Mapping of putative quantitative trait loci controlling the total oligosaccharide and sucrose content of Glycine max seeds

Although oligosaccharides and sucrose are very important nutritional components of soybean seeds, little information is available about inheritance of oligosaccharide and sucrose content. The objective of this study was to identify quantitative trait loci (QTLs) that determine the oligosaccharide and sucrose content of soybean. The 117 F_2:10 recombinant inbred lines developed from a cross of “Keunolkong” and “Shinpaldalkong” were used. Narrow-sense heritability estimates, on a plot mean basis, of oligosaccharide and sucrose content were 79.07 and 74.84%, respectively. Four QTLs for oligosaccharide content were located on linkage groups (LG) C2, H, J, and L. Sucrose content was related with two QTLs located on LG H and J. Total oligosaccharide and sucrose content have two common QTLs on LG H and J.     

Comparative mapping of fiber,protein, and mineral content QTLs in two interspecific <Emphasis Type="Italic">Leymus</Emphasis> wildrye full-sib families

Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and mineral content are important components of forage quality in grasses. Elevated [K]/([Ca] + [Mg]) ratios (KRAT) substantially increase the risk of grass tetany (hypomagnesemia) in grazing animals, which is a serious problem associated with some cool-season grasses. The objectives of this study were to map and compare QTLs controlling concentrations of CP, NDF, ADF, Al, B, Ca, Cl, Cu, Fe, K, Mg, Mn, Na, P, S, Si, Zn, and KRAT in two full-sib Leymus triticoides × (L. triticoides × L. cinereus) TTC1 and TTC2 families. Significant genetic variation and QTLs were detected for all traits, with evidence of conserved QTLs for ADF (LG1a, LG5Xm, LG7a), NDF (LG7a), Ca (LG1b), CP, (LG5Xm), KRAT (LG3b, LG6b, LG7a, LG7b), Mn (LG2b, LG3b, LG4Xm), and S (LG3a) content in both TTC1 and TTC2 families. Moreover, the direction of QTL effects was the same for 13 of the 14 homologous QTLs in both families. The TTC1 and TTC2 KRAT QTLs on LG7a and LG7b were located near markers defining homoeologous relationships between the sub-genomes of allotetraploid Leymus, suggesting possible QTL homoeology. Another 88 QTLs were unique to one family or the other, but many of these clustered in genome regions common between the two families. These results will support development of new Leymus wildrye forages and help characterize genes controlling mineral uptake and fiber synthesis.     

Genetic mapping of QTLs conditioning soybean sprout yield and quality

Soybean sprouts have been used as a food in the Orient since ancient times. In this study, 92 restriction fragment length polymorphism (RFLP) loci and two morphological markers (W1 and T) were used to identify quantitative trait loci (QTLs) associated with soybean sprout-related traits in 100 F₂-derived lines from the cross of ’Pureunkong’×’Jinpumkong 2’. The genetic map consisted of 76 loci which covered about 756 cM and converged into 20 linkage groups. Eighteen markers remained unlinked. Phenotypic data were collected in 1996 and 1997 for hypocotyl length, percentage of abnormal seedlings, and sprout yield 6 days after germination at 20°C. Hypocotyl length was determined as the average length from the point of initiation of the first secondary root to the point of attachment of the cotyledons. The number of decayed seeds and seedlings, plus the number of stunted seedlings (less than 2-cm growth), was recorded a s abnormal seedlings. Seed weight was determined based on the 50-seed sample. Sprout yield was recorded as the total fresh weight of soybean sprouts produced from the 50-seed sample divided by the dry weight of the 50-seed sample. Four QTLs were associated with sprout yield in the combined analysis across 2 years. For the QTL linked to L154 on the Linkage Group (LG) G the positive allele was derived from Pureunkong (R ² = 0.19), whereas at the other three QTLs (A089 on LG B1, A668n on LG K and B046 on LG L) the positive alleles were from Jinpumkong 2. QTLs conditioning seed weight were linked to markers A802n (LG B1), A069 (LG E), Cr321 (LG F) and A235 (LG G). At these four markers, the Jinpumkong allele increased seed weight. Markers K011n on LG B1, W1 on LG F and A757 on LG L were linked to QTLs conditioning hypocotyl length; and Bng119, K455n and K418n to QTLs conditioning the abnormal seedlings. The QTLs conditioning sprout yield were in the same genomic locations as the QTLs for seed weight identified in this population or from previously published research, indicating that QTLs for sprout yield are genetically linked to seed-weight QTLs or else that seed-weight QTLs pleiotropically condition sprout yield. These data demonstrate that effective marker-assisted selection may be feasible for enhancing sprout yield in a soybean. The transgressive segregation of sprout yield, as well as the existence of two QTLs conditioning greater than 10% of the phenotypic variation in sprout yields provides an opportunity to select for progeny lines with a greater sprout yield than currently preferred cultivars such as Pureunkong. Received: 23 August 2000 / Accepted: 23 January 2001     

