Quantitative trait loci associated with soybean seed weight and composition under different phosphorus levels

Seed size and composition are important traits in food crops and can be affected by nutrient availability in the soil. Phosphorus (P) is a non‐renewable, essential macronutrient, and P deficiency limits soybean (Glycine max) yield and quality. To investigate the associations of seed traits in low‐ and high‐P environments, soybean recombinant inbred lines (RILs) from a cross of cultivars Fiskeby III and Mandarin (Ottawa) were grown under contrasting P availability environments. Traits including individual seed weight, seed number, and intact mature pod weight were significantly affected by soil P levels and showed transgressive segregation among the RILs. Surprisingly, P treatments did not affect seed composition or weight, suggesting that soybean maintains sufficient P in seeds even in low‐P soil. Quantitative trait loci (QTLs) were detected for seed weight, intact pods, seed volume, and seed protein, with five significant QTLs identified in low‐P environments and one significant QTL found in the optimal‐P environment. Broad‐sense heritability estimates were 0.78 (individual seed weight), 0.90 (seed protein), 0.34 (seed oil), and 0.98 (seed number). The QTLs identified under low P point to genetic regions that may be useful to improve soybean performance under limiting P conditions.     

Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus

Background and Aims

One of the key targets of breeding programmes in rapeseed (Brassica napus) is to develop high-yield varieties. However, the lack of available phosphorus (P) in soils seriously limits rapeseed production. The aim of this study was to dissect the genetic control of seed yield and yield-related traits in B. napus grown with contrasting P supplies.

Methods

Two-year field trials were conducted at one site with normal and low P treatments using a population of 124 recombinant inbred lines derived from a cross between ‘B104-2’ and ‘Eyou Changjia’. Seed yield, seed weight, seed number, pod number, plant height, branch number and P efficiency coefficient (PEC) were investigated. Quantitative trait locus (QTL) analysis was performed by composite interval mapping.

Key Results

The phenotypic values of most of the tested traits were reduced under the low P conditions. In total, 74 putative QTLs were identified, contributing 7·3–25·4 % of the phenotypic variation. Of these QTLs, 16 (21·6 %) were detected in two seasons and in the mean value of two seasons, and eight QTLs for two traits were conserved across P levels. Low-P-specific QTLs were clustered on chromosomes A1, A6 and A8. By comparative mapping between Arabidopsis and B. napus, 161 orthologues of 146 genes involved in Arabidopsis P homeostasis and/or yield-related trait control were associated with 45 QTLs corresponding to 23 chromosomal regions. Four gene-based markers developed from genes involved in Arabidopsis P homeostasis were mapped to QTL intervals.

Conclusions

Different genetic determinants were involved in controlling seed yield and yield-related traits in B. napus under normal and low P conditions. The QTLs detected under reduced P supply may provide useful information for improving the seed yield of B. napus in soils with low P availability in marker-assisted selection.     

Quantitative trait locus analysis of multiple agronomic traits in the model legume Lotus japonicus.

The first quantitative trait locus (QTL) analysis of multiple agronomic traits in the model legume Lotus japonicus was performed with a population of recombinant inbred lines derived from Miyakojima MG-20 x Gifu B-129. Thirteen agronomic traits were evaluated in 2004 and 2005: traits of vegetative parts (plant height, stem thickness, leaf length, leaf width, plant regrowth, plant shape, and stem color), flowering traits (flowering time and degree), and pod and seed traits (pod length, pod width, seeds per pod, and seed mass). A total of 40 QTLs were detected that explained 5%-69% of total variation. The QTL that explained the most variation was that for stem color, which was detected in the same region of chromosome 2 in both years. Some QTLs were colocated, especially those for pod and seed traits. Seed mass QTLs were located at 5 locations that mapped to the corresponding genomic positions of equivalent QTLs in soybean, pea, chickpea, and mung bean. This study provides fundamental information for breeding of agronomically important legume crops.     

