Conditional Quantitative Trait Loci Research Articles

Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Grain filling is an important growth process in formation of yield and quality for barley final yield determination. To explore the grain development behavior during grain filling period in barley, a high-density genetic map with 1962 markers deriving from a doubled haploid (DH) population of 122 lines was used to identify dynamic quantitative trait locus (QTL) for grain filling rate (GFR) and five grain size traits: grain area (GA), grain perimeter (GP), grain length (GL), grain width (GW) and grain diameter (GD). Unconditional QTL mapping is to detect the cumulative effect of genetic factors on a phenotype from development to a certain stage. Conditional QTL mapping is to detect a net effect of genetic factors on the phenotype at adjacent time intervals. Using unconditional, conditional and covariate QTL mapping methods, we successfully detected 34 major consensus QTLs. Moreover, certain candidate genes related to grain size, plant height, yield, and starch synthesis were identified in six QTL clusters, and individual gene was specifically expressed in different grain filling stages. These findings provide useful information for understanding the genetic basis of the grain filling dynamic process and will be useful for molecular marker-assisted selection in barley breeding.

Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population.

Plant height (PH) is an important trait in soybean, as taller plants may have higher yields but may also be at risk for lodging. Many genes act jointly to influence PH throughout development. To map the quantitative trait loci (QTL) controlling PH, we used the unconditional variable method (UVM) and conditional variable method (CVM) to analyze PH data for a four-way recombinant inbred line (FW-RIL) population derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19). We identified 7, 8, 16, 19, 15, 27, 17, 27, 22, and 24 QTL associated with PH at 10 developmental stages, respectively. These QTL mapped to 95 genomic regions. Among these QTL, 9 were detected using UVM and CVM, and 89 and 66 were only detected by UVM or CVM, respectively. In total, 36 QTL controlling PH were detected at multiple developmental stages and these made unequal contributions to genetic variation throughout development. Among 19 novel regions discovered in our study, 7 could explain over 10% of the phenotypic variation and contained only one single QTL. The unconditional and conditional QTL detected here could be used in molecular design breeding across the whole developmental procedure.

Linkage mapping and quantitative trait loci analysis of sweetness and other fruit quality traits in papaya

BackgroundThe identification and characterisation of quantitative trait loci (QTL) is an important step towards identifying functional sequences underpinning important crop traits and for developing accurate markers for selective breeding strategies. In this study, a genotyping-by-sequencing (GBS) approach detected QTL conditioning desirable fruit quality traits in papaya.ResultsFor this, a linkage map was constructed comprising 219 single nucleotide polymorphism (SNP) loci across 10 linkage groups and covering 509 centiMorgan (cM). In total, 21 QTLs were identified for seven key fruit quality traits, including flesh sweetness, fruit weight, fruit length, fruit width skin freckle, flesh thickness and fruit firmness. Several QTL for flesh sweetness, fruit weight, length, width and firmness were stable across harvest years and individually explained up to 19.8% of the phenotypic variance of a particular trait. Where possible, candidate genes were proposed and explored further for their application to marker-assisted breeding.ConclusionsThis study has extended knowledge on the inheritance and genetic control for key papaya physiological and fruit quality traits. Candidate genes together with associated SNP markers represent a valuable resource for the future of strategic selective breeding of elite Australian papaya cultivars.

Identification of a gene encoding a sugar transporter as a potential target of a novel LysR‐type transcriptional regulator rluD from rhizobium required for symbiotic nodulation in soybean (Glycine max Merr.)

