Mapping and validation of quantitative trait loci for spikelets per panicle and 1,000-grain weight in rice (Oryza sativa L.)

This study identified four and five quantitative trait loci (QTLs) for 1,000-grain weight (TGW) and spikelets per panicle (SPP), respectively, using rice recombinant inbred lines. QTLs for the two traits (SPP3a and TGW3a, TGW3b and SPP3b) were simultaneously identified in the two intervals between RM3400 and RM3646 and RM3436 and RM5995 on chromosome 3. To validate QTLs in the interval between RM3436 and RM5995, a BC₃F₂ population was obtained, in which TGW3b and SPP3b were simultaneously mapped to a 2.6-cM interval between RM15885 and W3D16. TGW3b explained 50.4% of the phenotypic variance with an additive effect of 1.81 g. SPP3b explained 29.1% of the phenotypic variance with an additive effect of 11.89 spikelets. The interval had no effect on grain yield because it increased SPP but decreased TGW and vice versa. Grain shape was strongly associated with TGW and was used for QTL analysis in the BC₃F₂ population. Grain length, grain width, and grain thickness were also largely controlled by TGW3b. At present, it is not clear whether one pleiotropic QTL or two linked QTLs were located in the interval. However, the conclusion could be made ultimately by isolation of TGW3b. The strategy for TGW3b isolation is discussed.     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

Molecular mapping of QTLs for resistance to the greenbug <Emphasis Type="Italic">Schizaphis graminum</Emphasis> (Rondani) in <Emphasis Type="Italic">Sorghum bicolor</Emphasis> (Moench)

Sorghum is a worldwide important cereal crop and widely cultivated for grain and forage production. Greenbug, Schizaphis graminum (Rondani) is one of the major insect pests of sorghum and can cause serious damage to sorghum plants, particularly in the US Great Plains. Identification of chromosomal regions responsible for greenbug resistance will facilitate both map-based cloning and marker-assisted breeding. Thus, a mapping experiment was conducted to dissect sorghum genetic resistance to greenbug biotype I into genomic regions. Two hundred and seventy-seven (277) F(2) progeny and their F(2:3) families from a cross between Westland A line (susceptible parent) and PI550610 (resistant parent) combined with 118 polymorphic simple sequence repeat (SSR) markers were used to map the greenbug resistance QTLs. Composite interval mapping (CIM) and multiple interval mapping (MIM) revealed two QTLs on sorghum chromosome nine (SBI-09) consistently conditioned the resistance of host plant to the greenbug. The two QTLs were designated as QSsgr-09-01 (major QTL) and QSsgr-09-02 (minor QTL), accounting for approximately 55-80%, and 1-6% of the phenotypic variation for the resistance to greenbug feeding, respectively. These resistance QTLs appeared to have additive and partially dominant effects. The markers Xtxp358, Xtxp289, Xtxp67 and Xtxp230 closely flanked the respective QTLs, and can be used in high-throughput marker-assisted selections (MAS) for breeding new resistant parents and producing commercial hybrids.     

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

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

QTL mapping analysis of plant height and ear height of maize (Zea mays L.)

Genetic map containing 103 microsatellite loci obtained on 200 F2 plants derived from the cross R15 x 478 was used for quantitative trait loci (QTL) mapping in maize. QTL were characterized in a population of 200 F2:4 lines, derived from selfing the F2 plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTL affecting plant height and ear height. The QTL explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8 and 9.     

