Epistatic Effects Research Articles

Epistatic effects of IGHG and FCGRIIB genes on the development of Alzheimer’s disease in African Americans

Genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have identified a large number of susceptibility genes, but most of AD heritability remains unexplained, implying the existence of additional genes. Furthermore, the majority of the GWAS have been conducted in people of European descent, and the genes important for AD susceptibility in people of African descent have been underexplored. In this hypothesis-generating prospective cohort study, we genotyped 191 African Americans (AAs) from three longitudinal cohorts on aging for the IgG3 allotype GM6, which is expressed exclusively in people of African descent, and assessed its interaction with IGHG, FCGRIIB, and HLA-DRB1 genes. Cox proportional hazards modeling showed that GM6 by itself was not significantly associated with AD development. However, there was evidence of epistatic interaction: The risk of developing AD associated with GM6 positivity was significantly different (p = 0.0098) in non-GM17/GM17 participants compared with GM 17/GM17 participants. Specifically, in non-GM17/GM17 participants, the risk of AD was over fourfold higher in GM6-positive participants compared with GM 6-negative participants (HR = 4.63). Similarly, risk of developing AD associated with GM6 positivity was marginally different in non-FCGRIIB TT participants compared with FCGRIIB TT participants. In non-FCGRIIB TT participants, the risk of developing AD was over twofold higher in GM6-positive participants compared with GM6-negative participants (HR = 2.44). This is the first report suggesting that immunoglobulin GM allotypes might play a role in AD etiology among AAs; however, since this was largely a hypothesis-generating study, replication in larger cohorts would be required to confirm this finding.

Dopaminergic Epistases in Schizophrenia

Background: The dopaminergic theory, the oldest and most comprehensively analyzed neurotransmitter theory of schizophrenia, remains a focal point of research. Methods: This systematic review examines the association between combinations of 14 dopaminergic genes and the risk of schizophrenia. The selected genes include dopamine receptors (DRD1–5), metabolizing enzymes (COMT, MAOA, MAOB, DBH), synthesizing enzymes (TH, DDC), and dopamine transporters (DAT, VMAT1, and VMAT2). Results: Recurring functional patterns show combinations with either hyperdopaminergic effects in limbic and striatal regions or high striatal and low prefrontal dopamine levels. The protective statuses of certain alleles or genotypes are often maintained in epistatic effects; however, exceptions exist. This complexity could explain the inconsistent results in previous genetic studies. Investigating individual alleles may be insufficient due to the heterozygous advantage observed in some studies. Conclusions: Schizophrenia may not be a monolithic disease, but rather a sum of different phenotypes which respond uniquely to different treatment and prevention approaches.

Validation of QTLs associated with corn borer resistance and grain yield: implications in maize breeding.

Validations of previously detected quantitative trait loci (QTLs) to assess their reliability are crucial before implementing breeding programs. The objective of this study was to determine the reliability and practical usefulness of previously reported QTLs for resistance to stem tunneling by the Mediterranean stem borer (MSB) and yield. These authors used approximately 600 recombinant inbred lines (RILs) from a multiparent advanced generation intercross (MAGIC) population to map QTL using a genome-wide association study (GWAS) approach. We identified RILs situated at the extremes of resistance and yield distributions within the whole MAGIC, and those QTLs were evaluated per se and crossed to a tester (A638) using lattice designs. In each set, a significant single-nucleotide polymorphism (SNP) was considered validated if (1) the same SNP was associated with the trait with a p-value < 0.02, or (2) within a ±2-Mbp interval, an SNP associated with the trait exhibited a p-value < 0.02 and demonstrated linkage disequilibrium (r2 > 0.2) with the SNPs previously reported. The novel QTL validation approach was implemented using improved experimental designs that led to higher heritability estimates for both traits compared to those estimated with the whole MAGIC population. The procedure used allowed us to jointly validate several QTL and to ascertain their possible contribution to hybrid improvement. Specifically, nearly three-quarters of the QTLs for tunnel length were confirmed. Notably, QTLs located in the genomic region 6.05-6.07 were consistently validated across different sets and have been previously linked to resistance against stalk tunneling in various mapping populations. For grain yield, approximately 10 out of 16 QTLs were validated. The validation rate for yield was lower than for tunnel length, likely due to the influence of dominance and/or epistatic effects. Overall, 9 out of 21 QTLs for tunnel length and 6 out of 17 QTLs for grain yield identified in our previous research were validated across both validation sets, indicating a moderate genetic correlation between per se and testcross performance of inbred lines. These findings offer insights into the reliability of QTL and genomic predictions, both derived from assessments conducted on the entire MAGIC population. Genomic predictions for tunnel length based on inbred line evaluations could be useful to develop more resistant hybrids; meanwhile, genomic prediction for yield could only be valid in a homozygous background.

