Non-additive Interaction Effects Research Articles

Interactive Effects of Climate Change and Pathogens on Plant Performance: A Global Meta-Analysis.

Plant health is increasingly threatened by abiotic and biotic stressors linked to anthropogenic global change. These stressors are frequently studied in isolation. However, they might have non-additive (antagonistic or synergistic) interactive effects that affect plant communities in unexpected ways. We conducted a global meta-analysis to summarize existing evidence on the joint effects of climate change (drought and warming) and biotic attack (pathogens) on plant performance. We also investigated the effect of drought and warming on pathogen performance, as this information is crucial for a mechanistic interpretation of potential indirect effects of climate change on plant performance mediated by pathogens. The final databases included 1230 pairwise cases extracted from 117 recently published scientific articles (from 2006) on a global scale. We found that the combined negative effects of drought and pathogens on plant growth were lower than expected based on their main effects, supporting the existence of antagonistic interactions. Thus, the larger the magnitude of the drought, the lower the pathogen capacity to limit plant growth. On the other hand, the combination of warming and pathogens caused larger plant damage than expected, supporting the existence of synergistic interactions. Our results on the effects of drought and warming on pathogens revealed a limitation of their growth rates and abundance invitro but an improvement under natural conditions, where multiple factors operate across the microbiome. Further research on the impact of climate change on traits explicitly defining the infective ability of pathogens would enhance the assessment of its indirect effects on plants. The evaluated plant and pathogen responses were conditioned by the intensity of drought or warming and by moderator categorical variables defining the pathosystems. Overall, our findings reveal the need to incorporate the joint effect of climatic and biotic components of global change into predictive models of plant performance to identify non-additive interactions.

The context dependency of multiple stressor effects on biodiversity: A commentary on Kefford et al. (2022).

The context dependency of multiple stressor effects on biodiversity: A commentary on Kefford et al. (2022).

The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies.

The X‐chromosome is often excluded from genome‐wide association studies because of analytical challenges. Some of the problems, such as the random, skewed, or no X‐inactivation model uncertainty, have been investigated. Other considerations have received little to no attention, such as the value in considering nonadditive and gene–sex interaction effects, and the inferential consequence of choosing different baseline alleles (i.e., the reference vs. the alternative allele). Here we propose a unified and flexible regression‐based association test for X‐chromosomal variants. We provide theoretical justifications for its robustness in the presence of various model uncertainties, as well as for its improved power when compared with the existing approaches under certain scenarios. For completeness, we also revisit the autosomes and show that the proposed framework leads to a more robust approach than the standard method. Finally, we provide supporting evidence by revisiting several published association studies. Supporting Information for this article are available online.

Zooplankton carcasses stimulate microbial turnover of allochthonous particulate organic matter

Carbon turnover in aquatic environments is dependent on biochemical properties of organic matter (OM) and its degradability by the surrounding microbial community. Non-additive interactive effects represent a mechanism where the degradation of biochemically persistent OM is stimulated by the provision of bioavailable OM to the degrading microbial community. Whilst this is well established in terrestrial systems, whether it occurs in aquatic ecosystems remains subject to debate. We hypothesised that OM from zooplankton carcasses can stimulate the degradation of biochemically persistent leaf material, and that this effect is influenced by the daphnia:leaf OM ratio and the complexity of the degrading microbial community. Fresh Daphnia magna carcasses and 13C-labelled maize leaves (Zea mays) were incubated at different ratios (1:1, 1:3 and 1:5) alongside either a complex microbial community (<50 µm) or solely bacteria (<0.8 µm). 13C stable-isotope measurements of CO2 analyses were combined with phospholipid fatty acids (PLFA) analysis and DNA sequencing to link metabolic activities, biomass and taxonomic composition of the microbial community. Our experiments indicated a significantly higher respiration of leaf-derived C when daphnia-derived OM was most abundant (i.e. daphnia:leaf OM ratio of 1:1). This process was stronger in a complex microbial community, including eukaryotic microorganisms, than a solely bacterial community. We concluded that non-additive interactive effects were a function of increased C–N chemodiversity and microbial complexity, with the highest net respiration to be expected when chemodiversity is high and the degrading community complex. This study indicates that identifying the interactions and processes of OM degradation is one important key for a deeper understanding of aquatic and thus global carbon cycle.

