Search

Genome-wide association studies (GWAS) have been a powerful tool for exploring potential relationships between single-nucleotide polymorphisms (SNPs) and biological traits. For screening out important genetic variants, it is desired to perform an exhaustive scan over a whole genome. However, this is usually a challenging and daunting task in computation, due mainly to the large number of SNPs in GWAS. In this article, we propose a computationally effective algorithm for highly homozygous genomes. Pseudo standard error (PSE) is known to be a highly efficient and robust estimator for the standard deviation of a quantitative trait. We thus develop a statistical testing procedure for determining significant SNP main effects and SNP × SNP interactions associated with a quantitative trait based on PSE. A simulation study is first conducted to evaluate its empirical size and power. It is shown that the proposed PSE-based method can generally maintain the empirical size sufficiently close to the nominal significance level. However, the power investigation indicates that the PSE-based method might lack power in identifying significant effects for low-frequency variants if their true effect sizes are not large enough. A software is provided for implementing the proposed algorithm and its computational efficiency is evaluated through another simulation study. An exhaustive scan is usually done within a very reasonable runtime and a rice genome data set is analyzed by the software. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity, and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty can be problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here, we report an advanced recombinant inbred line (RIL) quantitative trait locus mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs at present, was created by hybridization of 16 wild isolates, experimental evolution for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of singlenucleotide polymorphisms known to segregate in natural populations, and complements existing C. elegans mapping resources by providing fine resolution and high nucleotide diversity across > 95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad-sense heritability in the CeMEE. While simulations show that we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits do not feature such alleles. We instead find that a significant fraction of trait variance, approaching 40% for fertility, can be explained by sign epistasis with main effects below the detection limit. In congruence, phenotype prediction from genomic similarity, while generally poor (r² <10%), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms. [ABSTRACT FROM AUTHOR]

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene– gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders. [ABSTRACT FROM AUTHOR]

Plant architecture is important for crape myrtle ( Lagerstroemia indica ) cultivation and breeding. To investigate the genetic inheritance of plant architecture traits in Lagerstroemia , a mixed major gene plus polygene inheritance model was employed to analyze three traits in six generations (P 1 , P 2 , F 1 , B 1 , B 2 and F 2 ), including plant height (PH), internode length (IL) and primary lateral branch height (PLBH). The three traits showed extensive coefficient of variation and high broad sense heritability (0.60–0.84). Though transgressive segregations were present, transgressive heterosis was not observed in all the generations. The results showed that the IL fitted the D-0 model, which was followed by a one additive-dominance major gene plus additive-dominance-epistasis polygene genetic model. In addition, the PH and PLBH were quantitative traits fitting the E-0 model, namely two additive-dominance-epistasis major genes plus additive-dominance-epistasis polygene genetic model. The highest heritability of the major gene (83.62%) for the IL was observed in F 2 population, indicating that the IL trait was mainly controlled by genetic factors with a low effect of environmental factors and should be selected in early generations. However, the highest heritabilities of the major genes for the PH and PLBH were 43.23% and 70.29% in B 1 population, indicating that the PH and PLBH should be selected in late generations due to the large effect of environmental factors. Therefore, these findings will provide a theoretical instruction for the QTL mapping and breeding of desirable plant architecture in crape myrtle. [ABSTRACT FROM AUTHOR]

Background: Forkhead box O3A (FOXOA3) and apolipoprotein E (APOE) are arguably the strongest gene candidates to influence human exceptional longevity (EL, i.e., being a centenarian), but inconsistency exists among cohorts. Epistasis, defined as the effect of one locus being dependent on the presence of 'modifier genes', may contribute to explain the missing heritability of complex phenotypes such as EL. We assessed the potential association of epistasis among candidate polymorphisms related to physical capacity, as well as antioxidant defense and cardiometabolic traits, and EL in the Japanese population. A total of 1565 individuals were studied, subdivided into 822 middle-aged controls and 743 centenarians. Results: We found a FOXOA3 rs2802292 T-allele-dependent association of fibronectin type III domain-containing 5 (FDNC5) rs16835198 with EL: the frequency of carriers of the FOXOA3 rs2802292 T-allele among individuals with the rs16835198 GG genotype was significantly higher in cases than in controls (P < 0.05). On the other hand, among noncarriers of the APOE 'risk' ε4-allele, the frequency of the FDNC5 rs16835198 G-allele was higher in cases than in controls (48.4% vs. 43.6%, P < 0.05). Among carriers of the 'non-risk' APOE ε2-allele, the frequency of the rs16835198 G-allele was higher in cases than in controls (49% vs. 37.3%, P < 0.05). Conclusions: The association of FDNC5 rs16835198 with EL seems to depend on the presence of the FOXOA3 rs2802292 T-allele and we report a novel association between FNDC5 rs16835198 stratified by the presence of the APOE ε2/ε4-allele and EL. More research on 'gene*gene' and 'gene*environment' effects is needed in the field of EL. [ABSTRACT FROM AUTHOR]

