Learning epistatic polygenic phenotypes with Boolean interactions.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Modeling within and between Sub-Genomes Epistasis of Synthetic Hexaploid Wheat for Genome-Enabled Prediction of Diseases.

Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.

Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus.

Pathologic type I interferon (T1IFN) expression is a key feature in systemic lupus erythematosus (SLE) that associates with disease activity. When compared to adult-onset disease, juvenile-onset (j)SLE is characterized by increased disease activity and damage, which likely relates to increased genetic burden. To identify T1IFN-associated gene polymorphisms (TLR7, IRAK1, miR-3142/miR-146a, IRF5, IRF7, IFIH1, IRF8, TYK2, STAT4), identify long-range linkage disequilibrium and gene:gene interrelations, 319 jSLE patients were genotyped using panel sequencing. Coupling phenotypic quantitative trait loci (QTL) analysis identified 10 jSLE QTL that associated with young age at onset (<12 years; IRAK1 [rs1059702], TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760, rs3747517], STAT4 [rs3021866], TYK2 [rs280501], IRF8 [rs1568391, rs6638]), global disease activity (SLEDAI-2 K >10; IFIH1 [rs1990760], STAT4 [rs3021866], IRF8 [rs903202, rs1568391, rs6638]), and mucocutaneous involvement (TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760]). This study suggests T1IFN-associated polymorphisms and gene:gene interrelations in jSLE. Genotyping of jSLE patients may allow for individualized treatment and care.

Dominance and multi-locus interaction.

Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position effects or dominance modifiers that are sometimes associated with pathogen resistance, lineage, sex, or mating type. Theoretical models have illustrated how variable dominance appears in the context of multi-locus interaction (epistasis). Here, we review empirical evidence for variable dominance and how the observed patterns may be captured by proposed epistatic models. We highlight how integrating epistasis and dominance is crucial for comprehensively understanding adaptation and speciation.

Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction.

The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.

Parental histone transfer caught at the replication fork.

In eukaryotes, DNA compacts into chromatin through nucleosomes1,2. Replication of the eukaryotic genome must be coupled to the transmission of the epigenome encoded in the chromatin3,4. Here we report cryo-electron microscopy structures of yeast (Saccharomyces cerevisiae) replisomes associated with the FACT (facilitates chromatin transactions) complex (comprising Spt16 and Pob3) and an evicted histone hexamer. In these structures, FACT is positioned at the front end of the replisome by engaging with the parental DNA duplex to capture the histones through the middle domain and the acidic carboxyl-terminal domain of Spt16. The H2A-H2B dimer chaperoned by the carboxyl-terminal domain of Spt16 is stably tethered to the H3-H4 tetramer, while the vacant H2A-H2B site is occupied by the histone-binding domain of Mcm2. The Mcm2 histone-binding domain wraps around the DNA-binding surface of one H3-H4 dimer and extends across the tetramerization interface of the H3-H4 tetramer to the binding site of Spt16 middle domain before becoming disordered. This arrangement leaves the remaining DNA-binding surface of the other H3-H4 dimer exposed to additional interactions for further processing. The Mcm2 histone-binding domain and its downstream linker region are nested on top of Tof1, relocating the parental histones to the replisome front for transfer to the newly synthesized lagging-strand DNA. Our findings offer crucial structural insights into the mechanism of replication-coupled histone recycling for maintaining epigenetic inheritance.

Epistasis between mutator alleles contributes to germline mutation spectrum variability in laboratory mice.

Maintaining germline genome integrity is essential and enormously complex. Although many proteins are involved in DNA replication, proofreading, and repair, mutator alleles have largely eluded detection in mammals. DNA replication and repair proteins often recognize sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum of de novo mutations - the frequencies of C>T, A>G, etc. - will differ between genomes that harbor either a mutator or wild-type allele. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair gene Mutyh that increased the C>A germline mutation rate in a family of inbred mice known as the BXDs (Sasani et al., 2022, Ashbrook et al., 2021). In this study we developed a new method to detect alleles associated with mutation spectrum variation and applied it to mutation data from the BXDs. We discovered an additional C>A mutator locus on chromosome 6 that overlaps Ogg1, a DNA glycosylase involved in the same base-excision repair network as Mutyh (David et al., 2007). Its effect depends on the presence of a mutator allele near Mutyh, and BXDs with mutator alleles at both loci have greater numbers of C>A mutations than those with mutator alleles at either locus alone. Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles and may be applicable to mutation data from humans and other model organisms.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution.

