Epistasis correlates to genomic complexity.
Ontology highlight
ABSTRACT:
SUBMITTER: Sanjuan R
PROVIDER: S-EPMC1599975 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The study of genetic interactions (epistasis) is central to the understanding of genome organization and evolution. A general correlation between epistasis and genomic complexity has been recently shown, such that in simpler genomes epistasis is antagonistic on average (mutational effects tend to cancel each other out), whereas a transition towards synergistic epistasis occurs in more complex genomes (mutational effects strengthen each other). Here, we use a simple network model to identify basic features explaining this correlation. We show that, in small networks with multifunctional nodes, lack of redundancy, and absence of alternative pathways, epistasis is antagonistic on average. In contrast, lack of multi-functionality, high connectivity, and redundancy favor synergistic epistasis. Moreover, we confirm the previous finding that epistasis is a covariate of mutational robustness: in less robust networks it tends to be antagonistic whereas in more robust networks it tends to be synergistic. We argue that network features associated with antagonistic epistasis are typically found in simple genomes, such as those of viruses and bacteria, whereas the features associated with synergistic epistasis are more extensively exploited by higher eukaryotes.
| S-EPMC2481279 | biostudies-literature
Project description:Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species.
| S-EPMC4596682 | biostudies-literature
Project description:The rapid development of sequencing technologies makes thousands to millions of genetic attributes available for testing associations with various biological traits. Searching this enormous high-dimensional data space imposes a great computational challenge in genome-wide association studies. We introduce a network-based approach to supervise the search for three-locus models of disease susceptibility. Such statistical epistasis networks (SEN) are built using strong pairwise epistatic interactions and provide a global interaction map to search for higher-order interactions by prioritizing genetic attributes clustered together in the networks. Applying this approach to a population-based bladder cancer dataset, we found a high susceptibility three-way model of genetic variations in DNA repair and immune regulation pathways, which holds great potential for studying the etiology of bladder cancer with further biological validations. We demonstrate that our SEN-supervised search is able to find a small subset of three-locus models with significantly high associations at a substantially reduced computational cost.
| S-EPMC3587773 | biostudies-literature
Project description:Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes.
| S-EPMC5517071 | biostudies-literature
Project description:Epistasis is defined as a statistical interaction between two or more genomic loci in terms of their association with a phenotype of interest. Epistatic loci that are identified using data from Genome-Wide Association Studies (GWAS) provide insights into the interplay among multiple genetic factors, with applications including assessment of susceptibility to complex diseases, decision making in precision medicine, and gaining insights into disease mechanisms. Since the number of genomic loci assayed by GWAS is extremely large (usually in the order of millions), identification of epistatic loci is a statistically difficult and computationally intensive problem. Even when only pairwise interactions are considered, the size of the search space ranges from hundreds of millions to billions of locus pairs. The large number of statistical tests performed also makes sufficient type one error correction imperative. Consequently, efficient algorithms are required to filter the tests that are performed and evaluate large GWAS data sets in a reasonable amount of computation time. It has been observed that many pairwise tests are redundant due to correlations in their genotype values across samples, known as linkage disequilibrium. However, algorithms that have been developed for efficient identification of epistatic loci do not systematically exploit linkage disequilibrium. Here, we propose a new algorithm for fast epistasis detection based on hierarchical representation of linkage disequilibrium (LinDen). We utilize redundancies in genotype patterns between neighboring loci to generate a hierarchical structure and execute a branch-and-bound search to prioritize loci testing based on approximations of a test statistic for pairs of locus groups. The hierarchical organization of tests performed by LinDen allows for efficient scaling based on the screened loci. We test LinDen comprehensively on three data sets obtained from the Wellcome Trust Case Control Consortium: type two diabetes, psoriasis, and hypertension. Our results show that, as compared other state-of-the-art tools for fast epistasis detection, LinDen drastically reduces the number of tests performed while discovering statistically significant locus pairs. LinDen is implemented in C++ and is available as open source at http://compbio.Caseedu/linden/.
