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Author  |
Allum, F.; Shao, X.; Guénard, F.; Simon, M.M.; Busche, S.; Caron, M.; Lambourne, J.; Lessard, J.; Tandre, K.; Hedman, Å.K.; Kwan, T.; Ge, B.; Multiple, T.H.E.R.C.; Rönnblom, L.; McCarthy, M.I.; Deloukas, P.; Richmond, T.; Burgess, D.; Spector, T.D.; Tchernof, A.; Marceau, S.; Lathrop, M.; Vohl, M.C.; Pastinen, T.; Grundberg, E. |
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Abstract |
Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targeted arrays or enrichment methodologies preferentially covering CpG-dense regions, to characterize sufficiently large samples. To overcome this limitation, we present here a new customizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), for sequencing functional methylomes, while simultaneously providing genetic variation information. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adipose tissue (AT) samples and public databases to design AT-specific panels. We establish its efficiency for high-density interrogation of methylome variability by systematic comparisons with other approaches and demonstrate its applicability by identifying novel methylation variation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol, including at CD36. Our more comprehensive AT panel assesses tissue methylation and genotypes in parallel at ∼4 and ∼3 M sites, respectively. Our study demonstrates that MCC-Seq provides comparable accuracy to alternative approaches but enables more efficient cataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS. |
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