The Experts below are selected from a list of 21624 Experts worldwide ranked by ideXlab platform
Yousry A Elkassaby - One of the best experts on this subject based on the ideXlab platform.
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modeling realized gains in douglas fir pseudotsuga menziesii using laser scanning data from unmanned aircraft systems uas
Forest Ecology and Management, 2020Co-Authors: Samuel Grubinger, Nicholas C Coops, Michael Stoehr, Yousry A Elkassaby, Arko Lucieer, Darren TurnerAbstract:Abstract Tree Breeding programs form an integral part of sustainable forest management by providing genetically improved stock for reforestation. These programs rely on accurate phenotyping of forest trials, which become increasingly difficult to assess as Trees grow larger and canopy closure occurs. Airborne laser scanning (ALS) provides three-dimensional point cloud information on forest structure which can be used to characterize phenotypes of forest Trees. We analyzed 22-year-old realized gain trials of coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco var. menziesii) at three sites in coastal British Columbia, Canada, using dense point clouds produced from ALS acquired by unmanned aircraft system (UAS). We assessed the accuracy of ALS data against ground estimates of stand maximum height (r2 = 0.90, p
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single step blup with varying genotyping effort in open pollinated picea glauca
G3: Genes Genomes Genetics, 2017Co-Authors: Blaise Ratcliffe, Omnia Gamal Eldien, Charles Chen, Jaroslav Klápště, Ilga Porth, Eduardo P. Cappa, Yousry A ElkassabyAbstract:Maximization of genetic gain in forest Tree Breeding programs is contingent on the accuracy of the predicted Breeding values and precision of the estimated genetic parameters. We investigated the effect of the combined use of contemporary pedigree information and genomic relatedness estimates on the accuracy of predicted Breeding values and precision of estimated genetic parameters, as well as rankings of selection candidates, using single-step genomic evaluation (HBLUP). In this study, two traits with diverse heritabilities [Tree height (HT) and wood density (WD)] were assessed at various levels of family genotyping efforts (0, 25, 50, 75, and 100%) from a population of white spruce (Picea glauca) consisting of 1694 Trees from 214 open-pollinated families, representing 43 provenances in Quebec, Canada. The results revealed that HBLUP bivariate analysis is effective in reducing the known bias in heritability estimates of open-pollinated populations, as it exposes hidden relatedness, potential pedigree errors, and inBreeding. The addition of genomic information in the analysis considerably improved the accuracy in Breeding value estimates by accounting for both Mendelian sampling and historical coancestry that were not captured by the contemporary pedigree alone. Increasing family genotyping efforts were associated with continuous improvement in model fit, precision of genetic parameters, and Breeding value accuracy. Yet, improvements were observed even at minimal genotyping effort, indicating that even modest genotyping effort is effective in improving genetic evaluation. The combined utilization of both pedigree and genomic information may be a cost-effective approach to increase the accuracy of Breeding values in forest Tree Breeding programs where shallow pedigrees and large testing populations are the norm.
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estimates of pollen contamination and selfing in a coastal douglas fir seed orchard
Scandinavian Journal of Forest Research, 2015Co-Authors: Tony Kess, Yousry A ElkassabyAbstract:Seed orchards are the link from Tree Breeding to reforestation programs and are theoretically expected to function as closed, perfect populations, ensuring gain and diversity are consistently and predictively delivered as improved seed and seedlings. Seed orchard populations often deviate from panmixia due to fertility variation, reproductive asynchrony, and gene flow, leading to reduced seed crops genetic quality. Here, as a part of multiyear monitoring study, we used DNA fingerprinting (simple sequence repeat markers) to assess a Douglas-fir (Pseudotsuga menziesii) seed orchard's seed crop genetic quality (2009 seed crop). The studied seed crop was produced under ambient temperature (i.e. no reproductive phenology manipulation) and pollination was augmented by pollen from within orchard's pollen donors. DNA fingerprinting of the parental population (66 parents) along with 207 gametophyte (1n) – embryo (2n) pairs of random bulk sample of seed allowed parentage (maternal and paternal) assignment and the d...