Evolutionary prediction is of deep practical and philosophical importance. Here we show, using a simple computational protein model, that protein evolution remains unpredictable, even if one knows the effects of all mutations in an ancestral protein background. We performed a virtual deep mutational scan--revealing the individual and pairwise epistatic effects of every mutation to our model protein--and then used this information to predict evolutionary trajectories. Our predictions were poor. This is a consequence of statistical thermodynamics. Proteins exist as ensembles of similar conformations. The effect of a mutation depends on the relative probabilities of conformations in the ensemble, which in turn, depend on the exact amino acid sequence of the protein. Accumulating substitutions alter the relative probabilities of conformations, thereby changing the effects of future mutations. This manifests itself as subtle but pervasive high-order epistasis. Uncertainty in the effect of each mutation accumulates and undermines prediction. Because conformational ensembles are an inevitable feature of proteins, this is likely universal. [ABSTRACT FROM AUTHOR]

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene-gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated. [ABSTRACT FROM AUTHOR]

Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions - an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution. [ABSTRACT FROM AUTHOR]

Interferon-γ-induced aminopeptidase ERAP1 trims peptides within the endoplasmic reticulum so that they can be loaded onto MHC class I and presented to the CD8 + T-cells. ERAP1 association and its interaction with HLA-C∗06 is controversial across different populations. We have investigated the association and possible functional role of non-synonymous SNPs at different exons of ERAP1 (rs26653: Arg127Pro, rs30187: Lys528Arg and rs27044: Gln730Glu) and their interactions with HLA-C∗06 in psoriasis. Significant associations of HLA-C∗06 (OR = 5.47, P < 2.2 × 10 −16 ), rs30187 (OR 1.35, P = 7.4 × 10 −4 ) and rs27044 (OR = 1.24, P = 5.8 × 10 −3 ) were observed. All three ERAP1 SNPs showed significant association only for HLA-C∗06 positive patients, while rs30187 and rs27044 showed significant association only for early onset patients (rs30187: OR = 1.47, P = 9.6 × 10 −5 ; rs27044: OR = 1.36, P = 3.3 × 10 −4 ). No differential expression of ERAP1 was observed either between paired uninvolved and involved skin tissues of psoriasis patients or between non-risk and risk variants in the involved skin. Significant epistatic interaction was observed between HLA-C∗06 and the SNP (rs27044) located at the peptide-binding cavity of ERAP1. Evolutionary conservation analysis among mammals showed confinement of Lys528 and Gln730 within highly conserved regions of ERAP1 and suggested the possible detrimental effect of this allele in ERAP1 regulation. [ABSTRACT FROM AUTHOR]

A mutation-accumulation (MA) experiment with Caenorhabditis elegans nematodes was conducted in which replicate, independently evolving lines were initiated from a low-fitness mitochondrial electron transport chain mutant, gas-1. The original intent of the study was to assess the effect of electron transport chain dysfunction involving elevated reactive oxygen species production on patterns of spontaneous germline mutation. In contrast to results of standard MA experiments, gas-1 MA lines evolved slightly higher mean fitness alongside reduced among-line genetic variance compared with their ancestor. Likewise, the gas-1 MA lines experienced partial recovery to wildtype reactive oxygen species levels. Whole-genome sequencing and analysis revealed that the molecular spectrum but not the overall rate of nuclearDNAmutation differed from wildtype patterns. Further analysis revealed an enrichment of mutations in loci that occur in a gas-1-centric region of the C. elegans interactome, and could be classified into a small number of functional-genomic categories. Characterization of a backcrossed four-mutation set isolated from one gas-1 MA line revealed this combination to be beneficial on both gas-1 mutant and wildtype genetic backgrounds. Our combined results suggest that selection favoring beneficial mutations can be powerful even under unfavorable population genetic conditions, and agree with fitness landscape theory predicting an inverse relationship between population fitness and the likelihood of adaptation. [ABSTRACT FROM AUTHOR]

Lesch-Nyhan disease (LND) is a rare X-linked inherited neurogenetic disorder of purine metabolism in which the enzyme, hypoxanthine-guanine phosphoribosyltransferase (HGprt) is defective. The authors report a novel point mutation that led to HGprt-related neurological dysfunction (HND) in a family in which there was a missense mutation in exon 6 of the coding region of theHPRT1gene: g.34938G>T, c.403G>T, p.D135Y. Molecular diagnosis is consistent with the genetic heterogeneity of theHPRT1gene responsible for HGprt deficiency. It allows fast, accurate carrier detection and genetic counseling. [ABSTRACT FROM AUTHOR]

