In this article, we proposed the methodology of analyzing the distribution of gene functional properties in the context of statistical epistasis networks. The gene interaction network was constructed by first identifying the network of strong and significant pairwise SNP epistatic interactions and then building gene network on top of the SNP interaction network. After annotating genes as vertices based on their functional Gene Ontology, dyadicity and heterophilicity analysis was performed for each GO term to investigate to what degree the vertex characteristics correlate with the underlying interaction network topology. Using a population-based bladder cancer dataset and its previously identified SNP statistical epistasis network, we performed the dyadicity and heterophilicity analysis on enriched GO terms for the genes in the gene interaction network associated with bladder cancer. We were able to find 12 GO categories with significant dyadicity or heterophilicity, which indicated the ...
Our results indicate that epistasis can still facilitate adaptive divergence at longer timescales, even if epistasis is negative. Furthermore, our results indicate a counterintuitive principle that even strong negative epistasis can facilitate diversification on an intermediate timescale, because after a diversification event, one lineage accumulates positive additive effects and the other lineage accumulates negative additive effects, and in combination with a strong negative correlation between additive and epistatic effects, one of these lineages is actually likely to generate a descendant lineage that occupies the niche space between the two lineages. Although strong negative epistasis facilitates diversification, the lineages generated are not very stable and tend to merge. Furthermore, after 10 000 generations, and once dynamics stabilize, a population with a strongly negative epistatic architecture has lower diversity than a weaker negative epistatic architecture.. An important ...
We present a new parameterization of physiological epistasis that allows the measurement of epistasis separate from its effects on the interaction (epistatic) genetic variance component. Epistasis is the deviation of two-locus genotypic values from the sum of the contributing single-locus genotypic values. This parameterization leads to statistical tests for epistasis given estimates of two-locus genotypic values such as can be obtained from quantitative trait locus studies. The contributions of epistasis to the additive, dominance and interaction genetic variances are specified. Epistasis can make substantial contributions to each of these variance components. This parameterization of epistasis allows general consideration of the role of epistasis in evolution by defining its contribution to the additive genetic variance. ...
In evolutionary genetics, the sign of epistasis is usually more significant than the magnitude of epistasis. This is because magnitude epistasis (positive and negative) simply affects how beneficial mutations are together, however sign epistasis affects whether mutation combinations are beneficial or deleterious.[33] A fitness landscape is a representation of the fitness where all genotypes are arranged in 2D space and the fitness of each genotype is represented by height on a surface. It is frequently used as a visual metaphor for understanding evolution as the process of moving uphill from one genotype to the next, nearby, fitter genotype.[15] If all mutations are additive, they can be acquired in any order and still give a continuous uphill trajectory. The landscape is perfectly smooth, with only one peak (global maximum) and all sequences can evolve uphill to it by the accumulation of beneficial mutations in any order. Conversely, if mutations interact with one another by epistasis, the ...
Epistasis:. 1. Epistasis refers to interaction of two or more genes. Thus it involves two or more loci. 2. Epistasis may involve both homo and heterozygotes. Hence it is fixable in homozygotes. 3. Epistasis is of several types such as dominant, recessive, duplicate, etc. 4. Epistasis modifies the normal dihybrid phenotypic ratios in F2. 5. Epistasis is also known as inter genie or inter-locus gene interaction. 6. Recessive gene can also exhibit masking effect. Dominance: ...
QUANTITATIVE traits are affected by many genes that act singly and in interaction with each other. Epistasis, the interaction between genes at different loci, may exert important effects on (1) the dynamic of evolving populations (Cheverud and Routman 1996), (2) changes of genetic variances caused by long-term selection (Carlborg et al. 2006) or by a population bottleneck (Goodnight 1987), and (3) heterosis (Doebley et al. 1995; Yu et al. 1997; Li et al. 2001; Xing et al. 2002; Hua et al. 2003; Mei et al. 2003; Syed and Chen 2005; Kusterer et al. 2007; Melchinger et al. 2007a).. To detect epistatic quantitative trait loci (QTL) in conventional mapping studies with segregating populations such as recombinant inbred lines (RILs), methods have been applied to search multiple QTL simultaneously (for review see Carlborg and Haley 2004). Such multidimensional scans are hampered by the problem of multiple tests, which increases for digenic epistasis in a quadratic manner compared to tests for presence ...
