scholarly journals Mapping QTL for Grain Yield under Moisture Stress Environments in Rice (Oryza sativa L.)

2011 ◽  
Vol 3 (4) ◽  
pp. 129-133
Author(s):  
Supriyo CHAKRABORTY ◽  
Sheng-Chu WANG ◽  
Zhao-Bang ZENG
Keyword(s):  
Grain Yield ◽  
Genetic Variance ◽  
Oryza Sativa L ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Common Qtl ◽  
Rice Chromosomes ◽  
Stress Environments

Polygenes (QTLs) for grain yield were mapped on rice chromosomes under two moisture stress environments by multiple interval mapping (MIM) method in a double haploid (DH) population derived from a cross between a deep-rooted japonica and a shallow-rooted indica genotype. In environment 1 (E1), the MIM detected a total of six QTLs for grain yield on chromosomes-two QTLs on chromosome 1 and four QTLs on chromosome 5 along with one additive x additive epistasis. But in environment 2 (E2), the MIM detected five QTLs for grain yield on two chromosomes-three QTLs on chromosome 1 and two QTLs on chromosome 7. One common QTL on chromosome 1 flanked by the markers RG109-ME1014 was detected in both the environments, although the other detected QTLs differed between environments. The magnitude of QTL effect, percent genetic variance and percent phenotypic variance explained by each QTL was also estimated in both environments. The common QTL explained about 26.05 and 13.93% of genetic variance in E1 and E2, respectively. Estimated broad sense heritability for grain yield was 48.01 in E1 and 25.27% in E2.

scholarly journals Genetic Mapping Identifies Consistent Quantitative Trait Loci for Yield Traits of Rice under Greenhouse Drought Conditions

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 62
Author(s):  
Niranjan Baisakh ◽  
Jonalyn Yabes ◽  
Andres Gutierrez ◽  
Venkata Mangu ◽  
Peiyong Ma ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Grain Yield ◽  
Drought Resistance ◽  
Quantitative Trait ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Yield Traits ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Trait Loci

Improving drought resistance in crops is imperative under the prevailing erratic rainfall patterns. Drought affects the growth and yield of most modern rice varieties. Recent breeding efforts aim to incorporate drought resistance traits in rice varieties that can be suitable under alternative irrigation schemes, such as in a (semi)aerobic system, as row (furrow-irrigated) rice. The identification of quantitative trait loci (QTLs) controlling grain yield, the most important trait with high selection efficiency, can lead to the identification of markers to facilitate marker-assisted breeding of drought-resistant rice. Here, we report grain yield QTLs under greenhouse drought using an F2:3 population derived from Cocodrie (drought sensitive) × Nagina 22 (N22) (drought tolerant). Eight QTLs were identified for yield traits under drought. Grain yield QTL under drought on chromosome 1 (phenotypic variance explained (PVE) = 11.15%) co-localized with the only QTL for panicle number (PVE = 37.7%). The drought-tolerant parent N22 contributed the favorable alleles for all QTLs except qGN3.2 and qGN5.1 for grain number per panicle. Stress-responsive transcription factors, such as ethylene response factor, WD40 domain protein, zinc finger protein, and genes involved in lipid/sugar metabolism were linked to the QTLs, suggesting their possible role in drought tolerance mechanism of N22 in the background of Cocodrie, contributing to higher yield under drought.

Genetic analysis of tolerance to maize streak virus in maize

Genome ◽  
1999 ◽  
Vol 42 (1) ◽  
pp. 20-26 ◽  
Author(s):  
D T Kyetere ◽  
R Ming ◽  
M D McMullen ◽  
R C Pratt ◽  
J Brewbaker ◽  
...  
Keyword(s):  
Zea Mays ◽  
Field Experiments ◽  
Zea Mays L ◽  
Recombinant Inbred Lines ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Rflp Markers ◽  
Streak Virus ◽  
Phenotypic Variance ◽  
Progress Curve

