A genome-wide approach to identify pharmacogenetic contributions to adverse drug
reactions in real time
Matthew R. Nelson, Silviu-Alin Bacanu, Li Li, Clive E. Bowman, Michael Mosteller, Allen
D. Roses, Eric H. Lai, Margaret G. Ehm
Supplementary methods
Whole-Genome Association Quality Assurance Using Probe Intensity Data (iQC)
Genetic association studies are increasingly carried out on a genome-wide scale,
wherein up to a million single nucleotide polymorphisms (SNPs) may be genotyped and
tested. Although the development of technology and calling algorithms have resulted in
relatively high average genotyping accuracy, various sources of experimental variability
can result in genotyping errors, which may lead to false positive associations. This is
particularly true with small sample sizes, such as in many pharmacogenetic studies, in
which many of the most significantly associated results may be enriched with genotyping
errors.
We developed a quality index for rapid genotype quality assessment and detection of
genotype errors that likely result in false positive association results using Affymetrix
500K intensity data. For a given marker, the probe intensity data for each allele is
summarized in a 2-dimensional space. Only perfect-match data on a log2 scale is used.
The mean log2 normalized intensity for each subject is calculated for alleles A and B,
respectively, by averaging across all probes, subtracting the chip (subject) median to
account for chip (subject) variation, and adding the global median across chips
(subjects) to keep it positive. The normalized intensities are used to calculate the
Euclidean distance matrix over all subjects. Based on the distance matrix and individual
genotypes, the Silhouette width1 is obtained for each subject to measure the quality of
genotype classification. A well classified subject is the one in the appropriate genotype
cluster with Silhouette width ≥ 0. A quality index for each marker could be defined as the
proportion of well classified genotypes of all subjects. For the case-control design
considered here, the quality index is defined as the minimum of the proportion of well
classified genotypes in cases or controls. By considering cases and controls separately,
it is more likely to detect genotyping errors that have the greatest impact on association
results, which can be beneficial for data with disproportional samples of cases and
controls, particularly in PGx studies. Based on empirical data analyses, a cut off of
quality index at 90% was recommended for a case-control design.
For visualization, the mean adjusted intensities for each allele are plotted against each
other, where each color represented a different genotype in the cluster plots. For a high
quality marker, clusters of different colors (genotypes) should be separated from each
other, indicating a high confidence in genotype calling. For a low quality marker, clusters
of different colors (genotypes) might not have clear boundaries or part of the subjects
may be misclassified to the wrong cluster. There are instances where the clustering
suggests that the genotype calls are of poor quality even though the quality index may
be high. Visual inspection is recommended for all top associations.
References
1. Rousseeuw, P.J Silhouettes: a graphical aid to the interpretation and validation of
cluster analysis. J. Comput. Appl. Math., 20, 53–65