A General Test for Gene-Environment Interaction in Sib Pair-based Association Analysis of Quantitative Traits

Several association studies support the hypothesis that genetic variants can modify the influence of environmental factors on behavioral outcomes, i.e., G 9 E interaction. The case-control design used in these studies is powerful, but population stratification with respect to allele frequencies can give rise to false positive or false negative associations. Stratification with respect to the environmental factors can lead to false positives or false negatives with respect to environmental main effects and G 9 E interaction effects as well. Here we present a model based on Fulker et al. (1999) and Purcell (2002) for the study of G 9 E interaction in family-based association designs, in which the effects of stratification can be controlled. Simulations illustrate the power to detect genetic and environmental main effects, and G 9 E interaction effects for the sib pair design. The power to detect interaction was studied in eight different situations, both with and without the presence of population stratification, and for categorical and continuous environmental factors. Results show that the power to detect genetic and environmental main effects, and G 9 E interaction effects, depends on the allele frequencies and the distribution of the environmental moderator. Admixture effects of realistic effect size lead only to very small stratification effects in the G 9 E component, so impractically large numbers of sib pairs are required to detect such stratification

A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country

In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes

A Note on False Positives and Power in G × E Modelling of Twin Data

The variance components models for gene–environment interaction proposed by Purcell in 2002 are widely used. In both the bivariate and the univariate parameterization of these models, the variance decomposition of trait T is a function of moderator M. We show that if M and T are correlated, and moderator M is correlated between twins as well, the univariate parameterization produces a considerable increase in false positive moderation effects. A simple extension of this univariate moderation model prevents this elevation of the false positive rate provided the covariance between M and T is itself not also subject to moderation. If the covariance between M and T varies as a function of M, then moderation effects observed in the univariate setting should be interpreted with care as these can have their origin in either moderation of the covariance between M and T or in moderation of the unique paths of T. We conclude that researchers should use the full bivariate moderation model to study the presence of moderation on the covariance between M and T. If such moderation can be ruled out, subsequent use of the extended univariate moderation model, as proposed in this paper, is recommended as this model is more powerful than the full bivariate moderation model

Individual Differences in Processing Speed and Working Memory Speed as Assessed with the Sternberg Memory Scanning Task

The Sternberg Memory Scanning (SMS) task provides a measure of processing speed (PS) and working memory retrieval speed (WMS). In this task, participants are presented with sets of stimuli that vary in size. After a delay, one item is presented, and participants indicate whether or not the item was part of the set. Performance is assessed by speed and accuracy for both the positive (item is part of the set) and the negative trials (items is not part of the set). To examine the causes of variation in PS and WMS, 623 adult twins and their siblings completed the SMS task. A non-linear growth curve (nLGC) model best described the increase in reaction time with increasing set size. Genetic analyses showed that WMS (modeled as the Slope in the nLGC model) has a relatively small variance which is not due to genetic variation while PS (modeled as the Intercept in the nLGC model) showed large individual differences, part of which could be attributed to additive genetic factors. Heritability was 38% for positive and 32% for negative trials. Additional multivariate analyses showed that the genetic effects on PS for positive and negative trials were completely shared. We conclude that genetic influences on working memory performance are more likely to act upon basic processing speed and (pre)motoric processes than on the speed with which an item is retrieved from short term memory

Evolutionary history and leaf succulence as explanations for medicinal use in aloes and the global popularity of Aloe vera

BACKGROUND: Aloe vera supports a substantial global trade yet its wild origins, and explanations for its popularity over 500 related Aloe species in one of the world\u27s largest succulent groups, have remained uncertain. We developed an explicit phylogenetic framework to explore links between the rich traditions of medicinal use and leaf succulence in aloes. RESULTS: The phylogenetic hypothesis clarifies the origins of Aloe vera to the Arabian Peninsula at the northernmost limits of the range for aloes. The genus Aloe originated in southern Africa ~16 million years ago and underwent two major radiations driven by different speciation processes, giving rise to the extraordinary diversity known today. Large, succulent leaves typical of medicinal aloes arose during the most recent diversification ~10 million years ago and are strongly correlated to the phylogeny and to the likelihood of a species being used for medicine. A significant, albeit weak, phylogenetic signal is evident in the medicinal uses of aloes, suggesting that the properties for which they are valued do not occur randomly across the branches of the phylogenetic tree. CONCLUSIONS: Phylogenetic investigation of plant use and leaf succulence among aloes has yielded new explanations for the extraordinary market dominance of Aloe vera. The industry preference for Aloe vera appears to be due to its proximity to important historic trade routes, and early introduction to trade and cultivation. Well-developed succulent leaf mesophyll tissue, an adaptive feature that likely contributed to the ecological success of the genus Aloe, is the main predictor for medicinal use among Aloe species, whereas evolutionary loss of succulence tends to be associated with losses of medicinal use. Phylogenetic analyses of plant use offer potential to understand patterns in the value of global plant diversity

Phenotypic Complexity, Measurement Bias, and Poor Phenotypic Resolution Contribute to the Missing Heritability Problem in Genetic Association Studies

Background The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. Methodology We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. Conclusion We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99

The Heritability of Aptitude and Exceptional Talent Across Different Domains in Adolescents and Young Adults

The origin of individual differences in aptitude, defined as a domain-specific skill within the normal ability range, and talent, defined as a domain specific skill of exceptional quality, is under debate. The nature of the variation in aptitudes and exceptional talents across different domains was investigated in a population based twin sample. Self-report data from 1,685 twin pairs (12–24 years) were analyzed for Music, Arts, Writing, Language, Chess, Mathematics, Sports, Memory, and Knowledge. The influence of shared environment was small for both aptitude and talent. Additive and non-additive genetic effects explained the major part of the substantial familial clustering in the aptitude measures with heritability estimates ranging between .32 and .71. Heritability estimates for talents were higher and ranged between .50 and .92. In general, the genetic architecture for aptitude and talent was similar in men and women. Genetic factors contribute to a large extent to variation in aptitude and talent across different domains of intellectual, creative, and sports abilities