QTL analysis of yield traits in an advanced backcross population derived from a cultivated Andean × wild common bean (Phaseolus vulgaris L.) cross

Advanced backcross QTL analysis was used to identify quantitative trait loci (QTL) for agronomic performance in a population of BC₂F_3:5 introgression lines created from the cross of a Colombian large red-seeded commercial cultivar, ICA Cerinza, and a wild common bean accession, G24404. A total of 157 lines were evaluated for phenological traits, plant architecture, seed weight, yield and yield components in replicated trials in three environments in Colombia and genotyped with microsatellite, SCAR, and phaseolin markers that were used to create a genetic map that covered all 11 linkage groups of the common bean genome with markers spaced at an average distance of every 10.4 cM. Segregation distortion was most significant in regions orthologous for a seed coat color locus (R-C) on linkage group b08 and two domestication syndrome genes, one on linkage group b01 at the determinacy (fin) locus and the other on linkage group b02 at the seed-shattering (st) locus. Composite interval mapping analysis identified a total of 41 significant QTL for the eight traits measured of which five for seed weight, two for days to flowering, and one for yield were consistent across two or more environments. QTL were located on every linkage group with b06 showing the greatest number of independent loci. A total of 13 QTL for plant height, yield and yield components along with a single QTL for seed size showed positive alleles from the wild parent while the remaining QTL showed positive alleles from the cultivated parent. Some QTL co-localized with regions that had previously been described to be important for these traits. Compensation was observed between greater pod and seed production and smaller seed size and may have resulted from QTL for these traits being linked or pleiotropic. Although wild beans have been used before to transfer biotic stress resistance traits, this study is the first to attempt to simultaneously obtain a higher yield potential from wild beans and to analyze this trait with single-copy markers. The wild accession was notable for being from a unique center of diversity and for contributing positive alleles for yield and other traits to the introgression lines showing the potential that advanced backcrossing has in common bean improvement.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

QTL mapping for seedling traits in wheat grown under varying concentrations of N,P and K nutrients

Nutrient use efficiency (NuUE), comprising nutrient uptake and utilization efficiency, is regarded as one of the most important factors for wheat yield. In the present study, six morphological, nine nutrient content and nine nutrient utilization efficiency traits were investigated at the seedling stage using a set of recombinant inbred lines (RILs), under hydroponic culture of 12 treatments including single nutrient levels and two- and three-nutrient combinations treatments of N, P and K. For the 12 designed treatments, a total of 380 quantitative trait loci (QTLs) on 20 chromosomes for the 24 traits were detected. Of these, 87, 149 and 144 QTLs for morphological, nutrient content and nutrient utilization efficiency traits were found, respectively. Using the data of the average value (AV) across 12 treatments, 70 QTLs were detected for 23 traits. Most QTLs were located in new marker regions. Twenty-six important QTL clusters were mapped on 13 chromosomes, 1A, 1B, 1D, 2B, 3A, 3B, 4A, 4B, 5D, 6A, 6B, 7A and 7B. Of these, ten clusters involved 147 QTLs (38.7%) for investigated traits, indicating that these 10 loci were more important for the NuUE of N, P and K. We found evidence for cooperative uptake and utilization (CUU) of N, P and K in the early growth period at both the phenotype and QTL level. The correlation coefficients (r) between nutrient content and nutrient utilization efficiency traits for N, P and K were almost all significantly positive correlations. A total of 32 cooperative CUU loci (L1–L32) were found, which included 190 out of the 293 QTLs (64.8%) for the nutrient uptake and utilization efficiency traits, indicating that the CUU-QTLs were common for N, P and K. The CUU-QTLs in L3, L7, L16 and L28 were relatively stable. The CUU-QTLs may explain the CUU phenotype at the QTL level.     

