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Epistasis is a ubiquitous phenomenon in genetics, and is considered to be one of main factors in current efforts to unveil missing heritability of complex diseases. Simulation data is crucial for evaluating epistasis detection tools in genome‐wide association studies (GWAS). Existing simulators normally suffer from two limitations: absence of support for high‐order epistasis models containing multiple single nucleotide polymorphisms (SNPs), and inability to generate simulation SNP data independently. In this study, we proposed a simulator SimHOEPI, which is capable of calculating penetrance tables of high‐order epistasis models depending on either prevalence or heritability, and uses a resampling strategy to generate simulation data independently. Highlights of SimHOEPI are the preservation of realistic minor allele frequencies in sampling data, the accurate calculation and embedding of high‐order epistasis models, and acceptable simulation time. A series of experiments were carried out to verify these properties from different aspects. Experimental results show that SimHOEPI can generate simulation SNP data independently with high‐order epistasis models, implying that it might be an alternative simulator for GWAS. [ABSTRACT FROM AUTHOR]

A simple agent‐based model is presented that produces results matching the experimental data found by Lenski's group for ≤50,000 generations of Escherichia coli bacteria under continuous selective pressure. Although various mathematical models have been devised previously to model the Lenski data, the present model has advantages in terms of overall simplicity and conceptual accessibility. The model also clearly illustrates a number of features of the evolutionary process that are otherwise not obvious, such as the roles of epistasis and historical contingency in adaptation and why evolution is time irreversible ('Dollo's law'). The reason for this irreversibility is that genomes become increasingly integrated or organized, and this organization becomes a novel selective factor itself, against which future generations must compete. Selection for integrated or synergistic networks, systems or sets of mutations or traits, not for individual mutations, confers the main adaptive advantage. The result is a punctuated form of evolution that follows a logarithmic occurrence probability, in which evolution proceeds very quickly when interactomes begin to form but which slows as interactomes become more robust and the difficulty of integrating new mutations increases. Sufficient parameters exist in the game to suggest not only how equilibrium or stasis is reached but also the conditions in which it will be punctuated, the factors governing the rate at which genomic organization occurs and novel traits appear, and how population size, genome size and gene variability affect these. [ABSTRACT FROM AUTHOR]

Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network has a nested multilayer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify noncoding variant pairs associated with pathogenic genes in diseases beyond genome-wide association studies analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases. [ABSTRACT FROM AUTHOR]

Epistasis can markedly affect evolutionary trajectories. In recent decades, protein-level fitness landscapes have revealed extensive idiosyncratic epistasis among specific mutations. By contrast, other work has found ubiquitous and apparently nonspecific patterns of global diminishing-returns and increasing-costs epistasis among mutations across the genome. Here, we used a hierarchical CRISPR gene drive system to construct all combinations of 10 missense mutations from across the genome in budding yeast and measured their fitness in six environments. We show that the resulting fitness landscapes exhibit global fitness-correlated trends but that these trends emerge from specific idiosyncratic interactions. We thus provide experimental validation of recent theoretical work arguing that fitness-correlated trends can emerge as the generic consequence of idiosyncratic epistasis. [ABSTRACT FROM AUTHOR]

Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at . [ABSTRACT FROM AUTHOR]

A plausible explanation for statistical epistasis revealed in genome wide association analyses is the presence of high order linkage disequilibrium (LD) between the genotyped markers tested for interactions and unobserved functional polymorphisms. Based on findings in experimental data, it has been suggested that high order LD might be a common explanation for statistical epistasis inferred between local polymorphisms in the same genomic region. Here, we empirically evaluate how prevalent high order LD is between local, as well as distal, polymorphisms in the genome. This could provide insights into whether we should account for this when interpreting results from genome wide scans for statistical epistasis. An extensive and strong genome wide high order LD was revealed between pairs of markers on the high density 250k SNP-chip and individual markers revealed by whole genome sequencing in the Arabidopsis thaliana 1001-genomes collection. The high order LD was found to be more prevalent in smaller populations, but present also in samples including several hundred individuals. An empirical example illustrates that high order LD might be an even greater challenge in cases when the genetic architecture is more complex than the common assumption of bi-allelic loci. The example shows how significant statistical epistasis is detected for a pair of markers in high order LD with a complex multi allelic locus. Overall, our study illustrates the importance of considering also other explanations than functional genetic interactions when genome wide statistical epistasis is detected, in particular when the results are obtained in small populations of inbred individuals. [ABSTRACT FROM AUTHOR]