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。     

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)     

Genotypic variation and identification of QTLs for agronomic traits,using AFLP and SSR markers in RILs of sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

A population of 77 recombinant inbred lines (RILs) were developed through single-seed descent from a cross between PAC-2 and RHA-266. Seeds of the above-mentioned RILs and their parents were planted in the field in a randomised complete block design with two replications. Genetic control for some agronomical traits—sowing-to-flowering date, plant height, stem diameter (SD), head diameter (HD), grain weight per plant, 1,000-grain weight (TGW) and the percentage of oil in grains—were measured for RILs and their parents. Genetic variability was observed among 77 RILs for all traits studied. Transgressive segregation occurred for some traits, and the comparison between 10% of selected RILs with the best parent showed significant difference for SD and HD as well as for TGW. A set of 123 RILs from the same cross, including the 77 above-mentioned RILs and their two parents, were screened with 409 AFLP and SSR markers, and a linkage map was constructed based on 367 markers. Several QTLs associated with the studied traits were identified. The effects of each QTL are moderate, ranging from 7% to 37%, but a high percentage of phenotypic variance is explained when considering all the covariants (TR² mean around 80% in each trait). Although the detected regions need to be more precisely mapped, the information obtained should help in marker-assisted selection.     

The genetic basis of flowering time and photoperiod sensitivity in rapeseed (Brassica napus L.)

The objective of this study was to dissect the genetic control of days to flowering (DTF) and photoperiod sensitivity (PS) into the various components including the main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions (QEs). Doubled haploid (DH) fines were produced from an F1 between two spring Brassica napus cultivars Hyola 401 and Q2. DTF of the DH lines and parents were investigated in two locations, one location with a short and the other with a long photoperiod regime over two years. PS was calculated by the delay in DTF under long day as compared to that under short day. A genetic linkage map was constructed that comprised 248 marker loci including SSR, SRAP and AFLP markers. Further QTL analysis resolved the genetic components of flowering time and PS into the main-effect QTLs, epistatic QTLs and QEs. A total of 7 main-effect QTLs and 11 digenic interactions involving 21 loci located on 13 out of the 19 linkage groups were detected for the two traits. 3 main-effect QTLs and 4 pairs of epistatic QTLs were involved in QEs conferring DTF. One QTL on linkage group (LG) 18 was revealed to simultaneously affect DTF and PS and explain for the highest percentage of the phenotypic variation. The implications of the results for B. napus breeding have been discussed.     

Mapping QTLs for root morphological traits inBrassica rapa L. based on AFLP and RAPD markers

Root growth and thickening plays a key role in the final productivity and even the quality of storage roots in root crops. This study was conducted to identify and map quantitative trait loci (QTLs) affecting root morphological traits in Brassica rapa by using molecular markers. An F2 population was developed from a cross between Chinese cabbage (Brassica rapa ssp. chinensis) and turnip (B. rapa ssp. rapifera), which differed greatly in root characters. A genetic map covering 1837.1 cM, with 192 marker loci and 11 linkage groups, was constructed by using this F2 population. The F3 families derived from F2 plants were grown in the field and evaluated for taproot traits (thickness, length, and weight). QTL analysis via simple interval mapping detected 18 QTLs for the 3 root traits, including 7 QTLs for taproot thickness, 5 QTLs for taproot length, and 6 QTLs for taproot weight. Individually, the QTLs accounted for 8.4-27.4% of the phenotypic variation. The 2 major QTLs, qTRT4b for taproot thickness and qTRW4 for taproot weight, explained 27.4% and 24.8% of the total phenotypic variance, respectively. The QTLs for root traits, firstly detected in Brassica crops, may provide a basis for marker-assisted selection to improve productivity in root-crop breeding.     

云南软米直链淀粉含量的遗传分析与基因定位

以低直链淀粉( 软米)品种毫木细与高直链淀粉品种桂朝2号杂交F2代分离群体为试验材料,研究水稻低直链淀粉含量的遗传规律和基因定位。结果表明,软米品种毫木细直链淀粉含量受一个隐性主效基因/QTL控制,该基因位点（QTL）与糯性基因(wx)为非等位。除主效QTL外,可能还有微效基因的作用。用SSR标记检测到该主效基因/QTL位于11号染色体上RM224附近,LOD值为15.4,可解释的表型变异率为32%。     