An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma

BackgroundAsthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables.ResultsWe evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 233 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to quantify interaction effects using an information-theory metric known as Information Gain (IG). We performed analyses in the full dataset and in sex-stratified subsets. Our analyses identified several interaction models significantly, and suggestively, associated with BDR. The strongest interaction significantly associated with BDR was a pairwise interaction between pre-natal smoke exposure and socioeconomic status (full dataset IG: 2.78%, p = 0.001; female IG: 7.27%, p = 0.004)). Sex-stratified analyses yielded divergent results for females and males, indicating the presence of sex-specific effects.ConclusionsOur study identified novel interaction effects significantly, and suggestively, associated with BDR in African American children with asthma. Notably, we found that all of the interactions identified by ViSEN were “pure” interaction effects, in that they were not the result of strong main effects on BDR, highlighting the complexity of the network of biological and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods, thus emphasizing the importance of employing statistical methods optimized to detect both additive and non-additive interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.

Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach.

A major controversy in psychiatric genetics is whether nonadditive genetic interaction effects contribute to the risk of highly polygenic disorders. We applied a support vector machines (SVMs) approach, which is capable of building linear and nonlinear models using kernel methods, to classify cases from controls in a large schizophrenia case–control sample of 11,853 subjects (5,554 cases and 6,299 controls) and compared its prediction accuracy with the polygenic risk score (PRS) approach. We also investigated whether SVMs are a suitable approach to detecting nonlinear genetic effects, that is, interactions. We found that PRS provided more accurate case/control classification than either linear or nonlinear SVMs, and give a tentative explanation why PRS outperforms both multivariate regression and linear kernel SVMs. In addition, we observe that nonlinear kernel SVMs showed higher classification accuracy than linear SVMs when a large number of SNPs are entered into the model. We conclude that SVMs are a potential tool for assessing the presence of interactions, prior to searching for them explicitly.

Ensemble learning for detecting gene-gene interactions in colorectal cancer.

Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease.

Genetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks

BackgroundCommon metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body’s metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors.MethodsWe analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group.ResultsIn contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing β-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension.ConclusionsOur results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

Genetic landscape of interactive effects of HLA-DRB1 alleles on susceptibility to ACPA(+) rheumatoid arthritis and ACPA levels in Japanese population

BackgroundHLA-DRB1 is the strongest susceptibility gene to rheumatoid arthritis (RA). HLA-DRB1 alleles showed significant non-additive and interactive effects on susceptibility to RA in the European population, but these effects on...

Large-scale interaction effects reveal missing heritability in schizophrenia, bipolar disorder and posttraumatic stress disorder

Genetic susceptibility factors behind psychiatric disorders typically contribute small effects individually. A possible explanation for the missing heritability is that the effects of common variants are not only polygenic but also non-additive, appearing only when interactions within large groups are taken into account. Here, we tested this hypothesis for schizophrenia (SZ) and bipolar disorder (BP) disease risks, and identified genetic factors shared with posttraumatic stress disorder (PTSD). When considered independently, few single-nucleotide polymorphisms (SNPs) reached genome-wide significance. In contrast, when SNPs were selected in groups (containing up to thousands each) and the collective effects of all interactions were estimated, the association strength for SZ/BP rose dramatically with a combined sample size of 7187 cases and 8309 controls. We identified a large number of genes and pathways whose association was significant only when interaction effects were included. The gene with highest association was CSMD1, which encodes a negative regulator of complement activation. Pathways for glycosaminoglycan (GAG) synthesis exhibited strong association in multiple contexts. Taken together, highly associated pathways suggested a pathogenesis mechanism where maternal immune activation causes disruption of neurogenesis (compounded by impaired cell cycle, DNA repair and neuronal migration) and deficits in cortical interneurons, leading to symptoms triggered by synaptic pruning. Increased risks arise from GAG deficiencies causing complement activation and excessive microglial action. Analysis of PTSD data sets suggested an etiology common to SZ/BP: interneuron deficiency can also lead to impaired control of fear responses triggered by trauma. We additionally found PTSD risk factors affecting synaptic plasticity and fatty acid signaling, consistent with the fear extinction model. Our results suggest that much of the missing heritability of psychiatric disorders resides in non-additive interaction effects.

