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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Updated: Jun 24, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Missing call bias in high-throughput genotyping.

Wenqing Fu¹, Yi Wang, Ying Wang

¹MOE Key Laboratory of Contemporary Anthropology and Center for Evolutionary Biology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, PR China. didle.fu@gmail.com

|March 17, 2009

Summary

This summary is machine-generated.

Missing call bias (MCB) significantly impacts genome-wide association (GWA) studies, leading to power loss and biased results. Adjusting quality control thresholds for call rates and genotyping errors is crucial for minimizing bias in GWA analyses.

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Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

High-throughput genotyping platforms have enabled genome-wide association (GWA) studies.
The impact of missing calls on GWA studies is often overlooked compared to genotyping errors.

Purpose of the Study:

To investigate the effects of missing call bias (MCB) on GWA studies.
To compare MCB with genotyping errors in terms of bias and power loss.

Main Methods:

Experimental demonstration of MCB prevalence across four genotyping technologies.
Theoretical analysis of MCB's impact on allele/genotype frequencies, HWE, and association tests.
Comparison of bias introduced by MCB versus genotyping errors.

Main Results:

MCB was experimentally observed across Affymetrix 500 K SNP array, SNPstream, TaqMan, and Illumina Beadlab technologies.
MCB leads to biased conclusions in frequency estimation, HWE, and association tests.
MCB causes greater power loss in association tests than unbiased sample size reduction.

Conclusions:

The standard 'no-call' procedure for borderline quality data requires modification.
Coupling call-rate and genotyping error rate cut-offs is essential for minimizing bias in GWA studies.
Increase the call-rate QC cut-off while appropriately reducing the genotyping error rate cut-off.