Identification of QTL for increased fibrous roots in soybean

Drought stress adversely affects soybean at various developmental stages, which collectively results in yield reduction. Unpredictable rainfall has been reported to contribute about 36% to variation of yield difference between the rain-fed and irrigated fields. Among the drought resistance mechanisms, drought avoidance in genotypes with fibrous roots was recognized to be associated with drought resistance in soybean. Plant introduction PI416937 was shown to possess fibrous roots and has been used as a parent in breeding programs to improve soybean productivity. Little information is available on relative contribution and chromosomal location of quantitative trait loci (QTL) conditioning fibrous roots in soybean. To identify the genomic locations and genetic bases of this trait, a recombinant inbred line population was derived from a cross between PI416937 and ‘Benning’. To detect associated QTLs, phenotypic data were collected and analyzed for 2 years under rain-fed field conditions. The selective genotyping approach was used to reduce the costs and work associated with conducting the QTL analysis. A total of five QTLs were identified on chromosomes Gm01 (Satt383), Gm03 (Satt339), Gm04 (Sct_191), Gm08 (Satt429), and Gm20 (Sat_299), and together explained 51% of the variation in root score. Detected QTLs were co-localized with QTLs related to root morphology, suggesting that fibrous roots QTL may be associated with other morpho-physiological traits and seed yield in soybean. Genetic dissection of the fibrous roots trait at the individual marker loci will allow for marker-assisted selection to develop soybean genotypes with enhanced levels of fibrous roots.     

两种供氮水平下水稻生长后期相关性状的QTL定位

以特青为母本与Lemont杂交,然后用特青为轮回亲本回交,建立特青背景下的染色体片段置换系（CSSL）群体。在正常和低氮条件下分别在生长后期对株高（PH）、单株穗数（PN）、叶绿素含量（CC）、地上部干物重（SDW）和单株籽粒产量（YD）等性状进行了QTL分析,共检测到31个QTL。其中在正常供氮水平下控制PH、PN、CC、SDW和YD的QTL数目均为3个;在低氮水平下检测到5、4、5和2个影响PH、PN、CC和SDW的QTL,在低氮水平下没有检测到控制YD的位点。大部分QTL集中在第2、3、7、11和12染色体上,影响不同性状或在两种供氮水平下影响同一性状的QTL在染色体上成串或成簇分布。其中RM30-RM439、RM18-RM478、RM309-RM270、RM235-RM17等区域同时检测到控制两个以上性状的QTL,表现出明显的一因多效现象。推测仅在低氮水平下检测到的QTL可能跟水稻对低氮胁迫耐性有一定的关联。     

Identification of Related QTLs at Late Developmental Stage in Rice (Oryza sativa L.) Under Two Nitrogen Levels

QTL underlying related traits at the late developmental stage under two different nitrogen levels were investigated in rice using a population of chromosome segment substitution lines (CSSL) derived from a cross between Teqing and Lemont. A total of 31 QTLs referring 5 traits, that is, plant height (PH), panicle number per plant (PN), chlorophyll content (CC), shoot dry weight (SDW) and grain yield per plant (YD), were detected. Under normal N level, 3 QTLs were detected for each trait, while under low N level, 5,4, 5 and 2 QTLs were detected for PH, PN, CC and SDW respectively. Most of the QTLs were located on chromosome 2, 3, 7, 11 and 12. QTLs controlling different traits or the same trait under different N levels were mapped on the same or adjacent intervals, forming several clusters in rice chromosomes. More than two traits were controlled by QTLs on one of four intervals (RM30-RM439, RM18-RM478, RM309-RM270, and RM235-RM17), suggesting that there were some pleiotropic effects. It was supposed that some QTLs only detected at low N level might be associated with the ability to tolerate the low N stress in rice.     

Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize

The first objective of this study was to map and characterize quantitative trait loci (QTL) for grain yield (GY) and for secondary traits under varying nitrogen (N) supply. To achieve this objective, a segregating F_2:3 population previously developed for QTL mapping under water-limited conditions was used. The population was evaluated in Mexico under low N conditions in the dry winter season and under low and high N conditions in the wet summer season. From eight QTLs identified for GY under low N conditions, two were also detected under high N conditions. Five QTLs were stable across the two low N environments and five co-localized with QTLs identified for the anthesis-silking interval (ASI) or for the number of ears per plant (ENO) under low N conditions. The percentage of the phenotypic variance expressed by all QTLs for ASI and ENO was quite different when evaluated under low N conditions during the dry winter (40% for ASI and 22% for ENO) and the wet summer seasons (22% for ASI and 46% for ENO). The results suggest optimizing different breeding strategies based on selection index depending on the growing season. Good QTL colocalization was observed for ASI (four QTLs) and ENO (three QTLs) when looking at QTL identified under low N and water-limited conditions in the same population. The results suggest that that both secondary traits can be used in breeding programs for simultaneous improvement of maize against low N and drought stresses.     

Interval mapping of QTLs controlling yield-related traits and seed protein content in Pisum sativum

A linkage map of garden pea was constructed on the basis of 114 plants (F2 generation) derived from a cross combination Wt10245 x Wt11238. The map, consisting of 204 morphological, isozyme, AFLP, ISSR, STS, CAPS and RAPD markers, was used for interval mapping of quantitative trait loci (QTLs) controlling seed number, pod number, 1000-seed weight, 1000-yield, and seed protein content. Characterization of each QTL included identification of QTL position with reference to the flanking markers, estimation of the part of variance explained by this QTL, and determination of its gene action. The yield-related traits were measured in F2 plants and in F4 recombinant inbred lines (RILs). The interval mapping revealed two to six QTLs per trait, demonstrating linkage to seven pea chromosomes. A total of 37 detected QTLs accounted for 9.1-55.9% of the trait's phenotypic variation and showed different types of gene action. As many as eight and ten QTLs influencing the analysed traits were mapped in linkage groups III and V, respectively, indicating an important role of these regions of the pea genome in the control of yield and seed protein content.     

QTLs for days to silking in a recombinant inbred line maize population subjected to high and low nitrogen regimes

Days to silking (DTS) is one of the most important traits in maize (Zea mays). To investigate its genetic basis, a recombinant inbred line population was subjected to high and low nitrogen (N) regimes to detect quantitative trait loci (QTLs) associated with DTS. Three QTLs were identified under the high N regime; these explained 25.4% of the phenotypic variance. Due to additive effects, the QTL on chromosome 6 increased DTS up to 0.66 days; while the other two QTLs mapped on chromosome 9 (one linked with Phi061 and the other linked with Nc134) decreased DTS 0.89 and 0.91 days, respectively. Under low N regime, two QTLs were mapped on chromosomes 6 and 9, which accounted for 25.9% of the phenotypic variance. Owing to additive effects, the QTL on chromosome 6 increased DTS 0.67 days, while the other QTL on chromosome 9 decreased it 1.48 days. The QTL on chromosome 6, flanked by microsatellite markers Bnlg1600 and Phi077, was detected under both N regimes. In conclusion, we identified four QTLs, one on chromosome 6 and three on chromosome 9. These results contribute to our understanding of the genetic basis of DTS and will be useful for developing marker-assisted selection in maize breeding programs.     

Genetic analysis and QTL mapping of the seed hardness trait in a black common bean (<Emphasis Type="Italic">Phaseolus vulgaris</Emphasis>) recombinant inbred line (RIL) population

Seed hardness trait has a profound impact on cooking time and canning quality in dry beans. This study aims to identify the unknown genetic factors and associated molecular markers to better understand and tag this trait. An F_2:7 recombinant inbred line (RIL) population was derived from a cross between the hard and soft seeded black bean parents (H68-4 and BK04-001). Eighty-five RILs and the parental lines were grown at two locations in southern Manitoba during years 2014–2016. Seed samples were harvested manually at maturity to test for seed hardness traits. The hydration capacity and stone seed count were estimated by soaking the seeds overnight at room temperature following AACC method 56-35.01. Seed samples from 2016 tests were also cooked to determine effect of seed hardness on cooking quality. For mapping of genomic regions contributing to the traits, the RIL population was genotyped using the genotype by sequencing (GBS) approach. The QTL mapping revealed that in addition to the major QTL on chromosome 7 at a genomic location previously reported to affect seed-hydration, two novel QTL with significant effects were also detected on chromosomes 1 and 2. In addition, a major QTL affecting the visual appeal of cooked bean was mapped on chromosome 4. This multi-year-site study shows that despite large environmental effects, seed hardness is an oligo-genic and highly heritable trait, which is inherited independently of the cooking quality scored as visual appeal of cooked beans. The identification of the QTLs and development of SNP markers associated with seed hardness can be applied for common bean variety improvement and genetic exploitation of these traits.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Identification of loci governing eight agronomic traits using a GBS‐GWAS approach and validation by QTL mapping in soya bean

