scholarly journals SNPxE: SNP-environment interaction pattern identifier

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hui-Yi Lin ◽  
Po-Yu Huang ◽  
Tung-Sung Tseng ◽  
Jong Y. Park
Keyword(s):  
Environmental Factors ◽  
Specific Interaction ◽  
False Negative ◽  
Interaction Model ◽  
Interaction Pattern ◽  
Detection Accuracy ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Full Interaction ◽  
Environment Factor

Abstract Background Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. Results SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. Conclusions SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

scholarly journals A Method for Analysis and Visualization of Similar Hotspot Flow Patterns between Different Regional Groups

2018 ◽  
Author(s):  
Haiping Zhang ◽  
Xingxing Zhou ◽  
Xin Gu ◽  
Genlin Ji ◽  
Guoan Tang
Keyword(s):  
Spatial Interaction ◽  
Interaction Model ◽  
Flow Patterns ◽  
Interaction Pattern ◽  
Interaction Patterns ◽  
Flow Data ◽  
Regional Group ◽  
Neighborhood Relationship ◽  
Point To Point ◽  
Flow Of Information

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

CGRP in a gene–environment interaction model for depression: effects of antidepressant treatment

2018 ◽  
Vol 31 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Francesco Angelucci ◽  
Bart A Ellenbroek ◽  
Aram El Khoury ◽  
Aleksander A. Mathé
Keyword(s):  
Environmental Factors ◽  
Rat Model ◽  
Maternal Separation ◽  
Antidepressant Treatment ◽  
Interaction Model ◽  
Brain Regions ◽  
Environment Interaction ◽  
Brain Levels ◽  
Gene Environment ◽  
Genetic And Environmental Factors

AbstractObjectiveGenetic and environmental factors interact in the development of major depressive disorder (MDD). While neurobiological correlates have only partially been elucidated, altered levels of calcitonin gene-related peptide (CGRP)-like immunoreactivity (LI) in animal models and in the cerebrospinal fluid of depressed patients were reported, suggesting that CGRP may be involved in the pathophysiology and/or be a trait marker of MDD. However, changes in CGRP brain levels resulting from interactions between genetic and environmental risk factors and the response to antidepressant treatment have not been explored.MethodsWe therefore superimposed maternal separation (MS) onto a genetic rat model (Flinders-sensitive and -resistant lines, FSL/FRL) of depression, treated these rats with antidepressants (escitalopram and nortriptyline) and measured CGRP-LI in selected brain regions.ResultsCGRP was elevated in the frontal cortex, hippocampus and amygdala (but not in the hypothalamus) of FSL rats. However, MS did not significantly alter levels of this peptide. Likewise, there were no significant interactions between the genetic and environmental factors. Most importantly, neither escitalopram nor nortriptyline significantly altered brain CGRP levels.ConclusionOur data demonstrate that increased brain levels of CGRP are present in a well-established rat model of depression. Given that antidepressants have virtually no effect on the brain level of this peptide, our study indicates that further research is needed to evaluate the functional role of CGRP in the FSL model for depression.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Gene–gene and gene-environment interactions on cord blood total IgE in Chinese Han children

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Li Hua ◽  
Quanhua Liu ◽  
Jing Li ◽  
Xianbo Zuo ◽  
Qian Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Cord Blood ◽  
Synergistic Effects ◽  
Gene Interaction ◽  
Independent Effect ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Background IL13, IL4, IL4RA, FCER1B and ADRB2 are susceptible genes of asthma and atopy. Our previous study has found gene–gene interactions on asthma between these genes in Chinese Han children. Whether the interactions begin in fetal stage, and whether these genes interact with prenatal environment to enhance cord blood IgE (CBIgE) levels and then cause subsequent allergic diseases have yet to be determined. This study aimed to determine whether there are gene–gene and gene-environment interactions on CBIgE elevation among the aforementioned five genes and prenatal environmental factors in Chinese Han population. Methods 989 cord blood samples from a Chinese birth cohort were genotyped for nine single-nucleotide polymorphisms (SNPs) in the five genes, and measured for CBIgE levels. Prenatal environmental factors were collected using a questionnaire. Gene–gene and gene-environment interactions were analyzed with generalized multifactor dimensionality methods. Results A four-way gene–gene interaction model (IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713) was regarded as the optimal one for CBIgE elevation (testing balanced accuracy = 0.5805, P = 9.03 × 10–4). Among the four SNPs, only IL13 rs20541 was identified to have an independent effect on elevated CBIgE (odds ratio (OR) = 1.36, P = 3.57 × 10–3), while the other three had small but synergistic effects. Carriers of IL13 rs20541 TT, IL13 rs1800925 CT/TT, IL4 rs2243250 TT and ADRB2 rs1042713 AA were estimated to be at more than fourfold higher risk for CBIgE elevation (OR = 4.14, P = 2.69 × 10–2). Gene-environment interaction on elevated CBIgE was found between IL4 rs2243250 and maternal atopy (OR = 1.41, P = 2.65 × 10–2). Conclusions Gene–gene interaction between IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713, and gene-environment interaction between IL4 rs2243250 and maternal atopy begin in prenatal stage to augment IgE production in Chinese Han children.

Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model

2021 ◽  
Vol 50 (2) ◽  
pp. 343-350
Author(s):  
Meijin Ye ◽  
Zhaoyang Chen ◽  
Bingbing Liu ◽  
Haiwang Yue
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Kernel Weight ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Ammi Model ◽  
The Ideal

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

Water Mapping and Scoring approaches to predict the role of Hydration sites in Binding Affinity of PAK1 inhibitors

2021 ◽  
Vol 24 ◽  
Author(s):  
Jayashree Biswal ◽  
Prajisha Jayaprakash ◽  
Suresh Kumar Rayala ◽  
Ganesh Venkatraman ◽  
Raghu Rangasamy ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Computational Model ◽  
Active Compound ◽  
Scoring Function ◽  
Binding Mode ◽  
Interaction Pattern ◽  
Dynamics Simulation ◽  
Interaction Patterns ◽  
Highly Active ◽  
Group I

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1). Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: To establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Method: A congeneric series of 27 compounds was considered for this study with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking, to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound, to understand the conformational variability and complex’s stability. We then evaluate the predictable binding pose obtained from the docking studies. Result: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated to understand the significance of correlation in the folds of activity of highly (19 and 17), moderate (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72, due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute towards development of the most potent PAK1 inhibitors.

scholarly journals The Behavior Of Bureaucracy in Formulation Annual Government Planning Process

2013 ◽  
Vol 3 (3) ◽  
pp. 108
Author(s):  
Jamaluddin Ahmad
Keyword(s):  
Planning Process ◽  
External Environment ◽  
Interaction Model ◽  
Social Model ◽  
Environment Interaction ◽  
Regional Government ◽  
Government Budget ◽  
Individual Decision ◽  
Government Planning ◽  
Bureaucratic Behavior

The consistency of regional government budget is actually reflected from adjustment planning and budgeting program, which is really determined by formulating process of annual government planning every year. Formulating process is bureaucracy responsibility, so most of behaviors of bureaucracy where dominated. This research aimed at: (1) describing behaviors of bureaucracy types in formulating process annual government planning, (2) describing external environment models interaction with behaviors of bureaucracy types in formulating process annual government planning, and (3) explain the formulating process of annual government planning principles with behaviors of bureaucracy types. The results of the research showed that between behaviors of bureaucracy types consists of: career staff type, politics type, professional type, and missioner type, which dominated by the career staff type in formulating process annual government planning. This happens because of the perception, individual decision making, communication patterns, unit leadership, internal organization and culture. While the external environment interaction model of  bureaucratic behavior is a social model the determined the regency leadership factor, factor structure, factor of bureaucratic authority, and cultural factors. While the application of the principles annual government planning formulation based on the type of bureaucratic behavior has basically done but still needs improvement.

scholarly journals Genotype by environment interaction for physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids using additive main effects and multiplicative interaction model

2021 ◽  
Author(s):  
Zahra Abbasi ◽  
Jan Bocianowski
Keyword(s):  
Sugar Beet ◽  
Beta Vulgaris ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Physiological Traits ◽  
Environment Interaction ◽  
Extraction Coefficient ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Main Effects

AbstractThe objective of this study was to assess genotype by environment interaction for 21 physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids grown in Rodasht Agricultural Research Station in Iran by the additive main effects and multiplicative interaction model. The study comprised of 51 sugar beet genotypes [10 multigerm pollen parents, four monogerm seed parents and 36 F1 hybrids], evaluated at four environments in a randomized complete block design, with three replicates. The additive main effects and multiplicative interaction analyses revealed significant environment main effects with respect to all observed traits, except extraction coefficient of sugar. The additive main effects and multiplicative interaction stability values ranged from 0.009 (G17 for leaf Ca2+) to 9.698 (G09 for extraction coefficient of sugar). The parental forms 2 7233-P.29 (G38) and C CMS (G49) as well as hybrids 2(6)*C (G27) and 5*C (G33) are recommended for further inclusion in the breeding programs because of their stability and good average values of observed traits.

Gene–environment interplay in the etiology of psychosis

2018 ◽  
Vol 48 (12) ◽  
pp. 1925-1936 ◽  
Author(s):  
Alyson Zwicker ◽  
Eileen M. Denovan-Wright ◽  
Rudolf Uher
Keyword(s):  
Environmental Factors ◽  
Genetic Risk ◽  
Environmental Exposures ◽  
Response To Treatment ◽  
Human Potential ◽  
Specific Gene ◽  
Environment Interaction ◽  
Gene Environment ◽  
Increased Risk ◽  
The Impact

AbstractSchizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene–environment interactions. Only a few specific gene–environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene–environment interaction is a common polymorphism in theAKT1gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.

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