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mining conifers mega genome using rapid and efficient multiplexed high throughput genotyping by sequencing gbs snp discovery platform
Tree Genetics & Genomes, 2013Co-Authors: Charles Chen, Sharon E Mitchell, Robert J Elshire, Edward S Buckler, Yousry A ElkassabyAbstract:Next-generation sequencing (NGS) technologies are revolutionizing both medical and biological research through generation of massive SNP data sets for identifying heritable genome variation underlying key traits, from rare human diseases to important agronomic phenotypes in crop species. We evaluated the performance of genotyping-by-sequencing (GBS), one of the emerging NGS-based platforms, for genotyping two economically important conifer species, lodgepole pine (Pinus contorta) and white spruce (Picea glauca). Both species have very large genomes (>20,000 Mbp), are highly heterozygous, and lack reference sequences. From a small set (six accessions each) of independent replicated DNA samples and a 48-plex read depth, we obtained ~60,000 SNPs per species. After stringent filtering, we obtained 17,765 and 17,845 high-coverage SNPs without missing data for lodgepole pine and white spruce, respectively. Our results demonstrated that GBS is a robust and suitable method for genotyping conifers. The application of GBS to forest Tree Breeding and genomic selection is discussed.
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populus trichocarpa cell wall chemistry and ultrastructure trait variation genetic control and genetic correlations
New Phytologist, 2013Co-Authors: Ilga Porth, Jaroslav Klápště, Yousry A Elkassaby, Oleksandr Skyba, Ben S K Lai, Armando Geraldes, Wellington Muchero, Gerald A Tuskan, Carl J Douglas, Shawn D MansfieldAbstract:Summary The increasing ecological and economical importance of Populus species and hybrids has stimulated research into the investigation of the natural variation of the species and the estimation of the extent of genetic control over its wood quality traits for traditional forestry activities as well as the emerging bioenergy sector. A realized kinship matrix based on informative, high-density, biallelic single nucleotide polymorphism (SNP) genetic markers was constructed to estimate trait variance components, heritabilities, and genetic and phenotypic correlations. Seventeen traits related to wood chemistry and ultrastructure were examined in 334 9-yr-old Populus trichocarpa grown in a common-garden plot representing populations spanning the latitudinal range 44° to 58.6°. In these individuals, 9342 SNPs that conformed to Hardy–Weinberg expectations were employed to assess the genomic pair-wise kinship to estimate narrow-sense heritabilities and genetic correlations among traits. The range-wide phenotypic variation in all traits was substantial and several trait heritabilities were > 0.6. In total, 61 significant genetic and phenotypic correlations and a network of highly interrelated traits were identified. The high trait variation, the evidence for moderate to high heritabilities and the identification of advantageous trait combinations of industrially important characteristics should aid in providing the foundation for the enhancement of poplar Tree Breeding strategies for modern industrial use.
Andres J Cortes - One of the best experts on this subject based on the ideXlab platform.
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inheritance of rootstock effects in avocado persea americana mill cv hass
Frontiers in Plant Science, 2020Co-Authors: Paula H Reyesherrera, Laura Munozbaena, Valeria Velasquezzapata, Laura Patino, Oscar A Delgadopaz, Cipriano A Diazdiez, Alejandro A Navasarboleda, Andres J CortesAbstract:Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique Tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering Tree Breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit Tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015-2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a "genetic prediction" model to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in "Hass" avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit Tree crop. Ultimately, our work highlights the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit Tree Breeding programs while enhancing our understanding of the consequences of grafting.
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inheritance of rootstock effects in avocado persea americana mill cv hass
bioRxiv, 2020Co-Authors: Paula H Reyesherrera, Laura Munozbaena, Valeria Velasquezzapata, Laura Patino, Oscar A Delgadopaz, Cipriano A Diazdiez, Alejandro A Navasarboleda, Andres J CortesAbstract:Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique Tree phenotype. Yet, the interconnection of both genotypes obscures individual contributions to phenotypic variation (i.e. rootstock-mediated heritability), hampering Tree Breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as model fruit Tree. We characterized 240 rootstocks from 8 avocado cv. Hass orchards in three regions of the province of Antioquia, in the northwest Andes of Colombia, using 13 microsatellite markers (simple sequence repeats − SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, eco-physiological, and fruit yield and quality traits) in the scions for three years (2015−2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a ′genetic prediction′ model in order to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (R2) that oscillated between 0.58 and 0.74 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e. total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), while the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in ′Hass′ avocado. They also reinforce the utility of SSR markers for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit Tree crop. Ultimately, our work reinforces the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit Tree Breeding programs, while enhancing our understanding of the consequences of grafting.
