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Open Access Methodology article

Allelotyping of pooled DNA with 250 K SNP microarrays

Stefan Wilkening1*, Bowang Chen1, Michael Wirtenberger1, Barbara Burwinkel12, Asta Försti13, Kari Hemminki13 and Federico Canzian1

Author Affiliations

1 Department of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

2 Helmholtz University Group Molecular Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

3 Center for Family Medicine, Karolinska Institute, SE-14183 Huddinge, Sweden

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BMC Genomics 2007, 8:77  doi:10.1186/1471-2164-8-77

Published: 16 March 2007

Abstract

Background

Genotyping technologies for whole genome association studies are now available. To perform such studies to an affordable price, pooled DNA can be used. Recent studies have shown that GeneChip Human Mapping 10 K and 50 K arrays are suitable for the estimation of the allele frequency in pooled DNA. In the present study, we tested the accuracy of the 250 K Nsp array, which is part of the 500 K array set representing 500,568 SNPs. Furthermore, we compared different algorithms to estimate allele frequencies of pooled DNA.

Results

We could confirm that the polynomial based probe specific correction (PPC) was the most accurate method for allele frequency estimation. However, a simple k-correction, using the relative allele signal (RAS) of heterozygous individuals, performed only slightly worse and provided results for more SNPs. Using four replicates of the 250 K array and the k-correction using heterozygous RAS values, we obtained results for 104.141 SNPs. The correlation between estimated and real allele frequency was 0.983 and the average error was 0.046, which was comparable to the results obtained with the 10 K array. Furthermore, we could show how the estimation accuracy depended on the SNP type (average error for A/T SNPs: 0.043 and for G/C SNPs: 0.052).

Conclusion

The combination of DNA pooling and analysis of single nucleotide polymorphisms (SNPs) on high density microarrays is a promising tool for whole genome association studies.