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Neurological disorders, such as ALS, Alzheimer’s, epilepsy, Parkinson’s Disease, Autism, Atrial Fibrillation, and Sclerosis, affect the central nervous system, including the brain, nerves, spinal cords, muscles, and Neuromuscular joint. These disorders are investigated by detecting the genetic variations in Single Nucleotide Polymorphism (SNP) in Genome-Wide Association Studies (GWAS). In the human genome sequence, one SNP influences the effects of another SNP. These SNP-SNP interactions or Gene-Gene interaction (Epistasis) significantly increase the risk of disease susceptibility to neurological disorders. The manual analyses of various genetic interactions related to neurological diseases are cumbersome. Hence, the computational system is effective for the discovery of Epistasis effects in neurological syndromes. This study aims to explore various techniques of statistical, machine learning, optimization so far applied to find the epistasis effect for neurological disorders. This study finds several genetic interaction models involving different loci, various candidate genes, and SNP interactions involved in numerous neurological diseases. The gene APOE and its polymorphism increase Alzheimer's disease pathology. The gene GAB2 and its SNPs play a vital role in Alzheimer’s disease. The genes GABRA4, ITGB3, and SLC64A highly influence the genetic interactions for Autism disorder. In schizophrenia, the SNPs of NRG1 increase the disease risk. The benefits, limitations, and issues of the various computational techniques implemented for epistasis evaluation of neurological disease are deeply discussed.

Background For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to investigate the interactions between distant loci, or epistasis. Among them, the recently proposed EpiGWAS models the interactions between a target variant and the rest of the genome. However, applying this approach to studying interactions along all genes of a disease map is not straightforward. Here, we propose a pipeline to that effect, which we illustrate by investigating a multiple sclerosis GWAS dataset from the Wellcome Trust Case Control Consortium 2 through 19 disease maps from the MetaCore pathway database. Results For each disease map, we build an epistatic network by connecting the genes that are deemed to interact. These networks tend to be connected, complementary to the disease maps and contain hubs. In addition, we report 4 epistatic gene pairs involving missense variants, and 25 gene pairs with a deleterious epistatic effect mediated by eQTLs. Among these, we highlight the interaction of GLI-1 and SUFU, and of IP10 and NF-$$\kappa$$ κ B, as they both match known biological interactions. The latter pair is particularly promising for therapeutic development, as both genes have known inhibitors. Conclusions Our study showcases the ability of EpiGWAS to uncover biologically interpretable epistatic interactions that are potentially actionable for the development of combination therapy.

RecA is essential for double-strand-break repair (DSBR) and the SOS response in Escherichia coli K-12. RecN is an SOS protein and a member of the Structural Maintenance of Chromosomes family of proteins thought to play a role in sister chromatid cohesion/interactions during DSBR. Previous studies have shown that a plasmid-encoded recA4190 (Q300R) mutant had a phenotype similar to ∆recN (mitomycin C sensitive and UV resistant). It was hypothesized that RecN and RecA physically interact, and that recA4190 specifically eliminated this interaction. To test this model, an epistasis analysis between recA4190 and ∆recN was performed in wild-type and recBC sbcBC cells. To do this, recA4190 was first transferred to the chromosome. As single mutants, recA4190 and ∆recN were Rec+ as measured by transductional recombination, but were 3-fold and 10-fold decreased in their ability to do I-SceI-induced DSBR, respectively. In both cases, the double mutant had an additive phenotype relative to either single mutant. In the recBC sbcBC background, recA4190 and ∆recN cells were very UVS (sensitive), Rec−, had high basal levels of SOS expression and an altered distribution of RecA-GFP structures. In all cases, the double mutant had additive phenotypes. These data suggest that recA4190 (Q300R) and ∆recN remove functions in genetically distinct pathways important for DNA repair, and that RecA Q300 was not important for an interaction between RecN and RecA in vivo. recA4190 (Q300R) revealed modest phenotypes in a wild-type background and dramatic phenotypes in a recBC sbcBC strain, reflecting greater stringency of RecA’s role in that background.

Marssonina brunnea, the causative pathogen of Marssonina leaf spot of poplars (MLSP), devastates poplar plantations by forming black spots on leaves and defoliating trees. Although MLSP has been studied for over 30 years, the key genes that function during M. brunnea infection and their effects on plant growth are poorly understood. Here, we used multigene association studies to investigate the effects of key genes in the plant-pathogen interaction pathway, as revealed by transcriptome analysis, on photosynthesis and growth in a natural population of 435 Populus tomentosa individuals. By analyzing transcriptomic changes during three stages of infection, we detected 628 transcription factor genes among the 7,611 differentially expressed genes that might be associated with basal defense responses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that transcriptomic changes across different stages of infection lead to the reprogramming of metabolic processes possibly related to defense activation. We identified 29,399 common single-nucleotide polymorphisms (SNPs) within 221 full-length genes in plant-pathogen interaction pathways that were significantly associated with photosynthetic and growth traits. We also detected 4,460 significant epistatic pairs associated with stomatal conductance, tree diameter, and tree height. Epistasis analysis uncovered significant interactions between 2,561 SNP-SNP pairs from different functional modules in the plant-pathogen interaction pathway, revealing possible genetic interactions. This analysis revealed many key genes that function during M. brunnea infection and their potential roles in mediating photosynthesis and plant growth, shedding light on genetic interactions between functional modules in the plant-pathogen interaction pathway.

