Sensory bias as an explanation for the evolution of mate preferences

The sensory bias model of sexual selection posits that female mating preferences are by-products of natural selection on sensory systems. Although sensory bias was proposed 20 years ago, its critical assumptions remain untested. This paradox arises because sensory bias has been used to explain two different phenomena. First, it has been used as a hypothesis about signal design, that is, that males evolve traits that stimulate female sensory systems. Second, sensory bias has been used as a hypothesis for the evolution of female preference itself, that is, to explain why females exhibit particular preferences. We focus on this second facet. First, we clarify the unique features of sensory bias relative to the alternative models by considering each in the same quantitative genetic framework. The key assumptions of sensory bias are that natural selection is the predominant evolutionary mechanism that affects preference and that sexual selection on preferences is quantitatively negligible. We describe four studies that would test these assumptions and review what we can and cannot infer about sensory bias from existing studies. We suggest that the importance of sensory bias as an explanation for the evolution of female preferences remains to be determined.     

Quantifying the magnitude of baseline covariate imbalances resulting from selection bias in randomized clinical trials

Selection bias is most common in observational studies, when patients select their own treatments or treatments are assigned based on patient characteristics, such as disease severity. This first-order selection bias, as we call it, is eliminated by randomization, but there is residual selection bias that may occur even in randomized trials which occurs when, subconsciously or otherwise, an investigator uses advance knowledge of upcoming treatment allocations as the basis for deciding whom to enroll. For example, patients more likely to respond may be preferentially enrolled when the active treatment is due to be allocated, and patients less likely to respond may be enrolled when the control group is due to be allocated. If the upcoming allocations can be observed in their entirety, then we will call the resulting selection bias second-order selection bias. Allocation concealment minimizes the ability to observe upcoming allocations, yet upcoming allocations may still be predicted (imperfectly), or even determined with certainty, if at least some of the previous allocations are known, and if restrictions (such as randomized blocks) were placed on the randomization. This mechanism, based on prediction but not observation of upcoming allocations, is the third-order selection bias that is controlled by perfectly successful masking, but without perfect masking is not controlled even by the combination of advance randomization and allocation concealment. Our purpose is to quantify the magnitude of baseline imbalance that can result from third-order selection bias when the randomized block procedure is used. The smaller the block sizes, the more accurately one can predict future treatment assignments in the same block as known previous assignments, so this magnitude will depend on the block size, as well as on the level of certainty about upcoming allocations required to bias the patient selection. We find that a binary covariate can, on average, be up to 50% unbalanced by third-order selection bias.     

Adjusting for selection bias in retrospective, case-control studies

Retrospective case–control studies are more susceptibleto selection bias than other epidemiologic studies as by designthey require that both cases and controls are representativeof the same population. However, as cases and control recruitmentprocesses are often different, it is not always obvious thatthe necessary exchangeability conditions hold. Selection biastypically arises when the selection criteria are associatedwith the risk factor under investigation. We develop a methodwhich produces bias-adjusted estimates for the odds ratio. Ourmethod hinges on 2 conditions. The first is that a variablethat separates the risk factor from the selection criteria canbe identified. This is termed the "bias breaking" variable.The second condition is that data can be found such that a bias-correctedestimate of the distribution of the bias breaking variable canbe obtained. We show by means of a set of examples that suchbias breaking variables are not uncommon in epidemiologic settings.We demonstrate using simulations that the estimates of the oddsratios produced by our method are consistently closer to thetrue odds ratio than standard odds ratio estimates using logisticregression. Further, by applying it to a case–controlstudy, we show that our method can help to determine whetherselection bias is present and thus confirm the validity of studyconclusions when no evidence of selection bias can be found.     

