Evaluation of Power of the Illumina HumanOmni5M-4v1 BeadChip to Detect Risk Variants for Human Complex Diseases. C. Xing1, J. Huang2, Y.-H. Hsu3,4,5, A. L. DeStefano1,6,7, N. L. Heard-Costa6,7, P. A. Wolf6,7, S. Seshadri6,7, D. P. Kiel3,4, L. A. Cupples1,7, J. Dupuis1,7 1) Biostatistics, Boston University, Boston, MA, USA; 2) Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK; 3) Hebrew SeniorLife, Institute for Aging Research, Boston, MA, USA; 4) Harvard Medical School, Boston, MA, USA; 5) Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, USA; 6) Neurology, Boston University School of Medicine, Boston, MA, USA; 7) Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA.
Although genome-wide association studies (GWAS) have successfully identified thousands of disease-risk loci that harbor common variants associated with complex disease traits, a large portion of heritability is not explained. Emerging sequencing technologies can characterize all the variants. However, the cost is still high. Illumina recently released the HumanOmni5M-4v1 (Omni5) genotype array with ~ 4.3 million assayed SNPs, a much denser array compared to other arrays. Omni5 balances both cost and array density. In this article, we investigated the power of Omni5 to detect genetic associations. The Omni5 included variants in a wide range of minor allele frequencies (MAF) down to less than 1%. Theoretical power calculations indicated that Omni5 has increased power compared to other arrays with lower density when evaluating associations with some known loci, although there are some exceptions. We further evaluated the genetic associations between known loci and several traits in the Framingham Heart Study (FHS): femoral neck bone mineral density (FNBMD), lumbar spine bone mineral density (LSBMD), and hippocampal volume (HV). Finally, we searched genome-wide for novel associations using the Omni5 genotypes. We compared our associations with the ones obtained on the same participants from Affymetrix 500K + MIPS 50K arrays and two imputed datasets based on Affymetrix 500K + MIPS 50K arrays: (1) HapMap Phase II and (2) 1000 Genomes as reference panels. We observed increased evidence for genotype-phenotype associations with smaller p-values for known loci using the Omni5 genotypes. With limited sample sizes, we also identified novel variants with small p-values close to or at genome-wide significant levels. Our observations support the notion that dense genotyping using the Omni5 can be powerful in detecting novel variants. Comparison with imputed data with higher density also suggests that imputation helps but can not replace genotyping especially when imputation ratio is low.
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