Shusuke Numata, Tianzhang Ye, Thomas M

Slides:



Advertisements
Similar presentations
The Structure of Common Genetic Variation in United States Populations
Advertisements

Figure 3. Active enhancers located in intergenic DMRs
Michael Dannemann, Janet Kelso  The American Journal of Human Genetics 
Volume 63, Issue 3, Pages (August 2009)
Comprehensively Evaluating cis-Regulatory Variation in the Human Prostate Transcriptome by Using Gene-Level Allele-Specific Expression  Nicholas B. Larson,
Volume 2, Issue 2, Pages (February 2016)
Genomic Patterns of Homozygosity in Worldwide Human Populations
Influence of RNA Labeling on Expression Profiling of MicroRNAs
Common Variants of Large Effect in F12, KNG1, and HRG Are Associated with Activated Partial Thromboplastin Time  Lorna M. Houlihan, Gail Davies, Albert.
Total-Genome Analysis of BRCA1/2-Related Invasive Carcinomas of the Breast Identifies Tumor Stroma as Potential Landscaper for Neoplastic Initiation 
Linkage, Association, and Gene-Expression Analyses Identify CNTNAP2 as an Autism- Susceptibility Gene  Maricela Alarcón, Brett S. Abrahams, Jennifer L.
Detection, Imputation, and Association Analysis of Small Deletions and Null Alleles on Oligonucleotide Arrays  Lude Franke, Carolien G.F. de Kovel, Yurii.
Volume 23, Issue 7, Pages (May 2018)
Michael Cullen, Stephen P
Model-free Estimation of Recent Genetic Relatedness
Roger B. Deal, Steven Henikoff  Developmental Cell 
Discovery and Characterization of piRNAs in the Human Fetal Ovary
Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits  Nicholas Mancuso, Huwenbo Shi, Pagé.
C. -H. Chou, M. T. M. Lee, I. -W. Song, L. -S. Lu, H. -C. Shen, C. -H
Daniel Greene, Sylvia Richardson, Ernest Turro 
Loss of imprinting at the 14q32 domain is associated with microRNA overexpression in acute promyelocytic leukemia by Floriana Manodoro, Jacek Marzec, Tracy.
Haplotype Estimation Using Sequencing Reads
DNA Methylation Signatures within the Human Brain
Volume 25, Issue 4, Pages e6 (April 2017)
Volume 63, Issue 3, Pages (August 2009)
Genome-wide Analysis of Body Proportion Classifies Height-Associated Variants by Mechanism of Action and Implicates Genes Important for Skeletal Development 
Thomas Willems, Melissa Gymrek, G
Volume 44, Issue 1, Pages (October 2011)
A Massively Parallel Reporter Assay of 3′ UTR Sequences Identifies In Vivo Rules for mRNA Degradation  Michal Rabani, Lindsey Pieper, Guo-Liang Chew,
Prognostic Gene Expression Signatures Can Be Measured in Tissues Collected in RNAlater Preservative  Dondapati Chowdary, Jessica Lathrop, Joanne Skelton,
Volume 20, Issue 4, Pages e6 (April 2017)
Weight Loss after Gastric Bypass Is Associated with a Variant at 15q26
Gene-Expression Variation Within and Among Human Populations
Allele-Specific Methylome and Transcriptome Analysis Reveals Widespread Imprinting in the Human Placenta  Hirotaka Hamada, Hiroaki Okae, Hidehiro Toh,
Michael Dannemann, Janet Kelso  The American Journal of Human Genetics 
Volume 3, Issue 1, Pages (July 2016)
Mapping Global Histone Acetylation Patterns to Gene Expression
RNA-Stabilized Whole Blood Samples but Not Peripheral Blood Mononuclear Cells Can Be Stored for Prolonged Time Periods Prior to Transcriptome Analysis 
