Rheumatoid Arthritis Genotype Analysis

Analyses using real data have also been performed. Rheumatoid arthritis (RA) genotype data on 1999 cases and 1504 controls have been obtained from WTCCC [44 (link)]. Genotypes from the Affymetrix GeneChip 500 K Mapping Array Set have been filtered using the usual quality controls tests on DNA quality (percentage of genotyped marker for any given individual above 90%), markers quality (percentage of genotyped individuals for any given marker above 90%), genotypes frequencies (markers with a p-value below a Bonferroni adjusted 5% threshold under the hypothesis of Hardy-Weinberg equilibrium in the controls cohort have been discarded). Missing genotypes for the GeneChip markers have been imputed using impute2 software [45 (link)]. This procedure led to 312583 SNP to be analyzed for the 2 cohorts. Zhang et al. [46 ] and [47 (link)] also used this dataset to infer potential interactions. These studies will therefore serve as a comparison for the results obtained with our approach.

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Abo Alchamlat S, & Farnir F. (2017). KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies. BMC Bioinformatics, 18, 184.

Publication 2017

GeneChip 500 K Mapping Array Set

Genechip Genotypes Rheumatoid arthritis

Corresponding Organization :

Other organizations : University of Liège

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Variable analysis

independent variables

Rheumatoid arthritis (RA) genotype data

dependent variables

None explicitly mentioned

control variables

Percentage of genotyped marker for any given individual above 90%
Percentage of genotyped individuals for any given marker above 90%
Markers with a p-value below a Bonferroni adjusted 5% threshold under the hypothesis of Hardy-Weinberg equilibrium in the controls cohort

controls

Positive control: None mentioned
Negative control: None mentioned

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