Identification of QTL for agronomic traits of importance for olive breeding

Development of methodologies for early selection is one of the most important goals of olive breeding programs at present. In this context, the identification of molecular markers associated with beneficial alleles could allow the development of marker-assisted selection (MAS) strategies in olive breeding programs. Fruit-related and plant vigor traits, which are of key importance for olive selection and breeding, were analyzed during two seasons in a progeny derived from the cross ‘Picual’ × ‘Arbequina.’ Quantitative trait loci (QTL) analyses were performed using MapQTL 4.0. A total of 22 putative QTLs were identified in the map of ‘Arbequina.’ QTLs clustered in linkage groups (LG) 1, 10 and 17. QTLs for oil-related traits located in LG 1 and 10 explained around 20–30 % of the phenotypic variability depending on the season and the trait. QTL for moisture-related traits were detected in LG 1, 10 and 17, and QTLs for the ratio pulp to stone were identified in LG 10 and 17 explaining around 15–20 %. Interaction between QTLs for the same trait was investigated. The significance of these results was discussed considering the co-localization of QTLs and Pearson correlations among traits. Five additional QTLs were detected in the map of ‘Picual.’ Four of them clustered in LG 17 indicating the presence of a QTL for fruit weight explaining around 12.7–15.2 % of the variability. Additionally, a QTL for trunk diameter was detected in LG 14 explaining 16 % of the variation. These results represent an important step toward the application of MAS in olive breeding programs.     

QTL Mapping for Resistance to Iridovirus in Asian Seabass Using Genotyping-by-Sequencing

Identifying quantitative trait loci (QTL) for viral disease resistance is of particular importance in selective breeding programs of fish species. Genetic markers linked to QTL can be useful in marker-assisted selection (MAS) for elites resistant to specific pathogens. Here, we conducted a genome scan for QTL associated with Singapore grouper iridovirus (SGIV) resistance in an Asian seabass (Lates calcarifer) family, using a high-density linkage map generated with genotyping-by-sequencing. One genome-wide significant and three suggestive QTL were detected at LG21, LG6, LG13, and LG15, respectively. The phenotypic variation explained (PVE) by the four QTL ranged from 7.5 to 15.6%. The position of the most significant QTL at LG21 was located between 31.88 and 36.81 cM. The SNP marker (SNP130416) nearest to the peak of this QTL was significantly associated with SGIV resistance in an unrelated multifamily population. One candidate gene, MECOM, close to the peak of this QTL region, was predicted. Evidence of alternative splicing was observed for MECOM and one specific category of splicing variants was differentially expressed at 5 days post-SGIV infection. The QTL detected in this study are valuable resources and can be used in the selective breeding programs of Asian seabass with regard to resistance to SGIV.     

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.     

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.     

Identification of quantitative trait loci (QTL) for fruit-quality traits and number of weeks of flowering in the cultivated strawberry

Fruit quality and repeat flowering are two major foci of several strawberry breeding programs. The identification of quantitative trait loci (QTL) and molecular markers linked to these traits could improve breeding efficiency. In this work, an F₁ population derived from the cross ‘Delmarvel’ × ‘Selva’ was used to develop a genetic linkage map for QTL analyses of fruit-quality traits and number of weeks of flowering. Some QTL for fruit-quality traits were identified on the same homoeologous groups found in previous studies, supporting trait association in multiple genetic backgrounds and utility in multiple breeding programs. None of the QTL for soluble solids colocated with a QTL for titratable acids, and, although the total soluble solid contents were significantly and positively correlated with titratable acids, the correlation coefficient value of 0.2452 and independence of QTL indicate that selection for high soluble solids can be practiced independently of selection for low acidity. One genomic region associated with the total number of weeks of flowering was identified quantitatively on LG IV-S-1. The most significant marker, FxaACAO2I8C-145S, explained 43.3 % of the phenotypic variation. The repeat-flowering trait, scored qualitatively, mapped to the same region as the QTL. Dominance of the repeat-flowering allele was demonstrated by the determination that the repeat-flowering parent was heterozygous. This genomic region appears to be the same region identified in multiple mapping populations and testing environments. Markers linked in multiple populations and testing environments to fruit-quality traits and repeat flowering should be tested widely for use in marker-assisted breeding.     