MicroRNAs (miRNAs) play crucial roles in all aspects of plant growth and development, but the genetic interactions of miRNAs and their target genes in woody plants are largely unknown. Here, we integrated association genetics and expression profiling to decipher the allelic variations and interactions of the Pto-MIR319 family of miRNAs and 12 putative Pto-miR319 target genes related to wood formation in 435 unrelated individuals of Populus tomentosa Carriere (Chinese white poplar). Expression pattern analysis showed that among all pairings between expressions of pre-miRNA of Pto-MIR319 members and targets, 70.0% showed negative correlation of expression levels (r = – 0.944 to 0.674, P

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic, but it has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in the cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr .

Motivation Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. Results In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. Availability and implementation The software is available at github.com/shilab/parEBEN.

Despite the large number of noncoding RNAs in human genome and their roles in many diseases include cancer, we know very little about them due to lack of structural clues. The centerpiece of the structural clues is the full RNA base-pairing structure of secondary and tertiary contacts that can be precisely obtained only from costly and time-consuming 3D structure determination. Here, we performed deep mutational scanning of self-cleaving CPEB3 ribozyme by error-prone PCR and showed that a library of

The 53BP1-RIF1 pathway antagonizes resection of DNA broken ends and confers PARP inhibitor sensitivity on BRCA1-mutated tumors. However, it is unclear how this pathway suppresses initiation of resection. Here, we identify ASF1 as a partner of RIF1 via an interacting manner similar to its interactions with histone chaperones CAF-1 and HIRA. ASF1 is recruited to distal chromatin flanking DNA breaks by 53BP1-RIF1 and promotes non-homologous end joining (NHEJ) using its histone chaperone activity. Epistasis analysis shows that ASF1 acts in the same NHEJ pathway as RIF1, but via a parallel pathway with the shieldin complex, which suppresses resection after initiation. Moreover, defects in end resection and homologous recombination (HR) in BRCA1- deficient cells are largely suppressed by ASF1 deficiency. Mechanistically, ASF1 compacts adjacent chromatin by heterochromatinization to protect broken DNA ends from BRCA1-mediated resection. Taken together, our findings identified a RIF1-ASF1 histone chaperone complex that promotes changes in high-order chromatin structure to stimulate the NHEJ pathway for DSB repair.

Understanding how neural circuits underlie behaviour is challenging even in the era of the connectome because it requires a combined approach encompassing anatomical and functional analyses. This is exemplified in studying the circuit underlying the light-avoidance behaviour displayed by the larvae of the fruit fly Drosophila melanogaster. While this behaviour is robust and the nervous system relatively simple, only bits and pieces of the circuit have been delineated1. Indeed, some studies resulted in contradicting conclusions regarding the contributions of various neuronal types to this behaviour2,3. Here we devise trans-Tango MkII, a new version of the transsynaptic circuit tracing and manipulation tool trans-Tango4. We implement trans-Tango MkII in anatomical tracing and combine it with circuit epistasis analysis. We use neuronal inhibition to test necessity of particular neuronal types for light-avoidance. We complement these experiments by selective neuronal activation to examine sufficiency in rescuing light-avoidance deficiencies exhibited by photoreceptor mutants. Together, our studies reveal a four-order, linear circuit for light-avoidance behaviour connecting the light-detecting photoreceptors with a pair of neuroendocrine cells via two types of clock neurons. Our combined approach could be readily expanded to other larval circuits. Further, this strategy provides the framework for studying more complex nervous systems and behaviours.

MicroRNAs (miRNAs), important posttranscriptional regulators of gene expression, play a crucial role in plant growth and development. A single miRNA can regulate numerous target genes, making the determination of its function and interaction with targets challenging. We identified PtomiR403b target to PtoGT31B-1, which encodes a galactosyltransferase responsible for the biosynthesis of cell wall polysaccharides. We performed an association study and epistasis and Mendelian randomization (MR) analyses to explore how the genetic interaction between PtoMIR403b and its target PtoGT31B-1 underlies wood formation. Single nucleotide polymorphism (SNP)-based association studies identified 25 significant associations (P < 0.01, Q < 0.05), and PtoMIR403b and PtoGT31B-1 were associated with five traits, suggesting a role for PtomiR403b and PtoGT31B-1 in wood formation. Epistasis analysis identified 93 significant pairwise epistatic associations with 10 wood formation traits, and 37.89% of the SNP-SNP pairs indicated interactions between PtoMIR403b and PtoGT31B-1. We performed an MR analysis to demonstrate the causality of the relationships between SNPs in PtoMIR403b and wood property traits and that PtoMIR403b modulates wood formation by regulating expression of PtoGT31B-1. Therefore, our findings will facilitate dissection of the functions and interactions with miRNA-targets.

Ionising radiation (IR) is an environmental factor known to alter genomes and therefore challenge organisms to adapt. Lithuanian clean-up workers of the Chernobyl nuclear disaster (LCWC) experienced high doses of IR, leading to different consequences. This study aims to characterise a unique protective genomic variation in a relatively healthy LCWC group. This variation influenced their individual reaction to IR and potentially protects against certain diseases such as exfoliation syndrome and glaucoma. Clinical and IR dosage data were collected using a questionnaire to characterise the cohort of 93 LCWC. Genome-wide genotyping using Illumina beadchip technology was performed. The control group included 466 unrelated, self-reported healthy individuals of Lithuanian descent. Genotypes were filtered out from the microarray dataset using a catalogue of SNPs. The data were used to perform association, linkage disequilibrium, and epistasis analysis. Phenotype data analysis showed the distribution of the most common disease groups among the LCWC. A genomic variant of statistical significance (Fishers’ exact test, p = 0.019), rs3825942, was identified in LOXL1 (NM_005576.4:c.458G&gt, A). Linkage disequilibrium and epistasis analysis for this variant identified the genes LHFPL3, GALNT6, PIH1D1, ANKS1B, and METRNL as potentially involved in the etiopathogenesis of exfoliation syndrome and glaucoma, which were not previously associated with the disease. The LOXL1 variant is mostly considered a risk factor in the development of exfoliation syndrome and glaucoma. The influence of recent positive selection, the phenomenon of allele-flipping, and the fact that only individuals with the homozygous reference allele have glaucoma in the cohort of the LCWC suggest otherwise. The identification of rs3825942 and other potentially protective genomic variants may be useful for further analysis of the genetic architecture and etiopathogenetic mechanisms of other multifactorial diseases.