Divergent Whole-Genome Methylation Maps of Human and Chimpanzee Brains Reveal Epigenetic Basis of Human Regulatory Evolution  Jia Zeng, Genevieve Konopka,
Towfique Raj, Manik Kuchroo, Joseph M
Integrative Multi-omic Analysis of Human Platelet eQTLs Reveals Alternative Start Site in Mitofusin 2  Lukas M. Simon, Edward S. Chen, Leonard C. Edelstein,
Hippocampal “Time Cells”: Time versus Path Integration
Biased Allelic Expression in Human Primary Fibroblast Single Cells
Volume 20, Issue 4, Pages e6 (April 2017)
Wei Zhang, Shiwei Duan, Emily O. Kistner, Wasim K. Bleibel, R
Structural Architecture of SNP Effects on Complex Traits
Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression 
Catarina D. Campbell, Nick Sampas, Anya Tsalenko, Peter H
Volume 81, Issue 2, Pages (January 2014)
Matthieu Foll, Oscar E. Gaggiotti, Josephine T
GeMes, Clusters of DNA Methylation under Genetic Control, Can Inform Genetic and Epigenetic Analysis of Disease  Yun Liu, Xin Li, Martin J. Aryee, Tomas J.
A Three–Single-Nucleotide Polymorphism Haplotype in Intron 1 of OCA2 Explains Most Human Eye-Color Variation  David L. Duffy, Grant W. Montgomery, Wei.
Epigenomic Profiling Reveals DNA-Methylation Changes Associated with Major Psychosis  Jonathan Mill, Thomas Tang, Zachary Kaminsky, Tarang Khare, Simin.
Highly Punctuated Patterns of Population Structure on the X Chromosome and Implications for African Evolutionary History  Charla A. Lambert, Caitlin F.
Volume 19, Issue 5, Pages (May 2017)
Tobacco-Smoking-Related Differential DNA Methylation: 27K Discovery and Replication  Lutz P. Breitling, Rongxi Yang, Bernhard Korn, Barbara Burwinkel,
Volume 29, Issue 5, Pages (May 2016)
Varying Intolerance of Gene Pathways to Mutational Classes Explain Genetic Convergence across Neuropsychiatric Disorders  Shahar Shohat, Eyal Ben-David,
Haplotype Diversity across 100 Candidate Genes for Inflammation, Lipid Metabolism, and Blood Pressure Regulation in Two Populations  Dana C. Crawford,
Volume 1, Issue 1, Pages (July 2015)
Epigenetic Inactivation of Tumor Suppressor Genes in Serum of Patients with Cutaneous Melanoma  Alessandra Marini, Alireza Mirmohammadsadegh, Sandeep.
L-GATOR: Genetic Association Testing for a Longitudinally Measured Quantitative Trait in Samples with Related Individuals  Xiaowei Wu, Mary Sara McPeek 
Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L. S
Leslie S. Emery, Kevin M. Magnaye, Abigail W. Bigham, Joshua M
Pleiotropic Effects of Trait-Associated Genetic Variation on DNA Methylation: Utility for Refining GWAS Loci  Eilis Hannon, Mike Weedon, Nicholas Bray,
Genetic and Epigenetic Regulation of Human lincRNA Gene Expression
A Definitive Haplotype Map as Determined by Genotyping Duplicated Haploid Genomes Finds a Predominant Haplotype Preference at Copy-Number Variation Events 
Common Variants of Large Effect in F12, KNG1, and HRG Are Associated with Activated Partial Thromboplastin Time  Lorna M. Houlihan, Gail Davies, Albert.
Sarah A. Gagliano, Carolyn Ptak, Denise Y. F
Beyond GWASs: Illuminating the Dark Road from Association to Function
Presentation transcript:

