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.     

Identification of main effects, epistatic effects and their environmental interactions of QTLs for yield traits in rice

Quantitative trait loci (QTLs) controlling yield and yield components were identified by using a doubled haploid (DH) population of 120 lines from a sub-specific cross between ‘Samgang’ (Indica) and ‘Nagdong’ (Japonica). Main effects, epistatic effects, their environment interactions of QTLs were analyzed via mixed linear model approach across different environments. A total of 17 putative QTLs were identified on 8 chromosomes and five QTLs were detected over two years. 7 QTLs of main effects and 23 epistatic interactions were observed for five traits. Epistatic interactions played an important role in controlling the expression of yield related traits. The epistatic effects explained higher percentages of phenotype variation for panicles per plant, seed set percentage and yield. Significant QTL×environment (QE) interactions effects were identified for all traits, including 5 main effect QTLs. However, the present study failed to identify the significant interactions between epistatic loci containing main effect QTLs and the environment. The information provided in the present study could be used in the marker-assisted selection to enhance selection efficiency and to improve yield in rice.     

Bulk segregant analysis with molecular markers and its use for improving drought resistance in maize

The usual method to locate and compare loci regulating quantitative traits (QTLs) requires a segregating population of plants with each one genotyped with molecular markers. However, plants from such segregating populations can also be grouped according to phenotypic expression of a trait and tested for differences in allele frequency between the population bulks: bulk segregant analysis (BSA). The same probes used for making a genetic map (e.g. isozyme, RFLP, RAPD, etc) can be used for BSA. A molecular marker showing polymorphism between the parents of the population and which is closely-linked to a major QTL regulating a particular trait will mainly co-segregate with that QTL, i.e. segregate according to the phenotype if the QTL has a large effect. Thus, if plants are grouped according to expression of the trait and extreme groups tested with that polymorphic marker, the frequency of the two marker alleles present within each of the two bulks should deviate significantly from the ratio of 1 : 1 expected for most populations. As chromosomal locations of many molecular markers have now been determined in many species, the map location of closely-linked QTLs can therefore be deduced without having to genotype every individual in segregating populations. This has been used successfully with composite populations of maize to locate QTLs associated with yield under severe drought. An inbred line derived from one of the populations selected for higher drought yield has been crossed with a drought-susceptible inbred line to produce a mapping population for QTL analysis of physiological and developmental traits likely to regulate yield under drought. Future work to identify traits having QTLs with flanking markers showing significant allele frequency differences in the GSA studies will indicate those traits likely to be important in determining yield under drought.Key words: Bulk segregant analysis (BSA), drought resistance, genetic maps, maize, molecular markers, Zea mays (L.).      

Insect resistance in recombinant inbred soybean lines derived from non-resistant parents

Larval performance of Helicoverpa zea (Boddie) (corn earworm) (Lepidoptera: Noctuidae) was examined on 240 recombinant inbred (RI) soybean, Glycine max (L.) Merrill, lines. These homozygous RI were derived from an intraspecific cross of genetically distant, non-resistant, parents, Minsoy from China and Noir 1 from Hungary. Based upon a genetic map of more than 500 molecular markers, each RI line presented a unique genotype composed of a mixture of different parental alleles. The RI lines exhibited transgressive segregation with respect to their defensive effects on H. zea, such that the range of RI phenotypes far exceeded that of the parents. Similar effects were observed on the soybean looper, Pseudoplusia includens (Walker) (Lepidoptera: Noctuidae). We identified several independent quantitative trait loci (QTLs) linked to molecular markers that were associated with H. zea larval development parameters. Two QTLs affected several different traits including larval weight and developmental rate; other QTLs affected only a single trait each, i.e., larval weight, pupal weight, developmental rate, nutritional efficiency or survival. The results demonstrate that the increased range of defensive effects among the segregant RI lines is due to recombination among several parental genes that together quantitatively control plant defensive traits.Several alternative responses by herbivores have been proposed relative to plant hybrid swarms, hybrid avoidance due to higher hybrid resistance than either parent, hybrid preference due to lower resistance than either parent, hybrid equivalency to one or the other parent, or hybrid intermediacy. Within this RI population, we observed all of the proposed responses by H. zea, as might be expected when defensive traits are controlled by several genes.     

