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23 results for “genetic gain”
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
High genetic gains in wood volume and fecundity can be both achieved by direct selection in half-sib families of Pinus yunnanensis Franch.
<p><strong><span>Experiment background</span></strong></p> <p><span>This study focused on characterizing phenotypic variation among and within provenances of <em>Pinus yunnanensis</em><span> Franch. aged 16 years in </span></span><span>a common garden</span><span>, with an emphasis on key traits such as cone production, trunk straightness, and crown health, as well as their relationships with traditional growth traits like tree height, diameter at breast height, and wood volume. Specifically, the objectives were to (1) characterize the variation of each trait within and among provenances; (2) assess inter-trait relationships, exploring patterns of co-variation and potential trade-offs; and (3) evaluate the feasibility of multi-trait selection strategies that aim for simultaneous improvements in growth, trunk straightness, and fecundity, contributing valuable insights for advancing <em>P. yunnanensis</em><span> </span>breeding efforts.</span></p> <p><strong><span>Experimental Design</span></strong></p> <p><span>This study was conducted in a common garden for <em>P. yunnanensis</em> located in Lufeng County, central Yunnan Province (102°12' E, 25°13' N) at an altitude of 1860 meters. The site lies in the transition zone between the subtropical humid climate of eastern Yunnan and the sub-humid climate of southwest Yunnan. The climate is characterized by warm and dry winters, humid and hot summers, with a mean annual temperature of 15.5°C and annual precipitation ranging between 900–1000 mm. The dry season extends from November to April, accounting for 6%-17% of the total annual precipitation.</span></p> <p><span>The common garden was established in 2006, with progeny from 179 superior trees selected from six provenance regions, including Anning County (AN), Qujing City (QJ), Yongren County (YR), Yulong County (YL), Tengchong County (TC), and Ninglang County (NL). Each provenance includes 30 families, except for one provenance with 29 families. </span></p> <p><span>The common garden has an area of about 3 ha, with a random block design, and a planting scheme of 2 m × 3 m</span><span>. </span><a name="_Hlk181695359"></a><span>To minimize environmental variation across the study site, a horizontal banding method was used for land preparation prior to planting.</span><span> </span><span>To reduce environmental variation across the study site, a horizontal banding method was used during land preparation. In each block, six provenances were randomly arranged, and families were randomly assigned within each provenance. Five plants from each family were planted in rows, and the design was replicated four times. A total of 3467 progeny from 179 superior trees across six provenances were included in the trial.</span></p> <p><strong><span>Experimental Variables</span></strong></p> <p><span>The study measured nine phenotypic traits, which included both quantitative and qualitative traits, as outlined below:</span></p> <p><span>Tree Height (H): Measured directly with a Vertex Laser instrument (DZH-30, Harbin, China) in meters (m).</span></p> <p><span>Diameter at Breast Height (D): Measured using a circumference tape in centimeters (cm).</span></p> <p><span>Crown Diameter (LCD, SCD): Long crown diameter (LCD) and short crown diameter (SCD), representing the maximum and minimum tree crown diameter, respectively, measured in meters (m) using a tower ruler.</span></p> <p><span>Height Under the Branch (TH): Measured in meters (m) using a tower ruler.</span></p> <p><span>Wood Volume (V): Estimated using the formula based on the forestry industry standard for <em>P. yunnanensis</em> (Agriculture and Forestry Ministry of China, 1977), with units in cubic meters (m³).</span></p> <p><span>Cone Production (CP): The number of open and closed cones in the canopy, including both serotinous and non-serotinous cones, recorded in counts to assess fecundity.</span></p> <p><span>Trunk Straightness (ST): A subjective visual assessment using a classification system: 1 for a highly twisted stem, 5 for a perfectly straight stem.</span></p> <p><span>Crown Health (CH): Visual assessment of the tree's crown, considering damage from abiotic and biotic stresses, with a grading scale from 1 (severely damaged) to 5 (perfectly healthy).</span></p> <p><strong><span>Data Analysis Methods</span></strong></p> <p><span>Data analysis was performed using R (version 3.6.3). The following statistical methods were employed:</span></p> <p><span>Variance Analysis: Nested variance analysis was used to evaluate the significance of differences and partition phenotypic variation among and within provenances. </span></p> <p><span>Principal Component Analysis (PCA): PCA was performed on the standardized matrix of nine phenotypic traits to reveal the dimensional structure and patterns of the data.</span></p> <p><span>Structural Equation Modeling (SEM): SEM was used to examine the direct and indirect relationships among traits, such as growth (H, D, V), crown size (LCD, SCD, TH), fecundity (CP), trunk straightness (ST), and crown health (CH). This analysis helped identify the causal pathways between the traits.</span></p> <p><span>Random Forest Analysis (RF): RF analysis was conducted to assess the importance of specific traits in predicting fecundity (CP) and trunk straightness (ST). Regression and classification methods were used for these analyses, with 1000 decision trees to ensure stable importance measures.</span></p> <p><strong><span>Dataset Description</span></strong></p> <p><span>The excel file (Raw Data) includes the following sheets: 1- Variables: Details on all the variables. 2- </span><span>Values of phenotypic traits</span><span>. 3- </span><span>Variance components </span><span>of phenotypic traits among and within provenances</span><span>. 4-</span><span> </span><span>Coefficient of variance</span><span> for phenotypic traits</span><span>. 5-</span><span> <span>The</span> <span>average membership function values (SFM) and </span>comprehensive weight of each principal component (PCA)<span> of </span></span><span>phenotypic traits</span><span>.</span></p>
Development of whole-genome prediction models to increase the rate of genetic gain in intermediate wheatgrass (Thinopyrum intermedium) breeding
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Data and code for: Realized genetic gains via recurrent selection in a tropical maize haploid inducer population and optimizing simultaneous selection for the next cycles
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Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