Paulo De Souza Goncalves - One of the best experts on this subject based on the ideXlab platform.
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genomic selection in rubber Tree Breeding a comparison of models and methods for managing g e interactions
Frontiers in Plant Science, 2019Co-Authors: Livia Moura De Souza, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Felipe O Francisco, Vincent Le Guen, Roberto Fritscheneto, Ana Paula De SouzaAbstract:Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant Breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant Breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber Trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of Tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber Tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic Breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic Breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened Breeding cycles, we expect GS to be implemented in rubber Tree Breeding programs.
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deep expression analysis reveals distinct cold response strategies in rubber Tree hevea brasiliensis
BMC Genomics, 2019Co-Authors: Camila Campos Mantello, Lucas Boatwright, Carla Cristina Da Silva, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Brad W Barbazuk, Anete Pereira De SouzaAbstract:Natural rubber, an indispensable commodity used in approximately 40,000 products, is fundamental to the tire industry. The rubber Tree species Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg., which is native the Amazon rainforest, is the major producer of latex worldwide. Rubber Tree Breeding is time consuming, expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. In this work, transcriptome sequencing was used to identify a full set of transcripts and to evaluate the gene expression involved in the different cold-response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. We built a comprehensive transcriptome using multiple database sources, which resulted in 104,738 transcripts clustered in 49,304 genes. The RNA-seq data from the leaf tissues sampled at four different times for each genotype were used to perform a gene-level expression analysis. Differentially expressed genes (DEGs) were identified through pairwise comparisons between the two genotypes for each time series of cold treatments. DEG annotation revealed that RRIM600 and GT1 exhibit different chilling tolerance strategies. To cope with cold stress, the RRIM600 clone upregulates genes promoting stomata closure, photosynthesis inhibition and a more efficient reactive oxygen species (ROS) scavenging system. The transcriptome was also searched for putative molecular markers (single nucleotide polymorphisms (SNPs) and microsatellites) in each genotype. and a total of 27,111 microsatellites and 202,949 (GT1) and 156,395 (RRIM600) SNPs were identified in GT1 and RRIM600. Furthermore, a search for alternative splicing (AS) events identified a total of 20,279 events. The elucidation of genes involved in different chilling tolerance strategies associated with molecular markers and information regarding AS events provides a powerful tool for further genetic and genomic analyses of rubber Tree Breeding.
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genomic selection in rubber Tree Breeding a comparison of models and methods for dealing with g e
bioRxiv, 2019Co-Authors: Livia Moura De Souza, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Felipe O Francisco, Vincent Le Guen, Roberto Fritscheneto, Ana Paula De SouzaAbstract:Abstract Several genomic prediction models incorporating genotype × environment (G×E) interactions have recently been developed and used in genomic selection (GS) in plant Breeding programs. G×E interactions decrease selection accuracy and limit genetic gains in plant Breeding. Two genomic data sets were used to compare the prediction ability of multi-environment G×E genomic models and two kernel methods (a linear kernel (genomic best linear unbiased predictor, GBLUP) (GB) and a nonlinear kernel (Gaussian kernel, GK)) and prediction accuracy (PA) of five genomic prediction models: (1) one without environmental data (BSG); (2) a single-environment, main genotypic effect model (SM); (3) a multi-environment, main genotypic effect model (MM); (4) a multi-environment, single variance GxE deviation model (MDs); and (5) a multi-environment, environment-specific variance GxE deviation model (MDe). We evaluated the utility of GS with 435 rubber Tree individuals in two sites and genotyped the individuals with genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were estimated for diameter (DAP) and height (AP) at different ages, with a heritability ranging from 0.59 to 0.75 for both traits. Applying the model (BSG, SM, MM, MDs, and MDe) and kernel method (GBLUP and GK) combinations to rubber Tree data showed that models with the nonlinear GK and linear GBLUP kernel had similar PAs. Multi-environment models were superior to single-environment genomic models regardless the kernel (GBLUP or GK), suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. In the best scenario (well-watered (WW / GK), an increase of 6.7 and 8.7 fold of genetic gain can be obtained for AP and DAP, respectively, with multi-environment GS (MM, MDe and MDS) than by conventional genetic Breeding model (CBM). Furthermore, GS resulted in a more balanced selection response in DAP and AP and if used in conjunction with traditional genetic Breeding programs will contribute to a reduction in selection time. With the rapid advances in and declining costs of genotyping methods, balanced against the overall costs of managing large progeny trials and potential increased gains per unit time, we are hopeful that GS can be implemented in rubber Tree Breeding programs.