Maize streak, incited by maize streak geminivirus (MSV), is a major disease limiting maize (Zea mays L.) production over widespread areas of Africa. To understand the genetic basis of tolerance to MSV, recombinant inbred lines (RILs) derived from the cross of the MSV tolerant inbred Tzi4 with the MSV susceptible inbred Hi34, were evaluated for MSV tolerance. Experiments were conducted using controlled leafhopper (Cicadulina spp.) infestation in one glasshouse experiment at Namulonge, Uganda, and two field experiments at Centro Internacional de Mejoramiento de Maiz y Trigo, Harare, Zimbabwe. Eighty-seven RILs were genotyped at 82 loci by restriction fragment length polymorphism (RFLP) analysis. The association between genotype at RFLP marker loci and MSV tolerance was determined using single-factor analysis of variance (SFAOV), multiple regression, and interval mapping procedures. There was a significant association of MSV tolerance with RFLP markers on the short arm of chromosome 1. By SFAOV, the portion of the phenotypic variance explained by genotype class (R2) for the association between npi262 and the area under disease progress curve (AUDPC) measure of MSV tolerance was as high as 76% in field experiments. Interval mapping analyses (Knapp and Bridges 1990; Nelson 1997) identified the chromosome region bracketed by bnl12.06a and npi262 as explaining the largest proportion of the variation in MSV tolerance. After classification of symptom responses from the final field ratings into resistant and susceptible classes, qualitative analysis of data fit a chi-square test to a 1:1 Mendelian ratio, further indicating presence of a single major gene. Multipoint linkage analysis placed this gene, designated msv1, at a genetic distance of 3 cM distal to npi262. Identification of the tightly linked molecular marker locus npi262 should greatly aid ongoing conversion of susceptible African varieties to maize streak resistance.Key words: Zea mays L., Cicadulina spp., host resistance, gene mapping, molecular markers.

scholarly journals QTL Mapping for Days to Flowering under Drought Condition in Rice (Oryza sativa L.) Genome

2011 ◽  
Vol 39 (1) ◽  
pp. 58 ◽  
Author(s):  
Supriyo CHAKRABORTY ◽  
Zhao Bang ZENG
Keyword(s):  
Qtl Mapping ◽  
Oryza Sativa L ◽  
Chromosome 1 ◽  
Drought Condition ◽  
Days To Flowering ◽  
Common Qtl ◽  
Drought Tolerant ◽  
Negative Effect ◽  
Mapping Models ◽  
Positive Effect

QTL for days to flowering in rice under drought condition were mapped using a DH population derived from a cross between a deep-rooted upland adapted japonica genotype CT9993-5-10-1-M and a lowland adapted shallow-rooted moderately drought tolerant indica genotype IR62266-42-6-2. QTL mapping was performed following three different mapping models viz. simple (SIM), composite (CIM) and multiple mapping model (MIM) using WinQTL Cartographer version 2.5.006. SIM located 12 QTL for days to flowering spread over nine chromosomes whereas CIM and MIM each located 5 QTL with a threshold LOD score of 2.5. A comparison of the QTL detected by three different models identified five QTL that were common across at least two models for days to flowering. In MIM analysis, the detected QTL (qHD-1-b) between flanking markers (RG109 – ME1014) located on chromosome 1 recorded positive effect (1.4090) but the remaining four QTL had negative effect. The QTL (qHD-3-a) detected between flanking markers (RG104 – RG409) by both MIM and SIM in the present study was also reported earlier as linked with the marker RG104. The five common QTL detected by at least two models could be considered as stable QTL for days to flowering under drought and might be of practical use in marker assisted selection.

GENETIC VARIANCES IN AN EIGHT PARENT HALF DIALLEL OF OATS

1976 ◽  
Vol 18 (3) ◽  
pp. 419-427 ◽  
Author(s):  
D. R. Sampson ◽  
I. Tarumoto
Keyword(s):  
Grain Yield ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Heading Date ◽  
Phenotypic Variance ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Additive Component ◽  
Tests Of Fit ◽  
Half Diallel

