A finite mixture model for X‐chromosome association with an emphasis on microbiome data analysis
Bibliographic record
Abstract
Analysis of the X chromosome has been largely neglected in genetic studies mainly because of complex underlying biological mechanisms. On the other hand, the study of human microbiome data (typically over-dispersed counts with an excess of zeros) has generated great interest recently because of advancements in next-generation sequencing technologies. We propose a novel approach to infer the association between host genetic variants in the X-chromosome and microbiome data. The method accounts for random X-chromosome inactivation (XCI), skewed (or nonrandom) XCI (XCI-S), and escape of XCI (XCI-E). The inference is performed through a finite mixture model (FMM), in which an indicator variable denoting the "true" biological mechanism is treated as missing data. An expectation-maximization algorithm on zero-inflated and two-part models is implemented to estimate genetic effects. We investigate the performance of the FMM along with strategies that assume XCI and XCI-E mechanisms for all subjects compared with alternative approaches. Briefly, an XCI mechanism codes males' genotypes as homozygous females, whereas under XCI-E, males are treated as heterozygous females. By comprehensive simulations, we evaluate tests of the hypothesis under a computationally efficient score statistic. In summary, the FMM renders reduced bias and commensurate power compared to XCI, XCI-E, and alternative strategies while maintaining adequate Type 1 error control. The proposed method has far-reaching applications. In particular, we illustrate its usage on a large-scale human microbiome study, the Genetic, Environmental and Microbial (GEM) project, to test for the genetic association on the X chromosome.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".