1LocusSim a mobile-friendly simulator for teaching population genetics

Motivation

Biology students often struggle with the fundamental concepts of evolutionary genetics, including genetic drift, mutation and selection. To address this problem, 1LocusSim was developed to simulate the interaction of different factors, such as population size, mutation, selection and dominance, to study their effect on allelic frequency during evolution. With 1LocusSim, students can compare theoretical results with simulation outputs and solve and analyze different problems of population genetics. The 1LocusSim web has a responsive design which means that it has been specifically designed to be used on smartphones.

Results

To demonstrate its use, I review the classical overdominance model of population genetics and highlight a characteristic that is often not explicitly stated. Specifically, it is emphasized that the equilibrium of the model does not depend on the homozygous selection coefficients but rather on the ratio of the selection coefficients. This is already clear from the classical formula but maybe not so much for students. Also it implies that the equilibrium can be expressed solely in terms of the dominance coefficient h. To verify this theoretical prediction, I utilize the simulator and calculate the equilibrium for the well-known case of sickle cell anemia. By utilizing this tool, students can learn at their own pace and convenience, anywhere and anytime.

Availability and implementation

1LocusSim if freely available at https://1LocusSim-biosdev.pythonanywhere.com/. Website implemented under the Bottle micro web-framework for Python, with all major browsers supported.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Detecting Local Adaptation between North and South European Atlantic Salmon Populations

Pollution and other anthropogenic effects have driven a decrease in Atlantic salmon (Salmo salar) in the Iberian Peninsula. The restocking effort carried out in the 1980s, with salmon from northern latitudes with the aim of mitigating the decline of native populations, failed, probably due to the deficiency in adaptation of foreign salmon from northern Europe to the warm waters of the Iberian Peninsula. This result would imply that the Iberian populations of Atlantic salmon have experienced local adaptation in their past evolutionary history, as has been described for other populations of this species and other salmonids. Local adaptation can occur by divergent selections between environments, favoring the fixation of alleles that increase the fitness of a population in the environment it inhabits relative to other alleles favored in another population. In this work, we compared the genomes of different populations from the Iberian Peninsula (Atlantic and Cantabric basins) and Scotland in order to provide tentative evidence of candidate SNPs responsible for the adaptive differences between populations, which may explain the failures of restocking carried out during the 1980s. For this purpose, the samples were genotyped with a 220,000 high-density SNP array (Affymetrix) specific to Atlantic salmon. Our results revealed potential evidence of local adaptation for North Spanish and Scottish populations. As expected, most differences concerned the comparison of the Iberian Peninsula with Scotland, although there were also differences between Atlantic and Cantabric populations. A high proportion of the genes identified are related to development and cellular metabolism, DNA transcription and anatomical structure. A particular SNP was identified within the NADP-dependent malic enzyme-2 (_mMEP-2*), previously reported by independent studies as a candidate for local adaptation in salmon from the Iberian Peninsula. Interestingly, the corresponding SNP within the _mMEP-2* region was consistent with a genomic pattern of divergent selection.

A Simulation Study of the Ecological Speciation Conditions in the Galician Marine Snail Littorina saxatilis

In the last years, the interest in evolutionary divergence at small spatial scales has increased and so did the study of speciation caused by ecologically based divergent natural selection. The evolutionary interplay between gene flow and local adaptation can lead to low-dispersal locally adapted specialists. When this occurs, the evolutionary interplay between gene flow and local adaptation could eventually lead to speciation. The L. saxatilis system consists of two ecotypes displaying a microhabitat-associated intraspecific dimorphism along the wave-exposed rocky shores of Galicia. Despite being a well-known system, the dynamics of the ecotype formation remain unclear and cannot be studied from empirical evidence alone. In this study, individual-based simulations were used to incorporate relevant ecological, spatial, and genetic information, to check different evolutionary scenarios that could evolve non-random mating preferences and finally may facilitate speciation. As main results, we observed the evolution of intermediate values of choice which matches the estimates from empirical data of L. saxatilis in Galician shores and coincides with previous theoretical outcomes. Also, the use of the mating correlation as a proxy for assortative mating led to spuriously inferring greater reproductive isolation in the middle habitat than in the others, which does not happen when directly considering the choice values from the simulations. We also corroborate the well-known fact that the occurrence of speciation is influenced by the strength of selection. Taken together, this means, also according to other L. saxatilis systems, that speciation is not an immediate consequence of local divergent selection and mating preferences, but a fine tuning among several factors including the ecological conditions in the shore levels, the selection strength, the mate choice stringency, and cost to choosiness. The L. saxatilis system could correspond to a case of incomplete reproductive isolation, where the choice intensity is intermediate and local adaptation within the habitat is strong. These results support previous interpretations of the L. saxatilis model system and indicate that further empirical studies would be interesting to test whether the mate choice mechanism functions as a similarity-like mechanism as has been shown in other littorinids.

