Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method

To develop a model synthesis method for parsimoniously modeling gene-environmental interactions (GEI) associated with clinical outcomes and phenotypes. The AMBROSIA model synthesis approach utilizes the k-way interaction information (KWII), an information-theoretic metric capable of identifying variable combinations associated with GEI. For model synthesis, AMBROSIA considers relevance of combinations to the phenotype, it precludes entry of combinations with redundant information, and penalizes for unjustifiable complexity; each step is KWII based. The performance and power of AMBROSIA were evaluated with simulations and Genetic Association Workshop 15 (GAW15) data sets of rheumatoid arthritis (RA). AMBROSIA identified parsimonious models in data sets containing multiple interactions with linkage disequilibrium present. For the GAW15 data set containing 9187 single-nucleotide polymorphisms, the parsimonious AMBROSIA model identified nine RA-associated combinations with power >90%. AMBROSIA was compared with multifactor dimensionality reduction across several diverse models and had satisfactory power. Software source code is available from http://www.cse.buffalo.edu/DBGROUP/bioinformatics/resources.html. AMBROSIA is a promising method for GEI model synthesis.     

AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes

We developed a computationally efficient algorithm AMBIENCE, for identifying the informative variables involved in gene-gene (GGI) and gene-environment interactions (GEI) that are associated with disease phenotypes. The AMBIENCE algorithm uses a novel information theoretic metric called phenotype-associated information (PAI) to search for combinations of genetic variants and environmental variables associated with the disease phenotype. The PAI-based AMBIENCE algorithm effectively and efficiently detected GEI in simulated data sets of varying size and complexity, including the 10K simulated rheumatoid arthritis data set from Genetic Analysis Workshop 15. The method was also successfully used to detect GGI in a Crohn's disease data set. The performance of the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR), and pedigree disequilibrium test (PDT) methods. Furthermore, we assessed the computational speed of AMBIENCE for detecting GGI and GEI for data sets varying in size from 100 to 10(5) variables. Our results demonstrate that the AMBIENCE information theoretic algorithm is useful for analyzing a diverse range of epidemiologic data sets containing evidence for GGI and GEI.     

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.     

A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.     

Genetic and phenotypic characterization of a Pseudomonas aeruginosa population with high frequency of genomic islands

Various genomic islands, PAPI-1, PAPI-2, PAGI-1, PAGI-2, PAGI-3, and PAGI-4, and the element pKLC102 have been characterized in different P. aeruginosa strains from diverse habitats and geographical locations. Chromosomal DNA macroarray of 100 P. aeruginosa strains isolated from 85 unrelated patients hospitalized in an intensive care unit was created to assess the occurrence of these genomic islands (GEIs). The macroarray was then hybridized with labeled probes derived from each genomic island. In addition, PFGE patterns with SpeI, frequency of virulence genes, and antimicrobial resistance patterns of the strains were studied. Our results showed that almost all P. aeruginosa strains presented up to eight virulence genes. By SpeI macrorestriction fragment analysis we were able to identify 49 restriction patterns; 35 patterns correspond to single strains and the remaining 14 to strains subgroup (a-n). Most of the strains showed variation in number or composition of GEIs and a specific antimicrobial pattern indicating that each strain was an unrelated isolate. In terms of the number of genomic islands per strain, 7 GEIs were found in 34% of the strains, 6 in 18%, 5 in 12%, 4 in 14%, 3 in 10%, 2 in 7%, and 1 in 4%; only one isolate did not present any GEI. The genomic islands PAPI-1 and PAPI-2 and the element pKLC102 were the most frequently detected. The analysis of the location of each GEI in the chromosome of two strains show that the islands PAGI-3, PAPI-1, PAPI-2 and pKLC102 are present in the insertion site previously reported, but that PAGI-2 and PAGI-4 are inserted in another chromosome place in a site not characterized yet. In conclusion our data show that P. aeruginosa strains exhibited an epidemic population structure with horizontal transfer of DNA resulting in a high frequency of GEIs.     

A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction.     