It is well established that gene interactions influence common human diseases, but to date linkage studies have been constrained to searching for single genes across the genome. We applied a novel approach to uncover significant gene-gene interactions in a systematic two-dimensional (2D) genome-scan of essential hypertension. The study cohort comprised 2076 affected sib-pairs and 66 affected half-sib-pairs of the British Genetics of HyperTension study. Extensive simulations were used to establish significance thresholds in the context of 2D genome-scans. Our analyses found significant and suggestive evidence for loci on chromosomes 5, 9, 11, 15, 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 and 11q22.1, two-locus lod score=5.72; 5q13.1 and 19q12, two-locus lod score=5.35; 9q22.3 and 15q12, two-locus lod score=4.80; 16p12.3 and 16q23.1, two-locus lod score=4.50). For each significant and suggestive pairwise interaction, the two-locus genetic ...
Tobacco consumption has been implicated as the most relevant risk factor for the development of bladder cancer. Among various carcinogens identified in tobacco smoke, the polycyclic aromatic hydrocarbons (PAHs) are well characterized and their risk associations with bladder cancer are well studied [1-4]. In addition to these strong environmental risk factors, recent genome-wide association studies have identified several highly significant genetic factors with small effects [5, 6]. Some of these have been shown to modify the effects of smoking on risk of bladder cancer [7].. Most existing genetic association studies have focused on the independent effects of individual genes. That is, they have by design ignored the context of human ecology and the extensive variability in the human genome. As a result, much of the heritability of common human diseases such as bladder cancer remains unexplained. Multiple approaches have been proposed to account for this missing heritability including sequencing ...
How to catch epistasis: theory and practice Elena S. Gusareva, PhD [email protected] (1) Systems and Modeling Unit, Montefiore Institute (2) Bioinformatics and Modeling, GIGA-R Université de Liège Belgium Outline  Epistasis: gene interaction and phenotype effects  Protocol for genome-wide association interaction analysis (GWAI) Data collection Quality control Choosing a strategy for GWAI (exhaustive and selective epistasis screening) Tests of association Interpretation and follow-up (replication analysis and validation)  GWAI screening: an example on Alzheimer disease Missing heritability Monogenic disease - Phenylketonuria (Phenylalanine hydroxylase - PAH gene) mutation Single Linkage analysis Ph: disease gene Complex disease - Crohns disease (99 disease susseptibility loci ~ 25% of heritability of CD) Gene 1 Gene2 Environmental factor 1…n Gene …n Gender Age missing heritability !!! GWA analysis Ph: disease Biological epistasis William Bateson, 1909 - "compositional epistasis" ...
... typically applies to a certain allele, or version, of a gene. Epistasis depends on how the protein allele codes for actually functions. In our analogy, epistasis depends on what the workers do in our process.. Now well add epistasis to our example. Lets say a version (or allele) of a is broken so that it contains no instructions. Worker A wouldnt be able to put paint into the tray, and we would end up with a blank poster-even though workers B and C are still doing their jobs.. This broken version of a is epistatic to b and c: the final product (a blank poster) shows no evidence of what B and C have been told to do. We cant tell if Bs instructions said to add red or blue, or if Cs said to draw a circle or a square.. The important aspect of epistasis is that it doesnt just influence the phenotype, it hides the output of another gene or genes.. Now lets imagine that a is working, but c is broken. This version, or "allele," of c is epistatic to a and b: the output shows no evidence ...
There is a new paper in PLoS Genetics by Clayton that highlights the challenges of making biological inferences from statistical models of interaction. I was surprised to see our 2006 paper in the Journal of Theoretical Biology cited as an example of confusing mathematical and biological interaction. Clayton interpreted our paper as saying that we can make causal statements from statistical models. Quite to the contrary, we highlight in our paper the enormous challenges faced when trying to make inferences about the biology happening at the cellular level from a statistical model summarizing population-level data. He also misinterpreted our use of information theory in this paper. We very clearly state in this paper and many others that entropy measures are useful for "statistical" interpretation. We never say anywhere that this is any type of biological interpretation. Clayton should have read and cited our 2005 BioEssays paper that goes through the difference between biological and statistical ...
Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. These regulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasis within an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for 20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that control fitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership within the same or different phenotypic sub-networks. Extensive epistasis was observed between the transcription factors, regardless of sub-network membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presence of a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue and environment and shifted between ...
Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. These regulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasis within an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for 20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that control fitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership within the same or different phenotypic subnetworks. Extensive epistasis was observed between the transcription factors, regardless of subnetwork membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presence of a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue and environment and shifted between both ...
If synergistic epistasis occurs, each mutation added to a genome has a greater deleterious effect than preceding mutations. Without this effect it is difficult to explain how small populations can survive in the face of genetic drift, or how larger populations can survive a high mutation rate. In the 27 July Nature Peck and Waxman use a mathematical model to deduce that competition in small groups does, indeed, lead to synergistic epistasis (Nature 2000, 406:399-404). This competition also produ. 0 Comments. ...
Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, per se, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the ...
Dynamic epistasis for different alleles of the same gene Lin Xu, Brandon Barker, Zhenglong Gu (Submitted on 24 Nov 2014) Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including…
Epistasis is the sensation whereby a single polymorphisms influence on a characteristic depends upon other polymorphisms within the genome. Right here we present that, using advanced computation10 and a gene appearance study style, many cases of epistasis are located between common one nucleotide polymorphisms (SNPs). Within a cohort of 846 people 1062169-56-5 manufacture with 7339 gene appearance levels assessed in peripheral bloodstream, we discovered 501 significant pairwise connections between common SNPs influencing the appearance of 238 genes (< 2.91 10?16). Replication of the connections in two indie data pieces11,12 demonstrated both concordance of path of epistatic results (= 5.56 10?31) 1062169-56-5 manufacture and enrichment of relationship < 0.05/501. Forty-four from the hereditary connections can be found within 2Mb of parts of known physical chromosome connections13 (= 1.8 10?10). Epistatic systems of three SNPs or even more impact the appearance degrees of 129 genes, whereby one ...
Abstract: Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks ...
Are Genetic Interactions Influencing Gene Expression Evidence for Biological Epistasis or Statistical Artifacts? Alexandra Fish, John A. Capra, William S Bush doi: http://dx.doi.org/10.1101/020479 Interactions between genetic variants, also called epistasis, are pervasive in model organisms; however, their importance in humans remains unclear because statistical interactions in observational studies can be explained by processes other than…
The toll-like receptor 4 (TLR4)-myeloid differentiation factor 88 (MyD88)-dependent signaling pathway plays a role in the initiation and progression of coronary artery disease (CAD). We investigated SNP-SNP interactions between the TLR4 and MyD88 genes in CAD susceptibility and assessed whether the effects of such interactions were modified by confounding risk factors (hyperglycemia, hyperlipidemia and Helicobacter pylori (H. pylori) infection). Participants with CAD (n = 424) and controls (n = 424) without CAD were enrolled. Polymerase chain restriction-restriction fragment length polymorphism was performed on genomic DNA to detect polymorphisms in TLR4 (rs10116253, rs10983755, and rs11536889) and MyD88 (rs7744). H. pylori infections were evaluated by enzyme-linked immunosorbent assays, and the cardiovascular risk factors for each subject were evaluated clinically. The significant interaction between TLR4 rs11536889 and MyD88 rs7744 was associated with an increased CAD risk (p value for interaction = 0
Author Summary Epistasis describes non-additive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background. Epistatic mutations in viruses and bacteria that live under severe conditions, such as antibiotic treatments or immune pressure, often allow pathogens to develop drug resistance or escape the immune system. In this paper we develop a new phylogenetic method for detecting epistasis, and we apply this method to the surface proteins of the influenza A virus, which are important targets of the immune system and drug treatments. The authors identify and characterize hundreds of epistatic mutations in these proteins. Among those identified, we find the specific epistatic mutations that were recently shown, experimentally, to confer resistance to the drug Tamiflu. The results of this study may help to
Selfish genetic elements and coevolved suppressors are often invoked as sources of hybrid incompatibility [9,16,48], but direct evidence for a specific role of genomic conflict in the evolution of BDM incompatibilities is rare. Cryptic CMS in plants, where mismatch between organellar and nuclear genes results in hybrid male sterility, epitomizes this gap. Despite abundant evidence that cryptic CMS is common [21,23,24] and robust theory that it should evolve selfishly [28], the links between pattern and process have been circumstantial to date [31]. Here, we present, to our knowledge, the first direct evidence that mitochondrial CMS loci and associated nuclear restorers have evolved under the positive selection predicted by the conflict model. Our findings strongly point to selfish evolution/coevolution within one parental species, rather than negative epistasis limited only to hybrids, as the source of cytonuclear incompatibilites in crosses between hermaphroditic plant ...
