The conflict between natural and artificial selection in finite populations

Summary A single locus model of the interaction between natural selection and artificial selection for a quantitative character in a finite population, assuming heterozygote superiority in natural fitness but additive action on the character, has been studied using transition probability matrices.If natural selection is strong enough to create a selection plateau in which genetic variance declines relatively slowly, then the total response to artificial selection prior to the plateau will be much less than that expected in the absence of natural selection, and the half-life of response will be shorter. Such a plateau is likely to have a large proportion, if not all, of the original genetic variance still present. In selection programmes using laboratory animals, it seems likely that the homozygote favoured by artificial selection must be very unfit before such a plateau will occur. A significant decrease in population fitness as a result of artificial selection does not necessarily imply that the metric character is an important adaptive character.These implications of this model of natural selection are very similar to those derived by James (1962) for the optimum model of natural selection. In fact, there seems to be no aspect of the observable response to artificial selection that would enable anyone to distinguish between these two models of natural selection.     

Selection for phenotypic divergence between diploid and autotetraploid Heuchera grossulariifolia

Much of the diversity of flowering plants is associated with genomic duplication through polyploidy. Little is known, however, about the evolutionary mechanisms responsible for the diversification of novel polyploid lineages. We evaluated the possibility that divergence is driven by natural selection by estimating the strength of phenotypic selection acting on three floral traits in sympatric populations of diploid and autotetraploid Heuchera grossulariifolia over three years. Our results demonstrate consistent directional selection for increasing scape length and floral display in both diploid and tetraploid populations. In contrast, selection acting on flowering phenology varied across year and ploidy. Specifically, selection was found to favor late-flowering diploids in 2001 and 2002 but early-flowering tetraploids in 2003. We investigated the mechanistic basis of divergent selection for flowering phenology in 2003 by estimating the relationship between plant flowering phenology and the probability of intercytotype pollinator movement. The results demonstrated that less divergent tetraploids were significantly more likely to experience intercytotype flights than were more divergent tetraploids. This result is consistent with the pattern of phenotypic selection observed. Taken together, our results suggest that divergence of polyploids and their diploid progenitors may be driven by a process analogous to reinforcement whereby selection favors phenotypes that reduce the probability of intercytotype matings with reduced fertility.     

A new method to uncover signatures of divergent and stabilizing selection in quantitative traits

While it is well understood that the pace of evolution depends on the interplay between natural selection, random genetic drift, mutation, and gene flow, it is not always easy to disentangle the relative roles of these factors with data from natural populations. One popular approach to infer whether the observed degree of population differentiation has been influenced by local adaptation is the comparison of neutral marker gene differentiation (as reflected in FST) and quantitative trait divergence (as reflected in QST). However, this method may lead to compromised statistical power, because FST and QST are summary statistics which neglect information on specific pairs of populations, and because current multivariate tests of neutrality involve an averaging procedure over the traits. Further, most FST-QST comparisons actually replace QST by its expectation over the evolutionary process and are thus theoretically flawed. To overcome these caveats, we derived the statistical distribution of population means generated by random genetic drift and used the probability density of this distribution to test whether the observed pattern could be generated by drift alone. We show that our method can differentiate between genetic drift and selection as a cause of population differentiation even in cases with FST=QST and demonstrate with simulated data that it disentangles drift from selection more accurately than conventional FST-QST tests especially when data sets are small.     

Phenology and phenotypic natural selection on the flowering time of a deceit-pollinated tropical orchid, Myrmecophila christinae

BACKGROUND AND AIMS: Flowering phenology is described and the effect of flowering time on pollination success is evaluated in the deceit-pollinated tropical orchid, Myrmecophila christinae. It was expected that, due to this species' deceit pollination strategy and low observed pollinator visit rate, there would be a higher probability of natural selection events favouring individuals flowering away from the population flowering peak. METHODS: The study covers two consecutive years and four populations of M. christinae located along the north coast of the Yucatán Peninsula. For phenological and pollination success data, a total of 110 individuals were monitored weekly in 1998, and 83 individuals in 1999, during all the flowering and fruiting season. KEY RESULTS: The results showed significant differences in the probability of donating and receiving pollen throughout the flowering season. The probability of receiving or donating pollen increased the further an individual flowering was from the flowering peak. Regression analysis showed directional and disruptive phenotypic natural selection gradients, suggesting the presence of selection events unfavourable to flowering during flowering peak, for both male success (pollen removal) and female success (fruit production). However, the intensity and significance of the natural selection events varied between populations from year to year. The variation between seasons and populations was apparently due to variations in the density of reproductive individuals in each population and each season. CONCLUSIONS: As in other deceit-pollinated orchids, natural selection in M. christinae favours individuals flowering early or late in relation to population peak flowering. However, results also suggested a fluctuating regime of selective events act on flowering time of M. christinae.     