Data and models from multi-model inference of non-random mating from an information theoretic approach

This is a co-submission with Multi-model inference of non-random mating from an information theoretic approach [1]. These data corresponds to the complete simulated data set jointly with the set of models defined for analysing the data. The simulated data set was obtained using the program MateSim [2].

The simulated cases correspond to one-sex competition and mate choice models. For each simulation run, the population frequencies (premating individuals) and the sample of 500 mating pairs were generated randomly for a hypothetical trait with two classes at each sex.

Some datasets represent larger population size species (n = 10 000) and the mating process was represented as a sampling with replacement, and the population frequencies were constant over the mating season. The minimum phenotype frequency (MPF) allowed was 0.1.

Five different model cases were simulated, namely random mating, female competition with mate choice (with independent or compound parameters) and male competition with mate choice (with independent or compound parameters). Each case was simulated 1000 times.

Other datasets represent monogamous species (with large or small population size) and the mating process was without replacement (from the point of view of the available phenotypes).

These data sets were used to test the performance of the multi-model inference methodology proposed in [1]. The data may be useful for testing any new/old statistics for measuring sexual selection or assortative mating patterns.

Multi-model inference of non-random mating from an information theoretic approach

Non-random mating has a significant impact on the evolution of organisms. Here, I developed a modelling framework for discrete traits (with any number of phenotypes) to explore different models connecting the non-random mating causes (mate competition and/or mate choice) and their consequences (sexual selection and/or assortative mating). I derived the formulaefor the maximum likelihood estimates of each model and used information criteria to perform multi-model inference. Simulation results showed a good performance of both model selection and parameter estimation. The methodology was applied to ecotypes data of the marine gastropod Littorina saxatilis from Galicia (Spain), to show that the mating pattern is better described by models with two parameters that involve both mate choice and competition, generating positive assortative mating plus female sexual selection. As far as I know, this is the first standardized methodology for model selection and multi-model inference of mating parameters for discrete traits. The advantages of this framework include the ability of setting up models from which the parameters connect causes, as mate competition and mate choice, with their outcome in the form of data patterns of sexual selection and assortative mating. For some models, the parameters may have a double effect i.e. they produce sexual selection and assortative mating, while for others there are separated parameters for one kind of pattern or another. From an empirical point of view, it is much easier to study patterns than processes and, for this reason, the causal mechanisms of sexual selection are not so well known as the patterns they produce. The goal of the present work is to propose a new tool that helps to distinguish among different alternative processes behind the observed mating pattern. The full methodology was implemented in a software called InfoMating (available at http://acraaj.webs6.uvigo.es/InfoMating/Infomating.htm).

Myriads: P-value-based multiple testing correction

Motivation

There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P-values as currently demanded by microarray and other omic technologies.

Results

The program Myriads, formerly SGoF+, provides some of the most important P-value-based correction methods jointly with a test of dependency and a P-value simulator. Myriads easily manage hundreds of thousands of P-values.

Availability and implementation

http://myriads.webs.uvigo.es.

Contact

myriads@uvigo.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Geographic and Temporal Variation of Distinct Intracellular Endosymbiont Strains of Wolbachia sp. in the Grasshopper Chorthippus parallelus: a Frequency-Dependent Mechanism?

