ObjectiveThe capacity of the Affymetrix drug metabolism enzymes and transporters (DMET) Plus pharmacogenomics genotyping chip to estimate population substructure and cryptic relatedness was evaluated. The results were compared with estimates using genome-wide HapMap data for the same individuals.MethodsFor 301 unrelated individuals, spanning three continental populations and one admixed population, genotypic data were collected using the Affymetrix DMET Plus microarray. Genome-wide data on these individuals were obtained from HapMap release 3. Population substructure was assessed using Eigenstrat and ADMIXTURE software for both platforms. Cryptic relatedness was explored by inbreeding coefficient estimation. Nonparametric tests were used to determine correlations of the analytical results of the two genotyping platforms.ResultsPrincipal components analysis identified population substructure for both datasets, with 15.8 and 16.6% of the total variance explained in the first two principal components for DMET Plus and HapMap data, respectively. ADMIXTURE results correctly identified four subpopulations within each dataset. Nonparametric rank correlations indicated significant associations between analyses with an average =0.7272 (P<10(-16)) across the three continental populations and =0.4888 for the admixed population. Concordance correlation coefficients (average (c)=0.9693 across all four subpopulations) strongly indicate concordance between ADMIXTURE results. Inbreeding coefficients were slightly inflated (16 individuals>0.15) using DMET Plus data and no cryptic relatedness was indicated using HapMap data. The inflated inbreeding estimation could be because of the limited number of markers provided by DMET as a random sample of 1832 markers from HapMap also yielded inflated estimates of cryptic relatedness (39 individuals>0.15). Furthermore, use of single nucleotide polymorphisms located in genes involved in metabolism and transport may have different allele frequencies in subpopulations than single nucleotide polymorphisms sampled from the whole genome.ConclusionThe DMET Plus pharmacogenomics genotyping chip is effective in quantifying population substructure across the three continental populations and inferring the presence of an admixed population. On the basis of our results, these microarrays offer sufficient depth for covariate adjustment of population substructure in genomic association studies.

Rapid progress in genotyping technologies, including the scaling up of assay technologies to genome-wide levels and next generation sequencing, has motivated a burst in methods development and application to detect genotype-phenotype associations in a wide array of diseases and other phenotypes. In this chapter, the authors review the study design and genotyping options that are used in association mapping, along with the appropriate methods to perform mapping within these study designs. The authors discuss both candidate gene and genome-wide studies, focused on DNA level variation. Quality control, genotyping technologies, and single-SNP and multiple-SNP analyses have facilitated the successes in identifying numerous loci influence disease risk. However, variants identified have generally explained only a small fraction of the heritable component of disease risk. The authors discuss emerging trends and future directions in performing analysis for rare variants to detect these variants that predict these traits with more complex etiologies.