Identification of SSR markers associated with saccharification yield using pool-based genome-wide association mapping in sorghum
Bibliographic record
Abstract
Saccharification describes the conversion of plant biomass by cellulase into glucose. Because plants have never been selected for high saccharification yield, cellulosic ethanol production faces a significant bottleneck. To improve saccharification yield, it is critical to identify the genes that affect this process. In this study, we used pool-based genome-wide association mapping to identify simple sequence repeat (SSR) markers associated with saccharification yield. Screening of 703 SSR markers against the low and high saccharification pools identified two markers on the sorghum chromosomes 2 (23-1062) and 4 (74-508c) associated with saccharification yield. The association was significant at 1% using either general or mixed linear models. Localization of these markers based on the whole genome sequence indicates that 23-1062 is 223 kb from a β-glucanase (Bg) gene and 74-508c is 81 kb from a steroid-binding protein (Sbp) gene. Bg is critical for cell wall assembly and degradation, but Sbp can suppress the expression of Bg as demonstrated in Arabidopsis (Yang et al. 2005). These markers are found physically close to genes encoding plant cell wall synthesis enzymes such as xyloglucan fucosyltransferase (149 kb from 74-508c) and UDP-D-glucose 4-epimerase (46 kb from 23-1062). Genetic transformation of selected candidate genes is in progress to examine their effect on saccharification yield in plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".