THE DETECTION OF PLASTICITY GENES IN HETEROGENEOUS ENVIRONMENTS

The molecular genetic mechanisms for phenotypic plasticity across heterogeneous macro- and microenvironments were examined using the Populus genomic map constructed by DNA-based markers. Three hypotheses have been suggested to explain genetic variation in phenotypic response to varying environments (i.e., reaction norm): Lerner's homeostasis, allelic sensitivity, and gene regulation. The homeostasis hypothesis, which predicts that heterozygotes are less sensitive to the environment than homozygotes, was supported for phenotypic plasticity to unpredictable environments (microenvironmental plasticity) at the whole-genome level, but for phenotypic plasticity to predictable environments (macroenvironmental plasticity) the hypothesis was supported only at functioning quantitative trait loci (QTLs). For all growth traits studied, gene regulation was suggested to play a prevailing role in determining the norms of reaction to environments. Indirect evidence for gene regulation is that there tend to be more QTLs with larger effects on the phenotype in optimal growing conditions than suboptimal growing conditions because the expression of these QTLs identified is mediated by regulatory genes. Direct evidence for gene regulation is the identification of some loci that differ from QTLs for trait values within environments and exert an environmentally dependent control over structural gene expression. In this study, fewer environmentally sensitive QTLs were detected that display unparalleled allelic effects across environments. For stem height, there were more regulatory loci and more structural loci (whose expression is determined by gene regulation) affecting phenotypic plasticity than for basal area. It was found that microenvironmental plasticity was likely controlled by different genetic systems than those for macroenvironmental plasticity.     

Mapping QTLs contributing to <Emphasis Type="Italic">Ustilago maydis</Emphasis> resistance in specific plant tissues of maize

Quantitative trait loci (QTL) contributing to the frequency and severity of Ustilago maydis infection in the leaf, ear, stalk, and tassel of maize plants were mapped using an A188 × CMV3 and W23 × CMV3 recombinant inbred (RI) populations. QTLs mapped to genetic bins 2.04 and 9.04–9.05 of the maize genome contributed strongly (R ² = 18–28%) to variation in the frequency and severity of U. maydis infection over the entire plant in both populations and within the majority of environments. QTLs mapped to bins 3.05, 3.08, and 8.00 in the A188 × CMV3 population and bin 4.05 in both populations significantly contributed to the frequency or severity of infection in only the tassel tissue. QTLs mapped to bin 1.07 in the A188 × CMV3 population and bin 7.00 in the W23 × CMV3 population contributed to U. maydis resistance in only the ear tissue. Interestingly, the CMV3 allele of the QTL mapped to bin 1.10 in the A188 × CMV3 population significantly contributed to U. maydis susceptibility in the ear and stalk but significantly increased resistance in the tassel tissue. Digenic epistatic interactions between the QTL mapped to bin 5.08 and four distinct QTLs significantly contributed to the frequency and severity of infection over the entire plant and within the tassel tissue of the A188 × CMV3 population. Several QTLs detected in this study mapped to regions of the maize genome containing previously mapped U. maydis resistance QTLs and genes involved in plant disease resistance. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

An informative linkage map of soybean reveals QTLs for flowering time, leaflet morphology and regions of segregation distortion.

A genetic linkage map covering a large region of the genome with informative markers is essential for plant genome analysis, including identification of quantitative trait loci (QTLs), map-based cloning, and construction of a physical map. We constructed a soybean genetic linkage map using 190 F2 plants derived from a single cross between the soybean varieties Misuzudaizu and Moshidou Gong 503, based on restriction-fragment-length polymorphisms (RFLPs) and simple-sequence-repeat polymorphisms (SSRPs). This linkage map has 503 markers, including 189 RFLP markers derived from expressed sequence tag (EST) clones, and consists of 20 major linkage groups that may correspond to the 20 pairs of soybean chromosomes, covering 2908.7 cM of the soybean genome in the Kosambi function. Using this linkage map, we identified 4 QTLs--FT1, FT2, FT3, and FT4--for flowering time, the QTLs for the 5 largest principal components determining leaflet shape, 6 QTLs for single leaflet area, and 18 regions of segregation distortion. All 503 analyzed markers identified were located on the map, and almost all phenotypic variations in flowering time were explained by the detected QTLs. These results indicate that this map covers a large region of the soybean genome.     

