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 |
Abstract
¿
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).
Publications
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, 2018-11-12 00:00:00.0; , .
EPub date: 2018-11-12 00:00:00.0.
PMID: 30421436
Related Citations
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 00:00:00.0; 8(1), p. 8789.
EPub date: 2018-06-08 00:00:00.0.
PMID: 29884837
Related Citations
Estimation Of Indirect Effect When The Mediator Is A Censored Variable
Authors: Wang J.
, Shete S.
.
Source: Statistical Methods In Medical Research, 2017-01-01 00:00:00.0; , p. 962280217690414.
PMID: 28132585
Related Citations
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 00:00:00.0.
PMID: 28678827
Related Citations
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 00:00:00.0.
PMID: 29308008
Related Citations
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 00:00:00.0; 6, p. 34206.
PMID: 27670397
Related Citations
Empirical estimation of sequencing error rates using smoothing splines.
Authors: Zhu X.
, Wang J.
, Peng B.
, Shete S.
.
Source: Bmc Bioinformatics, 2016-04-22 00:00:00.0; 17, p. 177.
EPub date: 2016-04-22 00:00:00.0.
PMID: 27102907
Related Citations
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 00:00:00.0; 17, p. 40.
EPub date: 2016-02-13 00:00:00.0.
PMID: 26872611
Related Citations
Informative gene network for chemotherapy-induced peripheral neuropathy.
Authors: Reyes-Gibby C.C.
, Wang J.
, Yeung S.C.
, Shete S.
.
Source: Biodata Mining, 2015; 8, p. 24.
PMID: 26269716
Related Citations