Mate choice for genetic quality when environments vary: suggestions for empirical progress

Mate choice for good-genes remains one of the most controversial evolutionary processes ever proposed. This is partly because strong directional choice should theoretically deplete the genetic variation that explains the evolution of this type of female mating preference (the so-called lek paradox). Moreover, good-genes benefits are generally assumed to be too small to outweigh opposing direct selection on females. Here, we review recent progress in the study of mate choice for genetic quality, focussing particularly on the potential for genotype by environment interactions (GEIs) to rescue additive genetic variation for quality, and thereby resolve the lek paradox. We raise five questions that we think will stimulate empirical progress in this field, and suggest directions for research in each area: (1) How is condition-dependence affected by environmental variation? (2) How important are GEIs for maintaining additive genetic variance in condition? (3) How much do GEIs reduce the signalling value of male condition? (4) How does GEI affect the multivariate version of the lek paradox? (5) Have mating biases for high-condition males evolved because of indirect benefits?     

A test for linkage and association in general pedigrees: the pedigree disequilibrium test

Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.     

Understanding microbial community diversity metrics derived from metagenomes: performance evaluation using simulated data sets

Metagenomics holds the promise of greatly advancing the study of diversity in natural communities, but novel theoretical and methodological approaches must first be developed and adjusted for these data sets. We evaluated widely used macroecological metrics of taxonomic diversity on a simulated set of metagenomic samples, using phylogenetically meaningful protein-coding genes as ecological proxies. To our knowledge, this is the first approach of this kind to evaluate taxonomic diversity metrics derived from metagenomic data sets. We demonstrate that abundance matrices derived from protein-coding marker genes reproduce more faithfully the structure of the original community than those derived from SSU-rRNA gene. We also found that the most commonly used diversity metrics are biased estimators of community structure and differ significantly from their corresponding real parameters and that these biases are most likely caused by insufficient sampling and differences in community phylogenetic composition. Our results suggest that the ranking of samples using multidimensional metrics makes a good qualitative alternative for contrasting community structure and that these comparisons can be greatly improved with the incorporation of metrics for both community structure and phylogenetic diversity. These findings will help to achieve a standardized framework for community diversity comparisons derived from metagenomic data sets.     

Environmental heterogeneity,genotype-by-environment interactions and the reliability of sexual traits as indicators of mate quality

Exaggerated sexual displays are often supposed to indicate the indirect benefits females may receive from sexual reproduction with displaying males, but empirical evidence for positive relationships between the genetic quality and sexual trait quality is scant. The explanation for this might lie in the fact that mixing of reproductive individuals whose development has been influenced by genotype-by-environment interactions (GEIs) can blur the relationship between the individual male genetic quality and phenotype as perceived by females. Strong GEIs can generate an ecological crossover, where different genotypes are superior in environments that are separated either in space or time. Here, we use a stochastic simulation model to show that even a weak GEI, which does not generate an obvious ecological crossover, can neutralize or even reverse the relationship between genetic quality and sexual trait size in the presence of environmental heterogeneity during development. Our model highlights the importance of developmental selection in evolution of traits and allows us to predict the situations in which sexual displays might not be reliable indicators of genetic quality.     

A dual functional origin of transfer in the ICEclc genomic island of Pseudomonas knackmussii B13

Genomic islands (GEIs) are large DNA segments, present in most bacterial genomes, that are most likely acquired via horizontal gene transfer. Here, we study the self-transfer system of the integrative and conjugative element ICEclc of Pseudomonas knackmussii B13, which stands model for a larger group of ICE/GEI with syntenic core gene organization. Functional screening revealed that unlike conjugative plasmids and other ICEs ICEclc carries two separate origins of transfer, with different sequence context but containing a similar repeat motif. Conjugation experiments with GFP-labelled ICEclc variants showed that both oriTs are used for transfer and with indistinguishable efficiencies, but that having two oriTs results in an estimated fourfold increase of ICEclc transfer rates in a population compared with having a single oriT. A gene for a relaxase essential for ICEclc transfer was also identified, but in vivo strand exchange assays suggested that the relaxase processes both oriTs in a different manner. This unique dual origin of transfer system might have provided an evolutionary advantage for distribution of ICE, a hypothesis that is supported by the fact that both oriT regions are conserved in several GEIs related to ICEclc.     

On the use of multifactor dimensionality reduction (MDR) and classification and regression tree (CART) to identify haplotype-haplotype interactions in genetic studies

Haplotype-based approaches may have greater power than single-locus analyses when the SNPs are in strong linkage disequilibrium with the risk locus. To overcome potential complexities owing to large numbers of haplotypes in genetic studies, we evaluated two data mining approaches, multifactor dimensionality reduction (MDR) and classification and regression tree (CART), with the concept of haplotypes considering their haplotype uncertainty to detect haplotype-haplotype (HH) interactions. In evaluation of performance for detecting HH interactions, MDR had higher power than CART, but MDR gave a slightly higher type I error. Additionally, we performed an HH interaction analysis with a publicly available dataset of Parkinson's disease and confirmed previous findings that the RET proto-oncogene is associated with the disease. In this study, we showed that using HH interaction analysis is possible to assist researchers in gaining more insight into identifying genetic risk factors for complex diseases.     