Humans tend to form social relationships with others who resemble them. Whether this sorting of like with like arises from historical patterns of migration, meso-level social structures in modern society, or individual-level selection of similar peers remains unsettled. Recent research has evaluated the possibility that unobserved genotypes may play an important role in the creation of homophilous relationships. We extend this work by using data from 5,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarities among pairs of friends. Although there is some evidence that friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), further analysis suggests that meso-level forces, such as school assignment, are a principal source of genetic similarity between friends. We also observe apparent social- genetic effects in which polygenic scores of an individual's friends and schoolmates predict the individual's own educational attainment. In contrast, an individual's height is unassociated with the height genetics of peers. [ABSTRACT FROM AUTHOR]

The genetic basis of earlobe attachment has been a matter of debate since the early 20 th century, such that geneticists argue both for and against polygenic inheritance. Recent genetic studies have identified a few loci associated with the trait, but large-scale analyses are still lacking. Here, we performed a genome-wide association study of lobe attachment in a multiethnic sample of 74,660 individuals from four cohorts (three with the trait scored by an expert rater and one with the trait self-reported). Meta-analysis of the three expert-rater-scored cohorts revealed six associated loci harboring numerous candidate genes, including EDAR , SP5 , MRPS22 , ADGRG6 ( GPR126 ), KIAA1217 , and PAX9 . The large self-reported 23andMe cohort recapitulated each of these six loci. Moreover, meta-analysis across all four cohorts revealed a total of 49 significant (p < 5 × 10 −8 ) loci. Annotation and enrichment analyses of these 49 loci showed strong evidence of genes involved in ear development and syndromes with auricular phenotypes. RNA sequencing data from both human fetal ear and mouse second branchial arch tissue confirmed that genes located among associated loci showed evidence of expression. These results provide strong evidence for the polygenic nature of earlobe attachment and offer insights into the biological basis of normal and abnormal ear development. [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]

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]

The article focuses on the challenges regarding the use of genetic mapping to detect epistasis. It is noted that multiple testing is a common challenge in genome-wide scans for epistasis while statistical power is a related difficulty that genome-wide scans for epistasis face. It is mentioned that as inbred lines allows unambiguous cataloging of the variants, using them as the founders of crosses is desirable.

The article discusses how researcher Skwark along with colleagues harnessed population genomic data to approach the challenge of epistasis analysis .Topics discussed include developing a computational method termed genome direct coupling analysis (DCA) to detect epistasis using geno type and allele frequencies estimated from genome sequences of thousands of individuals of the same species.

Background: A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects. Results: In this paper we propose an original approach for detecting epistasis at the gene level, without systematically filtering on significant genes. We first compute interaction variables for each gene pair by finding its Eigen-Epistasis component, defined as the linear combination of Gene SNPs having the highest correlation with the phenotype. The selection of significant effects is done using a penalized regression method based on Group Lasso controlling the False Discovery Rate. Conclusion: The method is tested against two recent alternative proposals from the literature using synthetic data, and shows good performances in different settings. We demonstrate the power of our approach by detecting new gene-gene interactions on three genome-wide association studies. [ABSTRACT FROM AUTHOR]

Detecting epistasis, such as 2-SNP interactions, in genome-wide association studies (GWAS) is an important but time consuming operation. Consequently, GPUs have already been used to accelerate these studies, reducing the runtime for moderately-sized datasets to less than 1 hour. However, single-GPU approaches cannot perform large-scale GWAS in reasonable time. In this work we present multiEpistSearch, a tool to detect epistasis that works on GPU clusters. While CUDA is used for parallelization within each GPU, the workload distribution among GPUs is performed with Unified Parallel C++ (UPC++), a novel extension of C++ that follows the Partitioned Global Address Space (PGAS) model. multiEpistSearch is able to analyze large-scale datasets with 5 million SNPs from 10,000 individuals in less than 3 hours using 24 NVIDIA GTX Titans. [ABSTRACT FROM AUTHOR]

