Adaption,neutrality and life-course diversity

Heterogeneity among individuals in fitness components is what selection acts upon. Evolutionary theories predict that selection in constant environments acts against such heterogeneity. But observations reveal substantial non-genetic and also non-environmental variability in phenotypes. Here, we examine whether there is a relationship between selection pressure and phenotypic variability by analysing structured population models based on data from a large and diverse set of species. Our findings suggest that non-genetic, non-environmental variation is in general neither truly neutral, selected for, nor selected against. We find much variations among species and populations within species, with mean patterns suggesting nearly neutral evolution of life-course variability. Populations that show greater diversity of life courses do not show, in general, increased or decreased population growth rates. Our analysis suggests we are only at the beginning of understanding the evolution and maintenance of non-genetic non-environmental variation.     

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations

In discovery proteomics, as well as many other “omic” approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.     

Inference for set-based effects in genetic association studies with interval-censored outcomes

The rapid acceleration of genetic data collection in biomedical settings has recently resulted in the rise of genetic compendiums filled with rich longitudinal disease data. One common feature of these data sets is their plethora of interval-censored outcomes. However, very few tools are available for the analysis of genetic data sets with interval-censored outcomes, and in particular, there is a lack of methodology available for set-based inference. Set-based inference is used to associate a gene, biological pathway, or other genetic construct with outcomes and is one of the most popular strategies in genetics research. This work develops three such tests for interval-censored settings beginning with a variance components test for interval-censored outcomes, the interval-censored sequence kernel association test (ICSKAT). We also provide the interval-censored version of the Burden test, and then we integrate ICSKAT and Burden to construct the interval censored sequence kernel association test—optimal (ICSKATO) combination. These tests unlock set-based analysis of interval-censored data sets with analogs of three highly popular set-based tools commonly applied to continuous and binary outcomes. Simulation studies illustrate the advantages of the developed methods over ad hoc alternatives, including protection of the type I error rate at very low levels and increased power. The proposed approaches are applied to the investigation that motivated this study, an examination of the genes associated with bone mineral density deficiency and fracture risk.     

Clustered restricted mean survival time regression

For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time-to-event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model-free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within-cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster-robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small-sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias-corrected sandwich variance estimators to reduce the negative bias of the cluster-robust sandwich variance estimator. We study the finite-sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials.     

Bias in genetic variance estimates due to spatial autocorrelation

Summary A central problem in the analysis of genetic field trials is the dichotomy of genetic and environmental effects because one cannot be defined without the other. Results from 768,000 simulated family trials in complete randomized block designs demonstrated a serious upward bias in estimates of family variance components from multi-unit plot designs when the phenotypic observations were compatible with a first-order autoregressive (AR1) process. The inflation of family variances and, thus, additive genetic variance and narrow sense individual heritabilities progressed exponentially with an increase in the nearest neighbor correlation () in the AR1 process. Significant differences in inflation rates persisted among various plot configurations. At = 0.2 the inflation of family variances reached 48–73%. Inflation rates were independent of the level of heritability. Modified Papadakis nearest neighbor (NN) adjustment procedures were tested for their ability to remove the bias in family variances. A NN-adjustment based on Mead's coefficient of interplant interaction and one derived from Bartlett's simultaneous autoregressive scheme removed up to 97% of the bias introduced by the phenotypic correlations. NN-adjusted estimates had slightly (5–8%) higher relative errors than did unadjusted estimates.     

Growth patterns and endogenous indole-3-acetic acid concentrations in current-year coppice shoots and seedlings of two Betula species

Apical dominance, internode elongation, radial growth and xylem cell size in coppice and apical shoots of Betula pubescens B. Pendula were determined and related to endogenous indole-3-acetic acid (IAA) levels, measured by gas chromatography-selected ion monitoring in the apical bud and at three positions along the stem. The effects of defoliation and debudding on morphological and anatomical characters and endogenous IAA levels were also investigated. The coppice shoots displayed superior stem elongation and increased branching during the initial phase of growth, after which their growth pattern was similar to that of the seedlings; however, their radial growth was greater throughout the experiment. Both plant types produced smaller-sized xylem cells at the top of the shoot than at the bottom with coppice shoots tending to form larger tracheids and smaller vessels than the seedlings. There was no consistent difference in IAA concentration between the coppice shoots and the seedlings. Defoliation and debudding reduced the IAA level in the stem within 36 h and it was still low after 25 days. Although the extent of the IAA decrease was similar in both coppice shoots and seedlings, the treatments affected the morphological and anatomical characters differently in the two plant types. The results suggest that the observed differences between seedlings and coppice shoots were not mediated through a drastic change in IAA level.     