Background: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus ...
Background: The statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effect of model architecture on detection difficulty, and (3) Explore how adjusting GAMETES constraints influences diversity in the generated models.. Results: In this study we utilized a geometric approach ...
View Notes - supplementary from BIO 390 at McMaster University. BIO 390 - GENETICS FROM GENE TO PHENOTYPE DOMINANCE, EPISTASIS, GENE INTERACTION O VERVIEW - Genetic and environmental contributions
Sohail M, Vakhrusheva OA, Sul JH, Pulit SL, Francioli LC; Genome of the Netherlands Consortium; Alzheimers Disease Neuroimaging Initiative, van den Berg LH, Veldink JH, de Bakker PIW, Bazykin GA, Kondrashov AS, Sunyaev SR. Negative selection in humans and fruit flies involves synergistic epistasis.\\ Science 356(6337):​539-542 (2017). PubMed [[http://​www.ncbi.nlm.nih.gov/​pubmed/​28473589,PMID:​28473589 ...
Even if both genes have mutants with the same phenotype, there may be other mutations that enable pathway ordering via epistasis analysis. Specifically, if you can find a mutation that causes a gain of function - for example, by constitutively activating a gene product that normally requires activation. Consider the genes that specify the fates of cells at the termini of the Drosophila embryo so that they are distinct from those in the central region of the embryo. A ligand present only at the termini activates a receptor tyrosine kinase, encoded by the torso gene (Figure 4). The activated kinase initiates a signal transduction cascade that ultimately activates transcription of the tailless gene in the termini. The tailless gene encodes a transcriptional regulator that directs terminal-cell fates and represses central-cell fates in the termini. Thus, loss-of-function mutations in torso (torso lof ) and tailless (tailless lof ) have very similar phenotypes: the cells at the termini adopt central ...
EPISTASIS. Dr. S. Ramgopal Rao. Epistatic Gene Interactions. Gene interactions occur when two or more different genes influence the outcome of a single trait Most morphological traits (height, weight, color) are affected by multiple genes Slideshow 4764171 by hazina
Gene-Gene interaction: Comparison between the number of interacting locus/loci and the odds ratio (relative risk) pertaining to each SNP combination.Testing A
Collins SR, Miller KM, Maas NL, Roguev A, Fillingham J, Chu CS, Schuldiner M, Gebbia M, Recht J, Shales M, Ding H, Xu H, Han J, Ingvarsdottir K, Cheng B, Andrews B, Boone C, Berger SL, Hieter P, Zhang Z, Brown GW, Ingles CJ, Emili A, Allis CD, Toczyski DP, Weissman JS, Greenblatt JF, Krogan NJ. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature. 2007 Apr 12; 446(7137):806-10. Epub 2007 Feb 21 ...
Questions and answers on biology exam preparations. MCQs or objective questions are based on Epistasis, polytene chromosomes, number of chromosomes in plasmodium etc.
Daily News How Gaining and Losing Weight Affects the Body Millions of measurements from 23 people who consumed extra calories every day for a month reveal changes in proteins, metabolites, and gut microbiota that accompany shifts in body mass.. ...