<p>Studies characterizing the effect of a century of plant breeding on physiological traits are highly needed to identify candidate traits for future improvement in maize (<em>Zea mays</em> L.). A global evaluation of kernel weight progress over time requires the assembly of large and reliable data documenting genetic improvements in this trait across commercial breeding programs in different regions. We compiled a global dataset of yield and yield components from 34 published and unpublished studies comparing two or more maize cultivars from different decades of commercial release under field conditions. The dataset includes 750 entries of kernel weight data (requirement to be included in the systematic review), of which 642 and 666 include data entries of grain yield and kernel number, respectively. We also extracted the metadata describing experimental site information, agronomic management practices, and genotypic information. This dataset can be useful to identify trends of yield improvement across management conditions, with proper consideration of the trade-off between kernel number and kernel weight in maize.</p>
Data from Preliminary estimates of genetic parameters and familial selection for non-native poplars show good potential for genetic gains on growth, cold hardiness, trunk quality and Sphaerulina musiva susceptibility
<p>Data from Preliminary estimates of genetic parameters and familial selection for non-native poplars show good potential for genetic gains on growth, cold hardiness, trunk quality and <em>Sphaerulina musiva</em> susceptibility</p> <p><br> Abstract<br> Genetic parameters for growth, trunk quality and susceptibility to frost and <em>Sphaerulina musiva</em> attack was estimated from 34 half-sib families of hybrid poplar from the crossing of non-native parents, <em>Populus maximowiczii</em> A. Henry and <em>Populus trichocarpa</em> Torr. & Gray, 3 and 6 years after planting. The use of spatial analysis proved to be the best method for quantitative growth data. The proportion of the among-family variance to the total (phenotypic) variance as well as the high heritabilities of growth and susceptibility to frost and <em>Spaherulina musiva</em> showed a high potential for selection for these traits while the quality traits were under low genetic control. Some families showed gains for several traits, suggesting the possibility of developing a selection index to obtain superior families that show gain for not only growth but quality and adaptive traits as well. Type B correlations were high, suggesting that families responded in the same way regardless of the site. High type A correlation between growth traits at 3 and 6 years showed early selection potential, although these relationships should be confirmed with future measurements to evaluate this effect at maturity. These results can be integrated into the strategy for improving hybrid poplar parental populations and, in the longer term, will make it possible to optimize the selection of individuals with traits of interest for the operational deployment of hybrid poplar clones.</p>
Data for: Accuracy of genomic selection and long‐term genetic gain for resistance to Verticillium wilt in strawberry
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Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
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Data from: A dominance hypothesis argument for historical genetic gains and the fixation of heterosis in octoploid strawberry
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A Retrospective Epidemiologic Registry to Gain Insight Into the Characteristics and Prognosis of AML Patients According to the Routinely Used Genetic and Biologic Markers
ClinicalTrials.gov study NCT05541224. IPD Sharing: NO. Countries: 1. Publications: 13.
Accuracy of genomic selection and long-term genetic gain for resistance to Verticillium wilt in a genetically diverse strawberry population
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SELECTION METHODS TO OPTIMIZE THE GAIN AND GENETIC DIVERSITY IN Pinus caribaea var. caribaea
The proposal of this work was to estimate the genetic variability in orchards of Pinus caribaea var. caribaea based on growth traits and to analyze the best selection method. This study was conducted in two areas of P. caribaea var. caribaea situated in Savannah biome. The first orchard was a randomized complete block design with 76 progenies and 4 controls (area 1), the second orchard, the lattice design was 10x10 with 99 progenies and one control (area 2), 28 and 27 years old, respectively. The software SELEGEN was used to estimate genetic parameters trough REML/BLUP method. Significant variation was observed between and with progeny all traits in area 2 and only between plants within plots for height in area 1. The highest estimates of genetic variation and heritability were obtained for area 1. Without the optimization of selection, the highest gain (4.8%) in the selection between and within with a selection intensity of 52%, for area 1. In area 2, the highest gain (2.86%) in individual selection. We conclude that there is low genetic variability in seedlings orchards of P. caribaea var. caribaea. However, area 1 presents higher genetic control than area 2, and should be better explored. For the next generations, it is recommended the infusion of new genetic material to proceed with a forest improvement program, since it was observed low variability and low gains in the selection of P. caribaea var. caribaea.
Gain-of-function genetic alterations of G9a drive oncogenesis I
GEO Series GSE147419. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Gain-of-function genetic perturbationssimultaneouslyacross500 barcoded cancer cell lines
GEO Series GSE238126. Homo sapiens. 66 samples. Type: Other.
Gain-of-function genetic alterations of G9a drive oncogenesis II
GEO Series GSE147427. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
TLR7 gain-of-function genetic variation causes human lupus
GEO Series GSE196316. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A Bilingual Virtually-based Intervention (PEDALL) for the Prevention of Weight Gain in Childhood ALL Patients Considering Key Genetic and Sociodemographic Risk Factors
ClinicalTrials.gov study NCT05963971. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Transcriptome characterization of CHOPS syndrome, a novel genetic disorder caused by gain-of-function mutations of AFF4
GEO Series GSE64031. Homo sapiens. 10 samples. Type: Expression profiling by array.
Gain-of-function genetic alterations of G9a drive oncogenesis
GEO Series GSE147429. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Genetic screen for suppressors of lncRNA-mediated transcription interference identifies a gain-of-function mutation in the essential Pol2 termination factor Seb1
GEO Series GSE168898. Schizosaccharomyces pombe. 6 samples. Type: Expression profiling by high throughput sequencing.
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