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Data quality control in genetic case-control association studies

by Clarke, Geraldine M , Anderson, Carl A , Pettersson, Fredrik H , Cardon, Lon R , Morris, Andrew P , Zondervan, Krina T

in 631/114/1767 / 631/114/794 / 631/1647/2217/2138 / Algorithms / Analytical Chemistry / Animals / Applications software / Biological Techniques / Case studies / Case-Control Studies / Computational Biology - methods / Computational Biology/Bioinformatics / Control data (computers) / Data analysis / Deoxyribonucleic acid / Disease control / DNA / Failure rates / Female / Genetic research / Genetic Techniques / Genome-Wide Association Study - methods / Genomes / Genotype & phenotype / Humans / Life Sciences / Male / Management / Methods / Microarrays / Organic Chemistry / Outliers (statistics) / protocol / Quality assessment / Quality Control / Quality management / Single-nucleotide polymorphism / Software

2010

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Data quality control in genetic case-control association studies

by Clarke, Geraldine M , Anderson, Carl A , Pettersson, Fredrik H , Cardon, Lon R , Morris, Andrew P , Zondervan, Krina T

2010

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Data quality control in genetic case-control association studies

by Clarke, Geraldine M , Anderson, Carl A , Pettersson, Fredrik H , Cardon, Lon R , Morris, Andrew P , Zondervan, Krina T

2010

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Journal Article

Data quality control in genetic case-control association studies

Clarke, Geraldine M,

Anderson, Carl A,

Pettersson, Fredrik H,

Cardon, Lon R,

Morris, Andrew P,

Zondervan, Krina T

2010

Overview

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.

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Publisher

Nature Publishing Group UK,Nature Publishing Group

Subject

/ Analytical Chemistry

/ Animals

/ Applications software