scholarly journals PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics

2022 ◽  
Author(s):  
Joanna von Berg ◽  
Michelle ten Dam ◽  
Sander W. van der Laan ◽  
Jeroen de Ridder
Keyword(s):  
Effect Sizes ◽  
Polar Coordinates ◽  
P Value ◽  
Biological Processes ◽  
Summary Statistics ◽  
Large Collection ◽  
Multiple Traits ◽  
Biologically Relevant ◽  
Insight Into ◽  
Shared Genetic

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from GWAS summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a p-value per SNP that can be used for further analysis. We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. This network shows how PolarMorphism can be used to gain insight into relationships between traits and trait domains. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

An Insight into Oligopeptide Transporter 3 (OPT3) family proteins

2020 ◽  
Vol 27 ◽  
Author(s):  
Fırat Kurt
Keyword(s):  
Stress Responses ◽  
Chelating Agent ◽  
Biological Processes ◽  
Biotic And Abiotic Stress ◽  
Oligopeptide Transporter ◽  
Binding Residues ◽  
Fe Homeostasis ◽  
Proteins Interactions ◽  
Insight Into ◽  
Selection Of

: Oligopeptide transporter 3 (OPT3) proteins are one of the subsets of OPT clade, yet little is known about these transporters. Therefore, homolog OPT3 proteins in several plant species were investigated and characterized using bioinformatical tools. Motif and co-expression analyses showed that OPT3 proteins may be involved in both biotic and abiotic stress responses as well as growth and developmental processes. AtOPT3 usually seemed to take part in Fe homeostasis whereas ZmOPT3 putatively interacted with proteins involved in various biological processes from plant defense system to stress responses. Glutathione (GSH), as a putative alternative chelating agent, was used in the AtOPT3 and ZmOPT3 docking analyses to identify their putative binding residues. The information given in this study will contribute to the understanding of OPT3 proteins’ interactions in various pathways and to the selection of potential ligands for OPT3s.

scholarly journals Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

2016 ◽  
Vol 7 ◽  
Author(s):  
Dominic Holland ◽  
Yunpeng Wang ◽  
Wesley K. Thompson ◽  
Andrew Schork ◽  
Chi-Hua Chen ◽  
...  
Keyword(s):  
Effect Sizes ◽  
Summary Statistics

Recent advances in multivalent carbon nanoform-based glycoconjugates

2021 ◽  
Vol 28 ◽  
Author(s):  
Javier Ramos-Soriano ◽  
Mattia Ghirardello ◽  
M. Carmen Galan
Keyword(s):  
Carbon Nanotubes ◽  
Molecular Level ◽  
Biological Processes ◽  
Biologically Relevant ◽  
Disease Mechanisms ◽  
Recent Advances

: Multivalent carbohydrate-mediated interactions are fundamental to many biological processes, including disease mechanisms. To study these significant glycan-mediated interactions at a molecular level, carbon nanoforms such as fullerenes, carbon nanotubes, or graphene and their derivatives have been identified as promising biocompatible scaffolds that can mimic the multivalent presentation of biologically relevant glycans. In this minireview, we will summarize the most relevant examples of the last few years in the context of their applications.

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

2021 ◽  
Vol 23 ◽  
Author(s):  
Pei He ◽  
Rong- Rong Cao ◽  
Fei- Yan Deng ◽  
Shu- Feng Lei
Keyword(s):  
Association Study ◽  
Canonical Correlation ◽  
Genetic Factors ◽  
Genome Wide Association Study ◽  
Summary Statistics ◽  
Immune Disease ◽  
Histocompatibility Complex ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
Shared Genetic

Background: Immune and skeletal systems physiologically and pathologically interact with each other. The immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown Objective: This study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia and fracture) Methods: The canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. Versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA. Results: About 157 (p<8.19E-6), 319 (p<3.90E-6) and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune disease, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1 and TSBP1-AS1 (p<E-300) were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, majority (18) of these 19 putative validated pleiotropic genes were associated with RA. Conclusion: The metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.

scholarly journals Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

2015 ◽  
Author(s):  
Dominic Holland ◽  
Yunpeng Wang ◽  
Wesley K Thompson ◽  
Andrew Schork ◽  
Chi-Hua Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Sample Sizes ◽  
Genetic Components ◽  
Complex Phenotypes ◽  
Genome Wide ◽  
Z Scores