Twenty-eight progenies with their eight parent cultivars of Avena saliva L. (2n = 6x = 42) were grown in F1, F2 and F3 in separate years; the F1 as spaced plants, the F2 and F3 as dense seeded populations. Additive genetic variance constituted most of the phenotypic variance of eight traits (heading date, plant height, stem diameter, grain yield and four components of yield) according to a Griffing Method 4, Model II analysis. Similarly, additive × year interactions were more important than nonadditive × year interactions. A Hayman-Jinks analysis of the same material but with the parents included showed that the additive component was 2 to 16 times larger than the dominance components in the F1 However in the F2 and F3 the dominance components became larger than the additive components for most traits instead of declining in importance as expected. Further, tests of fit to the hypotheses underlying the Hayman-Jinks analysis were negative in 8 of 24 cases. It is postulated that these discrepancies result from epistatic variance which caused an upward bias in the dominance estimates. The calculation and uses of two estimates of narrow-sense heritability are discussed.

Discovery of Genomic Regions Associated With Resistance to Late Wilt Disease Caused by Harpophora Maydis (Samra, Sabet and Hing) in Maize (Zea mays L.)

2021 ◽  
Author(s):  
N. C. Sunitha ◽  
E. Gangappa ◽  
R. P. Veeresh Gowda ◽  
Ramesh S ◽  
Sunil Biradar ◽  
...  
Keyword(s):  
Interval Mapping ◽  
Interaction Effects ◽  
Snp Markers ◽  
Wilt Disease ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Resistance Level ◽  
Resistant Parent ◽  
Harpophora Maydis ◽  
Location Interaction

Abstract Late wilt disease (LWD) caused by Harpophora maydis (Samra, Sabet and Hing) is emerging as major production constraint in maize across the world. As a prelude to develop maize hybrids resistance to LWD, genetic basis of resistance was investigated. Two F2:3 mapping populations (derived from CV156670 × 414-33 (P-1) and CV156670 × CV143587 (P-2)) were challenged with LWD at two locations (Kallinayakanahalli and Muppadighatta) during 2017 post-rainy season. Wider range of LWD scores were observed at both locations in both the populations. LWD response was influenced by significant Genotype × location interaction. Six and 56 F2:3 progeny families showed resistance level better than resistant parent. 150 and 199 polymorphic SNP markers were used to genotype P-1 and P-2, respectively. Inclusive composite interval mapping was performed to detect significant QTL, QTL × QTL, QTL × Location interaction effects. Three major and four minor QTL controlling LWD resistance were detected on chromosome-1. Position and effect of the QTL varied with the location. Significant di-QTL interactions involving QTL (with significant and/or non-significant effects) located within and between all the ten chromosomes were detected. Five of the seven detected QTL in our study showed significant QTL × location interaction. Though two major QTL (q-lw-1.5 and q-lw-1.6) with lower Q×L interaction effects could be considered as stable, their phenotypic variance is not large enough to deploy them in MAS. Based on these results, strategies to breed maize for resistance to LWD are discussed.

scholarly journals Mapping QTLs for phenotypic and morpho-physiological traits related to grain yield under late sown conditions in wheat (Triticum aestivum L.)

2021 ◽  
Author(s):  
Yaswant Kumar Pankaj ◽  
Lalit Pal ◽  
Ragupathi Nagarajan ◽  
Kulvinder Singh Gill ◽  
Vishnu Kumar ◽  
...  
Keyword(s):  
Heat Stress ◽  
Grain Yield ◽  
Heat Tolerance ◽  
Canopy Temperature ◽  
Triticum Aestivum L ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Breeding Lines ◽  
Positive Correlations ◽  
Genomic Regions

The elevating temperature makes heat stress one of the major issues for wheat production globally. To elucidate genetic basis and map heat tolerance traits, a set of 166 doubled haploid lines (DHLs) derived from the cross between PBW3438/IC252874 was used. The population was evaluated under Normal sown (NS) and late sown (LS) conditions, by exposing to heat stress during rabi season. The canopy temperature (CT) showed positive correlations with grain yield, whereas Soil plant analysis development (SPAD) was not significantly correlated and associated with GY in both the normal and late sown conditions. Composite interval mapping (CIM) identified total 12 Quantitative trait loci (QTLs) viz., 2 (Normal sown), 10 (late sown) mapped on linkage groups 1A, 1D, 2B, 2D, 3B, 4D, 5B, and 6D, during both the crop seasons 2017-18 and 2018-19. Combining the results of these QTLs revealed a major stable QTL for grain yield (GY) on chromosome 3B with 11.84% to 21.24% explaining phenotypic variance under both sowing conditions. QTL for CT and SPAD was detected on chromosome 1A while QTL for GY on chromosomes 3B and 5B. The identified QTLs in the genomic regions could be targeted for genetic improvement and marker-assisted selection for heat tolerance in wheat. The tools like SPAD and CT could be exploited to screen a large number of breeding lines.

