The Genetic Basis of the Ecological Amplitude of Spartina patens. II. Variance and Correlation Analysis

Evolution ◽  
1985 ◽  
Vol 39 (5) ◽  
pp. 1034 ◽  
Author(s):  
John A. Silander
Keyword(s):  
Correlation Analysis ◽  
Genetic Basis ◽  
Spartina Patens ◽  
Ecological Amplitude

scholarly journals Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis

2021 ◽  
Author(s):  
Xiaoyun Zhang ◽  
Ying Song ◽  
Xiao Chen ◽  
Xiaojia Zhuang ◽  
Zhiqiang Wei ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Genetic Basis ◽  
Demyelinating Disease ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Ms Pathogenesis ◽  
Brain Tissues

Abstract Background: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. MS pathogenesis is closely related to the environment, genetic, and immune system, but the underlying interactions have not been clearly elucidated. This study aims to unveil the genetic basis and immune landscape of MS pathogenesis with bioinformatics.Methods:Gene matrix wasretrieved from the gene expression database NCBI GEO. Then, bioinformatics was used to standardize the samples and obtain differentially expressed genes (DEGs). The protein-protein interaction network was constructed with DEGs on the STRING website. Cytohubbaplug-in and MCODE plug-in were used to mine hub genes. Meanwhile, the CIBERSORTX algorithm was used to explore the characteristics of immune cellinfiltration in MS brain tissues. Spearman correlation analysis was performed between genes and immune cells, and the correlation between genes and different types of brain tissues was also analyzed using the WGCNA method.Results:A total of 90 samples from 2 datasetswere included, and 882 DEGs and 10 hub genes closely related to MS were extracted. Functional enrichment analysis suggested the roleof immune response in MS. Besides,CIBERSORTX algorithm results showed that MS brain tissuescontained a variety of infiltrating immune cells. Correlation analysis suggested that the hub genes were highly relevant to chronic active white matter lesions.Certain hub genes played a role in the activation of immune cells such as macrophages and natural killer cells.Conclusions: Our study shall provideguidance for the further study of the genetic basis and immune infiltration mechanism of MS.

scholarly journals Phosphate/Zinc Interaction Analysis in Two Lettuce Varieties Reveals Contrasting Effects on Biomass, Photosynthesis, and Dynamics of Pi Transport

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nadia Bouain ◽  
Mushtak Kisko ◽  
Aida Rouached ◽  
Myriam Dauzat ◽  
Benoit Lacombe ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Inorganic Phosphate ◽  
Genetic Basis ◽  
Interaction Analysis ◽  
Biological Significance ◽  
Normal Plant ◽  
Strong Negative Correlation ◽  
Pi Transport ◽  
Zn Concentration ◽  
Zn Homeostasis

Inorganic phosphate (Pi) and Zinc (Zn) are essential nutrients for normal plant growth. Interaction between these elements has been observed in many crop plants. Despite its agronomic importance, the biological significance and genetic basis of this interaction remain largely unknown. Here we examined the Pi/Zn interaction in two lettuce (Lactuca sativa) varieties, namely, “Paris Island Cos” and “Kordaat.” The effects of variation in Pi and Zn supply were assessed on biomass and photosynthesis for each variety. Paris Island Cos displayed better growth and photosynthesis compared to Kordaat under all the conditions tested. Correlation analysis was performed to determine the interconnectivity between Pi and Zn intracellular contents in both varieties. Paris Island Cos showed a strong negative correlation between the accumulation levels of Pi and Zn in shoots and roots. However, no relation was observed for Kordaat. The increase of Zn concentration in the medium causes a decrease in dynamics of Pi transport in Paris Island Cos, but not in Kordaat plants. Taken together, results revealed a contrasting behavior between the two lettuce varieties in terms of the coregulation of Pi and Zn homeostasis and provided evidence in favor of a genetic basis for the interconnection of these two elements.

scholarly journals Integration of Genetic and Immune Infiltration Insights Into Data Mining of Multiple Sclerosis Pathogenesis

2021 ◽  
Author(s):  
Xiaoyun Zhang ◽  
Ying Song ◽  
Xiao Chen ◽  
Xiaojia Zhuang ◽  
Qizhi Xie ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Genetic Basis ◽  
Demyelinating Disease ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Multiple Sclerosis Pathogenesis ◽  
Brain Tissues

Abstract Background: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. Pathogenesis is closely related to environment, genetic and immune system, but the underlying interactions are still not clearly elucidated. Our study aims to uncover the genetic basis and immune landscape of multiple sclerosis pathogenesis with bioinformatics.Methods: In our study, gene matrix were retrieceed from gene expression database NCBI GEO. We then used bioinformatics to standardize the samples and obtain differential gene expressions (DEGs). We constructed a protein-protein interaction network with DEGs on the STRING website, and used Cytohubba plug-in and MCODE plug-in to to mine hub genes. Then we had a functional enrichment hub genes and DEGs. Meanwhile, we use CIBERSORTX algorithm to explore the characteristics of immune cells infiltration in brain tissues of multiple sclerosis, and did a Spearman correlation analysis between genes and immune cells. We also analyzed the correlation between genes and types of brain tissues with WGCNA method.Results: We included a total of 90 samples from 2 datasets in the study, extracted 882 differential genes and 10 hub genes closely related to multiple sclerosis. Functional enrichment analysis suggested roles of immune response in multiple sclerosis. Besides, with CIBERSORTX algorithm we found brain tissues of MS contain a variety of infiltrating immune cells. Correlation analysis suggested that hub genes are highly relevant to chronic active white matter lesions and certain hub genes in our study may play a role in the activation of immune cells such as macrophages and natural killer cells.Conclusions: Our study provides guidance for the study of genetic basis and immune infiltration mechanism of multiple sclerosis.

scholarly journals LAVA: An integrated framework for local genetic correlation analysis

2021 ◽  
Author(s):  
J. Werme ◽  
S. van der Sluis ◽  
D. Posthuma ◽  
C. A. de Leeuw
Keyword(s):  
Correlation Analysis ◽  
Genetic Correlation ◽  
Multiple Regression ◽  
Partial Correlation ◽  
Genetic Basis ◽  
Global Scale ◽  
Considerable Heterogeneity ◽  
Integrated Framework ◽  
Genomic Regions ◽  
Shared Genetic

ABSTRACTGenetic correlation (rg) analysis is commonly used to identify traits that may have a shared genetic basis. Traditionally, rg is studied on a global scale, considering only the average of the shared signal across the genome; though this approach may fail to detect scenarios where the rg is confined to particular genomic regions, or show opposing directions at different loci. Tools dedicated to local rg analysis have started to emerge, but are currently restricted to analysis of two phenotypes. For this reason, we have developed LAVA, an integrated framework for local rg analysis which, in addition to testing the standard bivariate local rg’s between two traits, can evaluate the local heritability for all traits of interest, and analyse conditional genetic relations between several traits using partial correlation or multiple regression. Applied to 20 behavioural and health phenotypes, we show considerable heterogeneity in the bivariate local rg’s across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations between traits.

scholarly journals Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

2019 ◽  
Vol 35 (14) ◽  
pp. i474-i483 ◽  
Author(s):  
Lei Du ◽  
Kefei Liu ◽  
Lei Zhu ◽  
Xiaohui Yao ◽  
Shannon L Risacher ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Genetic Basis ◽  
Imaging Data ◽  
Genetic Patterns ◽  
Neuroimaging Data ◽  
Sparse Canonical Correlation Analysis ◽  
Longitudinal Imaging ◽  
The Brain

Abstract Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

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