scholarly journals Association Study and Mendelian Randomization Analysis Reveal Effects of the Genetic Interaction Between PtoMIR403b and PtoGT31B-1 on Wood Formation in Populus tomentosa

2021 ◽  
Vol 12 ◽  
Author(s):  
Liang Xiao ◽  
Liting Man ◽  
Lina Yang ◽  
Jinmei Zhang ◽  
Baoyao Liu ◽  
...  
Keyword(s):  
Association Study ◽  
Target Genes ◽  
Mendelian Randomization ◽  
Genetic Interaction ◽  
Association Studies ◽  
Wood Property ◽  
Wood Formation ◽  
Populus Tomentosa ◽  
Cell Wall Polysaccharides ◽  
Epistasis Analysis

MicroRNAs (miRNAs), important posttranscriptional regulators of gene expression, play a crucial role in plant growth and development. A single miRNA can regulate numerous target genes, making the determination of its function and interaction with targets challenging. We identified PtomiR403b target to PtoGT31B-1, which encodes a galactosyltransferase responsible for the biosynthesis of cell wall polysaccharides. We performed an association study and epistasis and Mendelian randomization (MR) analyses to explore how the genetic interaction between PtoMIR403b and its target PtoGT31B-1 underlies wood formation. Single nucleotide polymorphism (SNP)-based association studies identified 25 significant associations (P < 0.01, Q < 0.05), and PtoMIR403b and PtoGT31B-1 were associated with five traits, suggesting a role for PtomiR403b and PtoGT31B-1 in wood formation. Epistasis analysis identified 93 significant pairwise epistatic associations with 10 wood formation traits, and 37.89% of the SNP-SNP pairs indicated interactions between PtoMIR403b and PtoGT31B-1. We performed an MR analysis to demonstrate the causality of the relationships between SNPs in PtoMIR403b and wood property traits and that PtoMIR403b modulates wood formation by regulating expression of PtoGT31B-1. Therefore, our findings will facilitate dissection of the functions and interactions with miRNA-targets.

Transcriptome analysis and association mapping reveal the genetic regulatory network response to cadmium stress in Populus tomentosa

2020 ◽  
Author(s):  
Mingyang Quan ◽  
Xin Liu ◽  
Liang Xiao ◽  
Panfei Chen ◽  
Fangyuan Song ◽  
...  
Keyword(s):  
Stress Responses ◽  
Target Genes ◽  
Leaf Growth ◽  
Association Studies ◽  
Cadmium Stress ◽  
Populus Tomentosa ◽  
Genetic Networks ◽  
Hormone Regulation ◽  
Lncrna Gene ◽  
Cd Tolerance

Abstract Long non-coding RNAs (lncRNAs) play essential roles in plant abiotic stress responses, but the response of lncRNA-mediated genetic networks to cadmium (Cd) treatment remain elusive in trees, the promising candidates for phytoremediation of Cd contamination. We identified 172 Cd-responsive lncRNAs and 295 differentially expressed target genes in the leaves of Cd-treated Populus tomentosa. Functional annotation revealed that these lncRNAs were involved in various processes, including photosynthesis, hormone regulation, and phenylalanine metabolism. Association studies identified 78 significant associations, representing 14 Cd-responsive lncRNAs and 28 target genes for photosynthetic and leaf physiological traits. Epistasis uncovered 83 pairwise interactions among these traits, revealing Cd-responsive lncRNA-mediated genetic networks for photosynthesis and leaf physiology in P. tomentosa. We focused on the roles of two Cd-responsive lncRNA–gene pairs, MSTRG.22608.1–PtoMYB73 and MSTRG.5634.1–PtoMYB27, in Cd tolerance of Populus, and detected insertions/deletions within lncRNAs as polymorphisms driving target gene expression. Genotype analysis of lncRNAs and heterologous overexpression of PtoMYB73 and PtoMYB27 in Arabidopsis indicated their effects on enhancing Cd tolerance, photosynthetic rate, and leaf growth, and the potential interaction mechanisms of PtoMYB73 with abiotic stresses. Our study identifies the genetic basis for the response of Populus to Cd treatment, facilitating genetic improvement of Cd tolerance in trees.

scholarly journals Association Studies in Populus tomentosa Reveal the Genetic Interactions of Pto-MIR156c and Its Targets in Wood Formation

2016 ◽  
Vol 7 ◽  
Author(s):  
Mingyang Quan ◽  
Qingshi Wang ◽  
Souksamone Phangthavong ◽  
Xiaohui Yang ◽  
Yuepeng Song ◽  
...  
Keyword(s):  
Association Studies ◽  
Wood Formation ◽  
Populus Tomentosa ◽  
Genetic Interactions

scholarly journals Genome Cytosine Methylation May Affect Growth and Wood Property Traits in Populations of Populus tomentosa

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 828
Author(s):  
Kaifeng Ma ◽  
Yuepeng Song ◽  
Dong Ci ◽  
Daling Zhou ◽  
Min Tian ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Level ◽  
Cytosine Methylation ◽  
Wood Property ◽  
Tree Height ◽  
Wood Formation ◽  
Populus Tomentosa ◽  
Phenotypic Traits ◽  
Regulatory Processes ◽  
Tree Development