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population's mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn.

Positive epistasis drives clavulanic acid resistance in double mutant libraries of BlaC ß-lactamase.

Phenotypic effects of mutations are highly dependent on the genetic backgrounds in which they occur, due to epistatic effects. To test how easily the loss of enzyme activity can be compensated for, we screen mutant libraries of BlaC, a ß-lactamase from Mycobacterium tuberculosis, for fitness in the presence of carbenicillin and the inhibitor clavulanic acid. Using a semi-rational approach and deep sequencing, we prepare four double-site saturation libraries and determine the relative fitness effect for 1534/1540 (99.6%) of the unique library members at two temperatures. Each library comprises variants of a residue known to be relevant for clavulanic acid resistance as well as residue 105, which regulates access to the active site. Variants with greatly improved fitness were identified within each library, demonstrating that compensatory mutations for loss of activity can be readily found. In most cases, the fittest variants are a result of positive epistasis, indicating strong synergistic effects between the chosen residue pairs. Our study sheds light on a role of epistasis in the evolution of functional residues and underlines the highly adaptive potential of BlaC.

Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aß42 ratio in ADNI cohort.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Genetic architecture of trait variance in craniofacial morphology.

The genetic architecture of trait variance has long been of interest in genetics and evolution. One of the earliest attempts to understand this architecture was presented in Lerner's Genetic Homeostasis (1954). Lerner proposed that heterozygotes should be better able to tolerate environmental perturbations because of functional differences between the alleles at a given locus, with each allele optimal for slightly different environments. This greater robustness to environmental variance, he argued, would result in smaller trait variance for heterozygotes. The evidence for Lerner's hypothesis has been inconclusive. To address this question using modern genomic methods, we mapped loci associated with differences in trait variance (vQTL) on 1,101 individuals from the F34 of an advanced intercross between LG/J and SM/J mice. We also mapped epistatic interactions for these vQTL in order to understand the influence of epistasis for the architecture of trait variance. We did not find evidence supporting Lerner's hypothesis, that heterozygotes tend to have smaller trait variances than homozygotes. We further show that the effects of most mapped loci on trait variance are produced by epistasis affecting trait means and that those epistatic effects account for about a half of the differences in genotypic-specific trait variances. Finally, we propose a model where the different interactions between the additive and dominance effects of the vQTL and their epistatic partners can explain Lerner's original observations but can also be extended to include other conditions where heterozygotes are not the least variable genotype.

Global epistasis in plasmid-mediated antimicrobial resistance.

Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.

QTL epistasis plays a role of homeostasis on heading date in rice.

If there was no gene interaction, the gene aggregation effect would increase infinitely with the increase of gene number. Epistasis avoids the endless accumulation of gene effects, playing a role of homeostasis. To confirm the role, QTL epistases were analyzed by four single-segment substitution lines with heading date QTLs in this paper. We found that QTLs of three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the relationship "positive QTLs-negative one order interactions-positive two order interactions". In this way, the aggregation effect of QTLs was partially neutralized by the opposite epistatic effect sum. There also were two exceptions, QTL OsMADS50 and gene Hd3a-2 were always with consistent effect directions with their epistases, implying they could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

Wright was right: leveraging old data and new methods to illustrate the critical role of epistasis in genetics and evolution.