玉米雌雄开花间隔天数、结穗率与产量的数量性状位点(QTL)分析

采用SSR标记连锁图谱和复合区间作图法在山西灌溉和干旱胁迫条件下,对玉米(Zea mays L.)自交系黄早四×掖107组合的F3群体雌雄开花间隔天数(ASI)、结穗率和籽粒产量进行了数量性状位点(QTL)定位及基因效应分析.结果表明,在两种水分处理下,ASI、结穗率与籽粒产量的相关性均达到显著水平(P＜0.05).在灌溉和干旱胁迫下,分别检测到3个和2个控制ASI的QTL,位于第1、2、3和第2、5染色体上.在灌溉条件下,在第3和第6染色体上各检测到1个控制结穗率的QTL,基因作用方式呈加性或部分显性,可解释19.9%的表型变异;在干旱条件下,在第3、 7、10染色体上共检测到4个控制结穗率的QTL,基因作用方式为显性或部分显性,可解释60.4%的表型变异.在灌溉和干旱胁迫下,控制产量的QTL分别定位在第3、6、7和第1、2、4、8染色体上,基因作用方式均以加性或部分显性为主,可解释的表型变异为7.3%～22.0%.在干旱条件下,借助连锁分子标记和基因效应分析,可构建包含ASI、结穗率和产量QTL的选择指数,用于分子标记辅助育种.     

四倍体栽培棉种产量和纤维品质性状的QTL定位

陆地棉和海岛棉是两个不同的四倍体栽培种 ,但在生产上各有其特点 ,陆地棉丰产性强 ,海岛棉纤维品质优良 ,利用其种间杂交群体定位产量和品质性状的QTL ,对于分子标记辅助的海岛棉优质纤维向陆地棉转移很有意义。以SSR和RAPD为分子标记 ,陆地棉与海岛棉杂种 (邯郸 2 0 8×Pima90 )F2 群体为作图群体 ,构建了一张含 12 6个标记的遗传图谱 ,包括 6 8个SSR标记和 5 8个RAPD标记 ,可分为 2 9个连锁群 ,标记间平均距离为 13 7cM ,总长1717 0cM ,覆盖棉花总基因组约 34 34% ;以遗传图 12 6个标记为基础 ,对F2 :3 家系符合正态分布的 10个农艺性状及纤维品质性状进行全基因组QTL扫描 ,结果发现 2 9个QTL分别与产量和品质性状有关。其中与衣指、籽指、皮棉产量、子棉产量、衣分等产量性状相关的QTL分别有 1、3、5、6和 1个 ,与纤维长度、整齐度、强度、伸长率和马克隆值等品质性状相关的QTL分别有 2、4、2、4和 1个。各QTL解释的变异量在 12 4 2 %～ 47 0 1%之间。其中比强度有关的 2个QTL能够解释的表型变异率分别为 34 15 %和 13 86 %。     

QTLs for resistance to Setosphaeria turcica in an early maturing Dent×Flint maize population

Quantitative trait loci (QTLs) for resistance to the fungal pathogen Setosphaeria turcica, the cause of northern corn leaf blight (NCLB), were mapped in a population of 220 F₃ families derived from a cross between two moderately resistant European inbred lines, D32 (dent) and D145 (flint). The population was genotyped with 87 RFLP and 7 SSR markers. Trials were conducted in the field in Switzerland, and in the greenhouse with selected F₃ families in Germany. The F₃ population segregated widely for resistance with transgression of the parents. By composite interval mapping, a total of 13 QTLs were detected with two disease ratings (0 and 3 weeks after flowering). Together these QTLs explained 48% and 62% of the phenotypic variation. Gene action at most QTLs was partially dominant. Eight out of the 13 QTL alleles for resistance were contributed by the more-resistant parent, D145. On chromosomes 3, 5 and 8, QTLs were located in the same chromosomal regions as QTLs in tropical and U.S. Corn Belt germplasm. Some QTLs affected NCLB, head smut and common rust at the same time, with alleles at these loci acting isodirectionally. Received: 25 January 1999 / Accepted: 20 Februar 1999     

Molecular mapping of quantitative trait loci in japonica rice.