Identification of QTLs Associated with Conversion of Sucrose to Hexose in Mature Fruit of Japanese Pear

Sweetness is the most important trait for fruit breeding and is fundamentally determined by both total and individual sugar contents. We analyzed the contents of sucrose, fructose, glucose, and sorbitol in mature fruit in an F₁ population derived from crossing modern Japanese pear cultivar ‘Akizuki’ and breeding line ‘373-55’. A genetic linkage map was constructed using simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNP). We identified two regions associated with individual sugar contents on linkage group (LG) 1 and LG 7. The percentages of the variance in sucrose, fructose, and glucose explained by the quantitative trait loci (QTLs) were 26.6, 15.9, and 18.5%, respectively, for the region on LG 1, and 22.2, 20.0, and 9.5%, respectively, for the region on LG 7. In both regions, genotypes associated with increases in sucrose were associated with decreases in both fructose and glucose. The 1.5-LOD support intervals of the QTLs on LGs 1 and 7 include SSRs within the regions flanking acid invertase genes PPAIV3 and PPAIV1, respectively. Because acid invertase is a key enzyme in the conversion of sucrose to hexose, these are promising candidates for genes underlying those QTLs and controlling individual sugar contents. We also found one region on LG 11 that explained 21.4% of the variation in total sugar content but was not significantly associated with variation for individual sugars. The information obtained in this study will accelerate research and breeding programs to improve fruit sweetness.     

Identification of quantitative trait loci for plant height, lodging, and maturity in a soybean population segregating for growth habit

The use of molecular markers to identify quantitative trait loci (QTLs) has the potential to enhance the efficiency of trait selection in plant breeding. The purpose of the present study was to identify additional QTLs for plant height, lodging, and maturity in a soybean, Glycine max (L.) Merr., population segregating for growth habit. In this study, 153 restriction fragment length polymorphisms (RFLP) and one morphological marker (Dt1) were used to identify QTLs associated with plant height, lodging, and maturity in 111 F₂-derived lines from a cross of PI 97100 and Coker 237. The F₂-derived lines and two parents were grown at Athens, Ga., and Blackville, S.C., in 1994 and evaluated for phenotypic traits. The genetic linkage map of these 143 loci covered about 1600 cM and converged into 23 linkage groups. Eleven markers remained unlinked. Using interval-mapping analysis for linked markers and single-factor analysis of variance (ANOVA), loci were tested for association with phenotypic data taken at each location as well as mean values over the two locations. In the combined analysis over locations, the major locus associated with plant height was identified as Dt1 on linkage group (LG) L. The Dt1 locus was also associated with lodging. This locus explained 67.7% of the total variation for plant height, and 56.4% for lodging. In addition, two QTLs for plant height (K007 on LG H and A516b on LG N) and one QTL for lodging (cr517 on LG J) were identified. For maturity, two independent QTLs were identified in intervals between R051 and N100, and between B032 and CpTI, on LG K. These QTLs explained 31.2% and 26.2% of the total variation for maturity, respectively. The same QTLs were identified for all traits at each location. This consistency of QTLs may be related to a few QTLs with large effects conditioning plant height, lodging, and maturity in this population.     

