Impact of epistasis and QTL × environmental interaction on the oil filling rate of soybean seed at different developmental stages

The oil accumulation in the developing soybean seed has been shown to be a dynamic process with different rates and activities at different phases affected by both genotype and environment. The objective of the present study was to investigate additive, epistatic and quantitative trait loci (QTL) × environment interaction (QE) effects of the QTL controlling oil filling rate in soybean seed. A total of 143 recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used in this study to obtain 2 years of field data (2004 and 2005). A total of 26 QTL with significantly unconditional and conditional additive (a) effect and/or additive × environment interaction (ae) effect at different filling stages were identified on 14 linkage groups. Among the QTL with significant a effects, 18 QTL showed positive effects and 6 QTL had negative effects on seed filling rate of oil content during seed development. A total of 29 epistatic pairwise QTL underlying seed filling rate were identified at different filling stages. About 28 pairs of the QTL showed additive × additive epistatic (aa) effects and 14 pairs of the QTL showed aa × environment interaction (aae) effects at different filling stages. QTL with aa and aae (additive × additive × environment) effects appeared to vary at different filling stages. Our results demonstrated that oil filling rate in soybean seed were under genetic, developmental and environmental control.     

Population-specific QTLs and their different epistatic interactions for pod dehiscence in soybean [<Emphasis Type="Italic">Glycine max</Emphasis> (L.) Merr.]

Pod dehiscence (PD) prior to harvest results in serious yield loss in soybean. Two linkage maps with simple sequence repeat (SSR) markers were independently constructed using recombinant inbred lines (RILs) developed from Keunolkong (pod-dehiscent) × Sinpaldalkong (pod-indehiscent) and Keunolkong × Iksan 10 (pod-indehiscent). These soybean RIL populations were used to identify quantitative trait loci (QTLs) conditioning resistance to PD. While a single major QTL on linkage group (LG) J explained 46% of phenotypic variation in PD in the Keunolkong × Sinpaldalkong population with four minor QTLs, three minor QTLs were identified in the Keunolkong × Iksan 10 population. Although these two populations share the pod dehiscent parent, no common QTL has been identified. In addition, epistatic interactions among three marker loci partially explained phenotypic variation in PD in both populations. The result of this study indicates that different breeding strategies will be required for PD depending on genetic background.     

QTL identification for molecular breeding of fibre yield and fibre quality traits in jute

In jute (Corchorus olitorius), quantitative trait loci (QTL) analysis was conducted to study the genetics of eight fibre yield traits and two fibre quality traits. For this purpose, we used a mapping population consisting of 120 recombinant inbred lines (RILs) and also used a linkage map consisting of 36 SSR markers that was developed by us earlier (Das et al. 2011). The RIL population was derived from the cross JRO 524 (coarse fibre) × PPO4 (fine fibre) following single seed descent. Using single-locus analysis involving composite interval mapping, a total of 21 QTLs were identified for eight fibre yield traits whereas for fibre quality (fibre fineness), only one QTL was detected. The QTL for fibre fineness explained 8.31–10.56% of the phenotypic variation and was detected in two out of three environments. Using two-locus analysis involving QTLNetwork, as many as 11 M-QTLs were identified for seven fibre yield traits (excluding top diameter) and one M-QTL was identified for fibre fineness which accounted for 4.57% of the phenotypic variation. For six fibre yield traits, we detected 16 E-QTLs involved in nine QQ epistatic interactions. For fibre fineness, four E-QTLs involved in two QQ epistatic interactions and for fibre strength, six E-QTLs involved in three QQ epistatic interactions were identified. Eight out of the 11 M-QTLs observed for the fibre yield traits were also involved in QE interactions; for fibre fineness and fibre strength, no QE interactions were observed.     

Quantitative trait loci (QTL) mapping of silique length and petal colour in Brassica rapa

Recombinant inbred lines (RILs) derived from a cross between Brassica rapa L. cv. ‘Sampad’, and an inbred line 3‐0026.027 was used to map the loci controlling silique length and petal colour. The RILs were evaluated under four environments. Variation for silique length in the RILs ranged from normal, such as ‘Sampad’, to short silique, such as 3‐0026.027. Three QTL, SLA3, SLA5 and SLA7, were detected on the linkage groups A3, A5 and A7, respectively. These QTL explained 36.0 to 42.3% total phenotypic variance in the individual environments and collectively 32.5% phenotypic variance. No additive × additive epistatic interaction was detected between the three QTL. Moreover, no QTL × environment interaction was detected in any of the four environments. The number of loci for silique length detected based on QTL mapping agrees well with the results from segregation analysis of the RILs. In case of petal colour, a single locus governing this trait was detected on the linkage group A2.     