The space-lifetime hypothesis: viewing organisms in four dimensions, literally

Much of the debate about alternative scaling exponents may result from unawareness of the dimensionality appropriate for different data and questions; in some cases, analysis has to include a fourth temporal dimension, and in others, it does not. Proportional scaling simultaneously applied to an organism and its generation time, treating the latter as a natural fourth dimension, produces a simple explanation for the 3/4 power in large-scale interspecies comparisons. Analysis of data sets of reduced dimensionality (e.g., data sets constructed such that one or more of the four dimensions are fixed), results in predictably lower metabolic exponents of 2/3 and 1/2 under one and two constraints, respectively. Our space-lifetime view offers a predictive framework that may be useful in developing a more complete mechanistic theory of metabolic scaling.     

Effects of larval crowding on quantitative variation for development time and viability in Drosophila melanogaster

Competition between individuals belonging to the same species is a universal feature of natural populations and is the process underpinning organismal adaptation. Despite its importance, still comparatively little is known about the genetic variation responsible for competitive traits. Here, we measured the phenotypic variation and quantitative genetics parameters for two fitness‐related traits—egg‐to‐adult viability and development time—across a panel of Drosophila strains under varying larval densities. Both traits exhibited substantial genetic variation at all larval densities, as well as significant genotype‐by‐environment interactions (GEIs). GEI was attributable to changes in the rank order of reaction norms for both traits, and additionally to differences in the between‐line variance for development time. The coefficient of genetic variation increased under stress conditions for development time, while it was higher at both high and low densities for viability. While development time also correlated negatively with fitness at high larval densities—meaning that fast developers have high fitness—there was no correlation with fitness at low density. This result suggests that GEI may be a common feature of fitness‐related genetic variation and, further, that trait values under noncompetitive conditions could be poor indicators of individual fitness. The latter point could have significant implications for animal and plant breeding programs, as well as for conservation genetics.     

Genotype‐by‐environment interactions underlie the expression of pre‐ and post‐copulatory sexually selected traits in guppies

The role that genotype‐by‐environment interactions (GEIs) play in sexual selection has only recently attracted the attention of evolutionary biologists. Yet GEIs can have profound evolutionary implications by compromising the honesty of sexual signals, maintaining high levels of genetic variance underlying their expression and altering the patterns of genetic covariance among fitness traits. In this study, we test for GEIs in a highly sexually dimorphic freshwater fish, the guppy Poecilia reticulata. We conducted an experimental quantitative genetic study in which male offspring arising from a paternal half‐sibling breeding design were assigned to differing nutritional ‘environments’ (either high or low feed levels). We then determined whether the manipulation of diet quantity influenced levels of additive genetic variance and covariance for several highly variable and condition‐dependent pre‐ and post‐copulatory sexual traits. In accordance with previous work, we found that dietary limitation had strong phenotypic effects on numerous pre‐ and post‐copulatory sexual traits. We also report evidence for significant GEI for several of these traits, which in some cases (area of iridescence and sperm velocity) reflected a change in the rank order of genotypes across different nutritional environments (i.e. ecological crossover). Furthermore, we show that genetic correlations vary significantly between nutritional environments. Notably, a highly significant negative genetic correlation between iridescent coloration and sperm viability in the high food treatment broke down under dietary restriction. Taken together, these findings are likely to have important evolutionary implications for guppies; ecological crossover may influence sexual signal reliability in unstable (nutritional) environments and contribute towards the extreme levels of polymorphism in sexual traits typically reported for this species. Furthermore, the presence of environment‐specific genetic covariance suggests that trade‐offs measured in one environment may not be indicative of genetic constraints in others.     

水稻光合生产与干物质累积的动态模拟

在综合已有研究成果的基础上,兼顾模型的机理性与实用性的平衡,构建了水稻光合生产与干物质累积的模拟模型．模型采用高斯积分法有效地计算冠层每日的总光合量,并考虑了冠层消光系数随生理发育时间（PDT)的动态变化,模型较充分地量化了生理年龄、温度、叶片含氮量及水分亏缺因子等对光合作用的影响及维持呼吸系数与PDT的动态变化关系、利用独立的试验资料对模型核实的结果显示,模型可以较好地预测不同生长条件下的生物量累积动态,具有较强的机理性与实用性．     

Genetic variation and causes of genotype-environment interaction in the body size of blue tit (Parus caeruleus).

