The genetic relationship among plant-height traits found using multiple-trait QTL mapping of a dent corn and popcorn cross

Genome ◽  
2007 ◽  
Vol 50 (4) ◽  
pp. 357-364 ◽  
Author(s):  
Yuling Li ◽  
Yongbin Dong ◽  
Suzhun Niu ◽  
Dangqun Cui
Keyword(s):  
Plant Height ◽  
Genetic Relationship ◽  
Plant Traits ◽  
Analysis Data ◽  
Interval Mapping ◽  
Joint Analysis ◽  
Maize Breeding ◽  
Multiple Trait ◽  
Dent Corn ◽  
Additional Qtls

Plant height (PH) is one of the most important traits in maize breeding programs. In popcorn, inferior plant traits can be improved with the dent/flint corn germplasm. In the current study, a total of 259 F2:3 families, developed from a cross between a dent corn inbred and a popcorn inbred, were evaluated for 4 PH traits. Quantitative trait loci (QTLs) for each trait were detected using composite interval mapping methods. In addition, genetic interrelationships were investigated using multiple-trait joint analysis for PH with ear height (EH), and for PH with top height (TH). In total, 6, 5, 2, and 6 QTLs were identified for PH, EH, TH, and TH/PH in single-trait analysis, respectively. Joint-analysis data suggest a strong and complex genetic relationship between PH and EH, and between PH and EH, with no QTLs controlling any single trait independently. In addition, 4 kinds of QTLs detected were classified as closely linked QTLs, pleiotropic QTLs, QTLs with opposite effects, and additional QTLs. It was, consequently, difficult to improve lodge resistance through selection on any individual PH trait. The current study demonstrates that multiple-trait joint analysis not only identified additional QTLs, but also revealed the genetic relationship among different highly correlated traits at the molecular level.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

scholarly journals Growth and production of irrigated vitória pineapple grown in semi-arid conditions

2014 ◽  
Vol 36 (3) ◽  
pp. 693-703 ◽  
Author(s):  
Rodinei Facco Pegoraro ◽  
Bruna Aparecida Madureira de Souza ◽  
Victor Martins Maia ◽  
Uirá do Amaral ◽  
Marlon Cristian Toledo Pereira
Keyword(s):  
Plant Height ◽  
Biomass Production ◽  
Plant Traits ◽  
Block Design ◽  
Leaf Length ◽  
Stem Diameter ◽  
Plant Residue ◽  
Arid Conditions ◽  
Vegetative Biomass ◽  
Semi Arid

This study aimed to evaluate the growth characteristics of irrigated Vitória pineapple plants grown in semi-arid conditions and determine its developmental stages based on those characteristics. It was used a randomized block design with four replicates. The experimental treatments were: plant harvest at 270, 330, 390, 450, 510, 570, 690, 750, and 810 days after planting (DAP). The following variables were determined: plant height, stem diameter, D-leaf length, D-leaf fresh and dry mass, biomass production of plants and plant parts (organs), and vegetative biomass. Five phenological stages are proposed based on vegetative biomass production: < 20% biomass production (V1); 21-40% (V2); 41-60% (V3); 61-80% (V4); and > 80% (V5). The maximum growth rate for plant height, D-leaf length, and stem diameter was observed at the end of the phenological stage V1 (390-411 DAP), and at the end of stage V5 these plant traits had average values of 106, 82, and 7 cm, respectively. The maximum biomass accumulation rates were observed at stages V4 and V5, resulting in a final fruit yield and total fresh biomass of 72 t ha-1 and 326 t ha-1, respectively. Finally, we estimated that 80% of the accumulated biomass may remain in the field after fruit and slip harvest, and could be incorporated as plant residue into the soil.

scholarly journals Parameter genetik kultivar-kultivar jagung local pada cekaman salinitas sedang

Jurnal Agro ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 55-67
Author(s):  
Sakka Bin Samudin ◽  
Jeki Moh. Adnan Khalik ◽  
Ruli Akbar ◽  
Muliati Muliati ◽  
Mustakin Mustakin
Keyword(s):  
Salinity Stress ◽  
Coefficient Of Variation ◽  
Genetic Parameters ◽  
Plant Traits ◽  
Genetic Advance ◽  
Maize Breeding ◽  
Green House ◽  
Moderate Salinity ◽  
Genetic Coefficient Of Variation ◽  
Genetic Coefficient

