scholarly journals Large-scale trans-eQTLs affect hundreds of transcripts and mediate patterns of transcriptional co-regulation Short: trans-eQTLs reveal patterns of transcriptional co-regulation

2016 ◽  
Author(s):  
Boel Brynedal ◽  
JinMyung Choi ◽  
Towfique Raj ◽  
Robert Bjornson ◽  
Barbara E Stranger ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Large Scale ◽  
Biological Processes ◽  
Confidence Set ◽  
Substantial Evidence ◽  
Limited Sample ◽  
Phenotypic Differences ◽  
Multiple Transcripts ◽  
A Cell ◽  
Variance Explained

AbstractGenetic variation affecting gene regulation is a driver of phenotypic differences between individuals and can be used to uncover how biological processes are organized in a cell. Although detecting cis-eQTLs is now routine, trans-eQTLs have proven more challenging to find due to the modest variance explained and the multiple testing burden when comparing millions of SNPs for association to thousands of transcripts. Here, we provide evidence for the existence of trans-eQTLs by looking for SNPs associated with the expression of multiple genes simultaneously. We find substantial evidence of trans-eQTLs, with an 1.8-fold enrichment in nominally significant markers in all three populations and significant overlap between results across the populations. These trans-eQTLs target the same genes and show the same direction of effect across populations. We define a high-confidence set of eight independent trans-eQTLs which are associated to multiple transcripts in all three populations, and affect the same targets in all three populations with the same direction of effect. We then show that target transcripts of trans-eQTLs encode proteins that interact more frequently than expected by chance, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. Thus, we have demonstrated that trans-eQTLs can be accurately identified even in studies of limited sample size.

scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

Intraclass Correlation Coefficients for Designing Cluster-Randomized Trials in Sub-Saharan Africa Education

2016 ◽  
Vol 40 (6) ◽  
pp. 500-525 ◽  
Author(s):  
Ben Kelcey ◽  
Zuchao Shen ◽  
Jessaca Spybrook
Keyword(s):  
Large Scale ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Educational Interventions ◽  
Sub Saharan Africa ◽  
Design Stage ◽  
Education Outcomes ◽  
Randomized Studies ◽  
Sub Saharan ◽  
Variance Explained

Objective: Over the past two decades, the lack of reliable empirical evidence concerning the effectiveness of educational interventions has motivated a new wave of research in education in sub-Saharan Africa (and across most of the world) that focuses on impact evaluation through rigorous research designs such as experiments. Often these experiments draw on the random assignment of entire clusters, such as schools, to accommodate the multilevel structure of schooling and the theory of action underlying many school-based interventions. Planning effective and efficient school randomized studies, however, requires plausible values of the intraclass correlation coefficient (ICC) and the variance explained by covariates during the design stage. The purpose of this study was to improve the planning of two-level school-randomized studies in sub-Saharan Africa by providing empirical estimates of the ICC and the variance explained by covariates for education outcomes in 15 countries. Method: Our investigation drew on large-scale representative samples of sixth-grade students in 15 countries in sub-Saharan Africa and includes over 60,000 students across 2,500 schools. We examined two core education outcomes: standardized achievement in reading and mathematics. We estimated a series of two-level hierarchical linear models with students nested within schools to inform the design of two-level school-randomized trials. Results: The analyses suggested that outcomes were substantially clustered within schools but that the magnitude of the clustering varied considerably across countries. Similarly, the results indicated that covariance adjustment generally reduced clustering but that the prognostic value of such adjustment varied across countries.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

Dehydration-induced proteomic landscape of mitochondria in chickpea reveals large-scale coordination of key biological processes

2019 ◽  
Vol 192 ◽  
pp. 267-279 ◽  
Author(s):  
Dipak Gayen ◽  
Saurabh Gayali ◽  
Pragya Barua ◽  
Nilesh Vikram Lande ◽  
Swati Varshney ◽  
...  
Keyword(s):  
Large Scale ◽  
Biological Processes

Dynamic Measurement Modeling: Using Nonlinear Growth Models to Estimate Student Learning Capacity

2017 ◽  
Vol 46 (6) ◽  
pp. 284-292 ◽  
Author(s):  
Denis G. Dumas ◽  
Daniel M. McNeish
Keyword(s):  
Multiple Testing ◽  
Large Scale ◽  
Growth Models ◽  
Dynamic Assessment ◽  
The United States ◽  
Dynamic Measurement ◽  
Data Sets ◽  
Data Systems ◽  
Longitudinal Assessment ◽  
Assessment Practice

Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate opportunity to develop various academic skills but can nonetheless do so in the future. One long-standing approach to the partial rectification of this issue is dynamic assessment (DA), a technique that features multiple testing occasions integrated with learning opportunities. However, DA is extremely resource intensive to incorporate into educational assessment practice and cannot be applied to extant large-scale data sets. In this article, the authors describe a recently developed statistical technique, dynamic measurement modeling (DMM), which is capable of estimating quantities associated with DA—including student capacity for learning a particular skill—from existing large-scale longitudinal assessment data, allowing the core concepts of DA to be scaled up for use with secondary data sets such as those collected by Statewide Longitudinal Data Systems in the United States. The authors show that by considering several assessments over time, student capacity can be reliably estimated, and these capacity estimates are much less affected by student race/ethnicity, gender, and socioeconomic status than are single-timepoint assessment scores, thereby improving the consequential validity of measurement.

scholarly journals Hidden in plain sight: What remains to be discovered in the eukaryotic proteome?