Substitution rates in Drosophila nuclear genes: implications for translational selection

The relationships between synonymous and nonsynonymous substitution rates and between synonymous rate and codon usage bias are important to our understanding of the roles of mutation and selection in the evolution of Drosophila genes. Previous studies used approximate estimation methods that ignore codon bias. In this study we reexamine those relationships using maximum-likelihood methods to estimate substitution rates, which accommodate the transition/transversion rate bias and codon usage bias. We compiled a sample of homologous DNA sequences at 83 nuclear loci from Drosophila melanogaster and at least one other species of Drosophila. Our analysis was consistent with previous studies in finding that synonymous rates were positively correlated with nonsynonymous rates. Our analysis differed from previous studies, however, in that synonymous rates were unrelated to codon bias. We therefore conducted a simulation study to investigate the differences between approaches. The results suggested that failure to properly account for multiple substitutions at the same site and for biased codon usage by approximate methods can lead to an artifactual correlation between synonymous rate and codon bias. Implications of the results for translational selection are discussed.     

Natural selection on synonymous sites is correlated with gene length and recombination in Drosophila.

Evolutionary analysis of codon bias in Drosophila indicates that synonymous mutations are not neutral, but rather are subject to weak selection at the translation level. Here we show that the effectiveness of natural selection on synonymous sites is strongly correlated with the rate of recombination, in accord with the nearly neutral hypothesis. This correlation, however, is apparent only in genes encoding short proteins. Long coding regions have both a lower codon bias and higher synonymous substitution rates, suggesting that they are affected less efficiently by selection. Therefore, both the length of the coding region and the recombination rate modulate codon bias. In addition, the data indicate that selection coefficients for synonymous mutations must vary by a minimum of one or two orders of magnitude. Two hypotheses are proposed to explain the relationship among the coding region length, the codon bias, and the synonymous divergence and polymorphism levels across the range of recombination rates in Drosophila. The first hypothesis is that selection coefficients on synonymous mutations are inversely related to the total length of the coding region. The second hypothesis proposes that interference among synonymous mutations reduces the efficacy of selection on these mutations. We investigated this second hypothesis by carrying out forward simulations of weakly selected mutations in model populations. These simulations show that even with realistic recombination rates, this interference, which we call the "small-scale" Hill-Robertson effect, can have a moderately strong influence on codon bias.     

Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies

Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.     

Codon usage in regulatory genes in Escherichia coli does not reflect selection for ''rare'' codons.

It has often been suggested that differential usage of codons recognized by rare tRNA species, i.e. "rare codons", represents an evolutionary strategy to modulate gene expression. In particular, regulatory genes are reported to have an extraordinarily high frequency of rare codons. From E. coli we have compiled codon usage data for highly expressed genes, moderately/lowly expressed genes, and regulatory genes. We have identified a clear and general trend in codon usage bias, from the very high bias seen in very highly expressed genes and attributed to selection, to a rather low bias in other genes which seems to be more influenced by mutation than by selection. There is no clear tendency for an increased frequency of rare codons in the regulatory genes, compared to a large group of other moderately/lowly expressed genes with low codon bias. From this, as well as a consideration of evolutionary rates of regulatory genes, and of experimental data on translation rates, we conclude that the pattern of synonymous codon usage in regulatory genes reflects primarily the relaxation of natural selection.     

Performance tradeoffs in target‐group bias correction for species distribution models

Species distribution models (SDMs) are often calibrated using presence‐only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially‐explicit knowledge of sampling effort is rarely available. In multi‐species studies, sampling effort has been inferred following the target‐group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species‐ specific response to this type of bias correction. The present study aims at evaluating the impacts of sampling bias and bias correction on SDM performance. To this end, we designed a realistic system of sampling bias and virtual species based on 92 terrestrial mammal species occurring in the Mediterranean basin. We manipulated presence and background data selection to calibrate four SDM types. Unbiased (unbiased presence data) and biased (biased presence data) SDMs were calibrated using randomly distributed background data. We used real and TG‐estimated sampling efforts in background selection to correct for sampling bias in presence data. Overall, environmental sampling bias had a deleterious effect on SDM performance. In addition, bias correction improved model accuracy, and especially when based on spatially‐explicit knowledge of sampling effort. However, our results highlight important species‐specific variations in susceptibility to sampling bias, which were largely explained by range size: widely‐distributed species were most vulnerable to sampling bias and bias correction was even detrimental for narrow‐ranging species. Furthermore, spatial discrepancies in SDM predictions suggest that bias correction effectively replaces an underestimation bias with an overestimation bias, particularly in areas of low sampling intensity. Thus, our results call for a better estimation of sampling effort in multispecies system, and cautions the uninformed and automatic application of TG bias correction.     