Comparison of QTLs mapped in RILs and their test-cross progenies of tropical maize for insect resistance and agronomic traits

Quantitative trait loci (QTL) affecting resistance to south-western corn borer Diatraea grandiosella (SWCB) and sugarcane borer Diatraea saccharalis (SCB) have been identified previously in F_2:3 lines and recombinant inbred lines (RILs) of tropical maize using restriction fragment length polymorphism (RFLP) analyses. Our objective was to determine whether QTLs identified in these generations are also expressed in test crosses (TC) of RILs. A population of 166 TC progenies was developed by crossing RILs from the cross CML131 (susceptible) × CML67 (resistant) with the unrelated, susceptible tester line CML216. Resistance to first-generation SWCB, measured as leaf-feeding damage (LFD) under artificial infestation, and other agronomic traits were evaluated in two environments for the TC progenies and three environments for 183 RILs. The correlation between line per se and TC performance was low for LFD and intermediate for most agronomic traits. Estimates of the genotypic variance and heritabilities were smaller in the TC progenies than in the RILs for all traits. Quantitative trait loci were identified using an RFLP linkage map with 136 loci. For LFD, four QTLs were detected in the TC progenies, of which two were in common with nine QTLs previously mapped in the RILs. Few QTLs for agronomic traits were common to the two types of progeny, because of the low consistency of QTL positions for all traits in RIL and TC progenies, the use of TC progenies should be considered in QTL mapping studies as the first step for marker-assisted selection in hybrid breeding.     

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

Seed protein content at the harvest stage is the sum of protein accumulation during seed filling. The aim of our investigation was to identify loci underlying the filling rate of seed protein at different developmental stages. To this end, we used 143 recombinant inbred lines (RILs) derived from the cross of soybean cultivars ‘Charleston’ and ‘Dongnong 594’ and composite interval mapping with a mixed genetic model. The genotype × environment interactions of the quantitative trait loci (QTL) were also evaluated. Thirty-nine unconditional QTL underlying the filling rate of seed protein at five developmental stages were mapped onto 14 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 4.88 to 26.05%. Thirty-eight conditional QTL underlying the filling rate of seed protein were mapped onto 16 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 1.87 to 31.34%. The numbers and types of QTL and their genetic effects on the filling rate of seed protein were different at each developmental stage. A G × E interaction effect was observed for some QTL.     

QTL analysis for grain weight in common wheat

Quantitative trait loci (QTL) analysis for grain weight (GW = 1000 grain weight) in common wheat was conducted using a set of 100 recombinant inbred lines (RILs) derived from a cross ‘Rye Selection 111 (high GW) × Chinese Spring (low GW)’. The RILs and their two parental genotypes were evaluated for GW in six different environments (three locations × two years). Genotyping of RILs was carried out using 449 (30 SSRs, 299 AFLP and 120 SAMPL) polymorphic markers. Using the genotyping data of RILs, framework linkage maps were prepared for three chromosomes (1A, 2B, 7A), which were earlier identified by us to carry important/major genes for GW following monosomic analysis. QTL analysis for GW was conducted following genome-wide single marker regression analysis (SMA) and composite interval mapping (CIM) using molecular maps for the three chromosomes. Following SMA, 12 markers showed associations with GW, individual markers explaining 6.57% to 10.76% PV (phenotypic variation) for GW in individual environments. The high grain weight parent, Rye Selection111, which is an agronomically superior genotype, contributed favourable alleles for GW at six of the 12 marker loci identified through SMA. The CIM identified two stable and definitive QTLs, one each on chromosome arms 2BS and 7AS, which were also identified through SMA, and a third suggestive QTL on 1AS. These QTLs explained 9.06% to 19.85% PV for GW in different environments. The QTL for GW on 7AS is co-located with a QTL for heading date suggesting the occurrence of a QTL having a positive pleiotropic effect on the two traits. Some of the markers identified during the present study may prove useful for marker-assisted selection, while breeding for high GW in common wheat.     

