Inference of evolutionary forces acting on human biological pathways

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald–Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

A growth factor-induced, spatially organizing cytoskeletal module enables rapid and persistent fibroblast migration

Directional migration requires robust front/back polarity. We find that fibroblasts treated with platelet-derived growth factor (PDGF) and prepolarized by plating on a fibronectin line substrate exhibit persistent migration for hours. This does not occur in the absence of PDGF or on uniformly coated fibronectin substrates. Persistent migration arises from establishment of two functional modules at cell front and back. At the front, formation of a zone containing podosome-like structures (PLS) dynamically correlates with low RhoA and myosin activity and absence of a contractile lamella. At the back, myosin contractility specifically controls tail retraction with minimal crosstalk to the front module. The PLS zone is maintained in a dynamic steady state that preserves size and position relative to the cell front, allowing for long-term coordination of front and back modules. We propose that front/back uncoupling achieved by the PLS zone is crucial for persistent migration in the absence of directional cues.

An intercontinental comparison of chironomid palaeotemperature inference models: Europe vs North America

Chironomid-temperature inference models based on North American, European and combined surface sediment training sets were compared to assess the overall reliability of their predictions. Between 67 and 76 of the major chironomid taxa in each data set showed a unimodal response to July temperature, whereas between 5 and 22 of the common taxa showed a sigmoidal response. July temperature optima were highly correlated among the training sets, but the correlations for other taxon parameters such as tolerances and weighted averaging partial least squares (WA-PLS) and partial least squares (PLS) regression coefficients were much weaker. PLS, weighted averaging, WA-PLS, and the Modern Analogue Technique, all provided useful and reliable temperature inferences. Although jack-knifed error statistics suggested that two-component WA-PLS models had the highest predictive power, intercontinental tests suggested that other inference models performed better. The various models were able to provide good July temperature inferences, even where neither good nor close modern analogues for the fossil chironomid assemblages existed. When the models were applied to fossil Lateglacial assemblages from North America and Europe, the inferred rates and magnitude of July temperature changes varied among models. All models, however, revealed similar patterns of Lateglacial temperature change. Depending on the model used, the inferred Younger Dryas July temperature decrease ranged between 2.5 and 6°C.

LORENZ: Stata module to estimate and display Lorenz curves and concentration curves

Lorenz estimates Lorenz and concentration curves from individual-level data and, optionally, displays the results in a graph. Relative as well as generalized, absolute, unnormalized, or custom-normalized Lorenz or concentration curves are supported, and tools for computing contrasts between different subpopulations or outcome variables are provided. Variance estimation for complex samples is fully supported.

PANELS: Stata module to count panels and apply a command to panel units

panels provides a quick way to count the number of panels (groups) in a dataset and display some basic information about the sizes of the panels. Furthermore, -panels- can be used as a prefix command to other Stata commands to apply them to panel units instead of individual observations. This is useful, for example, if you want to compute frequency distributions or summary statistics for panel characteristics.

TEXDOC: Stata module to create a LaTeX document including Stata output

texdoc provides tools to create a LaTeX document from within Stata in a weaving fashion. This is especially useful if you want to produce a LaTeX document that contains Stata output, such as, e.g., a Stata Journal article or solutions to statistics homework assignments.

TABLETUTORIAL: Stata module to provide tutorial on automated table generation and reporting

-tabletutorial- illustrates how Stata can be used to export statistical results and generate customized reports. Part 1 explains how results from Stata routines can be accessed and how they can be exported using the -file- comand or a wrapper such as, e.g., -mat2txt-. Part 2 shows how model estimation results can be archived using -estwrite- and how models can be tabulated and exported to LaTeX, MS Excel, or MS Word using -estout-. Part 3 illustrates how to set up automatic reports in LaTeX or MS Word. The tutorial is based on a talk given at CEPS/INSTEAD in Luxembourg in October 2008. After install, type -help tabletutorial- to start the tutorial (in Stata 8, type -whelp tabletutorial-). The -mat2txt-, -estwrite-, and -estout- packages, also available from SSC, are required to run the examples.

PSPLINE: Stata module providing a penalized spline scatterplot smoother based on linear mixed model technology

Pspline uses xtmixed to fit a penalized spline regression and plots the smoothed function. Additional covariates can be specified to adjust the smooth and plot partial residuals.

NLCHECK: Stata module to check linearity assumption after model estimation

nlcheck is a simple diagnostic tool that can be used after fitting a model to quickly check the linearity assumption for a given predictor. nlcheck categorizes the predictor into bins, refits the model including dummy variables for the bins, and then performs a joint Wald test for the added parameters. Alternative, nlcheck uses linear splines for the adaptive model. Support for discrete variables is also provided. Optionally, nlcheck also displays a graph of the adjusted linear predictions from the original model and the adaptive model

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth