Genome-wide association studies (GWAS) have been successful in identifying genetic markers associated with complex diseases. GWAS usually focus on autosomal markers and exclude X-chromosome markers. However, many complex diseases show sex biases in disease frequency, suggesting potential associations between sex chromosomes and these diseases. Particularly, X-chromosome genes are involved in many cancers, including both sex-organ-specific (e.g., ovarian and prostate) and non-sex-organ-specific (e.g., renal cell carcinoma) cancers. Therefore, ignoring X-chromosome markers in association studies might lead to the loss of potential signals for complex diseases. Nonetheless, the development of statistical tests for X- chromosome analysis based on a mixed-sex sample has received surprisingly little attention, perhaps due to the complexity of the X-chromosome inactivation (XCI) process. XCI on female X-chromosome loci states that in females during early embryonic development, 1 of the 2 copies of the X-chromosome present in each cell is randomly inactivated to achieve dosage compensation of X-linked genes in males and females. The XCI process is in general random; however, skewed, or non-random, XCI is also a biological plausibility. Skewed XCI has been defined using an arbitrary threshold of inactivation of 1 of the alleles in > 75% of cells. Another complexity in analyzing X-chromosome data is the escape from XCI outside the pseudo-autosomal regions of the X-chromosome, which results in both alleles remaining active (i.e., no dosage compensation). Statistical approaches designed for autosomal chromosomes have been used for X-chromosome analysis. However, because they ignore XCI, these approaches are not based on biologically plausible models and, therefore, are likely to lose power to detect X-chromosome-associate genetic variants. In this grant, we propose to develop a novel statistical approach for analyzing X-chromosomal genetic data that will account for different XCI processes, including random XCI, skewed XCI, and escape from XCI (Aim 1). Since individual markers only explain a small fraction of the expected heritability and the experimental evidence has shown that multiple markers/genes tend to function together on complex diseases, we will also develop gene-based and, even further, pathway-based approaches for analyzing X-chromosome data (Aim 1). We will analyze the head and neck cancer X-chromosome genetic data using the proposed and existing approaches, based on the existing data from an ongoing GWAS at The University of Texas MD Anderson Cancer Center (R01 CA131324, PI: Sanjay Shete, co-investigator of this grant) (Aim 2).

Public Health Relevance

We will investigate X-chromosome genetic association in head and neck cancer study using the statistical methods developed in this grant. We will potentially identify novel genetic signals associated with head and neck cancer risk that could improve our knowledge of the etiology and pathogenesis of head and neck cancer and complete the genetic profiles of individuals, which are essential for head and neck cancer risk assessment and prevention.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Research Grants (R03)
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Special Emphasis Panel (ZCA1)
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Verma, Mukesh
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University of Texas MD Anderson Cancer Center
Biostatistics & Other Math Sci
United States
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Wang, Jian; Shete, Sanjay (2018) Estimation of indirect effect when the mediator is a censored variable. Stat Methods Med Res 27:3010-3025
Reyes-Gibby, Cielito C; Wang, Jian; Yeung, Sai-Ching J et al. (2018) Genome-wide association study identifies genes associated with neuropathy in patients with head and neck cancer. Sci Rep 8:8789
Wang, Jian; Talluri, Rajesh; Shete, Sanjay (2017) Selection of X-chromosome Inactivation Model. Cancer Inform 16:1176935117747272
Reyes-Gibby, Cielito C; Melkonian, Stephanie C; Wang, Jian et al. (2017) Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis. PLoS One 12:e0180396
Zhu, Xuan; Wang, Jian; Peng, Bo et al. (2016) Empirical estimation of sequencing error rates using smoothing splines. BMC Bioinformatics 17:177
Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients. Sci Rep 6:34206
Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients. BMC Genet 17:40
Reyes-Gibby, Cielito C; Wang, Jian; Yeung, Sai-Ching J et al. (2015) Informative gene network for chemotherapy-induced peripheral neuropathy. BioData Min 8:24