scholarly journals Genome-wide sequencing-based identification of methylation quantitative trait loci and their role in schizophrenia risk

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kira A. Perzel Mandell ◽  
Nicholas J. Eagles ◽  
Richard Wilton ◽  
Amanda J. Price ◽  
Stephen A. Semick ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Postmortem Brain ◽  
Environment Interaction ◽  
Human Postmortem Brain ◽  
Risk Variants ◽  
Postmortem Brain Tissue ◽  
Trait Loci ◽  
Risk Snps ◽  
Methylation Quantitative Trait Loci

AbstractDNA methylation (DNAm) is an epigenetic regulator of gene expression and a hallmark of gene-environment interaction. Using whole-genome bisulfite sequencing, we have surveyed DNAm in 344 samples of human postmortem brain tissue from neurotypical subjects and individuals with schizophrenia. We identify genetic influence on local methylation levels throughout the genome, both at CpG sites and CpH sites, with 86% of SNPs and 55% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting the genes and regions with which these loci are epigenetically associated. These findings can be used to better characterize schizophrenia GWAS-identified variants as epigenetic risk variants. Regions differentially methylated by schizophrenia risk-SNPs explain much of the heritability associated with risk loci, despite covering only a fraction of the genomic space. We provide a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.

scholarly journals Quantitative Trait Loci Controlling Light and Hormone Response in Two Accessions ofArabidopsis thaliana

Genetics ◽  
2002 ◽  
Vol 160 (2) ◽  
pp. 683-696 ◽  
Author(s):  
Justin O Borevitz ◽  
Julin N Maloof ◽  
Jason Lutes ◽  
Tsegaye Dabi ◽  
Joanna L Redfern ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Natural Variation ◽  
Red Light ◽  
Light Response ◽  
Environment Interaction ◽  
Isogenic Line ◽  
Hormone Response ◽  
New Genes ◽  
Trait Loci

AbstractWe have mapped quantitative trait loci (QTL) responsible for natural variation in light and hormone response between the Cape Verde Islands (Cvi) and Landsberg erecta (Ler) accessions of Arabidopsis thaliana using recombinant inbred lines (RILs). Hypocotyl length was measured in four light environments: white, blue, red, and far-red light and in the dark. In addition, white light plus gibberellin (GA) and dark plus the brassinosteroid biosynthesis inhibitor brassinazole (BRZ) were used to detect hormone effects. Twelve QTL were identified that map to loci not previously known to affect light response, as well as loci where candidate genes have been identified from known mutations. Some QTL act in all environments while others show genotype-by-environment interaction. A global threshold was established to identify a significant epistatic interaction between two loci that have few main effects of their own. LIGHT1, a major QTL, has been confirmed in a near isogenic line (NIL) and maps to a new locus with effects in all light environments. The erecta mutation can explain the effect of the HYP2 QTL in the blue, BRZ, and dark environments, but not in far-red. LIGHT2, also confirmed in an NIL, has effects in white and red light and shows interaction with GA. The phenotype and map position of LIGHT2 suggest the photoreceptor PHYB as a candidate gene. Natural variation in light and hormone response thus defines both new genes and known genes that control light response in wild accessions.

Methylation quantitative trait loci analysis in Korean exposome study

2020 ◽  
Vol 16 (2) ◽  
pp. 175-183
Author(s):  
Jaehyun Park ◽  
Sung Ok Kwon ◽  
San-Ha Kim ◽  
Seung Jun Kim ◽  
Eun Jung Koh ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

scholarly journals Quantitative Trait Loci × Maternal Cytoplasmic Environment Interaction for Development Rate inOncorhynchus mykiss

Genetics ◽  
2006 ◽  
Vol 175 (1) ◽  
pp. 335-347 ◽  
Author(s):  
Krista M. Nichols ◽  
Karl W. Broman ◽  
Kyle Sundin ◽  
Jennifer M. Young ◽  
Paul A. Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Development Rate ◽  
Environment Interaction ◽  
Trait Loci

scholarly journals DNMIVD: DNA methylation interactive visualization database

2019 ◽  
Vol 48 (D1) ◽  
pp. D856-D862 ◽  
Author(s):  
Wubin Ding ◽  
Jiwei Chen ◽  
Guoshuang Feng ◽  
Geng Chen ◽  
Jun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Interactive Visualization ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Differentially Methylated Genes ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

Abstract Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

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