Power for detecting genetic divergence: differences between statistical methods and marker loci

Information on statistical power is critical when planning investigations and evaluating empirical data, but actual power estimates are rarely presented in population genetic studies. We used computer simulations to assess and evaluate power when testing for genetic differentiation at multiple loci through combining test statistics or P values obtained by four different statistical approaches, viz. Pearson's chi-square, the log-likelihood ratio G-test, Fisher's exact test, and an F(ST)-based permutation test. Factors considered in the comparisons include the number of samples, their size, and the number and type of genetic marker loci. It is shown that power for detecting divergence may be substantial for frequently used sample sizes and sets of markers, also at quite low levels of differentiation. The choice of statistical method may be critical, though. For multi-allelic loci such as microsatellites, combining exact P values using Fisher's method is robust and generally provides a high resolving power. In contrast, for few-allele loci (e.g. allozymes and single nucleotide polymorphisms) and when making pairwise sample comparisons, this approach may yield a remarkably low power. In such situations chi-square typically represents a better alternative. The G-test without Williams's correction frequently tends to provide an unduly high proportion of false significances, and results from this test should be interpreted with great care. Our results are not confined to population genetic analyses but applicable to contingency testing in general.     

SCN outputs and the hypothalamic balance of life

The circadian clock in the suprachiasmatic nucleus (SCN) is composed of thousands of oscillator neurons, each dependent on the cell-autonomous action of a defined set of circadian clock genes. Still, the major question remains how these individual oscillators are organized into a biological clock producing a coherent output able to time all the different daily changes in behavior and physiology. In the present review, the authors discuss the anatomical connections and neurotransmitters used by the SCN to control the daily rhythms in hormone release. The efferent SCN projections mainly target neurons in the medial hypothalamus surrounding the SCN. The activity of these preautonomic and neuroendocrine target neurons is controlled by differentially timed waves of, among others, vasopressin, GABA, and glutamate release from SCN terminals. Together, the data on the SCN control of neuroendocrine rhythms provide clear evidence not only that the SCN consists of phenotypically (i.e., according to neurotransmitter content) different subpopulations of neurons but also that subpopulations should be distinguished (within phenotypically similar groups of neurons) based on the acrophase of their (electrical) activity. Moreover, the specialization of the SCN may go as far as a single body structure, that is, the SCN seems to contain neurons that specifically target the liver, pineal, and adrenal.     

Distribution of transferrin binding protein B gene (<Emphasis Type="Italic">tbpB</Emphasis>) variants among <Emphasis Type="Italic">Neisseria</Emphasis>species

Background  

Transferrin binding protein B (tbpB), an outer membrane lipoprotein, is required for the acquisition of iron from human transferrin. Two tbpB families have been documented in Neisseria meningitidis: an isotype I tbpB gene of 1.8 kb and an isotype II tbpB gene of 2.1 kb, the former expressed by meningococci in the disease-associated ST-11 clonal complex and the latter found among meningococci belonging to the hyper-invasive clonal complexes including ST-8, ST-18, ST-32, ST-41/44 as well as N. gonorrhoeae isolates. The origin of the isotype I tbpB gene is unknown, however several features in common with non-pathogenic Neisseria and the ST-11 clonal complex N. meningitidis isolate FAM18 have been documented leading to the hypothesis that the isotype I tbpB gene may also be shared between non-pathogenic Neisseria and ST-11 meningococci. As a result, the diversity of the tbpB gene was investigated in a defined collection of Neisseria species.     

<Emphasis Type="Italic">C. elegans dss-1</Emphasis>is functionally conserved and required for oogenesis and larval growth

Background  

Dss1 (or Rpn15) is a recently identified subunit of the 26S proteasome regulatory particle. In addition to its function in the protein degradation machinery, it has been linked to BRCA2 (breast cancer susceptibility gene 2 product) and homologous DNA recombination, mRNA export, and exocytosis. While the fungal orthologues of Dss1 are not essential for viability, the significance of Dss1 in metazoans has remained unknown due to a lack of knockout animal models.     

Surface ultrastructure of the elasmobranchia parasitizing <Emphasis Type="Italic">Grillotiella exilis</Emphasis> and <Emphasis Type="Italic">Pseudonybelinia odontacantha</Emphasis> (Trypanorhyncha,Cestoda)

The surface ultrastructure of two monotypic trypanorhynch genera is described based on new material of Grillotiella exilis (Linton, 1909) and type material of Pseudonybelinia odontacantha Dollfus 1966. In G. exilis, spiniform microtriches cover the bothrial surfaces and the anterior part of the pars vaginalis posterior to the bothria. Bifurcate microtriches adorn the bothrial margins, filiform microtriches the scolex peduncle, and capilliform microtriches the posterior scolex end. This microthrix pattern resembles that found in, e.g., Grillotia erinaceus (van Beneden, 1858), with the difference that the anterior part of the pars vaginalis is covered with a collar of multidigitate palmate microtriches. The position of Grillotiella within the Grillotiinae, Lacistorhynchidae is supported based on these data. The bothria and scolex peduncle of P. odontacantha are covered with acerosate and unciniform microtriches on the distal bothrial surface and capilliform microtriches on the scolex peduncle. Short filiform microtriches cover the appendix. The microthrix pattern resembles that of the Tentaculariidae but with unciniform and acerosate microtriches densely covering the entire distal bothrial surface. Tegumental grooves are present on the posterior bothrial margin. They can be distinguished from bothrial pits in otobothrioid trypanorhynchs in having similar unciniform microtriches compared to the other parts of the bothrial surface and in lacking any spiniform microtriches. With the absence of bothrial pits as characteristic for the otobothrioids and its characteristic microthrix pattern, P. odontacantha together with Paranybelinia otobothrioides Dollfus 1966, both belonging to the Paranybeliniidae change their position in the most recent system from the Otobothrioidea into the Tentacularioidea.     

