Genotype-dependent associations between serotonin transporter gene (SLC6A4) DNA methylation and late-life depression

International audienceBACKGROUND: Disrupted serotonergic signaling is often a feature of depression and the role of the serotonin transporter gene (SLC6A4), responsible for serotonin re-uptake, has received much attention in this regard. Most studies have focused on the polymorphic 5-HTTLPR upstream repeat, or DNA methylation at the promoter CpG island. Few studies have explored the influence of genetic variation across the gene on DNA methylation, and their combined association with depression risk. The aim of this study was to determine whether genetic variation in the SLC6A4 gene influences promoter DNA methylation, and whether these are associated with depression status.METHOD: The ESPRIT study involves a community-based population of older individuals (> 65 years of age). Major depressive disorder (MDD) was diagnosed according to DSM-IV (American Psychiatric Association, 1994) criteria, and severe depressive symptoms assessed by the Centre for Epidemiological Studies Depression (CES-D) Scale. Sequenom MassARRAY was used to measure SLC6A4 methylation status (n = 302).RESULTS: Nominally significant associations were observed between SLC6A4 genetic variants (5-HTTLPR, rs140700, rs4251417, rs6354, rs25528, rs25531) and DNA methylation at several CpG sites. In multivariate regression, DNA methylation was associated with depression status, but only in the presence of specific genotypes. In individuals homozygous for the short 5-HTTLPR and 5-HTTLPR/r25531 alleles, lower methylation at two CpGs was associated with depression (β = - 0.44 to β = - 0.31; p = 0.001 to p = 0.038).CONCLUSION: We present evidence for genotype-dependent associations between SLC6A4 methylation and depression. Genetic variants may also play a role in influencing promoter methylation levels and its association with depression

Noisy Inference and Oracles

A learner noisily infers a function or set, if every correct item is presented infinitely often while in addition some incorrect data ("noise") is presented a finite number of times. It is shown that learning from a noisy informant is equal to finite learning with K-oracle from a usual informant. This result has several variants for learning from text and using different oracles. Furthermore, partial identification of all r.e. sets can cope also with noisy input

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