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transcriptome analysis of distinct cold tolerance strategies in the rubber Tree hevea brasiliensis
bioRxiv, 2018Co-Authors: Camila Campos Mantello, Lucas Boatwright, Carla Cristina Da Silva, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Brad W Barbazuk, Anete Pereira De SouzaAbstract:Natural rubber is an indispensable commodity used in approximately 40,000 products and is fundamental to the tire industry. Among the species that produce latex, the rubber Tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg.], a species native to the Amazon rainforest, is the major producer of latex used worldwide. The Amazon Basin presents optimal conditions for rubber Tree growth, but the occurrence of South American leaf blight, which is caused by the fungus Microcyclus ulei (P. Henn) v. Arx, limits rubber Tree production. Currently, rubber Tree plantations are located in scape regions that exhibit suboptimal conditions such as high winds and cold temperatures. Rubber Tree Breeding programs aim to identify clones that are adapted to these stress conditions. However, rubber Tree Breeding is time-consuming, taking more than 20 years to develop a new variety. It is also expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. Transcriptome sequencing using next-generation sequencing (RNA-seq) is a powerful tool to identify a full set of transcripts and for evaluating gene expression in model and non-model species. In this study, we constructed a comprehensive transcriptome to evaluate the cold response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. Furthermore, we identified putative microsatellite (SSR) and single-nucleotide polymorphism (SNP) markers. Alternative splicing, which is an important mechanism for plant adaptation under abiotic stress, was further identified, providing an important database for further studies of cold tolerance.
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rubber Tree ortet ramet genetic correlation and early selection efficiency to reduce rubber Tree Breeding cycle
Industrial Crops and Products, 2015Co-Authors: Andre Luis Bombonato, Ligia Regina Lima Gouvea, Cecilia Khusala Verardi, Guilherme Augusto Peres Silva, Paulo De Souza GoncalvesAbstract:Abstract The present study aimed to correlate the performance of rubber Tree [Hevea brasiliensis (Willd. ex. Adr. de Juss.) Muell-Arg] ortets selected in progenies tests and the performance of their respective ramets in the clonal test. In the progenies tests three early measurements were carried out for the traits early rubber yield (ERP), number of vassel latex rings (NVR) and bark thickness (BKT). Using the HMMm (Hamaker Morris-Mann modified) test the best ortets were selected based on rubber yield and cloned to set the clonal test. In the clonal test the same traits were studied, but the dry rubber production (DRP) was assessed in the normal tapping (from the seventh year of age). Statistical analyses were carried out using the REML/BLUP (restricted maximum likelihood/best linear unbiased prediction) method. After studying the progenies and clonal tests, the correlation between the performance of ortets and ramets was estimated and the performance of the ortets in the second and third early tests (ERP2 and ERP3) has proven to be reliable, since the observed estimate of the correlation coefficient with its ramets was significant. The study showed that if taken into account the second and/or third early test, there is reliability that the most productive ortets selected in nursery stage will originate more productive clones. It is efficient to select rubber Tree based on the early rubber production, thus reducing the long rubber Tree Breeding cycle.
Jean Beaulieu - One of the best experts on this subject based on the ideXlab platform.
