|Title||Effective SNP ranking improves the performance of eQTL mapping.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||X Jeng, Jessie, Jacob Rhyne, Teng Zhang, and Jung-Ying Tzeng|
|Date Published||2020 Sep|
|Keywords||Computer Simulation, Data Analysis, Gene Expression Regulation, Genome-Wide Association Study, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Quantitative Trait Loci|
Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.
|Alternate Journal||Genet Epidemiol|
|Original Publication||Effective SNP ranking improves the performance of eQTL mapping.|
|PubMed Central ID||PMC7725394|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Effective SNP ranking improves the performance of eQTL mapping.