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Grant Details

Grant Number: 1R03CA192197-01A1 Interpret this number
Primary Investigator: Wang, Jian
Organization: University Of Tx Md Anderson Can Ctr
Project Title: Method to Analyze X-Chromosomal Genetic Data
Fiscal Year: 2015


¿ DESCRIPTION (provided by applicant): 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).


Mediation analysis in a case-control study when the mediator is a censored variable.
Authors: Wang J. , Ning J. , Shete S. .
Source: Statistics in medicine, 2019-03-30; 38(7), p. 1213-1229.
EPub date: 2018-11-12.
PMID: 30421436
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Estimation of indirect effect when the mediator is a censored variable.
Authors: Wang J. , Shete S. .
Source: Statistical methods in medical research, 2018 Oct; 27(10), p. 3010-3025.
EPub date: 2017-01-30.
PMID: 28132585
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Genome-wide association study identifies genes associated with neuropathy in patients with head and neck cancer.
Authors: Reyes-Gibby C.C. , Wang J. , Yeung S.J. , Chaftari P. , Yu R.K. , Hanna E.Y. , Shete S. .
Source: Scientific reports, 2018-06-08; 8(1), p. 8789.
EPub date: 2018-06-08.
PMID: 29884837
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Selection of X-chromosome Inactivation Model.
Authors: Wang J. , Talluri R. , Shete S. .
Source: Cancer informatics, 2017; 16, p. 1176935117747272.
EPub date: 2017-12-17.
PMID: 29308008
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Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.
Authors: Reyes-Gibby C.C. , Melkonian S.C. , Wang J. , Yu R.K. , Shelburne S.A. , Lu C. , Gunn G.B. , Chambers M.S. , Hanna E.Y. , Yeung S.J. , et al. .
Source: PloS one, 2017; 12(7), p. e0180396.
EPub date: 2017-07-05.
PMID: 28678827
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Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients.
Authors: Reyes-Gibby C.C. , Wang J. , Silvas M.R. , Yu R.K. , Hanna E.Y. , Shete S. .
Source: Scientific reports, 2016-09-27; 6, p. 34206.
EPub date: 2016-09-27.
PMID: 27670397
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Empirical estimation of sequencing error rates using smoothing splines.
Authors: Zhu X. , Wang J. , Peng B. , Shete S. .
Source: BMC bioinformatics, 2016-04-22; 17, p. 177.
EPub date: 2016-04-22.
PMID: 27102907
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MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients.
Authors: Reyes-Gibby C.C. , Wang J. , Silvas M.R. , Yu R. , Yeung S.C. , Shete S. .
Source: BMC genetics, 2016-02-13; 17, p. 40.
EPub date: 2016-02-13.
PMID: 26872611
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Informative gene network for chemotherapy-induced peripheral neuropathy.
Authors: Reyes-Gibby C.C. , Wang J. , Yeung S.J. , Shete S. .
Source: BioData mining, 2015; 8, p. 24.
EPub date: 2015-08-12.
PMID: 26269716
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