AbstractRhizobia can fine‐tune activating key genes involved in background regulation network of nodulation in effective symbiosis processes. Detecting the mechanism of the soybean‐rhizobium interaction plays a pivotal role in aiding the agricultural environment. Here, we report that LysR family transcriptional factor rluD is a novel putative gene that supports symbiosis of broad host‐range strain Sinorhizobium fredii HH103 (S. fredii HH103) during soybean nodulation. The nodule numbers (NN) and nodule dry weight (NDW) of ‘Charleston’ and ‘Dongnong594’ (parents of recombinant inbred line [RIL] population) were reduced after inoculation with S. fredii HH103ΩrluD compared to HH103. To further elucidate the mechanism of rluD in the establishment of symbiosis, unconditional and conditional quantitative trait loci (QTLs) underlying NN and NDW were detected via a high‐generation RILs inoculated with HH103ΩrluD and wild‐type HH103. A major locus of the overlapping region between unconditional and conditional QTLs on Chromosome 6 was identified. The candidate gene Glyma.06g166800 encodes a novel sugar transporter, which might interact with rluD based on qRT‐PCR analysis. This result is valuable for elucidating the mechanism of symbiosis.

Identification of genomic regions and diagnostic markers for resistance to aflatoxin contamination in peanut (Arachis hypogaea L.)

BackgroundAflatoxin contamination caused by Aspergillus flavus is a major constraint to peanut industry worldwide due to its toxicological effects to human and animals. Developing peanut varieties with resistance to seed infection and/or aflatoxin accumulation is the most effective and economic strategy for reducing aflatoxin risk in food chain. Breeding for resistance to aflatoxin in peanut is a challenging task for breeders because the genetic basis is still poorly understood. To identify the quantitative trait loci (QTLs) for resistance to aflatoxin contamination in peanut, a recombinant inbred line (RIL) population was developed from crossing Zhonghua 10 (susceptible) with ICG 12625 (resistant). The percent seed infection index (PSII), the contents of aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2) of RILs were evaluated by a laboratory kernel inoculation assay.ResultsTwo QTLs were identified for PSII including one major QTL with 11.32–13.00% phenotypic variance explained (PVE). A total of 12 QTLs for aflatoxin accumulation were detected by unconditional analysis, and four of them (qAFB1A07 and qAFB1B06.1 for AFB1, qAFB2A07 and qAFB2B06 for AFB2) exhibited major and stable effects across multiple environments with 9.32–21.02% PVE. Furthermore, not only qAFB1A07 and qAFB2A07 were co-localized in the same genetic interval on LG A07, but qAFB1B06.1 was also co-localized with qAFB2B06 on LG B06. Conditional QTL mapping also confirmed that there was a strong interaction between resistance to AFB1 and AFB2 accumulation. Genotyping of RILs revealed that qAFB1A07 and qAFB1B06.1 interacted additively to improve the resistance to both AFB1 and AFB2 accumulation. Additionally, validation of the two markers was performed in diversified germplasm collection and four accessions with resistance to aflatoxin accumulation were identified.ConclusionsSingle major QTL for resistance to PSII and two important co-localized intervals associated with major QTLs for resistance to AFB1 and AFB2. Combination of these intervals could improve the resistance to aflatoxin accumulation in peanut. SSR markers linked to these intervals were identified and validated. The identified QTLs and associated markers exhibit potential to be applied in improvement of resistance to aflatoxin contamination.

A high-density genetic map and QTL mapping on growth and latex yield-related traits in Hevea brasiliensis Müll.Arg

