INCREASED RESIDUAL VARIANCE AT DEVELOPMENTAL SWITCH POINTS: STATISTICAL ARTIFACT OR INDICATOR OF EXPOSED GENOTYPIC INFLUENCE?

Discussing the organization of developmental switches, West-Eberhard (2003) proposed the use of regression residual plots to locate the neutral point of the switch, which is characterized by maximum genotypic influence on the resulting phenotype. However, statistical artifacts due to measurement error in nonlinear models might account for a substantial proportion of the increase of residuals variance at the switch point. Simulations based on field data show that increases in residual variance occur as artifacts when normal amounts of measurement error are present, even in absence of any genotypic variance. The results suggest that interpretation of nonlinear variation in thresold traits is problematic and requires considering this statistical effect. A method to estimate the weight of genotypic contribution to residual variance is proposed, and its assumptions and limitations are discussed.     

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.     

Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance–covariance matrices ( G ). Large‐sample theory shows that maximum‐likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G . This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G , and of functions of G . We refer to this as the REML‐MVN method. This has been implemented in the mixed‐model program WOMBAT. Estimates of sampling variances from REML‐MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20‐dimensional data set for Drosophila wings. REML‐MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best‐estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML‐MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.     

Can we manage fisheries with the inherent uncertainty from eDNA?

Environmental (e)DNA, as a general approach in aquatic systems, seeks to connect the presence of species' genetic material in the water and hence to infer the species' physical presence. However, fisheries managers face making decisions with risk and uncertainty when eDNA indicates a fish is present but traditional methods fail to capture the fish. In comparison with traditional methods such as nets, electrofishing and piscicides, eDNA approaches have more sources of underlying error that could give rise to false positives. This has resulted in some managers to question whether eDNA can be used to make management decisions because there is no fish in hand. As a relatively new approach, the methods and techniques have quickly evolved to improve confidence in eDNA. By evaluating an eDNA based research programmes through the pattern of the eDNA signal, assay design, experimental design, quality assurance and quality control checks, data analyses and concurrent search for fish using traditional gears, the evidence for fish presence can be evaluated to build confidence in the eDNA approach. The benefits for fisheries management from adopting an eDNA approach are numerous but include cost effectiveness, broader geographic coverage of habitat occupancy, early detection of invasive species, non-lethal stock assessments, exploration of previously inaccessible aquatic environments and discovery of new species hidden beneath the water's surface. At a time when global freshwater and marine fisheries are facing growing threats from over-harvest, pollution and climate change, we anticipate that growing confidence in eDNA will overcome the inherent uncertainty of not having a fish in hand and will empower the informed management actions necessary to protect and restore our fisheries.     

Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume

Optical motion capture is commonly used in biomechanics to measure human kinematics. However, no studies have yet examined the accuracy of optical motion capture in a large capture volume (>100 m³), or how accuracy varies from the center to the extreme edges of the capture volume. This study measured the dynamic 3D errors of an optical motion capture system composed of 42 OptiTrack Prime 41 cameras (capture volume of 135 m³) by comparing the motion of a single marker to the motion reported by a ThorLabs linear motion stage. After spline interpolating the data, it was found that 97% of the capture area had error below 200 μm. When the same analysis was performed using only half (21) of the cameras, 91% of the capture area was below 200 μm of error. The only locations that exceeded this threshold were at the extreme edges of the capture area, and no location had a mean error exceeding 1 mm. When measuring human kinematics with skin-mounted markers, uncertainty of marker placement relative to underlying skeletal features and soft tissue artifact produce errors that are orders of magnitude larger than the errors attributed to the camera system itself. Therefore, the accuracy of this OptiTrack optical motion capture system was found to be more than sufficient for measuring full-body human kinematics with skin-mounted markers in a large capture volume (>100 m³).     

Estimation of uncertainties in X-ray refinement results by use of perturbed structures

The uncertainties in the refined parameters for a 1.5-A X-ray structure of carbon-monoxy (FeII) myoglobin are estimated by combining energy minimization with least-squares refinement against the X-ray data. The energy minimizations, done without reference to the X-ray data, provide perturbed structures which are used to restart conventional X-ray refinement. The resulting refined structures have the same, or better, R-factor and stereochemical parameters as the original X-ray structure, but deviate from it by 0.13 A rms for the backbone atoms and 0.31 A rms for the sidechain atoms. Atoms interacting with a disordered sidechain, Arg 45 CD3, are observed to have larger positional uncertainties. The uncertainty in the B-factors, within the isotropic harmonic motion approximation, is estimated to be 15%. The resulting X-ray structures are more consistent with the energy parameters used in simulations.     

Non-destructive sampling of maternal DNA from the external shell of bird eggs

The use of non-destructive sampling methods to collect genetic material from wildlife allows researchers to minimize disturbance. Most avian studies employ capturing and handling of young and parents to draw blood for DNA analysis. In some cases adult female birds are difficult to catch, so maternal genotyping has required collection of contour feathers from nests, or destructive sampling of eggs. Many species do not leave contour feathers in the nest, and destructive sampling has been unreliable due to contamination with embryonic DNA. Alternative field sampling techniques for collection of maternal DNA from birds are therefore desirable. Here we demonstrate that avian maternal DNA can be isolated in a non-invasive and non-destructive way from the external surface of eggs. We used cotton swabs to collect maternal DNA from the external shells of herring gull (Larus argentatus) and Caspian tern (Sterna caspia) eggs. DNA was then amplified by the polymerase chain reaction (PCR) for microsatellite genotyping. We verified that the DNA samples were maternal by comparing microsatellite profiles to those obtained from adults and chicks from the same nests. In 100% of Caspian tern (n=16) and herring gull families (n=12), the egg swabs that amplified matched the maternal microsatellite genotype. In a screening of many nests of both species, we successfully amplified microsatellite markers from 101/115 (88%) egg swabs. Swabs from eggs with blood stains on the shell were more likely to amplify successfully than those from clean eggs. The advantages of this new method include increased parentage assignment/exclusion power, and increased availability of maternal DNA for genotyping of species that do not deposit contour feathers in nests.