ArticlePloS one2014
RNA-Seq using two populations reveals genes and alleles controlling wood traits and growth in Eucalyptus nitens.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.Scientific reports · 2021Article
- Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population ofG3 (Bethesda, Md.) · 2020Article
- Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata.Scientific reports · 2019Article
- Co-expression network of transcription factors reveal ethylene-responsive element-binding factor as key regulator of wood phenotype in3 Biotech · 2018Article
- Article
- Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Application of Genomic Technologies to the Breeding of Trees.Frontiers in genetics · 2016Review
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Eucalyptus nitens is a perennial forest tree species grown mainly for kraft pulp production in many parts of the world. Kraft pulp yield (KPY) is a key determinant of plantation profitability and increasing the KPY of trees grown in plantations is a major breeding objective. To speed up the breeding process, molecular markers that can predict KPY are desirable. To achieve this goal, we carried out RNA-Seq studies on trees at extremes of KPY in two different trials to identify genes and alleles whose expression correlated with KPY. KPY is positively correlated with growth measured as diameter at breast height (DBH) in both trials. In total, six RNA bulks from two treatments were sequenced on an Illumina HiSeq platform. At 5% false discovery rate level, 3953 transcripts showed differential expression in the same direction in both trials; 2551 (65%) were down-regulated and 1402 (35%) were up-regulated in low KPY samples. The genes up-regulated in low KPY trees were largely involved in biotic and abiotic stress response reflecting the low growth among low KPY trees. Genes down-regulated in low KPY trees mainly belonged to gene categories involved in wood formation and growth. Differential allelic expression was observed in 2103 SNPs (in 1068 genes) and of these 640 SNPs (30%) occurred in 313 unique genes that were also differentially expressed. These SNPs may represent the cis-acting regulatory variants that influence total gene expression. In addition we also identified 196 genes which had Ka/Ks ratios greater than 1.5, suggesting that these genes are under positive selection. Candidate genes and alleles identified in this study will provide a valuable resource for future association studies aimed at identifying molecular markers for KPY and growth.
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