Background: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles. Results: This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach. Conclusions: In this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is a leading cause of worldwide cancer death. It has become clear that microRNAs (miRNAs) play a role in the progress of colon cancer and understanding the effect of miRNAs on tumorigenesis could lead to better prognosis and improved treatment. However, most studies have focused on studying differentially expressed miRNAs between tumor and non-tumor samples or between stages in tumor tissue. Limited work has conducted to study the interactions or epistasis between miRNAs and how the epistasis brings about effect on tumor progression. In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas. Results: We develop a workflow composed of multiple steps for feature selection based on the Empirical Bayesian Elastic Net (EBEN) method. First, we identify the main effects using a model with only main effect on the phenotype. Second, a corrected phenotype is calculated by removing the significant main effect from the original phenotype. Third, we select features with epistatic effect on the corrected phenotype. Finally, we run the full model with main and epistatic effects on the previously selected main and epistatic features. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer. Many of miRNAs with main effect on colon cancer have been previously reported to be associated with colon cancer, and the majority of the epistatic miRNAs share common target genes that could explain their epistasis effect on the pathological stages of colon cancer. We also find many of the target genes of detected miRNAs are associated with colon cancer. Go Ontology Enrichment Analysis of the experimentally validates targets of main and epistatic miRNAs, shows that these target genes are enriched for biological processes associated with cancer progression. Conclusion: Our results provide a set of candidate miRNAs associated with colon cancer progression that could have potential translational and therapeutic utility. Our analysis workflow offers a new opportunity to efficiently explore epistatic interactions among genetic and epigenetic factors that could be associated with human diseases. Furthermore, our workflow is flexible and can be applied to analyze the main and epistatic effect of various genetic and epigenetic factors on a wide range of phenotypes. [ABSTRACT FROM AUTHOR]

The resistance of soybean ( Glycine max L. Merr.) cultivars varies with the different races of the soybean cyst nematode ( SCN), Heterodera glycines, referred to as HG types (biotypes). Resistant cultivars with durable resistance are emphasized in recent years. The aim here was to identify quantitative trait loci ( QTLs) for resistance to two SCN HG types ( HG type 2.5.7, race 1; and HG type 1.2.3.5.7, race 4) in resistant cultivar 'L-10' and to analyse the additive and epistatic effects of the identified QTLs. A total of 140 F5-derived F10 recombinant inbred lines (F5:10 RILs) were advanced via single-seed-descent from the cross between 'L-10' (broadly resistant to SCN) and 'Heinong 37' ( SCN-susceptible). For SCN HG type 2.5.7 and HG type 1.2.3.5.7 resistance, three and six QTLs for resistance to SCN HG type 2.5.7 and HG type 1.2.3.5.7 were identified, respectively, most of which could explain <10% of the phenotypic variation. Among these QTLs, five were identified over 2 years, while the other QTLs were detected in either 2009 or 2010. QSCN1-2, located near the SSR marker Sat_069 of linkage group D1b (Chromosome, 2), was responsible for the largest proportion of phenotypic variation (16.01% in 2009 and 18.94% in 2010), suggested that it could effectively be used as a candidate QTL for the marker-assisted selection ( MAS) of soybean lines resistant to SCN. Additionally, for SCN HG type 2.5.7 and HG type 1.2.3.5.7 resistance, two and four QTLs showed an additive effect ( a), respectively. One epistatic pair of QTLs ( QSCN1-1- QSCN1-3) for SCN HG type 2.5.7 resistance and eight epistatic pairs of QTLs for SCN HG type 1.2.3.5.7 resistance were found to have significant aa effects, among which one pair of QTLs ( QSCN4-4 and QSCN4-5) contributed a large proportion of aa effects (3%). The results indicated that additive and epistatic effects could significantly affect SCN resistance. Therefore, both of a and aa effects should be considered in MAS programmes. [ABSTRACT FROM AUTHOR]

Systems biology presents an integrated view of biological systems, focusing on the relations between elements, whether functional or evolutionary, and providing a rich framework for the comprehension of life. At the same time, many low-throughput experimental studies are performed without influence from this integrated view, whilst high-throughput experiments use low-throughput results in their validation and interpretation. We propose an inversion in this logic, and ask which benefits could be obtained from a holistic view coming from high-throughput studies--and systems biology in particular--in interpreting and designing low-throughput experiments. By exploring some key examples from the renal and adrenal physiology, we try to show that network and modularity theory, along with observed patterns of association between elements in a biological system, can have profound effects on our ability to draw meaningful conclusions from experiments. [ABSTRACT FROM AUTHOR]

Frequency-independent selection is generally considered as a force that acts to reduce the genetic variation in evolving populations, yet rigorous arguments for this idea are scarce. When selection fluctuates in time, it is unclear whether frequencyindependent selection may maintain genetic polymorphism without invoking additional mechanisms. We show that constant frequency-independent selection with arbitrary epistasis on a well-mixed haploid population eliminates genetic variation if we assume linkage equilibrium between alleles. To this end, we introduce the notion of frequency-independent selection at the level of alleles, which is sufficient to prove our claim and contains the notion of frequency-independent selection on haploids. When selection and recombination are weak but of the same order, there may be strong linkage disequilibrium; numerical calculations show that stable equilibria are highly unlikely. Using the example of a diallelic two-locus model, we then demonstrate that frequency-independent selection that fluctuates in time can maintain stable polymorphism if linkage disequilibrium changes its sign periodically. We put our findings in the context of results from the existing literature and point out those scenarios in which the possible role of frequencyindependent selection in maintaining genetic variation remains unclear. [ABSTRACT FROM AUTHOR]