Evolving the improbable

The debate about the evolutionary roles of natural selection and molecular drive cannot be resolved by assigning probabilities with hind-sight to unique events in the evolution of complex adaptations. Hoyle dismisses natural selection by calculating, erroneously, the improbability of assembling by chance a string of amino acids in the order in which they occur in a given protein. Dawkins dismisses molecular drive on the same false grounds of the improbability of making the right decision, by chance, at each step in a long series of steps in the evolution of a given trait. Calculation of the probability, at any given step, of a successful match between fortuitous genetic variation and fortuitous environmental heterogeneity requires detailed knowledge of all parameters at that step. Such information is an essential requirement for quantifying the roles of natural selection and molecular drive in the evolution of one actual series of steps out of many potential series.     

Adaptation or biased gene conversion? Extending the null hypothesis of molecular evolution

The analysis of evolutionary rates is a popular approach to characterizing the effect of natural selection at the molecular level. Sequences contributing to species adaptation are expected to evolve faster than nonfunctional sequences because favourable mutations have a higher fixation probability than neutral ones. Such an accelerated rate of evolution might be due to factors other than natural selection, in particular GC-biased gene conversion. This is true of neutral sequences, but also of constrained sequences, which can be illustrated using the mouse Fxy gene. Several criteria can discriminate between the natural selection and biased gene conversion models. These criteria suggest that the recently reported human accelerated regions are most likely the result of biased gene conversion. We argue that these regions, far from contributing to human adaptation, might represent the Achilles' heel of our genome.     

The probability of parallel evolution

Abstract How often will natural selection drive parallel evolution at the DNA sequence level? More precisely, what is the probability that selection will cause two populations that live in identical environments to substitute the same beneficial mutation? Here I show that, under fairly general conditions, the answer is simple: if a wild‐type sequence can mutate to n different beneficial mutations, replicate populations will on average fix the same mutation with probability P= 2/(n + 1). This probability, which is derived using extreme value theory, is independent of most biological details, including the length of the gene in question and the precise distribution of fitness effects among alleles. I conclude that the probability of parallel evolution under natural selection is nearly twice as large as that under neutrality.     

The non-existence of a principle of natural selection

The theory of natural selection is a rich systematization of biological knowledge without a first principle. When formulations of a proposed principle of natural selection are examined carefully, each is seen to be exhaustively analyzable into a proposition about sources of fitness and a proposition about consequences of fitness. But whenever the fitness of an organic variety is well defined in a given biological situation, its sources are local contingencies together with the background of laws from disciplines other than the theory of natural selection; and the consequences of fitness for the long range fate of organic varieties are essentially applications of probability theory. Hence there is no role and no need for a principle of the theory of natural selection, and any generalities that may hold in that theory are derivative rather than fundamental.     

Cooperating in the face of uncertainty: a consistent framework for understanding the evolution of cooperation

The evolution of cooperative behaviour, whereby individuals enhance the fitness of others at an apparent cost to themselves, represents one of the greatest paradoxes of evolution. Individuals that engage in such cooperative behaviour can, however, be favoured by natural selection if cooperative actions confer higher fitness than alternative actions. To understand the evolution of cooperative behaviour, the direct and indirect genetic benefits that individuals accrue in the present and future must be summed - this can be accomplished without any reference to the colorful vocabulary typically associated with studies of cooperation. When benefits are accrued indirectly through relatives or directly in the future individuals must be able to assess and enhance their probability of accruing those benefits and behave accordingly. We suggest that, in the same way that studies of kin recognition systems improved our understanding of how individuals assess and enhance their probability of accruing indirect benefits, studies of various forms of inheritance and reciprocation recognition systems will improve our understanding of how individuals assess and enhance their probability of accruing future benefits. Recognizing the parallel between studies of indirect fitness and future fitness, at multiple levels of analysis, will move us toward a simpler and more consistent framework for understanding the evolution of cooperative behaviour.     

