Dataset Information

Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

ABSTRACT: BACKGROUND: Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms with similar design features. This procedure deals with both extreme and subtle biases and is intended to be applied before standard genotype calling algorithms. RESULTS: Application of the spatial bias adjustments on HapMap samples resulted in higher genotype call rates with equal or even better accuracy for thousands of SNPs. Consequently the normalization procedure is expected to lead to more meaningful biological inferences and could be valuable for genome-wide SNP analysis. CONCLUSIONS: Spatial normalization can potentially rescue thousands of SNPs in a genetic study at the small cost of computational time. The approach is implemented in R and available from the authors upon request.

SUBMITTER: Chai HS

PROVIDER: S-EPMC2910027 | biostudies-other | 2010

REPOSITORIES: biostudies-other

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Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

Chai High Seng HS Therneau Terry M TM Bailey Kent R KR Kocher Jean-Pierre A JP

BMC bioinformatics 20100629

<h4>Background</h4>Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms ...[more]

PMID: 20587065

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Project description:Background: Single-nucleotide polymorphism (SNP) arrays are an ideal technology for genotyping genetic variants in mass screening. However, using SNP arrays to detect rare variants [with a minor allele frequency (MAF) of <1%] is still a challenge because of noise signals and batch effects. An approach that improves the genotyping quality is needed for clinical applications. Methods: We developed a quality-control procedure for rare variants which integrates different algorithms, filters, and experiments to increase the accuracy of variant calling. Using data from the TWB 2.0 custom Axiom array, we adopted an advanced normalization adjustment to prevent false calls caused by splitting the cluster and a rare het adjustment which decreases false calls in rare variants. The concordance of allelic frequencies from array data was compared to those from sequencing datasets of Taiwanese. Finally, genotyping results were used to detect familial hypercholesterolemia (FH), thrombophilia (TH), and maturity-onset diabetes of the young (MODY) to assess the performance in disease screening. All heterozygous calls were verified by Sanger sequencing or qPCR. The positive predictive value (PPV) of each step was estimated to evaluate the performance of our procedure. Results: We analyzed SNP array data from 43,433 individuals, which interrogated 267,247 rare variants. The advanced normalization and rare het adjustment methods adjusted genotyping calling of 168,134 variants (96.49%). We further removed 3916 probesets which were discordant in MAFs between the SNP array and sequencing data. The PPV for detecting pathogenic variants with 0.01%<MAF≤1% exceeded 99.37%. PPVs for those with an MAF of ≤0.01% improved from 95% to 100% for FH, 42.11% to 85.19% for TH, and 18.24% to 72.22% for MODY after adopting our rare variant quality-control procedure and experimental verification. Conclusion: Adopting our quality-control procedure, SNP arrays can adequately detect variants with MAF values ranging 0.01%∼0.1%. For variants with MAF values of ≤0.01%, experimental validation is needed unless sequencing data from a homogeneous population of >10,000 are available. The results demonstrated our procedure could perform correct genotype calling of rare variants. It provides a solution of pathogenic variant detection through SNP array. The approach brings tremendous promise for implementing precision medicine in medical practice.

Dataset Information

Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

Publications

Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

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