The number one goal of most rice improvement programmes around the world is breeding for high potential to take care of the increasing consumption of rice. The objective of this experiment was to study the inheritance of yield and yield components and to estimate the heritabilities of important quantitative traits in rice (Oryza sativa L.). Six generations viz., P1, P2, F1, F2, BCP1 and BCP2 of a cross between IET6279 and IR70445-146-3-3 were used for the study. Generation mean analysis suggested that additive effects had a major role for the expression of plant height, number of panicles, number of spikelets per panicle, percentage spikelets fertility per plant and grain yield per plant, which further suggested that phenotypic selection was possible at an early stage. Both additive and non-additive gene actions were important for the expression of number of fertile spikelets per panicle and 100-grain weight. Therefore, selection for these characters would be fruitful, if delayed till epistasis effects are reduced to a minimum. High broad sense heritability estimates were observed for characters viz. plant height and 100-grain weight, suggesting that the characters under study are less influenced by environment in their expression. Thus, selection based on phenotypic value of these characters would be reliable and effective. Low estimates of broad sense heritability were observed for number of panicles per plant, number of spikelets per panicle, number of fertile spikelets per panicle, percentage spikelets fertility per plant and grain yield per plant, indicating that influence of the environment was high for these characters. Therefore, superior genotypes selection based on phenotypic performance for these characters may not effective. [ABSTRACT FROM AUTHOR]

Despite the efficacy of antibiotics to protect humankind against many deadly pathogens, such as Mycobacterium tuberculosis, nothing can prevent the emergence of drug-resistant strains. Several mechanisms facilitate drug resistance in M. tuberculosis including compensatory evolution, epistasis, clonal interference, cell wall integrity, efflux pumps, and target mimicry. In this study, we present recent findings relevant to these mechanisms, which can enable the discovery of new drug targets and subsequent development of novel drugs for treatment of drug-resistant M. tuberculosis. [ABSTRACT FROM AUTHOR]

The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation. [ABSTRACT FROM AUTHOR]

Motivation: The existing epistasis analysis approaches have been criticized mainly for their: (i) ignoring heterogeneity during epistasis analysis; (ii) high computational costs; and (iii) volatility of performances and results. Therefore, they will not perform well in general, leading to lack of reproducibility and low power in complex disease association studies. In this work, a fast scheme is proposed to accelerate exhaustive searching based on multi-objective optimization named ESMO for concurrently analyzing heterogeneity and epistasis phenomena. In ESMO, mutual entropy and Bayesian network approaches are combined for evaluating epistatic SNP combinations. In order to be compatible with heterogeneity of complex diseases, we designed an adaptive framework based on non-dominant sort and top k selection algorithm with improved time complexity O(k*M*N). Moreover, ESMO is accelerated by strategies such as trading space for time, calculation sharing and parallel computing. Finally, ESMO is nonparametric and model-free. Results:We compared ESMO with other recent or classic methods using different evaluating measures. The experimental results show that our method not only can quickly handle epistasis, but also can effectively detect heterogeneity of complex population structures. [ABSTRACT FROM AUTHOR]

Evo-devo is a theory proposed to study how phenotypes evolve by comparing the developmental processes of different organisms or the same organism experiencing changing environments. It has been recognized that nonallelic interactions at different genes or quantitative trait loci, known as epistasis, may play a pivotal role in the evolution of development, but it has proven difficult to quantify and elucidate this role into a coherent picture. We implement a high-dimensional genomewide association study model into the evo-devo paradigm and pack it into the R-based Evo-Devo-EpiR, aimed at facilitating the genome-wide landscaping of epistasis for the diversification of phenotypic development. By analyzing a highthroughput assay of DNA markers and their pairs simultaneously, Evo-Devo-EpiR is equipped with a capacity to systematically characterize various epistatic interactions that impact on the pattern and timing of development and its evolution. Enabling a global search for all possible genetic interactions for developmental processes throughout the whole genome, Evo-Devo-EpiR provides a computational tool to illustrate a precise genotype-phenotype map at interface between epistasis, development and evolution. [ABSTRACT FROM AUTHOR]

The discovery of higher-order epistatic interactions is an important task in the field of genome wide association studies which allows for the identification of complex interaction patterns between multiple genetic markers. Some existing bruteforce approaches explore the whole space of k-interactions in an exhaustive manner resulting in almost intractable execution times. Computational cost can be reduced drastically by restricting the search space with suitable preprocessing filters which prune unpromising candidates. Other approaches mitigate the execution time by employing massively parallel accelerators in order to benefit from the vast computational resources of these architectures. In this paper, we combine a novel preprocessing filter, namely SingleMI, with massively parallel computation on modern GPUs to further accelerate epistasis discovery. Our implementation improves both the runtime and accuracy when compared to a previous GPU counterpart that employs mutual information clustering for prefiltering. SingleMI is open source software and publicly available at: . [ABSTRACT FROM AUTHOR]

