furthermore, 13 major QTLs and five important QTL hotspots were classified and highlighted from the detected QTLs. Finally, Glyma01g15506, Glyma02g08910, Glyma02g11110, Glyma07g15960, Glyma15g19670 and Glyma15g19810 were predicted from the genetic intervals of the major QTLs and important QTL hotspots.ConclusionsThe detected QTLs and candidate genes may facilitate to gain a better understanding of the hereditary basis of soybean leaf chlorophyll-content traits and may be valuable to pave the way for the marker-assisted selection (MAS) breeding of the target traits.