Patterns of divergence among conifer ESTs and polymorphism in Pinus sylvestris identify putative selective sweeps

Finding genes that are under positive selection is a difficult task, especially in non-model organisms. Here, we have analyzed expressed sequence tag (EST) data from 4 species (Pinus pinaster, Pinus taeda, Picea glauca, and Pseudotsuga menziesii) to investigate selection patterns during their evolution and to identify genes likely to be under positive selection. To confirm selection, population samples of these genes have been sequenced in Pinus sylvestris, a species that was not included in the EST data set. The estimates of branch-specific Ka/Ks (nonsynonymous/synonymous substitution rates) across all genes in the EST data set were similar or smaller than estimates from other higher plant species. There was no evidence for the traditional indication of positive selection, Ka/Ks above 1. However, several lines of evidence based on polymorphism patterns suggest that genes with high Ka/Ks (0.20-0.52) in the EST data set are in fact more affected by positive selection in P. sylvestris than genes with low Ka/Ks (0.01-0.04). The high Ka/Ks genes have a lower level of polymorphism and more negative Tajima's D than the low Ka/Ks genes. Further, in the high Ka/Ks group, the Hudson-Kreitman-Aguade test is significant. This suggests that the EST data set is a good starting point for finding genes under positive selection in conifers and that even moderate Ka/Ks values could be indicative of selection. A group of 5 genes with high Ka/Ks collectively show evidence for positive selection within P. sylvestris.     

What is signal and what is noise in the brain?

The response of a cortical neuron to a stimulus can show a very large variability when repeatedly stimulated by exactly the same stimulus. This has been quantified in terms of inter-spike-interval (ISI) statistics by several researchers (e.g., [Softky, W., Koch, C., 1993. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13(1), 334-350.]). The common view is that this variability reflects noisy information processing based on redundant representation in large neuron populations. This view has been challenged by the idea that the apparent noise inherent in brain activity that is not strictly related or temporally coupled to the experiment could be functionally significant. In this work we examine the ISI statistics and discuss these views in a recently published model of interacting cortical areas [Knoblauch, A., Palm, G., 2002. Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback. Biol. Cybernet. 87(3), 151-167.]. From the results of further single neuron simulations we can isolate temporally modulated synaptic input as a main contributor for high ISI variability in our model and possibly in real neurons. In contrast to alternative mechanisms, our model suggests a function of the temporal modulations for short-term binding and segmentation of figures from background. Moreover, we show that temporally modulated inputs lead to ISI statistics which fit better to the neurophysiological data than alternative mechanisms.     

Spatial genetic structure of northern pike (Esox lucius) in the Baltic Sea

The genetic relationships among 337 northern pike (Esox lucius) collected from the coastal zone of the central Baltic region and the Finnish islands of Aland were analysed using five microsatellite loci. Spatial structure was delineated using both traditional F-statistics and individually based approaches including spatial autocorrelation analysis. Our results indicate that the observed genotypic distribution is incompatible with that of a single, panmictic population. Isolation by distance appears important for shaping the genetic structure of pike in this region resulting in a largely continuous genetic change over the study area. Spatial autocorrelation analysis (Moran's I) of individual pairwise genotypic data show significant positive genetic correlation among pike collected within geographical distances of less than c. 100-150 km (genetic patch size). We suggest that the genetic patch size may be used as a preliminary basis for identifying management units for pike in the Baltic Sea.     

Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback

 To investigate scene segmentation in the visual system we present a model of two reciprocally connected visual areas using spiking neurons. Area P corresponds to the orientation-selective subsystem of the primary visual cortex, while the central visual area C is modeled as associative memory representing stimulus objects according to Hebbian learning. Without feedback from area C, a single stimulus results in relatively slow and irregular activity, synchronized only for neighboring patches (slow state), while in the complete model activity is faster with an enlarged synchronization range (fast state). When presenting a superposition of several stimulus objects, scene segmentation happens on a time scale of hundreds of milliseconds by alternating epochs of the slow and fast states, where neurons representing the same object are simultaneously in the fast state. Correlation analysis reveals synchronization on different time scales as found in experiments (designated as tower, castle, and hill peaks). On the fast time scale (tower peaks, gamma frequency range), recordings from two sites coding either different or the same object lead to correlograms that are either flat or exhibit oscillatory modulations with a central peak. This is in agreement with experimental findings, whereas standard phase-coding models would predict shifted peaks in the case of different objects. Received: 22 August 2001 / Accepted in revised form: 8 April 2002