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA- B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene ontology biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the central nervous system malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes. [ABSTRACT FROM AUTHOR]

Motivation: The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate.Method: In this work, we present a multi-objective heuristic optimization methodology named MACOED for detecting genetic interactions. In MACOED, we combine both logistical regression and Bayesian network methods, which are from opposing schools of statistics. The combination of these two evaluation objectives proved to be complementary, resulting in higher power with a lower false-positive rate than observed for optimizing either objective independently. To solve the space and time complexity for high-dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED that is able to retain non-dominated solutions found in past iterations.Results: We compared MACOED with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method outperforms others in both detection power and computational feasibility for large datasets.Availability and implementation: Codes and datasets are available at: www.csbio.sjtu.edu.cn/bioinf/MACOED/.Contact: hbshen@sjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits. [ABSTRACT FROM AUTHOR]

Summary Intraspecific genetic incompatibilities prevent the assembly of specific alleles into single genotypes and influence genome- and species-wide patterns of sequence variation. A common incompatibility in plants is hybrid necrosis, characterized by autoimmune responses due to epistatic interactions between natural genetic variants. By systematically testing thousands of F 1 hybrids of Arabidopsis thaliana strains, we identified a small number of incompatibility hot spots in the genome, often in regions densely populated by nucleotide-binding domain and leucine-rich repeat (NLR) immune receptor genes. In several cases, these immune receptor loci interact with each other, suggestive of conflict within the immune system. A particularly dangerous locus is a highly variable cluster of NLR genes, DM2 , which causes multiple independent incompatibilities with genes that encode a range of biochemical functions, including NLRs. Our findings suggest that deleterious interactions of immune receptors limit the combinations of favorable disease resistance alleles accessible to plant genomes. [ABSTRACT FROM AUTHOR]

Genome-scale genetic interaction networks are progressively contributing to map the molecular circuitry that determines cellular behavior. To what extent this mapping changes in response to different environmental or genetic conditions is, however, largely unknown. Here, we assembled a geneticnetwork using aninsilicomodel of metabolism inyeast toexplicitly askhowseparate genetic backgrounds alternetwork structure. Backgrounds definedby singledeletionsofmetabolically activeenzymes induce strongrewiring when the deletion corresponds to a catabolic gene, evidencing a broad redistribution of fluxes to alternative pathways. We also show how change is more pronounced in interactions linking genes in distinct functional modules and in those connections that present weak epistasis. These patterns reflect overall the distributed robustness of catabolism. In a second class of genetic backgrounds, in which a number of neutral mutations accumulate, we dominantly observe modifications in the negative interactions that together with an increase in the number of essential genes indicate a global reduction in buffering. Notably, neutral trajectories that originate considerable changes in the wild-type network comprise mutations that diminished the environmentalplasticity of the corresponding metabolism, what emphasizes a mechanistic integration of genetic and environmental buffering. More generally, our work demonstrates howthe specificmechanistic causes of robustness influence the architecture of multiconditional genetic interactionmaps. [ABSTRACT FROM AUTHOR]

A combinatorially complete data set consists of studies of all possible combinations of a set of mutant sites in a gene or mutant alleles in a genome. Among the most robust conclusions from these studies is that epistasis between beneficial mutations often shows a pattern of diminishing returns, in which favorable mutations are less fit when combined than would be expected. Another robust inference is that the number of adaptive evolutionary paths is often limited to a relatively small fraction of the theoretical possibilities, owing largely to sign epistasis requiring evolutionary steps that would entail a decrease in fitness. Here we summarize these and other results while also examining issues that remain unresolved and future directions that seem promising. [ABSTRACT FROM AUTHOR]

