Acid phosphatase gene <Emphasis Type="Italic">GmHAD1</Emphasis> linked to low phosphorus tolerance in soybean,through fine mapping

Key message

Map-based cloning identified GmHAD1, a gene which encodes a HAD-like acid phosphatase, associated with soybean tolerance to low phosphorus stress.

Abstract

Phosphorus (P) deficiency in soils is a major limiting factor for crop growth worldwide. Plants may adapt to low phosphorus (LP) conditions via changes to root morphology, including the number, length, orientation, and branching of the principal root classes. To elucidate the genetic mechanisms for LP tolerance in soybean, quantitative trait loci (QTL) related to root morphology responses to LP were identified via hydroponic experiments. In total, we identified 14 major loci associated with these traits in a RIL population. The log-likelihood scores ranged from 2.81 to 7.43, explaining 4.23–13.98% of phenotypic variance. A major locus on chromosome 08, named qP8-2, was co-localized with an important P efficiency QTL (qPE8), containing phosphatase genes GmACP1 and GmACP2. Another major locus on chromosome 10 named qP10-2 explained 4.80–13.98% of the total phenotypic variance in root morphology. The qP10-2 contains GmHAD1, a gene which encodes an acid phosphatase. In the transgenic soybean hairy roots, GmHAD1 overexpression increased P efficiency by 8.4–16.5% relative to the control. Transgenic Arabidopsis plants had higher biomass than wild-type plants across both short- and long-term P reduction. These results suggest that GmHAD1, an acid phosphatase gene, improved the utilization of organic phosphate by soybean and Arabidopsis plants.

     

Genetic analysis of shoot fresh weight in a cross of wild (<Emphasis Type="Italic">G. soja</Emphasis>) and cultivated (<Emphasis Type="Italic">G. max</Emphasis>) soybean

Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. In the present study, a total of 188 F_5:8 recombinant inbred lines (RIL) derived from an interspecific cross of PI 483463 (Glycine soja) and Hutcheson (Glycine max) were investigated for SFW variation in the field for three consecutive years. The parental lines and RILs were phenotyped in the field at the R6 stage by measuring total biomass in kg/plot to identify the QTLs for SFW. Three QTLs qSFW6_1, qSFW15_1, and qSFW19_1 influencing SFW were identified on chromosome 6, 15, and 19, respectively. The QTL qSFW19_1 flanked between the markers BARC-044913-08839 and BARC-029975-06765 was the stable QTL expressed in all the three environments. The phenotypic variation explained by the QTLs across all environments ranged from 6.56 to 21.32 %. The additive effects indicated contribution of alleles from both the parents and additive × environment interaction effects affected the expression of SFW QTL. Screening of the RIL population with additional SSRs from the qSFW19_1 region delimited the QTL between the markers SSR19-1329 and BARC-29975-06765. QTL mapping using bin map detected two QTLs, qSFW19_1A and qSFW19_1B. The QTL qSFW19_1A mapped close to the Dt1 gene locus, which affects stem termination, plant height, and floral initiation in soybean. Potential candidate genes for SFW were pinpointed, and sequence variations within their sequences were detected using high-quality whole-genome resequencing data. The findings in this study could be useful for understanding genetic basis of SFW in soybean.     

QTL mapping of physiological traits associated with salt tolerance in Medicago truncatula Recombinant Inbred Lines

In this study, QTL mapping of physiological traits in the model Legume (Medicago truncatula) was performed using a set of RILs derived from LR5. Twelve parameters associated with Na+ and K+ content in leaves, stems and roots were measured. Broad-sense heritability of these traits was ranged from 0.15 to 0.83 in control and from 0.14 to 0.61 in salt stress. Variation among RILs was dependent on line, treatment and line by treatment effect. We mapped 6 QTLs in control, 2 in salt stress and 5 for sensitivity index. No major QTL was identified indicating that tolerance to salt stress is governed by several genes with low effects. Detected QTL for leaf, stem and root traits did not share the same map locations, suggesting that genes controlling transport of Na+ and K+ may be different. The maximum of QTL was observed on chromosome 1, no QTL was detected on chromosomes 5 and 6.     