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genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits
Evolutionary Applications, 2020Co-Authors: Jean Beaulieu, Simon Nadeau, Chen Ding, Jose M Celedon, Aida Azaiez, Carol Ritland, Jeanphilippe Laverdiere, Marie Deslauriers, Greg Adams, Michele FullartonAbstract:With climate change, the pressure on Tree Breeding to provide varieties with improved resilience to biotic and abiotic stress is increasing. As such, pest resistance is of high priority but has been neglected in most Tree Breeding programs, given the complexity of phenotyping for these traits and delays to assess mature Trees. In addition, the existing genetic variation of resistance and its relationship with productivity should be better understood for their consideration in multitrait Breeding. In this study, we evaluated the prospects for genetic improvement of the levels of acetophenone aglycones (AAs) in white spruce needles, which have been shown to be tightly linked to resistance to spruce budworm. Furthermore, we estimated the accuracy of genomic selection (GS) for these traits, allowing selection at a very early stage to accelerate Breeding. A total of 1,516 progeny Trees established on five sites and belonging to 136 full-sib families from a mature Breeding population in New Brunswick were measured for height growth and genotyped for 4,148 high-quality SNPs belonging to as many genes along the white spruce genome. In addition, 598 Trees were assessed for levels of AAs piceol and pungenol in needles, and 578 for wood stiffness. GS models were developed with the phenotyped Trees and then applied to predict the trait values of unphenotyped Trees. AAs were under moderate-to-high genetic control (h2: 0.43-0.57) with null or marginally negative genetic correlations with other traits. The prediction accuracy of GS models (GBLUP) for AAs was high (PAAC: 0.63-0.67) and comparable or slightly higher than pedigree-based (ABLUP) or BayesCπ models. We show that AA traits can be improved and that GS speeds up the selection of improved Trees for insect resistance and for growth and wood quality traits. Various selection strategies were tested to optimize multitrait gains.
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factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced Breeding population of black spruce picea mariana
BMC Genomics, 2017Co-Authors: Jean Beaulieu, Patrick R N Lenz, Shawn D Mansfield, Sebastien Clement, Mireille Desponts, Jean BousquetAbstract:Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate Tree Breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long Breeding cycles. In the present study, we tested GS in an advanced-Breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy. The study relied on 734 25-year-old Trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of Trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were identified to carry large effects, indicating a minor role for short-range LD in this population. This study supports the integration of GS models in advanced-generation Tree Breeding programs, given that high genomic prediction accuracy was obtained with a relatively small number of markers due to high relatedness and family structure in the population. In boreal spruce Breeding programs and similar ones with long Breeding cycles, much larger gain per unit of time can be obtained from genomic selection at an early age than by the conventional approach. GS thus appears highly profitable, especially in the context of forward selection in species which are amenable to mass vegetative propagation of selected stock, such as spruces.
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genetic improvement of white spruce mechanical wood traits early screening by means of acoustic velocity
Forests, 2013Co-Authors: Patrick Lenz, David Auty, Alexis Achim, Jean Beaulieu, John MackayAbstract:There is a growing interest to use acoustic sensors for selection in Tree Breeding to ensure high wood quality of future plantations. In this study, we assessed acoustic velocity as a selection trait for the improvement of mechanical wood properties in two 15- and 32-year-old white spruce (Picea glauca [Moench.] Voss) genetic tests. Individual heritability of acoustic velocity was moderate and of the same magnitude as heritability of wood density. Considerable genetic gain could be expected for acoustic velocity and a measure combining velocity and wood density. The relationship between acoustic velocity and cellulose microfibril angle (MFA) was strong on the genetic level and selection based on velocity could effectively improve MFA, which is one of the most important determinants of wood mechanical properties. Although low, the positive relationship between acoustic velocity and Tree height presents an interesting opportunity for the improvement of both Tree growth and wood quality. On the phenotypic level, MFA was more strongly correlated to acoustic velocity in mature Trees than in young Trees. The addition of easily obtainable traits such as diameter at breast height (DBH), height-to-diameter ratio as well as wood density to velocity determinations could improve models of MFA at the young and the mature age. We conclude that juvenile acoustic velocity is an appropriate trait to select for wood quality in a Tree Breeding context.