The cis-regulatory effects responsible for cancer development have not been as extensively studied as the perturbations of the protein coding genome in tumorigenesis. To better characterize colorectal cancer (CRC) development we conducted an RNA-sequencing experiment of 103 matched tumour and normal colon mucosa samples from Danish CRC patients, 90 of which were germline-genotyped. By investigating allele-specific expression (ASE) we show that the germline genotypes remain important determinants of allelic gene expression in tumours. Using the changes in ASE in matched pairs of samples we discover 71 genes with excess of somatic cis-regulatory effects in CRC, suggesting a cancer driver role. We correlate genotypes and gene expression to identify expression quantitative trait loci (eQTLs) and find 1,693 and 948 eQTLs in normal samples and tumours, respectively. We estimate that 36% of the tumour eQTLs are exclusive to CRC and show that this specificity is partially driven by increased expression of specific transcription factors and changes in methylation patterns. We show that tumour-specific eQTLs are more enriched for low CRC genome-wide association study (GWAS) P values than shared eQTLs, which suggests that some of the GWAS variants are tumour specific regulatory variants. Importantly, tumour-specific eQTL genes also accumulate more somatic mutations when compared to the shared eQTL genes, raising the possibility that they constitute germline-derived cancer regulatory drivers. Collectively the integration of genome and the transcriptome reveals a substantial number of putative somatic and germline cis-regulatory cancer changes that may have a role in tumorigenesis. [ABSTRACT FROM AUTHOR]

The Bateson–Dobzhansky–Muller (BDM) model of reproductive isolation by genetic incompatibility is a widely accepted model of speciation. Because of the exceptionally rich biological information about the budding yeast Saccharomyces cerevisiae, the identification of BDM incompatibilities in yeast would greatly deepen our understanding of the molecular genetic basis of reproductive isolation and speciation. However, despite repeated efforts, BDM incompatibilities between nuclear genes have never been identified between S. cerevisiae and its sister species S. paradoxus. Such negative results have led to the belief that simple nuclear BDM incompatibilities do not exist between the two yeast species. Here, we explore an alternative explanation that such incompatibilities exist but were undetectable due to limited statistical power. We discover that previously employed statistical methods were not ideal and that a redesigned method improves the statistical power. We determine, under various sample sizes, the probabilities of identifying BDM incompatibilities that cause F1 spore inviability with incomplete penetrance, and confirm that the previously used samples were too small to detect such incompatibilities. Our findings call for an expanded experimental search for yeast BDM incompatibilities, which has become possible with the decreasing cost of genome sequencing. The improved methodology developed here is, in principle, applicable to other organisms and can help detect epistasis in general. [ABSTRACT FROM PUBLISHER]

Background: The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses. Results: In this work, we propose a computational efficient approach based on complete enumeration of two-locus epistasis models. This approach uses a two-stage (screening and testing) search strategy and guarantees the enumeration of all epistasis patterns. The implementation is done on graphic processing units (GPU), which can finish the analysis on a GWAS data (with around 5, 000 subjects and around 350, 000 markers) within two hours. Source code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST. Conclusions: This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS. [ABSTRACT FROM AUTHOR]

One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions. [Copyright &y& Elsevier]

The vast application of fluorescent semiconductor nanoparticles (NPs) or quantum dots (QDs) has prompted the development of new, cheap and safer methods that allow generating QDs with improved biocompatibility. In this context, green or biological QDs production represents a still unexplored area. This work reports the intracellular CdTe QDs biosynthesis in bacteria. Escherichia coli overexpressing the gshA gene, involved in glutathione (GSH) biosynthesis, was used to produce CdTe QDs. Cells exhibited higher reduced thiols, GSH and Cd/Te contents that allow generating fluorescent intracellular NP-like structures when exposed to CdCl2 and K2TeO3. Fluorescence microscopy revealed that QDs-producing cells accumulate defined structures of various colors, suggesting the production of differently-sized NPs. Purified fluorescent NPs exhibited structural and spectroscopic properties characteristic of CdTe QDs, as size and absorption/emission spectra. Elemental analysis confirmed that biosynthesized QDs were formed by Cd and Te with Cd/Te ratios expected for CdTe QDs. Finally, fluorescent properties of QDs-producing cells, such as color and intensity, were improved by temperature control and the use of reducing buffers. [ABSTRACT FROM AUTHOR]