Gene-by-gene comparison of expression levels for mixed asexual versus sporozoite stages and mixed asexual parasites versus heat shock treated mixed asexual para
scriptsize \begin{align*} a&= w_{000}-w_{010}-w_{100}+w_{110} & m&=w_{001}+w_{010}+w_{100}-w_{111}-2w_{000}\\ b&=w_{001}-w_{011}-w_{101}+w_{111} & n&=w_{011}+w_{101}+w_{110}-w_{000}-2w_{111}\\ c&=w_{000}-w_{001}-w_{100}+w_{101} & o&=w_{010}+w_{100}+w_{111}-w_{001}-2w_{110}\\ d&=w_{010}-w_{011}-w_{110}+w_{111} & p&=w_{000}+w_{011}+w_{101}-w_{110}-2w_{001}\\ e&=w_{000}-w_{001}-w_{010}+w_{011} & q&=w_{001}+w_{100}+ w_{111}-w_{010}-2w_{101}\\ f&=w_{100}-w_{101}-w_{110}+w_{111} & r&=w_{000}+w_{011}+ w_{110}-w_{101}-2w_{010}\\ g&=w_{000}-w_{011}-w_{100}+w_{111} & s&=w_{000}+w_{101}+ w_{110}-w_{011}-2w_{100}\\ h&=w_{001}-w_{010}-w_{101}+w_{110} & t&=w_{001}+w_{010}+w_{111}-w_{100}-2w_{011}\\ i&=w_{000}-w_{010}-w_{101}+w_{111}\\ j&=w_{001}-w_{011}-w_{100}+w_{110}\\ k&=w_{000}-w_{001}-w_{110}+w_{111}\\ l&=w_{010}-w_{011}-w_{100}+w_{101}\\ \end{align*} $$ Beerenwinkel, Niko, Lior Pachter, and Bernd Sturmfels. "Epistasis and shapes of fitness landscapes." Statistica Sinica (2007): 1317-1342. ...
C. Loucoubar, A.V. Grant, J.-F. Bureau, I. Casademont, N. Ardo Bar, A. Bar-Hen, M.T. Diop, J. Faye, F. Diene Sarr, A. Badiane, A. Tall, J.-F. Trape, F. Cliquet, B. Schwikowski, M. Lathrop, R.E. Paul, A. Sakuntabhai (2016). Detecting multi-way epistasis in family-based association studies. Accepté dans Briefings in Bioinformatics ...
There is increasing recognition that nutrients have the capacity to directly regulate metabolic processes through impacting on the expression of enzymes, receptors, hormones and other proteins. As such they can impact on growth, ageing and susceptibility to non-communicable diseases. Our interests span the impact of macro- and micro-nutrients on food intake, tissue differentiation, growth and repair and diseases processes such as atherosclerosis and muscular-skeletal degeneration. A greater understanding of such nutrient: gene interactions should lead to improved nutritional advice and pharmacological interventions to maintain lifelong health and prevent, or delay the onset of, chronic diseases commonly associated with the aging process. ...
Multifactor Dimensionality Reduction (MDR) Analysis to Detect Single Nucleotide Polymorphisms Associated with a Carcass Trait in a Hanwoo Population - Multifactor-dimensionality Reduction (MDR);SNP;Hanwoo;Association Study;
Epistasis arising from physiological interactions between gene products often contributes to species differences, particularly those involved in reproductive isolation. In social organisms, phenotypes are influenced by the genotypes of multiple interacting individuals. In theory, social interactions can give rise to an additional type of epistasis between the genomes of social partners that can contribute to species differences. Using a full-factorial cross-fostering design with three species of closely related Temnothorax ants, I found that adult worker size was determined by an interaction between the genotypes of developing brood and care-giving workers, i.e. intergenomic epistasis. Such intergenomic social epistasis provides a strong signature of coevolution between social partners. These results demonstrate that just as physiologically interacting genes coevolve, diverge, and contribute to species differences, so do socially interacting genes. Coevolution and conflict between social partners,
TY - JOUR. T1 - Investigation of potential gene-gene interactions between APOE and RELN contributing to autism risk. AU - Ashley-Koch, Allison E.. AU - Jaworski, James. AU - Ma, De Qiong. AU - Mei, Hao. AU - Ritchie, Marylyn D.. AU - Skaar, David A.. AU - Robert Delong, G.. AU - Worley, Gordon. AU - Abramson, Ruth K.. AU - Wright, Harry H.. AU - Cuccaro, Michael L.. AU - Gilbert, John R.. AU - Martin, Eden R.. AU - Pericak-Vance, Margaret A.. PY - 2007/8/1. Y1 - 2007/8/1. N2 - BACKGROUND: Several candidate gene studies support RELN as susceptibility gene for autism. Given the complex inheritance pattern of autism, it is expected that gene-gene interactions will exist. A logical starting point for examining potential gene-gene interactions is to evaluate the joint effects of genes involved in a common biological pathway. RELN shares a common biological pathway with APOE, and Persico et al. have observed transmission distortion of the APOE-2 allele in autism families. OBJECTIVE: We evaluated RELN ...
