Paul Joyce

Performance-Based Selection of Likelihood Models for Phylogeny Estimation

Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen and that that choice be justified. To date, justification has largely been accomplished through use of likelihood-ratio tests (LRTs) to assess the relative fit of a nested series of reversible models. While this approach certainly represents an important advance over arbitrary model selection, the best fit of a series of models may not always provide the most reliable phylogenetic estimates for finite real data sets, where all available models are surely incorrect. Here, we develop a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. This DT method includes a penalty for overfitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered and thus does not rely on a series of pairwise comparisons of models to traverse model space. We evaluate this method by examining four real data sets and by using those data sets to define simulation conditions. In the real data sets, the DT method selects the same or simpler models than conventional LRTs. In order to lend generality to the simulations, codon-based models (with parameters estimated from the real data sets) were used to generate simulated data sets, which are therefore more complex than any of the models we evaluate. On average, the DT method selects models that are simpler than those chosen by conventional LRTs. Nevertheless, these simpler models provide estimates of branch lengths that are more accurate both in terms of relative error and absolute error than those derived using the more complex (yet still wrong) models chosen by conventional LRTs. This method is available in a program called DT-ModSel.

Differences in the composition of vaginal microbial communities found in healthy Caucasian and black women

The maintenance of a low pH in the vagina through the microbial production of lactic acid is known to be an important defense against infectious disease in reproductive age women. Previous studies have shown that this is largely accomplished through the metabolism of lactic acid bacteria, primarily species of Lactobacillus. Despite the importance of this defense mechanism to womens health, differences in the species composition of vaginal bacterial communities among women have not been well defined, nor is it known if and how these differences might be linked to differences in the risk of infection. In this study, we defined and compared the species composition of vaginal bacterial communities in 144 Caucasian and black women in North America. This was carried out based on the profiles of terminal restriction fragments of 16S rRNA genes, and phylogenetic analysis of 16S rRNA gene sequences of the numerically dominant microbial populations. Among all the women sampled, there were eight major kinds of vaginal communities (‘supergroups’) that occurred in the general populace at a frequency of at least 0.05 (P=0.99). From the distribution of these supergroups among women, it was possible to draw several conclusions. First, there were striking, statistically significant differences (P=0.0) in the rank abundance of community types among women in these racial groups. Second, the incidence of vaginal communities in which lactobacilli were not dominant was higher in black women (33%) as compared to Caucasian women (7%). Communities not dominated by lactobacilli had Atopobium and a diverse array of phylotypes from the order Clostridiales. Third, communities dominated by roughly equal numbers of more than one species of Lactobacillus were rare in black women, but common in Caucasian women. We postulate that because of these differences in composition, not all vaginal communities are equally resilient, and that differences in the vaginal microbiota of Caucasian and black women may at least partly account for known disparities in the susceptibility of women in these racial groups to bacterial vaginosis and sexually transmitted diseases.

A new method for estimating the size of small populations from genetic mark-recapture data

The use of non‐invasive genetic sampling to estimate population size in elusive or rare species is increasing. The data generated from this sampling differ from traditional mark–recapture data in that individuals may be captured multiple times within a session or there may only be a single sampling event. To accommodate this type of data, we develop a method, named capwire, based on a simple urn model containing individuals of two capture probabilities. The method is evaluated using simulations of an urn and of a more biologically realistic system where individuals occupy space, and display heterogeneous movement and DNA deposition patterns. We also analyse a small number of real data sets. The results indicate that when the data contain capture heterogeneity the method provides estimates with small bias and good coverage, along with high accuracy and precision. Performance is not as consistent when capture rates are homogeneous and when dealing with populations substantially larger than 100. For the few real data sets where N is approximately known, capwires estimates are very good. We compare capwires performance to commonly used rarefaction methods and to two heterogeneity estimators in program capture: Mh‐Chao and Mh‐jackknife. No method works best in all situations. While less precise, the Chao estimator is very robust. We also examine how large samples should be to achieve a given level of accuracy using capwire. We conclude that capwire provides an improved way to estimate N for some DNA‐based data sets. Capwire is available at http://www.cnr.uidaho.edu/lecg/.