2018 ◽  
Author(s):  
Valerie Wood ◽  
Antonia Lock ◽  
Midori A. Harris ◽  
Kim Rutherford ◽  
Jürg Bähler ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Fission Yeast ◽  
Genome Sequencing ◽  
Large Scale ◽  
Biological Process ◽  
Blind Spot ◽  
Model Organisms ◽  
Biological Processes ◽  
Health And Disease

AbstractThe first decade of genome sequencing stimulated an explosion in the characterization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model organisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes. To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences.We use a simple yet powerful metric based on Gene Ontology (GO) biological process terms to define characterized and uncharacterized proteins for human, budding yeast, and fission yeast. We then identify a set of conserved but unstudied proteins in S. pombe, and classify them based on a combination of orthogonal attributes determined by large-scale experimental and comparative methods. Finally, we explore possible reasons why these proteins remain neglected, and propose courses of action to raise their profile and thereby reap the benefits of completing the catalog of proteins’ biological roles.

scholarly journals Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning

2019 ◽  
Author(s):  
Vanessa E. Gray ◽  
Katherine Sitko ◽  
Floriane Z. Ngako Kameni ◽  
Miriam Williamson ◽  
Jason J. Stephany ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Amino Acid ◽  
Alanine Scanning Mutagenesis ◽  
Aggregation Assay ◽  
Disease Etiology ◽  
Charged Amino Acids ◽  
Molecular Determinants ◽  
Aβ Aggregation ◽  
A Cell

AbstractDespite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ~75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.

scholarly journals A collection of genetic mouse lines and related tools for inducible and reversible intersectional misexpression

2019 ◽  
Author(s):  
Elham Ahmadzadeh ◽  
N. Sumru Bayin ◽  
Xinli Qu ◽  
Aditi Singh ◽  
Linda Madisen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Genetic Manipulation ◽  
Cell Types ◽  
Biological Processes ◽  
Control Of Gene Expression ◽  
A Cell ◽  
Summary Statement ◽  
Spatiotemporal Control ◽  
Mouse Lines

AbstractThanks to many advances in genetic manipulation, mouse models have become very powerful in their ability to interrogate biological processes. In order to precisely target expression of a gene of interest to particular cell types, intersectional genetic approaches utilizing two promoter/enhancers unique to a cell type are ideal. Within these methodologies, variants that add temporal control of gene expression are the most powerful. We describe the development, validation and application of an intersectional approach that involves three transgenes, requiring the intersection of two promoter/enhancers to target gene expression to precise cell types. Furthermore, the approach utilizes available lines expressing tTA/rTA to control timing of gene expression based on whether doxycycline is absent or present, respectively. We also show that the approach can be extended to other animal models, using chicken embryos. We generated three mouse lines targeted at the Tigre (Igs7) locus with TRE-loxP-tdTomato-loxP upstream of three genes (p21, DTA and Ctgf) and combined them with Cre and tTA/rtTA lines that target expression to the cerebellum and limbs. Our tools will facilitate unraveling biological questions in multiple fields and organisms.Summary statementAhmadzadeh et al. present a collection of four mouse lines and genetic tools for misexpression-mediated manipulation of cellular activity with high spatiotemporal control, in a reversible manner.

scholarly journals Haploinsufficiency of the psychiatric risk gene Cyfip1 causes abnormal postnatal hippocampal neurogenesis through microglial and Arp2/3 mediated actin dependent mechanisms

2018 ◽  
Author(s):  
Niels Haan ◽  
Laura J Westacott ◽  
Jenny Carter ◽  
Michael J Owen ◽  
William P Gray ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Hippocampal Neurons ◽  
Hippocampal Neurogenesis ◽  
Actin Dynamics ◽  
Biological Processes ◽  
Risk Gene ◽  
A Cell ◽  
Adult Born Neurons ◽  
Newborn Neurons ◽  
And Migration

AbstractGenetic risk factors can significantly increase chances of developing psychiatric disorders, but the underlying biological processes through which this risk is effected remain largely unknown. Here we show that haploinsufficiency of Cyfip1, a candidate risk gene present in the pathogenic 15q11.2(BP1-BP2) deletion may impact on psychopathology via abnormalities in cell survival and migration of newborn neurons during postnatal hippocampal neurogenesis. We demonstrate that haploinsufficiency of Cyfip1 leads to increased numbers of adult born hippocampal neurons due to reduced apoptosis, without altering proliferation. We confirm this is due to a cell autonomous failure of microglia to induce apoptosis through the secretion of the appropriate factors. Furthermore, we show an abnormal migration of adult-born neurons due to altered Arp2/3 mediated actin dynamics. Together, our findings throw new light on how the genetic risk candidate Cyfip1 may influence the hippocampus, a brain region with strong evidence for involvement in psychopathology.

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