Wolbachia is an intracellular endosymbiont that can produce a range of effects on host fitness, but the temporal dynamics of Wolbachia strains have rarely been experimentally evaluated. We compare interannual strain frequencies along a geographical region for understanding the forces that shape Wolbachia strain frequency in natural populations of its host, Chorthippus parallelus (Orthoptera, Acrididae). General linear models show that strain frequency changes significantly across geographical and temporal scales. Computer simulation allows to reject the compatibility of the observed patterns with either genetic drift or sampling errors. We use consecutive years to estimate total Wolbachia strain fitness. Our estimation of Wolbachia fitness is significant in most cases, within locality and between consecutive years, following a negatively frequency-dependent trend. Wolbachia spp. B and F strains show a temporal pattern of variation that is compatible with a negative frequency-dependent natural selection mechanism. Our results suggest that such a mechanism should be at least considered in future experimental and theoretical research strategies that attempt to understand Wolbachia biodiversity.

A generalization of the informational view of non-random mating: Models with variable population frequencies

The mating distribution caused by mate choice can be expressed as a gain in information with respect to random mating. In that view, the population phenotype frequencies had been considered constant during the breeding season. Here, such restriction was relaxed to consider encounter-mating processes in which first, the encounter between partners depends on the phenotype distribution of the population, and second, the mating after the encounter depends on the mutual mating propensities. Under this setting, the population phenotype frequencies are no longer constant because the process of pair formation occurs in discrete intervals of time, called mating rounds where at least one mating pair is formed, and the frequency of phenotypes available for the next mating, changes. Provided that there are more than one mating round per breeding season, the population phenotype frequencies are no longer constant. Similarly to the constant case, we describe the change in the mating phenotypes by the flow of information with respect to random mating. This information can be partitioned into sexual selection, assortative mating (sexual isolation) and their mixed effect. Likewise, the pairwise statistics for total change, sexual selection and assortative mating are generalized for variable population phenotype frequencies. The new tests had more power to detect the effects of non-random mating when the phenotype frequencies varied during the breeding season. The differences in power were high for sexual selection but minor for assortative mating scenarios. However, the application of the new formulas requires the estimation of phenotype frequencies at each mating round. Therefore, choosing one or another type of statistics would depend on the biological scenario as well as the availability and easiness to split the sampling in more than one mating round.

MateSim: Monte Carlo simulation for the generation of mating tables

In species with sexual reproduction, the mating pattern is an important element for understanding evolutionary and speciation processes. Given a mating pool where individuals can encounter each other randomly, the individual mating preferences define the mating frequencies in the population. However, in every mating process we can distinguish two different steps. First, the encounter between partners, and second, the actual mating once the encounter has occurred. Yet, we cannot always assume that the observed population patterns accurately reflect the individuals' preferences. In some scenarios, individuals may have difficulties to achieve their preferred matings, such as in monogamous species with low population size, where the mating process is similar to a sampling without replacement. In this case, the encounter process will introduce some noise that may disconnect the individual preferences from the obtained mating pattern. Such a difference between mating patterns and mating preferences has previously been shown by different modeling scenarios. Here I present a program that simulates the mating process for both discrete and continuous traits, under different encounter models and individual preference functions, and including effects such as time dependence and aging. The utility of the software is demonstrated by replicating and extending a recent study that showed how patterns of positive assortative mating, or marriage in human societies, may arise from non-assortative individual preferences. The previous result is confirmed and is shown to be caused by the marriage among the "ugliest" and oldest individuals, who after many attempts were finally able to mate among themselves. In fact, I show that the assortative pattern vanishes if an aging process prevents these individuals from mating altogether. The software MateSim is available jointly with the user's manual, at http://acraaj.webs.uvigo.es/MateSim/matesim.htm.

