Model misspecification and multipoint linkage analysis.

Pairwise linkage analysis is robust to genetic model misspecification provided dominance is correctly specified, the primary effect being inflation of the recombination fraction. By contrast, we show that multipoint analysis under misspecified models is not robust when a putative disease locus is placed between close flanking markers, with potentially spuriously negative multipoint lod scores being produced. The problem is due to incorrect attribution of segregation of a disease allele and the consequent conclusion of (unlikely) double crossovers between flanking markers. As a possible solution, we propose the use of high disease allele frequencies, as this allows probabilistically for nonsegregation (through parental homozygosity or dual matings). We show analytically and through analysis of pedigree data simulated under a two-locus heterogeneity model that using a disease allele frequency of 0.05 in the dominant case and 0.25 in the recessive case is quite robust in producing positive multipoint lod scores with close flanking markers across a broad range of conditions including varying allele frequencies, epistasis, genetic heterogeneity and phenocopies.     

Application of two tests of multivariate discordancy to fisheries data sets

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k = 5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data.     

Treasures and traps in genome-wide data sets: case examples from yeast

Since the publication of the Saccharomyces cerevisiae genome sequence, much effort has been dedicated to developing high-throughput techniques to generate comprehensive information about the function and dynamics of all genes in this yeast's genome. These techniques have generated data sets that typically contain large amounts of reliable and valuable biological information. Nevertheless, there are also uncertainties that are associated with such large-scale studies, which we discuss in this review. These uncertainties increase with the complexity of the organism under study. On the basis of the results from yeast, we should learn much from human and mouse genomic data sets. However, as with yeast data sets, they might also contain misleading results.     

Quinean social skills: empirical evidence from eye-gaze against information encapsulation

Since social skills are highly significant to the evolutionary success of humans, we should expect these skills to be efficient and reliable. For many Evolutionary Psychologists efficiency entails encapsulation: the only way to get an efficient system is via information encapsulation. But encapsulation reduces reliability in opaque epistemic domains. And the social domain is darkly opaque: people lie and cheat, and deliberately hide their intentions and deceptions. Modest modularity [Currie and Sterelny (2000) Philos Q 50:145–160] attempts to combine efficiency and reliability. Reliability is obtained by placing social skills in un-encapsulated central cognition; efficiency by having the social system sensitive to encapsulated socially tagged cues. In this paper, I argue that this approach fails. I focus on eye-gaze as a plausible example of a socially significant encapsulated cue. I demonstrate contra modest modularity that eye-gaze is subject to influence from central cognition.

Hamilton goes empirical: estimation of inclusive fitness from life-history data

Hamilton's theory of kin selection is one of the most important advances in evolutionary biology since Darwin. Central to the kin-selection theory is the concept of inclusive fitness. However, despite the importance of inclusive fitness in evolutionary theory, empirical estimation of inclusive fitness has remained an elusive task. Using the concept of individual fitness, I present a method for estimating inclusive fitness and its components for diploid organisms with age-structured life histories. The method presented here: (i) allows empirical estimation of inclusive fitness from life-history data; (ii) simultaneously considers all components of fitness, including timing and magnitude of reproduction; (iii) is consistent with Hamilton's definition of inclusive fitness; and (iv) adequately addresses shortcomings of existing methods of estimating inclusive fitness. I also demonstrate the application of this new method for testing Hamilton's rule.     

Computational mate choice: theory and empirical evidence

The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice.     

Predicting protein function from protein/protein interaction data: a probabilistic approach

MOTIVATION:The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. RESULTS: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.     

Evaluation of methods for detecting recombination from DNA sequences: empirical data

The performance of 14 different recombination detection methods was evaluated by analyzing several empirical data sets where the presence of recombination has been suggested or where recombination is assumed to be absent. In general, recombination methods seem to be more powerful with increasing levels of divergence, but different methods showed distinct performance. Substitution methods using summary statistics gave more accurate inferences than most phylogenetic methods. However, definitive conclusions about the presence of recombination should not be derived on the basis of a single method. Performance patterns observed from the analysis of real data sets coincided very well with previous computer simulation results. Previous recombination inferences from some of the data sets analyzed here should be reconsidered. In particular, recombination in HIV-1 seems to be much more widespread than previously thought. This finding might have serious implications on vaccine development and on the reliability of previous inferences of HIV-1 evolutionary history and dynamics.     

SAINT: probabilistic scoring of affinity purification-mass spectrometry data

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.     