Rice (Oryza sativa L.) molecular maps have previously been constructed using interspecific crosses or crosses between the two major subspecies: indica and japonica. For japonica breeding programs, however, it would be more suitable to use intrasubspecific crosses. A linkage map of 129 random amplified polymorphic DNA (RAPD) and 18 restriction fragment length polymorphism (RFLP) markers was developed using 118 F2 plants derived from a cross between two japonica cultivars with high and low seedling vigor, Italica Livorno (IL) and Labelle (LBL), respectively. The map spanned 980.5 cM (Kosambi function) with markers on all 12 rice chromosomes and an average distance of 7.6 cM between markers. Codominant (RFLP) and coupling phase linkages (among RAPDs) accounted for 79% of total map length and 71% of all intervals. This map contained a greater percentage of markers on chromosome 10, the least marked of the 12 rice chromosomes, than other rice molecular maps, but had relatively fewer markers on chromosomes 1 and 2. We used this map to detect quantitative trait loci (QTL) for four seedling vigor related traits scored on 113 F3 families in a growth chamber slantboard test at 18 degrees C. Two coleoptile, five root, and five mesocotyl length QTLs, each accounting for 9-50% of the phenotypic variation, were identified by interval analysis. Single-point analysis confirmed interval mapping results and detected additional markers significantly influencing each trait. About two-thirds of alleles positive for the putative QTLs were from the high-vigor parent, IL. One RAPD marker (OPAD13720) was associated with a IL allele that accounted for 18.5% of the phenotypic variation for shoot length, the most important determinant of seedling vigor in water-seeded rice. Results indicate that RAPDs are useful for map development and QTL mapping in rice populations with narrow genetic base, such as those derived from crosses among japonica cultivars. Other potential uses of the map are discussed. Key words : QTL mapping, RAPD, RFLP, seedling vigor, japonica, Oryza sativa.     

测序水稻品种SSR遗传连锁图谱的构建及其农艺性状基因座分析

利用测序水稻品种"Nipponbare（粳）/广陆矮4号（籼）"杂交F2群体90个单株为作图群体,构建含148个SSR标记的水稻遗传连锁图谱,覆盖基因组全长1737.81cM,标记间平均距11.90cM。利用该图谱及Excel2000和Mapmaker/QTL1.1b软件对分蘖数、穗数、穗长、主穗长、株高、剑叶长等六个农艺性状间的相互关系和基因位点进行分析,结果在LOD>2.2和P<0.005的条件下共检测到28个QTLs,它们分布在水稻所有染色体上,单个QTL对性状的分子贡献率11.1%-42.9%,其中大于20%有10个,并对选用已测序材料为亲本构建图谱来探讨水稻农艺性状的分子基础及其育种意义进行了讨论。     

Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture

A previous genetic map containing 117 microsatellite loci and 400 F(2) plants was used for quantitative trait loci (QTL) mapping in tropical maize. QTL were characterized in a population of 400 F(2:3) lines, derived from selfing the F(2) plants, and were evaluated with two replications in five environments. QTL determinations were made from the mean of these five environments. Grain yield (GY), plant height (PH), ear height (EH) and grain moisture (GM) were measured. Variance components for genotypes (G), environments (E) and GxE interaction were highly significant for all traits. Heritability was 0.69 for GY, 0.66 for PH, 0.67 for EH and 0.23 for GM. Using composite interval mapping (CIM), a total of 13 distinct QTLs were identified: four for GY, four for PH and five for EH. No QTL was detected for GM. The QTL explained 32.73 % of the phenotypic variance of GY, 24.76 % of PH and 20.91 % of EH. The 13 QTLs displayed mostly partial dominance or overdominance gene action and mapped to chromosomes 1, 2, 7, 8 and 9. Most QTL alleles conferring high values for the traits came from line L-14-4B. Mapping analysis identified genomic regions associated with two or more traits in a manner that was consistent with correlation among traits, supporting either pleiotropy or tight linkage among QTL. The low number of QTLs found, can be due to the great variation that exists among tropical environments.     