Genetic Variation and QTL Analysis of Crude Fibre and Quality Traits in the Doubled Haploid Winter Oilseed Rape (Brassica napus L.) Population R19 x Lord

ABSTRACTThe oil‐extracted meal of oilseed rape is used for feeding livestock and is a potential source of vegetable protein for human consumption. Its protein quality is limited by glucosinolates, phenolic compounds, phytates and crude fibres. The reduction of cellulose, hemicellulose and indigestible lignin in black‐seeded oilseed rape may enhance the protein content, colour, sensory quality and digestibility. The objective of this study was to analyse the inheritance of seed fibre components in relation to oil, protein and other quality traits in a DH population derived from the cross of low lignin lines R19 and cv. Lord. The population of 143 lines was tested in field experiments in six environments and was genotyped with Illumina 15K Brassica SNP chip. QTL and candidate genes were identified using the high‐quality long‐read reference genome of Express 617. Major QTL for fibre components were identified on chromosomes C01 and C03 with opposite additive and epistatic effects. Fibre QTL colocated with each other and QTL for seed colour, oil and protein. Epistatic effects were identified, which resulted in a further decrease of fibre content.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

Diverse epistatic effects in barley-powdery mildew interactions localize to host chromosome hotspots

Diverse epistatic effects in barley-powdery mildew interactions localize to host chromosome hotspots

Epistatic interactions between oxytocin- and dopamine-related genes and trust.

Trust is an essential human trait. Although research suggests that the interplay between oxytocinergic and dopaminergic systems affects trust formation, little research has focused on epistatic (i.e., gene by gene) interaction effects of oxytocin- and dopamine-related genes on trust. Using a sample of 348 adults (114 men), we aimed to investigate the associations between genetic variants in oxytocin- and dopamine-related genes and the general, neighborhood, and institutional trust with consideration of sex differences. Three-way interaction between oxytocin-related gene genotypes, dopamine-related genotypes, and sex was found for the oxytocin receptor gene (OXTR)rs1042778 and the Catechol-O-Methyltransferase gene (COMT) rs4680 genotypes (p = 0.02) and for OXTR rs2254298 and the dopamine D2 receptor gene (DRD2) rs1800497 genotypes (p = 0.01). Further sex-stratified analyses revealed that the interaction between OXTR rs1042778 and COMT rs4680 genotypes was associated with neighborhood trust among men (p = 0.0007). Also, the interaction between OXTR rs2254298 and DRD2 rs1800497 genotypes was associated with institutional trust among men (p = 0.005). Post-hoc analyses found that men with OXTR rs1042778 TG/TT and COMT rs4680 GG genotypes reported higher neighborhood trust than those with GG + AG/AA (B = 13.49, SE = 4.68, p = 0.02), TG/TT + AG/AA (B = 23.00, SE = 5.99, p = 0.001), and GG + GG (B = 18.53, SE = 5.25, p = 0.003). Similarly, men with OXTR rs2254298 AG/AA and DRD2 rs1800497 CC genotypes showed higher institutional trust than those with AG/AA + TT/TC (B = 15.67, SE = 5.30, p = 0.02). We could not find any interacting associations among women. While we note that our sample size and candidate gene approach have a potential risk of chance findings, our study provides an important foundation toward further exploration of sex-specific epistatic interaction effects of oxytocin- and dopamine-related genes on trust, indicating the importance of both systems in trust formation.