Non-random mating and information theory

In this work, mate choice is modeled by means of the abstract concept of mutual mating propensity. This only assumes that different types of couples can have different mating success. The model is adequate for any population where mating occurs among distinct types. There is no extra assumption about particular mating scheme or preference model. The concept of mutual mating propensity permits to express the observed change in the mating phenotypes as the gain in information with respect to random mating. The obtained expression is a form of the Price equation in which the mapping between ancestral and descendant population is substituted by a mapping between random mating and non random mating population. At the same time, this framework provides the connection between mate choice and the exact mathematical partition of the choice effects, namely sexual isolation, sexual selection and a mixed effect. The sexual selection component is the sum of the intra-sexual male and female selection. The proposed framework helps to unveil previously hidden invariants. For example, if the mutual preference between partner types is multiplicative there is no sexual isolation (inter-sexual selection) effect on the frequencies, i.e. the only possible effect of mate choice is intra-sexual selection. On the contrary, whatever the contribution of each partner to the mutual preference, if it comes as a non-multiplicative factor, there is at least an inter-sexual selection detectable effect. This new view over the mate choice problem, permits to develop general mating propensity models and to make predictions of the mate choice effects that may emerge from such models. This possibility opens up the way for setting a general theory of model fitting and multimodel inference for mate choice. Thus, it is suggested that the proposed framework, by describing mate choice as the flow of information due to non-random mating, provides a new important setting for exploring different mating models and their consequences.

HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations

The detection of genomic regions involved in local adaptation is an important topic in current population genetics. There are several detection strategies available depending on the kind of genetic and demographic information at hand. A common drawback is the high risk of false positives. In this study we introduce two complementary methods for the detection of divergent selection from populations connected by migration. Both methods have been developed with the aim of being robust to false positives. The first method combines haplotype information with inter-population differentiation (FST). Evidence of divergent selection is concluded only when both the haplotype pattern and the FST value support it. The second method is developed for independently segregating markers i.e. there is no haplotype information. In this case, the power to detect selection is attained by developing a new outlier test based on detecting a bimodal distribution. The test computes the FST outliers and then assumes that those of interest would have a different mode. We demonstrate the utility of the two methods through simulations and the analysis of real data. The simulation results showed power ranging from 60-95% in several of the scenarios whilst the false positive rate was controlled below the nominal level. The analysis of real samples consisted of phased data from the HapMap project and unphased data from intertidal marine snail ecotypes. The results illustrate that the proposed methods could be useful for detecting locally adapted polymorphisms. The software HacDivSel implements the methods explained in this manuscript.

Impact of deep coalescence and recombination on the estimation of phylogenetic relationships among species using AFLP markers

Deep coalescence and the nongenealogical pattern of descent caused by recombination have emerged as a common problem for phylogenetic inference at the species level. Here we use computer simulations to assess whether AFLP-based phylogenies are robust to the uncertainties introduced by these factors. Our results indicate that phylogenetic signal can prevail even in the face of extensive deep coalescence allowing recovering the correct species tree topology. The impact of recombination on tree accuracy was related to total tree depth and species effective population size. The correct tree topology could be recovered upon many simulation settings due to a trade-off between the conflicting signals resulting from intra-locus recombination and the benefits of the joint consideration of unlinked loci that better matched overall the true species tree. Errors in tree topology were not only determined by deep coalescence, but also by the timing of divergence and the tree-building errors arising from an insufficient number of characters. DNA sequences generally outperformed AFLPs upon any simulated scenario, but this difference in performance was nearly negligible when a sufficient number of AFLP characters were sampled. Our simulations suggest that the impact of deep coalescence and intra-locus recombination on the reliability of AFLP trees could be minimal for effective population sizes equal to or lower than 10,000 (typical of many vertebrates and tree plants) given tree depths above 0.02 substitutions per site.

Teaching the fluctuation test in silico by using mutate: a program to distinguish between the adaptive and spontaneous mutation hypotheses

Mutate is a program developed for teaching purposes to impart a virtual laboratory class for undergraduate students of Genetics in Biology. The program emulates the so-called fluctuation test whose aim is to distinguish between spontaneous and adaptive mutation hypotheses in bacteria. The plan is to train students in certain key multidisciplinary aspects of current genetics such as sequence databases, DNA mutations, and hypothesis testing, while introducing the fluctuation test. This seminal experiment was originally performed studying Escherichia coli resistance to the infection by bacteriophage T1. The fluctuation test initiated the modern bacterial genetics that 25 years later ushered in the era of the recombinant DNA. Nowadays we know that some deletions in fhuA, the gene responsible for E. coli membrane receptor of T1, could cause the E. coli resistance to this phage. For the sake of simplicity, we will introduce the assumption that a single mutation generates the resistance to T1. During the practical, the students use the program to download some fhuA gene sequences, manually introduce some stop codon mutations, and design a fluctuation test to obtain data for distinguishing between preadaptative (spontaneous) and induced (adaptive) mutation hypotheses. The program can be launched from a browser or, if preferred, its executable file can be downloaded from http://webs.uvigo.es/acraaj/MutateWeb/Mutate.html. It requires the Java 5.0 (or higher) Runtime Environment (freely available at http://www.java.com).