The genetics of yield and its contributing traits was studied through generation mean analysis by using ten inbreds, their 45 F1 crosses by following diallel mating design, their corresponding F2, BC1 and BC2 progenies. The scaling test of A, B, C were significant indicating role of epistatic effects. In general, the magnitude of dominance effect was higher than additive effect indicating that yield and its components are largely governed by dominance effect. A breeding procedure which exploit both additive and dominance components, such as family selection with intermating, will be suitable for improvement of yield and its traits. The epistasis was complementary and duplicate type in the crosses for grain yield and its contributing traits. [ABSTRACT FROM AUTHOR]

Key message : Complementing genomic data with other 'omics' predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Abstract: Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream 'omics' data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of 'omics' data. Here, we evaluate several 'omics' predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream 'omics' data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding. [ABSTRACT FROM AUTHOR]

Understanding genome and gene function in a whole organism requires us to fully comprehend the life cycle and the physiology of the organism in question. Caenorhabditis elegans XX animals are hermaphrodites that exhaust their sperm after 3 d of egg-laying. Even though C. elegans can live for many days after cessation of egg-laying, the molecular physiology of this state has not been as intensely studied as other parts of the life cycle, despite documented changes in behavior and metabolism. To study the effects of sperm depletion and aging of C. elegans during the first 6 d of adulthood, we measured the transcriptomes of first-day adult hermaphrodites and sixth-day sperm-depleted adults, and, at the same time points, mutant fog-2(lf) worms that have a feminized germline phenotype. We found that we could separate the effects of biological aging from sperm depletion. For a large subset of genes, young adult fog-2(lf) animals had the same gene expression changes as sperm-depleted sixth-day wild-type hermaphrodites, and these genes did not change expression when fog-2(lf) females reached the sixth day of adulthood. Taken together, this indicates that changing sperm status causes a change in the internal state of the worm, which we call the female-like state. Our data provide a high-quality picture of the changes that happen in global gene expression throughout the period of early aging in the worm. [ABSTRACT FROM AUTHOR]

Epistasis and genetic variance heterogeneity are two non-additive genetic inheritance patterns that are often, but not always, related. Here we use theoretical examples and empirical results from earlier analyses of experimental data to illustrate the connection between the two. This includes an introduction to the relationship between epistatic gene action, statistical epistasis, and genetic variance heterogeneity, and a brief discussion about how genetic processes other than epistasis can also give rise to genetic variance heterogeneity. [ABSTRACT FROM AUTHOR]

Background: The effect of epistasis on response to selection is a highly debated topic. Here, we investigated the impact of epistasis on response to sequence-based selection via genomic best linear prediction (GBLUP) in a regime of strong non-symmetrical epistasis under divergent selection, using real Drosophila sequence data. We also explored the possible advantage of including epistasis in the evaluation model and/or of knowing the causal mutations. Results: Response to selection was almost exclusively due to changes in allele frequency at a few loci with a large effect. Response was highly asymmetric (about four phenotypic standard deviations higher for upward than downward selection) due to the highly skewed site frequency spectrum. Epistasis accentuated this asymmetry and affected response to selection by modulating the additive genetic variance, which was sustained for longer under upward selection whereas it eroded rapidly under downward selection. Response to selection was quite insensitive to the evaluation model, especially under an additive scenario. Nevertheless, including epistasis in the model when there was none eventually led to lower accuracies as selection proceeded. Accounting for epistasis in the model, if it existed, was beneficial but only in the medium term. There was not much gain in response if causal mutations were known, compared to using sequence data, which is likely due to strong linkage disequilibrium, high heritability and availability of phenotypes on candidates. Conclusions: Epistatic interactions affect the response to genomic selection by modulating the additive genetic variance used for selection. Epistasis releases additive variance that may increase response to selection compared to a pure additive genetic action. Furthermore, genomic evaluation models and, in particular, GBLUP are robust, i.e. adding complexity to the model did not modify substantially the response (for a given architecture). [ABSTRACT FROM AUTHOR]

Motivation: Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in largescale SNP datasets. Approach: We propose a new algorithm which combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR), termed DECMDR. DECMDR uses the CMDR as a fitness measure to evaluate values of solutions in DE process for scanning the potential statistical epistasis in GWAS. Results: The results indicated that DECMDR outperforms the existing algorithms in terms of detection success rate by the large simulation and real data obtained from the Wellcome Trust Case Control Consortium. For running time comparison, DECMDR can efficient to apply the CMDR to detect the significant association between cases and controls amongst all possible SNP combinations in GWAS. [ABSTRACT FROM AUTHOR]

Adaptive diversification is an essential evolutionary process, one that produces phenotypic innovations including the colonization of available ecological niches. Bacteria can diverge in sympatry when ecological opportunities allow, but the underlying genetic mechanisms are often unknown. Perhaps, the longest-lasting adaptive diversification seen in the laboratory occurred during the long-term evolution experiment, in which 12 populations of Escherichia coli have been evolving independently for more than 65,000 generations from a common ancestor. In one population, two lineages, S and L, emerged at ~6500 generations and have dynamically coexisted ever since by negative frequency-dependent interactions mediated, in part, by acetate secretion by L. Mutations in spoT, arcA, and gntR promoted the emergence of the S lineage, although they reproduced only partially its phenotypic traits. Here, we characterize the evolved mechanism of acetate consumption by the S lineage that enabled invasion and coexistence with the L lineage. We identified an additional mutation in acs that, together with the arcA mutation, drove an early restructuring of the transcriptional control of central metabolism in S, leading to improved acetate consumption. Pervasive epistatic interactions within the S genome contributed to the exploitation of this new ecological opportunity. The emergence and maintenance of this long-term polymorphism is a complex multi-step process. [ABSTRACT FROM AUTHOR]

