Table 1.

Eight analytical considerations and challenges, C1–C8, present in X‐chromosome‐inclusive association studies

Problem	Solution	Relevant sections
C1: Quantitative traits vs. binary outcomes
C2: Genotype‐based vs. allele‐based association methods Allele‐based association tests, comparing allele frequency differences between cases and controls, are locally most powerful. However, they analyze binary outcomes only and are sensitive to the Hardy–Weinberg equilibrium (HWE) assumption (Sasieni, 1997).	Genotype‐based regression models, $Y$‐on‐$G$, support various types of outcome data, account for covariate effects with ease, and are robust to the HWE assumption.	Sections 1 and 2
C3: The choice of the baseline allele for association analysis, r vs. R For the autosomes, switching the two alleles does not affect the association inference. Is this true for the X‐chromosome?	It is not always true for the X‐chromosome, unless $S$ is included in the model.	Sections 2.1 and 2.2, and C4
C4: Sex as a covariate vs. no S main effect Unlike the autosomes, sex is a confounder when analyzing the X‐chromosome for traits exhibiting sexual dimorphism (e.g., height and weight). Even for the autosomes, sex can be a confounder if allele frequencies differ significantly between males and females.×	To maintain the correct type I error rate control, the sex main effect must be considered particular when analyzing the X‐chromosome. The resulting association test is also invariant to the choice of the baseline allele.	Section 2.2 and C3
C5: Gene–sex interaction vs. no G × S interaction effect Gene–sex interaction might exist, but there is a concern over loss of power due to increased degrees of freedom. In addition, what is the interpretation of gene–sex interaction effect in the presence of X‐inactivation?	Under no interaction, power loss of modeling interaction is capped at 11.4%. Models including the $G\times S$ covariate also lead to tests invariant to the assumption of X‐chromosome inactivation status.	Sections 2.3 and 3, and C6
C6: X‐chromosome inactivation (XCI) vs. no XCI XCI occurs if one of the two alleles in a genotype of a female is silenced. Individual‐level XCI status requires additional biological information that are not typically available to genetic association studies. Assuming XCI or no XCI at the sample level leads to different genotype coding strategies (Table 2), and it was thought that this will always lead to different association results.	XCI uncertainty implies sex‐stratified genetic effect which can be analytically represented by the $G\times S$ interaction effect. Teasing apart these different biological phenomenon require other “omic” data and additional analyses.	Sections 2.3 and 5, and C5
C7: If XCI, random vs. skewed X‐inactivation If the choice of the silenced allele in females is skewed toward a specific allele, the average effect of the $rR$ genotype is no longer the average of those of $r$ and $R$.	XCI skewness is statistically equivalent to a dominance genetic effect.	Section 2.4, and C8
C8: Dominance effect vs. no GD dominance effect For both the autosomes and X‐chromosome, the most common practice is to use the additive test which has better power than the genotypic test under (approximate) additivity, but it cannot capture dominance effects. The exact trade‐off, however, is not clear.	We provide analytical and empirical evidence supporting the use of genotypic model when analyzing either the autosomes or X‐chromosome. For an X‐chromosomal variant, including the dominance effect term has the added benefit of resolving of the skewed X‐inactivation uncertainty issue.	Sections 2.4, 3 and 4, and C7