AFLPMax: a user-friendly application for computing the optimal number of amplified fragment length polymorphism markers needed in phylogenetic reconstruction

Amplified fragment length polymorphisms (AFLPs) are widely used for phylogenetic inference especially in non-model species. Frequently, trees obtained with other nuclear or mitochondrial markers or with morphological information need additional resolution, increased branch support, or independent data sources (i.e. unlinked loci). In such cases, the use of AFLPs is a quick and cheap option. Computer simulation has shown that dominant AFLP markers lead to less accurate tree topologies than bi-allelic codominant markers such as SNPs, but this difference becomes negligible for shallow trees when using AFLP data sets that include a sufficiently large number of characters. Thus, determining how many AFLP characters are required to recover a given phylogeny is a key issue regarding the appropriateness of AFLPs for phylogenetic reconstruction. Here, we present a user-friendly, java-based graphical interface, AFLPMax, which executes an automatic pipeline of different programs providing the user with the optimal number of AFLP characters needed to recover a given phylogeny with high accuracy and support. Executables for Windows, linux and MacOS X operating systems, source code and user manual are available from: http://webs.uvigo.es/acraaj/AFLPMax.htm.

Multiple hypothesis testing in proteomics: a strategy for experimental work

In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate method that deal with this problem have been disseminated for more than one decade. However a survey of proteomics journals shows that such tests are not widely implemented in one commonly used technique, quantitative proteomics using two-dimensional electrophoresis. We outline a selection of multiple hypothesis testing methods, including some that are well known and some lesser known, and present a simple strategy for their use by the experimental scientist in quantitative proteomics work generally. The strategy focuses on the desirability of simultaneous use of several different methods, the choice and emphasis dependent on research priorities and the results in hand. This approach is demonstrated using case scenarios with experimental and simulated model data.

Simulation of genes and genomes forward in time

The importance of simulation software in current and future evolutionary and genomic studies is just confirmed by the recent publication of several new simulation tools. The forward-in-time simulation strategy has, therefore, re-emerged as a complement of coalescent simulation. Additionally, more efficient coalescent algorithms, the same as new ideas about the combined use of backward and forward strategies have recently appeared. In the present work, a previous review is updated to include some new forward simulation tools. When simulating at the genome-scale the conflict between efficiency (i.e. execution speed and memory usage) and flexibility (i.e. complex modeling capabilities) emerges. This is the pivot around which simulation of evolutionary processes should improve. In addition, some effort should be made to consider the process of developing simulation tools from the point of view of the software engineering theory. Finally, some new ideas and technologies as general purpose graphic processing units are commented.

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

BACKGROUND

The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.

RESULTS

It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.

CONCLUSION

Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.

The importance of bio-computational tools for predicting HIV drug resistance

The battle against retrovirus HIV-1 has reached a critical point. Antiretroviral resistance appears under highly active anti-retroviral therapy and the use of new drug combinations capable to overcome the emerged resistance is necessary. After detecting drug resistance two main approaches are possible. The phenotypic assays study in vitro the replication ability of virus variants in the presence or absence of drugs. This approach is expensive and time consuming. The genotypic assays try to obtain information from viral sequences coding for the drug targets in order to detect mutations with low susceptibility to drugs. Although this approach is faster and cheaper, a clear interpretation of the results is not always possible. In this work, I comment and analyze some new patents that point towards more efficient resistance detection and integral data management and prediction systems performing an efficient personalized combined therapy. In the future, computational tools will be essential as exploratory and interpretation systems in order to obtain a better support of clinical decisions concerning both the prediction and the evolution of drug resistance. Importantly, the revised patents conform to this trend.