甘蓝型油菜千粒重性状的QTL初步定位研究

千粒重是油菜重要的产量相关性状之一,构建油菜遗传连锁图谱是研究其产量性状基因的前提。本研究利用小孢子培养技术,选育出了甘蓝型油菜大粒品系(G-42)和小粒品系(7-9)的纯合DH系DH-G-42和DH-7-9,其千粒重分别为6.24 g和2.42 g,二者比值达2.58。以DH-G-42为母本、DH-7-9为父本,构建了含190个单株的F2遗传作图群体,利用SSR和SRAP标记技术绘制遗传连锁图谱,该图谱共包含20个连锁群,涉及128个SSR标记和100个SRAP标记,图谱总长1546.6cM,标记间平均图距为6.78cM。本研究共检测到3个与千粒重性状相关的QTL,分别位于A9和C1连锁群,其中qSW-A9-1和qSW-A9-2贡献率分别达到10.98%和27.45%,均可视为控制粒重的主效QTL。本研究为后续进行油菜千粒重性状QTL的精细定位分析、分子标记辅助选择育种及新基因的克隆等奠定了基础。     

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 Underlying Water-Logging Tolerance in Soybean

Soil water-logging can cause severe damage to soybean [Glycine max (L.) Merr.] and results in significant yield reduction. The objective of this study was to identify quantitative trait loci (QTL) that condition water-logging tolerance (WLT) in soybean. Two populations with 103 and 67 F_6:11 recombinant inbred lines (RILs) from A5403 × Archer (Population 1) and P9641 × Archer (Population 2), respectively, were used as the mapping populations. The populations were evaluated for WLT in manually flooded fields in 2001, 2002, and 2003. Significant variation was observed for WLT among the lines in the two populations. No transgressive tolerant segregants were observed in either population. Broad-sense heritability of WLT for populations 1 and 2 were 0.59 and 0.43, respectively. The tolerant and sensitive RILs from each population were selected to create a tolerant bulk and a sensitive bulk, respectively. The two bulks and the parents of each population were tested with 912 simple sequence repeat (SSR) markers to select candidate regions on the linkage map that were associated with WLT. Markers from the candidate regions were used to genotype the RILs in both populations. Both single marker analysis (SMA) and composite interval mapping (CIM) were used to identify QTL for WLT. Seventeen markers in Population 1 and 15 markers in Population 2 were significantly (p <0.0001) associated with WLT in SMA. Many of these markers were linked to Rps genes or QTL conferring resistance to Phytophthora sojae Kaufmann and Gerdemann. Five markers, Satt599 on linkage group (LG) A1, Satt160, Satt269, and Satt252 on LG F, and Satt485 on LG N, were significant (p <0.0001) for WLT in both populations. With CIM, a WLT QTL was found close to the marker Satt385 on LG A1 in Population 1 in 2003. This QTL explained 10% of the phenotypic variation and the allele that increased WLT came from Archer. In Population 2 in 2002, a WLT QTL was located near the marker Satt269 on LG F. This QTL explained 16% of the phenotypic variation and the allele that increased WLT also came from Archer.     

