Genomic insights into selection for heterozygous alleles and woody traits in <i>Populus tomentosa</i>
Bibliographic record
Abstract
Heterozygous alleles are widespread in outcrossing and clonally propagated woody plants. The variation in heterozygosity that underlies population adaptive evolution and phenotypic variation, however, remains largely unknown. Here, we describe a de novo chromosome-level genome assembly of Populus tomentosa, an economic and ecologically important native tree in northern China. By resequencing 302 natural accessions, we determined that the South subpopulation (Pop_S) encompasses the ancestral strains of P. tomentosa, while the Northwest subpopulation (Pop_NW) and Northeast subpopulation (Pop_NE) experienced different selection pressures during population evolution, resulting in significant population differentiation and a decrease in the extent of heterozygosity. Analysis of heterozygous selective sweep regions (HSSR) suggested that selection for lower heterozygosity contributed to the local adaptation of P. tomentosa by dwindling gene expression and genetic load in the Pop_NW and Pop_NE subpopulations. Genome-wide association studies (GWAS) revealed that 88 single nucleotide polymorphisms (SNPs) within 63 genes are associated with nine wood composition traits. Among them, the selection for the homozygous AA allele in PtoARF8 is associated with reductions in cellulose and hemicellulose contents by attenuating PtoARF8 expression, and the increase in lignin content is attributable to the selection for decreases in exon heterozygosity in PtoLOX3 during adaptive evolution of natural populations. This study provides novel insights into allelic variations in heterozygosity associated with adaptive evolution of P. tomentosa in response to the local environment and identifies a series of key genes for wood component traits, thereby facilitating genomic-based breeding of important traits in perennial woody plants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".