Considering dependence among genes and markers for false discovery control in eQTL mapping
Liang Chen1, Tiejun Tong, Hongyu Zhao
1Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA. liang.chen@usc.edu
Bioinformatics (Oxford, England)
|July 19, 2008
Summary
We developed a new statistical method to account for gene dependence in large-scale biological studies. This approach improves the accuracy of expression quantitative trait loci (eQTL) identification and controls false positives more effectively.
Area of Science:
- Genomics
- Statistical Genetics
- Bioinformatics
Background:
- Multiple comparison adjustment is critical in large-scale biological studies.
- Dependence among genes is often ignored but significant in genetical genomics (e.g., expression quantitative trait loci mapping).
- Gene expression dependence impacts data analysis and interpretation.
Purpose of the Study:
- To address the challenge of multiple comparison adjustment in the presence of gene dependence.
- To develop methods for controlling false discoveries while accounting for hypothesis dependence.
- To enhance statistical power for expression quantitative trait loci (eQTL) identification.
Main Methods:
- Proposed a method considering both mean and variance of false discovery number for adjustment.
- Developed a variance estimator for false discovery number.
- Introduced weighted upper bound of false discovery proportion (wuFDP) for improved eQTL power and false positive control.
Main Results:
- The proposed wuFDP control improves statistical power in eQTL identification.
- The wuFDP approach offers better control of false positives compared to FDR and uFDP, especially with linked markers.
- Simulation studies and real data analysis demonstrate the effectiveness of uFDP and wuFDP controls.
Conclusions:
- Accounting for gene dependence is crucial for accurate multiple comparison adjustment in genomics.
- The wuFDP method provides a powerful and robust approach for eQTL analysis.
- This work offers improved statistical tools for large-scale biological data analysis.
Related Concept Videos
01:29Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
01:09Epistasis Analysis
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...