| S-EPMC5737499 | biostudies-literature
Project description:Medulloblastoma is an aggressively growing tumour, arising in the cerebellum or medulla/brain stem. It is the most common malignant brain tumour in children, and shows tremendous biological and clinical heterogeneity. Despite recent treatment advances, approximately 40% of children experience tumour recurrence, and 30% will die from their disease. Those who survive often have a significantly reduced quality of life. Four tumour subgroups with distinct clinical, biological and genetic profiles are currently identified. WNT tumours, showing activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis. Group 3 and 4 tumours are molecularly less well characterized, and also present the greatest clinical challenges. The full repertoire of genetic events driving this distinction, however, remains unclear. Here we describe an integrative deep-sequencing analysis of 125 tumour-normal pairs, conducted as part of the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project. Tetraploidy was identified as a frequent early event in Group 3 and 4 tumours, and a positive correlation between patient age and mutation rate was observed. Several recurrent mutations were identified, both in known medulloblastoma-related genes (CTNNB1, PTCH1, MLL2, SMARCA4) and in genes not previously linked to this tumour (DDX3X, CTDNEP1, KDM6A, TBR1), often in subgroup-specific patterns. RNA sequencing confirmed these alterations, and revealed the expression of what are, to our knowledge, the first medulloblastoma fusion genes identified. Chromatin modifiers were frequently altered across all subgroups. These findings enhance our understanding of the genomic complexity and heterogeneity underlying medulloblastoma, and provide several potential targets for new therapeutics, especially for Group 3 and 4 patients.
| S-EPMC3662966 | biostudies-literature
Project description:Chronic lymphocytic leukemia (CLL) has a variable clinical course. Presence of specific genomic aberrations has been shown to impact survival outcomes and can help categorize CLL into clinically distinct subtypes. We studied 178 CLL patients enrolled in a prospective study at the University of Michigan, of whom 139 and 39 were previously untreated and previously treated, respectively. We obtained unbiased, high-density, genome-wide measurements of subchromosomal copy number changes in highly purified DNA from sorted CD19(+) cells and buccal cells using the Affymetrix 50kXbaI SNP array platform (Santa Clara, CA). Genomic complexity scores were derived and correlated with the surrogate clinical end points time to first therapy (TTFT) and time to subsequent therapy (TTST): measures of disease aggressiveness and/or therapy efficaciousness. In univariate analysis, progressively increasing complexity scores in previously untreated CLL patients identified patients with short TTFT at high significance levels. Similarly, TTST was significantly shorter in pretreated patients with high as opposed to low genomic complexity. In multivariate analysis, genomic complexity emerged as an independent risk factor for short TTFT and TTST. Finally, algorithmic subchromosomal complexity determination was developed, facilitating automation and future routine clinical application of CLL whole-genome analysis.
| S-EPMC2518900 | biostudies-other
Project description:Recently, cortical areas with motor properties have attracted attention widely to their involvement in both action generation and perception. Inferior frontal gyrus (IFG), ventral premotor cortex (PMv) and inferior parietal lobule (IPL), presumably consisting of motor-related areas, are of particular interest, given that they respond to motor behaviors both when they are performed and observed. Converging neuroimaging evidence has shown the functional roles of IFG, PMv and IPL in action understanding. Most studies have focused on the effects of modulations in goals and kinematics of observed actions on the brain response, but little research has explored the effects of manipulations in motor complexity. To address this, we used fNIRS to examine the brain activity in the frontal, motor, parietal and occipital regions, aiming to better understand the brain correlates involved in encoding motor complexity. Twenty-one healthy adults executed and observed two hand actions that differed in motor complexity. We found that motor complexity sensitive brain regions were present in the pars opercularis IFG/PMv, primary motor cortex (M1), IPL/supramarginal gyrus and middle occipital gyrus (MOG) during action execution, and in pars opercularis IFG/PMv and M1 during action observation. Our findings suggest that the processing of motor complexity involves not only M1 but also pars opercularis IFG, PMv and IPL, each of which plays a critical role in action perception and execution.
| S-EPMC7335074 | biostudies-literature
Project description:Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene-gene interactions in the second stage. We applied this two-stage analysis strategy to late-onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in leucine-rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in three genes--urokinase-type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)--were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.
| S-EPMC2804868 | biostudies-literature
Project description:Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density ("Phantom Epistasis"). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.
| S-EPMC7466977 | biostudies-literature