scholarly journals Genetic variants influence on the placenta regulatory landscape

2018 ◽  
Author(s):  
F. Delahaye ◽  
C. Do ◽  
Y. Kong ◽  
R. Ashkar ◽  
M. Sala ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
High Resolution ◽  
Quantitative Trait ◽  
Association Studies ◽  
Expression Data ◽  
Healthy Women ◽  
Trait Loci ◽  
The Impact ◽  
Regulatory Landscape

AbstractBackgroundFrom genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications.ResultsBy producing and analyzing DNA sequence variation (n=303), DNA methylation (n=303) and mRNA expression data (n=80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations.ConclusionsThese findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.Author summaryThe placenta is a critical organ playing multiple roles including oxygen and metabolite transfer from mother to fetus, hormone production, and vascular perfusion. With this study, we aimed to deliver a placenta-specific regulatory map based on a combination of publicly available and newly generated data. To complete this reference, we obtained genotype information (n=303), DNA methylation (n=303) and expression data (n=80) for placentas from healthy women. Our analysis of methylation and expression quantitative trait loci (QTLs) and correlations between methylation and expression data were designed to identify fundamental associations between genome, transcriptome, and epigenome in this key fetal organ. The results provide high-resolution genetic and epigenetic maps specific to the placenta based on a representative ethnically diverse cohort. As interest and efforts are growing to better understand the etiology of placental disease and the impact of the environment on placental function these data will provide a reference and enhance future investigations.

scholarly journals Predicting the impact of non-coding variants on DNA methylation

2016 ◽  
Author(s):  
Haoyang Zeng ◽  
David K. Gifford
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Sequence Variation ◽  
Specific Gene ◽  
Single Nucleotide ◽  
Trait Loci ◽  
Allele Specific ◽  
Coding Variants ◽  
The Impact

AbstractDNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.

scholarly journals A resource for the simultaneous high-resolution mapping of multiple quantitative trait loci in rats: The NIH heterogeneous stock

2008 ◽  
Vol 19 (1) ◽  
pp. 150-158 ◽  
Author(s):  
M. Johannesson ◽  
R. Lopez-Aumatell ◽  
P. Stridh ◽  
M. Diez ◽  
J. Tuncel ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
High Resolution ◽  
Quantitative Trait ◽  
Heterogeneous Stock ◽  
High Resolution Mapping ◽  
Trait Loci ◽  
Resolution Mapping

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

scholarly journals High Resolution Melting and Insertion Site-Based Polymorphism Markers for Wheat Variability Analysis and Candidate Genes Selection at Drought and Heat MQTL Loci

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1294 ◽  
Author(s):  
Rosa Mérida-García ◽  
Sergio Gálvez ◽  
Etienne Paux ◽  
Gabriel Dorado ◽  
Laura Pascual ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
High Resolution ◽  
Quantitative Trait ◽  
High Resolution Melting ◽  
Insertion Site ◽  
Cost Effective ◽  
Wheat Breeding ◽  
Reference Sequence ◽  
Detection Techniques ◽  
Trait Loci

The practical use of molecular markers is facilitated by cost-effective detection techniques. In this work, wheat insertion site-based polymorphisms (ISBP) markers were set up for genotyping using high-resolution melting analysis (HRM). Polymorphic HRM-ISBP assays were developed for wheat chromosomes 4A and 3B and used for wheat variability assessment. The marker sequences were mapped against the wheat genome reference sequence, targeting interesting genes. Those genes were located within or in proximity to previously described quantitative trait loci (QTL) or meta-quantitative trait loci (MQTL) for drought and heat stress tolerance, and also yield and yield related traits. Eighteen of the markers used tagged drought related genes and, interestingly, eight of the genes were differentially expressed under different abiotic stress conditions. These results confirmed HRM as a cost-effective and efficient tool for wheat breeding programs.

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

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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