Detecting recombination and diversifying selection in human alpha-papillomavirus

Intragenic recombination and selection analyses were performed in DNA sequences of human alpha-papillomavirus. Recombination was estimated and the corresponding breakpoints obtained by re-analyzing data grouped by phylogenetic and epidemiological criteria, using different alignment methods. Diversifying or positive selection has been scarcely studied in these viruses probably due to the high divergence rates. We have applied maximum likelihood, empirical Bayesian and maximum parsimony methods to detect the presence of positive selection. Within the HPV 16 type, significant positive selection was detected at the time of the separation of the African 1 and African 2 branches from the other populations. At the inter-type level, positive selection can be traced in some codons of the gene L2 of the high and low risk groups. These results indicate that positive selection could have been important in the evolution of HPV both at inter- and intra-type levels.

Simulation of genomes: a review

There is an increasing role of population genetics in human genetic research linking empirical observations with hypotheses about sequence variation due to historical and evolutionary causes. In addition, the data sets are increasing in size, with genome-wide data becoming a common place in many empirical studies. As far as more information is available, it becomes clear that simplest hypotheses are not consistent with data. Simulations will provide the key tool to contrast complex hypotheses on real data by generating simulated data under the hypothetical historical and evolutionary conditions that we want to contrast. Undoubtedly, developing tools for simulating large sequences that at the same time allow simulate natural selection, recombination and complex demography patterns will be of great interest in order to better understanding the trace left on the DNA by different interacting evolutionary forces. Simulation tools will be also essential to evaluate the sampling properties of any statistics used on genome-wide association studies and to compare performance of methods applied at genome-wide scales. Several recent simulation tools have been developed. Here, we review some of the currently existing simulators which allow for efficient simulation of large sequences on complex evolutionary scenarios. In addition, we will point out future directions in this field which are already a key part of the current research in evolutionary biology and it seems that it will be a primary tool in the future research of genome and post-genomic biology.

GENOMEPOP: a program to simulate genomes in populations

Background

There are several situations in population biology research where simulating DNA sequences is useful. Simulation of biological populations under different evolutionary genetic models can be undertaken using backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally efficient. The reason is that they are based on the history of lineages with surviving offspring in the current population. On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. Hence, there is an increasing interest in forward population genetic simulation and efficient new tools have been developed recently. Software tools that allow efficient simulation of large DNA fragments under complex evolutionary models will be very helpful when trying to better understand the trace left on the DNA by the different interacting evolutionary forces. Here I will introduce GenomePop, a forward simulation program that fulfills the above requirements. The use of the program is demonstrated by studying the impact of intracodon recombination on global and site-specific dN/dS estimation.

Results

I have developed algorithms and written software to efficiently simulate, forward in time, different Markovian nucleotide or codon models of DNA mutation. Such models can be combined with recombination, at inter and intra codon levels, fitness-based selection and complex demographic scenarios.

Conclusion

GenomePop has many interesting characteristics for simulating SNPs or DNA sequences under complex evolutionary and demographic models. These features make it unique with respect to other simulation tools. Namely, the possibility of forward simulation under General Time Reversible (GTR) mutation or GTR×MG94 codon models with intra-codon recombination, arbitrary, user-defined, migration patterns, diploid or haploid models, constant or variable population sizes, etc. It also allows simulation of fitness-based selection under different distributions of mutational effects. Under the 2-allele model it allows the simulation of recombination hot-spots, the definition of different frequencies in different populations, etc. GenomePop can also manage large DNA fragments. In addition, it has a scaling option to save computation time when simulating large sequences and population sizes under complex demographic and evolutionary situations. These and many other features are detailed in its web page [1].