Identification of QTLs associated with resistance to soybean cyst nematode races 2, 3 and 5 in soybean PI 90763

Soybean cyst nematode (SCN) is a major soybean pest throughout the soybean growing regions in the world, including the USA. Soybean PI 90763 is an important SCN resistance source. It is resistant to several SCN populations including races 2, 3 and 5. But its genetics of resistance is not well known. The objectives of this study were to: (1) confirm quantitative trait loci (QTLs) for resistance to SCN race 3 in PI 90763 and (2) identify QTLs for resistance to SCN races 2 and 5. QTLs were searched in Hamilton × PI 90763 F_2:3populations using 193 polymorphic simple sequence repeats (SSRs) covering 20 linkage groups (LGs). QTLs for resistance to SCN were identified on LGs A2, B1, E, G, J and L. The same QTL was suggested for resistance to different SCN races where their 1-LOD support intervals of QTL positions highly overlapped. The QTL on LG G was associated with resistance to races 2, 3 and 5. The QTL on LG B1 was associated with resistance to races 2 and 5. The QTL on LG J was associated with resistance to races 2 and 3. The QTLs on LGs A2 and L were associated with resistance to race 3. The QTL on LG E was associated with resistance to race 5. We conclude that LGs A2 and B1 may represent an important distinction between resistance to SCN race 3 and resistance to SCN races 2 and 5 in soybean.     

Mapping quantitative trait loci associated with aluminum toxin tolerance in NJRIKY recombinant inbred line population of soybean (Glycine max)

To investigate the genetic mechanism of AI-tolerance in soybean,a recombinant inbred line population (RIL) with 184 F2:7:11 lines derived from the cross of Kefeng No.1 x Nannong 1138-2 (AI-tolerant x AI-sensitive) were tested in pot experimentwith sand culture medium in net room in Nanjing.Four traits,i.e.plant height,number of leaves,shoot dry weight and root dry weight at seedling stage,were evaluated and used to calculate the average membership index (FAi) as the indicator of AI-tolerance.The composite interval mapping (ClM) under WinQTL Cartographer v.2.5 detected five QTLs (i.e.qFAiol,qFAi-2,qFAi-3,qFAi-4 and qFAi-5),explaining 5.20%-9.07% of the total phenotypic variation individually.While with the multiple interval mapping (MIM) of the same software,five QTLs (qFAi-1,qFAi-5,qFAi-6,qFAi-7,and qFAi-8) explaining 5.7%-24.60% of the total phenotypic variation individually were mapped.Here qFAi-1 and qFAi-5 were detected by both CIM and MIM with the locations in a same flanking marker region,GMKF046-GMKF080 on B1 and satt278-sat_95 on L,respectively.While qFAi-2 under CIM and qFAi-6 under MIM both on D1b2 were located in neighboring regions with their confidence intervals overlapped and might be the same locus.Segregation analysis under major gene plus polygene inheritance model showed that Al-tolerance was controlled by two major genes (h2mg =33.05%) plus polygenes (h2pg=52.73%).Both QTL mapping and segregation analysis confirmed two QTLs responsible for Al-tolerance with relatively low heritability,and there might be a third QTL,confounded with the polygenes in segregation analysis.     

Quantitative trait loci and candidate gene mapping of aluminum tolerance in diploid alfalfa

Aluminum (Al) toxicity in acid soils is a major limitation to the production of alfalfa (Medicago sativa subsp. sativa L.) in the USA. Developing Al-tolerant alfalfa cultivars is one approach to overcome this constraint. Accessions of wild diploid alfalfa (M. sativa subsp. coerulea) have been found to be a source of useful genes for Al tolerance. Previously, two genomic regions associated with Al tolerance were identified in this diploid species using restriction fragment length polymorphism (RFLP) markers and single marker analysis. This study was conducted to identify additional Al-tolerance quantitative trait loci (QTLs); to identify simple sequence repeat (SSR) markers that flank the previously identified QTLs; to map candidate genes associated with Al tolerance from other plant species; and to test for co-localization with mapped QTLs. A genetic linkage map was constructed using EST-SSR markers in a population of 130 BC₁F₁ plants derived from the cross between Al-sensitive and Al-tolerant genotypes. Three putative QTLs on linkage groups LG I, LG II and LG III, explaining 38, 16 and 27% of the phenotypic variation, respectively, were identified. Six candidate gene markers designed from Medicago truncatula ESTs that showed homology to known Al-tolerance genes identified in other plant species were placed on the QTL map. A marker designed from a candidate gene involved in malic acid release mapped near a marginally significant QTL (LOD 2.83) on LG I. The SSR markers flanking these QTLs will be useful for transferring them to cultivated alfalfa via marker-assisted selection and for pyramiding Al tolerance QTLs.     