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing (GBS) approach was used to provide dense genome‐wide marker coverage (>47 000 SNPs) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.     

Genetic analysis of grain and malt quality in an elite barley population

Quantitative trait loci (QTLs) associated with grain weight, grain width, kernel hardness and malting quality were mapped in a doubled haploid population derived from two elite Australian malting barley varieties, Navigator and Admiral. A total of 30 QTLs for grain weight, grain width and kernel hardness were identified in three environments, and 63 QTLs were identified for ten malting quality traits in two environments. Three malting quality traits, namely β-amylase, diastatic power and apparent attenuation limit, were mainly controlled by a QTL linked to the Bmy1 gene at the distal end of chromosome 4H encoding a β-amylase enzyme. Six other malting quality traits, namely α-amylase, soluble protein, Kolbach index, free amino-acid nitrogen, wort β-glucan and viscosity, had coincident QTL clustered on chromosomes 1HS, 4HS, 7HS and 7HL, which demonstrated the interdependence of these traits. There was a strong association between these malt quality QTL clusters on chromosomes 1HS and 7HL and the major QTL for kernel hardness, suggesting that the use of this trait to enable early selection for malting quality in breeding programs would be feasible. In contrast, the majority of QTLs for hot-water extract were not coincident with those identified for other malt quality traits, which suggested differences in the mechanism controlling this trait. Novel QTLs have been identified for kernel hardness on chromosomes 2HL and 7HL, hot-water extract on 7HL and wort β-glucan on 6HL, and the resulting markers may be useful for marker-assisted selection in breeding programs.     

Analysis of chromosomal localization of loci controlling milk production traits in cattle

Analysis of the pattern of the chromosomal localization of quantitative trait loci (QTLs) is necessary for comprehensively understanding their functions. The chromosomal localization of QTLs controlling milk production traits has been studied in cattle chromosomes. The distribution of QTLs between chromosomes has proved to be binomial. Their distribution along each chromosome was, in general, uniform, except for the QTLs controlling the somatic cell score (SCS), which tended towards telomeric location. However, there are chromosomes either enriched with or particularly poor in QTLs. The QTL distribution patters are the most similar for the milk yield (M) and milk protein yield (P) and for milk fat yield (F) and milk fat content (%F). The pattern of the SCS QTLs stands out among those of other QTLs. The distance between the QTLs of contrasting traits is the shortest for M and P QTLs, longer for M and milk protein content (%P) QTLs, and still longer for M and %F QTLs, which may be explained by QTL pleiotropy, a common phenomenon in cattle.     

Mapping QTLs for yield and nitrogen-related traits in wheat: influence of nitrogen and phosphorus fertilization on QTL expression

Key message

The present study identified some new important genomic regions and demonstrated the availability of conditional analysis in dissecting QTLs induced by environmental factors.