标记辅助回交育种中所需最小样本容量的近似估计

回交育种是把有利基因从供体亲本向受体亲本转移的一种有效方法,标记辅助选择可加速其进程。为了制定合理的标记辅助选择计划,育种家必须知道所需的后代群体大小。该文提出了一种估算在标记辅助回交育种中同时进行前景选择和背景选择所需群体大小的方法。在假定所需转移的目标基因座与遗传背景之间为相互独立的简化假设下,可以通过将解析方法（针对前景选择）与基于回交亲本图示基因型的模拟方法（针对背景选择）相结合,近似地估计出在每一世代中选到所需基因型的概率,进而估算出在一定概率水平下至少获得一个符合要求的个体所需的最小样本容量,用假想的例子演示了该方法的使用情况。该方法可以很方便地应用于实际的回交育种。     

On the probability of correct selection for large k populations, with application to microarray data

One frontier of modern statistical research is the problems arising from data sets with extremely large k (>1000) populations, e.g. microarray and neuroimaging data. For many such problems the focus shifts from testing for significance to selecting, filtering, or screening. Classical Ranking and Selection Methodology (RSM) studied the probability of correct selection (PCS). PCS is the probability that the "best" (t = 1) of k populations is truly selected, according to some specified criteria of best. This paper extends and adapts two selection goals from the RSM literature that are suitable for large k problems (d-best and G-best selection). It is then shown how estimation of PCS for selecting multiple (t > 1) populations with d-best and G-best selection can be implemented to provide a useful measure of the quality of a given selection. A simulation study and the application of the proposed method to a benchmark microarray data set show it is an effective and versatile tool for assessing the probability that a particular gene selection or gene filtering step truly obtains the best genes. Moreover, the proposed method is fully general and may be applied to any such extremely large k problem.     

Measuring spatial variation in natural selection using randomly-sown seeds of Arabidopsis thaliana

We describe a simple method of measuring spatial variation in fitness using randomly sown seeds. Given a random initial distribution of seeds, any spatial variation in the final distribution of genotypes must be the result of spatial variation in natural selection. Departures from spatial randomness are tested using a modification of join-count statistics, based on the probability that two randomly chosen plants separated by a given distance carry the same genetic marker. Monte Carlo simulations showed that for the randomly sown seeds design the join-count test had higher power to detect spatial variation in fitness than analysis of variance. The power was relatively independent of the number of genotypes used or the number of microsites sampled. The method was tested in the field using three standard lines of Arabidopsis thaliana. At the end of the life cycle, adjacent plants were significantly more likely to be the same line than randomly chosen pairs and the spatial dependence extended to distances of nearly 50 cm. Simple experiments like this may be especially useful for “comparative ecological genetics”, allowing systematic studies of natural selection among many species or habitats.     

Natural Selection for within-Generation Variance in Offspring Number

In this paper it is shown that natural selection can act on the within-generation variance in offspring number. The fitness of a genotype will increase as its variance in offspring number decreases. The intensity of selection on the variance component is inversely proportional to population size, although the fixation probability of a gene which differs from its allele only in the variance in its offspring number is independent of population size. The concept of effective population size is shown to be of limited use when there is genetic variation in the variance in offspring number.     

Existence of steady-state probability distributions in multilocus models for genotype evolution

It is shown that a representative Fisher-Wright model withn(≥3) diallelic loci admits a necessary condition for existence of a time-independent steady-state probability distribution. This necessary condition states that a global integral depending on the phenotype fitness functions of natural selection must be larger than a certain quantity depending on the parameters associated with genetic drift.     