Sexual Selection and Breeding Patterns: Insights from Salmonids (Salmonidae)

Although "intrasexual selection" has been accepted as the mechanism by which males evolve elaborate secondary sexual traits which are used in aggressive contests, the importance of "intersexual selection" as a mechanism by which males have acquired exaggerated traits to display to females during courtship was less readily accepted. In spite of this scepticism, several genetic models have supported the latter idea, and many empirical studies showed that females were generally more discriminating in mate choice than males, because of differences in relative investment between sexes. Nowadays, this idea is reinforced by various concepts (parental investment, potential reproductive rate, environmental potential for polygamy...) which stress that the strength of sexual selection is related to many interdependent factors, such as mating systems, resource distribution (food, habitat, mate), life history and other ecological characteristics. The case of Salmonids is presented here to show how novel information on sexual selection has contributed to the understanding of the plasticity of breeding patterns in the context of evolutionary biology.     

Confidence intervals and P-values for meta-analysis with publication bias

We study publication bias in meta-analysis by supposing there is a population (y, sigma) of studies which give treatment effect estimates y approximately N(theta, sigma(2)). A selection function describes the probability that each study is selected for review. The overall estimate of theta depends on the studies selected, and hence on the (unknown) selection function. Our previous paper, Copas and Jackson (2004, Biometrics 60, 146-153), studied the maximum bias over all possible selection functions which satisfy the weak condition that large studies (small sigma) are as likely, or more likely, to be selected than small studies (large sigma). This led to a worst-case sensitivity analysis, controlling for the overall fraction of studies selected. However, no account was taken of the effect of selection on the uncertainty in estimation. This article extends the previous work by finding corresponding confidence intervals and P-values, and hence a new sensitivity analysis for publication bias. Two examples are discussed.     

植物的亲缘选择

亲缘选择是指在一个随机交配群体中的个体基于亲缘关系而以一种非随机性的方式相互作用,其作用结果是亲缘个体得到更大的广义适合度。综述了亲缘选择和亲缘竞争两种观点以及各自的试验支持证据;分析了导致亲缘选择试验结果出现分歧的原因,认为这主要是由于对亲缘选择理解上的模糊以及试验设计的不严谨所致。植物间的亲缘选择研究不仅相对较少,对亲缘选择的机制研究更为欠缺,这就造成了目前对此问题在科学认识上出现不少盲点。综合前期研究,提出今后对亲缘选择的研究应该首先界定"亲缘"程度,同时改良试验设计方案,选择多种不同生境下的物种对亲缘选择进行深入研究,并且考虑环境因子对植物亲缘选择的影响。同时,对植物亲缘识别机制的研究应该从生理生化方面出发,通过定性定量地分析探索植物根系分泌物在植物亲缘识别中的作用和作用途径。     

The Method Quality of Cross-Over Studies Involved in Cochrane Systematic Reviews

Background

It is possible that cross-over studies included in current systematic reviews are being inadequately assessed, because the current risk of bias tools do not consider possible biases specific to cross-over design. We performed this study to evaluate whether this was being done in cross-over studies included in Cochrane Systematic Reviews (CSRs).

Methods

We searched the Cochrane Library (up to 2013 issue 5) for CSRs that included at least one cross-over trial. Two authors independently undertook the study selection and data extraction. A random sample of the CSRs was selected and we evaluated whether the cross-over trials in these CSRs were assessed according to criteria suggested by the Cochrane handbook. In addition we reassessed the risk of bias of these cross-over trials by a checklist developed form the Cochrane handbook.

