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Updated: May 10, 2026
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A Method to Study the C924T Polymorphism of the Thromboxane A2 Receptor Gene
Published on: April 1, 2019
Catching the genomic wave in oligonucleotide single-nucleotide polymorphism arrays by modeling sequence binding
Yalu Wen1, Ming Li, Wenjiang J Fu
1The Computational Genomics Lab, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824, USA.
Summary
The genomic wave, a genome data artifact correlated with GC content, was studied to understand its mechanism. A new model successfully separated biological signals from this artifact, identifying GC content and fragment length as key sources.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- The genomic wave is a significant artifact in genome data, strongly linked to sequence GC content.
- Existing statistical methods filter this artifact, but its underlying mechanism remains unstudied.
- Understanding the genomic wave's sources is crucial for separating biological signals from artifacts in genomic studies.
Purpose of the Study:
- To investigate the mechanism and sources of the genomic wave artifact in oligonucleotide single-nucleotide polymorphism (SNP) arrays.
- To develop a method for separating biological signals from the genomic wave artifact.
- To improve genome data quality for array design, modeling, and association studies.
Main Methods:
- Developed a novel Probe Intensity Composite Representation (PICR) model to separate biological signals from array background.
- The PICR model decomposes probe intensity into target concentrations, SNP-specific background, and measurement error.
- Applied the PICR model to Affymetrix GeneChip 500K HapMap and Wellcome Trust Case-Control Study data.
Main Results:
- The PICR model successfully captured the genomic wave within the SNP-specific background term.
- Biological signals (allelic target concentrations) were successfully separated from the genomic wave artifact.
- Identified GC content and fragment length (FL) as two primary sources of the genomic wave artifact.
Conclusions:
- The PICR model effectively removes the genomic wave artifact from genome data.
- The genomic wave is influenced not only by GC content but also by a nonlinear effect of fragment length.
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