A NOVEL COMPARATIVE METHOD FOR IDENTIFYING SHIFTS IN THE RATE OF CHARACTER EVOLUTION ON TREES

Evolutionary biologists since Darwin have been fascinated by differences in the rate of trait‐evolutionary change across lineages. Despite this continued interest, we still lack methods for identifying shifts in evolutionary rates on the growing tree of life while accommodating uncertainty in the evolutionary process. Here we introduce a Bayesian approach for identifying complex patterns in the evolution of continuous traits. The method (auteur) uses reversible‐jump Markov chain Monte Carlo sampling to more fully characterize the complexity of trait evolution, considering models that range in complexity from those with a single global rate to potentially ones in which each branch in the tree has its own independent rate. This newly introduced approach performs well in recovering simulated rate shifts and simulated rates for datasets nearing the size typical for comparative phylogenetic study (i.e., ≥64 tips). Analysis of two large empirical datasets of vertebrate body size reveal overwhelming support for multiple‐rate models of evolution, and we observe exceptionally high rates of body‐size evolution in a group of emydid turtles relative to their evolutionary background. auteur will facilitate identification of exceptional evolutionary dynamics, essential to the study of both adaptive radiation and stasis.

An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus

The primary impediment to formulating a general theory for adaptive evolution has been the unknown distribution of fitness effects for new beneficial mutations. By applying extreme value theory, Gillespie circumvented this issue in his mutational landscape model for the adaptation of DNA sequences, and Orr recently extended Gillespies model, generating testable predictions regarding the course of adaptive evolution. Here we provide the first empirical examination of this model, using a single-stranded DNA bacteriophage related to φX174, and find that our data are consistent with Orrs predictions, provided that the model is adjusted to incorporate mutation bias. Orrs work suggests that there may be generalities in adaptive molecular evolution that transcend the biological details of a system, but we show that for the model to be useful as a predictive or inferential tool, some adjustments for the biology of the system will be necessary.

Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells

Green fluorescent protein (GFP) is used extensively as a reporter protein to monitor cellular processes, including intracellular protein trafficking and secretion. In general, this approach depends on GFP acting as a passive reporter protein. However, it was recently noted that GFP oligomerizes in the secretory pathway of endocrine cells. To characterize this oligomerization and its potential role in GFP transport, cytosolic and secretory forms of enhanced GFP (EGFP) were expressed in GH4C1 and AtT-20 endocrine cells. Biochemical analysis showed that cytosolic EGFP existed as a 27 kDa monomer, whereas secretory forms of EGFP formed disulphide-linked oligomers. EGFP contains two cysteine residues (Cys(49) and Cys(71)), which could play a role in this oligomerization. Site-directed mutagenesis of Cys(49) and Cys(71) showed that both cysteine residues were involved in disulphide interactions. Substitution of either cysteine residue resulted in a reduction or loss of oligomers, although dimers of the secretory form of EGFP remained. Mutation of these residues did not adversely affect the fluorescence of EGFP. EGFP oligomers were stored in secretory granules and secreted by the regulated secretory pathway in endocrine AtT-20 cells. Similarly, the dimeric mutant forms of EGFP were still secreted via the regulated secretory pathway, indicating that the higher-order oligomers were not necessary for sorting in AtT-20 cells. These results suggest that the oligomerization of EGFP must be considered when the protein is used as a reporter molecule in the secretory pathway.