Abstract

The high input and low use efficiency of nutrient fertilizers require knowledge of the genetic control of crop reaction to nutrient supplements. In this study, 14 morphological and 8 physiological traits of a set of 182 wheat (Triticum aestivum L.) recombinant inbred lines (Xiaoyan 54 × Jing 411) were investigated in six environments to map quantitative trait loci (QTLs). The influence of nitrogen (N) and phosphorus (P) fertilization on QTL expression was studied by unconditional and conditional analysis. A total of 117 and 30 QTLs were detected by unconditional and conditional analysis, respectively, among which 21 were common for both methods. Thirty-four QTL clusters were identified. Eighteen conserved QTLs (15.4 % of the 117 QTLs) between years, but within nutritional treatment were found. The three major QTLs on chromosomes 2D, 4B and 6A were coincident with Rht8, Rht-B1b and TaGW2, respectively. The other two important intervals on chromosomes 4B and 7A for yield component traits were newly detected QTLs that warrant further study. By conditional analysis, spikelet number per spike was found to be induced by P fertilization mostly, whereas N fertilization had more effects on the expression of the QTLs for nitrogen concentration and utilization efficiency traits. QTLs that respond to N and P interactions were also detected. The results are helpful for understanding the genetic basis of N utilization efficiency in wheat under different N and P supplement environments and provide evidence for the availability of conditional analysis in dissecting QTLs induced by environmental factors.     

Mapping QTLs for seed yield and drought susceptibility index in soy-bean（Glycine max L.） across different environments

Drought stress has long been a major constraint in maintaining yield stability of soybean （Glycine max （L.） Merr.） in rainfed ecosystems. The identification of consistent quantitative trait loci （QTL） involving seed yield per plant （YP） and drought susceptibility index （DSI） in a population across different environments would therefore be important in molecular marker-assisted breeding of soybean cultivars suitable for rainfed regions. The YP of a recombinant line population of 184 F2：7：11 lines from a cross of Kefengl and Nannong1138-2 was studied under water-stressed （WS） and well-watered （WW） conditions in field （F） and greenhouse （G） trials, and DSI for yield was calculated in two trials. Nineteen QTLs associated with YP-WS and YP-WW, and 10 QTLs associated with DSI, were identi- fied. Comparison of these QTL locations with previous findings showed that the majority of these regions control one or more traits re- lated to yield and other agronomic traits. One QTL on molecular linkage group （MLG） K for YP-F, and two QTLs on MLG C2 for YP-G, remained constant across different water regimes. The regions on MLG C2 for YP-WW-F and MLG H for YP-WS-F had a pleiotropic effect on DSI-F, and MLG A1 for YP-WS-G had a pleiotropic effect on DSI-G. The identification of consistent QTLs for YP and DSI across different environments will significantly improve the efficiency of selecting for drought tolerance in soybean.     

Identification of QTLs for seed and pod traits in soybean and analysis for additive effects and epistatic effects of QTLs among multiple environments

Soybean seed and pod traits are important yield components. Selection for high yield style in seed and pod along with agronomic traits is a goal of many soybean breeders. The intention of this study was to identify quantitative trait loci (QTL) underlying seed and pod traits in soybean among eleven environments in China. 147 recombinant inbred lines were advanced through single-seed-descent method. The population was derived from a cross between Charleston (an American high yield soybean cultivar) and DongNong594 (a Chinese high yield soybean cultivar). A total of 157 polymorphic simple sequence repeat markers were used to construct a genetic linkage map. The phenotypic data of seed and pod traits [number of one-seed pod, number of two-seed pod, number of three-seed pod, number of four-seed pod, number of (two plus three)-seed pod, number of (three plus four)-seed pod, seed weight per plant, number of pod per plant] were recorded in eleven environments. In the analysis of single environment, fourteen main effect QTLs were identified. In the conjoint analysis of multiple environments, twenty-four additive QTLs were identified, and additive QTLs by environments interactions (AE) were evaluated and analyzed at the same time among eleven environments; twenty-three pairs of epistatic QTLs were identified, and epistasis (additive by additive) by environments interactions (AAE) were also analyzed and evaluated among eleven environments. Comparing the results of identification between single environment mapping and multiple environments conjoint mapping, three main effect QTLs with positive additive values and another three main effect QTLs with negative additive values, had no interactions with all environments, supported that these QTLs could be used in molecular assistant breeding in the future. These different effect QTLs could supply a good foundation to the gene clone and molecular asisstant breeding of soybean seed and pod traits.