Despite the growing number of studies showing that genotype × environment and epistatic interactions control fitness, the influences of epistasis × environment interactions on adaptive trait evolution remain largely uncharacterized., Across three field trials, we quantified aliphatic glucosinolate ( GSL) defense chemistry, leaf damage, and relative fitness using mutant lines of Arabidopsis thaliana varying at pairs of causal aliphatic GSL defense genes to test the impact of epistatic and epistasis × environment interactions on adaptive trait variation., We found that aliphatic GSL accumulation was primarily influenced by additive and epistatic genetic variation, leaf damage was primarily influenced by environmental variation and relative fitness was primarily influenced by epistasis and epistasis × environment interactions. Epistasis × environment interactions accounted for up to 48% of the relative fitness variation in the field. At a single field site, the impact of epistasis on relative fitness varied significantly over 2 yr, showing that epistasis × environment interactions within a location can be temporally dynamic., These results suggest that the environmental dependency of epistasis can profoundly influence the response to selection, shaping the adaptive trajectories of natural populations in complex ways, and deserves further consideration in future evolutionary studies. [ABSTRACT FROM AUTHOR]

Beneficial mutations are the driving force of adaptive evolution. In asexual populations, the identification of beneficial alleles is confounded by the presence of genetically linked hitchhiker mutations. Parallel evolution experiments enable the recognition of common targets of selection; yet these targets are inherently enriched for genes of large target size and mutations of large effect. A comprehensive study of individual mutations is necessary to create a realistic picture of the evolutionarily significant spectrum of beneficial mutations. Here we use a bulk-segregant approach to identify the beneficial mutations across 11 lineages of experimentally evolved yeast populations. We report that nearly 80% of detected mutations have no discernible effects on fitness and less than 1% are deleterious. We determine the distribution of driver and hitchhiker mutations in 31 mutational cohorts, groups of mutations that arise synchronously from low frequency and track tightly with one another. Surprisingly, we find that one-third of cohorts lack identifiable driver mutations. In addition, we identify intracohort synergistic epistasis between alleles of hsl7 and kel1, which arose together in a low-frequency lineage. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Plant Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects—the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the “MArginal ePIstasis Test”, or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium. [ABSTRACT FROM AUTHOR]

Background: Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing. Results: In this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis. Conclusions: Experiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The experiment comprised of six F1's, which were selected based on the combinations of low yield × high yield and low ASI × high ASI. The selected six F1's were crossed in full diallel mating design to synthesise 16 double crosses was estimated and validated on the basis of performance of their constituent single crosses. Out of 16 hybrids, DCH 15 recorded lower tasseling and silking days and hence it can be used further in breeding programme. While, DCH 13 recorded narrow mean for ASI are suggested for use in deriving superior inbred lines with a combination of narrow ASI and higher ear circumference and kernel rows ear-1.The expected yield performance of DCH 5 (125.88), DCH 8 (124.23), DCH 10 (123.96), DCH 15 (123.73), DCH 6 (122.77) and DCH 12 (120.76) out yielded best check, Hema (119.00) for grain yield plant-1respectively. DCH 12 predicted better for most of the characters viz., ASI, ear length, ear circumference, kernel rows-1 and 100 grain weight. While, DCH 15 predicted better for characters viz., days to tasseling, days to silking, plant height (cm), ear height (cm), kernels row-1 and shelling percentage. Therefore, these two crosses may be used as double cross hybrids for breeding programme. In both prediction methods, nearly 14 out of 16 double cross hybrids manifested significant differences between realised and predicted performance suggesting involvement of epistasis in genetic control of grain yield plant-1. [ABSTRACT FROM AUTHOR]

Many mutations have deleterious phenotypic effects that can be alleviated by suppressor mutations elsewhere in the genome. High-throughput approaches have facilitated the large-scale identification of these suppressors and have helped shed light on core functional mechanisms that give rise to suppression. Following reports that suppression occurs naturally within species, it is important to determine how our understanding of this phenomenon based on lab experiments extends to genetically diverse natural populations. Although suppression is typically mediated by individual genetic changes in lab experiments, recent studies have shown that suppression in natural populations can involve combinations of genetic variants. This difference in complexity suggests that sets of variants can exhibit similar functional effects to individual suppressors found in lab experiments. In this review, we discuss how characterizing the way in which these variants jointly lead to suppression could provide important insights into the genotype-phenotype map that are relevant to evolution and health. [ABSTRACT FROM AUTHOR]

Gupta et al., in their article in this issue ('Niche construction in evolutionary theory: the construction of an academic niche?'. doi:), lament 'serious problems with the way science is being done' and suggest that 'niche construction theory exemplifies this state of affairs.' However, their aggressively confrontational but superficial critique of niche construction theory (NCT) only contributes to these problems by attacking claims that NCT does not make. This is unfortunate, as their poor scholarship has done a disservice to the evolutionary biology community through propagating misinformation. We correct Gupta et al.'s misunderstandings, stressing that NCT does not suggest that the fact that organisms engage in niche construction is neglected, nor does it make strong claims on the basis of its formal theory. Moreover, the treatment of niche construction as an evolutionary process has been highly productive, and is both theoretically and empirically well-validated. We end by reflecting on the potentially deleterious implications of their publication for evolutionary science. [ABSTRACT FROM AUTHOR]

For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the "missing heritability" of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson's disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts. [ABSTRACT FROM AUTHOR]

Recognition and rejection of heterospecific male gametes occurs in a broad range of taxa, although the complexity of mechanisms underlying these components of postmating cryptic female choice is poorly understood. In plants, the arena for postmating interactions is the female reproductive tract (pistil), within which heterospecific pollen tube growth can be arrested via active molecular recognition and rejection. Unilateral incompatibility (UI) is one such postmating barrier in which pollen arrest occurs in only one direction of an interspecific cross. We investigated the genetic basis of pistil-side UI between Solanum species, with the specific goal of understanding the role and magnitude of epistasis between UI QTL. Using heterospecific introgression lines (ILs) between Solanum pennellii and S. lycopersicum, we assessed the individual and pairwise effects of three chromosomal regions (ui1.1, ui3.1, and ui12.1) previously associated with interspecific UI among Solanum species. Specifically, we generated double introgression ('pyramided') genotypes that combined ui12.1 with each of ui1.1 and ui3.1, and assessed the strength of UI pollen rejection in the pyramided lines, compared to single introgression genotypes. We found that none of the three QTL individually showed UI rejection phenotypes, but lines combining ui3.1 and ui12.1 showed significant pistil-side pollen rejection. Furthermore, double ILs (DILs) that combined different chromosomal regions overlapping ui3.1 differed significantly in their rate of UI, consistent with at least two genetic factors on chromosome three contributing quantitatively to interspecific pollen rejection. Together, our data indicate that loci on both chromosomes 3 and 12 are jointly required for the expression of UI between S. pennellii and S. lycopersicum, suggesting that coordinated molecular interactions among a relatively few loci underlie the expression of this postmating prezygotic barrier. In addition, in conjunction with previous data, at least one of these loci appears to also contribute to conspecific self-incompatibility (SI), consistent with a partially shared genetic basis between inter- and intraspecific mechanisms of postmating prezygotic female choice. [ABSTRACT FROM AUTHOR]

A low-activity variant of endoplasmic reticulum aminopeptidase 1 (ERAP1), Hap10, is associated with the autoinflammatory disorder Behçet's disease (BD) in epistasis with HLA-B*51, which is the main risk factor for this disorder. The role of Hap10 in BD pathogenesis is unknown. We sought to define the effects of Hap10 on the HLA-B*51 peptidome and to distinguish these effects from those due to HLA-B*51 polymorphisms unrelated to disease. The peptidome of the BD-associated HLA-B*51:08 subtype expressed in a Hap10-positive cell line was isolated, characterized by mass spectrometry, and compared with the HLA-B*51:01 peptidome from cells expressing more active ERAP1 allotypes. We additionally performed synthetic peptide digestions with recombinant ERAP1 variants and estimated peptide-binding affinity with standard algorithms. In the BD-associated ERAP1 context of B*51:08, longer peptides were generated; of the two major HLA-B*51 subpeptidomes with Pro-2 and Ala-2, the former one was significantly reduced, and the latter was increased and showed more ERAP1-susceptible N-terminal residues. These effects were readily explained by the low activity of Hap10 and the differential susceptibility of X-Pro and X-Ala bonds to ERAP1 trimming and together resulted in a significantly altered peptidome with lower affinity. The differences due to ERAP1 were clearly distinguished from those due to HLA-B*51 subtype polymorphism, which affected residue frequencies at internal positions of the peptide ligands. The alterations in the nature and affinity of HLA-B*51·peptide complexes probably affect T-cell and natural killer cell recognition, providing a sound basis for the joint association of ERAP1 and HLA-B*51 with BD. [ABSTRACT FROM AUTHOR]

