Literature DB >> 30232923

A robust and powerful test for case-control genetic association study on X chromosome.

Peng Wang1, Si-Qi Xu1, Bei-Qi Wang1, Wing Kam Fung2, Ji-Yuan Zhou1.   

Abstract

Hundreds of genome-wide association studies were conducted to map the disease genes on autosomes in human beings. It is known that many complex diseases are sex-determined and X chromosome is expected to play an important role. However, only a few single-nucleotide polymorphisms on X chromosome were found to be significantly associated with the diseases under study. On the other hand, to balance the genetic effect between two sexes, X chromosome inactivation occurs in most of X-linked genes by silencing one copy of two X chromosomes in females and dosage compensation is achieved. A few association studies on X chromosome incorporated the information on dosage compensation. However, some of them require the assumption of Hardy-Weinberg equilibrium and some need to specify the underlying genetic model. Therefore, in this article, we propose a novel method for association by taking account of different dosage compensation patterns. The proposed test is a robust approach because it requires neither specifying the underlying genetic models nor the assumption of Hardy-Weinberg equilibrium. Further, the proposed method allows for different deviations from Hardy-Weinberg equilibrium between cases and controls. Simulation results demonstrate that our proposed method generally outperforms the existing methods in terms of controlling the size and the test power. Finally, we apply the proposed test to the meta-analysis of the Graves' disease data for its practical use.

Entities:  

Keywords:  Graves' disease; X chromosome; association analysis; dosage compensation; genetic model

Mesh:

Substances:

Year:  2018        PMID: 30232923     DOI: 10.1177/0962280218799532

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  9 in total

1.  X-chromosome genetic association test incorporating X-chromosome inactivation and imprinting effects.

Authors:  Wei Liu; Bei-Qi Wang; Guojun Liu-Fu; Wing Kam Fung; Ji-Yuan Zhou
Journal:  J Genet       Date:  2019-11       Impact factor: 1.166

2.  Robust association tests for quantitative traits on the X chromosome.

Authors:  Zi-Ying Yang; Wei Liu; Yu-Xin Yuan; Yi-Fan Kong; Pei-Zhen Zhao; Wing Kam Fung; Ji-Yuan Zhou
Journal:  Heredity (Edinb)       Date:  2022-09-10       Impact factor: 3.832

3.  BEXCIS: Bayesian methods for estimating the degree of the skewness of X chromosome inactivation.

Authors:  Wen-Yi Yu; Yu Zhang; Meng-Kai Li; Zi-Ying Yang; Wing Kam Fung; Pei-Zhen Zhao; Ji-Yuan Zhou
Journal:  BMC Bioinformatics       Date:  2022-05-24       Impact factor: 3.307

4.  XCMAX4: A Robust X Chromosomal Genetic Association Test Accounting for Covariates.

Authors:  Youpeng Su; Jing Hu; Ping Yin; Hongwei Jiang; Siyi Chen; Mengyi Dai; Ziwei Chen; Peng Wang
Journal:  Genes (Basel)       Date:  2022-05-09       Impact factor: 4.141

5.  A statistical measure for the skewness of X chromosome inactivation based on family trios.

Authors:  Si-Qi Xu; Yu Zhang; Peng Wang; Wei Liu; Xian-Bo Wu; Ji-Yuan Zhou
Journal:  BMC Genet       Date:  2018-12-05       Impact factor: 2.797

6.  Testing for goodness rather than lack of fit of an X-chromosomal SNP to the Hardy-Weinberg model.

Authors:  Stefan Wellek; Andreas Ziegler
Journal:  PLoS One       Date:  2019-02-21       Impact factor: 3.240

7.  Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder.

Authors:  William A Jons; Colin L Colby; Susan L McElroy; Mark A Frye; Joanna M Biernacka; Stacey J Winham
Journal:  Biol Sex Differ       Date:  2019-12-09       Impact factor: 5.027

8.  A robust test for X-chromosome genetic association accounting for X-chromosome inactivation and imprinting.

Authors:  Yu Zhang; Si-Qi Xu; Wei Liu; Wing Kam Fung; Ji-Yuan Zhou
Journal:  Genet Res (Camb)       Date:  2020-04-01       Impact factor: 1.588

9.  A statistical measure for the skewness of X chromosome inactivation for quantitative traits and its application to the MCTFR data.

Authors:  Bao-Hui Li; Wen-Yi Yu; Ji-Yuan Zhou
Journal:  BMC Genom Data       Date:  2021-07-02
  9 in total

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