Genome-Wide Association Study for Seed Quality Traits in Chickpea
Bibliographic record
Abstract
Chickpeas (Cicer arietinum L.) are an important source of nutrition for the world’s population. Global demand for high quality chickpeas is growing [1]. Canadian chickpea production is expected to increase as a result. Breeding efforts for Canadian varieties are increasingly targeting seed quality. Seed protein and oil content are important, quantitatively inherited traits [2]. To improve nutrition and functional quality of chickpeas an understanding of the genetic basis and underlying traits is crucial. Genome-wide association studies (GWAS) have been employed in breeding populations to aid marker assisted selection (MAS) and genomic selection (GS) applications [3]. A panel of diverse chickpea accessions including materials developed at the Canadian breeding program were grown during the summers of 2016-18. The population was grown at two locations each year, with three replicates per location. The panel was phenotyped for total crude protein and oil content. Over 60K single nucleotide polymorphisms (SNP) derived from whole genome sequences were used to analyze genetic diversity and population structure of the accessions. After data quality screening 170 out of 184 accessions remained, consisting of 45 Desis and 125 Kabulis. Roughly 30 600 SNPs remained after filtering for quality and a 10% minor allele frequency (MAF). Population structure analysis revealed several distinct groups primarily divided by market class. Linkage disequilibrium decay was determined to be between 390 and 380 kilobases. There was a moderate negative correlation shared between the traits (r=-0.57) as well as a genotype by environment interaction. It was determined that year was a greater source of phenotypic variation than growing location. QTLs and candidate genes associated with total protein and oil content were identified through GWAS, with potential markers for both traits on the q arm of chromosome 5.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".