DNA Methylation Signatures in Development and Aging of the Human Prefrontal Cortex  Shusuke Numata, Tianzhang Ye, Thomas M. Hyde, Xavier Guitart-Navarro, Ran Tao, Michael Wininger, Carlo Colantuoni, Daniel R. Weinberger, Joel E. Kleinman, Barbara K. Lipska  The American Journal of Human Genetics  Volume 90, Issue 2, Pages 260-272 (February 2012) DOI: 10.1016/j.ajhg.2011.12.020 Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 1 Depiction of Global DNA Methylation Patterns in Human Subjects across the Lifespan (A) Histograms of subject ages in the brain collection. All fetal samples (left panel) are between 14 and 20 gestational weeks. The following color scale is used: red, male fetal samples (n = 14); orange, female fetal samples (n = 16); light blue, male children (n = 10); green, female children (n = 5); blue, males older than 10 years (n = 32); and purple, females older than 10 years (n = 31). The same color scale is used in (B and C). (B) Global DNA methylation pattern examined by multidimensional scaling for autosomal CpG sites. Each DNA methylation sample is represented as a single point colored by the age of the subject. There is a tight cluster of the fetal samples, a progression of methylation levels from the fetal period to childhood, and another cluster for postchildhood. (C) Global DNA methylation pattern of CpG sites on the X chromosome examined by multidimensional scaling. Each DNA methylation sample is represented as a single point colored by the age of the subject and sex. Samples were distinctly segregated by sex. A progression of methylation was also observed from the fetal period to postchildhood within each sex group. The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 2 Depiction of Rates of DNA Methylation in Three Life Stages (A) Rates of methylation change with age during three developmental life stages (the fetal period, childhood, and postchildhood/adult) at each CpG site for 27,578 CpG loci. A rate of change is plotted on the x axis. The −log10 p value is on the y axis, such that increasing values indicate more significant changes in methylation with age within a given stage. Red dots represent significant age-related methylation changes at FDR < 0.05. The fastest rates of methylation changes occurred during the fetal period, involving predominantly decreases in methylation (negative values on the x axis). In contrast, during the childhood and adulthood the changes were much slower and involved mainly increased methylation with aging (positive values on the x axis). (B) Average rates of significant methylation changes with age within three developmental stages (red = the fetal period, green = childhood, blue = postchildhood). The absolute rate of the methylation change per year for the significant age-related CpG loci (FDR < 0.05) is on the y axis. The fastest changes in methylation occurred during the prenatal period, followed by childhood and postchildhood. The error bars represent standard deviations. (C) Diagram showing the numbers of the CpG sites with significant age-related changes during the life stages. Of 27,578 CpG sites, significant age-related changes in DNA methylation (FDR < 0.05) were observed at 865 sites during the fetal period, 5,506 sites during childhood, and 10,578 sites during postchildhood. One hundred and thirty-eight sites out of the 252 sites that overlapped between the fetal period and the childhood and 188 sites out of the 3,529 sites that overlapped between children and postchildhood showed the opposite patterns of methylation (an increase followed by a decrease or vice versa) between these life stages. The transition from the fetal life to childhood is frequently associated with a reversal of direction in DNA methylation profile. The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 3 Depiction of Individual Loci's DNA Methylation Signatures across the Human Lifespan (A) Depiction of individual loci's DNA methylation signatures across the human lifespan, illustrating three patterns of DNA methylation across the fetal, child and adult life periods. Methylation levels are on the y axis, with age on the x axis. The CpG site of CDH22 (cg04640913) showed significant age-related increases during the fetal period, childhood, and postchildhood (p = 2 × 10−6, p = 1 × 10−3, and p = 2.3 × 10−4, respectively). The CpG site of SCGB3A2 (cg19530885) showed significant age-related decreases during the fetal period, childhood, and postchildhood (p = 4.5 × 10−13, p = 9.5 × 10−3, and p = 2.4 × 10−4, respectively).The CpG site of NOS1 (cg21006686) showed a significant age-related decrease during the fetal period (p = 8.6 × 10−4) and increases during childhood and postchildhood (p = 2 × 10−8 and p = 1.1 × 10−2, respectively). (B) Depiction of individual loci's DNA methylation signatures across the human lifespan, illustrating sex differences. Females are red and males are blue. The CpG sites of MAOA (cg19441691) and TLE1 (cg15915418) showed significant sex differences (p = 1.04 × 10−62 and 1.27 × 10−65, respectively). The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 4 Depiction of Age-Related Changes in DNA Methylation in Three Life Stages as a Function of CpGI Context Black lines show density distributions of regression coefficients for age term for the CpG loci located in the CpGIs. Red lines show density distribution of regression coefficients for age term for the CpG loci located outside the CpGIs. More loci are demethylated than hypermethylated during the fetal period, whereas increased methylation with age dominates in postnatal life (childhood and postchildhood). The effect sizes are larger for loci outside the CpGIs than those located in the CpGIs. The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 5 Depiction of Hierarchical Clustering with the Weighted Correlation Network Analysis (A) Hierarchical clustering dendrogram obtained with the weighted correlation network analysis. The first color row underneath (labeled Group) shows the module assignment determined by the Dynamic Tree Cut. The second color row (labeled chrX) represents autosomal (gray) or X chromosomal (red) location of the loci in the module. The third row (labeled CpGI) shows whether the loci are located in the CpGIs (red) or outside the CpGIs (gray). (B) Heat map visualization of DNA methylation status of six modules. A hierarchical clustering was conducted across 108 subjects shown in rows (a vertical color bar on the left indicates sex: red = females, blue = males; a vertical color bar on the right of the picture indicates age groups: red = fetal, green = childhood, blue = postchildhood) and six modules in columns (CpG loci were clustered into comethylation modules: groups 0–5, with group 0 containing unclassified loci). Color key on top indicates DNA methylation values: lowest, pink highest, blue lowest. The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 6 Depiction of Correlations between DNA Methylation and Gene Expression (A) Histogram of correlation coefficients for Pearson's correlations between DNA methylation and gene expression. Green line shows correlations of expression with methylation at the loci located in CpGIs, red line, with loci outside CpGIs. (B) An example of opposite patterns of DNA methylation (left) and expression (right) changes with age (x axis) for NNAT (neuronatin). The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions

Figure 7 Depiction of Associations between the Genotype and DNA Methylation (A) Depiction of the mQTL for the CpG site cg01889448 and rs1063355 in HLA-DQB1 (p = 10−29). Three genotypes of rs1063355 are 1/1 in red (n = 21), 1/2 in green (n = 57), and 2/2 in blue (n = 30). The lines in the bar graph indicate the median, and the bars (whiskers) represent the minimum and maximum of the data after the removal of the outliers. (B) The relationship between the strength of association and the distance of the SNP from the CpG site. Only significant cis-SNPs associations are plotted (FDR < 0.05). The distance of SNPs from the CpG sites in base pairs is on the x axis and the −log10 p values of SNP association with DNA methylation. Plotting p values against the distance of SNPs from each CpG site showed that the highest associations were with the SNPs located close to CpG site. The American Journal of Human Genetics 2012 90, 260-272DOI: (10.1016/j.ajhg.2011.12.020) Copyright © 2012 The American Society of Human Genetics Terms and Conditions