Attractor characterization from scaled doublet structures: simulations for small data sets

 Further developments are presented in the technique for analysing attractor behaviour from small data sets, based on the observation of scaled structures in families of slope curves of correlation integrals. Scaled doublet structures are investigated systematically for short time series obeying the Mackey and Glass delay differential equation. At an attractor correlation dimension close to 5, ranges of values of T (the length of the time sequence) and of f (the recording frequency) are described in which the scaled doublet structures are unambiguously identified and distinguished from structures that can occasionally be found with randomized time sequences. Implications for the characterization of low-dimension attractors, notably from electroencephalographic recordings, are discussed, including in particular the advantage to be gained from moderately oversampling the data. Received: 11 July 1994/Accepted: 4 August 1994     

Enemy-mediated apparent competition: empirical patterns and the evidence

Apparent competition arises when two victim species negatively affect each other (−,  −) by enhancing the equilibrium density or changing the foraging behaviour of a shared natural enemy. Shared enemies can also mediate non-reciprocal (−,  0) indirect effects, i.e. indirect amensalism, whenever one prey species is not affected by the presence of alternative prey. We review 34 studies on terrestrial and freshwater systems to evaluate the extent to which apparent competition has been perceived as a reciprocal (−,  −) or non-reciprocal (−,  0) interaction. We found only three studies showing reciprocal effects between apparent competitors. Indirect amensalism was documented in 10 studies and could be inferred for 16 other cases (76% in total). The remaining five studies provided insufficient data to determine the form of indirect interaction. The apparent prevalence of non-reciprocal enemy-mediated interactions resembles that observed for resource-based interspecific competition. Amensal indirect effects via shared predation may result from differences in population size, nutritional value, susceptibility to attack, or asynchronous dynamics of alternative prey, or the predator's feeding preferences. Moreover, experimental protocols may confound the actual form of apparent competition through short-term observations, incomplete designs, or biased consideration of conspicuous interactions, leading to reciprocal effects being overlooked. We conclude that, at present, it is still difficult to determine the relative role of apparent competition vs indirect amensalism in natural food webs because most published studies have failed to document in full interactions via shared enemies.     

Resolution of the laurasiatherian phylogeny: evidence from genomic data

Despite great progress over the past decade, some portions of the mammalian tree of life remain unresolved. In particular, relationships among the different orders included within the supraordinal group Laurasiatheria have been proven difficult to determine, and have received poor support in the vast majority of phylogenomic studies of mammalian systematics. We estimated interordinal relationships within Laurasiatheria using sequence data from 3733 protein-coding genes. Our study included data from from 11 placental mammals, corresponding to five of the six orders of Laurasiatheria, plus five outgroup species. Ingroup and outgroup species were chosen to maximize the number single-copy ortholog genes for which sequence data was available for all species in our study. Phylogenetic analyses of the concatenated dataset using maximum likelihood and Bayesian methods resulted on an identical and well supported topology in all alignment strategies compared. Our analyses provide high support for the sister relationship between Chiroptera and Cetartiodactyla and also provide support for placing Perissodactyla as sister to Carnivora. We obtained maximal estimates of bootstrap support (100%) and posterior probability (1.00) for all nodes within Laurasiatheria. Our study provides a further demonstration of the utility of very large and conserved genomic dataset to clarify our understanding of the evolutionary relationships among mammals.     

Transgenerational inheritance of longevity: Theoretical framework and empirical evidence

A number of experimental and epidemiological investigations have provided evidence that the health status and aging rate may largely depend on the conditions of early development. Several recent studies provided data suggesting that effects of stresses in early development can be inherited transgenerationally, causing changes of various characteristics in subsequent generations. It has been shown that epigenetic factors associated with regulation of genetic expression, including DNA methylation and modifications of histones and microRNAs, can play a key role in transgenerational inheritance. Until now, it has been generally accepted that the complete erasure of epigenetic marks takes place during gametogenesis and early embryogenesis. In recent years, however, several papers obtained data demonstrating that, in certain cases, epigenetic modifications induced during early ontogenesis could not be erased completely and be transmitted to descendants, affecting their phenotype over several generations. This review provides data of epidemiological and experimental studies showing the possibility of transgenerational inheritance of life expectancy and longevity-associated traits in several generations.     

Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets

Data analysis--not data production--is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.     

Along signal paths: an empirical gene set approach exploiting pathway topology

Gene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis clipper, that tries to fill in this gap. clipper implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expression datasets. Our results demonstrate the efficacy of clipper in the identification of signal transduction paths totally coherent with the biological problem.