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions. [ABSTRACT FROM AUTHOR]

We surveyed gene-gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E−08) and a Bonferroni corrected threshold (P=1.1E−12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E−08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E−08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings. [ABSTRACT FROM AUTHOR]

In evolution, the effects of a single deleterious mutation can sometimes be compensated for by a second mutation which recovers the original phenotype. Such epistatic interactions have implications for the structure of genome space—namely, that networks of genomes encoding the same phenotype may not be connected by single mutational moves. We use the folding of RNA sequences into secondary structures as a model genotype–phenotype map and explore the neutral spaces corresponding to networks of genotypes with the same phenotype. In most of these networks, we find that it is not possible to connect all genotypes to one another by single point mutations. Instead, a network for a phenotypic structure with n bonds typically fragments into at least 2n neutral components, often of similar size. While components of the same network generate the same phenotype, they show important variations in their properties, most strikingly in their evolvability and mutational robustness. This heterogeneity implies contingency in the evolutionary process. [ABSTRACT FROM AUTHOR]

Heterosis is widely used in genetic crop improvement; however, the genetic basis of heterosis is incompletely understood. The use of whole-genome segregating populations poses a problem for establishing the genetic basis of heterosis, in that interactions often mask the effects of individual loci. However, introgression line (IL) populations permit the partitioning of heterosis into defined genomic regions, eliminating a major part of the genome-wide epistasis. In our previous study, based on mid-parental heterosis (HMP) value with single-point analysis, 42 heterotic loci (HLs) associated with six yield-related traits were detected in wild and cultivated rice using a set of 265 ILs of Dongxiang common wild rice (Oryza rufipogon Griff.). In this study, the genetic effects of HLs were determined as the combined effects of both additive and dominant gene actions, estimated from the performance values of testcross F1s and the dominance effects estimated from the HMP values of testcross F1s. We characterized the gene action type at each HL. Thirty-eight of the 42 HLs were over-dominant, and in the absence of epistasis, four HLs were dominant. Therefore, we favour that over-dominance is a major genetic basis of ‘wild-cultivar’ crosses at the single functional Mendelian locus level. [ABSTRACT FROM AUTHOR]

The human mutation rate per nucleotide site per generation (μ) can be estimated from data on mutation rates at loci causing Mendelian genetic disease, by comparing putatively neutrally evolving nucleotide sequences between humans and chimpanzees and by comparing the genome sequences of relatives. Direct estimates from genome sequencing of relatives suggest that μ is about 1.1 x 10-8, which is about twofold lower than estimates based on the human-chimp divergence. This implies that an average of ~70 new mutations arise in the human diploid genome per generation. Most of these mutations are paternal in origin, but the male:female mutation rate ratio is currently uncertain and might vary even among individuals within a population. On the basis of a method proposed by Kondrashov and Crow, the genome-wide deleterious mutation rate (U) can be estimated from the product of the number of nucleotide sites in the genome, μ, and the mean selective constraint per site. Although the presence of many weakly selected mutations in human noncoding DNA makes this approach somewhat problematic, estimates are U ≈ 2.2 for the whole diploid genome per generation and ~0.35 for mutations that change an amino acid of a protein-coding gene. A genome-wide deleterious mutation rate of 2.2 seems higher than humans could tolerate if natural selection is "hard," but could be tolerated if selection acts on relative fitness differences between individuals or if there is synergistic epistasis. I argue that in the foreseeable future, an accumulation of new deleterious mutations is unlikely to lead to a detectable decline in fitness of human populations. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated genegene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results: In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion: iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. [ABSTRACT FROM AUTHOR]