Identification of genome regions controlling cotyledon,pod wall/seed coat and pod wall resistance to pea weevil through QTL mapping

Pea weevil, Bruchus pisorum, is one of the limiting factors for field pea (Pisum sativum) cultivation in the world with pesticide application the only available method for its control. Resistance to pea weevil has been found in an accession of Pisum fulvum but transfer of this resistance to cultivated pea (P. sativum) is limited due to a lack of easy-to-use techniques for screening interspecific breeding populations. To address this problem, an interspecific population was created from a cross between cultivated field pea and P. fulvum (resistance source). Quantitative trait locus (QTL) mapping was performed to discover the regions associated with resistance to cotyledon, pod wall/seed coat and pod wall resistance. Three major QTLs, located on linkage groups LG2, LG4 and LG5 were found for cotyledon resistance explaining approximately 80 % of the phenotypic variation. Two major QTLs were found for pod wall/seed coat resistance on LG2 and LG5 explaining approximately 70 % of the phenotypic variation. Co-linearity of QTLs for cotyledon and pod wall/seed coat resistance suggested that the mechanism of resistance for these two traits might act through the same pathways. Only one QTL was found for pod wall resistance on LG7 explaining approximately 9 % of the phenotypic variation. This is the first report on the development of QTL markers to probe Pisum germplasm for pea weevil resistance genes. These flanking markers will be useful in accelerating the process of screening when breeding for pea weevil resistance.     

Identification of germplasm with stacked QTL underlying seed traits in an inbred soybean population from cultivars Essex and Forrest

Soybean (Glycine max (L.) Merr.) seed provides valuable oil (~200 g/kg) and protein (~400 g/kg) co-products. Seed composition variations result from several quantitative trait loci (QTL) that act through development. The objectives here were to identify loci underlying seed traits in the Essex × Forrest (EF94)-derived recombinant inbred line (RIL) population which has low frequencies of marker polymorphisms. Seed weight, protein, and oil were measured over 3 years: 2001, 2003, and 2005. Essex’s seeds were larger (141 mg/seed), higher in protein (406 g/kg), and lower in oil (190 g/kg) than Forrest’s (115 mg/seed, 395 g protein/kg, and 203 g oil/kg). Marker analysis included 413 markers for trait associations used for ANOVA, interval mapping, and composite interval mapping. Eleven QTL in nine genomic regions were associated (LOD >2; P < 0.0077) with seed traits. Two QTL, for mean protein and seed size, were clustered on linkage group (LG) E (chromosome Gm16). QTL for protein content alone were found on LG C2 (Gm6), LG D1b (Gm2), LG H (Gm12), and LG I (Gm20). The alleles from Essex, the high-protein parent, underlay higher protein (4–10 g/kg) at four of five loci. A QTL for mean oil was found on LG A2 (Gm18) and on LG I (Gm 20). The alleles from Forrest underlay higher oil (3–4 g/kg). Five separate QTL for mean seed weight were found on LG A1 (Gm05), LG N (Gm15), two on LG B1 (Gm11) and one on LG N (Gm3). The alleles from Essex underlay greater seed weight (0.4–0.66 g/100 seeds). The QTL positions were consistent with reported loci. Germplasm that contained all five beneficial alleles at the QTL underlying protein was significantly higher in protein and yield than Essex (409.7–412.3 g/kg) and included RILs 49 and 62. The germplasm identified can be useful for further breeding of the many traits and QTL measured in each line.     

Genetic control of soybean seed oil: II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield

Soybean [Glycine max (L.) Merrill] seed oil is the primary global source of edible oil and a major renewable and sustainable feedstock for biodiesel production. Therefore, increasing the relative oil concentration in soybean is desirable; however, that goal is complex due to the quantitative nature of the oil concentration trait and possible effects on major agronomic traits such as seed yield or protein concentration. The objectives of the present study were to study the relationship between seed oil concentration and important agronomic and seed quality traits, including seed yield, 100-seed weight, protein concentration, plant height, and days to maturity, and to identify oil quantitative trait loci (QTL) that are co-localized with the traits evaluated. A population of 203 F_4:6 recombinant inbred lines, derived from a cross between moderately high oil soybean genotypes OAC Wallace and OAC Glencoe, was developed and grown across multiple environments in Ontario, Canada, in 2009 and 2010. Among the 11 QTL associated with seed oil concentration in the population, which were detected using either single-factor ANOVA or multiple QTL mapping methods, the number of QTL that were co-localized with other important traits QTL were six for protein concentration, four for seed yield, two for 100-seed weight, one for days to maturity, and one for plant height. The oil-beneficial allele of the QTL tagged by marker Sat_020 was positively associated with seed protein concentration. The oil favorable alleles of markers Satt001 and GmDGAT2B were positively correlated with seed yield. In addition, significant two-way epistatic interactions, where one of the interacting markers was solely associated with seed oil concentration, were identified for the selected traits in this study. The number of significant epistatic interactions was seven for yield, four for days to maturity, two for 100-seed weight, one for protein concentration, and one for plant height. The identified molecular markers associated with oil-related QTL in this study, which also have positive effects on other important traits such as seed yield and protein concentration, could be used in the soybean marker breeding programs aimed at developing either higher seed yield and oil concentration or higher seed protein and oil concentration per hectare. Alternatively, selecting complementary parents with greater breeding values due to positive epistatic interactions could lead to the development of higher oil soybean cultivars.     

