Silk_40K SNP array facilitates the genomic prediction of complex traits in silkworm (Bombyx mori)
Bibliographic record
Abstract
• Silk_40K array is the first set of genome wide SNP chip of silkworm. • QTL mapping provides new insights into the genetic architecture of silk yield. • BmCOPB2 and BmOCIAD1 were identified as the new genes for silk yield. • Genomic prediction based on Silk_40K showed high ability in silkworm. It is highly attractive to use silkworms producing natural fibres with excellent properties and insect-based nutrients on a large scale. Traits related to these often have complex genetic bases. Genomic selection (GS) has exhibited high efficiency in improving complex traits. However, for silkworm, there is a lack of low-cost, high-throughput genotyping tools, and the effectiveness of GS has yet to be studied. To develop a genome-wide SNP chip, evaluate its genotyping capabilities and potential applications in genetic analysis and genomic selection of complex traits in silkworm. We designed the Silk_40K array containing 41,887 SNPs. Two backcross (BC 1 ) populations were genotyped using this array. The quantitative trait locus (QTL) mapping and gene expression analysis were conducted to identify silk yield-related genes and CRISPR/Cas9 was used to validate candidate function. Multimodel genomic prediction (GP) based on different SNP sets was performed in BC1 and germplasms. The SNPs of Silk_40K array evenly distributed across the genome. The calling ratio reached 94.01 % across different samples, with an average polymorphism rate of 37.42 %. We identified 61 QTLs associated with silk yield, and two novel genes BmOCIAD1 and BmCOPB2 within the QTL of cocoon shell ratio (CSR) on chromosome 1. Knockout of BmOCIAD1 significantly increased cocoon shell weight (CSW) by 36.36 % (females) and 50.00 % (males), and CSR by 19.17 % (females) and 13.49 % (males). Silk_40K also showed a high genomic predictive ability in both BC 1 populations and germplasms, with maximum correlation (R) of 0.866 (CSW) and 0.891 (CSR) in germplasms. Silk_40K array is the first whole-genome SNP chip for silkworm. Based on this, two new genes controlling silk yield were identified, providing new insights into the genetic architecture of silk yield. We also demonstrated for the first time that genomic selection may achieve high efficiency in selecting for complex traits in silkworms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".