Abstract C015: Selective and pairwise epistatic effects of somatic mutations in KRAS wild-type pancreatic cancer

Abstract About 8 to 10% of all pancreatic adenocarcinomas (PAAD) do not possess canonical mutations in KRAS. A sizable proportion of these KRAS wild-type (WT) pancreatic cancers harbor mutations in alternative MAPK-pathway driver genes. These alternative MAPK-pathway genes represent a set of therapeutic targets that might be exploited to treat individuals in this PAAD subset. Previous studies have prioritized the mutational- target landscape of KRAS WT PAAD by ranking genes determined by significance thresholds to be drivers by the prevalence of their somatic mutations in tumors. These prevalences usefully inform the size of the patient population that would be candidates for targeted therapeutics. However, prevalence is markedly influenced by underlying mutation rate. It is not a metric of cancer effect; it does not directly quantify the degree to which a driver mutation increases cancer cell survival and proliferation, nor do observations of co-occurrence or mutual exclusivity quantify epistasis. Therefore, to better guide the development of targeted therapeutics, we assembled a rich dataset of 6,391 PAAD tumor sequences from TCGA, UTSW, ICGC, QCMG, Yale-Gilead, and the AACR Project GENIE.We analyzed the cancer effect sizes of somatic variants in PAADs. Furthermore, we determined the selective epistatic interactions between KRAS and other PAAD driver genes. Nine of the 20 highest-effect variants were nonsynonymous substitutions in KRAS, with the prevalence of these mutations ranging from 18 to 2390 (median 93). This high frequency of highly oncogenic KRAS mutants supports the longstanding conception that KRAS mutations dominate the oncogenic landscape of PAAD. However, of the 6,391 cases analyzed, 627 (9.8%) were KRAS WT. To further investigate the oncogenic landscape of these samples, we employed a pairwise epistasis model of selection. We discovered that not only are alternative MAPK drivers (BRAF and NRAS) present in KRAS WT PAAD, but that they show antagonistic epistasis with KRAS mutation. Moreover, we found that non-MAPK drivers CTNNB1 and IDH1 also exhibit substantial antagonistic epistasis with KRAS. Also, in a KRAS-substituted background, selection for mutations in tumor suppressor genes TP53, CDKN2A, and SMAD4 was increased. Furthermore, mutations in BRAF, NRAS, CTNNB1, TP53, CDKN2A, and SMAD4, genes commonly considered to be drivers of PAAD, in addition to a previously unrecognized driver IDH1, suppressed selection for KRAS mutations. Not only MAPK drivers, but also other oncogenic drivers of PAAD at both high and low prevalence exhibit antagonistic somatic selective epistasis. Treatments targeting these variants that are antagonistically epistatic with KRAS may rescue the full strength of selection on KRAS variants, warranting continued monitoring for KRAS variants in resurgent cancer. Moreover, as resistance to KRAS inhibitors is likely caused by gained alterations in alternative MAPK drivers, exploration of treatments targeting these mutations may additionally provide an avenue to overcome therapeutic resistance in PAADs. Citation Format: Rishi M Shah, Can M Ersahin, Jeffrey D Mandell, Vincent L Cannataro, Jeffrey P Townsend. Selective and pairwise epistatic effects of somatic mutations in KRAS wild-type pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C015.

AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants: Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance.

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that protect and restore ACE2-binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class 1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.

Assessing the opportunity for selection to impact morphological traits in crosses between two Solanum species.

Within biology, there have been long-standing goals to understand how traits impact fitness, determine the degree of adaptation, and predict responses to selection. One key step in answering these questions is to study the mode of gene action or genetic architecture of traits. The genetic architecture underlying a trait will ultimately determine whether selection can lead to a change in the phenotype. Theoretical and empirical research have shown that additive architectures are most responsive to selection. The genus Solanum offers a unique system to quantify the genetic architecture of traits. Crosses between Solanum pennellii and S. lycopersicum, which have evolved unique adaptive traits for very different environments, offer an opportunity to investigate the genetic architecture of a variety of morphological traits that often are not variable within species. We generated cohorts between strains of these two Solanum species and collected phenotypic data for eight morphological traits. The genetic architectures underlying these traits were estimated using an information-theoretic approach to line cross analysis. By estimating the genetic architectures of these traits, we were able to show a key role for maternal and epistatic effects and infer the accessibility of these traits to selection.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Background Rhizomania counts as the most important disease in sugar beet Beta vulgaris L. for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that carries resistances at the two known resistance clusters. The sugar beet population was genotyped using single nucleotide polymorphism (SNP) markers. Methods First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. Results From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the conclusion that more than the two known resistance clusters are involved in rhizomania resistance and that genetic interactions affect rhizomania resistance. Finally, we have analysed which SNPs were repeatedly detected in the feature selection process and discovered four SNPs, two of which are located on chromosomes that were previously not associated with rhizomania resistance.