Camila Campos Mantello - One of the best experts on this subject based on the ideXlab platform.
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deep expression analysis reveals distinct cold response strategies in rubber Tree hevea brasiliensis
BMC Genomics, 2019Co-Authors: Camila Campos Mantello, Lucas Boatwright, Carla Cristina Da Silva, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Brad W Barbazuk, Anete Pereira De SouzaAbstract:Natural rubber, an indispensable commodity used in approximately 40,000 products, is fundamental to the tire industry. The rubber Tree species Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg., which is native the Amazon rainforest, is the major producer of latex worldwide. Rubber Tree Breeding is time consuming, expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. In this work, transcriptome sequencing was used to identify a full set of transcripts and to evaluate the gene expression involved in the different cold-response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. We built a comprehensive transcriptome using multiple database sources, which resulted in 104,738 transcripts clustered in 49,304 genes. The RNA-seq data from the leaf tissues sampled at four different times for each genotype were used to perform a gene-level expression analysis. Differentially expressed genes (DEGs) were identified through pairwise comparisons between the two genotypes for each time series of cold treatments. DEG annotation revealed that RRIM600 and GT1 exhibit different chilling tolerance strategies. To cope with cold stress, the RRIM600 clone upregulates genes promoting stomata closure, photosynthesis inhibition and a more efficient reactive oxygen species (ROS) scavenging system. The transcriptome was also searched for putative molecular markers (single nucleotide polymorphisms (SNPs) and microsatellites) in each genotype. and a total of 27,111 microsatellites and 202,949 (GT1) and 156,395 (RRIM600) SNPs were identified in GT1 and RRIM600. Furthermore, a search for alternative splicing (AS) events identified a total of 20,279 events. The elucidation of genes involved in different chilling tolerance strategies associated with molecular markers and information regarding AS events provides a powerful tool for further genetic and genomic analyses of rubber Tree Breeding.
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transcriptome analysis of distinct cold tolerance strategies in the rubber Tree hevea brasiliensis
bioRxiv, 2018Co-Authors: Camila Campos Mantello, Lucas Boatwright, Carla Cristina Da Silva, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Brad W Barbazuk, Anete Pereira De SouzaAbstract:Natural rubber is an indispensable commodity used in approximately 40,000 products and is fundamental to the tire industry. Among the species that produce latex, the rubber Tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg.], a species native to the Amazon rainforest, is the major producer of latex used worldwide. The Amazon Basin presents optimal conditions for rubber Tree growth, but the occurrence of South American leaf blight, which is caused by the fungus Microcyclus ulei (P. Henn) v. Arx, limits rubber Tree production. Currently, rubber Tree plantations are located in scape regions that exhibit suboptimal conditions such as high winds and cold temperatures. Rubber Tree Breeding programs aim to identify clones that are adapted to these stress conditions. However, rubber Tree Breeding is time-consuming, taking more than 20 years to develop a new variety. It is also expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. Transcriptome sequencing using next-generation sequencing (RNA-seq) is a powerful tool to identify a full set of transcripts and for evaluating gene expression in model and non-model species. In this study, we constructed a comprehensive transcriptome to evaluate the cold response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. Furthermore, we identified putative microsatellite (SSR) and single-nucleotide polymorphism (SNP) markers. Alternative splicing, which is an important mechanism for plant adaptation under abiotic stress, was further identified, providing an important database for further studies of cold tolerance.
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microsatellite marker development for the rubber Tree hevea brasiliensis characterization and cross amplification in wild hevea species
BMC Research Notes, 2012Co-Authors: Camila Campos Mantello, Paulo De Souza Goncalves, Fernando Suzuki, Livia Moura De Souza, Anete Pereira De SouzaAbstract:Background The rubber Tree (Hevea brasiliensis) is native to the Amazon region and it is the major source of natural rubber in the world. Rubber Tree Breeding is time-consuming and expensive. However, molecular markers such as microsatellites can reduce the time required for these programs. This study reports new genomic microsatellite markers developed and characterized in H. brasiliensis and the evaluation of their transferability to other Hevea species.
