Leveraging LASSO-based methodologies for enhanced SNP analysis in plant genomes
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Bibliographic record
Abstract
Abstract Summary Genome-wide association studies (GWAS) have been widely used to reveal the associations between genetic variations and phenotypes in a population of individuals. However, they have been criticized for missing important genetic markers usually due to the fact that the data may not fit the statistical models well. In this study, we address the challenge of identifying significant single nucleotide polymorphisms (SNPs) in GWAS by harnessing the capabilities of two sophisticated regression models, BIGLASSO and AUTALASSO. They are both variants of the least absolute shrinkage and selection operator (LASSO). Our research contributes to the field of genomics through detailed comparative analysis of Arabidopsis thaliana, revealing how each method specializes in uncovering SNPs for different trait types. Our findings indicate that BIGLASSO shows stronger alignment with GWAS results, particularly excelling in the analysis of binary traits, even when these are derived from categorical phenotypes. AUTALASSO could be effective for quantitative traits and complement GWAS. We demonstrate that these LASSO-based methods can significantly enhance the identification of genetic markers, offering a potent complement to traditional GWAS approaches. Our findings not only bridge the gap between statistical and machine learning methodologies in genetic studies but also provide a practical framework for researchers seeking to validate reported SNPs or explore new genomic regions for trait association. This work stands as a pivotal step toward the integration of advanced computational techniques in genomics, paving the way for more precise and comprehensive genetic analyses. Availability and implementation Key results from the paper are available at the https://github.com/DongdongHou006/LASSO-SNP. The program was implementated using Python and R, and was tested using the Digital Research Alliance of Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it