Allele-specific expression analysis methods for high-density SNP microarray data.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Genome-wide allele-specific expression (ASE) quantification technologies have advanced significantly.
• Single-nucleotide polymorphism (SNP) microarrays are explored as a tool for ASE analysis.
• Data from cell lines and primary tissues with available RNA and DNA profiles were utilized.

Purpose of the Study:

• To investigate the efficacy of SNP microarrays for quantifying allele-specific expression (ASE).
• To compare the performance of different statistical methods for ASE detection.
• To identify optimal approaches for ASE analysis using high-density genotyping arrays.

Main Methods:

• Utilized high-density Illumina Infinium II genotyping arrays for ASE measurement.
• Preprocessed data included outlier removal, normalization, and filtering of SNPs and genotyping calls.
• Compared three ASE detection tests: one published and two novel approaches, varying in operational level and input data requirements.

Main Results:

• Analyzed data from two experiments using SNP microarrays for ASE quantification.
• Identified varying sensitivity among ASE detection methods, with performance improving with increased sample size.
• Methods relying solely on RNA signal demonstrated superior performance across multiple metrics.
• Top-ranked SNPs identified by all methods are potential candidates for ASE.

Conclusions:

• SNP microarrays are a viable technology for genome-wide ASE quantification.
• Methods utilizing RNA signal alone are most effective for ASE detection.
• Increasing sample size enhances the sensitivity of ASE detection methods.