Produktivitas jagung di Sulawesi Tengah masih relatif rendah dibanding produksi nasional sehingga perlu ditingkatkan melalui pemuliaan tanaman. Penelitian bertujuan untuk mengkaji parameter genetik tanaman jagung pada cekaman salinitas sedang.  Penelitian dilaksanakan pada Juni sampai Agustus 2019, di Green House Fakultas Pertanian, Universitas Tadulako, Palu. Rancangan percobaan yang digunakan adalah Rancangan Acak Lengkap (RAL) terdiri atas 6 perlakuan genotip dan diulang 3 kali serta 5 unit tanaman per perlakuan sehingga terdapat 90 unit percobaan. Parameter genetik yang di analisis adalah koefisiean keragaman genotipik, koefisien keragaman fenotipik, heritabilitas, kemajuan genetik, korelasi, dan analisis sidik lintas. Hasil penelitian menunjukkan bahwa kehijauan daun, bobot tongkol berkelobot, berat tongkol tidak berkelobot, dan berat biji pertongkol memiliki koefisien keragaman genetik tinggi. Kehijauan daun, berat tongkol berkelobot, bobot tongkol tidak berkelobot, panjang tongkol tidak berkelobot, diamater tongkol, berat biji per tongkol dan bobot 100 biji memiliki nilai heritabilitas dan kemajuan genetik tinggi. Seleksi secara tidak langsung dapat dilakukan pada umur panen agar diperoleh hasil jagung lokal yang tinggi pada kondisi tercekam salinitas sedang. Karakter-karakter tersebut dapat dijadikan acuan dalam menyeleksi tanaman jagung dengan cekaman salinitas sedang untuk program pemuliaan jagung.AbstractThe productivity of maize in Central Sulawesi is relatively low compared to national production and needs to be improved by plant breeding. The study aimed to examine the genetic parameters of the maize plant traits at moderate salinity stress. The research was conducted from June to August 2019, at the Green House of the Faculty of Agriculture, Tadulako University, Palu. The genetic parameters analyzed were genetic coefficient of variation, phenotypic coefficient of variation, heritability, genetic advance, correlation, and path analysis. The experimental design used a completely randomized design consisting of six genotypic treatments and repeated three times. The results showed that the leaves greenness, the weight of the cob with and without husk, and weight of seeds per cob had a high genetic coefficient of variation. Leaf greenness, the weight of the ear with and without husk, ear length without husk, ear diameter, seed weight per ear and yield have a high value of heritability and genetic advance. Indirect selection can be applied through harvest time trait to obtain a high local maize yield in moderate salinity stress condition. These traits can be used as a reference in selecting maize plants with moderate salinity stress for maize breeding programs.

scholarly journals MOLECULAR CHARACTERIZATION OF QUALITY PROTEIN MAIZE AND DOWNY MILDEW RESISTANCE LINES BASED ON SIMPLE SEQUENCE REPEATS (SSRs) MARKER

Zuriat ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
D. Ruswandi ◽  
N. Wicaksana ◽  
M. B. Pabendon ◽  
M. Azrai ◽  
M. Rachmadi ◽  
...  
Keyword(s):  
Molecular Characterization ◽  
Genetic Relationship ◽  
Genetic Relatedness ◽  
Genotypic Variation ◽  
Group Method ◽  
Maize Breeding ◽  
Pair Group ◽  
Group A ◽  
Ssrs Markers ◽  
Relationship Of

The information on germplasm diversity and genetic relatedness among elite breeding materials is an important element in maize breeding. Molecular characterization and genetic relationship of 11 QPM-DMR lines were analysed using thirty three SSRs markers. Genetic relationship was determined using Jaccard’s similarity coefficient, and dendogram was then constructed based on the unweighted pair-group method with arithmetical averages (UPGMA). Result showed that (i) all SSRs loci were informative for describing the genotypic variation as showed by their PIC, which ranged from 0.19 for umc1304 to 0.93 for phi112; (ii) the eleven maize inbred lines were clustered into one major group A and small groups B and C that corresponds well with the breeding programs adopted at different institutes of release, and (iii) thus, SSRs marker system is a valuable marker for varietals identification and for genetic diversity study of elite breeding materials.