The BH3-only family of proteins is key for initiating apoptosis in a variety of contexts, and may also contribute to non-apoptotic cellular processes. Historically, the nematode Caenorhabditis elegans has provided a powerful system for studying and identifying conserved regulators of BH3-only proteins. In C. elegans, the BH3-only protein egl-1 is expressed during development to cell-autonomously trigger most developmental cell deaths. Here we provide evidence that egl-1 is also transcribed after development in the sensory neuron pair URX without inducing apoptosis. We used genetic screening and epistasis analysis to determine that its transcription is regulated in URX by neuronal activity and/or in parallel by orthologs of Protein Kinase G and the Salt-Inducible Kinase family. Because several BH3-only family proteins are also expressed in the adult nervous system of mammals, we suggest that studying egl-1 expression in URX may shed light on mechanisms that regulate conserved family members in higher organisms.

Using gene-set association test and epistasis analysis, this research achieved higher statistical power with potentially high accuracy, and detected significant genes and gene networks that influence flowering time in barley., Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene–phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene–phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.

Flowering time is an important agronomic trait, attributed by multiple genes, gene–gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein–protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

Detecting single nucleotide polymorphisms (SNPs) interactions is crucial to identify susceptibility genes associated with complex human diseases in genome-wide association studies. Clustering-based approaches are widely used in reducing search space and exploring potential relationships between SNPs in epistasis analysis. However, these approaches all only use a single measure to filter out nonsignificant SNP combinations, which may be significant ones from another perspective. In this paper, we propose a two-stage approach named EpiMC (Epistatic Interactions detection based on Multiple Clusterings) that employs multiple clusterings to obtain more precise candidate sets and more comprehensively detect high-order interactions based on these sets. In the first stage, EpiMC proposes a matrix factorization based multiple clusterings algorithm to generate multiple diverse clusterings, each of which divide all SNPs into different clusters. This stage aims to reduce the chance of filtering out potential candidates overlooked by a single clustering and groups associated SNPs together from different clustering perspectives. In the next stage, EpiMC considers both the single-locus effects and interaction effects to select high-quality disease associated SNPs, and then uses Jaccard similarity to get candidate sets. Finally, EpiMC uses exhaustive search on the obtained small candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpiMC has a better performance in detecting high-order interactions than state-of-the-art solutions. On the Wellcome Trust Case Control Consortium (WTCCC) dataset, EpiMC detects several significant epistatic interactions associated with breast cancer (BC) and age-related macular degeneration (AMD), which again corroborate the effectiveness of EpiMC.

Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single-nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms.

Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.

Fanconi Anemia (FA) is a prototypic genetic disease signified by heterogeneous phenotypes including cancer, bone marrow failure, short stature, congenital abnormalities, infertility, sub-mendelian birth rate, genome instability and high cellular sensitivity to cancer therapeutics1-4. Clinical diagnosis is confirmed by identifying biallelic, homo- or hemizygous mutations in any one of twenty-three FANC genes1,5. Puzzlingly, inactivation of one single Fanc gene in mice fails to faithfully model the human disease manifestations6-8. We here delineate a preclinical Fanc mouse model with mutations in two genes, Fancd1/Brca2 and Fanco/Rad51c, that recapitulates the severity and heterogeneity of the human disease manifestations including death by cancer at young age. Surprisingly, these grave phenotypes cannot be explained by the sum of phenotypes seen in mice with single gene inactivation, which are unremarkable. In contrast to expectations from classic epistasis analysis of genetic pathways, the data instead reveal an unexpected functional synergism of polygenic Fanc mutations. Importantly in humans, whole exome sequencing uncovers that FANC co-mutation in addition to the identified inactivating FANC gene mutation is a frequent event in FA patients. Collectively, the data establish a concept of polygenic stress as an important contributor to disease manifestations, with implications for molecular diagnostics.

Controlling chromatin state constitutes a major regulatory step in gene expression regulation across eukaryotes. While global cellular features or processes are naturally impacted by chromatin state alterations, little is known about how chromatin regulatory genes interact in networks to dictate downstream phenotypes. Using the activity of the canonical galactose network in yeast as a model, here, we measured the impact of the disruption of key chromatin regulatory genes on downstream gene expression, genetic noise and fitness. Using Trichostatin A and nicotinamide, we characterized how drug-based modulation of global histone deacetylase activity affected these phenotypes. Performing epistasis analysis, we discovered phenotype-specific genetic interaction networks of chromatin regulators. Our work provides comprehensive insights into how the galactose network activity is affected by protein interaction networks formed by chromatin regulators.