Partitioning of covariance as a method for studying kin selection

Covariance models of selection predict gene frequency change in terms of the relationship between the fitness of an individual and its phenotype, and therefore provide a convenient method for studying evolution in natural systems. When these models are partitioned into effects acting within and between hierarchical levels of selection, they can be directly applied to questions of kin selection. In many cases, the partitioning of covariance approach to the study of kin selection is a more useful alternative to the traditional hamiltonian or inclusive fitness approach.     

Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression

This article investigates an augmented inverse selection probability weighted estimator for Cox regression parameter estimation when covariate variables are incomplete. This estimator extends the Horvitz and Thompson (1952, Journal of the American Statistical Association 47, 663-685) weighted estimator. This estimator is doubly robust because it is consistent as long as either the selection probability model or the joint distribution of covariates is correctly specified. The augmentation term of the estimating equation depends on the baseline cumulative hazard and on a conditional distribution that can be implemented by using an EM-type algorithm. This method is compared with some previously proposed estimators via simulation studies. The method is applied to a real example.     

Selection for territory acquisition is modulated by social network structure in a wild songbird

The social environment may be a key mediator of selection that operates on animals. In many cases, individuals may experience selection not only as a function of their phenotype, but also as a function of the interaction between their phenotype and the phenotypes of the conspecifics they associate with. For example, when animals settle after dispersal, individuals may benefit from arriving early, but, in many cases, these benefits will be affected by the arrival times of other individuals in their local environment. We integrated a recently described method for calculating assortativity on weighted networks, which is the correlation between an individual's phenotype and that of its associates, into an existing framework for measuring the magnitude of social selection operating on phenotypes. We applied this approach to large‐scale data on social network structure and the timing of arrival into the breeding area over three years. We found that late‐arriving individuals had a reduced probability of breeding. However, the probability of breeding was also influenced by individuals’ social networks. Associating with late‐arriving conspecifics increased the probability of successfully acquiring a breeding territory. Hence, social selection could offset the effects of nonsocial selection. Given parallel theoretical developments of the importance of local network structure on population processes, and increasing data being collected on social networks in free‐living populations, the integration of these concepts could yield significant insights into social evolution.     

A New Test for Selection Applied to Codon Usage in Drosophila simulans and D. mauritiana

In many organisms, synonymous codon usage is biased by a history of natural selection. However, codon bias, itself, does not indicate that selection is ongoing; it may be a vestige of past selection. Simple statistical tests have been devised to infer ongoing selection on codon usage by comparing the derived state frequency spectra at polymorphic sites segregating either derived preferred codons or derived unpreferred codons; if selection is effective, the frequency of derived states should be higher in the former. We propose a new test that uses the inferred degree of preference, essentially calculating the correlation of derived state frequency and the difference in preference between the derived and the ancestral states; the correlation should be positive if selection is effective. When implementing the test, derived and ancestral states can be assigned by parsimony or on the basis of relative probability. In either case, statistical significance is estimated by a simple permutation test. We explored the statistical power of the test by sampling polymorphism data from 14 loci in 16 strains of D. simulans, finding that the test retains 80% power even when quite a few of the data are discarded. The power of the test likely reflects better use of multiple features of the data, combining population frequencies of polymorphic variants and quantitative estimates of codon preferences. We also applied this novel test to 14 newly sequenced loci in five strains of D. mauritiana, showing for the first time ongoing selection on codon usage in this species.     

Within- and between-species DNA sequence variation and the 'footprint' of natural selection

Extensive DNA data emerging from genome-sequencing projects have revitalized interest in the mechanisms of molecular evolution. Although the contribution of natural selection at the molecular level has been debated for over 30 years, the relevant data and appropriate statistical methods to address this issue have only begun to emerge. This paper will first present the predominant models of neutral, nearly neutral, and adaptive molecular evolution. Then, a method to identify the role of natural selection in molecular evolution by comparing within- and between-species DNA sequence variation will be presented. Computer simulations show that such methods are powerful for detecting even very weak selection. Examination of DNA variation data within and between Drosophila species suggests that 'silent' sites evolve under a balance between weak selection and genetic drift. Simulated data also show that sequence comparisons are a powerful method to detect adaptive protein evolution, even when selection is weak or affects a small fraction of nucleotide sites. In the Drosophila data examined, positive selection appears to be a predominant force in protein evolution.     

The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.