scholarly journals Fine mapping of rice drought QTL and study on combined effect of QTL for their physiological parameters under moisture stress condition

2016 ◽  
Vol 8 (4) ◽  
pp. 1992-1998
Author(s):  
K. Baghyalakshmi ◽  
P. Jeyaprakash ◽  
S. Ramchander ◽  
M. Raveendran ◽  
S. Robin
Keyword(s):  
Grain Yield ◽  
Fine Mapping ◽  
Rice Variety ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Backcross Inbred Lines ◽  
Relative Water ◽  
House Condition ◽  
And Control ◽  
Better Than

The present investigation was undertaken to study the effect of different yield QTL (DTY2.2, DTY3.1 and DTY8.1) under drought and their physiological response to drought stress. Backcross Inbred Lines (BILs) of IR64 (CB-193 and CB-229) along with IR64, APO and the traditional rice variety Norungan were raised in green house condition under water stress and control to evaluate the effect of the QTL on grain yield. The BIL CB-193 recorded higher photosynthetic rate (22.051), transpiration rate (7.152) and Ci/Ca ratio (0.597) whereas the BIL CB-229 recorded high relative water content (80.76%). It was found that the combination of three QTL (CB-229) performed better than the susceptible parent and the line with two QTL (CB-193 Fine-mapping of two QTLs viz., qDTY2.2 and qDTY8.1, for grain yield (GY) were conducted using backcross derived lines. Composite interval mapping analyses resolved the originally identified qDTY2.2 region of 6.7 cM into a segment of 2.1 cM and two sub QTLs at region between RM23132 and RM1578 (75.75 cM- 77.66 cM), RM515 and RM1578 (75.11 cM-77.66 cM) were identified in qDTY8.1 region. However this study provides a unique opportunity to breeders to introgress such regions together as a unit into high-yielding drought-susceptible varieties through MAS.

Quantitative trait loci underlying hatching weight and growth traits in an F2 intercross between two strains of Japanese quail

2012 ◽  
Vol 52 (11) ◽  
pp. 1012 ◽  
Author(s):  
S. S. Sohrabi ◽  
A. K. Esmailizadeh ◽  
A. Baghizadeh ◽  
H. Moradian ◽  
M. R. Mohammadabadi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Japanese Quail ◽  
Quantitative Trait ◽  
Growth Traits ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Cross Model ◽  
Trait Loci ◽  
Qtl Effects

A three-generation resource population was developed using two distinct Japanese quail strains, wild and white, to map quantitative trait loci underlying hatching weight and growth traits. Eight pairs of white and wild birds were crossed reciprocally and 34 F1 birds were produced. The F1 birds were intercrossed to generate 422 F2 offspring. All of the animals from three generations (472 birds) were genotyped for eight microsatellite markers on chromosome 1. Liveweight data from hatch to 5 weeks of age were collected on the F2 birds. Quantitative trait loci (QTL) analysis was conducted applying the line-cross model and the least-squares interval mapping approach. The results indicated QTL affecting hatching weight and several growth related traits on chromosome 1. The F2 phenotypic variance explained by the detected additive QTL effects ranged from 1.0 to 3.7 for different traits. Modelling both additive and dominance QTL effects revealed additional QTL with significant dominance mode of action affecting pre-slaughter weight. However, there was no evidence for imprinting (parent-of-origin) effects. The variance due to the reciprocal cross effect ranged between 3.0 and 19.1% for weight at 1 week of age and hatching weight, respectively.

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