Hevea brasiliensis Müll.Arg., the rubber tree, is an important industrial crop, which is the major source of nature rubber. Recently, Simple Sequence Repeat (SSR)- and Single Nucleotide Polymorphism (SNP)-based genetic maps have been reported, however further trait-related markers are needed for efficient molecular breeding in rubber tree. In this study, a total of 206 F1 progeny derived from a cross of CATAS 8–79 × MT/C/11 9/67 were sequenced and genotyped by specific–locus amplified fragment sequencing (SLAF-seq) technology. Based on 268,592 SNPs obtained from SLAF-seq, a high-density genetic map with 4,543 SNPs was constructed, covering 2,670.27 cM of the whole rubber tree genome. This map has an average marker distance of 0.59 cM.Based on the high-density genetic map, dynamic quantitative trait loci (QTL) analysis of latex yield and stem growth was carried out by conditional and unconditional QTL mapping. To our knowledge, it is the first time to perform conditional QTL mapping of rubber tree for exploring QTLs expressed selectively at different growth stages. In total, eleven QTLs for stem growth and 12 QTLs for latex yield, distributing in 15 linkage groups (LGs), were detected, explaining 3.1%–18.4% of the phenotypic variance. Two QTLs, qSG-8-2 for stem growth and qLD-8-3 for latex yield, were mapped within the same marker interval of LG 8 with a genetic distance of 3.0 cM. Herein the high-density genetic map and trait-related QTLs in rubber tree were reported, which will facilitate the genetic research of rubber tree and enhance rubber tree molecular breeding.

Dissection of Pleiotropic QTL Regions Controlling Wheat Spike Characteristics Under Different Nitrogen Treatments Using Traditional and Conditional QTL Mapping.

Optimal spike characteristics are critical in improving the sink capacity and yield potential of wheat even in harsh environments. However, the genetic basis of their response to nitrogen deficiency is still unclear. In this study, quantitative trait loci (QTL) for six spike-related traits, including heading date (HD), spike length (SL), spikelet number (SN), spike compactness (SC), fertile spikelet number (FSN), and sterile spikelet number (SSN), were detected under two different nitrogen (N) supplies, based on a high-density genetic linkage map constructed by PCR markers, DArTs, and Affymetrix Wheat 660 K SNP chips. A total of 157 traditional QTLand 54 conditional loci were detected by inclusive composite interval mapping, among which three completely low N-stress induced QTL for SN and FSN (qSn-1A.1, qFsn-1B, and qFsn-7D) were found to maintain the desired spikelet fertility and kernel numbers even under N deficiency through pyramiding elite alleles. Twenty-eight stable QTL showing significant differencet in QTL detection model were found and seven genomic regions (R2D, R4A, R4B, R5A, R7A, R7B, and R7D) clustered by these stable QTL were highlighted. Among them, the effect of R4B on controlling spike characteristics might be contributed from Rht-B1. R7A harboring three major stable QTL (qSn-7A.2, qSc-7A, and qFsn-7A.3) might be one of the valuable candidate regions for further genetic improvement. In addition, the R7A was found to show syntenic with R7B, indicating the possibly exsting homoeologous candidate genes in both regions. The SNP markers involved with the above highlighted regions will eventually facilitate positional cloning or marker-assisted selection for the optimal spike characteristics under various N input conditions.

Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide association study in the Japanese population

To assess the use of plasma free amino acids (PFAAs) as biomarkers for metabolic disorders, it is essential to identify genetic factors that influence PFAA concentrations. PFAA concentrations were absolutely quantified by liquid chromatography–mass spectrometry using plasma samples from 1338 Japanese individuals, and genome-wide quantitative trait locus (QTL) analysis was performed for the concentrations of 21 PFAAs. We next conducted a conditional QTL analysis using the concentration of each PFAA adjusted by the other 20 PFAAs as covariates to elucidate genetic determinants that influence PFAA concentrations. We identified eight genes that showed a significant association with PFAA concentrations, of which two, SLC7A2 and PKD1L2, were identified. SLC7A2 was associated with the plasma levels of arginine and ornithine, and PKD1L2 with the level of glycine. The significant associations of these two genes were revealed in the conditional QTL analysis, but a significant association between serine and the CPS1 gene disappeared when glycine was used as a covariate. We demonstrated that conditional QTL analysis is useful for determining the metabolic pathways predominantly used for PFAA metabolism. Our findings will help elucidate the physiological roles of genetic components that control the metabolism of amino acids.

Conditional and unconditional QTL analyses of seed hardness in vegetable soybean (Glycine max L. Merr.)