应用AFLP与RFLP标记研究水稻磷吸收与利用率的数量性状位点

To identify genetic factors underlying phosphorus (P) uptake and use efficiency under low-P stress in rice (Oryza sativa L.), 84 selected genotypes (recombinant inbred lines) and their parents (which differed in tolerance for low-P stress) “IR20” and IR55178-3B-9-3, were cultured in liquid medium supplemented with adequate and low P levels in a greenhouse. Plants were sampled after 6 weeks in culture for measurements of plant dry weight, P concentration, P uptake and P use efficiency under both P sufficient and stress conditions. A total of 179 molecular markers, including 26 RFLPs and 153 AFLPs, mapped on all 12 chromosomes of rice based on the 84 selected genotypes were used to detect the quantitative trait loci (QTLs) underlying tolerance for low-P stress. Three QTLs were detected on chromosomes 6, 7 and 12, respectively, for relative plant dry weight (RPDW) and relative P uptake (RPUP). One of the QTLs flanked by RG9 and RG241 on chromosome 12 had a major effect which explained about 50% of the variations in the two parameters across the population. The results coincided with the QTLs for low-P stress based on relative tillering ability from the same population from a cross between Nipponbare and Kasalath under soil condition. The identical major QTL for P uptake and plant growth under low-P stress in both liquid medium and soil strongly suggests that the ability of P uptake mainly controls rice tolerance for low-P stress.     

Genetic mapping revealed two loci for soybean aphid resistance in PI 567301B

The soybean aphid (Aphis glycines Matsumura) is the most damaging insect pest of soybean [Glycine max (L.) Merr.] in North America. New soybean aphid biotypes have been evolving quickly and at least three confirmed biotypes have been reported in USA. These biotypes are capable of defeating most known aphid resistant soybean genes indicating the need for identification of new genes. Plant Introduction (PI) 567301B was earlier identified to have antixenosis resistance against biotype 1 and 2 of the soybean aphid. Two hundred and three F_7:9 recombinant inbred lines (RILs) developed from a cross of soybean aphid susceptible cultivar Wyandot and resistant PI 567301B were used for mapping aphid resistance genes using the quantitative trait loci (QTL) mapping approach. A subset of 94 RILs and 516 polymorphic SNP makers were used to construct a genome-wide molecular linkage map. Two candidate QTL regions for aphid resistance were identified on this linkage map. Fine mapping of the QTL regions was conducted with SSR markers using all 203 RILs. A major gene on chromosome 13 was mapped near the previously identified Rag2 gene. However, an earlier study revealed that the detached leaves of PI 567301B had no resistance against the soybean aphids while the detached leaves of PI 243540 (source of Rag2) maintained aphid resistance. These results and the earlier finding that PI 243540 showed antibiosis resistance and PI 567301B showed antixenosis type resistance, indicating that the aphid resistances in the two PIs are not controlled by the same gene. Thus, we have mapped a new gene near the Rag2 locus for soybean aphid resistance that should be useful in breeding for new aphid-resistant soybean cultivars. Molecular markers closely linked to this gene are available for marker-assisted breeding. Also, the minor locus found on chromosome 8 represents the first reported soybean aphid-resistant locus on this chromosome.     

Construction of a high-density genetic map based on large-scale markers developed by specific length amplified fragment sequencing (SLAF-seq) and its application to QTL analysis for isoflavone content in Glycine max

Background

Quantitative trait locus (QTL) mapping is an efficient approach to discover the genetic architecture underlying complex quantitative traits. However, the low density of molecular markers in genetic maps has limited the efficiency and accuracy of QTL mapping. In this study, specific length amplified fragment sequencing (SLAF-seq), a new high-throughput strategy for large-scale SNP discovery and genotyping based on next generation sequencing (NGS), was employed to construct a high-density soybean genetic map using recombinant inbred lines (RILs, Luheidou2 × Nanhuizao, F_5:8). With this map, the consistent QTLs for isoflavone content across various environments were identified.

Results

In total, 23 Gb of data containing 87,604,858 pair-end reads were obtained. The average coverage for each SLAF marker was 11.20-fold for the female parent, 12.51-fold for the male parent, and an average of 3.98-fold for individual RILs. Among the 116,216 high-quality SLAFs obtained, 9,948 were polymorphic. The final map consisted of 5,785 SLAFs on 20 linkage groups (LGs) and spanned 2,255.18 cM in genome size with an average distance of 0.43 cM between adjacent markers. Comparative genomic analysis revealed a relatively high collinearity of 20 LGs with the soybean reference genome. Based on this map, 41 QTLs were identified that contributed to the isoflavone content. The high efficiency and accuracy of this map were evidenced by the discovery of genes encoding isoflavone biosynthetic enzymes within these loci. Moreover, 11 of these 41 QTLs (including six novel loci) were associated with isoflavone content across multiple environments. One of them, qIF20-2, contributed to a majority of isoflavone components across various environments and explained a high amount of phenotypic variance (8.7% - 35.3%). This represents a novel major QTL underlying isoflavone content across various environments in soybean.

Conclusions

Herein, we reported a high-density genetic map for soybean. This map exhibited high resolution and accuracy. It will facilitate the identification of genes and QTLs underlying essential agronomic traits in soybean. The novel major QTL for isoflavone content is useful not only for further study on the genetic basis of isoflavone accumulation, but also for marker-assisted selection (MAS) in soybean breeding in the future.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1086) contains supplementary material, which is available to authorized users.     

Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure

Key message

Eighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker.

Abstract

A population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1?×?NN1138-2 (MGII?×?MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~?0.75?×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures “Restricted Two-stage Multi-locus Genome-wide Association Study” (RTM-GWAS), “Mixed Linear Model Genome-wide Association Study” (MLM-GWAS) and the linkage mapping procedure “Composite Interval Mapping” (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70–89.92% (within heritability 98.2%), 146.41–353.62% (overflowing) and 88.29–172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein–protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000–MGX should be explored further.

     

Mapping QTLs for chlorophyll content and chlorophyll fluorescence in wheat under heat stress

Heat stress, one of the major abiotic stresses in wheat, affects chlorophyll fluorescence and chlorophyll content and thereby photosynthesis. To identify quantitative trait loci (QTLs) associated with these traits under terminal heat stress, 251 recombinant inbred lines (RILs) derived from a cross HD 2808/HUW510 were phenotyped. Using composite interval mapping, 40 QTLs were identified; 17 were related to conditions after timely sowing and 23 to heat stress after late sowing. The various parameters of chlorophyll fluorescence were associated with 23 QTLs, which were located on chromosomes 1A, 2A, 3A, and 2D and explained 3.67 to 18.04 % of phenotypic variation, whereas chlorophyll content was associated with 17 QTLs on chromosomes 2A, 2B, 2D, 5B, and 7A explaining 3.49 to 31.36 % of phenotypic variation. Most of the identified QTLs were clustered on chromosome 2D followed by 2A and 1A. The QTL Qchc.iiwbr-2A for chlorophyll content linked with marker gwm372 was stable over conditions and explained 3.81 to 18.05 % of phenotypic variation. In addition, 7 epistatic QTL pairs were also detected which explained 1.67 to 11.0 % of phenotypic variance. These identified genomic regions can be used in marker assisted breeding after validation for heat tolerance in wheat.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

QTL analysis of root traits as related to phosphorus efficiency in soybean

Background and Aims

Low phosphorus (P) availability is a major constraint to soybean growth and production, especially in tropical and subtropical areas. Root traits have been shown to play critical roles in P efficiency in crops. Identification of the quantitative trait loci (QTLs) conferring superior root systems could significantly enhance genetic improvement in soybean P efficiency.

Methods

A population of 106 F₉ recombinant inbred lines (RILs) derived from a cross between BD2 and BX10, which contrast in both P efficiency and root architecture, was used for mapping and QTL analysis. Twelve traits were examined in acid soils. A linkage map was constructed using 296 simple sequence repeat (SSR) markers with the Kosambi function, and the QTLs associated with these traits were detected by composite interval mapping and multiple-QTL mapping.

Key Results

The first soybean genetic map based on field data from parental genotypes contrasting both in P efficiency and root architecture was constructed. Thirty-one putative QTLs were detected on five linkage groups, with corresponding contribution ratios of 9·1–31·1 %. Thirteen putative QTLs were found for root traits, five for P content, five for biomass and five for yield traits. Three clusters of QTLs associated with the traits for root and P efficiency at low P were located on the B1 linkage group close to SSR markers Satt519 and Satt519-Sat_128, and on the D2 group close to Satt458; and one cluster was on the B1 linkage group close to Satt519 at high P.

Conclusions

Most root traits in soybean were conditioned by more than two minor QTLs. The region closer to Satt519 on the B1 linkage group might have great potential for future genetic improvement for soybean P efficiency through root selection.     

Loci and candidate gene identification for resistance to Sclerotinia sclerotiorum in soybean (Glycine max L. Merr.) via association and linkage maps

Soybean white mold (SWM), caused by Sclerotinia sclerotiorum ((Lib.) W. Phillips), is currently considered to be the second most important cause of soybean yield loss due to disease. Research is needed to identify SWM‐resistant germplasm and gain a better understanding of the genetic and molecular basis of SWM resistance in soybean. Stem pigmentation after treatment with oxaloacetic acid is an effective indicator of resistance to SWM. A total of 128 recombinant inbred lines (RILs) derived from a cross of ‘Maple Arrow’ (partial resistant to SWM) and ‘Hefeng 25’ (susceptible) and 330 diverse soybean cultivars were screened for the soluble pigment concentration of their stems, which were treated with oxalic acid. Four quantitative trait loci (QTLs) underlying soluble pigment concentration were detected by linkage mapping of the RILs. Three hundred and thirty soybean cultivars were sequenced using the whole‐genome encompassing approach and 25 179 single‐nucleotide polymorphisms (SNPs) were detected for the fine mapping of SWM resistance genes by genome‐wide association studies. Three out of five SNP markers representing a linkage disequilibrium (LD) block and a single locus on chromosome 13 (Gm13) were significantly associated with the soluble pigment content of stems. Three more SNPs that represented three minor QTLs for the soluble pigment content of stems were identified on another three chromosomes by association mapping. A major locus with the largest effect on Gm13 was found both by linkage and association mapping. Four potential candidate genes involved in disease response or the anthocyanin biosynthesis pathway were identified at the locus near the significant SNPs (<60 kbp). The beneficial allele and candidate genes should be useful in soybean breeding for improving resistance to SWM.     

