Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time consuming. Here, we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in-utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify interindividual DNA methylation variation that is present both at birth and 3 yr later. These findings suggest that disease-relevant epigenetic variation could be detected at birth, i.e., before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of interindividual epigenomic variation in a range of common human diseases.
Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time-consuming. Here we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify inter-individual DNA methylation variation that is present both at birth and three years later. These findings suggest that disease-relevant epigenetic variation could be detected at birth i.e. before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of inter-individual epigenomic variation in a range of common human diseases.
Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time-consuming. Here we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify inter-individual DNA methylation variation that is present both at birth and three years later. These findings suggest that disease-relevant epigenetic variation could be detected at birth i.e. before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of inter-individual epigenomic variation in a range of common human diseases.
Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time-consuming. Here we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify inter-individual DNA methylation variation that is present both at birth and three years later. These findings suggest that disease-relevant epigenetic variation could be detected at birth i.e. before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of inter-individual epigenomic variation in a range of common human diseases. Bisulphite converted DNA was hybridised to the Illumina Infinium 450k Human Methylation Beadchip
Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time-consuming. Here we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify inter-individual DNA methylation variation that is present both at birth and three years later. These findings suggest that disease-relevant epigenetic variation could be detected at birth i.e. before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of inter-individual epigenomic variation in a range of common human diseases. Bisulphite converted DNA was sequenced
Project description:This research uses consecutive generations of two independent mutation accumulation (MA) lines in model organism A. thaliana to understand transgenerational stability of epialleles via self-fertilization. With whole-genome bisulfite sequencing, regions of instability were identified and quantified. The vast majority of the methylated genome is stably inherited to offspring and the identified unstable regions do not change frequently between generations. Additionally, an epigenetic cross of two MA lines was created to understand inheritance patterns of epialleles via outcrossing in the absence of genetic variation. Whole-genome bisulfite sequencing was used to predict epigenotype of the offspring without single nucleotide polymorphisms. In regions of differential methylation between the parents, about half of regions show predictable inheritance.
Project description:Epigenetic variation is a potential source of genomic and phenotypic variation among different individuals in a population, and among different varieties within a species. We used a two-tiered approach to identify naturally occurring epigenetic alleles in the flowering plant Arabidopsis: a primary screen for transcript level polymorphisms among three strains (Col, Cvi, Ler), followed by a secondary screen for epigenetic alleles. Here, we describe the identification of stable, meiotically transmissible epigenetic alleles that correspond to one member of a previously uncharacterized non-LTR retroposon family, which we have designated Sadhu. The pericentromeric At2g10410 element is highly expressed in strain Col, but silenced in Ler and 18 other strains surveyed. Transcription of this locus is inversely correlated with cytosine methylation and both the expression and DNA methylation states map in a Mendelian manner to stable cis-acting variation. The silent Ler allele can be converted by the epigenetic modifier mutation ddm1 to a meiotically stable expressing allele with an identical primary nucleotide sequence, demonstrating that the variation responsible for transcript level polymorphism among Arabidopsis strains is epigenetic. We extended our characterization of the Sadhu family members and show that different elements are subject to both genetic and epigenetic variation in natural populations. These findings support the view that an important component of natural variation in retroelements is epigenetic.
Project description:The Quartet Project aims to provide resources for QC of multiple types of omic technologies and the effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practices for their QC utilities were developed for whole process QC of large-scale, multi-center, and longitudinal multi-omics profiling.
Project description:Much is known about visual search for single targets, but relatively little about how participants "forage" for multiple targets. One important question is how long participants will search before moving to a new display. Evidence suggests that participants should leave when intake drops below the average rate ("optimal foraging," Charnov, 1976). However, the real world has temporal structure (e.g., seasons) that could influence behavior. Does it matter if winter is coming and the next display will be worse than the last? We gave participants a series of search displays and asked them to collect targets as fast as possible. Target density was structured-rising and falling systematically across trials. We measured the duration for which participants foraged in each display (trials were terminated by participants). Foraging behavior was affected by temporal structure-counter to a simple optimal foraging account, observers searched displays longer when quality was falling compared to rising (Experiments 1 and 2). Additionally, we found that temporal structure altered explicit predictions about display quality (Experiment 2). These results demonstrate that foraging theories need to consider richer models of observers' representations of the world.
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.