Much of evolutionary theory is predicated on assumptions about the relative importance of simple additive versus complex epistatic genetic architectures. Previous work suggests traits strongly associated with fitness will lack additive genetic variation, whereas traits less strongly associated with fitness are expected to exhibit more additive genetic variation. We use a quantitative genetics method, line cross analysis, to infer genetic architectures that contribute to trait divergence. By parsing over 1,600 datasets by trait type, clade, and cross divergence, we estimated the relative importance of epistasis across the tree of life. In our comparison between life-history traits and morphological traits, we found greater epistatic contributions to life-history traits. Our comparison between plants and animals showed that animals have more epistatic contribution to trait divergence than plants. In our comparison of within-species versus between-species crosses, we found that only animals exhibit a greater epistatic contribution to trait divergence as divergence increases. While many scientists have argued that epistasis is ultimately of little importance, our results show that epistasis underlies much of trait divergence and must be accounted for in theory and practical applications like domestication, conservation breeding design, and understanding complex diseases.

Evaluation of gene-gene interaction between the interleukin (IL)-2 and IL-2RA gene polymorphisms in schizophrenia patients in the Turkish Population.

OBJECTIVES: To evaluate the genetic polymorphisms in IL-2 and IL-2RA genes in schizophrenia (SCZ) patients by comparing them with healthy controls. METHODS: A sample of 127 patients with SCZ and 100 healthy volunteers were included in the case-control study. These individuals were consecutively selected from the Malazgirt State Hospital Psychiatry Outpatient Clinic in Mus, Turkey, over the three months from October 2020 to December 2020. The Structured Clinical Interview for DSM-5 Disorders, Clinician Version (SCID-5-CV) was used to confirm the diagnosis according to the DSM-5 criteria. In addition, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to determine gene polymorphisms from DNA material. RESULTS: Our findings indicated significant differences in the IL-2 genotype and allele frequencies between SCZ patients and the healthy control group. Specifically, the frequency of the homozygous GG genotype was notably higher in SCZ patients compared to the control group. Conversely, when comparing the IL-2RA genotype and allele frequencies of SCZ patients with the control group, no statistically significant differences were observed between the 2 groups. When compared to individuals with other genotypes, interaction analysis indicated that carriers of the GG/AG (IL-2/IL-2RA) genotype demonstrated a significantly increased risk of SCZ. CONCLUSION: In light of the analyses, our study indicates that while the IL-2 genotype polymorphism may be considered a risk factor for developing SCZ, the IL-2RA variant was not associated with SCZ among Turkish patients.

In vivo functional phenotypes from a computational epistatic model of evolution.

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Mitonuclear epistasis involving TP63 and haplogroup Uk: Risk of rapid progression of knee OA in patients from the OAI.

OBJECTIVE: To investigate genetic interactions between mitochondrial deoxyribonucleic acid (mtDNA) haplogroups and nuclear single nucleotide polymorphisms (nSNPs) to analyze their impact on the development of the rapid progression of knee osteoarthritis (OA). DESIGN: A total of 1095 subjects from the Osteoarthritis Initiative, with a follow-up time of at least 48-months, were included. Appropriate statistical approaches were performed, including generalized estimating equations adjusting for age, gender, body mass index, contralateral knee OA, Western Ontario and McMaster Universities Osteoarthritis Index pain, previous injury in target knee and the presence of the mtDNA variant m.16519C. Additional genomic data consisted in the genotyping of Caucasian mtDNA haplogroups and eight nSNPs previously associated with the risk of knee OA in robust genome-wide association studies. RESULTS: The simultaneous presence of the G allele of rs12107036 at TP63 and the haplogroup Uk significantly increases the risk of a rapid progression of knee OA (odds ratio = 1.670; 95% confidence interval [CI]: 1.031-2.706; adjusted p-value = 0.027). The assessment of the population attributable fraction showed that the highest proportion of rapid progressors was under the simultaneous presence of the G allele of rs12107036 and the haplogroup Uk (23.4%) (95%CI: 7.89-38.9; p-value < 0.05). The area under the curve of the cross-validation model (0.730) was very similar to the obtained for the predictive model (0.735). A nomogram was constructed to help clinicians to perform clinical trials or epidemiologic studies. CONCLUSIONS: This study demonstrates the existence of a mitonuclear epistasis in OA, providing new mechanisms by which nuclear and mitochondrial variation influence the susceptibility to develop different OA phenotypes.

Identifying potential genetic epistasis implicated in Alzheimer's disease via detection of SNP-SNP interaction on quantitative trait CSF Aß42.

Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aß42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aß42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.