The seed hardness of vegetable soybean (VSB) is generally regarded as an important quality characteristic that contributes largely to its processing and mouthfeel. However, the genetic control of it is still a gray area. To identify the dynamic quantitative trait loci (QTLs) and detect the genetic relationship between seed hardness and growth period traits at the QTL level, conditional and unconditional QTL analyses were performed using a population of 184 recombinant inbred lines in 2015, 2016 and 2017. For seed hardness at stage 1 to stage 4 (W1–W4), 12 and 8 QTLs were identified by unconditional and conditional mapping, respectively. The comparative analyses between unconditional and conditional mapping uncovered three types of dynamic QTLs underlying seed hardness in the development of VSB. These results demonstrated that genes underlying seed hardness were actively expressed in the interval between stage 2 and stage 3, which was in parallel with the mass filling period in the development of soybean seeds. For seed hardness at R6 stage (W), 11 unconditional QTLs were detected. When W was conditioned on the number of days after sowing to R6 stage (DAS), the number of days after flowering to R6 stage (DAF) and flower time (FT), 11, 2, and 2 conditional QTLs were identified, respectively. Four types of unique QTLs distinguished the contributions of DAS, DAF and FT to the genetic effects of W. Of these, uqW.D1b-1 and uqW.A1-2 control W with independent of DAS or FT. Hence, they would be valuable for the selection softer seed lines, without the simultaneous depression of growth period traits even though there exist significant positive correlation between them. The loci identified by conditional and unconditional QTL analyses may be useful for the improvements of decreased seed hardness with the best balance of growth period through marker assisted selection (MAS).

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map.

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding.

Dynamic QTL analysis and validation for plant height using maternal and paternal backcrossing populations in Upland cotton

Plant height determines plant biomass yield, harvest index and economic yield. We analyzed quantitative trait loci (QTL) and gene action controlling plant height. We generated the maternal and paternal testcrossing (TC/M and TC/P) populations based on a recombinant inbred line population. Data for plant height at t1, t2, t3, t4 or t5 stages were collected over 2 years from 3 TC/M field trials and 2 TC/P field trials. At single-locus level, 32 QTLs at five stages and 24 conditional QTLs at four intervals were detected, and 14 QTLs shared in different years or populations or stages. Plant height displayed dynamic characteristics through expression of QTLs. A total of 21 novel QTLs were detected and 11 QTLs validated the previous results. And 19 QTLs explained over 10% of phenotypic variation, such as qPH-Chr9-2, qPH-Chr19-4 and qPH-Chr22-4. The region of NAU5330-NAU1269 on chromosome 19 may be a desired target for genetic improvement of plant height in Upland cotton. In addition, five and eight heterotic loci were identified in TC/M and TC/P populations, respectively. Additive, partial dominance and overdominance effects were observed in both TC populations. We also identified 43 epistatic QTLs and QTLs by environment interactions by inclusive composite interval mapping method. Taken together, additive, partial dominance and overdominance effects together with epistasis explained the genetic basis of plant height in Upland cotton.

Mining for genes encoding proteins associated with NopL of Sinorhizobium fredii HH103 using quantitative trait loci in soybean (Glycine max Merr.) recombinant inbred lines