Collective Genetic Interaction Effects and the Role of Antigen-Presenting Cells in Autoimmune Diseases.

Autoimmune diseases occur when immune cells fail to develop or lose their tolerance toward self and destroy body’s own tissues. Both insufficient negative selection of self-reactive T cells and impaired development of regulatory T cells preventing effector cell activation are believed to contribute to autoimmunity. Genetic predispositions center around the major histocompatibility complex (MHC) class II loci involved in antigen presentation, the key determinant of CD4+ T cell activation. Recent studies suggested that variants in the MHC region also exhibit significant non-additive interaction effects. However, collective interactions involving large numbers of single nucleotide polymorphisms (SNPs) contributing to such effects are yet to be characterized. In addition, relatively little is known about the cell-type-specificity of such interactions in the context of cellular pathways. Here, we analyzed type 1 diabetes (T1D) and rheumatoid arthritis (RA) genome-wide association data sets via large-scale, high-performance computations and inferred collective interaction effects involving MHC SNPs using the discrete discriminant analysis. Despite considerable differences in the details of SNP interactions in T1D and RA data, the enrichment pattern of interacting pairs in reference epigenomes was remarkably similar: statistically significant interactions were epigenetically active in cell-type combinations connecting B cells to T cells and intestinal epithelial cells, with both helper and regulatory T cells showing strong disease-associated interactions with B cells. Our results provide direct genetic evidence pointing to the important roles B cells play as antigen-presenting cells toward CD4+ T cells in the context of central and peripheral tolerance. In addition, they are consistent with recent experimental studies suggesting that the repertoire of B cell-specific self-antigens in the thymus are critical to the effective control of corresponding autoimmune activation in peripheral tissues.

Additive genetic effect of APOE and BDNF on hippocampus activity

Human memory is a highly heritable polygenic trait with complex inheritance patterns. To study the genetics of memory and memory-related diseases, hippocampal functioning has served as an intermediate phenotype. The importance of investigating gene–gene effects on complex phenotypes has been emphasized, but most imaging studies still focus on single polymorphisms. APOE ε4 and BDNF Met, two of the most studied gene variants for variability in memory performance and neuropsychiatric disorders, have both separately been related to poorer episodic memory and altered hippocampal functioning. Here, we investigated the combined effect of APOE and BDNF on hippocampal activation (N=151). No non-additive interaction effects were seen. Instead, the results revealed decreased activation in bilateral hippocampus and parahippocampus as a function of the number of APOE ε4 and BDNF Met alleles present (neither, one, or both). The combined effect was stronger than either of the individual effects, and both gene variables explained significant proportions of variance in BOLD signal change. Thus, there was an additive gene–gene effect of APOE and BDNF on medial temporal lobe (MTL) activation, showing that a larger proportion of variance in brain activation attributed to genetics can be explained by considering more than one gene variant. This effect might be relevant for the understanding of normal variability in memory function as well as memory-related disorders associated with APOE and BDNF.