Identification of quantitative trait loci controlling soybean seed weight in recombinant inbred lines derived from PI 483463 (Glycine soja) × ‘Hutcheson’ (G. max)

The objective of this study was to identify quantitative trait loci (QTLs) controlling 100‐seed weight in soybean using 188 recombinant inbred lines (RIL) derived from a cross of PI 483463 and ‘Hutcheson’. The parents and RILs were grown for 4 years (2010–2013), and mature, dry seeds were used for 100‐seed weight measurement. The variance components of genotype (a), environment (e) and a × e interactions for seed weight were highly significant. The QTL analysis identified 14 QTLs explaining 3.83–12.23% of the total phenotypic variation. One of the QTLs, qSW17‐2, was found to be the stable QTL, being identified in all the environments with high phenotypic variation as compared to the other QTLs. Of the 14 QTLs, 10 QTLs showed colocalization with the seed weight QTLs identified in earlier reports, and four QTLs, qSW5‐1, qSW14‐1, qSW15‐1 and qSW15‐2, found to be the novel QTLs. A two‐dimensional genome scan revealed 11 pairs of epistatic QTLs across 11 chromosomes. The QTLs identified in this study may be useful in genetic improvement of soybean seed weight.     

Quantitative trait loci mapping for yield-related traits under low and high planting densities in maize (Zea mays)

Average maize yield per hectare has increased significantly because of the improvement in high-density tolerance, but little attention has been paid to the genetic mechanism of grain yield response to high planting density. Here, we used a population of 301 recombinant inbred lines (RILs) derived from the cross YE478 × 08–641 to detect quantitative trait loci (QTLs) for 16 yield-related traits under two planting densities (57,000 and 114,000 plants per ha) across four environments. These yield-related traits responded differently to high-density stress. A total of 110 QTLs were observed for these traits: 33 QTLs only under low planting density, 50 QTLs under high planting density and 27 QTLs across both densities. Only two major QTLs, qCD6 and qWKEL2-2, were identified across low- and high-density treatments. Seven environmentally stable QTLs were also observed containing qED6, qWKEL3, qRN3-3, qRN7-2, qRN9-2 and qRN10 across both densities, as well as qRN9-1 under low density. In addition, 16 and eight pairs of loci with epistasis interaction (EPI) were detected under low and high planting densities, respectively. Additionally, nine and 17 loci showed QTL × environment interaction (QEI) under low- and high-density conditions, respectively. These interactions are of lesser importance than the main QTL effects. We also observed 26 pleiotropic QTL clusters, and the hotspot region 3.08 concentrated nine QTLs, suggesting its great importance for maize yield. These findings suggested that multiple minor QTLs, loci with EPI and QEI, pleiotropy and the complex network of “crosstalk” among them for yield-related traits were greatly influenced by plant density, which increases our understanding of the genetic mechanism of yield-related traits for high-density tolerance.     

Genome-wide QTL analysis for pre-harvest sprouting tolerance in bread wheat

A framework linkage map comprising 214 molecular marker (SSR, AFLP, SAMPL) loci was prepared using an intervarietal recombinant inbred line (RIL) mapping population of bread wheat. The RIL population that was developed from the cross SPR8198 (red-grained and PHS tolerant genotype) × HD2329 (white-grained and PHS susceptible genotype) following single seed descent segregated for pre-harvest sprouting (PHS). The RIL population and parental genotypes were evaluated in six different environments and the data on PHS were collected. Using the linkage map and PHS data, genome-wide single-locus and two-locus QTL analyses were conducted for PHS tolerance (PHST). Single-locus analysis following composite interval mapping (CIM) detected a total of seven QTL, located on specific arms of five different chromosome (1AS, 2AL, 2DL, 3AL and 3BL). These seven QTL included two major QTL one each on 2AL and 3AL. Two of these seven QTL were also detected following two-locus analysis, which resolved a total of four main-effect QTL (M-QTL), and 12 epistatic QTL (E-QTL), the latter involved in 7 QTL × QTL interactions. Interestingly, none of these M-QTL and E-QTL detected by two-locus analysis was involved in Q × E and Q × Q × E interactions, supporting the results of ANOVA, where genotype × environment interaction were non-significant. The QTL for PHS detected in the present study may be efficiently utilized for marker-aided selection for enhancing PHST in bread wheat.     

Molecular markers to exploit genotype–environment interactions of relevance in organic growing systems

One of the substantial differences between conventional and organic growing systems is the degree to which the farmer can control biotic and abiotic stresses; for organic growing systems varieties are needed with a broad adaptation to annually varying factors, while at the same time a good specific adaptation is necessary with respect to more constant climate and soil conditions. This combination of requirements implies that varieties for organic farming need to be better characterised with respect to genotype × environment interactions than varieties for conventional farming. Such interactions, which often are found for quantitatively expressed traits, are in general difficult to deal with in phenotypic selection. New approaches for QTL analyses (e.g. using physiological models) facilitate estimation of effects of genes on a trait (the phenotype) as a response to environmental influences. From such analyses, markers can be identified which may help to predict the trait expression of a plant genotype in relation to defined environmental factors. The application of markers to select for loci with specific interactions with the environment could, therefore, be especially important for plant breeders targeting organic farming systems.     