In rhizobia, host specificity is controlled by the nodulation outer proteins (Nops). However, the host genes encoding proteins associated with Nops remain unknown. We constructed a Sinorhizobium fredii HH103ΩnopL mutant and analyzed the nodule number (NN) and nodule dry weight (NDW) of 10 germplasms inoculated with the mutant or wild-type strain. Using recombinant inbred lines (RILs), the conditional and unconditional quantitative trait loci (QTLs) associated with NopL were detected. The regions overlapping conditional and unconditional QTLs were identified. Additionally, the expression levels of selected candidate genes were analyzed by qRT-PCR. Depending on the germplasm, NopL positively or negatively affected NN and NDW. The NN and NDW of ‘Charleston’ and ‘Dongnong 594’ (RIL parents) were lower in samples inoculated with Sinorhizobium fredii HH103ΩnopL than in samples inoculated with the wild-type strain. In soybean inoculated independently with Sinorhizobium fredii HH103 and HH103ΩnopL, 11 and 5 unconditional QTLs were identified for NN and NDW, respectively, while five and two conditional QTLs were identified for NN and NDW, respectively. Two overlapping QTLs were identified among the conditional and unconditional QTLs. Furthermore, the enzymes encoded by Glyma.13g170500 and Glyma.07g099700 likely interacted with NopL. The Glyma.13g170500 and Glyma.07g099700 detected at QTLs represent candidate genes that encode enzymes that interact with NopL to regulate the establishment of symbiotic relationships between rhizobia and soybean.

Stable, Environmental Specific and Novel QTL Identification as Well as Genetic Dissection of Fatty Acid Metabolism in Brassica napus.

Fatty acid (FA) composition is the typical quantitative trait in oil seed crops, of which study is not only closely related to oil content, but is also more critical for the quality improvement of seed oil. The double haploid (DH) population named KN with a high density SNP linkage map was applied for quantitative trait loci (QTL) analysis of FA composition in this study. A total of 406 identified QTL were detected for eight FA components with an average confidence interval (CI) of 2.92 cM, the explained phenotypic variation (PV) value ranged from 1.49 to 45.05%. Totally, 204 consensus and 91 unique QTL were further obtained via meta-analysis method for the purpose of detecting multiple environment expressed and pleiotropic QTL, respectively. Of which, 74 stable expressed and 22 environmental specific QTL were also revealed, respectively. In order to make clear the genetic mechanism of FA metabolism at individual QTL level, conditional QTL analysis was also conducted and more than two thousand conditional QTL which could not be detected under the unconditional mapping were detected, which indicated the complex interrelationship of the QTL controlling FA content in rapeseed. Through comparative genomic analysis and homologous gene annotation, 61 candidates related to acyl lipid metabolism were identified underlying the CI of FA QTL. To further visualize the genetic mechanism of FA metabolism, an intuitive and meticulous network about acyl lipid metabolism was constructed and some closely related candidates were positioned. This study provided a more accurate localization for stable and pleiotropic QTL, and a deeper dissection of the molecular regulatory mechanism of FA metabolism in rapeseed.

Major Co-localized QTL for Plant Height, Branch Initiation Height, Stem Diameter, and Flowering Time in an Alien Introgression Derived Brassica napus DH Population.

Plant height (PH), branch initiation height (BIH), and stem diameter (SD) are three stem-related traits that play crucial roles in plant architecture and lodging resistance. Herein, we show one doubled haploid (DH) population obtained from a cross between Y689 (one Capsella bursa-pastoris derived Brassica napus intertribal introgression) and Westar (B. napus cultivar) that these traits were significantly positively correlated with one another and with flowering time (FT). Based on a high-density SNP map, a total of 102 additive quantitative trait loci (QTL) were identified across six environments. Seventy-two consensus QTL and 49 unique QTL were identified using a two-round strategy of QTL meta-analysis. Notably, a total of 19 major QTL, including 11 novel ones, were detected for these traits, which comprised two QTL clusters on chromosomes A02 and A07. Conditional QTL mapping was performed to preliminarily evaluate the genetic basis (pleiotropy or tight linkage) of the co-localized QTL. In addition, QTL by environment interactions (QEI) mapping was performed to verify the additive QTL and estimate the QEI effect. In the genomic regions of all major QTL, orthologs of the genes involved in phytohormone biosynthesis, phytohormone signaling, flower development, and cell differentiation in Arabidopsis were proposed as candidate genes. Of these, BnaA02g02560, an ortholog of Arabidopsis GASA4, was suggested as a candidate gene for PH, SD, and FT; and BnaA02g08490, an ortholog of Arabidopsis GNL, was associated with PH, BIH and FT. These results provide useful information for further genetic studies on stem-related traits and plant growth adaptation.

