Efficient Statistical Method for Association Analysis of X-Linked Variants

Unlike the gene-poor Y chromosome, the X chromosome contains over 1,000 genes that are essential for viability of cells. Females have 2 X chromosomes, and thus female X-linked gene expression would be expected to be twice that of males. To adjust this imbalance, one of the 2 X-linked genes is often inactivated, and this is known as X-chromosome inactivation (XCI). However, recent studies described that a gene can be nonrandomly selected for inactivation from 2 X-linked genes and that XCI is not observed in some X-linked genes. Since this complex biological process has prevented efficient statistical association analyses, we propose a new statistical method against this uncertain biological process.

The proposed method consists of 2 steps. First, p values for various biological processes are calculated and then combined into a single p value with the modified Fisher method and a minimum p value.

Our simulation results show that the proposed method is generally the most statistically efficient and is not sensitive to the unknown biological model.

Therefore, we can conclude that the proposed approaches are robust against the various XCI processes for testing the association of X-linked single nucleotide polymorphisms with the disease of interest and the proposed method is a practical solution.