[en] Background: Understanding the influence of genetic variantson DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects.
Results: Here, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements.
Conclusions: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.
Disciplines :
Genetics & genetic processes
Author, co-author :
Scherer, Michael
Gasparoni, Gilles
Rahmouni, Souad ; Université de Liège - ULiège > GIGA Medical Genomics - Unit of Animal Genomics
Shashkova, Tatiana
Arnoux, Marion
Louis, Edouard ; Université de Liège - ULiège > Département des sciences cliniques > Hépato-gastroentérologie
Nostaeva, Arina
Avalos, Diana
Dermitzakis, Emmanouil T.
Aulchenko, Yurii S.
Lengauer, Thomas
Lyons, Paul A.
Georges, Michel ; Université de Liège - ULiège > Dpt. de gestion vétérinaire des Ressources Animales (DRA) > Génomique animale
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