Bacteria carry out sophisticated developmental programs to colonize exogenous surfaces. The rotary flagellum, a dynamic machine that drives motility, is a key regulator of surface colonization. The specific signals recognized by flagella and the pathways by which those signals are transduced to coordinate adhesion remain subjects of debate. Mutations that disrupt flagellar assembly in the dimorphic bacterium Caulobacter crescentus stimulate the production of a polysaccharide adhesin called the holdfast. Using a genome-wide phenotyping approach, we compared surface adhesion profiles in wild-type and flagellar mutant backgrounds of C. crescentus. We identified a diverse set of flagellar mutations that enhance adhesion by inducing a hyper-holdfast phenotype and discovered a second set of mutations that suppress this phenotype. Epistasis analysis of the flagellar signaling suppressor (fss) mutations demonstrated that the flagellum stimulates holdfast production via two genetically distinct pathways. The developmental regulator PleD contributes to holdfast induction in mutants disrupted at both early and late stages of flagellar assembly. Mutants disrupted at late stages of flagellar assembly, which assemble an intact rotor complex, induce holdfast production through an additional process that requires the MotAB stator and its associated diguanylate cyclase, DgcB. We have assigned a subset of the fss genes to either the stator- or pleD-dependent networks and characterized two previously unidentified motility genes that regulate holdfast production via the stator complex. We propose a model through which the flagellum integrates mechanical stimuli into the C. crescentus developmental program to coordinate adhesion.ImportanceUnderstanding how bacteria colonize solid surfaces is of significant clinical, industrial and ecological importance. In this study, we identified genes that are required for Caulobacter crescentus to activate surface attachment in response to signals from a macromolecular machine called the flagellum. Genes involved in transmitting information from the flagellum can be grouped into separate pathways, those that control the C. crescentus morphogenic program and those that are required for flagellar motility. Our results support a model in which a developmental and a mechanical signaling pathway operate in parallel downstream of the flagellum and converge to regulate adhesion. We conclude that the flagellum serves as a signaling hub by integrating internal and external cues to coordinate surface colonization and emphasize the role of signal integration in linking complex sets of environmental stimuli to individual behaviors.

Nearest-neighbor Projected-Distance Regression (NPDR) is a feature selection technique that uses nearest-neighbors in high dimensional data to detect complex multivariate effects including epistasis. NPDR uses a regression formalism that allows statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provides a mechanism for NPDR to adjust for population structure, which we apply to a GWAS of systemic lupus erythematosus (SLE). We also test NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design and continuous outcomes. NPDR identifies potential interactions in an epistasis network that influences the SLE disorder.

Background Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD. Methods We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. Results We identified 2 candidate genetic interactions with PFDR RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR Conclusions In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

SummaryStudying epistatic gene interactions is important in understanding genetic architecture of complex traits in organisms. However, due to an enormous number of gene combinations to be analyzed, detection of epistatic gene-gene interactions has been computationally demanding. Here, we show a simple approach RIL-StEp, specialized to Recombinant Inbred Lines (RILs), to study epistasis using single nucleotide polymorphisms (SNPs) information of the genome. We applied the method to reveal epistasis affecting rice seed hull color phenotype, and successfully identified gene pairs that presumably control seed hull color. This method has a potential to enhancing our understanding of genetic architecture of various traits.

Genome-wide association studies have identified many loci associated with Alzheimer’s dementia. However, these variants only explain part of the heritability of Alzheimer’s disease (AD). As genetic epistasis can be a major contributor to the “missing heritability” of AD, we conducted genome-wide epistasis screening for AD pathologies in two independent cohorts. First, we performed a genome-wide epistasis study of AD-related brain pathologies (N(max) = 1,318) in ROS/MAP. Candidate interactions were validated using cerebrospinal fluid biomarkers of AD in ADNI (N(max) = 1,128). Further functional analysis tested the association of candidate interactions with neuroimaging phenotypes. For tau and amyloid-β (Aβ) pathology, we identified 2,803 and 464 candidate SNP-SNP interactions, respectively. Associations of candidate SNP-SNP interactions with brain volume and white matter changes from neuroimages provides additional insights into their molecular functions. Transcriptional analysis supported possible gene-gene interactions identified by statistical screening through their co-expression in the brain. In summary, we outlined an exhaustive epistasis analysis to identify novel genetic interactions with potential roles in AD pathologies. We further delved into the functional relevance of candidate interactions by association with neuroimaging phenotypes and analysis of co-expression between corresponding gene pairs.

It is necessary to consider what makes problems easy or hard for genetic algorithms. Davidor [Dav91] has examined this question by applying Epistasis Analysis. In this paper, we first give a review of Epistasis Analysis. Then, we apply Epistasis Analysis to certain functions and construct five functions with differing degrees of difficulty for genetic algorithms, some GA-easy and some GA-hard. Finally, experimental results and conclusion are described. Unfortunately, Epistasis Analysis can not accurately estimate the GA-hardness of a function.

A major challenge facing the genetics of autism spectrum disorders (ASDs) is the large and growing number of candidate risk genes and gene variants of unknown functional significance. Here, we used Caenorhabditis elegans to systematically functionally characterize ASD-associated genes in vivo. Using our custom machine vision system, we quantified 26 phenotypes spanning morphology, locomotion, tactile sensitivity, and habituation learning in 135 strains each carrying a mutation in an ortholog of an ASD-associated gene. We identified hundreds of genotype–phenotype relationships ranging from severe developmental delays and uncoordinated movement to subtle deficits in sensory and learning behaviors. We clustered genes by similarity in phenomic profiles and used epistasis analysis to discover parallel networks centered on CHD8•chd-7 and NLGN3•nlg-1 that underlie mechanosensory hyperresponsivity and impaired habituation learning. We then leveraged our data for in vivo functional assays to gauge missense variant effect. Expression of wild-type NLG-1 in nlg-1 mutant C. elegans rescued their sensory and learning impairments. Testing the rescuing ability of conserved ASD-associated neuroligin variants revealed varied partial loss of function despite proper subcellular localization. Finally, we used CRISPR-Cas9 auxin-inducible degradation to determine that phenotypic abnormalities caused by developmental loss of NLG-1 can be reversed by adult expression. This work charts the phenotypic landscape of ASD-associated genes, offers in vivo variant functional assays, and potential therapeutic targets for ASD.