Background: The QTLMAS XVth dataset consisted of the pedigrees, marker genotypes and quantitative trait performances of 2,000 phenotyped animals with a half-sib family structure. The trait was regulated by 8 QTL which display additive, imprinting or epistatic effects. This paper aims at comparing the QTL mapping results obtained by six participants of the workshop. Methods: Different regression, GBLUP, LASSO and Bayesian methods were applied for QTL detection. The results of these methods are compared based on the number of correctly mapped QTL, the number of false positives, the accuracy of the QTL location and the estimation of the QTL effect. Results: All the simulated QTL, except the interacting QTL on Chr5, were identified by the participants. Depending on the method, 3 to 7 out of the 8 QTL were identified. The distance to the real location and the accuracy of the QTL effect varied to a large extent depending on the methods and complexity of the simulated QTL. Conclusions: While all methods were fairly efficient in detecting QTL with additive effects, it was clear that for non-additive situations, such as parent-of-origin effects or interactions, the BayesC method gave the best results by detecting 7 out of the 8 simulated QTL, with only two false positives and a good precision (less than 1 cM away on average). Indeed, if LASSO could detect QTL even in complex situations, it was associated with too many false positive results to allow for efficient GWAS. GENMIX, a method based on the phylogenies of local haplotypes, also appeared as a promising approach, which however showed a few more false positives when compared with the BayesC method. [ABSTRACT FROM AUTHOR]

Background: The turkey (Meleagris gallopavo) is an important agricultural species and the second largest contributor to the world’s poultry meat production. Genetic improvement is attributed largely to selective breeding programs that rely on highly heritable phenotypic traits, such as body size and breast muscle development. Commercial breeding with small effective population sizes and epistasis can result in loss of genetic diversity, which in turn can lead to reduced individual fitness and reduced response to selection. The presence of genomic diversity in domestic livestock species therefore, is of great importance and a prerequisite for rapid and accurate genetic improvement of selected breeds in various environments, as well as to facilitate rapid adaptation to potential changes in breeding goals. Genomic selection requires a large number of genetic markers such as e.g. single nucleotide polymorphisms (SNPs) the most abundant source of genetic variation within the genome. Results: Alignment of next generation sequencing data of 32 individual turkeys from different populations was used for the discovery of 5.49 million SNPs, which subsequently were used for the analysis of genetic diversity among the different populations. All of the commercial lines branched from a single node relative to the heritage varieties and the South Mexican turkey population. Heterozygosity of all individuals from the different turkey populations ranged from 0.17-2.73 SNPs/Kb, while heterozygosity of populations ranged from 0.73-1.64 SNPs/Kb. The average frequency of heterozygous SNPs in individual turkeys was 1.07 SNPs/Kb. Five genomic regions with very low nucleotide variation were identified in domestic turkeys that showed state of fixation towards alleles different than wild alleles. Conclusion: The turkey genome is much less diverse with a relatively low frequency of heterozygous SNPs as compared to other livestock species like chicken and pig. The whole genome SNP discovery study in turkey resulted in the detection of 5.49 million putative SNPs compared to the reference genome. All commercial lines appear to share a common origin. Presence of different alleles/haplotypes in the SM population highlights that specific haplotypes have been selected in the modern domesticated turkey. [ABSTRACT FROM AUTHOR]

Interaction (nonadditive effects) between genetic variants has been highlighted as an important mechanism underlying phenotypic variation, but the discovery of genetic interactions in humans has proved difficult. In this study, we show that the spectrum of variation in the human genome has been shaped by modifier effects of cis-regulatory variation on the functional impact of putatively deleterious protein-coding variants. We analyzed 1000 Genomes population-scale resequencing data from Europe (CEU [Utah residents with Northern and Western European ancestry from the CEPH collection]) and Africa (YRI [Yoruba in Ibadan, Nigeria]) together with gene expression data from arrays and RNA sequencing for the same samples. We observed an underrepresentation of derived putatively functional coding variation on the more highly expressed regulatory haplotype, which suggests stronger purifying selection against deleterious coding variants that have increased penetrance because of their regulatory background. Furthermore, the frequency spectrum and impact size distribution of common regulatory polymorphisms (eQTLs) appear to be shaped in order to minimize the selective disadvantage of having deleterious coding mutations on the more highly expressed haplotype. Interestingly, eQTLs explaining common disease GWAS signals showed an enrichment of putative epistatic effects, suggesting that some disease associations might arise from interactions increasing the penetrance of rare coding variants. In conclusion, our results indicate that regulatory and coding variants often modify the functional impact of each other. This specific type of genetic interaction is detectable from sequencing data in a genome-wide manner, and characterizing these joint effects might help us understand functional mechanisms behind genetic associations to human phenotypes—including both Mendelian and common disease. [Copyright &y& Elsevier]

Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic. [ABSTRACT FROM AUTHOR]

We investigate the generation and decay of interspecific disequilibrium (ID) between organelle and symbiont genomes as a function of the rate of horizontal transmission. We show that rare horizontal transmission greatly diminishes the covariance between organelle and symbiont genomes. This result has two important implications. First, a low level of ID does not indicate low levels of vertical transmission. Second, even with low levels of horizontal transmission, the additive effects of host and symbiont loci will determine the response to selection, while epistatic effects will not be selectable. [ABSTRACT FROM AUTHOR]

Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations. [ABSTRACT FROM AUTHOR]

‘Disease-causing’ mutations do not cause disease in all individuals. One possible important reason for this is that the outcome of a mutation can depend upon other genetic variants in a genome. These epistatic interactions between mutations occur both within and between molecules, and studies in model organisms show that they are extremely prevalent. However, epistatic interactions are still poorly understood at the molecular level, and consequently difficult to predict de novo. Here I provide an overview of our current understanding of the molecular mechanisms that can cause epistasis, and areas where more research is needed. A more complete understanding of epistasis will be vital for making accurate predictions about the phenotypes of individuals. [Copyright &y& Elsevier]

Epistasis is defined as interactions between alleles of two or more genetic loci. Detection of epistatic interactions is the key to understand the genetic architecture and gene networks underlying complex traits. Here, we examined the extent of epistasis for seven quantitative traits with an association mapping approach in a large population of elite sugar beet lines. We found that correction for population stratification is required and that in terms of reducing the false-positive rate the mixed model approach including the kinship matrix performed best. In genome-wide scans, we detected both main effects and epistatic QTL. For physiological traits, the detected digenic and higher-order epistasis explained a considerable proportion of the genotypic variance. We illustrate that the identified epistatic interactions define comprehensive genetic networks, which may serve as starting points towards a systems-oriented approach to understand the regulation of complex traits. [ABSTRACT FROM AUTHOR]

Motivation: Hundreds of genome-wide association studies have been performed over the last decade, but as single nucleotide polymorphism (SNP) chip density has increased so has the computational burden to search for epistasis [for n SNPs the computational time resource is O(n(n−1)/2)]. While the theoretical contribution of epistasis toward phenotypes of medical and economic importance is widely discussed, empirical evidence is conspicuously absent because its analysis is often computationally prohibitive. To facilitate resolution in this field, tools must be made available that can render the search for epistasis universally viable in terms of hardware availability, cost and computational time.Results: By partitioning the 2D search grid across the multicore architecture of a modern consumer graphics processing unit (GPU), we report a 92× increase in the speed of an exhaustive pairwise epistasis scan for a quantitative phenotype, and we expect the speed to increase as graphics cards continue to improve. To achieve a comparable computational improvement without a graphics card would require a large compute-cluster, an option that is often financially non-viable. The implementation presented uses OpenCL—an open-source library designed to run on any commercially available GPU and on any operating system.Availability: The software is free, open-source, platform-independent and GPU-vendor independent. It can be downloaded from http://sourceforge.net/projects/epigpu/.Contact: gib.hemani@roslin.ed.ac.uk [ABSTRACT FROM AUTHOR]

US maize yield has increased eight-fold in the past 80 years, with half of the gain attributed to selection by breeders. During this time, changes in maize leaf angle and size have altered plant architecture, allowing more efficient light capture as planting density has increased. Through a genome-wide association study (GWAS) of the maize nested association mapping panel, we determined the genetic basis of important leaf architecture traits and identified some of the key genes. Overall, we demonstrate that the genetic architecture of the leaf traits is dominated by small effects, with little epistasis, environmental interaction or pleiotropy. In particular, GWAS results show that variations at the liguleless genes have contributed to more upright leaves. These results demonstrate that the use of GWAS with specially designed mapping populations is effective in uncovering the basis of key agronomic traits. [ABSTRACT FROM AUTHOR]