To the Editor:. These are exciting times for the genetic investigation of systemic lupus erythematosus (SLE), characterized by the discovery of many reproducibly associated loci1. Further progress will require research in many different directions, including investigation of how the effects of each locus integrate between them and with environmental exposures to cause SLE. We read with interest the report by Hellquist, et al2 that showed evidence of significant epistatic interaction between 2 SLE-associated loci, IRF5 and TYK2. The first is a definitively confirmed SLE susceptibility locus with one of the strongest, albeit complex, effects. The latter has been more contentious, but its association with SLE is becoming clearer2,3. Epistasis means that risk in subjects with susceptibility alleles at the 2 loci significantly exceeds the sum of the risks at each locus. This was rightly interpreted to mean that the 2 loci impinge in the type 1 interferon pathway2, which is an important insight ...
A four-stage approach was used to analyse epistasis based on the pre-corrected phenotypes where SNP genotypes were fitted as fixed factors: 1) single SNP regression to identify qSNPs (see above); 2) detect qSNP × qSNP pairs [9]; 3) detect qSNP × non-qSNP pairs; 4) detect non-qSNP × non-qSNP pairs. Nested tests were used to identify significant epistatic pairs; the first test compares the full model (y = μ+SNP1+SNP2+SNP1 *SNP2+e) with the NULL model (y = μ+e); the second test compares the full model with the two-SNP model (y = μ+SNP1+SNP2+e) (i.e. epistasis test). Only pairs that were significant for the first test enter the epistasis test. When either SNP1 or SNP2 is a qSNP, the first test is changed to ensure the full model is better than the single SNP model (y = μ+qSNP +e) before the epistasis test. When both SNP1 and SNP2 are qSNPs, only the epistasis test is needed. To avoid spurious interactions between closely located SNPs an arbitrary minimum distance of 10 cM was applied to any ...
BACKGROUND: Neuregulin1 (NRG1)-ErbB signaling has been implicated in the pathogenesis of cancer and schizophrenia. We have previously reported that NRG1-stimulated migration of B lymphoblasts is PI3K-AKT1dependent and impaired in patients with schizophrenia and significantly linked to the catechol-o-methyltransferase (COMT) Val108/158Met functional polymorphism. METHODOLOGY/PRINCIPAL FINDINGS: We have now examined AKT1 activation in NRG1-stimulated B lymphoblasts and other cell models and explored a functional relationship between COMT and AKT1. NRG1-induced AKT1 phosphorylation was significantly diminished in Val carriers compared to Met carriers in both normal subjects and in patients. Further, there was a significant epistatic interaction between a putatively functional coding SNP in AKT1 (rs1130233) and COMT Val108/158Met genotype on AKT1 phosphorylation. NRG1 induced translocation of AKT1 to the plasma membrane also was impaired in Val carriers, while PIP(3) levels were not decreased. Interestingly
The most noticeable finding of the present study based on a highly heterotic cross is the prevalence and importance of epistasis in the rice genome with two pronounced features. First, two-locus analyses resolved much larger numbers of loci contributing to trait expression than single-locus analyses. For example, counting only the interactions simultaneously detected in both years for grain number per panicle, the significant two-locus interactions involved a total of 25 loci located on 9 of the 12 rice chromosomes, compared with 5 and 7 QTLs detected in the two years for this trait. As a second feature, all three types of interactions (AA, AD, and DD) occurred among the various two-locus combinations. It is even more remarkable that multiple interaction terms were found in a considerable proportion of the interacting two-locus combinations in all traits examined (Tables 4 and 5).. Overdominance at the single-locus level was detected at many of the QTLs. More QTLs showed overdominance for yield ...