Quantitative trait loci associated with body weight and abdominal fat traits on chicken chromosomes 3, 5 and 7

Body weight and abdominal fat traits in meat-type chickens are complex and economically important factors. Our objective was to identify quantitative trait loci (QTL) responsible for body weight and abdominal fat traits in broiler chickens. The Northeast Agricultural University Resource Population (NEAURP) is a cross between broiler sires and Baier layer dams. We measured body weight and abdominal fat traits in the F(2) population. A total of 362 F(2) individuals derived from four F(1) families and their parents and F(0) birds were genotyped using 29 fluorescent microsatellite markers located on chromosomes 3, 5 and 7. Linkage maps for the three chromosomes were constructed and interval mapping was performed to identify putative QTLs. Nine QTL for body weight were identified at the 5% genome-wide level, while 15 QTL were identified at the 5% chromosome-wide level. Phenotypic variance explained by these QTL varied from 2.95 to 6.03%. In particular, a QTL region spanning 31 cM, associated with body weight at 1 to 12 weeks of age and carcass weight at 12 weeks of age, was first identified on chromosome 5. Three QTLs for the abdominal fat traits were identified at the 5% chromosome-wide level. These QTLs explained 3.42 to 3.59% of the phenotypic variance. This information will help direct prospective fine mapping studies and can facilitate the identification of underlying genes and causal mutations for body weight and abdominal fat traits.     

Mapping QTLs for alpha-amylase activity in rye grain

Genetic control of alpha-amylase activity in rye grain was investigated by QTL mapping based on DS2 x RXL10 intercross consisting of 99 F5-6 families propagated at one location during four vegetation seasons. A wide range of variation in alpha-amylase activity and transgression effects were found among families and parental lines. This variation was shown to be determined in 40.1% by 7 significant (LOD score not less than 2.5) and 2 putative QTLs (2 < LOD < 2.5) distributed on all rye chromosomes except 4R. Two significant QTLs located on 3RL and 5RL chromosome arms were expressed each year. The third significant QTL was detected in three years (1RL). The other four significant QTLs (2RL, 5RS, 6RL, 7RL) were found in one year of study. The number and composition of QTLs were specific for a given year varying from three to six. QTLs were not correlated with isoenzyme polymorphisms at the structural alpha-Amy1 loci. A QTL associated with a region containing the alpha-Amy3 locus was detected on chromosome 5RL. Both high- and low-activity QTL alleles were found in each parental line, which explains the appearance of transgressive recombinants in the segregating population.     

A genetic analysis of seed and berry weight in grapevine.

Fruit size and seedlessness are highly relevant traits in many fruit crop species, and both are primary targets of breeding programs for table grapes. In this work we performed a quantitative genetic analysis of size and seedlessness in an F1 segregating population derived from the cross between a classical seeded (Vitis vinifera L. 'Dominga') and a newly bred seedless ('Autumn Seedless') cultivar. Fruit size was scored as berry weight (BW), and for seedlessness we considered both seed fresh weight (SFW) and the number of seeds and seed traces (SN) per berry. Quantitative trait loci (QTL) analysis of BW detected 3 QTLs affecting this trait and accounting for up to 67% of the total phenotypic variance. QTL analysis for seedlessness detected 3 QTLs affecting SN (explaining up to 35% of total variance) and 6 affecting SFW (explaining up to 90% of total variance). Among them, a major effect QTL explained almost half of the phenotypic variation for SFW. Comparative analysis of QTLs for these traits reduced the number of grapevine genomic regions involved, one of them being a major effect QTL for seedlessness. Association analyses showed that microsatellite locus VMC7F2, closely linked to this QTL, is a useful marker for selection of seedlessnes.