| Literature DB >> 27325607 |
Sungkyoung Choi1, Sungyoung Lee1, Dandi Qiao2, Megan Hardin2,3, Michael H Cho2,3, Edwin K Silverman2,3, Taesung Park1,4, Sungho Won1,5,6.
Abstract
Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.Entities:
Keywords: X chromosome; X chromosome inactivation; extended families; genetic association analysis; rare variants
Mesh:
Year: 2016 PMID: 27325607 PMCID: PMC4981534 DOI: 10.1002/gepi.21979
Source DB: PubMed Journal: Genet Epidemiol ISSN: 0741-0395 Impact factor: 2.135