A Genome-Wide mQTL-seq Scan Identifies Potential Molecular Signatures Regulating Plant Height in Chickpea

The current study employed a high-throughput genome-wide next-generation sequencing-led multiple QTL-seq (mQTL-seq) strategy in two inter- and intra-specific recombinant inbred line (RIL) mapping populations to identify the major genomic regions underlying robust quantitative trait loci (QTLs) regulating plant height in chickpea. The whole genome resequencing discovered 446,475 and 150,434 high-quality homozygous single nucleotide polymorphisms (SNPs) exhibiting polymorphism between tall and dwarf/semi-dwarf mapping parents and bulk/homozygous individuals selected from each of two chickpea RIL populations. These SNP-led mQTL-seq assays in RIL mapping populations scaled-down two longer major genomic regions (1.26–1.34 Mb) underlying robust plant height QTLs into the shorter high-resolution QTL intervals (653.2–756.3 kb) on chickpea chromosomes 3 and 8. This essentially delineated regulatory novel natural SNP allelic variants from brassinosteroid insensitive 1-receptor kinase 1 (BAK1) and gibberellin (GA) 20-oxidase genes governing plant height in chickpea. A strong impact of evolutionary bottlenecks including strong artificial/natural selection on two plant height gene loci during chickpea domestication was observed. The shoot apical meristem-specific expression aside from down-regulation of two plant height genes especially in dwarf/semi-dwarf as compared to tall parents and homozygous mapping individuals of two aforementioned RIL populations was apparent. The integrated genomics-assisted breeding strategy combining mQTL-seq with differential gene expression profiling and functional allelic diversity-based trait domestication study collectively identified potential natural allelic variants of candidate genes underlying major plant height QTLs in chickpea. These functionally relevant molecular signatures can be of immense use for marker-aided genetic enhancement to develop high seed- and pod-yielding non-lodging cultivars restructured with desirable plant height in chickpea.     

Locating QTLs controlling overwintering seedling rate in perennial glutinous rice 89-1 (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A new cold tolerant germplasm resource named glutinous rice 89-1 (Gr89-1, Oryza sativa L.) can overwinter using axillary buds, with these buds being ratooned the following year. The overwintering seedling rate (OSR) is an important factor for evaluating cold tolerance. Many quantitative trait loci (QTLs) controlling cold tolerance at different growth stages in rice have been identified, with some of these QTLs being successfully cloned. However, no QTLs conferring to the OSR trait have been located in the perennial O. sativa L. To identify QTLs associated with OSR and to evaluate cold tolerance. 286 F₁₂ recombinant inbred lines (RILs) derived from a cross between the cold tolerant variety Gr89-1 and cold sensitive variety Shuhui527 (SH527) were used. A total of 198 polymorphic simple sequence repeat (SSR) markers that were distributed uniformly on 12 chromosomes were used to construct the linkage map. The gene ontology (GO) annotation of the major QTL was performed through the rice genome annotation project system. Three main-effect QTLs (qOSR2, qOSR3, and qOSR8) were detected and mapped on chromosomes 2, 3, and 8, respectively. These QTLs were located in the interval of RM14208 (35,160,202 base pairs (bp))–RM208 (35,520,147 bp), RM218 (8,375,236 bp)–RM232 (9,755,778 bp), and RM5891 (24,626,930 bp)–RM23608 (25,355,519 bp), and explained 19.6%, 9.3%, and 11.8% of the phenotypic variations, respectively. The qOSR2 QTL displayed the largest effect, with a logarithm of odds score (LOD) of 5.5. A total of 47 candidate genes on the qOSR2 locus were associated with 219 GO terms. Among these candidate genes, 11 were related to cell membrane, 7 were associated with cold stress, and 3 were involved in response to stress and biotic stimulus. OsPIP1;3 was the only one candidate gene related to stress, biotic stimulus, cold stress, and encoding a cell membrane protein. After QTL mapping, a total of three main-effect QTLs—qOSR2, qOSR3, and qOSR8—were detected on chromosomes 2, 3, and 8, respectively. Among these, qOSR2 explained the highest phenotypic variance. All the QTLs elite traits come from the cold resistance parent Gr89-1. OsPIP1;3 might be a candidate gene of qOSR2.