Genetic analysis of plant height using two immortalized populations of “CRI12 × J8891” in Gossypium hirsutum L.

Plant height is an important plant architecture trait that determines the canopy structure, photosynthetic capacity and lodging resistance of upland cotton populations. To understand the genetic basis of plant height for marker-assisted breeding, quantitative trait loci (QTL) analysis was conducted based on the genetic map of recombinant inbred lines (RILs) derived from the cross “CRI12 × J8891” (Gossypium hirsutum L.). Three methods, including composite interval mapping, multiple interval mapping and multi-marker joint analysis, were used to detect QTL across multiple environments in the RILs and in the immortalized F₂ population developed through intermating between RILs. A total of 19 QTL with genetic main effects and/or genetic × environment interaction effects were identified on 15 chromosomes or linkage groups, each explaining 5.8–14.3 % of the phenotypic variation. Five digenic epistatic QTL pairs, mainly involving additive × additive and/or dominance × dominance, were detected in different environments. Seven out of eight interacting loci were main-effect QTL, suggesting that these loci act as major genes as well as modifying genes in the expression of plant height. The results demonstrate that additive effects, dominance and epistasis are all important for the genetic constitution of plant height, with additive effects playing a more important role in reducing plant height. QTL showing stability across environments that were repeatedly detected by different methods can be used in marker-assisted breeding.     

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

In 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.     

Quantitative trait loci analysis of stem strength and related traits in soybean

Stem strength is one of the major influencing factors of lodging in soybean [Glycine max (L.) Merr.] as well as other crops. To identify quantitative trait loci (QTL) associated with stem strength and related traits in soybean, a recombinant inbred line (RIL) population consisting of 165 lines derived from Zhongdou No. 29 × Zhongdou No. 32 was used in 3 years. Significant positive correlations were found among the four traits (stem strength, stem diameter, number of nodes, root dry weight). A linkage map spanning 1,240.7 cM was constructed using 245 SSR (simple sequence repeat) markers and a phenotypic marker (leaflet shape). By composite interval mapping and two-round strategy of QTL meta-analysis, 32 consensus QTL and 19 unique QTL were identified, respectively. Of eight pleiotropic unique QTL, two QTL (uq.A2-2 and uq.A2-3) located at the intervals of 23.2–26.8 and 38.5–42.4 cM on linkage group A2, respectively, were associated with all the four traits. Additive × environment (ae) interaction effects, epistasis (aa) and epistasis × environment (aae) interaction effects of QTL were detected as well. The results provide useful information for further genetic studies on stem strength of soybean.     

水稻抗稻曲病数量性状座位及效应分析

利用157个家系组成的大关稻(japonica)/IR28 (indica)重组自交系(recombinant inbred lines, RIL)群体,采用高效引发稻曲病人工接种方法,以病情指数作为稻曲病的表型值。2007和2009年,鉴定亲本及RILs对水稻稻曲病的抗性。利用QTL Cartographer 软件,对水稻稻曲病抗性基因进行检测分析。两年共检测到qFsr1、qFsr2、qFsr4、qFsr8、qFsr10、qFsr11、qFsr12等7个QTL,分别位于第1、第2、第4、第8、第10、第11和第12染色体上,贡献率在9.8%~22.5%之间。其中,2007年检测到qFsr1、qFsr4、qFsr10、qFsr11、qFsr12等5个位点;2009年检测到qFsr2、qFsr8、qFsr10、qFsr11等4个位点,qFsr11、qFsr12在两年中均被检测到,对性状的解释率在18.0%~19.3%之间,使病情指数下降8.0%~16.3%,提高了抗病性。根据抗性位点加性效应方向,在qFsr1、qFsr2、qFsr8、qFsr10、qFsr11和qFsr12位点上,亲本IR28存在抗稻曲病的增效等位基因,大关稻具有减效等位基因,而位点qFsr4的抗性效应来源正好相反。qFsr11、qFsr12及其附近的标记可望在稻曲病抗性分子标记辅助选择育种中加以应用。     

Genotype-by-environment effects on the performance of recombinant inbred lines of Virginia-type peanut

