Comprehensive whole-genome analyses of the UK Biobank reveal significant sex differences in both genotype missingness and allele frequency on the X chromosome
Bibliographic record
Abstract
The UK Biobank is the most used dataset for genome-wide association studies (GWAS). GWAS of sex, essentially sex differences in minor allele frequencies (sdMAF), has identified autosomal SNPs with significant sdMAF, including in the UK Biobank, but the X chromosome was excluded. Our recent report identified multiple regions on the X chromosome with significant sdMAF, using short-read sequencing of other datasets. We performed a whole genome sdMAF analysis, with ~410 k white British individuals from the UK Biobank, using array genotyped, imputed or exome sequencing data. We observed marked sdMAF on the X chromosome, particularly at the boundaries between the pseudo-autosomal regions (PAR) and the non-PAR (NPR), as well as throughout the NPR, consistent with our earlier report. A small fraction of autosomal SNPs also showed significant sdMAF. Using the centrally imputed data, which relied mostly on low-coverage whole genome sequence, resulted in 2.1% of NPR SNPs with significant sdMAF. The whole exome sequencing also displays sdMAF on the X chromosome, including some NPR SNPs with heterozygous genotype calls in males. Genotyping, sequencing and imputation of X chromosomal SNPs requires further attention to ensure the integrity for downstream association analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".