RFLP tagging of QTLs conditioning specific leaf weight and leaf size in soybean

 Selection for high specific leaf weight (SLW) in soybean [Glycine max (L) Merr.] may increase apparent photosynthetic rate per unit leaf area (AP), which in turn may improve seed yield. In general, the SLW and leaf size are negatively correlated in soybean. To maximize total photosynthetic performance, and perhaps the seed yield, of a soybean cultivar, it would be necessary to establish a large leaf area rapidly while maintaining a high SLW. The objective of the present study was to identify quantitative trait loci (QTLs) conditioning SLW and leaf size in soybean. One hundred and twenty F₄-derived lines from a ‘Young’×PI416937 population were evaluated using restriction fragment length polymorphism (RFLP) markers. The genetic map consisted of 155 loci on 33 linkage groups (LGs) covering 973 cM of map distance. The phenotypic data were collected from two different environments – a greenhouse at Athens, Ga. and a field site at Windblow, N.C. The SLW and leaf-size measurements were made on leaves from the 8th and 9th node of soybean plants at the V12 stage of development. Combined over environments, six putative independent RFLP markers were associated with SLW, and four of these loci were consistent across environments. Individually, the six markers each explained between 8 and 18% of the phenotypic variation among lines for SLW. The Young alleles contributed to a greater SLW at four of the six independent marker loci, and transgressive segregation occurred among the progeny for SLW. Three putative independent RFLP markers were associated with leaf size, each explaining between 6 to 11% of the phenotypic variation in the trait, and one of these markers was identified in both environments. There was no correlation between SLW and leaf size in this population. Similarly, none of the six QTLs conditioning SLW were linked to any of the three QTLs for leaf size. In this soybean population, it is possible to select for progeny lines with greater SLW than either parent perhaps without affecting the leaf size. It is feasible to pyramid all of the desirable alleles for greater SLW and large leaf size in a single genetic background. Received: 16 August 1997 / Accepted: 20 October 1997     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。     

Mendelian Factors Underlying Quantitative Traits in Tomato: Comparison across Species, Generations, and Environments

As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 in only a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii).(ABSTRACT TRUNCATED AT 400 WORDS)     

Effects of genetic background and environment on QTLs and epistasis for rice (Oryza sativa L.) panicle number

A double-haploid (DH) population and a recombinant inbred (RI) line population, derived from a cross between a tropical japonica variety, Azucena, as male parent and two indica varieties, IR64 and IR1552, as female parents respectively, were used in both field and pot experiments for detecting QTLs and epistasis for rice panicle number in different genetic backgrounds and different environments. Panicle number (PN) was measured at maturity. A molecular map with 192 RFLP markers for the DH population and a molecular map with 104 AFLP markers and 103 RFLP markers for the RI population were constructed, in which 70 RFLP markers were the same. Six QTLs were identified in the DH population, including two detected from field experiments and four from pot experiments. The two QTLs, mapped on chromosomes 1 and 12, were identical in both field and pot experiments. In the RI population, nine QTLs were detected, five QTLs from field conditions and four from the pot experiments. Three of these QTLs were identical in both experimental conditions. Only one QTL, linked to CDO344 on chromosome 12, was detected across the populations and experiments. Different epistasitic interaction loci on PN were found under different populations and in different experimental conditions. One locus, flanked by RG323 and RZ801 on chromosome 1, had an additive effect in the DH population, but epistatic effects in the RI population. These results indicate that the effect of genetic background on QTLs is greater than that of environments, and epistasis is more sensitive to genetic background and environments than main-effect QTLs. QTL and epistatic loci could be interchangeable depending on the genetic backgrounds and probably on the environments where they are identified. Received: 26 May 2000 / Accepted: 19 October 2000     

不同遗传背景及环境中水稻（Oryza sativa L.）穗长的QTLs和上位性分析

以粳稻Ａｚｕｃｅｎａ为父本与灿稻ＩＲ６４杂交发展的一双单倍体（ＤＨ） 本,与灿稻ＩＲ１５５２杂交发展的一重组自交系（ＲＩ）群体为材料,应用分子标记图说对２个群体在大田答舅栽２个环境下的穗长进行ＱＴＬｓ及上位性效应分析,ＤＨ群体中共检测６个穗长ＱＴＬｓ,位于第１、４长染色体上的３个ＱＴＬｓ,,在２个环境中稳定表达,未检测一闰性效应,加性效应为穗长遗传主效应,ＲＩ群体中,共检测到３个穗长ＱＴＬｓ及６对     

Genetical genomics identifies the genetic architecture for growth and weevil resistance in spruce

