Published in

Springer Nature [academic journals on], European Journal of Human Genetics

DOI: 10.1038/ejhg.2015.244



Export citation

Search in Google Scholar

Evaluation of Power of the Illumina HumanOmni5M-4v1 BeadChip to Detect Risk Variants for Human Complex Diseases

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO


Although genome-wide association studies (GWAS) have identified thousands of disease-risk loci harboring common variants, 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. It is a denser array compared to the others and can balance both cost and array density. In this article, we investigated the power of Omni5 to detect genetic associations. The Omni5 included variants down to < 1% minor allele frequencies (MAFs). Theoretical power calculations indicated that Omni5 has increased power compared to other arrays with lower density for some known loci, although there are some exceptions. We further evaluated the genetic associations between known loci and three traits in the Framingham Heart Study (FHS). Finally, we searched genome-wide for novel associations using the Omni5 genotypes. We compared our associations with the ones from Affymetrix 500K + MIPS 50K arrays and two imputed datasets based on the same arrays: (1) HapMap Phase II and (2) 1000 Genomes as reference panels. We observed increased evidence with smaller p-values for known loci using the Omni5 genotypes. With limited sample sizes, we also identified novel variants with small p-values at genome-wide significant levels. Our observations support 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.