scholarly journals Genetic progress, adaptability and stability of maize cultivars for value of cultivation and use trials

2019 ◽  
Vol 41 (1) ◽  
pp. 42624
Author(s):  
Joaquim Vicente Uate ◽  
Joel Jorge Nunvuga ◽  
Carlos Pereira da Silva ◽  
Lauro Jose Moreira Guimarães ◽  
Renzo Garcia Von Pinho ◽  
...  
Keyword(s):  
Low Cost ◽  
Yield Potential ◽  
Joint Analysis ◽  
Stability Estimates ◽  
Maize Breeding ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Growing Seasons ◽  
Maize Cultivars ◽  
Maize Genotypes

Maize breeding programs conduct multi-environment trials every year to assess the performance of new cultivars in pre-releasing tests. The data are combined across sites and seasons to perform a joint analysis in order to obtain information that will help breeders to select the best cultivars for different environments. Beyond this, it is essential to understand the different factors that can hamper the selection and genetic progress (i.e., genetic variability, selection intensity and genotype-by-environment interactions). In this study, the genetic progress (GP) was estimated and the adaptability and stability of 81 maize genotypes were evaluated in a series of trials for the value of cultivation and use (VCU) between the 2010/11 and 2014/15 growing seasons. The genotypes were composed of open-pollinated varieties, topcross hybrids, intervarietal hybrids, and single, double and three-way cross hybrids and were assessed in 117 environments in the central region of Brazil, from which 22 presented environmental stresses. For grain yield, an annual GP of 331.5 kg ha-1 was observed, thus showing efficiency in the selection of superior cultivars. Additionally, it was observed that some low-cost seed cultivars showed yield potential, adaptability and stability estimates that were compatible with commercial hybrids, thus making them quite attractive for cultivation in environments with or without abiotic stresses.

When does gap filling of trait data confound taxonomic and functional analyses? 

2021 ◽  
Author(s):  
Julia Joswig ◽  
Jens Kattge ◽  
Guido Kraemer ◽  
Miguel Mahecha ◽  
Nadja Rüger ◽  
...  
Keyword(s):  
Functional Groups ◽  
Best Practice ◽  
Plant Traits ◽  
Analysis Data ◽  
Gap Filling ◽  
Global Database ◽  
Probabilistic Matrix Factorization ◽  
Filling Method ◽  
Functional Analyses ◽  
Trait Correlations

&lt;p&gt;Data on plant traits are increasingly used to understand relationships between biodiversity and ecosystem processes. Large trait databases are sparse because they are compiled from many smaller and usually more local databases. This sparsity severely limits the potential for both multivariate and global data analyses, and so &quot;gap-filling&quot; (imputation) approaches are commonly used to predict missing trait data prior to analysis. Data imputation can result in large biases and circularity; yet, no best practice has evolved for the appropriate use of gap-filled data. Here, we use the TRY database, the largest global database of plant traits, in combination with the commonly used gap-filling algorithm, BayesianHierarchical Probabilistic Matrix Factorization (BHPMF), to address opportunities and problems introduced by gap-filling. BHPMF is the gap-filling method of choice for both TRY, and the large and widely used database sPLOT. It predicts missing trait data using the taxonomic hierarchy and observed patterns of trait variance and trait-trait correlations. We use three metrics: root mean square error estimates, coefficient of variation to assess univariate deviation, and silhouette indices to assess multivariate deviation and clustering strength. We show that gap-filling results in deviation of these metrics calculated for groupings at lower taxonomic levels (intra-specific and intra-genera), but less so at higher taxonomic levels (family) and for functional groups. Trait-trait correlations are preserved at all levels. The strength of deviations depends both on the percentage of gaps, and on data characteristics, e.g. intra-taxa variability. Gap-filling with dataset-external trait data generally ameliorates prediction error, but the deviations of intra-taxonomic variation measures depend on the content of the added data. We conclude that BHPMF gap-filling introduces little bias if specifically used for analyses of traits within functional groups, including growth forms and plant functional types (PFTs), as well as trait-trait correlations. However, we generally discourage their use for analyses of taxonomic groupings at or below the family level. In summary, our study supports decisions on when and how to integrate BHPMF gap-filled trait data in future studies. We conclude with selected best practices when using sparse databases.&lt;/p&gt;

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