Population-based Analysis of Alzheimer’s Disease Risk Alleles Implicates Genetic Interactions

Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer's disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and nonadditive interactions on LOAD status prediction performance. 2419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and receiver operator characteristic analysis to assess the LOAD status prediction performance of these loci using additive and nonadditive models and compared odds ratios and PAFs between AlzGene.org and Cache County. Odds ratios were comparable between Cache County and AlzGene.org when identical single nucleotide polymorphisms were genotyped. PAFs from AlzGene.org ranged from 2.25% to 37%; those from Cache County ranged from .05% to 20%. Including non-APOE alleles significantly improved LOAD status prediction performance (area under the curve = .80) over APOE alone (area under the curve = .78) when not constrained to an additive relationship (p < .03). We identified potential allelic interactions (p values uncorrected): CD33-MS4A4E (synergy factor = 5.31; p < .003) and CLU-MS4A4E (synergy factor = 3.81; p < .016). Although nonadditive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer's disease etiology.

Net primary productivity and rain‐use efficiency as affected by warming, altered precipitation, and clipping in a mixed‐grass prairie

Grassland productivity in response to climate change and land use is a global concern. In order to explore the effects of climate change and land use on net primary productivity (NPP), NPP partitioning [fBNPP , defined as the fraction of belowground NPP (BNPP) to NPP], and rain-use efficiency (RUE) of NPP, we conducted a field experiment with warming (+3°C), altered precipitation (double and half), and annual clipping in a mixed-grass prairie in Oklahoma, USA since July, 2009. Across the years, warming significantly increased BNPP, fBNPP , and RUEBNPP by an average of 11.6%, 2.8%, and 6.6%, respectively. This indicates that BNPP was more sensitive to warming than aboveground NPP (ANPP) since warming did not change ANPP and RUEANPP much. Double precipitation stimulated ANPP, BNPP, and NPP but suppressed RUEANPP , RUEBNPP , and RUENPP while half precipitation decreased ANPP, BNPP, and NPP but increased RUEANPP , RUEBNPP , and RUENPP . Clipping interacted with altered precipitation in impacting RUEANPP , RUEBNPP , and RUENPP , suggesting land use could confound the effects of precipitation changes on ecosystem processes. Soil moisture was found to be a main factor in regulating variation in ANPP, BNPP, and NPP while soil temperature was the dominant factor influencing fBNPP . These findings suggest that BNPP is critical point to future research. Additionally, results from single-factor manipulative experiments should be treated with caution due to the non-additive interactive effects of warming with altered precipitation and land use (clipping).

Solvation thermodynamics and heat capacity of polar and charged solutes in water

The solvation thermodynamics and in particular the solvation heat capacity of polar and charged solutes in water is studied using atomistic molecular dynamics simulations. As ionic solutes we consider a F(-) and a Na(+) ion, as an example for a polar molecule with vanishing net charge we take a SPC/E water molecule. The partial charges of all three solutes are varied in a wide range by a scaling factor. Using a recently introduced method for the accurate determination of the solvation free energy of polar solutes, we determine the free energy, entropy, enthalpy, and heat capacity of the three different solutes as a function of temperature and partial solute charge. We find that the sum of the solvation heat capacities of the Na(+) and F(-) ions is negative, in agreement with experimental observations, but our results uncover a pronounced difference in the heat capacity between positively and negatively charged groups. While the solvation heat capacity ΔC(p) stays positive and even increases slightly upon charging the Na(+) ion, it decreases upon charging the F(-) ion and becomes negative beyond an ion charge of q = -0.3e. On the other hand, the heat capacity of the overall charge-neutral polar solute derived from a SPC/E water molecule is positive for all charge scaling factors considered by us. This means that the heat capacity of a wide class of polar solutes with vanishing net charge is positive. The common ascription of negative heat capacities to polar chemical groups might arise from the neglect of non-additive interaction effects between polar and apolar groups. The reason behind this non-additivity is suggested to be related to the second solvation shell that significantly affects the solvation thermodynamics and due to its large spatial extent induces quite long-ranged interactions between solvated molecular parts and groups.