Gauge your understanding of genetic epistasis with an interactive quiz and printable worksheet. The questions can be used as a pretest, study...
It is becoming increasingly evident that single-locus effects cannot explain complex multifactorial human diseases like cancer. We applied the multi-factor dimensionality reduction (MDR) method to a large cohort study on gene-environment and gene-gene interactions. The study (case-control nested in the EPIC cohort) was established to investigate molecular changes and genetic susceptibility in relation to air pollution and environmental tobacco smoke (ETS) in non-smokers. We have analyzed 757 controls and 409 cases with bladder cancer (n = 124), lung cancer (n = 116) and myeloid leukemia (n = 169). Thirty-six gene variants (DNA repair and metabolic genes) and three environmental exposure variables (measures of air pollution and ETS at home and at work) were analyzed. Interactions were assessed by prediction error percentage and cross-validation consistency (CVC) frequency. For lung cancer, the best model was given by a significant gene-environment association between the base excision repair (BER) XRCC1
Heck, Angela; Vogler, Christian; Gschwind, Leo; Ackermann, Sandra; Auschra, Bianca; Spalek, Klara; Rasch, Björn; de Quervain, Dominique; Papassotiropoulos, Andreas (2011). Statistical epistasis and functional brain imaging support a role of voltage-gated potassium channels in human memory. PLoS ONE, 6(12), e29337. Iijima, Takatoshi; Wu, Karen; Witte, Harald; Hanno-Iijima, Yoko; Glatter, Timo; Richard, Stéphane; Scheiffele, Peter (2011). SAM68 regulates neuronal activity-dependent alternative splicing of Neurexin-1. Cell, 147(7), 1601-14. Schötz, Thomas; Neher, Richard A.; Gerland, Ulrich (2011). Target search on a dynamic DNA molecule. Physical Review E, 84(5 Pt 1), 051911. Loewith, R.; Hall, M. N. (2011). Target of Rapamycin (TOR) in nutrient signaling and growth control. Genetics, 189(4), 1177-1201. Suffert, Guillaume; Malterer, Georg; Hausser, Jean; Viiliäinen, Johanna; Fender, Aurélie; Contrant, Maud; Ivacevic, Tomi; Benes, Vladimir; Gros, Frédéric; Voinnet, Olivier; Zavolan, Mihaela; ...
CiteSeerX - Scientific articles matching the query: eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study
The 3 loci that have been associated with CAC thus far (CDKN2A/B at 9p21, PHACTR1, and ADAMTS7) were first discovered for their association with CAD and MI. This observation motivated us to test the hypothesis that other loci associated with CAD/MI might also influence CAC. Because single-variant association testing has limited power to detect modest effects, we adopted a polygenic approach by aggregating the effects of ≤15 000 independent SNPs into a single genetic risk score and then tested each for association with CAC. The polygenic score is based on the assumption that markers act additively; that is, gene-gene interactions (epistasis) are ignored in these models. Our results demonstrate that there is at least a polygenic component with alleles acting additively, and its quantitative contribution may represent a lower bound estimate if we assume a non-negligible contribution because of epistatic effects.. In our data, the 3 known CAC SNP associations found by recent GWAS explained 2.4% of ...
Epistasis is defined as a non‐additive genetic interaction, where the interaction may be described as transgressive if the hybrid progeny is in some way either superior to the better or inferior to the worse parent. Transgression has been previously suggested to facilitate hybrid niche specialization and is particularly important in crop breeding (i.e., when hybrid yields are higher than those of either parent).. sRNAs play an important role in gene and genome regulation. MicroRNAs (miRNAs) and trans‐acting small interfering RNAs (tasiRNAs) regulate coding transcript levels, while small interfering RNAs (siRNAs) guide DNA methylation and stable chromatin modifications predominantly at TEs and other repeat sequences. These epigenetic marks keep TEs repressed, thereby limiting potentially detrimental transposition events. Epigenetic and sRNA differences between and within species are relatively poorly described compared with genetic and transcriptome variation. Nonetheless, since genomic ...
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