Results

We identified 688 CSRs that included one or more cross-over studies. We chose a random sample of 60 CSRs and these included 139 cross-over studies. None of these CSRs undertook a risk of bias assessment specific for cross-over studies. In fact items specific for cross-over studies were seldom considered anywhere in quality assessment of these CSRs. When we reassessed the risk of bias, including the 3 items specific to cross-over trials, of these 139 studies, a low risk of bias was judged for appropriate cross-over design in 110(79%), carry-over effects in 48(34%) and for reporting data in all stages of the trial in 114(82%).Assessment of biases in cross-over trials could affect the GRADE assessment of a review’s findings.

Conclusion

The current Cochrane risk of bias tool is not adequate to assess cross-over studies. Items specific to cross-over trials leading to potential risk of bias are generally neglected in CSRs. A proposed check list for the evaluation of cross-over trials is provided.     

Matched Case—Control Data Analysis with Selection Bias

Case-control studies offer a rapid and efficient way to evaluate hypotheses. On the other hand, proper selection of the controls is challenging, and the potential for selection bias is a major weakness. Valid inferences about parameters of interest cannot be drawn if selection bias exists. Furthermore, the selection bias is difficult to evaluate. Even in situations where selection bias can be estimated, few methods are available. In the matched case-control Northern Manhattan Stroke Study (NOMASS), stroke-free controls are sampled in two stages. First, a telephone survey ascertains demographic and exposure status from a large random sample. Then, in an in-person interview, detailed information is collected for the selected controls to be used in a matched case-control study. The telephone survey data provides information about the selection probability and the potential selection bias. In this article, we propose bias-corrected estimators in a case-control study using a joint estimating equation approach. The proposed bias-corrected estimate and its standard error can be easily obtained by standard statistical software.     

OVERCOMING STATISTICAL BIAS TO ESTIMATE GENETIC MATING SYSTEMS IN OPEN POPULATIONS: A COMPARISON OF BATEMAN'S PRINCIPLES BETWEEN THE SEXES IN A SEX‐ROLE‐REVERSED PIPEFISH

The genetic mating system is a key component of the sexual selection process, yet methods for the quantification of mating systems remain controversial. One approach involves metrics derived from Bateman's principles, which are based on variances in mating and reproductive success and the relationship between them. However, these measures are extremely difficult to measure for both sexes in open populations, because missing data can result in biased estimates. Here, we develop a novel approach for the estimation of mating system metrics based on Bateman's principles and apply it to a microsatellite‐based parentage analysis of a natural population of the dusky pipefish, Syngnathus floridae. Our results show that both male and female dusky pipefish have significantly positive Bateman gradients. However, females exhibit larger values of the opportunity for sexual selection and the opportunity for selection compared to males. These differences translate into a maximum intensity of sexual selection () for females three times larger than that for males. Overall, this study identifies a critical source of bias that affects studies of mating systems in open populations, presents a novel method for overcoming this bias, and applies this method for the first time in a sex‐role‐reversed pipefish.     

Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias — An Updated Review

Background

The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making.

Methodology/Principal Findings

In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40–62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.

Conclusions

This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.     

Synergistic selection and graded traits

Fitness interactions where benefits are shared only between individuals with similar traits are often referred to as synergistic. Examples include defence characters, like insect warning colouration and plant unpalatability, and joint activities needing the active participation of all group members, such as cooperative hunting. Previous analyses, assuming discrete variation in the trait, have shown that synergistic selection can be a sufficient explanation for the evolutionary stability of such traits. Here, we investigate the consequences of graded variation in the trait responsible for synergistic effects. Classifying the synergism as unbiased when an individual receives maximum associational benefit by having the same trait value as its neighbours, and letting a positive (negative) bias represent the maximum above (below) this value, we show that only positively biased synergistic selection can enhance a graded trait. Thus for graded traits, a synergistic benefit is not in itself sufficient for evolutionary stability. We study possible reasons for synergistic bias in a simple model of plant defences against herbivores, and suggest that the processes of herbivore avoidance learning and diet selection are probable causes of positive bias. We propose that mammalian herbivores exposed to a given level of toxicity will show stronger feeding aversion to higher toxicity, resulting in positively biased synergistic selection of plant defence traits. Positive bias produced by avoidance learning may, in a similar way, also select for defence signals.     

