Identification of phenological QTLs using a combination of High and Low-Coverage Whole Genome Sequencing in Japanese plum ( Prunus salicina Lindl.)

QTLs for seven different quality traits were mapped. Six QTLs were considered stable and major QTLs, and the genetic effects of the QTLs were validated. Wheat grain quality traits are the key factors for economic value and are largely influenced by genetics and the environment. In this study, a genetic linkage map consisting of 8329 markers spanning 4131.54cM was constructed using the Wheat55K SNP Array by genotyping a recombinant inbred line population of 304 lines. The quantitative trait loci (QTLs) for the swelling index of glutenin, SDS sedimentation volume (SDSS), wet gluten content, grain protein content, gluten index, grain starch content, and falling number were mapped for multiple years of experiments using the ICIM-BIP, ICIM-MET, and ICIM-EPI methods, respectively. A total of 92 QTLs, 194 cQTLs, and 117 pairs of eQTLs were mapped. Six QTLs, which were QGPC.sau-4A.1, QWGC.sau-4A, QSDSS.sau-1A.1, QGI.sau-1A, QFN.sau-4D, and QSIG.sau-1A, were considered major and stable QTLs. BLAST results showed that except QFN.sau-4D, the other 5 QTLs were new. Eight QTL clusters that contained 19 QTLs were also detected, and all the major and stable QTLs were located in these QTL clusters. Kompetitive allele-specific PCR markers closely linked to the six QTLs were designed. The genetic effects of the major and stable QTLs were successfully confirmed in different populations. These results provide new resources for breeding of high-quality wheat in the future.

BackgroundThe identification of quantitative trait loci (QTLs) that are stable and consistent across multiple environments and populations plays an essential role in marker-assisted selection (MAS). In the present study, we used 28,861 simple sequence repeat (SSR) markers, which included 12,560 Gossypium raimondii (D genome) sequence-based SSR markers to identify polymorphism between two upland cotton strains 0–153 and sGK9708. A total of 851 polymorphic primers were finally selected and used to genotype 196 recombinant inbred lines (RIL) derived from a cross between 0 and 153 and sGK9708 and used to construct a linkage map. The RIL population was evaluated for fiber quality traits in six locations in China for five years. Stable QTLs identified in this intraspecific cross could be used in future cotton breeding program and with fewer obstacles.ResultsThe map covered a distance of 4,110 cM, which represents about 93.2 % of the upland cotton genome, and with an average distance of 5.2 cM between adjacent markers. We identified 165 QTLs for fiber quality traits, of which 47 QTLs were determined to be stable across multiple environments. Most of these QTLs aggregated into clusters with two or more traits. A total of 30 QTL clusters were identified which consisted of 103 QTLs. Sixteen clusters in the At sub-genome comprised 44 QTLs, whereas 14 clusters in the Dt sub-genome that included 59 QTLs for fiber quality were identified. Four chromosomes, including chromosome 4 (c4), c7, c14, and c25 were rich in clusters harboring 5, 4, 5, and 6 clusters respectively. A meta-analysis was performed using Biomercator V4.2 to integrate QTLs from 11 environmental datasets on the RIL populations of the above mentioned parents and previous QTL reports. Among the 165 identified QTLs, 90 were identified as common QTLs, whereas the remaining 75 QTLs were determined to be novel QTLs. The broad sense heritability estimates of fiber quality traits were high for fiber length (0.93), fiber strength (0.92), fiber micronaire (0.85), and fiber uniformity (0.80), but low for fiber elongation (0.27). Meta-clusters on c4, c7, c14 and c25 were identified as stable QTL clusters and were considered more valuable in MAS for the improvement of fiber quality of upland cotton.ConclusionMultiple environmental evaluations of an intraspecific RIL population were conducted to identify stable QTLs. Meta-QTL analyses identified a common chromosomal region that plays an important role in fiber development. Therefore, QTLs identified in the present study are an ideal candidate for MAS in cotton breeding programs to improve fiber quality.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2560-2) contains supplementary material, which is available to authorized users.

Use of CropSyst as a tool to predict water use and crop coefficient in Japanese plum trees

Nitrogen and carbohydrate (soluble sugars and starch) reserves were analysed in twig samples of ‘Red Beaut’ and ‘Showtime’ Japanese plum (Prunus salicina Lindl.) cultivars cultivated in two similar experimental orchards under organic and conventional management. ‘Red Beaut’ is a vigorous cultivar while ‘Showtime’ is a middle to low vigorous cultivar. Both cultivars exhibited delayed flowering and premature defoliation under organic management. In general, there were no differences in the concentration of reserves between the two types of management for either of the two plum cultivars, and, likewise, no differences were observed between cultivars, regardless of their vigor. Additionally, reserves were also analysed in plantlets of ‘Mariana 2624’ rootstock (Prunus cerasifera Ehrh × Prunus munsoniana W. Wight & Hedrick) grown in a growth chamber that simulated conditions of spring and autumn periods and subjected to organic or mineral fertilisation. Plants subjected to organic fertilisation were smaller and defoliated earlier, but the sizes of carbohydrate and nitrogen reserves were similar to that of plants grown with mineral fertilisation.

