Evaluating the performance of Affymetrix SNP Array 6.0 platform with 400 Japanese individuals

BackgroundWith improvements in genotyping technologies, genome-wide association studies with hundreds of thousands of SNPs allow the identification of candidate genetic loci for multifactorial diseases in different populations. However, genotyping errors caused by genotyping platforms or genotype calling algorithms may lead to inflation of false associations between markers and phenotypes. In addition, the number of SNPs available for genome-wide association studies in the Japanese population has been investigated using only 45 samples in the HapMap project, which could lead to an inaccurate estimation of the number of SNPs with low minor allele frequencies. We genotyped 400 Japanese samples in order to estimate the number of SNPs available for genome-wide association studies in the Japanese population and to examine the performance of the current SNP Array 6.0 platform and the genotype calling algorithm "Birdseed".ResultsAbout 20% of the 909,622 SNP markers on the array were revealed to be monomorphic in the Japanese population. Consequently, 661,599 SNPs were available for genome-wide association studies in the Japanese population, after excluding the poorly behaving SNPs. The Birdseed algorithm accurately determined the genotype calls of each sample with a high overall call rate of over 99.5% and a high concordance rate of over 99.8% using more than 48 samples after removing low-quality samples by adjusting QC criteria.ConclusionOur results confirmed that the SNP Array 6.0 platform reached the level reported by the manufacturer, and thus genome-wide association studies using the SNP Array 6.0 platform have considerable potential to identify candidate susceptibility or resistance genetic factors for multifactorial diseases in the Japanese population, as well as in other populations.

[1]  Lisa J. Martin,et al.  Quality assessment of buccal versus blood genomic DNA using the Affymetrix 500 K GeneChip , 2007, BMC Genetics.

[2]  Jing Huang,et al.  Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array. , 2004, Genome research.

[3]  Hiroshi Sato,et al.  Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction , 2002, Nature Genetics.

[4]  Edward R. Dougherty,et al.  SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays , 2007, Bioinform..

[5]  Sudha Seshadri,et al.  The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports , 2007, BMC Medical Genetics.

[6]  J. Hampe,et al.  Efficacy assessment of SNP sets for genome-wide disease association studies , 2007, Nucleic acids research.

[7]  K. Gunderson,et al.  Whole genome genotyping technologies on the BeadArray™ platform , 2007 .

[8]  J. Brooks Multiple newly identified loci associated with prostate cancer susceptibility , 2008 .

[9]  Ali Amin Al Olama,et al.  Multiple newly identified loci associated with prostate cancer susceptibility , 2008, Nature Genetics.

[10]  S. Tsuji,et al.  Appropriate data cleaning methods for genome-wide association study , 2008, Journal of Human Genetics.

[11]  Lon R Cardon,et al.  Evaluating coverage of genome-wide association studies , 2006, Nature Genetics.

[12]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[13]  R. Mägi,et al.  Evaluating the performance of commercial whole-genome marker sets for capturing common genetic variation , 2007, BMC Genomics.

[14]  Yusuke Nakamura,et al.  A high-throughput SNP typing system for genome-wide association studies , 2001, Journal of Human Genetics.

[15]  D. Nickerson,et al.  Variation is the spice of life , 2001, Nature Genetics.

[16]  Ying Wang,et al.  Genomewide association study of leprosy. , 2009, The New England journal of medicine.