While many developmentally relevant enhancers act in a modular fashion, there is growing evidence for nonadditive interactions between distinct cis-regulatory enhancers. We investigated if nonautonomous enhancer interactions underlie transcription regulation of the Drosophila segment polarity gene, wingless.
We identified two wg enhancers active at the blastoderm stage: wg 3613u, located from -3.6 to -1.3 kb upstream of the wg transcription start site (TSS) and 3046d, located in intron two of the wg gene, from 3.0 to 4.6 kb downstream of the TSS. Genetic experiments confirm that Even Skipped (Eve), Fushi-tarazu (Ftz), Runt, Odd-paired (Opa), Odd-skipped (Odd), and Paired (Prd) contribute to spatially regulated wg expression. Interestingly, there are enhancer specific differences in response to the gain or loss of function of pair-rule gene activity. Although each element recapitulates aspects of wg expression, a composite reporter containing both enhancers more faithfully recapitulates wg regulation than would be predicted from the sum of their individual responses.
These results suggest that the regulation of wg by pair-rule genes involves nonadditive interactions between distinct cis-regulatory enhancers.

Fluorine (F) substitution on conjugated polymers in polymer solar cells (PSCs) has a diverse effect on molecular properties and device performance. We present a series of three D-A type conjugated polymers (PBT, PFBT, and PDFBT) based on dithienothiophene and benzothiadiazole units with different numbers of F atoms to explain the influence of F substitution by comparing the molecular interactions of the polymers and the recombination kinetics in PSCs. The preaggregation behavior of PFBT and PDFBT in o-DCB at the UV-vis absorption spectra proves that both polymers have strong intermolecular interactions. Besides, more closely packed structures and change into face-on orientation of fluorinated polymers are observed in polymer:PC71BM blends by GIXD which is beneficial for charge transport and, ultimately, for current density in PSCs (4.3, 13.0, and 14.5 mA cm-2 for PBT, PFBT, and PDFBT, respectively). Also, the introduction of F atoms on conjugated backbones affects the recombination kinetics by suppressing bimolecular recombination, thereby improving the fill factor (0.41, 0.68, and 0.69 for PBT, PFBT, and PDFBT, respectively). Consequently, the PCE of PSCs reached 7.3% without any additional treatment (annealing, solvent additive, etc.) in the polymer containing difluorinated BT (PDFBT) that is much higher than nonfluorinated BT (PBT ∼ 1%) and monofluorinated BT (PFBT ∼ 6%).

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

Mixture risk assessment is often hampered by the lack of dose-response information on the mixture being assessed, forcing reliance on component formulas such as dose addition. We present a four-step approach for evaluating chemical mixture data for consistency with dose addition for use in supporting a component based mixture risk assessment. Following the concepts in the U.S. EPA mixture risk guidance (U.S. EPA, 2000a,b), toxicological interaction for a defined mixture (all components known) is departure from a clearly articulated definition of component additivity. For the common approach of dose additivity, the EPA guidance identifies three desirable characteristics, foremost of which is that the component chemicals are toxicologically similar. The other two characteristics are empirical: the mixture components have toxic potencies that are fixed proportions of each other (throughout the dose range of interest), and the mixture dose term in the dose additive prediction formula, which we call the combined prediction model (CPM), can be represented by a linear combination of the component doses. A consequent property of the proportional toxic potencies is that the component chemicals must share a common dose-response model, where only the dose coefficients depend on the chemical components. A further consequence is that the mixture data must be described by the same mathematical function (\"mixture model\") as the components, but with a distinct coefficient for the total mixture dose. The mixture response is predicted from the component dose-response curves by using the dose additive CPM and the prediction is then compared with the observed mixture results. The four steps are to evaluate: (1) toxic proportionality by determining how well the CPM matches the single chemical models regarding mean and variance; (2) fit of the mixture model to the mixture data; (3) agreement between the mixture data and the CPM prediction; and (4) consistency between the CPM and the mixture model. Because there are four evaluations instead of one, some involving many parameters or dose groups, there are more opportunities to reject statistical hypotheses about dose addition, thus statistical adjustment for multiple comparisons is necessary. These four steps contribute different pieces of information about the consistency of the component and mixture data with the two empirical characteristics of dose additivity. We examine this four-step approach in how it can show empirical support for dose addition as a predictor for an untested mixture in a screening level risk assessment. The decision whether to apply dose addition should be based on all four of those evidentiary pieces as well as toxicological understanding of these chemicals and should include interpretations of the numerical and toxicological issues that arise during the evaluation. This approach is demonstrated with neurotoxicity data on carbamate mixtures.

Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a single nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r4, where r2 is the usual correlation between the causal and marker loci, in contrast to the well-known result that power to detect a multiplicative effect decays as a function of r2. We perform a simulation study with empirical patterns of LD to assess how this disease model distortion is likely to impact GWAS results. Among loci where association is detected, we observe that there is reasonable power to detect substantial deviations from the multiplicative model, such as for dominant and recessive models. Thus, it is worth explicitly testing for such deviations routinely.