11,048 research outputs found
netgwas: An R Package for Network-Based Genome-Wide Association Studies
Graphical models are powerful tools for modeling and making statistical
inferences regarding complex associations among variables in multivariate data.
In this paper we introduce the R package netgwas, which is designed based on
undirected graphical models to accomplish three important and interrelated
goals in genetics: constructing linkage map, reconstructing linkage
disequilibrium (LD) networks from multi-loci genotype data, and detecting
high-dimensional genotype-phenotype networks. The netgwas package deals with
species with any chromosome copy number in a unified way, unlike other
software. It implements recent improvements in both linkage map construction
(Behrouzi and Wit, 2018), and reconstructing conditional independence network
for non-Gaussian continuous data, discrete data, and mixed
discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely
occur in genetics and genomics such as genotype data, and genotype-phenotype
data. We demonstrate the value of our package functionality by applying it to
various multivariate example datasets taken from the literature. We show, in
particular, that our package allows a more realistic analysis of data, as it
adjusts for the effect of all other variables while performing pairwise
associations. This feature controls for spurious associations between variables
that can arise from classical multiple testing approach. This paper includes a
brief overview of the statistical methods which have been implemented in the
package. The main body of the paper explains how to use the package. The
package uses a parallelization strategy on multi-core processors to speed-up
computations for large datasets. In addition, it contains several functions for
simulation and visualization. The netgwas package is freely available at
https://cran.r-project.org/web/packages/netgwasComment: 32 pages, 9 figures; due to the limitation "The abstract field cannot
be longer than 1,920 characters", the abstract appearing here is slightly
shorter than that in the PDF fil
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Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery
Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant’s sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance
Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data
BACKGROUND: High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits. RESULTS: We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight. CONCLUSIONS: We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest
Whole-genome sequencing of Theileria parva strains provides insight into parasite migration and diversification in the african continent
The disease caused by the apicomplexan protozoan parasite Theileria parva, known as East Coast fever or Corridor disease, is one of the most serious cattle diseases in Eastern, Central, and Southern Africa. We performed whole-genome sequencing of nine T. parva strains, including one of the vaccine strains (Kiambu 5), field isolates from Zambia, Uganda, Tanzania, or Rwanda, and two buffalo-derived strains. Comparison with the reference Muguga genome sequence revealed 34 814–121 545 single nucleotide polymorphisms (SNPs) that were more abundant in buffalo-derived strains. High-resolution phylogenetic trees were constructed with selected informative SNPs that allowed the investigation of possible complex recombination events among ancestors of the extant strains. We further analysed the dN/dS ratio (non-synonymous substitutions per non-synonymous site divided by synonymous substitutions per synonymous site) for 4011 coding genes to estimate potential selective pressure. Genes under possible positive selection were identified that may, in turn, assist in the identification of immunogenic proteins or vaccine candidates. This study elucidated the phylogeny of T. parva strains based on genome-wide SNPs analysis with prediction of possible past recombination events, providing insight into the migration, diversification, and evolution of this parasite species in the African continent
Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana.
BackgroundGenome-wide association (GWA) is gaining popularity as a means to study the architecture of complex quantitative traits, partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of adaptive quantitative traits. GSL are key anti-herbivory defenses that impart adaptive advantages within field trials. While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA, GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA.Methodology/principal findingsTo understand how development and environment can influence GWA, we conducted a study using 96 Arabidopsis thaliana accessions, >40 GSL phenotypes across three conditions (one developmental comparison and one environmental comparison) and ∼230,000 SNPs. Developmental stage had dramatic effects on the outcome of GWA, with each stage identifying different loci associated with GSL traits. Further, while the molecular bases of numerous quantitative trait loci (QTL) controlling GSL traits have been identified, there is currently no estimate of how many additional genes may control natural variation in these traits. We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A. thaliana using single gene isogenic lines.Conclusions/significanceTogether, these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism. Additionally, while GWA is highly conditional upon genetics, the use of additional genomic information can rapidly identify causal loci en masse
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Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.
Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10-52) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression
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Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence.
Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation
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