In plants, relationships between resistance to herbivorous insect pests and growth are typically controlled by complex interactions between genetically correlated traits. These relationships often result in tradeoffs in phenotypic expression. In this study we used genetical genomics to elucidate genetic relationships between tree growth and resistance to white pine terminal weevil (Pissodes strobi Peck.) in a pedigree population of interior spruce (Picea glauca, P. engelmannii and their hybrids) that was growing at Vernon, B.C. and segregating for weevil resistance. Genetical genomics uses genetic perturbations caused by allelic segregation in pedigrees to co-locate quantitative trait loci (QTLs) for gene expression and quantitative traits. Bark tissue of apical leaders from 188 trees was assayed for gene expression using a 21.8K spruce EST-spotted microarray; the same individuals were genotyped for 384 SNP markers for the genetic map. Many of the expression QTLs (eQTL) co-localized with resistance trait QTLs. For a composite resistance phenotype of six attack and oviposition traits, 149 positional candidate genes were identified. Resistance and growth QTLs also overlapped with eQTL hotspots along the genome suggesting that: 1) genetic pleiotropy of resistance and growth traits in interior spruce was substantial, and 2) master regulatory genes were important for weevil resistance in spruce. These results will enable future work on functional genetic studies of insect resistance in spruce, and provide valuable information about candidate genes for genetic improvement of spruce.     

QTL mapping of ten agronomic traits on the soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L. Merr.) genetic map and their association with EST markers

A set of 184 recombinant inbred lines (RILs) derived from soybean vars. Kefeng No.1 × Nannong 1138-2 was used to construct a genetic linkage map. The two parents exhibit contrasting characteristics for most of the traits that were mapped. Using restricted fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) and expressed sequence tags (ESTs), we mapped 452 markers onto 21 linkage groups and covered 3,595.9 cM of the soybean genome. All of the linkage groups except linkage group F were consistent with those of the consensus map of Cregan et al. (1999). Linkage group F was divided into two linkage groups, F1 and F2. The map consisted of 189 RFLPs, 219 SSRs, 40 ESTs, three R gene loci and one phenotype marker. Ten agronomic traits—days to flowering, days to maturity, plant height, number of nodes on main stem, lodging, number of pods per node, protein content, oil content, 100-seed weight, and plot yield—were studied. Using winqtlcart, we detected 63 quantitative trait loci (QTLs) that had LOD>3 for nine of the agronomic traits (only exception being seed oil content) and mapped these on 12 linkage groups. Most of the QTLs were clustered, especially on groups B1 and C2. Some QTLs were mapped to the same loci. This pleiotropism was common for most of the QTLs, and one QTL could influence at most five traits. Seven EST markers were found to be linked closely with or located at the same loci as the QTLs. EST marker GmKF059a, encoding a repressor protein and mapped on group C2, accounted for about 20% of the total variation of days to flowering, plant height, lodging and nodes on the main stem, respectively.Communicated by H.F. LinskensW.-K. Zhang, Y.-J. Wang and G.-Z. Luo contributed equally to this investigation.     

Mapping of the genomic regions controlling seed storability in soybean (Glycine max L.)

Seed storability is especially important in the tropics due to high temperature and relative humidity of storage environment that cause rapid deterioration of seeds in storage. The objective of this study was to use SSR markers to identify genomic regions associated with quantitative trait loci (QTLs) controlling seed storability based on relative germination rate in the F_2:3 population derived from a cross between vegetable soybean line (MJ0004-6) with poor longevity and landrace cultivar from Myanmar (R18500) with good longevity. The F_2:4 seeds harvested in 2011 and 2012 were used to investigate seed storability. The F₂ population was genotyped with 148 markers and the genetic map consisted of 128 SSR loci which converged into 38 linkage groups covering 1664.3 cM of soybean genome. Single marker analysis revealed that 13 markers from six linkage groups (C1, D2, E, F, J and L) were associated with seed storability. Composite interval mapping identified a total of three QTLs on linkage groups C1, F and L with phenotypic variance explained ranging from 8.79 to 13.43%. The R18500 alleles increased seed storability at all of the detected QTLs. No common QTLs were found for storability of seeds harvested in 2011 and 2012. This study agreed with previous reports in other crops that genotype by environment interaction plays an important role in expression of seed storability.     

