Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of <i>Pinus taeda</i> L. in the Southern USA

Lauer, Edwin; Sims, Andrew D.; McKeand, Steven E.; Isik, Fikret

doi:10.1093/forsci/fxaa035

Cited by 13 publications

(4 citation statements)

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“…Improvement of economically important traits such as tree height, stem volume, wood quality, and resistance to fusiform rust disease through traditional breeding requires more than 15 years per cycle (Isik and McKeand, 2019 ). These complex traits vary substantially between populations (Lauer et al, 2020 ). Marker‐assisted selection (MAS) describes a technique in which a small collection of variants (typically single‐nucleotide polymorphisms [SNPs]), linked to known quantitative trait loci (QTL), are used to predict genetic merit.…”

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Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome

et al. 2021

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Premise An informatics approach was used for the construction of an Axiom genotyping array from heterogeneous, high‐throughput sequence data to assess the complex genome of loblolly pine ( Pinus taeda ). Methods High‐throughput sequence data, sourced from exome capture and whole genome reduced‐representation approaches from 2698 trees across five sequence populations, were analyzed with the improved genome assembly and annotation for the loblolly pine. A variant detection, filtering, and probe design pipeline was developed to detect true variants across and within populations. From 8.27 million variants, a total of 642,275 were evaluated and 423,695 of those were screened across a range‐wide population. Results The final informatics and screening approach delivered an Axiom array representing 46,439 high‐confidence variants to the forest tree breeding and genetics community. Based on the annotated reference genome, 34% were located in or directly upstream or downstream of genic regions. Discussion The Pita50K array represents a genome‐wide resource developed from sequence data for an economically important conifer, loblolly pine. It uniquely integrates independent projects that assessed trees sampled across the native range. The challenges associated with the large and repetitive genome are addressed in the development of this resource.

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Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome

et al. 2021

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“…To make selection more efficient by modeling the GE interaction by the heterogeneity variance-covariance structure among environments and by borrowing information across them, a multivariate mixed model approach has already been tested in fruit (Chavarría-Perez et al, 2020), grain (Hunt et al, 2020;Monteverde et al, 2018), forage (Gouveia et al, 2020), and tree crops (Lauer et al, 2021). In cassava, only Okeke et al (2017) investigated the GE interaction by modeling two distinct covariances for genotypes and residuals among environments.…”

Section: Crop Sciencementioning

confidence: 99%

Evaluation and selection of cassava clones and exploitation of genetic covariance across multiple environments

Santos,

Pereira,

Abreu

et al. 2024

Crop Science

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Clonal evaluation trials of cassava (Manihot esculenta Crantz), where the main selection of this crop takes place, are usually carried out in multiple environments. This study investigated the influence of genotype–environment (GE) interaction on selection and how to explore genetic information across environments in a mixed model approach by modeling different genetic covariance structures. Approximately 240 cassava clones were assessed in an augmented block design during the 2020/2021 growing season in Brazil. The unstructured model was the best suited and used to investigate several strategies of selection. The predicted genetic gains based on individual analyses varied greatly among environments (5.52%–12.62% for root yield; 1.00%–6.09% for dry matter content; and 4.01%–9.42% for dry matter yield), although the clones mean was similar. Moreover, most of the selected clones in each environment outperformed the best check (>80%), except for root yield and dry matter yield in one environment. By multi‐environment analysis, greater local gains were detected in each environment (means of 16.87% for root yield, 5.56% for dry matter content, and 17.27% for dry matter yield) and for mean heritability (0.52 for root yield, 0.76 for dry matter content, and 0.55 for dry matter yield). The coincidence of clones selected by individual and multi‐environment analyses was 64% for root yield, 73% for dry matter content, and 66% for dry matter yield. The best scenario for selection is when all environments are considered simultaneously, for which regional genetic gains of 16.71% were predicted for root yield, 5.40% for dry matter content, and 17.06% for dry matter yield.

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“…The targeting of genetic breeding programs to produce varieties with high SV is prioritized in most cultivated tree species, including LP (Lu et al 2018;Shalizi and Isik 2019). However, the realization of volume increases based on molecular data is hampered by the polygenic nature of this complex trait and substantial variation between populations (Lauer et al 2021). Statistical methods for associating genotypes with phenotypes have been of great interest for breeding, especially as a tool for genomic selection (GS) and a means of deciphering causative loci 4 using genome-wide association studies (GWASs).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association reveals novel insights into the molecular mechanisms regulating stem volume inPinus taeda

Bajay¹,

Aono²,

Francisco³

et al. 2022

Preprint

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Pinus taeda(loblolly pine [LP]), a long-lived tree species, is one of the world's most economically important forest trees. Genetic improvement programs for loblolly and otherPinusspecies have focused on survival, biomass growth, resistance to diseases and pests, and stem shape. Among the growth traits, volume is the most widely considered in tree improvement programs. Despite the great interest in increasing volume growth, there are significant challenges to unraveling the molecular mechanisms behind this quantitative trait since it is presumably influenced by the action of large numbers of genes that may interact epistatically through unknown molecular mechanisms. The challenge of uncovering the genetic variants involved in variation for growth traits and their interaction is even greater in conifers such as LP because of the extremely large size and high complexity ofPinusgenomes. Here we present a comprehensive genetic analysis of LP involving genetic markers in association with volume (via genome-wide association studies [GWAS] and machine learning [ML]) to uncover pathways involved with good phenotypes for volume. The objective of this data integration was to provide a functional characterization of the genetic marker regions selected by GWAS and ML. We used a population of LP in the 2nd cycle of breeding and testing composed of full-sib progenies established at seven sites by the Cooperative Forest Genetics Research Program at the University of Florida. A total of 1,692 individuals were phenotyped and genotyped using sequence capture probes targeting putative genes. Probes were developed based on an elite germplasm transcriptome from LP. A total of 31,589 SNPs were identified and used to perform a GWAS through a multilocus mixed model. A precision core marker set with 7,864 SNPs selected through ML, i.e., feature selection (FS), was used for genomic selection (GS), providing a predictive accuracy (R Pearson coefficient) of approximately 0.79. For genome annotation and the construction of gene coexpression networks, three transcriptomes were assembled based on data from different pine species (Pinus taeda,Pinus elliottiiandPinus radiata). We selected genes associated with the target trait and assessed the cascade of related molecular mechanisms within coexpression networks. These results advance our understanding of the genetics influencing wood traits and reveal candidate genes for future functional studies as well as increase our understanding of quantitative genetics and the genomics of complex phenotypic variations in LP.

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Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

Cited by 13 publications

References 21 publications

Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome

Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome

Evaluation and selection of cassava clones and exploitation of genetic covariance across multiple environments

Genome-wide association reveals novel insights into the molecular mechanisms regulating stem volume inPinus taeda

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