A Simulation Study of Permutation,Bootstrap, and Gene Dropping for Assessing Statistical Significance in the Case of Unequal Relatedness

We used simulations to evaluate methods for assessing statistical significance in association studies. When the statistical model appropriately accounted for relatedness among individuals, unrestricted permutation tests and a few other simulation-based methods effectively controlled type I error rates; otherwise, only gene dropping controlled type I error but at the expense of statistical power.     

Corrections for measurement error due to delayed onset of illness for case-crossover designs

Epidemiologic studies of the short-term effects of ambient particulate matter (PM) on the risk of acute cardiovascular or cerebrovascular events often use data from administrative databases in which only the date of hospitalization is known. A common study design for analyzing such data is the case-crossover design, in which exposure at a time when a patient experiences an event is compared to exposure at times when the patient did not experience an event within a case-control paradigm. However, the time of true event onset may precede hospitalization by hours or days, which can yield attenuated effect estimates. In this article, we consider a marginal likelihood estimator, a regression calibration estimator, and a conditional score estimator, as well as parametric bootstrap versions of each, to correct for this bias. All considered approaches require validation data on the distribution of the delay times. We compare the performance of the approaches in realistic scenarios via simulation, and apply the methods to analyze data from a Boston-area study of the association between ambient air pollution and acute stroke onset. Based on both simulation and the case study, we conclude that a two-stage regression calibration estimator with a parametric bootstrap bias correction is an effective method for correcting bias in health effect estimates arising from delayed onset in a case-crossover study.     

Assessing genetic diversity in Italian goat populations using AFLP® markers

Amplified fragment length polymorphism (AFLP) markers were used to investigate the genetic variation in a sample of seven goat (Capra hircus) populations. A total of 210 individuals (30 per population) were analysed using seven selected AFLP primer combinations that produced 219 clear polymorphisms. Four autochthonous goat breeds (Bionda dell'Adamello, Frisa, Orobica and Verzaschese), two primary populations, one from the Lombardy Alps (Val di Livo) and the other from Sardinia island (Sarda) and a reference cosmopolitan breed (Saanen) were included in the analysis. The expected heterozygosity (Het) did not differ significantly among breeds (range 0.21-0.24). No breed specific markers were identified. The variability at AFLP loci was largely maintained within breeds, as indicated by the coefficient of genetic differentiation (Gst) value (0.11). Dice similarities calculated between pairs of individuals belonging to the same or to different breeds largely overlapped. Bootstrapping on markers indicated that the coefficient of variation (CV) of the genetic indexes tested decreases only marginally by adding markers over 100 AFLPs. Cluster analysis based on standard genetic distance between breeds indicates that Sarda is the most distant population, while Bionda, Frisa, Verzaschese and Val di Livo seem to be highly related populations. Interestingly, Saanen is closer than Orobica to the other four goat populations of the Lombardy Alps. Principal co-ordinates analysis based on Dice similarities confirms these observations. Genetic diversity of the goat populations investigated confirms what is expected on the basis of their geographical location. Results from Orobica are not correlated with geographical distances and may reflect undocumented migrations and gene flows and identify an original genetic resource.     

Adjusting estimative prediction limits

This note presents a direct adjustment of the estimative predictionlimit to reduce the coverage error from a target value to third-orderaccuracy. The adjustment is asymptotically equivalent to thoseof Barndorff-Nielsen & Cox (1994, 1996) and Vidoni (1998).It has a simpler form with a plug-in estimator of the coverageprobability of the estimative limit at the target value.     

Robust spatial extent inference with a semiparametric bootstrap joint inference procedure

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain‐phenotype associations that inform our understanding of disease. Recent studies have shown that Gaussian random field (GRF)‐based tools can have inflated family‐wise error rates (FWERs). This has led to substantial controversy as to which processing choices are necessary to control the FWER using GRF‐based SEI. The failure of GRF‐based methods is due to unrealistic assumptions about the spatial covariance function of the imaging data. A permutation procedure is the most robust SEI tool because it estimates the spatial covariance function from the imaging data. However, the permutation procedure can fail because its assumption of exchangeability is violated in many imaging modalities. Here, we propose the (semi‐) parametric bootstrap joint (PBJ; sPBJ) testing procedures that are designed for SEI of multilevel imaging data. The sPBJ procedure uses a robust estimate of the spatial covariance function, which yields consistent estimates of standard errors, even if the covariance model is misspecified. We use the methods to study the association between performance and executive functioning in a working memory functional magnetic resonance imaging study. The sPBJ has similar or greater power to the PBJ and permutation procedures while maintaining the nominal type 1 error rate in reasonable sample sizes. We provide an R package to perform inference using the PBJ and sPBJ procedures.     