Cotton productivity under water-stressed conditions is controlled by multiple quantitative trait loci (QTL). Enhancement of these productivity traits under water deficit stress is crucial for the genetic improvement of upland cotton, Gossypium hirsutum. In the present study, we constructed a genetic map with 504 single nucleotide polymorphisms (SNPs) covering a total span length of 4,416 cM with an average inter-marker distance of 8.76 cM. A total of 181 intra-specific recombinant inbred lines (RILs) were derived from a cross between G. hirsutum var. MCU5 and TCH1218 were used. Although 2,457 polymorphic SNPs were detected between the parents using the CottonSNP50K assay, only 504 SNPs were found to be useful for the construction of the genetic map. In the SNP genotyping, a large number of SNPs showed either >20% missing data, duplication, or segregation distortion. However, the mapped SNPs of this study showed collinearity with the physical map of the reference genome (G. hirsutum var.TM-1), indicating that there was no chromosomal rearrangement within the studied mapping population. RILs were evaluated under multi-environments and seasons for which the phenotypic data were acquired. A total of 53 QTL controlling plant height (PH), number of sympodial branches, boll number (BN), and boll weight (BW) were dissected by QTL analysis under irrigated and water stress conditions. Additionally, it was found that nine QTL hot spots not only co-localized for more than one investigated trait but were also stable with major QTL, i.e., with > 10% of phenotypic variation. One QTL hotspot on chromosome 22 flanked by AX-182254626–AX-182264770 with a span length of 89.4 cM co-localized with seven major and stable QTL linked to a number of sympodial branches both under irrigated and water stress conditions. In addition, putative candidate genes associated with water stress in the QTL hotspots were identified. Besides, few QTL from the hotspots were previously reported across various genetic architects in cotton validating the potential applications of these identified QTL for cotton breeding and improvement. Thus, the major and stable QTL identified in the present study would improve the cotton productivity under water-limited environments through marker-assisted selection.

PPV was first detected in Spain in 1984 in Japanese plum (Prunus salicinaLindl) cv. Red Beaut and spread very quickly to other Japanese and European plums and apricot cultivars but left peach cultivars unaffected. In the years following the detection of PPV, the predominant aphid species visitingPrunusorchards in Mediterranean areas wereAphis gossypiifollowed byAphis spiraecola, the latter being the main aphid species found at present. Both species are considered to be the main vectors of PPV in Spanish earlyPrunusgrowing areas. Spatial analysis of the spread of PPV‐D in Japanese plum and apricot trees confirmed the lack of significant association between immediately adjacent trees. The observed spatial pattern of sharka suggests a lack of movement of PPV‐viruliferous aphid vectors to immediately adjacent trees and indicates their preferential movement to trees several tree spaces away. PPV‐D is the only type currently present in Spain, with the exception of a PPV‐M outbreak that was detected in and successfully eradicated from Aragón in 2002. The short‐distance spread of PPV‐M infection occurred as far as 12 m along the rows of peach trees. However, PPV‐D has not been observed to spread through peach cultivars, despite being grown in the vicinity of heavily infected plots of apricot or Japanese plum trees.