Detection of two quantitative trait loci for resistance to ascochyta blight in an intra-specific cross of chickpea (Cicer arietinum L.): development of SCAR markers associated with resistance

Two quantitative trait loci (QTLs), (QTL_AR1 and QTL_AR2) associated with resistance to ascochyta blight, caused by Ascochyta rabiei, have been identified in a recombinant inbred line population derived from a cross of kabuli×desi chickpea. The population was evaluated in two cropping seasons under field conditions and the QTLs were found to be located in two different linkage groups (LG4a and LG4b). LG4b was saturated with RAPD markers and four of them associated with resistance were sequenced to give sequence characterized amplified regions (SCARs) that segregated with QTL_AR2. This QTL explained 21% of the total phenotypic variation. However, QTL_AR1, located in LG4a, explained around 34% of the total phenotypic variation in reaction to ascochyta blight when scored in the second cropping season. This LG4a region only includes a few markers, the flower colour locus (B/b), STMS GAA47, a RAPD marker and an inter-simple-sequence-repeat and corresponds with a previously reported QTL. From the four SCARs tagging QTL_AR2, SCAR (SCY17₅₉₀) was co-dominant, and the other three were dominant. All SCARs segregated in a 1:1 (presence:absence) ratio and the scoring co-segregated with their respective RAPD markers. QTL_AR2 on LG4b was mapped in a highly saturated genomic region covering a genetic distance of 0.8 cM with a cluster of nine markers (three SCARs, two sequence-tagged microsatellite sites (STMS) and four RAPDs). Two of the four SCARs showed significant alignment with genes or proteins related to disease resistance in other species and one of them (SCK13₆₀₃) was sited in the highly saturated region linked to QTL_AR2. STMS TA72 and TA146 located in LG4b were described in previous maps where QTL for blight resistance were also localized in both inter and intraspecific crosses. These findings may improve the precision of molecular breeding for QTL_AR2 as they will allow the choice of as much polymorphism as possible in any population and could be the starting point for finding a candidate resistant gene for ascochyta blight resistance in chickpea.     

Identification of QTLs for root characteristics in maize grown in hydroponics and analysis of their overlap with QTLs for grain yield in the field at two water regimes

We investigated the overlap among quantitative trait loci (QTLs) in maize for seminal root traits measured in hydroponics with QTLs for grain yield under well-watered (GY-WW) and water-stressed (GY-WS) field conditions as well as for a drought tolerance index (DTI) computed as GY-WS/GY-WW. In hydroponics, 11, 7, 9, and 10 QTLs were identified for primary root length (R1L), primary root diameter (R1D), primary root weight (R1W), and for the weight of the adventitious seminal roots (R2W), respectively. In the field, 7, 8, and 9 QTLs were identified for GY-WW, GY-WS, and DTI, respectively. Despite the weak correlation of root traits in hydroponics with GY-WW, GY-WS, and DTI, a noticeable overlap between the corresponding QTLs was observed. QTLs for R2W most frequently and consistently overlapped with QTLs for GY-WW, GY-WS, and/or DTI. At four QTL regions, an increase in R2W was positively associated with GY-WW, GY-WS, and/or DTI. A 10 cM interval on chromosome 1 between PGAMCTA205 and php20644 showed the strongest effect on R1L, R1D, R2W, GY-WW, GY-WS, and DTI. These results indicate the feasibility of using hydroponics in maize to identify QTL regions controlling root traits at an early growth stage and also influencing GY in the field. A comparative analysis of the QTL regions herein identified with those described in previous studies investigating root traits in different maize populations revealed a number of QTLs in common.     

Identification of molecular markers linked to quantitative trait loci for soybean resistance to corn earworm

 One hundred and thirty nine restriction fragment length polymorphisms (RFLPs) were used to construct a soybean (Glycine max L. Merr.) genetic linkage map and to identify quantitative trait loci (QTLs) associated with resistance to corn earworm (Helicoverpa zea Boddie) in a population of 103 F₂-derived lines from a cross of ‘Cobb’ (susceptible) and PI229358 (resistant). The genetic linkage map consisted of 128 markers which converged onto 30 linkage groups covering approximately 1325 cM. There were 11 unlinked markers. The F₂-derived lines and the two parents were grown in the field under a plastic mesh cage near Athens, Ga., in 1995. The plants were artificially infested with corn earworm and evaluated for the amount of defoliation. Using interval-mapping analysis for linked markers and single-factor analysis of variance (ANOVA), markers were tested for an association with resistance. One major and two minor QTLs for resistance were identified in this population. The PI229358 allele contributed insect resistance at all three QTLs. The major QTL is linked to the RFLP marker A584 on linkage group (LG) ‘M’ of the USDA/Iowa State University public soybean genetic map. It accounts for 37% of the total variation for resistance in this cross. The minor QTLs are linked to the RFLP markers R249 (LG ‘H’) and Bng047 (LG ‘D1’). These markers explain 16% and 10% of variation, respectively. The heritability (h²) for resistance was estimated as 64% in this population. Received: 15 October 1997 / Accepted: 4 November 1997     