Genome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities that does not require raw genotype data, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype -- the proportion of SNPs (after uniform pruning, so that large LD blocks are not over-represented) likely to be in strong LD with causal/mechanistically associated SNPs -- and predicting the proportion of chip heritability explainable by genome wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and additionally, for purposes of illustration, putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We estimate the degree to which effect sizes are over-estimated when based on linear regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

scholarly journals Methodology Appraisal of Experimental Research Studies in Mathematics Teaching

2019 ◽  
Vol 5 (1) ◽  
pp. 40-45
Author(s):  
Capote Paul John
Keyword(s):  
Learning Strategies ◽  
Experimental Research ◽  
Teaching And Learning ◽  
Research Problem ◽  
Effect Sizes ◽  
P Value ◽  
Experimental Teaching ◽  
Teaching And Learning Strategies ◽  
The Mean ◽  
Research Studies

This study made use of Gene Glass Effect Size formula to estimate the mean effect sizes of the eleven (11) reviewed experimental research studies on the effect of experimental and conventional teaching and learning strategies to the academic performance of students in mathematics. A validated Inclusion Criteria was utilized in the selection of studies and a modified Methodology Appraisal Checklist was employed in the analysis of elements of the research problem and research methodology. The mean effect sizes and variances of the reviewed studies vary across the elements of the research problem and methodology, an indication that teaching and learning strategies are dependent on the quality of methodology used by the researcher. This study stressed that there is no enough evidence to prove that experimental teaching and learning strategies are more effective than conventional pedagogies in improving learnings in math, as the analysis of variance (ANOVA) revealed a p-value of 0.982 (critical value=0.05).

scholarly journals A cross-sectional, non-interventional study of the correlation between insight and adherence in schizophrenia in Nepal

2019 ◽  
Author(s):  
Imogen Henrietta Stokes ◽  
Uddhav Lama ◽  
Jai Bahadar Khattri
Keyword(s):  
Correlation Coefficient ◽  
Living Arrangements ◽  
Cultural Beliefs ◽  
Self Report ◽  
P Value ◽  
Interventional Study ◽  
Cross Sectional ◽  
Need For Treatment ◽  
Patient Demographics ◽  
Insight Into

Abstract Background: There is a significant lack of research in the Nepalese study population on adherence in patients with schizophrenia. This cross-sectional, non-interventional study aims to re-examine the recognised correlation between insight and adherence in this population, whilst exploring the association between patient demographics and adherence to help bring understanding to how familial and environmental factors may impact adherence. Methods: Patients were recruited upon attendance to outpatient’s appointments and admission to the psychiatry department of Manipal Teaching Hospital. A self-report questionnaire was used to collect data on patient demographics, including age, ethnicity, religion, employment status, current living arrangements and education level; the Birchwood Insight Scale (BIS); and the Drug Attitude Inventory (DAI-10) score. Descriptive statistics on the demographics, BIS and DAI-10 were collated using SPSS. An analysis of variance of DAI-10 scores according to participant demographics was performed using a one-way ANOVA analysis. Correlation between the BIS and BIS subscales and the DAI-10 was tested using Pearson’s 2-tailed analysis at 0.01 significance level.Results: 19 participants consented to participate in this study with 100% data obtained. 57.9% unemployed, 63.2% living with parents and 47.4% had only a basic education. 36.8% of participants had poor insight; 84.2% of participants had poor insight into their symptoms; 78.9% of participants had poor insight into their illness and 36.8% had poor insight into their need for treatment. 52.7% of participants were poor adherers. No significant differences in DAI-10 scores were found between demographic groups. A positive correlation was found between the total BIS score and DAI-10 score (Pearson correlation coefficient of 0.585; P value = 0.009). Furthermore, the awareness of need for treatment subscale score and the DAI-10 score found a correlation coefficient of 0.609 (P value = 0.006). Conclusions: In conclusion, this study found prevalent non-adherence to medication and demonstrated that insight correlates with adherence. Although study findings did not suggest that patient demographics were associated with non-adherence, it is important to consider the possibility that these high rates of non-adherence have other contributing factors; overcoming rural health inequality, cultural beliefs regarding psychiatric illness and unaffordability remain the great challenges for the Nepalese population.

