Data quality control in genetic case-control association studies

Abstract

This protocol details the data quality assessment and control steps 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 to the study. 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 carry out 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 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, as these are provided in a further protocol in the series. Issues concerning the study design and marker selection in case-control studies have been discussed in our earlier protocols. The protocol should take approximately 8 hours to complete.

Population-specific genotype imputations using minimac or IMPUTE2

“…The purpose of these filters is to exclude both markers and samples with low-quality data. Anderson et al 7 and Verma et al 10 provide a detailed protocol that deals with both per-maker and per-individual filtering.…”

“…To do this, complete the protocol for quality control, as described by Anderson et al 7 . We assume that the genotypes have been called by a genotyping center and returned in PLINK format named 'raw-GWA-data.ped, raw-GWA-data.map' .…”

“…Therefore, Anderson et al 7 have previously published a protocol dealing with quality control of genotype data, and our work can be seen as an extension of that protocol. A guideline for imputations with the Beagle 8 and IMPUTE2 (ref.…”

“…9) and minimac 12 . We describe the different pipelines for imputations using the genome-wide SNP data provided by Anderson et al 7 as a target data set. We will start with the quality control of this target set using the pipeline from Anderson et al 7 .…”

“…We describe the different pipelines for imputations using the genome-wide SNP data provided by Anderson et al 7 as a target data set. We will start with the quality control of this target set using the pipeline from Anderson et al 7 . We will show how to lift the target set over to the correct National Center for Biotechnology Information (NCBI) build and then provide pipelines for imputation using minimac 12 and IMPUTE2 (ref.…”