SSR-based genetic linkage map construction in pistachio using an interspecific F<Subscript>1</Subscript> population and QTL analysis for leaf and shoot traits

Pistachio is one of the most commercially important nut trees in the world. To characterize the genetic controls of horticultural traits and facilitate marker-assisted breeding in pistachio, we constructed an SSR-based linkage map using an interspecific F₁ population derived from a cross between the cultivar “Siirt” (Pistacia vera L.) and the monoecious Pa-18 genotype of Pistacia atlantica Desf. This population was also used for the first QTL analysis in pistachio on leaf and shoot characters. In total, 1312 SSR primers were screened, and 388 loci were successfully integrated into parental linkage maps. The Siirt maternal map contained 306 markers, while the “Pa-18” paternal map included 285 markers along the 15 linkage groups. The Siirt map spanned 1410.4 cM, with an average marker distance of 4.6 cM; the Pa-18 map covered 1362.5 cM with an average marker distance of 4.8 cM. Phenotypic data were collected during the growing seasons of 2015 and 2016 for four traits: leaf length (LL), leaf width (LW), leaf length/leaf width ratio (LWR), number of leaflet pairs (NLL), and young shoot color (YSC). A total of 17 QTLs were identified in the parental maps. Four QTLs for LL and LW were located on LG2 and LG4, while four QTLs for LWR ratio on LG13 and LG14, two QTLs for NLL and two QTLs for YSC were on LG7 and LG9, respectively, with similar positions in both parental maps. The SSR markers, linkage maps, and QTLs reported here will provide a valuable resource for future molecular and genetic studies in pistachio.     

A complete genetic linkage map and QTL analyses for bast fibre quality traits, yield and yield components in jute (Corchorus olitorius L.)

We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2 × = 14) using an F₆ recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as co-localized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest co-localized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those for fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.     

Multi-environment QTL analyses for drought-related traits in a recombinant inbred population of chickpea (Cicer arientinum L.)

A recombinant inbred line (RIL) population, comprising 181 lines derived from ILC588 × ILC3279, was evaluated in 10 environments across three locations with different moisture gradients. A drought resistance score (DRS) and three phenology traits—plant height (PLHT), days to flowering (DFLR), and days to maturity (MAT)—were recorded along with seven yield-related traits—grain yield (GY), biological yield (BY), harvest index (HI), the number of pods/3 plants (Pod), percentage of empty pods (%Epod), 100 seed weight (100 sw), and seed number/3 plants (SN). Two RILs (152, 162) showed the best GYs and DRSs under stressed and non-stressed environments. The quantitative trait loci (QTLs) analyses detected 93 significant QTLs (LOD ≥ 2.0) across the genome × environment interactions. The highest phenotypic variation (>24 %) was explained by the QTL_DFLR in Terbol-11. Four common possible pleiotropic QTLs on LG3 and LG4 were identified as associated with DFLR, DRS, GY, MAT, HI, SN, and Pod. No significant epistatic interactions were found between these QTLs and the other markers. However, the QTL for DRS was detected as a conserved QTL in three late planting environments. The markers H6C-07 (on LG3) and H5G01 (on LG4) were associated with QTLs for many traits in all environments studied except two. The allele ‘A’ of marker H6C07 (from the tolerant parent ILC588) explained 80 % of the yield increase under late planting and 29.8 % of that under dry environments. Concentrating on LG3 and LG4 in molecular breeding programs for drought could speed up improvement for these traits.     

Mapping QTLs for Swimming Ability Related Traits in Cyprinus carpio L.

