Estimation of heritability of different outcomes for genetic studies of TNFi response in patients with rheumatoid arthritis
SourceAnnals of the Rheumatic Diseases, 74, 12, (2015), pp. 2183-7
Article / Letter to editor
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Annals of the Rheumatic Diseases
SubjectRadboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 5: Inflammatory diseases RIHS: Radboud Institute for Health Sciences; Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience
OBJECTIVES: Pharmacogenetic studies of tumour necrosis factor inhibitors (TNFi) response in patients with rheumatoid arthritis (RA) have largely relied on the changes in complex disease scores, such as disease activity score 28 (DAS28), as a measure of treatment response. It is expected that genetic architecture of such complex score is heterogeneous and not very suitable for pharmacogenetic studies. We aimed to select the most optimal phenotype for TNFi response using heritability estimates. METHODS: Using two linear mixed-modelling approaches (Bayz and GCTA), we estimated heritability, together with genomic and environmental correlations for the TNFi drug-response phenotype DeltaDAS28 and its separate components: Delta swollen joint count (SJC), Delta tender joint count (TJC), Delta erythrocyte sedimentation rate (ESR) and Delta visual-analogue scale of general health (VAS-GH). For this, we used genome-wide single nucleotide polymorphism (SNP) data from 878 TNFi-treated Dutch patients with RA. Furthermore, a multivariate genome-wide association study (GWAS) approach was implemented, analysing separate DAS28 components simultaneously. RESULTS: The highest heritability estimates were found for DeltaSJC ([Formula: see text]=0.76 and [Formula: see text]=0.87) and DeltaTJC ([Formula: see text]=0.62 and [Formula: see text]=0.82); lower heritability was found for DeltaDAS28 ([Formula: see text]=0.59 and [Formula: see text]=0.71) while estimates for DeltaESR and DeltaVASGH were near or equal to zero. The highest genomic correlations were observed for DeltaSJC and DeltaTJC (0.49), and the highest environmental correlation was seen between DeltaTJC and DeltaVASGH (0.62). The multivariate GWAS did not generate excess of low p values as compared with a univariate analysis of DeltaDAS28. CONCLUSIONS: Our results indicate that multiple SNPs together explain a substantial portion of the variation in change in joint counts in TNFi-treated patients with RA. In conclusion, of the outcomes studied, the joint counts are most suitable for TNFi pharmacogenetics in RA.
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