QTL x environment interactions in rice. I. heading date and plant height

One hundred twenty six doubled-haploid (DH) rice lines were evaluated in nine diverse Asian environments to reveal the genetic basis of genotype × environment interactions (GEI) for plant height (PH) and heading date (HD). A subset of lines was also evaluated in four water-limited environments, where the environmental basis of G × E could be more precisely defined. Responses to the environments were resolved into individual QTL × environment interactions using replicated phenotyping and the mixed linear-model approach. A total of 37 main-effect QTLs and 29 epistatic QTLs were identified. On average, these QTLs were detectable in 56% of the environments. When detected in multiple environments, the main effects of most QTLs were consistent in direction but varied considerably in magnitude across environments. Some QTLs had opposite effects in different environments, particularly in water-limited environments, indicating that they responded to the environments differently. Inconsistent QTL detection across environments was due primarily to non- or weak-expression of the QTL, and in part to significant QTL × environment interaction effects in the opposite direction to QTL main effects, and to pronounced epistasis. QTL × environment interactions were trait- and gene-specific. The greater GEI for HD than for PH in rice were reflected by more environment-specific QTLs, greater frequency and magnitude of QTL × environment interaction effects, and more pronounced epistasis for HD than for PH. Our results demonstrated that QTL × environment interaction is an important property of many QTLs, even for highly heritable traits such as height and maturity. Information about QTL × environment interaction is essential if marker-assisted selection is to be applied to the manipulation of quantitative traits.Communicated by G. Wenzel     

Quantitative trait loci for growing degree days to flowering and photoperiod response in Sunflower (Helianthus annuus L.)

The number of days from seedling emergence to flowering (DTF) is a major consideration in sunflower breeding programs. This is a complex trait determined by the genotype, environmental conditions and interactions. Photoperiod and temperature have major effects on DTF and could be important sources of genotype× environment interaction. The objectives of this study were to locate quantitative trait loci (QTLs) associated with growing degree days (GDD) to flowering and photoperiod (PP) response in an elite sunflower population. Two hundred and thirty five F₂-generation plants and their F_2:3 and F_2:4 progenies of a single-cross population of two divergent inbred lines were evaluated in six environments (locations, years and sowing dates) with photoperiods known to elicit a PP response between the inbred lines. Detection of QTLs was facilitated with a genetic linkage map of 205 RFLP loci and composite interval mapping. The 205 restriction fragment length polymorphism (RFLP) loci covered 1380 cM and were arranged in 17 linkage groups, which is the haploid number of chromosomes in this species. The average interval size was 5.9 cM. Six QTLs in linkage groups A, B, F, I, J and L were associated with GDD to flowering and accounted for 76% of the genotypic variation in the mean environment. QTLs in linkage groups A and B accounted for 72% of the genetic variation. QTL×environment (QTL×E) interactions were highly significant for linkage groups A, B, F and J (P<0.01). QTLs in linkage groups A and B were highly dependent on PP. Also, QTL mapping of the ratio of the GDD required by a progeny to flower at a PP of 12.1 and 15.0 h, defined as the photoperiod response (PPR), suggested that alleles at QTLs in linkage groups A and B were responsive to PP. QTLs in linkage groups F and J showed QTL×E interaction but the LOD values were not associated with PP. QTL×E interactions for additive effects were highly significant (P<0.01) for linkage groups A, B and F. QTL×E interactions for QTLs with dominant effects were significant (P<0.01) for linkage groups A, B and J. The dominant effect of QTLs in linkage group B increased in environments with a longer PP. The knowledge of how these QTLs influence the GDD for flowering and how they interact with the environment will facilitate marker- assisted selection and backcross conversion of photoperiod-sensitive germplasm. Received: 7 February 2000 / Accepted: 13 June 2000     

Epistat : a computer program for identifying and testing interactions between pairs of quantitative trait loci

 We describe a computer program, Epistat, which combines statistical methods and color-graphic displays to facilitate the analysis of interactions between pairs of quantitative trait loci (QTLs). Epistat organizes genetic-mapping data and quantitative-trait values into graphic displays which illustrate the individual effects of single loci as well as the interactions between any two loci. Keyboard commands allow the user to search the data set for individual QTLs and to test for interactions between QTLs. For a given trait, the program displays the effects of the alleles at each of two loci on the quantitative-trait value, as well as the effects of the interactions between these alleles. Loglikelihood ratios are used to compare the likelihood of explaining the effects by null, additive, or epistatic models. Examples of interactions in soybean are presented for near-infrared transmittance (NIT), seed number, and reproductive period. Epistat has been used to find numerous interactions between QTLs in soybean in which trait variation at one locus is conditional upon a specific allele at another. Received: 16 January 1996 / Accepted: 27 September 1996     

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.     

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.