Tehokkaat ja tilastollisesti pätevät regressiomenetelmät genominlaajuisten assosiaatiotutkimusten suorittamiseen
Bibliographic record
Abstract
Genome-wide association studies (GWAS) aim to find genetic variants that are associated with a trait, such as height or the presence of a disease. Performing a GWAS is computationally demanding, and efficiency could be improved by utilizing high-performance computing (HPC). In addition, the structure of the genomic and phenotypic data, with properties such as case-control imbalance, population structure, and relatedness complicates the formulation of a valid statistical model and generates a high number of spurious associations. In recent years, many new tools have been developed to address these issues, but there is a lack of comprehensive evaluation of the tools and discussion of how HPC could be leveraged in GWAS. In this thesis, two tools, REGENIE and fastGWA, were compared in terms of their (1) statistical properties and (2) scalability to analyze increasing sample sizes and millions of variants, and their ability to take advantage of parallel processing. In addition, a portable, end-to-end GWAS workflow was developed in an HPC environment. This thesis shows that fastGWA is able to better control for confounding by relatedness and produce better calibrated test statistics for binary traits, while REGENIE has higher statistical power. Both fastGWA and REGENIE are able to make efficient use of parallel computing resources and analyze tens of thousands of individuals and millions of variants in a reasonable amount of time. The developed workflow demonstrates how HPC can be used to efficiently perform large-scale GWAS: An end-to-end GWAS pipeline, including file conversion, pre-processing, association testing and visualization, analyzed 77,053 individuals and over 18 million SNPs in 5.4 hours using 20 CPU cores.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".