Genetic analysis of seed size, yield and days to flowering in a chickpea recombinant inbred line population derived from a Kabuli × Desi cross

Quantitative traits, seed size, yield and days to flowering were studied in a chickpea intraspecific recombinant inbred line (RIL) population (F_6:7) derived from a Kabuli × Desi cross. The population was evaluated in two locations over 2 years. Days to flowering was also evaluated in the greenhouse under short-day conditions. Seed size was the most heritable trait (0.90), followed by days to flowering (0.36) and yield (0.14). Negative and significant correlation was found between yield and seed size in the second year where environmental homogeneity was tested by analysing the controls included in each assay. During the first year, the environment was not considered homogeneous for yield in either location. Quantitative trait loci (QTLs) for the three characters were detected in linkage group (LG) 4. In relation to seed size, two QTLs were located in LG4 (QTL_SW1) and LG8 (QTL_SW2). QTL_SW1 accounted 20.3% of the total phenotypic variation and QTL_SW2 explained 10.1%. A QTL for yield (QTL_YD) was located in LG4 explaining around 13% of variation. QTL_YD might be pleiotropic with QTL_SW1. For days to flowering, a QTL (QTL_DF1) was located in LG4 for all environments analysed explaining around 20% of variation. QTL_DF1 was closely linked to QTL_SW1 and QTL_YD in LG4.     

An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding

Key message

The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.

Abstract

The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.

     

甘蓝型油菜遗传图谱的构建及单株产量构成因素的QTL分析

采用常规品系04-1139与高产多角果品系05-1054构建的F2代群体为作图群体, 运用SSR(Simple sequence repeat)和SRAP(Sequence-related amplified polymorphism)构建分子标记遗传图谱并对甘蓝型油菜单株产量构成因素进行QTL分析。遗传图谱包含200个分子标记, 分布于19个连锁群上, 总长度1 700.23 cM, 标记间的平均距离8.50 cM。采用复合区间作图法(Composite interval mapping, CIM)对单株产量构成因素(单株有效角果数、每果粒数和千粒重)进行QTL分析, 共检测到12个QTL: 其中单株有效角果数4个QTL, 分别解释表型变异为35.64%、12.96%、28.71%和34.02%; 每果粒数获得5个QTL, 分别解释表型变异为8.41%、7.87%、24.37%、8.57%和14.31%; 千粒重获得３个QTL, 分别解释表型变异为2.33%、1.81%和1.86%。结果表明: 同一性状的等位基因增效作用可以同时来自高值亲本和低值亲本; 文章中与主效QTL连锁的标记可用于油菜产量性状的分子标记辅助选择和聚合育种。     