rRNA gene activity and control of expression mediated by methylation and imprinting during embryo development in wheat x rye hybrids

Ribosomal RNA genes originating from one parent are often suppressed in interspecific hybrids. We show that treatments during germination with the cytosine analogue 5-azacytidine stably reactivate the expression of the suppressed rRNA genes of rye origin in the wheat x rye amphiploid, triticale, by preventing methylation of sites in the rye rDNA. When 5-azacytidine is applied to embryos of triticale and wheat x rye F₁ hybrids nine, or more, days after fertilization, rye rRNA gene expression is stably reactivated in the resulting seedling. Earlier treatments have no effect on rye rRNA gene expression, indicating that undermethylation of DNA early in embryo development is reversible. After 9 days, the methylation status of rRNA genes in maintained throughout development. Since the change in expression follows a methylation change at particular restriction-enzyme sites, the data establish a clear correlation between gene activity and methylation in plants.     

Approximate Confidence Intervals for Contrasts in the Balanced Three Factor Mixed Model

When conducting a statistical analysis of data from a designed experiment, an investigator is often interested in confidence intervals for contrasts of the fixed effects. If the analysis involves a mixed linear model, exact confidence intervals for contrasts of the fixed effects are not always available. In such cases, confidence intervals with approximate coverage probabilities must be used. As will be shown, this problem may be generalized to that of constructing a confidence interval for the parameter μ, where X is a normal random variable with mean μ and variance ∑ a_qθ_q, where a₁…,a_Q are known constants, U_q = n_qS/θ_q is a chi-squared random variable with n_q degrees of freedom, for each q = 1,…, Q, and X,U₁,…, U_Q are mutually independent. In this paper, we consider the case where Q = 3 and a₃ ≤0.     

Genetic architecture of host use in a widely distributed, polyphagous butterfly (Lepidoptera: Papilionidae): adaptive inferences based on comparison of spatio-temporal populations

Genetic variance-covariance structure of larval performance within and among spatio-temporal populations of the widely distributed, polyphagous tiger swallowtail butterfly, Papilio glaucus , is described. Performance traits were assessed for full-sibling families on three host species: Liriodendron tulipifera, Magnolia virginiana and Prunus serotina . Mean performance varied across hosts, indicating these hosts present unique developmental environments. Although full-sibling families significantly differed in plasticity of across-hosts response in three of the five spatio-temporal populations, additive genetic variation was mostly associated with P. serotina or pupal mass. The relative lack of heritable variation in rate and length of larval development on L. tulipifera and M. virginiana was consistent with an earlier study that established host-associated geographic differentiation of P. glaucus populations. Performance appeared relatively independent across hosts and thus genetic constraints cannot be casually invoked to explain persistence of local adaptation and host specialization in the face of extensive gene flow. I promote the hypothesis that gene flow among geographically distant populations is relatively restricted and that previously established, allozyme-based estimates of panmixia are confounded by effects of Pleistocene glaciations. Significant heterogeneity of variance-covariance structure among spatio-temporal P. glaucus populations supports an interpretation of restricted gene flow and relative evolutionary independence. Despite low precision of estimates of genetic parameters, local variance-covariance structure was remarkably consistent with expectations given the presumed evolutionary history of regional populations.     

ADAPTIVE RADIATION ALONG GENETIC LINES OF LEAST RESISTANCE

Are measurements of quantitative genetic variation useful for predicting long-term adaptive evolution? To answer this question, I focus on g_max, the multivariate direction of greatest additive genetic variance within populations. Original data on threespine sticklebacks, together with published genetic measurements from other vertebrates, show that morphological differentiation between species has been biased in the direction of g_max for at least four million years, despite evidence that natural selection is the cause of differentiation. This bias toward the direction of evolution tends to decay with time. Rate of morphological divergence between species is inversely proportional to θ, the angle between the direction of divergence and the direction of greatest genetic variation. The direction of greatest phenotypic variance is not identical with g_max, but for these data is nearly as successful at predicting the direction of species divergence. I interpret the findings to mean that genetic variances and covariances constrain adaptive change in quantitative traits for reasonably long spans of time. An alternative hypothesis, however, cannot be ruled out: that morphological differentiation is biased in the direction g_max because divergence and g_max are both shaped by the same natural selection pressures. Either way, the results reveal that adaptive differentiation occurs principally along “genetic lines of least resistance.”