Recombination favors the evolution of drug resistance in HIV-1 during antiretroviral therapy

We studied the relationship between recombination and the fixation time of multiple drug resistance mutations after HIV-1 drug therapy, under a set of different realistic scenarios. We have generalized a previous model by Bretscher et al. [Bretscher, Althaus, Muller, Bonhoeffer, 2004. Recombination in HIV and the evolution of drug resistance: for better or for worse? Bioessays 26(2), 180-188] in order to explore different implementations of phenotypic mixing and more realistic demographic and selective regimes. Using computer simulations we show that the effect of recombination on the evolution of drug resistance depends strongly on the intensity of selection, as well as on the viral population size. Under the high selection pressure expected during antiretroviral therapy, the strength of the Hill-Robertson effect increases and recombination favors the evolution of resistance under a wide range of population sizes, independently of the sign of the epistatic interaction. Our results suggest that recombination plays an important role in the evolution of drug resistance in HIV-1 under various realistic scenarios. These findings could be taken into account in order to develop optimal HIV-1 drug treatments.

Recombination estimation under complex evolutionary models with the coalescent composite-likelihood method

The composite-likelihood estimator (CLE) of the population recombination rate considers only sites with exactly two alleles under a finite-sites mutation model (McVean, G. A. T., P. Awadalla, and P. Fearnhead. 2002. A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160:1231-1241). While in such a model the identity of alleles is not considered, the CLE has been shown to be robust to minor misspecification of the underlying mutational model. However, there are many situations where the putative mutation and demographic history can be quite complex. One good example is rapidly evolving pathogens, like HIV-1. First we evaluated the performance of the CLE and the likelihood permutation test (LPT) under more complex, realistic models, including a general time reversible (GTR) substitution model, rate heterogeneity among sites (Gamma), positive selection, population growth, population structure, and noncontemporaneous sampling. Second, we relaxed some of the assumptions of the CLE allowing for a four-allele, GTR + Gamma model in an attempt to use the data more efficiently. Through simulations and the analysis of real data, we concluded that the CLE is robust to severe misspecifications of the substitution model, but underestimates the recombination rate in the presence of exponential growth, population mixture, selection, or noncontemporaneous sampling. In such cases, the use of more complex models slightly increases performance in some occasions, especially in the case of the LPT. Thus, our results provide for a more robust application of the estimation of recombination rates.

Application of Relative Warp Analysis to the Evaluation of Two-Dimensional Gels in Proteomics: Studying Isoelectric Point and Relative Molecular Mass Variation

We propose a geometric-morphometrics method (relative warp analysis) to be used in proteomic comparisons. This approach was applied to a dataset from a comparison between 5 controls and 5 patients with colorectal cancer disease published elsewhere. The spots in the 2-D maps were used as landmarks in a morphometric study, and the differences in shape (spot distribution) among them were obtained. The shape variables were used to compare controls and patients. These components mostly ignore random or experimental effects affecting all the proteins in any of the two dimensions studied. Furthermore, the method allows the researcher to find those proteins which contributed the most to the local shape component detected. Applying this approach, we detected variations in the position (isoelectric point and/or relative molecular mass) of some spots that may reflect differences in the amino acidic sequence or post-translational modifications.

The evolutionary forces maintaining a wild polymorphism of Littorina saxatilis: model selection by computer simulations

Two rocky shore ecotypes of Littorina saxatilis from north-west Spain live at different shore levels and habitats and have developed an incomplete reproductive isolation through size assortative mating. The system is regarded as an example of sympatric ecological speciation. Several experiments have indicated that different evolutionary forces (migration, assortative mating and habitat-dependent selection) play a role in maintaining the polymorphism. However, an assessment of the combined contributions of these forces supporting the observed pattern in the wild is absent. A model selection procedure using computer simulations was used to investigate the contribution of the different evolutionary forces towards the maintenance of the polymorphism. The agreement between alternative models and experimental estimates for a number of parameters was quantified by a least square method. The results of the analysis show that the fittest evolutionary model for the observed polymorphism is characterized by a high gene flow, intermediate-high reproductive isolation between ecotypes, and a moderate to strong selection against the nonresident ecotypes on each shore level. In addition, a substantial number of additive loci contributing to the selected trait and a narrow hybrid definition with respect to the phenotype are scenarios that better explain the polymorphism, whereas the ecotype fitnesses at the mid-shore, the level of phenotypic plasticity, and environmental effects are not key parameters.