Genetic predisposition has been identified as one of the components contributing to the obesity epidemic in modern societies. The aetiology of polygenic obesity is multifactorial, which indicates that lifestyle and environmental factors may influence multiples genes to aggravate this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have been associated with BMI. However, identified loci only explain a small proportion of the variation observed for this phenotype. The linear nature of genome wide association studies (GWAS) used to identify associations between genetic variants and the phenotype have had limited success in explaining the heritability variation of BMI and shown low predictive capacity in classification studies. GWAS ignores the epistatic interactions that less significant variants have on the phenotypic outcome. In this paper we utilise a novel deep learning-based methodology to reduce the high dimensional space in GWAS and find epistatic interactions between SNPs for classification purposes. SNPs were filtered based on the effects associations have with BMI. Since Bonferroni adjustment for multiple testing is highly conservative, an important proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore, only SNPs with p-values

Roots perform vital roles for adaptation and productivity under water-deficit stress, even though their specific functions are poorly understood. In this study, the genetic control of the nodal-root architectural and anatomical response to water deficit were investigated among diverse spring barley accessions. Water deficit induced substantial variations in the nodal root traits. The cortical, stele, and total root cross-sectional areas of the main-shoot nodal roots decreased under water deficit, but increased in the tiller nodal roots. Root xylem density and arrested nodal roots increased under water deficit, with the formation of root suberization/lignification and large cortical aerenchyma. Genome-wide association study implicated 11 QTL intervals in the architectural and anatomical nodal root response to water deficit. Among them, three and four QTL intervals had strong effects across seasons and on both root architectural and anatomical traits, respectively. Genome-wide epistasis analysis revealed 44 epistatically interacting SNP loci. Further analyses showed that these QTL intervals contain important candidate genes, including ZIFL2, MATE, and PPIB, whose functions are shown to be related to the root adaptive response to water deprivation in plants. These results give novel insight into the genetic architectures of barley nodal root response to soil water deficit stress in the fields, and thus offer useful resources for root-targeted marker-assisted selection.

Epistasis detection represents a fundamental problem in bio-medicine to understand the reasons for occurrence of complex phenotypic traits (diseases) across a population of individuals. Exhaustively examining all possible interactions of multiple Single-Nucleotide Polymorphisms provides the most reliable way to identify accurate solutions, but it is both computationally and memory intensive task. To tackle this challenge, this work proposes a modular and self-adaptive framework for high-performance and energy-efficient epistasis analysis on modern tightly-coupled heterogeneous platforms composed of multi-core CPUs and integrated GPUs. To fully exploit the capabilities of these systems, the proposed framework incorporates both task- and data-parallel approaches specifically tailored to enhance single and multi-objective epistasis detection on each device architecture, along with allowing efficient collaborative execution across all devices. The experimental results show the ability of the proposed framework to handle the heterogeneity of an Intel CPU+iGPU system, achieving performance and energy-efficiency gains of up to 5\(\times \) and 6\(\times \) in different parallel execution scenarios.

Previous models suggested that regulation of telomere length in Saccharomyces cerevisiae by Tel1(ATM) and Mec1(ATR) would parallel the established pathways regulating the DNA damage response. Here, we provide evidence that telomere length regulation differs from the DNA damage response in both the Tel1 and Mec1 pathways. We found that Rad53 mediates a Mec1 telomere length regulation pathway but is dispensable for Tel1 telomere length regulation, whereas in the DNA damage response, Rad53 is regulated by both Mec1 and Tel1. Using epistasis analysis with a Tel1 hypermorphic allele, Tel1-hy909, we found that the MRX complex is not required downstream of Tel1 for telomere elongation but is required downstream of Tel1 for the DNA damage response. Our data suggest that nucleolytic telomere end processing is not a required step for telomerase to elongate telomeres.

Summary Lignin provides structural support in perennial woody plants and is a complex phenolic polymer derived from phenylpropanoid pathway. Lignin biosynthesis is regulated by coordinated networks involving transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). However, the genetic networks underlying the lignin biosynthesis pathway for tree growth and wood properties remain unknown. Here, we used association genetics (additive, dominant and epistasis) and expression quantitative trait nucleotide (eQTN) mapping to decipher the genetic networks for tree growth and wood properties in 435 unrelated individuals of Populus tomentosa. We detected 124 significant associations (P ≤ 6.89E‐05) for 10 growth and wood property traits using 30 265 single nucleotide polymorphisms from 203 lignin biosynthetic genes, 81 TF genes, 36 miRNA genes and 71 lncRNA loci, implying their common roles in wood formation. Epistasis analysis uncovered 745 significant pairwise interactions, which helped to construct proposed genetic networks of lignin biosynthesis pathway and found that these regulators might affect phenotypes by linking two lignin biosynthetic genes. eQTNs were used to interpret how causal genes contributed to phenotypes. Lastly, we investigated the possible functions of the genes encoding 4‐coumarate: CoA ligase and cinnamate‐4‐hydroxylase in wood traits using epistasis, eQTN mapping and enzymatic activity assays. Our study provides new insights into the lignin biosynthesis pathway in poplar and enables the novel genetic factors as biomarkers for facilitating genetic improvement of trees.