Growth and wood formation are crucial and complex biological processes during tree development. These biological regulatory processes are presumed to be controlled by DNA methylation. However, there is little direct evidence to show that genes taking part in wood regulation are affected by cytosine methylation, resulting in phenotypic variations. Here, we detected epimarkers using a methylation-sensitive amplification polymorphism (MSAP) method and performed epimarker–trait association analysis on the basis of nine growth and wood property traits within populations of 432 genotypes of Populus tomentosa. Tree height was positively correlated with relative full-methylation level, and 1101 out of 2393 polymorphic epimarkers were associated with phenotypic traits, explaining 1.1–7.8% of the phenotypic variation. In total, 116 epimarkers were successfully sequenced, and 96 out of these sequences were linked to putative genes. Among them, 13 candidate genes were randomly selected for verification using quantitative real-time PCR (qRT-PCR), and it also showed the expression of nine putative genes of PtCYP450, PtCpn60, PtPME, PtSCP, PtGH, PtMYB, PtWRKY, PtSTP, and PtABC were negatively correlated with DNA methylation level. Therefore, it suggested that changes in DNA methylation might contribute to regulating tree growth and wood property traits.

scholarly journals A transcriptome-wide association study of Alzheimer’s disease using prediction models of relevant tissues identifies novel candidate susceptibility genes

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yanfa Sun ◽  
Jingjing Zhu ◽  
Dan Zhou ◽  
Saranya Canchi ◽  
Chong Wu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Target Genes ◽  
Prediction Models ◽  
Association Studies ◽  
Strong Support ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer’s disease (AD), but the genes responsible for these associations remain largely unknown. Methods We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry. Results We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10−6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal. Conclusions Our study provides new insights into the etiology and underlying genetic architecture of AD.

scholarly journals Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

2021 ◽  
Author(s):  
Bryce Rowland ◽  
Sanan Venkatesh ◽  
Manuel Tardaguila ◽  
Jia Wen ◽  
Jonathan D Rosen ◽  
...  
Keyword(s):  
Association Study ◽  
Target Genes ◽  
Prediction Models ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Rna Seq ◽  
Genome Wide ◽  
Increased Sensitivity

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold (α = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold (α = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.

scholarly journals VAMPIRE: A model blood cell trait focused annotation tool for interpretation and prioritization of complex trait genome-wide association study results

2021 ◽  
Author(s):  
Cheynna Crowley ◽  
Quan Sun ◽  
Le Huang ◽  
Erik L. Bao ◽  
Paul Auer ◽  
...  
Keyword(s):  
Blood Cell ◽  
Association Study ◽  
Complex Traits ◽  
Web Application ◽  
Target Genes ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractThousands of genetic loci have been identified as associated with hematological indices (red blood cell, white blood cell, and platelet related traits), as well as other complex traits and disease. However, most loci identified are noncoding and not clearly linked to target genes, and tools are needed to prioritize the most likely functional variants for experimental follow-up. We here describe VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/) for blood cell trait associated loci from recent large multi-ethnic genome-wide association studies (GWAS). This tool efficiently displays information from blood cell relevant tissues on epigenomic signatures, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information from Hi-C and similar technologies, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Variants are classified into multiple prioritization categories according to these functional signatures. Leveraging data generated from independent functional validation experiments, we demonstrate that our prioritized variants are enriched within experimentally validated variant sets. VAMPIRE allows rapid prioritization and interpretation of blood cell trait GWAS variants and could be easily adapted for use with other complex trait GWAS results and extended to new annotation sources.Author SummaryMany large genome-wide association studies (GWAS) have recently been performed for blood cell traits, with thousands of associations identified. However, most of the associated variants are in noncoding regions and are often hard to interpret, link to genes, and prioritize for functional follow-up. Similar challenges exist for genetic studies of many other traits and diseases. Trying to translate knowledge of GWAS significant variants to target genes and biological insights, we here describe VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/) for blood cell trait associated loci from recent large multi-ethnic GWAS. This tool displays a variety of information including epigenomic signatures, variant impact on protein and gene expression, chromatin conformation information, and publicly available GWAS and phenome-wide association study (PheWAS) results for other traits. We classified variants into annotation categories using this information, and show that variants in the highest priority categories are enriched in likely causal variant sets from previous functional experiments. We anticipate this tool will guide appropriate variants to prioritize for experimental validation for researchers studying blood cell traits, as well as providing an easily adaptable model for the creation of similar annotation tools for other complex traits and diseases.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

2021 ◽  
Vol 22 (11) ◽  
pp. 6083
Author(s):  
Aintzane Rueda-Martínez ◽  
Aiara Garitazelaia ◽  
Ariadna Cilleros-Portet ◽  
Sergi Marí ◽  
Rebeca Arauzo ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Clear Cell ◽  
Association Studies ◽  
Epidemiological Data ◽  
Epidemiological Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genomic Analyses ◽  
Gynecological Disorder ◽  
Robust Evidence

Endometriosis is a common gynecological disorder that has been associated with endometrial, breast and epithelial ovarian cancers in epidemiological studies. Since complex diseases are a result of multiple environmental and genetic factors, we hypothesized that the biological mechanism underlying their comorbidity might be explained, at least in part, by shared genetics. To assess their potential genetic relationship, we performed a two-sample mendelian randomization (2SMR) analysis on results from public genome-wide association studies (GWAS). This analysis confirmed previously reported genetic pleiotropy between endometriosis and endometrial cancer. We present robust evidence supporting a causal genetic association between endometriosis and ovarian cancer, particularly with the clear cell and endometrioid subtypes. Our study also identified genetic variants that could explain those associations, opening the door to further functional experiments. Overall, this work demonstrates the value of genomic analyses to support epidemiological data, and to identify targets of relevance in multiple disorders.

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