Epistatic interaction has the reverse effects with its constitutive quantitative trait loci

Epistasis is one of important genetic components for a quantitative trait in plant. Eshed and Zamir found negative epistatic interactions of quantitative trait loci in Tomato first. We detected that positive (negative) QTLs generated mostly negative (positive) epistatic interactions on heading date in rice, and then proposed the hypothese that QTL epistasis plays a role of homeostasis in one of our recent papers. In order to further provide additional evidence, the effects of QTLs and their epistatic effects on two quantitative traits of plant height (ph) and thousand kernel weight (tkw) were analyzed in this study. The same regularity was verified again. We detected that positive ph QTLs and negative tkw QTLs always generated reverse epistatic effects, respectively. Moreover, high-order epistatic effects were estimated on these two traits. The sum of all epistatic effects would partially neutralize the additive of constitutive QTL effects. This feature of epistsis would be the mechanism for bionts to maintain homeostasis while the obstacle for human to achieve the pyramiding breeding objectives. More evidences are still being collected to support our assumption.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Analysis of Genetic Architecture through Generation Mean Analysis for Yield and Yield Contributing Traits in Crosses of Indian Mustard (Brassica juncea)

Generation mean analysis was deployed to study the inheritance of 12 yield and its component traits in two crosses viz. PR-2009-6 × ALBELI (Family A) and PR-2009-6 × RGN-73 (Family B) of Indian mustard [Brassica juncea (L.) Czern&amp; Coss] for parents P1 and P2, F1 , F2 , BC1 and BC2 generations. Analysis of variance showed presence of significant variability among families and also within families (among progenies) for various traits under study except for days to maturity, seeds per siliquae and siliquae length. Family A showed considerable interaction effects for siliquae on main raceme, number of seeds per siliquae and oil content. Family B displayed epistatic interactions for characters such as plant height, length of main raceme, siliquae on main raceme, siliquae length, oil content seed yield per plant and glucosinolate content. Fixable effects [d] and [i] were found important in the inheritance of plant height, seed yield per plant and glucosinolate content. Among non fixable effects, dominance [h] gene action was significantly important for determining length of main raceme, number of siliquae on main raceme, siliquae length, oil content, seed yield per plant and glucosinolate content. Interaction gene effects were found significant in controlling plant height, length of main raceme, number of secondary branches per plant, siliquae on main raceme, siliqua length,1000-seed weight, oil content, seed yield per plant and glucosinolate content. Thus, both main effects and epistatic effects were found important in controlling the various traits under study.

Massively parallel free energy calculations for in silico affinity maturation of designed miniproteins.

Computational protein design efforts continue to make remarkable advances, yet the discovery of high-affinity binders typically requires large-scale experimental screening of site-saturated mutant (SSM) libraries. Here, we explore how massively parallel free energy methods can be used for in silico affinity maturation of de novo designed binding proteins. Using an expanded ensemble (EE) approach, we perform exhaustive relative binding free energy calculations for SSM variants of three miniproteins designed to bind influenza A H1 hemagglutinin by Chevalier et al. (2017). We compare our predictions to experimental ΔΔ G values inferred from a Bayesian analysis of the high-throughput sequencing data, and to state-of-the-art predictions made using the Flex ddG Rosetta protocol. A systematic comparison reveals prediction accuracies around 2 kcal/mol, and identifies net charge changes, large numbers of alchemical atoms, and slow side chain conformational dynamics as key contributors to the uncertainty of the EE predictions. Flex ddG predictions are more accurate on average, but highly conservative. In contrast, EE predictions can better classify stabilizing and destabilizing mutations. We also explored the ability of SSM scans to rationalize known affinity-matured variants containing multiple mutations, which are non-additive due to epistatic effects. Simple electrostatic models fail to explain non-additivity, but observed mutations are found at positions with higher Shannon entropies. Overall, this work suggests that simulation-based free energy methods can provide predictive information for in silico affinity maturation of designed miniproteins, with many feasible improvements to the efficiency and accuracy within reach.

Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape.

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.

A complete chromosome substitution mapping panel reveals genome-wide epistasis in Arabidopsis

Chromosome substitution lines (CSLs) are tentatively supreme resources to investigate non-allelic genetic interactions. However, the difficulty of generating such lines in most species largely yielded imperfect CSL panels, prohibiting a systematic dissection of epistasis. Here, we present the development and use of a unique and complete panel of CSLs in Arabidopsis thaliana, allowing the full factorial analysis of epistatic interactions. A first comparison of reciprocal single chromosome substitutions revealed a dependency of QTL detection on different genetic backgrounds. The subsequent analysis of the complete panel of CSLs enabled the mapping of the genetic interactors and identified multiple two- and three-way interactions for different traits. Some of the detected epistatic effects were as large as any observed main effect, illustrating the impact of epistasis on quantitative trait variation. We, therefore, have demonstrated the high power of detection and mapping of genome-wide epistasis, confirming the assumed supremacy of comprehensive CSL sets.

The evolution of mammalian Rem2: unraveling the impact of purifying selection and coevolution on protein function, and implications for human disorders.

Rad And Gem-Like GTP-Binding Protein 2 (Rem2), a member of the RGK family of Ras-like GTPases, is implicated in Huntington's disease and Long QT Syndrome and is highly expressed in the brain and endocrine cells. We examine the evolutionary history of Rem2 identified in various mammalian species, focusing on the role of purifying selection and coevolution in shaping its sequence and protein structural constraints. Our analysis of Rem2 sequences across 175 mammalian species found evidence for strong purifying selection in 70% of non-invariant codon sites which is characteristic of essential proteins that play critical roles in biological processes and is consistent with Rem2's role in the regulation of neuronal development and function. We inferred epistatic effects in 50 pairs of codon sites in Rem2, some of which are predicted to have deleterious effects on human health. Additionally, we reconstructed the ancestral evolutionary history of mammalian Rem2 using protein structure prediction of extinct and extant sequences which revealed the dynamics of how substitutions that change the gene sequence of Rem2 can impact protein structure in variable regions while maintaining core functional mechanisms. By understanding the selective pressures, protein- and gene - interactions that have shaped the sequence and structure of the Rem2 protein, we gain a stronger understanding of its biological and functional constraints.

Rare Drivers at Low Prevalence with High Cancer Effects in T-Cell and B-Cell Pediatric Acute Lymphoblastic Leukemia.

The genomic analyses of pediatric acute lymphoblastic leukemia (ALL) subtypes, particularly T-cell and B-cell lineages, have been pivotal in identifying potential therapeutic targets. Typical genomic analyses have directed attention toward the most commonly mutated genes. However, assessing the contribution of mutations to cancer phenotypes is crucial. Therefore, we estimated the cancer effects (scaled selection coefficients) for somatic substitutions in T-cell and B-cell cohorts, revealing key insights into mutation contributions. Cancer effects for well-known, frequently mutated genes like NRAS and KRAS in B-ALL were high, which underscores their importance as therapeutic targets. However, less frequently mutated genes IL7R, XBP1, and TOX also demonstrated high cancer effects, suggesting pivotal roles in the development of leukemia when present. In T-ALL, KRAS and NRAS are less frequently mutated than in B-ALL. However, their cancer effects when present are high in both subtypes. Mutations in PIK3R1 and RPL10 were not at high prevalence, yet exhibited some of the highest cancer effects in individual T-cell ALL patients. Even CDKN2A, with a low prevalence and relatively modest cancer effect, is potentially highly relevant for the epistatic effects that its mutated form exerts on other mutations. Prioritizing investigation into these moderately frequent but potentially high-impact targets not only presents novel personalized therapeutic opportunities but also enhances the understanding of disease mechanisms and advances precision therapeutics for pediatric ALL.

Epistasis and pleiotropy-induced variation for plant breeding.

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.