Motivation For many traits, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this ‘missing heritability’ have been proposed. Single nucleotide polymorphism (SNP)–SNP interaction (epistasis), as one of the compelling models, has been widely studied. However, the genome-wide scan of epistasis, especially for quantitative traits, poses huge computational challenges. Moreover, covariate adjustment is largely ignored in epistasis analysis due to the massive extra computational undertaking. Results In the current study, we found striking differences among epistasis models using both simulation data and real biological data, suggesting that not only can covariate adjustment remove confounding bias, it can also improve power. Furthermore, we derived mathematical formulas, which enable the exhaustive epistasis scan together with full covariate adjustment to be expressed in terms of large matrix operation, therefore substantially improving the computational efficiency (∼104× faster than existing methods). We call the new method MatrixEpistasis. With MatrixEpistasis, we re-analyze a large real yeast dataset comprising 11 623 SNPs, 1008 segregants and 46 quantitative traits with covariates fully adjusted and detect thousands of novel putative epistasis with P-values Availability and implementation The method is implemented in R and available at https://github.com/fanglab/MatrixEpistasis. Supplementary information Supplementary data are available at Bioinformatics online.

Gene interactions, or epistasis, play a large role in determining evolutionary outcomes. The ruggedness of fitness landscapes, and thus the predictability of evolution and the accessibility of high-fitness genotypes, is determined largely by the pervasiveness of epistasis and the degree of correlation between similar genotypes. We created all possible pairings of three sets of five beneficial first-step mutations fixed during adaptive walks under three different regimes: selection on growth rate alone, on growth rate and thermal stability, and on growth rate and pH stability. All 30 double-mutants displayed negative, antagonistic epistasis with regard to growth rate and fitness, but positive epistasis and additivity were common for the stability phenotypes. This suggested that biophysically simple phenotypes, such as capsid stability, may on average behave more additively than complex phenotypes like viral growth rate. Growth rate epistasis was also smaller in magnitude when the individual effects of single mutations were smaller. Significant sign epistasis, such that the effect of a mutation that is beneficial in the wild-type background is deleterious in combination with a second mutation, emerged more frequently in intragenic mutational pairings than in intergenic pairs, and was evident in nearly half of the double-mutants, indicating that the fitness landscape is moderately uncorrelated and of intermediate ruggedness. Together, our results indicated that mutations may interact additively with regard to phenotype when considered at a basic, biophysical level, but that epistasis arises as a result of pleiotropic interactions between the individual components of complex phenotypes and diminishing returns arising from intermediate phenotypic optima.

Phosphorylated histone H2AX, termed ‘γH2AX’, mediates the chromatin response to DNA double strand breaks (DSBs) in mammalian cells. H2AX deficiency increases the numbers of unrepaired DSBs and translocations, which are partly associated with defects in non-homologous end joining (NHEJ) and contributing to genomic instability in cancer. However, the role of γH2AX in NHEJ of general DSBs has yet to be clearly defined. Here, we showed that despite little effect on overall NHEJ efficiency, H2AX deficiency causes a surprising bias towards accurate NHEJ and shorter deletions in NHEJ products. By analyzing CRISPR/Cas9-induced NHEJ and by using a new reporter for mutagenic NHEJ, we found that γH2AX, along with its interacting protein MDC1, is required for efficient classical NHEJ (C-NHEJ) but with short deletions and insertions. Epistasis analysis revealed that ataxia telangiectasia mutated (ATM) and the chromatin remodeling complex Tip60/TRRAP/P400 are essential for this H2AX function. Taken together, these data suggest that a subset of DSBs may require γH2AX-mediated short-range nucleosome repositioning around the breaks to facilitate C-NHEJ with loss of a few extra nucleotides at NHEJ junctions. This may prevent outcomes such as non-repair and translocations, which are generally more destabilizing to genomes than short deletions and insertions from local NHEJ.

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. Availability and implementation https://github.com/XiongLi2016/ESMO/tree/master/ESMO-common-master.

Long non-coding RNAs (lncRNAs) function in various biological processes. However, their roles in secondary growth of plants remain poorly understood. Here, 15,691 lncRNAs were identified from vascular cambium, developing xylem, and mature xylem of Populus tomentosa with high and low biomass using RNA-seq, including 1,994 lncRNAs that were differentially expressed (DE) among the six libraries. 3,569 cis-regulated and 3,297 trans-regulated protein-coding genes were predicted as potential target genes (PTGs) of the DE lncRNAs to participate in biological regulation. Then, 476 and 28 lncRNAs were identified as putative targets and endogenous target mimics (eTMs) of Populus known microRNAs (miRNAs), respectively. Genome re-sequencing of 435 individuals from a natural population of P. tomentosa found 34,015 single nucleotide polymorphisms (SNPs) within 178 lncRNA loci and 522 PTGs. Single-SNP associations analysis detected 2,993 associations with 10 growth and wood-property traits under additive and dominance model. Epistasis analysis identified 17,656 epistatic SNP pairs, providing evidence for potential regulatory interactions between lncRNAs and their PTGs. Furthermore, a reconstructed epistatic network, representing interactions of 8 lncRNAs and 15 PTGs, might enrich regulation roles of genes in the phenylpropanoid pathway. These findings may enhance our understanding of non-coding genes in plants.

