On the analysis of genetic association with long-read sequencing data
Bibliographic record
Abstract
Long-read sequencing (LRS) technologies have enhanced the ability to resolve complex genomic architecture and determine the 'phase' relationships of genetic variants over long distances. Although genome-wide association studies (GWAS) identify individual variants associated with complex traits, they do not typically account for whether multiple associated signals at a locus may act in cis or trans, or whether they reflect allelic heterogeneity. As a result, effects that arise specifically from phase relationships may remain hidden in analyses using short-read and microarray data. While the advent of LRS has enabled accurate measurement of phase in population cohorts, statistical methods that leverage phase in genetic association analysis remain underdeveloped. Here, we introduce the Regression on Phase (RoP) method, which directly models cis and trans phase effects between variants under a regression framework. In simulations, RoP outperforms genotype interaction tests that detect phase effects indirectly, and distinguishes in-cis from in-trans phase effects. We implemented RoP at two cystic fibrosis (CF) modifier loci discovered by GWAS. At the chromosome 7q35 trypsinogen locus, RoP confirmed that two variants contributed independently (allelic heterogeneity). At the SLC6A14 locus on chromosome X, phase analysis uncovered a coordinated regulatory mechanism in which a promoter variant modulates lung phenotypes in individuals with CF when acting in cis with a lung-specific enhancer (E2765449/enhD). This coordinated regulation was confirmed in functional studies. These findings highlight the potential of leveraging phase information from LRS in genetic association studies. Analyzing phase effects with RoP can provide deeper insights into the complex genetic architectures underlying disease phenotypes, ultimately guiding more informed functional investigations and potentially revealing new therapeutic targets.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".