Body height (BH), head length (HL), snout length (SL), and tail length (TL) are important traits related with swimming ability of fish. Therefore, improving these traits will increase the production which is the basic goal of aquaculture breeding. To understand the genetic basis of swimming ability related traits in Cyprinus carpio L., a high-density linkage map spanning 3,301 cM in 50 linkage groups was utilized for quantitative trait locus (QTL) mapping. Mapping family comprised 190 offspring and 627 molecular markers were genotyped with average distance of 5.6 cM. A total of 15 QTLs including four (qBH13, qBH30, qBH33, qBH48) for BH, four (qHL10, qHL18, qHL29, qHL48) for HL, three (qSL24, qSL27, qSL45) for SL, and four (qTL15, qTL17, qTL18, qTL44) for TL were detected on 13 linkage groups LG10, LG13, LG15, LG17, LG18, LG24, LG27, LG29, LG30, LG33, LG44, LG45, and LG48. Each LG consisted on single QTL except LG18 and LG48. LG18 was found with two QTLs associated with HL and TL. While LG48 was comprised, the QTLs related with BH and HL. The phenotype variance was recorded from 12.6 to 40.6 %. Five QTLs, qHL48, qSL45, qTL15, qTL18, and qTL44, explained phenotype variance of >20 % with a significant levels of 0.047, 0.049, 0.037, 0.025, and 0.023, respectively. The neighbored loci of these QTLs were considered as main region of chromosomes controlling the traits. These identified genetic regions will be the main source of discovering gene(s) associated with swimming ability related traits in C. carpio L.     

RFLP markers associated with soybean cyst nematode resistance and seed composition in a ‘Peking’בEssex’ population

 Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, causes severe damage to soybean [Glycine max (L.) Merr] throughout North America and worldwide. Molecular markers associated with loci conferring SCN resistance would be useful in breeding programs using marker-assisted selection (MAS). In this study, 200 F_2:3 families derived from two contrasting parents, SCN-resistant ‘Peking’ with relatively low protein and oil concentrations, and SCN-susceptible ‘Essex’ with high protein and oil concentrations, were used to determine loci underlying the SCN resistance and seed composition. Three different SCN Race isolates (1, 3, and 5) were used to screen both parents and F_2:3 families. The parents were surveyed with 216 restriction fragment length polymorphism (RFLP) probes with five different restriction enzymes. Fifty-six were polymorphic and contrasted with trait data from bioassays to identify molecular markers associated with loci controlling resistance to SCN and seed composition. Five RFLP markers, A593 and T005 on linkage group (LG) B, A018 on LG E, and K014 and B072 on LG H, were significantly linked to resistance loci for Race 1 isolate, which jointly explained 57.7% of the total phenotypic variation. Three markers (B072 and K014, both on LG H; T005 on LG B) were associated with resistance to the Race 3 isolate and jointly explained 21.4% of the total phenotypic variation. Two markers (K011 on LG I, A963 on LG E) associated with resistance to the Race 5 isolate together explained 14.0% of the total phenotypic variation. In the same population we also identified two RFLP markers (B072 on LG H, B148 on LG F) associated with loci conferring protein concentration, which jointly explained 32.3% of the total phenotypic variation. Marker B072 was also linked to loci controlling the concentration of seed oil, which explained 21% of the total phenotypic variation. Clustering among quantitative trait loci (QTLs) conditioning resistance to different SCN Race isolates and seed protein and oil concentrations may exist in this population. We believe that markers located near these QTLs could be used to select for new SCN resistance and higher levels of seed protein and oil concentrations in breeding improved soybean cultivars. Received: 3 March 1998 / Accepted: 18 August 1998     

鲤鱼(Cyprinus carpio L.)头长、眼径、眼间距QTL的定位

用265个AFLP标记、127个微卫星分子标记、37个EST-SSR标记和16个RAPD标记对大头鲤/荷包红鲤抗寒品系的F2雌核发育群体44个个体进行基因型检测, 构建鲤鱼遗传连锁图谱。利用软件WinQTLCart2.5采用复合区间作图法对头长、眼径、眼间距3个性状进行了QTL分析。结果显示, 共检测到5个与头长性状相关的QTL, 分别定位于鲤鱼连锁图谱的LG2(qHS-2-1)、LG3 (qHS-3-1)、LG40(qHS-40-1)和LG4 (qHS-4-2和qHS-4-3) 上。其中qHS-40-1拥有最大的 LOD值, 为7.94, 可解释的表型变异为 34.29%。qHS-4-2的LOD值最小, 为5.03, 可解释的表型变异为11.52%。5个与头长性状相关的 QTL 加性效应值均为负值。检测到两个与眼径性状相关的QTL, 分别定位于鲤鱼连锁图谱的LG39连锁群(qED-39-1)和LG40连锁群(qED-40-1)上, 其中qED-40-1的LOD值比较大, 为2.76, 其加性效应值为负值, 可以解释5.62%的表型变异; qED-39-1的LOD值为2.72, 加性效应值为正值, 可以解释9.77%的表型变异。检测到两个与眼间距性状相关的QTL, 分别定位到鲤鱼连锁图谱的LG20连锁群(qEC-20-1)和LG28 连锁群(qEC-28-1)上。其中qEC-20-1的LOD值比较大, 为3.77, 加性效应值为正值, 可以解释8.29%的表型变异; qEC-28-1的LOD值为2.79, 对应的加性效应值为负值, 可以解释8.88%的表型变异     

