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

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.

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

Current Biotechnology ◽

10.2174/2211550109999201008125800 ◽

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.

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Role of Genetic Interactions in Lung Diseases Detection using Computational Approaches: A Review

Current Chinese Computer Science ◽

10.2174/2665997201666210125091915 ◽

2021 ◽

Vol 01 ◽

Author(s):

S Priya ◽

R Manavalan

Keyword(s):

Lung Cancer ◽

Lung Diseases ◽

Computational Models ◽

Association Studies ◽

Genetic Interactions ◽

Genome Wide Association Studies ◽

Computational Approaches ◽

Genome Wide ◽

Human Disorders ◽

Brca1 Brca2

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lungs syndromes for diagnosis of acute respiratory. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent Epistasis effects for various lung diseases such as Asthma, Tuberculosis, lung cancer, and Nicotine drug dependence. The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are presented.

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Kmeans-Pillar-Salpepi: Genetic Interactions Detection Through K-Means Clustering with Pillar and Salp Optimization Techniques in Genome-Wide Association Studies

Bioscience Biotechnology Research Communications ◽

10.21786/bbrc/14.3.18 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1020-1025

Author(s):

S Priya

Keyword(s):

Association Studies ◽

Optimization Techniques ◽

Genetic Interactions ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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

Journal of Experimental Botany ◽

10.1093/jxb/eraa434 ◽

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.

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Detecting Genetic Interactions in Pathway-Based Genome-Wide Association Studies

Genetic Epidemiology ◽

10.1002/gepi.21803 ◽

2014 ◽

Vol 38 (4) ◽

pp. 300-309 ◽

Cited By ~ 10

Author(s):

Anhui Huang ◽

Eden R. Martin ◽

Jeffery M. Vance ◽

Xiaodong Cai

Keyword(s):

Association Studies ◽

Genetic Interactions ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Computational Methods Dedicated for Neurological Disorder Detection through Epistasis Analysis: A Review

Current Chinese Engineering Science ◽

10.2174/1573411016999201110101039 ◽

2020 ◽

Vol 01 ◽

Author(s):

Sridharan Priya ◽

Radhakrishnan Manavalana

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neurological Disorders ◽

Neurological Disorder ◽

Disease Risk ◽

Neurological Diseases ◽

Association Studies ◽

Genetic Interactions ◽

Computational Techniques ◽

Genome Wide Association Studies

Background: Neurological disorders diseases such as ALS, Alzheimer’s, epilepsy, Parkinson’s Disease, Autism, Atrial Fibrillation, and Sclerosis affect the central nervous system, including the brain, nerves, spinal cords, muscles, and Neuromuscular joint. These disorders are investigated by detecting the genetic variations in Single Nucleotide Polymorphism (SNP) in Genome-Wide Association Studies (GWAS). In the human genome sequence, one SNP influence the effects of another SNP. These SNP-SNP interactions or Gene-Gene interaction (Epistasis) significantly increases the risk of disease susceptibility to neurological disorders. Objective: The manual analyzes of various genetic interactions related to Neurological diseases are cumbersome. Hence, the computational system is effective for the discovery of Epistasis effects in Neurological syndromes. This study aims to explore various techniques of statistical, machine learning, optimization, so far applied to find the epistasis effect for neurological-disorder. Conclusion: This study finds several genetic interactions models involving different loci, various candidate genes, and SNP interactions involved in numerous neurological diseases. The gene APOE and its polymorphism increase Alzheimer's disease pathology. The gene GAB2 and its SNPs play a vital role in Alzheimer’s disease. The genes GABRA4, ITGB3, and SLC64A highly influence the genetic interactions for Autism disorder. In schizophrenia, the SNPs of NRG1 increases the disease risk. The benefits, limitations, and issues of the various computational techniques implemented for epistasis evaluation of neurological disease are deeply discussed.

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Identifying Genetic Interactions Associated with Late-Onset Alzheimer’s Disease

10.7287/peerj.preprints.123 ◽

2013 ◽

Author(s):

Charalampos S Floudas ◽

Nara Um ◽

M. Ilyas Kamboh ◽

Michael M Barmada ◽

Shyam Visweswaran

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Association Studies ◽

Univariate Analysis ◽

Genetic Interactions ◽

Supporting Evidence ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.

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Key Genes and Genetic Interactions of Plant-Pathogen Functional Modules in Poplar Infected by Marssonina brunnea

Molecular Plant-Microbe Interactions ◽

10.1094/mpmi-11-19-0325-r ◽

2020 ◽

Vol 33 (8) ◽

pp. 1080-1090

Author(s):

Sisi Chen ◽

Yanfeng Zhang ◽

Yiyang Zhao ◽

Weijie Xu ◽

Yue Li ◽

...

Keyword(s):

Plant Growth ◽

Plant Pathogen ◽

Association Studies ◽

Defense Responses ◽

Genetic Interactions ◽

Functional Modules ◽

Plant Pathogen Interaction ◽

Marssonina Brunnea ◽

Key Genes ◽

Transcriptomic Changes

Marssonina brunnea, the causative pathogen of Marssonina leaf spot of poplars (MLSP), devastates poplar plantations by forming black spots on leaves and defoliating trees. Although MLSP has been studied for over 30 years, the key genes that function during M. brunnea infection and their effects on plant growth are poorly understood. Here, we used multigene association studies to investigate the effects of key genes in the plant-pathogen interaction pathway, as revealed by transcriptome analysis, on photosynthesis and growth in a natural population of 435 Populus tomentosa individuals. By analyzing transcriptomic changes during three stages of infection, we detected 628 transcription factor genes among the 7,611 differentially expressed genes that might be associated with basal defense responses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that transcriptomic changes across different stages of infection lead to the reprogramming of metabolic processes possibly related to defense activation. We identified 29,399 common single-nucleotide polymorphisms (SNPs) within 221 full-length genes in plant-pathogen interaction pathways that were significantly associated with photosynthetic and growth traits. We also detected 4,460 significant epistatic pairs associated with stomatal conductance, tree diameter, and tree height. Epistasis analysis uncovered significant interactions between 2,561 SNP-SNP pairs from different functional modules in the plant-pathogen interaction pathway, revealing possible genetic interactions. This analysis revealed many key genes that function during M. brunnea infection and their potential roles in mediating photosynthesis and plant growth, shedding light on genetic interactions between functional modules in the plant-pathogen interaction pathway.

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