The circadian clock is a complex regulatory network that enhances plant growth and fitness in a constantly changing environment. In Arabidopsis (Arabidopsis thaliana), the clock is composed of numerous regulatory feedback loops in which REVEILLE8 (RVE8) and its homologs RVE4 and RVE6 act in a partially redundant manner to promote clock pace. Here, we report that the remaining members of the RVE8 clade, RVE3 and RVE5, play only minor roles in the regulation of clock function. However, we find that RVE8 clade proteins have unexpected functions in the modulation of light input to the clock and the control of plant growth at multiple stages of development. In seedlings, these proteins repress hypocotyl elongation in a daylength- and sucrose-dependent manner. Strikingly, adult rve4 6 8 and rve3 4 5 6 8 mutants are much larger than wild-type plants, with both increased leaf area and biomass. This size phenotype is associated with a faster growth rate and larger cell size and is not simply due to a delay in the transition to flowering. Gene expression and epistasis analysis reveal that the growth phenotypes of rve mutants are due to the misregulation of PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 expression. Our results show that even small changes in PIF gene expression caused by the perturbation of clock gene function can have large effects on the growth of adult plants.

Understanding the pattern of epistasis—the non-independence of mutations—is critical for relating genotype and phenotype. However, the combinatorial complexity of potential epistatic interactions has severely limited the analysis of this problem. Using new mutational approaches, we report a comprehensive experimental study of all 213 mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein—an opportunity to examine epistasis up to the 13th order. The data show the existence of many high-order epistatic interactions between mutations, but also reveal extraordinary sparsity, enabling novel experimental and computational strategies for learning the relevant epistasis. We demonstrate that such information, in turn, can be used to accurately predict phenotypes in practical situations where the number of measurements is limited. Finally, we show how the observed epistasis shapes the solution space of single-mutation trajectories between the parental fluorescent proteins, informative about the protein’s evolutionary potential. This work provides conceptual and experimental strategies to profoundly characterize epistasis in a protein, relevant to both natural and laboratory evolution., Epistasis underlies the complexity of genotype-phenotype maps. Here, the authors analyze 8,192 mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein, and show the existence, but also the sparsity, of high-order epistatic interactions.

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work., Author summary Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data, suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use two of the largest available bacterial population genome data sets on Streptococcus pneumoniae (the pneumococcus) and Streptococcus pyogenes (group A Streptococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. We also discover a number of plausible targets of co-selection in S. pyogenes linked to RNA pseudouridine synthases. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

SUMMARYA major challenge facing the genetics of Autism Spectrum Disorders (ASD) is the large and growing number of candidate risk genes and gene variants of unknown functional significance. Here, we used Caenorhabditis elegans to systematically functionally characterize ASD-associated genes in vivo. Using our custom machine vision system we quantified 26 phenotypes spanning morphology, locomotion, tactile sensitivity, and habituation learning in 87 strains each carrying a mutation in an ortholog of an ASD-associated gene. We identified hundreds of novel genotype-phenotype relationships ranging from severe developmental delays and uncoordinated movement to subtle deficits in sensory and learning behaviors. We clustered genes by similarity in phenomic profiles and used epistasis analysis to discover parallel networks centered on CHD8•chd-7 and NLGN3•nlg-1 that underlie mechanosensory hyper-responsivity and impaired habituation learning. We then leveraged our data for in vivo functional assays to gauge missense variant effect. Expression of wild-type NLG-1 in nlg-1 mutant C. elegans rescued their sensory and learning impairments. Testing the rescuing ability of all conserved ASD-associated neuroligin variants revealed varied partial loss-of-function despite proper subcellular localization. Finally, we used CRISPR-Cas9 auxin inducible degradation to determine that phenotypic abnormalities caused by developmental loss of NLG-1 can be reversed by adult expression. This work charts the phenotypic landscape of ASD-associated genes, offers novel in vivo variant functional assays, and potential therapeutic targets for ASD.

Previous models suggested that regulation of telomere length inS. cerevisiaeby Tel1(ATM) and Mec1(ATR) parallel the established pathways regulating the DNA damage response. Here we provide evidence that telomere length regulation differs from the DNA damage response in both the Tel1 and Mec1 pathways. We found that Rad53 mediates a Mec1 telomere length regulation pathway but is dispensable for Tel1 telomere length regulation, whereas in the DNA damage response Rad53 is regulated by both Mec1 and Tel1. Using epistasis analysis with a Tel1 hypermorphic allele, Tel1-hy909, we found that the MRX complex is not required downstream of Tel1 for telomere elongation but is required downstream of Tel1 for the DNA damage response. Since models that invoke a required end processing event for telomerase elongation are primarily based on the yeast pathways, our data call for a re-examination of the requirement for telomere end processing in both yeast and mammalian cells.

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these. ispartof: HUMAN GENETICS vol:138 issue:4 pages:293-305 ispartof: location:Germany status: published

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.

Aurora-A is a conserved mitotic kinase overexpressed in many types of cancer. Growing evidence shows that Aurora-A plays a crucial role in DNA damage response (DDR) although this aspect has been less characterized. We isolated a new aur-A mutation, named aur-A949, in Drosophila, and we showed that it causes chromosome aberrations (CABs). In addition, aur-A949 mutants were sensitive to X-ray treatment and showed impaired γ-H2Av foci dissolution kinetics. To identify the pathway in which Aur-A works, we conducted an epistasis analysis by evaluating CAB frequencies in double mutants carrying aur-A949 mutation combined to mutations in genes related to DNA damage response (DDR). We found that mutations in tefu (ATM) and in the histone variant H2Av were epistatic over aur-A949 indicating that Aur-A works in DDR and that it is required for γ-H2Av foci dissolution. More interestingly, we found that a mutation in lig4, a gene belonging to the non-homologous end joining (NHEJ) repair pathway, was epistatic over aur-A949. Based on studies in other systems, which show that phosphorylation is important to target Lig4 for degradation, we hypothesized that in aur-A949 mutant cells, there is a persistence of Lig4 that could be, in the end, responsible for CABs. Finally, we observed a synergistic interaction between Aur-A and the homologous recombination (HR) repair system component Rad 51 in the process that converts chromatid deletions into isochromatid deletions. Altogether, these data indicate that Aur-A depletion can elicit chromosome damage. This conclusion should be taken into consideration, since some anticancer therapies are aimed at reducing Aurora-A expression. This work was funded by Progetto di Ateneo (Sapienza University of Rome) to FV and by Progetto Avvio alla Ricerca (Sapienza University of Rome) to CM.