Mutations in TRNT1 cause congenital sideroblastic anemia with immunodeficiency, fevers, and developmental delay (SIFD)

Mutations in genes encoding proteins that are involved in mitochondrial heme synthesis, iron-sulfur cluster biogenesis, and mitochondrial protein synthesis have previously been implicated in the pathogenesis of the congenital sideroblastic anemias (CSAs). We recently described a syndromic form of CSA associated with B-cell immunodeficiency, periodic fevers, and developmental delay (SIFD). Here we demonstrate that SIFD is caused by biallelic mutations in TRNT1, the gene encoding the CCA-adding enzyme essential for maturation of both nuclear and mitochondrial transfer RNAs. Using budding yeast lacking the TRNT1 homolog, CCA1, we confirm that the patient-associated TRNT1 mutations result in partial loss of function of TRNT1 and lead to metabolic defects in both the mitochondria and cytosol, which can account for the phenotypic pleiotropy.

Beneficial Fitness Effects Are Not Exponential for Two Viruses

The distribution of fitness effects for beneficial mutations is of paramount importance in determining the outcome of adaptation. It is generally assumed that fitness effects of beneficial mutations follow an exponential distribution, for example, in theoretical treatments of quantitative genetics, clonal interference, experimental evolution, and the adaptation of DNA sequences. This assumption has been justified by the statistical theory of extreme values, because the fitnesses conferred by beneficial mutations should represent samples from the extreme right tail of the fitness distribution. Yet in extreme value theory, there are three different limiting forms for right tails of distributions, and the exponential describes only those of distributions in the Gumbel domain of attraction. Using beneficial mutations from two viruses, we show for the first time that the Gumbel domain can be rejected in favor of a distribution with a right-truncated tail, thus providing evidence for an upper bound on fitness effects. Our data also violate the common assumption that small-effect beneficial mutations greatly outnumber those of large effect, as they are consistent with a uniform distribution of beneficial effects.

Evaluating the performance of likelihood methods for detecting population structure and migration

A plethora of statistical models have recently been developed to estimate components of population genetic history. Very few of these methods, however, have been adequately evaluated for their performance in accurately estimating population genetic parameters of interest. In this paper, we continue a research program of evaluation of population genetic methods through computer simulation. Specifically, we examine the software migratee‐n 1.6.8 and test the accuracy of this software to estimate genetic diversity (Θ), migration rates, and confidence intervals. We simulated nucleotide sequence data under a neutral coalescent model with lengths of 500 bp and 1000 bp, and with three different per site Θ values of (0.00025, 0.0025, 0.025) crossed with four different migration rates (0.0000025, 0.025, 0.25, 2.5) to construct 1000 evolutionary trees per‐combination per‐sequence‐length. We found that while migratee‐n 1.6.8 performs reasonably well in estimating genetic diversity (Θ), it does poorly at estimating migration rates and the confidence intervals associated with them. We recommend researchers use this software with caution under conditions similar to those used in this evaluation.

Epistasis between beneficial mutations and the phenotype-to-fitness Map for a ssDNA virus.

Epistatic interactions between genes and individual mutations are major determinants of the evolutionary properties of genetic systems and have therefore been well documented, but few quantitative data exist on epistatic interactions between beneficial mutations, presumably because such mutations are so much rarer than deleterious ones. We explored epistasis for beneficial mutations by constructing genotypes with pairs of mutations that had been previously identified as beneficial to the ssDNA bacteriophage ID11 and by measuring the effects of these mutations alone and in combination. We constructed 18 of the 36 possible double mutants for the nine available beneficial mutations. We found that epistatic interactions between beneficial mutations were all antagonistic—the effects of the double mutations were less than the sums of the effects of their component single mutations. We found a number of cases of decompensatory interactions, an extreme form of antagonistic epistasis in which the second mutation is actually deleterious in the presence of the first. In the vast majority of cases, recombination uniting two beneficial mutations into the same genome would not be favored by selection, as the recombinant could not outcompete its constituent single mutations. In an attempt to understand these results, we developed a simple model in which the phenotypic effects of mutations are completely additive and epistatic interactions arise as a result of the form of the phenotype-to-fitness mapping. We found that a model with an intermediate phenotypic optimum and additive phenotypic effects provided a good explanation for our data and the observed patterns of epistatic interactions.