Bioturbating space enhances the effects of non‐additive interactions among benthic ecosystem engineers on cross‐habitat nutrient regeneration

Understanding the mechanisms that drive complementary interactions among species is key to the progress of the debate over the role of biodiversity in ecosystem functioning. In addition, interspecific interactions among physical ecosystem engineers have rarely been framed in the context of biodiversity experiments. Here, we provide an empirical test of how the physical niche space of species influences the effects of the biodiversity of bioturbators on cross‐habitat nutrient fluxes in benthic sediments. In the laboratory, we orthogonally manipulated the number and composition of three benthic invertebrate bioturbator species that differ in the dimensions of their bioturbating space niche; i.e. their vertical distribution in the sediment over a gradient of sediment depth and volume. The ammonium (NH4‐N) flux from the sediment to the water was positively related to bioturbator species richness only in the sediments with the deepest depth and greatest volume. The non‐additive effects of bioturbator species richness on the benthic–pelagic NH4‐N flux increased linearly with sediment depth and volume, but only in the three‐species mixtures. Furthermore, no individual species dominated the rates of H4‐N fluxes, indicating that biodiversity effects were mainly driven by complementarity. These results suggest that sediment bioturbating space mediates the magnitude of non‐additive effects among the three invertebrate species and sheds light on the importance of physical niche space in modulating the positive effects of biodiversity on ecosystem functioning.

Effects of Nonadditive Interactions on Ion Solvation at the Water/Vapor Interface: A Molecular Dynamics Study

Effects of Nonadditive Interactions on Ion Solvation at the Water/Vapor Interface: A Molecular Dynamics Study

Effects of Nonadditive Interactions on Ion Solvation at the Water/Vapor Interface: A Molecular Dynamics Study

The solvation of halide ions at the water/vapor interface is investigated by using molecular dynamics simulations with nonpolarizable molecular mechanical (MM), polarizable MM, and quantum mechanical (QM)/MM methods. The free energy profile of the ion solvation is decomposed into the energy and the entropic contributions along the ion displacement from inside to the surface of water. It is found that the surface affinity of the ion, relative to the bulk value, is determined by a subtle balance between the energetic destabilization and the entropic stabilization with the ion displacement. The amount of energetic destabilization is found to be reduced when nonadditive interactions are included, as in the polarizable MM and QM/MM models. The structure of water around the ion at the interface is also largely modified when the higher order effects are considered. For example, the induced dipole effect enhances the solvation structure around the ion at the interface significantly and thus reduces the amount of entropic stabilization at the interface, relative to in the bulk. It is found that this induced dipole effect causes the slowing in the ion-water hydrogen bond dynamics at the interface. On the other hand, the higher order induced multipole effects in the QM/MM method suppress both the excessive enhancement of the solvation structure and the slowing of the ion-water hydrogen bond dynamics at the interface. The present study demonstrates that not only the induced dipole moment but also the higher order induced multipole moments, which are neglected in standard empirical models, are essential for the correct description of the ion solvation at the water/vapor interface.

Weighted Explanations in History

Weighted explanations , whereby some causes are deemed more important than others, are ubiquitous in historical studies. Drawing from influential recent work on causation, I develop a definition of causal-explanatory strength. This makes clear exactly which aspects of explanatory weighting are subjective and which objective. It also sheds new light on several traditional issues, showing for instance that: underlying causes need not be more important than proximate ones; several different causes can each be responsible for most of an effect; small causes need not be less important than big ones; and non-additive interactive effects between causes present no particular difficulty.

Interactive effects of habitat modification and species invasion on native species decline

Different components of global environmental change are often studied and managed independently, but mounting evidence points towards complex non-additive interaction effects between drivers of native species decline. Using the example of interactions between land-use change and biotic exchange, we develop an interpretive framework that will enable global change researchers to identify and discriminate between major interaction pathways. We formalise a distinction between numerically mediated versus functionally moderated causal pathways. Despite superficial similarity of their effects, numerical and functional pathways stem from fundamentally different mechanisms of action and have fundamentally different consequences for conservation management. Our framework is a first step toward building a better quantitative understanding of how interactions between drivers might mitigate or exacerbate the net effects of global environmental change on biotic communities in the future.