Eight quick tips for including chromosome X in genome-wide association studies
Bibliographic record
Abstract
All individuals carry a minimum of 1 copy of chromosome X.Despite being a relatively long chromosome with more than 150 million base pairs [1], similar in length to chromosome 8, association testing of genetic variants on chromosome X is still not routinely conducted.Genome-wide association studies (GWAS) have been used to identify a vast range of genomic loci of interest for a variety of complex human diseases and traits by quantifying genetic variants that are statistically associated with a given disease/trait [2,3].However, a lack of testing for variants on the X chromosome limits our ability to identify vital loci and subsequently understand potential mechanisms linked to this chromosome.There was a call for the inclusion of chromosome X into genome-wide association analyses presented in 2013.At that time, a scan of published GWAS from 2010 and 2011 showed that only 33% of the studies had tested variants on the X chromosome in their analyses [4].Despite this call for inclusion, the lack of representation of this chromosome has not improved according to a 2023 study.Of the 136 publications that submitted at least 1 summary statistics file to the NHGRI-EBI GWAS Catalog in 2021, only 25% reported chromosome X results [5].Indeed, there are several characteristics of this chromosome that make it unique compared to the autosomes, which can pose analytical challenges in association testing.Such challenges include how to account for X inactivation in individuals with an XX karyotype, how to model the hemizygous state of genotypes in individuals with an XY karyotype, or how to best code genotypes at the 2 pseudo-autosomal regions, short stretches at either end of the X with high homology with the Y chromosome, known as PAR1 and PAR2.The non-pseudo-autosomal region (nonPAR) denotes the middle sequence of the X chromosome.Furthermore, there are many well-used software that take GWAS summary statistics as input and ignore chromosome X information [6,7].This practice can make it difficult and unintuitive for researchers to run association testing on the X chromosome.Inclusion of chromosome X routinely in GWAS and downstream analyses will serve to enhance our understanding of the genetic contributors to complex diseases and traits.Here, we propose 8 tips to help move towards the inclusion of X in GWAS to provide a suggested set of concrete actions that can be taken to overcome the challenges or obstacles preventing routine analysis of this chromosome.
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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 | 0.029 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.092 | 0.049 |
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".