Peanut (Arachis hypogaea L.) is known to be sensitive to genotype-by-environment interaction (GEI) effects. While previous studies have reported strong GEI effects on peanut yield, most of those studies involved a relatively small number of unrelated genotypes. We examined the extent of GEI effects in elite Virginia-type peanut using a large population of recombinant inbreed lines (RILs). Two-hundred-sixty-six F₇ RILs derived from different cultivars were grown in three environments. Net pod yield (NPY) was evaluated along with 11 other traits. ANOVA revealed that genotype and environment affected all of the examined traits, except for the triplet trait. The substantial influence of the environment was also demonstrated in a genetic-parameter analysis, in which the phenotypic variation coefficients were almost double those for genotypic variation. Still, relatively high heritability and genetic gain values were found for pod weight and NPY. Since NPY is the main target for selection, it was analyzed further. Path analysis showed that NPY is most directly influenced by pod weight and the shelling ratio. A significant GEI effect on NPY was identified using an AMMI model that described 42.7% of the total variation. This GEI component was subjected to a principal components analysis, which confirmed that the variability due to the different environments was greater than the variability that could be attributed to the different genotypes. Yet, several lines had stable yields across environments. These results demonstrate the importance of multi-location phenotyping for QTL analyses and crop improvement in peanut.     

Comparison and analysis of main effects,epistatic effects,and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice

Two genetic linkage maps based on doubled haploid (DH) and recombinant inbred lines (RILs) populations, derived from the same indica-japonica cross ‘Samgang × Nagdong’, were constructed to analyze the quantitative trait loci (QTLs) affecting agronomic traits in rice. The segregations of agronomic traits in RILs population showed larger variations than those in DH population. A total of 10 and 12 QTLs were identified on six chromosomes using DH population and seven chromosomes using RILs population, respectively. Three stable QTLs including pl9.1, ph1.1, and gwp11.1 were detected through different years. The percentages of phenotypic variation explained by individual QTLs ranged from 8 to 18% in the DH population and 9 to 33% in the RILs population. Twenty-three epistatic QTLs were identified in the DH population, while 21 epistatic QTLs were detected in the RILs population. Epistatic interactions played an important role in controlling the agronomic traits genetically. Four significant main-effect QTLs were involved in the digenic interactions. Significant interactions between QTLs and environments (QE) were identified in two populations. The QTLs affecting grain weight per panicle (GWP) were more sensitive to the environmental changes. The comparison and QTLs analysis between two populations across different years should help rice breeders to comprehend the genetic mechanisms of quantitative traits and improve breeding programs in marker-assisted selection (MAS).     

不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析

利用汕优63重组自交系与双亲回交产生的BC1F1和BC2F1群体,采用新发展的包括环境互作效应在内的多遗传体系QTL作图方法和基因定位软件,对稻米两种半必需氨基酸（组氨酸和精氨酸）进行三倍体胚乳和二倍体母体植株等不同遗传体系的QTL定位分析。共检测到10个控制组氨酸含量的QTL以及8个控制精氨酸含量的QTL。全部QTL均具有极显著的三倍体胚乳和二倍体母体植株基因的加性主效应,其中4个QTL具有显著或极显著的三倍体胚乳显性主效应,7个QTL还具有明显的环境互作效应。     

Identification of quantitative trait loci for seed isoflavone concentration in soybean (Glycine max) against soybean cyst nematode stress

Isoflavones are plant secondary metabolites produced in soybean (Glycine max), which provide plant defense against pathogens and are beneficial to human health. Soybean cyst nematode (SCN) is a major yield‐limiting pest in most soybean‐producing area across the world. Traits, seed isoflavones and SCN resistance are quantitative in nature, and their phenotypic evaluations are expensive. Quantitative trait loci (QTL) underlying the two traits will be helpful to develop SCN‐resistant lines with elevated isoflavones using marker‐assisted‐selection (MAS). This study aims to identify isoflavones and SCN‐related QTL in a soybean population consisting of 109 RILs, which was developed from a cross between two commercial soybean cultivars viz. ‘RCAT1004’ and ‘DH4202’ and grown in four non‐SCN and SCN‐infested fields during 2015 and 2016. While single marker ANOVA identified 10 QTL for isoflavones and five for SCN (p < 0.01), simple interval and multiple QTL mappings identified four QTL associated with isoflavones (LOD ≥ 2.2). These results contribute to a better understanding of the genetics of the two traits and provide molecular markers that can be used in MAS to facilitate developing SCN‐resistant soybeans with increased isoflavones.