Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex

Jaffe, Andrew E.; Gao, Yuan; Deep-Soboslay, Amy; Tao, Ran; Hyde, Thomas M.; Weinberger, Daniel R.; Kleinman, Joel E.

doi:10.1038/nn.4181

Cited by 450 publications

(504 citation statements)

References 62 publications

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“…There was evidence of heterogeneity in the FCO signature fraction in brain and muscle according to fetal gestational age. This observation, which is consistent with previous studies in fetal brain (Jaffe et al 2016) suggests that the transition observed postnatally in hematopoietic cells occurs prenatally in a tissue dependent fashion. This observation begs the question whether the kinetics of stem cell maturation are unique to each tissue.…”

Section: Cold Springsupporting

confidence: 93%

Tracing human stem cell lineage during development using DNA methylation

et al. 2018

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Abstract:Stem cell maturation is a fundamental, yet poorly understood aspect of human development. We devised a DNA methylation signature deeply reminiscent of embryonal stem cells (a fetal cell origin signature, FCO) to interrogate the evolving character of multiple human tissues. The cell fraction displaying this FCO signature was highly dependent upon developmental stage (fetal vs adult), and in leukocytes, it described a dynamic transition during the first 5 years of life. Significant individual variation in the FCO signature of leukocytes was evident at birth, in childhood, and throughout adult life. The genes characterizing the signature included transcription factors and proteins intimately involved in embryonic development. We defined and applied a DNA methylation signature common among human fetal hematopoietic progenitor cells, and have shown that this signature traces the lineage of cells and informs the study of stem cell heterogeneity in humans under homeostatic conditions.

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Section: Cold Springsupporting

confidence: 93%

Tracing human stem cell lineage during development using DNA methylation

et al. 2018

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“…Differences in latent RNA quality and the underlying cellular composition of homogenate tissue sources (20)(21)(22) are two of the strongest confounding factors in postmortem human studies. The qSVA approach here that uses quality-associated features is analogous to our previously proposed approach that uses celltype-associated features to untangle the confounding effects of cellular composition (sparse PCA) (23).…”

Section: Discussionmentioning

confidence: 99%

qSVA framework for RNA quality correction in differential expression analysis

Jaffe

Tao

Norris

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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108

125

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RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on highquality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a methodquality surrogate variable analysis (qSVA)-as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.RNA sequencing | differential expression analysis | statistical modeling | RNA quality M icroarrays and RNA sequencing (RNA-seq) can measure gene expression levels across hundreds of samples in a single experiment. As gene expression levels are measured with error, normalization procedures have been implemented for both microarray (1) and RNA sequencing (2) data to reduce technical variability, including controlling for variability associated with how and when the samples are run, so-called "batch" effects (3). Recent research has further characterized this expression variability in RNA-seq data (4-6), including demonstrating variability associated with technical factors involved in the preparation, sequencing, and analysis of samples. Variability in gene expression is particularly influenced by RNA quality (7) because accurately measuring gene expression levels strongly depends on the quality of the input RNA. This suggests that a portion of traditionally measured latent "batch" effects could actually be attributed to the underlying quality of the input RNA.Postmortem studies typically extract RNA from tissue that has been susceptible to a wide variety of antemortem and postmortem factors. Several approaches exist for quantifying the quality of the input RNA before sequencing library construction, including UV absorption ratios of 280 nm to 260 nm and RNA integrity numbers (RINs). RIN is a machine learning-derived measurement resulting from placing RNA on a Bioanalyzer and obtaining a tracing of fragment sizes per sample. RIN ranges from 10 (very high quality RNA) to 0 (completely degraded RNA), and the apparent intactness of ribosomal RNAs (which are two large peaks in the fragment size tracing) is one of the most discriminating factors that distinguishes very high quality from moderate quality RNA (8). Recommended RIN thresholds for sample exclusion before data generation have been suggested as low as 5.0 for PCR (7) and 7.0 for RNA-seq. (9). However, even high quality samples (RIN ...

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“…Nonetheless, a vast amount of methylation arrays of postmortem human brains have been released recently [23,24]. These latest advances may implicate the importance of methylation paterns in schizophrenic patients.…”

Section: Epigenetics and Dna Methylation Proiles In Schizophreniamentioning

confidence: 99%

Transcriptome Analysis of Systems Biology for Schizophrenia

Huang¹,

Tsao²,

Wang³

et al. 2016

Schizophrenia Treatment - The New Facets

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Transcriptome analysis of postmortem brain samples provides more insights to evaluate biological dysfunctions by analysis of diferential expression and genetic interactions in schizophrenia. The growing development of new technologies such as next-generation sequencing (NGS) helps to explore detailed and underlying molecular changes from global perspective of view, not only focus in single SNP variants. It is implicated that schizophrenia genetic and protein interactions may give rise to biological dysfunction not only in dopamine dysfunction but also in immune, energy metabolism, mitochondrial dysfunction and hemostasis. Epigenetic investigation of schizophrenia provides important information on how the environmental factors afect the genetic architecture of the disease. DNA methylation plays a pivotal role in etiology for schizophrenia. The schizophrenia diferential methylation genes and diferential expression genes were analyzed to ind the potential protein complexes related to the etiology of schizophrenia from alteration of DNA methylation. The protein complexes and pathways involved in schizophrenia diferential methylation network may be responsible for the etiology and potential treatment targets. It is implicated that the interaction between diferential expression candidate genes and diferential methylation genes may describe the global view of disease mechanisms and it has important roles in the pathogenesis for schizophrenia.

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Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex

Cited by 450 publications

References 62 publications

Tracing human stem cell lineage during development using DNA methylation

Tracing human stem cell lineage during development using DNA methylation

qSVA framework for RNA quality correction in differential expression analysis

Transcriptome Analysis of Systems Biology for Schizophrenia

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