Inheritance and QTL mapping of related root traits in soybean at the seedling stage

Key message

This study provides a foundation for further research on root genetic regulation and molecular breeding with emphasis on correlations among root traits to ensure robust root growth and well-developed root systems.

Abstract

A set of 447 recombinant inbred lines (RILs) derived from a cross between Jingdou23 (cultivar, female parent) and ZDD2315 (semi-wild, male parent) were used to analyze inheritance and detect QTLs related to root traits at the seedling stage using major gene plus polygene mixed inheritance analysis and composite interval mapping. The results showed that maximum root length (MRL) was controlled by three equivalent major genes, lateral root number (LRN) was controlled by two overlapping major genes, root weight (RW) and volume (RV) were controlled by four equivalent major genes. Hypocotyl length (HL) was controlled by four additive main genes, and hypocotyl weight (HW) was controlled by four additive and additive × additive epistatic, major genes; however, polygene effects were not detected in these traits. Shoot weight (SW) was controlled by multi-gene effects, but major gene effects were not detected. Twenty-four QTLs for MRL, LRN, RW, RV, SW, HL, HW were mapped on LG A1 (chromosome 5), LG A2 (chromosome 8), LG B1 (chromosome 11), LG B2 (chromosome 14), LG C2 (chromosome 6), LG D1b (chromosome 2), LG F_1 (chromosome 13), LG G (chromosome 18), LG H_1 (chromosome 12), LG H_2 (chromosome 12), LG I (chromosome 20), LG K_2 (chromosome 9), LG L (chromosome 19), LG M (chromosome 7), LG N (chromosome 3), LG O (chromosome 10), separately. Root traits were shown to have complex genetic mechanisms at the seedling stage, SW was controlled by multi-gene effects, and the other six traits were controlled by major gene effects. It is concluded that correlations among root traits must be considered to improve the development of beneficial root traits.     

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.     

Introgression of homeologous quantitative trait loci (QTLs) for resistance to the root-knot nematode [Meloidogyne arenaria (Neal) Chitwood] in an advanced backcross-QTL population of peanut (Arachis hypogaea L.)

Resistance to root-knot nematodes [Meloidogyne arenaria (Neal) Chitwood] is needed for cultivation of peanut in major peanut-growing areas, but significant resistance is lacking in the cultivated species (Arachis hypogaea L.). Markers to two closely-linked genes introgressed from wild relatives of peanut have been identified previously, but phenotypic evidence for the presence of additional genes in wild species and introgression lines has eluded quantitative trait locus (QTL) identification. Here, to improve sensitivity to small-effect QTLs, an advanced backcross population from a cross between a Florunner component line and the synthetic amphidiploid TxAG-6 [Arachis batizocoi × (A. cardenasii × A. diogoi)]^4× was screened for response to root-knot nematode infection. Composite interval mapping results suggested a total of seven QTLs plus three putative QTLs. These included the known major resistance gene plus a second QTL on LG1, and a potentially homeologous B-genome QTL on LG11. Additional potential homeologs were identified on linkage group (LG) 8 and LG18, plus a QTL on LG9.2 and putative QTLs on LG9.1 and 19. A QTL on LG15 had no inferred resistance-associated homeolog. Contrary to expectation, two introgressed QTLs were associated with susceptibility, and QTLs at some homeologous loci were found to confer opposite phenotypic responses. Long-term functional conservation accompanied by rapid generation of functionally divergent alleles may be a singular feature of NBS-LRR resistance gene clusters, contributing to the richness of resistance alleles available in wild relatives of crops. The significance for peanut evolution and breeding is discussed.