Studying complex traits in the post-GWAS era: application to asthma and allergy-related traits
Bibliographic record
Abstract
In the past few years, genome-wide association studies (GWAS) allowed to identify a largenumber of common variants associated with multiple complex traits. These studies were a greattool that really helped understanding the genetic basis of a large number of diseases allowing toidentify new pathways, better understand disease mechanisms and even pinpoint potential drugtargets. However, most of the SNP identified were located in the non-coding region of thegenome (~90%) and mainly had small effect size. Additionally, even the largest meta-analysiscombining thousands of samples could not explain most of the diseases heritability. This forcedthe research community to develop new strategies and tools to complement GWAS findings. Inthis thesis, we explored some of these strategies to study asthma and allergy-related traits. Thesediseases are highly heterogeneous, having important genetic and environmental components.They affect millions of people around the world resulting in many deaths and consist animportant economic burden. We used two strategies to understand the genetic basis of thesediseases: 1) exploring the impact of rare and low-frequency variants and 2) using DNAmethylation data to understand the functional impact of SNPs. We first developed a customcapture panel to assess both coding and non-coding rare and low-frequency regulatory variants toexplore their impact on autoimmune and inflammatory complex traits. We applied it to a familialasthma cohort from a founder population and identified three novel genes associated with relatedtraits (serum IgE levels and eosinophil percentage). We also used DNA methylation data tocomplement our findings as well as to identify new genes associated with allergic rhinitis. Theresults presented in this thesis represent a good example on how to learn from GWAS findingsand go beyond them to understand the genetic basis of complex traits.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".