Efficient Statistical Method for Association Analysis of X-Linked Variants

Abstract

Background/Aims: 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. Methods: 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. Results: Our simulation results show that the proposed method is generally the most statistically efficient and is not sensitive to the unknown biological model. Conclusion: 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.