scholarly journals The Expression of miR-513c and miR-3163 was Downregulated in Tumor Tissues Compared with Margin Tissues of Patients With Breast Cancer

2020 ◽  
Author(s):  
Soheila Delgir ◽  
Khandan Ilkhani ◽  
Asma Safi ◽  
Farhad Seif ◽  
Milad Bastami ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
In Silico ◽  
In Silico Analysis ◽  
Glutamine Metabolism ◽  
Expression Level ◽  
P Value ◽  
Biological Processes ◽  
Tumor Tissues ◽  
Silico Analysis

Abstract Background Breast cancer (BC) is the most common invasive cancer with different subtypes that its metabolism is unique compared with normal cells. Glutamine is considered a critical nutrition for tumor cell growth and therefore, targeting glutamine metabolism, especially Glutaminase, which catalyzed the conversion of glutamine to glutamate can be beneficial to design anti-cancer agents. Recently, evidence has shown that miRNAs with short length and single strand properties play a significant role in regulating the genes related to glutamine metabolism and may control the development of cancer.Methods Since, in-silico analysis confirmed that miR-513c and miR-3163 might be involved in glutamine metabolism, the expression level of these two miRNAs was evaluated in eighty BC tissues and margin tissues. The data were analyzed to evaluate the correlation between expression level of these miRNAs and patient’s characteristics such as abortion history, family history, and age. Furthermore, in-silico analysis was applied to predict the potential biological processes and molecular pathways of miR-513c and miR-3163 based on its gene targets.Results In-silico studies revealed the top categories of biological processes and pathways that play a critical role in cancer development were target genes for miR-513c and miR-3163. The current study showed that miR-513c (P-value = 0.02062 and fold change= -2.3801) and miR-3163 (P-value = 0.02034 and fold change= -2.3792) were downregulated in tumor tissues compared to margin tissues. Furthermore, the subgroup studies did not show any substantial relationship between expression levels of these two miRNAs and factors such as age, family history cancer, and abortion.Conclusion Based on our data, miR-513c and miR-3163 may be offered as a potential diagnosis and therapeutic targets for patients with BC.

scholarly journals eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

2020 ◽  
Author(s):  
Nurlan Kerimov ◽  
James D Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Cell Types ◽  
Summary Statistics ◽  
Ensembl Genome Browser ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Rest Api ◽  
Complex Human Traits ◽  
Insight Into

An increasing number of gene expression quantitative trait locus (eQTL) studies have made summary statistics publicly available, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and colocalisation. However, differences between these datasets, in their variants tested, allele codings, and in the transcriptional features quantified, are a barrier to their widespread use. Consequently, target genes for most GWAS signals have still not been identified. Here, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl/), a resource which contains quality controlled, uniformly re-computed QTLs from 21 eQTL studies. We find that for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies, enabling the integrative analysis of these data. Although most cis-eQTLs were shared between most bulk tissues, the analysis of purified cell types identified a greater diversity of cell-type-specific eQTLs, a subset of which also manifested as novel disease colocalisations. Our summary statistics can be downloaded by FTP, accessed via a REST API, and visualised on the Ensembl genome browser. New datasets will continuously be added to the eQTL Catalogue, enabling the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals Genetic overlap between idiopathic pulmonary fibrosis and COVID−19

2021 ◽  
Author(s):  
Richard J Allen ◽  
Beatriz Guillen-Guio ◽  
Emma Croot ◽  
Luke M Kraven ◽  
Samuel Moss ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Genetic Loci ◽  
Genome Wide ◽  
Genetic Overlap ◽  
Causal Variants ◽  
Shared Genetic

AbstractGenome-wide association studies (GWAS) of coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) have identified genetic loci associated with both traits, suggesting possible shared biological mechanisms. Using updated GWAS of COVID-19 and IPF, we evaluated the genetic overlap between these two diseases and identified four genetic loci (including one novel) with likely shared causal variants between severe COVID-19 and IPF. Although there was a positive genetic correlation between COVID-19 and IPF, two of these four shared genetic loci had an opposite direction of effect. IPF-associated genetic variants related to telomere dysfunction and spindle assembly showed no association with COVID-19 phenotypes. Together, these results suggest there are both shared and distinct biological processes driving IPF and severe COVID-19 phenotypes.

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