Mapping Quantitative Trait Loci for Omega-3 Fatty Acids in Asian Seabass

Omega-3 fatty acids are essential fatty acids for human health. Therefore, increasing both percentage of omega-3 and a better fatty acid profile in fish fillets is one of the breeding goals in aquaculture. However, it is difficult to increase the omega-3 content in fish fillets, as the phenotypic selection of these traits is not easily feasible. To facilitate the genetic improvement of the Asian seabass for optimal fatty acid profiles, a genome-wide scan for quantitative trait loci (QTL) affecting fatty acid level in the flesh of the Asian seabass was performed on an F₂ family containing 314 offspring. All family members were genotyped using 123 informative microsatellites and 22 SNPs. High percentages of n-3 polyunsaturated fatty acids (PUFA), especially C22:6 (DHA 16.48?±?3.09 %) and C20:5 (EPA 7.19?±?0.86 %) were detected in the flesh. One significant and 54 suggestive QTL for different fatty acids and a water content trait were detected on the whole genome. QTL for C18:0b was located on linkage groups (LG) 5. QTL for total n-3 PUFA content in flesh were mapped onto LG6 and LG23 with the phenotypic variance explained ranging from 3.8 to 6.3 %. Four QTL for C22:6 were detected on LG6, LG23, and LG24, explaining 3.9 to 4.9 % of the phenotypic variance, respectively. Mapping of QTL for contents of different fatty acids is the first step towards improving the omega-3 content in the fillets of fish by using marker-assisted selection and is important for understanding the biology of fatty acid deposition.     

陆地棉产量性状QTLs的分子标记及定位

用我国的高产栽培品种泗棉3号和美国栽培品种TM-1为材料,构建F₂和F_2∶3作图群体,应用301对SSR引物和1040个RAPD引物,对产量性状QTLs进行了分子标记筛选,结果共筛选出了37对SSR多态性引物和10个RAPD多态性引物的49个位点,鉴定出了控制产量性状变异的主效QTLs。定位于第9染色体的连锁群,分别具有控制铃重、衣分和籽指的主效QTLs,铃重的2个QTLs分别解释F_2∶3群体表型变异的18.2%和21.0%;在F₂群体检测到的1个衣分QTL解释表型变异的25%,另一个衣分QTL在F₂群体和F_2∶3群体都检测到,解释F₂群体衣分的24.9%的表型变异,解释F_2∶3群体衣分的5.9%的表型变异;在F_2∶3群体铃重的一个QTL的同一位置同时检测到一个籽指QTL,它解释15.6%的表型变异,是一因多效或是紧密连锁的两个QTLs,有待进一步研究。本研究标记的产量性状主效QTLs可用于棉花产量性状的标记辅助选择。     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

Genetic mapping of EST-derived simple sequence repeats (EST-SSRs) to identify QTL for leaf morphological characters in a Quercus robur full-sib family

The availability of genomic resources such as expressed sequence tag-derived simple sequence repeat (EST-SSR) markers in adaptive genes with high transferability across related species allows the construction of genetic maps and the comparison of genome structure and quantitative trait loci (QTL) positions. In the present study, genetic linkage maps were constructed for both parents of a Quercus robur × Q. robur ssp. slavonica full-sib pedigree. A total of 182 markers (61 AFLPs, 23 nuclear SSRs, 98 EST-SSRs) and 172 markers (49 AFLPs, 21 nSSRs, 101 EST-SSRs, 1 isozyme) were mapped on the female and male linkage maps, respectively. The total map length and average marker spacing were 1,038 and 5.7 cM for the female map and 998.5 and 5.8 cM for the male map. A total of 68 nuclear SSRs and EST-SSRs segregating in both parents allowed to define homologous linkage groups (LG) between both parental maps. QTL for leaf morphological traits were mapped on all 12 LG at a chromosome-wide level and on 6 LG at a genome-wide level. The phenotypic effects explained by each single QTL ranged from 4.0 % for leaf area to 15.8 % for the number of intercalary veins. QTL clusters for leaf characters that discriminate between Q. robur and Quercus petraea were mapped reproducibly on three LG, and some putative candidate genes among potentially many others were identified on LG3 and LG5. Genetic linkage maps based on EST-SSRs can be valuable tools for the identification of genes involved in adaptive trait variation and for comparative mapping.     

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

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