Genetic Dissection of Wheat Kernel Hardness Using Conditional QTL Mapping of Kernel Size and Protein-Related Traits

Kernel hardness (KH) is one of the primary quality parameters for common wheat (Triticum aestivum L.) and has a major impact on milling, flour quality, and end-product properties. In addition to Puroindoline (Pin) mutations and differences in Pin expression, other factors, such as kernel size and protein-related traits, play noticeable roles in determining hardness, but at the quantitative trait locus (QTL) level, the influence of these factors remains unclear. In this study, genetic relationships between KH and kernel size traits and between KH and protein-related traits were demonstrated by unconditional and conditional mapping using a wheat 90K genotyping assay with a segregating population of 173 recombinant inbred lines in four environments. Eight additive QTL for KH were detected using unconditional QTL mapping analysis; these QTL were primarily distributed on chromosomes 4B, 5A, 5B, and 6D, with phenotypic variation that ranged from 0.2 to 17.7%. In addition, one pair of epistatic QTL (QKH3B.4-65/QKH4B.6-2) was identified by unconditional mapping, and this pair accounted for 1.6% of the phenotypic variation. Through conditional mapping, after excluding the influences of kernel size and protein-related traits, 14 QTL were discovered and accounted for 0.6–18.5% of the phenotypic variation. Of them, the stable QTL QKH4B.4-17 made the largest contribution, which was partially contributed by the kernel length (KL), kernel thickness (KT), and dry gluten content (DGC). Furthermore, QKH4B.4-17 was crucially contributed by the kernel width (KW), kernel diameter (KD), kernel protein content (KPC), and wet gluten content (WGC) and was independent of the sedimentation volume (SV) and gluten index (GI). Another major QTL, QKH5B.10-63, was independent of the KW and KT; partly due to the variations in KL, KD, DGC, and WGC; and conclusively contributed by the KPC, SV, and GI. Seven additional QTL were only detected in the conditional analysis and were crucially contributed by kernel size or protein-related traits. These results demonstrated that kernel size and protein-related traits play significant roles in determining KH. The present study increases the understanding of the relationships between KH and kernel size and between KH and protein-related traits at the QTL level.

Quantitative trait loci for the diurnal flag leaf starch content during the early grain-filling stage in wheat (Triticum aestivum L.)

Starch is a product of photosynthetic activities in leaves. Wheat yields largely depend on photosynthetic carbon fixation and carbohydrate metabolism in flag leaves. The mapping of quantitative trait loci (QTLs) associated with flag leaf starch content (FLSC) in wheat (Triticum aestivum L.) was completed using unconditional and conditional QTL analyses. The FLSC of this population during the early grain-filling stage was measured at six stages in six environments. Combining unconditional and conditional QTL mapping methods, eight unconditional QTLs and nine conditional QTLs were detected, with five QTLs identified as unconditional and conditional QTLs. Four unconditional QTLs (i.e. qFLS-1B, qFLS-1D-1, qFLS-4A, and qFLS-7D-1) and one conditional QTL (i.e. qFLS-3A-1) were identified in two of six environments. Two QTLs (qFLS-1D-2 and qFLS-7D-1), which significantly affected the FLSC, were identified using the unconditional QTL mapping method, while three QTLs (i.e. qFLS-1A, qFLS-3A-1, and qFLS-7D-1) were detected using the conditional QTL mapping method. Our findings provide new insights into the genetic mechanism and regulatory network underlying the diurnal FLSC in wheat.

Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

AbstractIn wheat, strong genetic correlations have been found between grain yield (GY) and tiller number per plant (TN), fertile spikelet number per spike (FSN), kernel number per spike (KN) and thousand‐kernel weight (TKW). To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, we performed both normal and multivariate conditional QTL analysis based on two recombinant inbred lines (RILs) populations. A total of 79 and 48 normal QTLs were identified in the International Triticeae Mapping Initiative (ITMI)/SHW‐L1 × Chuanmai 32 (SC) populations, respectively, as well as 55 and 35 conditional QTLs. Thirty‐two QTL clusters in the ITMI population and 18 QTL clusters in the SC population explained 0.9%–46.2% of phenotypic variance for two to eight traits. A comparison between the normal and conditional QTL mapping analyses indicated that FSN made the smallest contribution to GY among the four GY components that were considered at the QTL level. The effects of TN, KN and TKW on GY were stronger at the QTL level.

Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum.

37 unconditional QTLs, 51 conditional QTLs and considerable epistatic QTLs were detected for waterlogging tolerance, and six favourable combinations were selected accelerating the possible application of MAS in chrysanthemum breeding. Chrysanthemum is seriously impacted by soil waterlogging. To determine the genetic characteristics of waterlogging tolerance (WAT) in chrysanthemum, a population of 162 F1 lines was used to construct a genetic map to identify the dynamic and epistatic quantitative trait loci (QTLs) for four WAT traits: wilting index (WI), dead leaf ratio (DLR), chlorosis score (Score) and membership function value of waterlogging (MFVW). The h B2 for the WAT traits ranged from 0.49 to 0.64, and transgressive segregation was observed in both directions. A total of 37 unconditional consensus QTLs with 5.81-18.21% phenotypic variation explanation (PVE) and 51 conditional consensus QTLs with 5.90-24.56% PVE were detected. Interestingly, three unconditional consensus QTLs were consistently identified across different stages, whereas no conditional consensus QTLs were consistently expressed. In addition, considerable epistatic QTLs, all with PVE values ranging from 0.01 to 8.87%, were detected by a joint analysis of WAT phenotypes. These results illustrated that the QTLs (genes) controlling WAT were environmentally dependent and selectively expressed at different times and indicated that both additive and epistatic effects underlie the inheritance of WAT in chrysanthemum. The findings of the current study provide insights into the complex genetic architecture of WAT, and the identification of favourable alleles represents an important step towards the application of molecular marker-assisted selection (MAS) and QTL pyramiding in chrysanthemum WAT breeding programmes.

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (Arachis hypogaea L.).

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments. Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in>100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13×Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93-43.63% of the phenotypic variations of the three traits were co-localized in a 5cM interval (1.48Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3cM genetic interval (280kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

Conditional and unconditional QTLs mapping of gluten strength in common wheat (Triticum aestivum L.)

AbstractDissecting the genetic relationships among gluten-related traits is important for high quality wheat breeding. Quantitative trait loci (QTLs) analysis for gluten strength, as measured by sedimentation volume (SV) and gluten index (GI), was performed using the QTLNetwork 2.0 software. Recombinant inbred lines (RILs) derived from the winter wheat varieties Shannong 01-35×Gaocheng 9411 were used for the study. A total of seven additive QTLs for gluten strength were identified using an unconditional analysis. QGi1D-13 and QSv1D-14 were detected through unconditional and conditional QTLs mapping, which explained 9.15–45.08% of the phenotypic variation. QTLs only identified under conditional QTL mapping were located in three marker intervals: WPT-3743–GLU-D1 (1D), WPT-7001–WMC258 (1B), and WPT-8682–WPT-5562 (1B). Six pairs of epistatic QTLs distributed nine chromosomes were identified. Of these, two main effect QTLs (QGi1D-13 and QSv1D-14) and 12 pairs of epistatic QTLs were involved in interactions with the environment. The results indicated that chromosomes 1B and 1D are important for the improvement of gluten strength in common wheat. The combination of conditional and unconditional QTLs mapping could be useful for a better understanding of the interdependence of different traits at the QTL molecular level.