Data quality control in genetic case-control association studies

Abstract

This protocol details the steps for data quality assessment and control that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that introduce bias. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to perform assessments of failure rate per individual and per SNP and to assess the degree of relatedness between individuals. We also detail other quality-control procedures, including the use of SMARTPCA software for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed here. Issues concerning study design and marker selection in case-control studies have been discussed in our earlier protocols. This protocol, which is routinely used in our labs, should take approximately 8 h to complete.

Figure 1

Genotype failure rate vs. heterozygosity across all individuals the study. Shading indicates sample density and dashed lines denote QC thresholds.

Figure 2

Ancestry clustering based on genome-wide association data. HapMap3 reference samples: CEU (red), CHB+JPT (purple) and YRI (green). GWA samples: black crosses. 11 cases and 19 controls with a 2^nd principal component score less than 0.072 (grey dashed line) were marked for removal.

Figure 3

Histogram of missing data rate across all individuals passing ‘per-individual’ QC. The dashed vertical line represents the threshold (3%) at which SNPs were removed from further analysis due to an excess failure rate.