Exploration of multi-locus evolution is critically important for understanding evolutionary dynamics. Recombination and epistasis lead to complex evolutionary dynamics. Quantifying the stability and function of such multi-locus evolutionary systems globally is a long-standing challenge for evolutionary biologists. The conventional Wright, Fisher and quasi-linkage equilibrium (QLE) theories can only be applied to highly restricted, simplified and special evolutionary scenarios. In this study, we developed a non-equilibrium potential and flux landscape theory to explore the multi-locus evolution beyond Wright, Fisher and the QLE. We found that the intrinsic potential landscape as a Lyapunov function under the zero noise limit can be used to describe the global stability of a deterministic multi-locus system. We identified and quantified two driving forces responsible for multi-locus evolution: the underlying landscape and the curl flux. We studied the evolution of different cases under recombination, epistasis and mutation. Recombination, frequency-dependent selection and mutation can give rise to non-zero curl flux. In particular, we investigated the dynamics of a simple example of a two-locus system. We explored the underlying potential landscape which shows stable basins attracting the system down to valleys and rotating curl flux, which aids the global communication under recombination, epistasis and mutation. Discontinuities in the first derivative of the non-equilibrium free energy functional shows the non-equilibrium phase transition as the recombination rate increases. We also explored the effect of epistasis on mono-stability and bi-stability evolution. Mutation may drive the system far from equilibrium and be another source of non-zero probability flux. Entropy production rate can quantify energy consumption or dissipation. We explained the origin of the Red Queen hypothesis for endless evolution using the curl flux. Our landscape and flux framework can be applied more generally to multi-locus evolutionary systems experiencing recombination and epistasis. [ABSTRACT FROM AUTHOR]

The role of epistasis in understanding the genetic architecture and variation of quantitative traits and its role, if any, in artificial selection and livestock improvement more generally has a long and sometimes controversial history. Its presence has been clearly demonstrated in, for example, laboratory experiments, but the amount of variation it contributes is likely to be small in outbred populations. In a finite population, although additive x additive epistatic variance is lost by genetic drift, it also contributes by conversion to additive variance through drift sampling and therefore has a potential indirect role in medium and long-term selection response, with superficial similarity to and hard to distinguish from mutation. Whilst predictions of response require knowledge of genetic parameters, an infinitesimal model provides some analytic results. Otherwise there is little quantitative information relevant to animal populations on which to judge this potential role of epistasis and reach firm conclusions. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis Beijing genotype strains increasingly circulate in different world regions, either as historical endemic, e.g. in East Asia, or recently imported, e.g. in South America, and this family is regarded as the most successful lineage of the global tuberculosis (TB) epidemic. Here we analysed the transmission capacity of these strains in the context of their phylogenetic background and drug resistance mutations. The study collection included all multidrug resistant (MDR) strains of Beijing genotype isolated in Beijing Chest Hospital, the largest tertiary TB facility in North China, in 2011–2013 ( n  = 278). Strains were subjected to NTF/IS 6110 and 24-loci MIRU-VNTR analysis. Drug resistance mutations were detected in rpoB , katG , inhA and oxyR–ahpC . A total of 58 and 220 strains were assigned to the ancient and modern Beijing sublineages, respectively. 24-MIRU-VNTR clustering was higher in modern versus ancient Beijing strains (35.9% vs. 12.1%; P  <   0.001). After taking into consideration the presence of rpoB and katG mutations, clustering decreased to 15.9% in modern and 0% in ancient strains. The most frequent combination of mutations ( rpoB 531-TTG and katG 315-ACC) was more prevalent in clustered versus non-clustered isolates in the modern sublineage (23/35 vs. 47/185; P  <   0.0001). To conclude, a combination of the known low-fitness-cost rpoB 531-TTG and katG 315-ACC mutations likely facilitates the increased transmission ability of MDR strains of the modern but not ancient Beijing sublineage. Accordingly, positive epistasis of major low-cost drug resistance-conferring mutations is influenced by the phylogenetic background of M. tuberculosis strains. [ABSTRACT FROM AUTHOR]

Background: Aspirin Exacerbated Respiratory Disease (AERD) is a chronic medical condition that encompasses asthma, nasal polyposis, and hypersensitivity to aspirin and other non-steroidal anti-inflammatory drugs. Several previous studies have shown that part of the genetic effects of the disease may be induced by the interaction of multiple genetic variants. However, heavy computational cost as well as the complexity of the underlying biological mechanism has prevented a thorough investigation of epistatic interactions and thus most previous studies have typically considered only a small number of genetic variants at a time. Methods: In this study, we propose a gene network based analysis framework to identify genetic risk factors from a genome-wide association study dataset. We first derive multiple single nucleotide polymorphisms (SNP)-based epistasis networks that consider marginal and epistatic effects by using different information theoretic measures. Each SNP epistasis network is converted into a gene-gene interaction network, and the resulting gene networks are combined as one for downstream analysis. The integrated network is validated on existing knowledgebase of DisGeNET for known gene-disease associations and GeneMANIA for biological function prediction. Results: We demonstrated our proposed method on a Korean GWAS dataset, which has genotype information of 440,094 SNPs for 188 cases and 247 controls. The topological properties of the generated networks are examined for scale-freeness, and we further performed various statistical analyses in the Allergy and Asthma Portal (AAP) using the selected genes from our integrated network. Conclusions: Our result reveals that there are several gene modules in the network that are of biological significance and have evidence for controlling susceptibility and being related to the treatment of AERD. [ABSTRACT FROM AUTHOR]

Background: Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. Methods: We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. Results: By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples. Conclusions: The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data. [ABSTRACT FROM AUTHOR]