In several studies of natural populations of birds, the heritability of body size estimated by parent-offspring regression has been lower when offspring have developed in poor feeding regimens than when they developed in good feeding regimens. This has led to the suggestion that adaptation under poor regimens may be constrained by lack of genetic variation. We examined the influence of environmental conditions on expression of genetic variation in body size of nestling blue tits (Parus caeruleus) by raising full sibs in artificially reduced and enlarged broods, corresponding to good and poor feeding regimens, respectively. Individuals grown in the poor regimen attained smaller body size than their sibs grown in the good regimen. However, there was among-family variation in response to the treatments--i.e., genotype-environment interactions (GEIs). Partitioning the GEI variance into contributions attributable to (1) differences in the among-family genetic variance between the treatments and (2) imperfect correlation of genotypic values across treatments identified the latter as the main cause of the GEI. Parent-offspring regressions were not significantly different when offspring were reared in the good environment (h2 = 0.75) vs. when they were reared in the poor environment (h2 = 0.63). Thus, there was little evidence that genetic variance in body size was lower under the poor conditions than under the good conditions. These results do not support the view that the genetic potential for adaptation to poor feeding conditions is less than that for adaptation to good conditions, but they do suggest that different genotypes may be favored under the different conditions.     

UGMDR: a unified conceptual framework for detection of multifactor interactions underlying complex traits

Biological outcomes are governed by multiple genetic and environmental factors that act in concert. Determining multifactor interactions is the primary topic of interest in recent genetics studies but presents enormous statistical and mathematical challenges. The computationally efficient multifactor dimensionality reduction (MDR) approach has emerged as a promising tool for meeting these challenges. On the other hand, complex traits are expressed in various forms and have different data generation mechanisms that cannot be appropriately modeled by a dichotomous model; the subjects in a study may be recruited according to its own analytical goals, research strategies and resources available, not only consisting of homogeneous unrelated individuals. Although several modifications and extensions of MDR have in part addressed the practical problems, they are still limited in statistical analyses of diverse phenotypes, multivariate phenotypes and correlated observations, correcting for potential population stratification and unifying both unrelated and family samples into a more powerful analysis. I propose a comprehensive statistical framework, referred as to unified generalized MDR (UGMDR), for systematic extension of MDR. The proposed approach is quite versatile, not only allowing for covariate adjustment, being suitable for analyzing almost any trait type, for example, binary, count, continuous, polytomous, ordinal, time-to-onset, multivariate and others, as well as combinations of those, but also being applicable to various study designs, including homogeneous and admixed unrelated-subject and family as well as mixtures of them. The proposed UGMDR offers an important addition to the arsenal of analytical tools for identifying nonlinear multifactor interactions and unraveling the genetic architecture of complex traits.     

Photobleaching kinetics, photoproduct formation, and dose estimation during ALA induced PpIX PDT of MLL cells under well oxygenated and hypoxic conditions.

Fluorescence photobleaching and photoproduct formation were investigated during delta-aminolevulinic acid (ALA) induced protoporphyrin IX (PpIX) PDT of MLL cells in vitro. Cells were incubated in either 0.1 or 1.0 mM ALA for 4 h and were treated with 532 nm or 635 nm light under well oxygenated or hypoxic conditions. Fluorescence spectra were acquired during treatment. Photobleaching and photoproduct formation were quantified using singular value decomposition fitting of fluorescence spectra to experimentally determined basis spectra for PpIX, photoprotoporphyrin (Ppp), product II (peak at 655 nm), and product III (peak at 618 nm). PpIX photobleaching occurred under both normal and hypoxic conditions. The photobleaching kinetics could not be explained by purely first- or second-order photobleaching kinetics, and were attributed to differences in PpIX binding at the two ALA incubation concentrations. Ppp was the main photoproduct and accumulated in higher levels in the absence of oxygen, likely a result of reduced Ppp photobleaching under hypoxia. Increases in product II fluorescence occurred mainly in the presence of oxygen. To assess potential fluorescence based PDT dose metrics, cell viability was measured at select times during treatment using a colony formation assay. Cell survival correlated well to changes in product II fluorescence, independent of oxygenation, sensitizer concentration, and treatment wavelength, suggesting that this product is primarily a result of singlet oxygen mediated reactions and may potentially be useful to quantify singlet oxygen dose during PDT.