Behçet’s Disease (BD) pathogenesis remains unclear, but some genetic loci and environmental factors are proposed to play a role. Here, we investigate the association of the endoplasmic reticulum aminopeptidase-1 (ERAP1) gene variants and HLA-B*51 with BD susceptibility and clinical manifestations in Iranian patients. In the study, 748 BD patients and 776 healthy individuals were included. The MGB-TaqMan Allelic Discrimination method was used to genotype 10 common missense single nucleotide polymorphisms (SNPs) and one intronic SNP in the ERAP1 gene region. We found no significant association between the 11 SNPs and BD in allelic and genotypic association tests. However, rs30187 showed the strongest association with BD in the recessive genotype model of the risk T allele in HLA-B*51 carriers. Although this became insignificant after correcting for multiple comparisons, the homozygous rs30187 risk allele genotype (TT) increased disease susceptibility in HLA-B*51 carriers in epistasis analysis, and the rs30187 TT recessive genotype showed a significant association with risk of cardiac involvement in the all patients and articular involvements in HLA-B*51 positive patients. Our findings suggest that gene-gene interactions between HLA-B*51 and ERAP1 variants is important for BD development, however, ERAP1 variants which interact with HLA-B*51 may differ among disease phenotypes or populations.

Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.

Overweight produces oxidative stress (OS) on the articular cartilage, with the subsequent risk of developing knee osteoarthritis (OA). Associations between genetic polymorphisms related to OS and OA have been reported, but it is currently unknown whether there exist interactions among them that affect OA development. To identify and evaluate interactions between multiple SNPs related to OS in Mexican knee OA patients. Ninety-two knee OA patients were included in the study, which were compared to 147 healthy controls. Nine variants of six genes (PEPD, AGER, IL6, ADIPOQ, PON1, and CA6) related to OS were genotyped in both study groups through the OpenArray system. Epistasis was analyzed with the multifactor dimensionality reduction (MDR) method. The MDR analysis revealed a significant interaction (p = 0.0107) between polymorphisms rs1501299 (ADIPOQ) and rs662 (PON1), with an entropy value of 9.84%; in addition, high and low risk genotypes were identified between these two polymorphisms. The effect of the interaction between rs1501299 (ADIPOQ) and rs662 (PON1) polymorphisms seems to play an important role in OA pathogenesis; so the epistasis analysis may provide an excellent tool for identifying individuals at high risk for developing OA.

In the yeast Saccharomyces cerevisiae, beneficial mutations selected during sulfate-limited growth are typically amplifications of the SUL1 gene, which encodes the high-affinity sulfate transporter, resulting in fitness increases of >35% . Cis-regulatory mutations have not been observed at this locus; however, it is not clear whether this absence is due to a low mutation rate such that these mutations do not arise, or they arise but have limited fitness effects relative to those of amplification. To address this question directly, we assayed the fitness effects of nearly all possible point mutations in a 493-base segment of the gene’s promoter through mutagenesis and selection. While most mutations were either neutral or detrimental during sulfate-limited growth, eight mutations increased fitness >5% and as much as 9.4%. Combinations of these beneficial mutations increased fitness only up to 11%. Thus, in the case of SUL1, promoter mutations could not induce a fitness increase similar to that of gene amplification. Using these data, we identified functionally important regions of the SUL1 promoter and analyzed three sites that correspond to potential binding sites for the transcription factors Met32 and Cbf1. Mutations that create new Met32- or Cbf1-binding sites also increased fitness. Some mutations in the untranslated region of the SUL1 transcript decreased fitness, likely due to the formation of inhibitory upstream open reading frames. Our methodology—saturation mutagenesis, chemostat selection, and DNA sequencing to track variants—should be a broadly applicable approach.

Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome segregation. Measuring homolog nondisjunction frequencies is laborious, and involves dissecting thousands of tetrads to detect missegregation of individually marked chromosomes. Here we describe a computational method (TetFit) to estimate the relative contributions of meiosis I nondisjunction and random-spore death to spore inviability in wild type and mutant strains. These values are based on finding the best-fit distribution of 4, 3, 2, 1, and 0 viable-spore tetrads to an observed distribution. Using TetFit, we found that meiosis I nondisjunction is an intrinsic component of spore inviability in wild-type strains. We show proof-of-principle that the calculated average meiosis I nondisjunction frequency determined by TetFit closely matches empirically determined values in mutant strains. Using these published data sets, TetFit uncovered two classes of mutants: Class A mutants skew toward increased nondisjunction death, and include those with known defects in establishing pairing, recombination, and/or synapsis of homologous chromosomes. Class B mutants skew toward random spore death, and include those with defects in sister-chromatid cohesion and centromere function. Epistasis analysis using TetFit is facilitated by the low numbers of tetrads (as few as 200) required to compare the contributions to spore death in different mutant backgrounds. TetFit analysis does not require any special strain construction, and can be applied to previously observed tetrad distributions.