BackgroundWheat (Triticum aestivum L.) is susceptible to abiotic factors during its growth period, such as salt and drought. The germination stage is the initial growth stage of wheat growth, when wheat is particularly sensitive to salt and drought stress.ResultsIn this study, the genetic basis of salt and drought tolerance in wheat was explored using a recombinant inbred lines (RILs) population consisting of 188 lines derived from a cross between the cultivars Kenong9204 (KN9204) and Jing411 (J411). The KJ-RIL lines and their parents were evaluated under 100 mM NaCl salt stress and 10% polyethylene glycol 6000 (PEG-6000) induced drought stress. A principal component analysis (PCA) showed that the cumulative contribution rate of the four principal components was 87.16% and 85.62% under salt and drought stress, respectively. A total of 16 salt-tolerant and five drought-tolerant lines were selected using the membership function and 52 putative additive quantitative trait loci (QTLs) and 251 epistatic QTLs (eQTLs) for eight traits were detected in the KJ-RILs. Among the 251 eQTLs, 19, 32, 67, 21, 27, 29, 32, and 24 pairwise eQTLs were observed for sheath length, seedling length, maximum root length, root number, seedling fresh weight, root fresh weight, seedling dry weight, and root dry weight, respectively. The 52 putative additive QTLs were found in all of the 21 wheat chromosomes except 1A, 2D, and 4A. The QTLs accounted for 3.92–13.26% of the phenotypic variation with logarithm of the odds values ranging from 2.52–6.91. A total of seven stable QTLs were detected: qShl-1D, qShl-3B, qSl-4D, qMrl-1D, qRn-2A.3, qRn-4B, and qRdw-7D, and three major QTLs were detected: qShl-4B, qRn-3B, and qRfw-7D. Among them, qShl-3B was a stable major QTL that explained 10.96%–12.02% of the variation rate and the most favorable allele came from KN9204. In addition, an InDel marker closely linked to qShl-3B was developed and validated in 188 RILs.ConclusionDifferent putative additive QTLs and epistatic QTLs (eQTLs) located on most of the 21 wheat chromosomes were detected for the studied traits. Of which, a total of seven stable QTLs and three major QTLs were detected. In addition, an InDel marker closely linked to the stable major QTL, qShl-3B was developed and validated in 188 RILs, providing important information for the genetic improvement and development of salt and drought tolerance wheat varieties.

Blood pressure (BP) in any human population exhibits as a continuous variable that fits a bell-shaped curve. Hypertensive individuals are those whose BP is maintained at one extreme of the curve and above a defined cutoff. Despite progress made in identifying the mechanisms underlying certain rare monogenic forms of hypertension,1,2 the etiology and pathogenesis of essential hypertension remain poorly understood. Because existing human populations are genetically heterogeneous, and because environmental factors impacting on the pathogenesis of hypertension cannot be controlled in a given population, it is difficult to identify the molecular mechanisms that transduce the sequela of essential hypertension via direct human studies.3 To alleviate the drawbacks of human investigations, animal models, especially inbred rodents, have been developed and experimentally manipulated to identify quantitative trait loci (QTLs) for BP, because major confounding environmental factors, such as diet and genetic background, can be systematically controlled. Once identified in animal models, the molecular basis may be translated into physiological understandings of essential hypertension in humans. It is with this expectation that efforts have been launched to identify the molecular basis of BP QTLs in animal models. Because the identification of individual QTLs is primarily based on their chromosome locations unbiased by, or unrestricted to, their physiological roles, positional cloning is believed to be the most efficient strategy. Before we embark on discussions regarding QTL discovery, a definition is in order. Semantic arguments abound as to exactly what a QTL, that is, a locus,4 entails. Is it 1 gene or a collection of genes? As genetic mapping progresses from a large chromosome segment to an interval of submegabase, several regions initially thought to contain 1 BP QTL5 appear to harbor >1 in each of them,6–10 whereas several other regions turned out to harbor 1 QTL as expected.11,12 …

BackgroundImproving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits.ResultsMolecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development.ConclusionWe constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.

Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.

BackgroundKernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes.ResultsThe analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61–0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%—14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites.ConclusionsMajor genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

<p>Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.</p>

<p>Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.</p>

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 13 major effect QTLs that contributed more than 10% toward phenotypic variation (PV) and 9 stable QTLs that could be repeatedly mapped in different generations. Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosomes D1 and D11. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18–21.45% of PV and qFS-A11-1 in F2:3, F2:4, and F2:5 with 6.05–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, while Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Seed size and shape are important traits determining yield and quality in soybean. Seed size and shape are also desirable for specialty soy foods like tofu, natto, miso, and edamame. In order to find stable quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight, the current study used vegetable type and seed soybean-derived F2 and F2:3 mapping populations. A total of 42 QTLs were mapped, which were dispersed across 13 chromosomes. Of these, seven were determined to be stable QTLs and five of them were major QTLs, namely qSL-10-1, qSW-4-1, qSV-4-1, qSLW-10-1, and qSLH-10-1. Thirteen of the 42 QTLs detected in the current study were found at known loci, while the remaining 29 were discovered for the first time. Out of these 29 novel QTLs, 17 were major QTLs. Based on Protein Analysis Through Evolutionary Relationships (PANTHER), gene annotation information, and literature search, 66 genes within seven stable QTLs were predicted to be possible candidate genes that might regulate seed shape and seed weight in soybean. The current study identified the key candidate genes and quantitative trait loci (QTLs) controlling soybean seed shape and weight, and these results will be very helpful in marker-assisted breeding for developing soybean varieties with improved seed weight and desired seed shape.