Mitogenomic circumscription of a novel percomorph fish clade mainly comprising “Syngnathoidei” (Teleostei)

Percomorpha, comprising about 60% of modern teleost fishes, has been described as the “(unresolved) bush at the top” of the tree, with its intrarelationships still being ambiguous owing to huge diversity (> 15,000 species). Recent molecular phylogenetic studies based on extensive taxon and character sampling, however, have revealed a number of unexpected clades of Percomorpha, and one of which is composed of Syngnathoidei (seahorses, pipefishes, and their relatives) plus several groups distributed across three different orders. To circumscribe the clade more definitely, we sampled several candidate taxa with reference to the previous studies and newly determined whole mitochondrial genome (mitogenome) sequences for 16 percomorph species across syngnathoids, dactylopterids, and their putatively closely-related fishes (Mullidae, Callionymoidei, Malacanthidae). Unambiguously aligned sequences (13,872 bp) from those 16 species plus 78 percomorphs and two outgroups (total 96 species) were subjected to partitioned Bayesian and maximum likelihood analyses. The resulting trees revealed a highly supported clade comprising seven families in Syngnathoidei (Gasterosteiformes), Dactylopteridae (Scorpaeniformes), Mullidae in Percoidei and two families in Callionymoidei (Perciformes). We herein proposed to call this clade “Syngnathiformes” following the latest nuclear DNA studies with some revisions on the included families.     

Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach

Fitting models with Bayesian likelihood-based parameter inference is becoming increasingly important in infectious disease epidemiology. Detailed datasets present the opportunity to identify subsets of these data that capture important characteristics of the underlying epidemiology. One such dataset describes the epidemic of bovine tuberculosis (bTB) in British cattle, which is also an important exemplar of a disease with a wildlife reservoir (the Eurasian badger). Here, we evaluate a set of nested dynamic models of bTB transmission, including individual- and herd-level transmission heterogeneity and assuming minimal prior knowledge of the transmission and diagnostic test parameters. We performed a likelihood-based bootstrapping operation on the model to infer parameters based only on the recorded numbers of cattle testing positive for bTB at the start of each herd outbreak considering high- and low-risk areas separately. Models without herd heterogeneity are preferred in both areas though there is some evidence for super-spreading cattle. Similar to previous studies, we found low test sensitivities and high within-herd basic reproduction numbers (R₀), suggesting that there may be many unobserved infections in cattle, even though the current testing regime is sufficient to control within-herd epidemics in most cases. Compared with other, more data-heavy approaches, the summary data used in our approach are easily collected, making our approach attractive for other systems.     

The complete mitochondrial genome of Pauropus longiramus (Myriapoda: Pauropoda): implications on early diversification of the myriapods revealed from comparative analysis

Myriapods are among the earliest arthropods and may have evolved to become part of the terrestrial biota more than 400 million years ago. A noticeable lack of mitochondrial genome data from Pauropoda hampers phylogenetic and evolutionary studies within the subphylum Myriapoda. We sequenced the first complete mitochondrial genome of a microscopic pauropod, Pauropus longiramus (Arthropoda: Myriapoda), and conducted comprehensive mitogenomic analyses across the Myriapoda. The pauropod mitochondrial genome is a circular molecule of 14,487 bp long and contains the entire set of thirty-seven genes. Frequent intergenic overlaps occurred between adjacent tRNAs, and between tRNA and protein-coding genes. This is the first example of a mitochondrial genome with multiple intergenic overlaps and reveals a strategy for arthropods to effectively compact the mitochondrial genome by overlapping and truncating tRNA genes with neighbor genes, instead of only truncating tRNAs. Phylogenetic analyses based on protein-coding genes provide strong evidence that the sister group of Pauropoda is Symphyla. Additionally, approximately unbiased (AU) tests strongly support the Progoneata and confirm the basal position of Chilopoda in Myriapoda. This study provides an estimation of myriapod origins around 555 Ma (95% CI: 444-704 Ma) and this date is comparable with that of the Cambrian explosion and candidate myriapod-like fossils. A new time-scale suggests that deep radiations during early myriapod diversification occurred at least three times, not once as previously proposed. A Carboniferous origin of pauropods is congruent with the idea that these taxa are derived, rather than basal, progoneatans.     

Analysis of ordered categorical data: two score-independent approaches

SUMMARY: A trend test is often employed to analyze ordered categorical data, in which a set of increasing scores is assigned a priori. There is a drawback in this approach, because how to choose a set of scores is not clear. There have been debates on which scores should be used (e.g., Graubard and Korn, 1987, Biometrics 43, 471-476; Ivanova and Berger, 2001, Biometrics 57, 567-570; Senn, 2007, Biometrics 63, 296-298). Conflicting conclusions are often obtained with different sets of scores. Two approaches, which have been applied to genetic case-control studies, are appealing for ordered categorical data, because they take into account the natural order in the data, are score independent, and not contingent on asymptotic theory. These two approaches are applied to a prospective study for detecting association between maternal drinking and congenital malformations.