The extent to which mitochondrial DNA (mtDNA) variation is involved in adaptive evolutionary change is currently being reevaluated. In particular, emerging evidence suggests that mtDNA genes coevolve with the nuclear genes with which they interact to form the energy producing enzyme complexes in the mitochondria. This suggests that intergenomic epistasis between mitochondrial and nuclear genes may affect whole-organism metabolic phenotypes. Here, we use crossed combinations of mitochondrial and nuclear lineages of the seed beetle Callosobruchus maculatus and assay metabolic rate under two different temperature regimes. Metabolic rate was affected by an interaction between the mitochondrial and nuclear lineages and the temperature regime. Sequence data suggests that mitochondrial genetic variation has a role in determining the outcome of this interaction. Our genetic dissection of metabolic rate reveals a high level of complexity, encompassing genetic interactions over two genomes, and genotype × genotype × environment interactions. The evolutionary implications of these results are twofold. First, because metabolic rate is at the root of life histories, our results provide insights into the complexity of life-history evolution in general, and thermal adaptation in particular. Second, our results suggest a mechanism that could contribute to the maintenance of nonneutral mtDNA polymorphism. [ABSTRACT FROM AUTHOR]

Sex-biased genes-genes that are differentially expressed within males and females-are nonrandomly distributed across animal genomes, with sex chromosomes and autosomes often carrying markedly different concentrations of male- and female-biased genes. These linkage patterns are often gene- and lineage-dependent, differing between functional genetic categories and between species. Although sex-specific selection is often hypothesized to shape the evolution of sex-linked and autosomal gene content, population genetics theory has yet to account for many of the gene- and lineage-specific idiosyncrasies emerging from the empirical literature. With the goal of improving the connection between evolutionary theory and a rapidly growing body of genome-wide empirical studies, we extend previous population genetics theory of sex-specific selection by developing and analyzing a biologically informed model that incorporates sex linkage, pleiotropy, recombination, and epistasis, factors that are likely to vary between genes and between species. Our results demonstrate that sex-specific selection and sex-specific recombination rates can generate, and are compatible with, the gene- and species-specific linkage patterns reported in the genomics literature. The theory suggests that sexual selection may strongly influence the architectures of animal genomes, as well as the chromosomal distribution of fixed substitutions underlying sexually dimorphic traits. [ABSTRACT FROM AUTHOR]

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic. [ABSTRACT FROM AUTHOR]

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a ''synthetic association study'' in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes. [ABSTRACT FROM AUTHOR]

Modern genomewide association studies are characterized by the problem of ''missing heritability.'' Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic ''subtypes,'' may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (e). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems. [ABSTRACT FROM AUTHOR]

Abstract: Genomewide association studies (GWAS) are being conducted to unravel the genetic etiology of complex diseases, in which complex epistasis may play an important role. One-stage method in which interactions are tested using all samples at one time may be computationally problematic, may have low power as the number of markers tested increases and may not be cost-efficient. A common two-stage method may be a reasonable and powerful approach for detecting interacting genes using all samples in both two stages. In this study, we introduce an alternative two-stage method, in which some promising markers are selected using a proportion of samples in the first stage and interactions are then tested using the remaining samples in the second stage. This two-stage method is called mixed two-stage method. We then investigate the power of both one-stage method and mixed two-stage method to detect interacting disease loci for a range of two-locus epistatic models in a case–control study design. Our results suggest that mixed two-stage method may be more powerful than one-stage method if we choose about 30% of samples for single-locus tests in the first stage, and identify less than and equal to 1% of markers for follow-up interaction tests. In addition, we compare both two-stage methods and find that our two-stage method will lose power because we only use part of samples in both two stages. [Copyright &y& Elsevier]