REPLICATED ORIGIN OF FEMALE‐BIASED ADULT SEX RATIO IN INTRODUCED POPULATIONS OF THE TRINIDADIAN GUPPY (POECILIA RETICULATA)

There are many theoretical and empirical studies explaining variation in offspring sex ratio but relatively few that explain variation in adult sex ratio. Adult sex ratios are important because biased sex ratios can be a driver of sexual selection and will reduce effective population size, affecting population persistence and shapes how populations respond to natural selection. Previous work on guppies (Poecilia reticulata) gives mixed results, usually showing a female‐biased adult sex ratio. However, a detailed analysis showed that this bias varied dramatically throughout a year and with no consistent sex bias. We used a mark‐recapture approach to examine the origin and consistency of female‐biased sex ratio in four replicated introductions. We show that female‐biased sex ratio arises predictably and is a consequence of higher male mortality and longer female life spans with little effect of offspring sex ratio. Inconsistencies with previous studies are likely due to sampling methods and sampling design, which should be less of an issue with mark‐recapture techniques. Together with other long‐term mark‐recapture studies, our study suggests that bias in offspring sex ratio rarely contributes to adult sex ratio in vertebrates. Rather, sex differences in adult survival rates and longevity determine vertebrate adult sex ratio.     

Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint

Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.     

Rapid divergence of codon usage patterns within the rice genome

Background

Synonymous codon usage varies widely between genomes, and also between genes within genomes. Although there is now a large body of data on variations in codon usage, it is still not clear if the observed patterns reflect the effects of positive Darwinian selection acting at the level of translational efficiency or whether these patterns are due simply to the effects of mutational bias. In this study, we have included both intra-genomic and inter-genomic comparisons of codon usage. This allows us to distinguish more efficiently between the effects of nucleotide bias and translational selection.

Results

We show that there is an extreme degree of heterogeneity in codon usage patterns within the rice genome, and that this heterogeneity is highly correlated with differences in nucleotide content (particularly GC content) between the genes. In contrast to the situation observed within the rice genome, Arabidopsis genes show relatively little variation in both codon usage and nucleotide content. By exploiting a combination of intra-genomic and inter-genomic comparisons, we provide evidence that the differences in codon usage among the rice genes reflect a relatively rapid evolutionary increase in the GC content of some rice genes. We also noted that the degree of codon bias was negatively correlated with gene length.

Conclusion

Our results show that mutational bias can cause a dramatic evolutionary divergence in codon usage patterns within a period of approximately two hundred million years.The heterogeneity of codon usage patterns within the rice genome can be explained by a balance between genome-wide mutational biases and negative selection against these biased mutations. The strength of the negative selection is proportional to the length of the coding sequences. Our results indicate that the large variations in synonymous codon usage are not related to selection acting on the translational efficiency of synonymous codons.

     

Bias in the introduction of variation as an orienting factor in evolution

SUMMARY According to New Synthesis doctrine, the direction of evolution is determined by selection and not by "internal causes" that act by way of propensities of variation. This doctrine rests on the theoretical claim that because mutation rates are small in comparison to selection coefficients, mutation is powerless to overcome opposing selection. Using a simple population-genetic model, this claim is shown to depend on assuming the prior availability of variation, so that mutation may act only as a "pressure" on the frequencies of existing alleles, and not as the evolutionary process that introduces novelty. As shown here, mutational bias in the introduction of novelty can strongly influence the course of evolution, even when mutation rates are small in comparison to selection coefficients. Recognizing this mode of causation provides a distinct mechanistic basis for an "internalist" approach to determining the contribution of mutational and developmental factors to evolutionary phenomena such as homoplasy, parallelism, and directionality.