Biology subjects

Bolker, B.

Publications and source records attributed to Bolker, B..

4 recordsLinked to original sources

bioRxiv2024

Investigating Statistical Power of Differential Abundance Studies

Identifying microbial taxa that differ in abundance between groups (control/treatment, healthy/diseased, etc.) is important for both basic and applied science. As in all scientific research, microbiome studies must have good statistical power to detect taxa with substantially different abundance between treatments; low power leads to poor precision and biased estimates via the "winners curse". Several studies have raised concerns about low power in microbiome studies. In this study, we investigate statistical power in differential abundance analysis. In particular, we present a novel approach for estimating the statistical power to detect effects at the level of individual taxa as a function of effect size (fold change) and mean abundance. We analysed seven real case-control microbiome datasets and developed a novel method for simulating microbiome data. We illustrate how power varies with effect size and mean abundance; our results suggest that typical differential abundance studies are underpowered for detecting changes in individual taxon.

microbiology↗

bioRxiv2024

Reassessing global historical R0 estimates of canine rabies

Rabies spread by domestic dogs continues to cause tens of thousands of human deaths every year in low- and middle-income countries. Nevertheless rabies is often neglected, perhaps because it has already been eliminated from high-income countries through dog vaccination. Estimates of canine rabiess intrinsic reproductive number ([R]0), a metric of disease spread, from a wide range of times and locations are relatively low (values < 2), with narrow confidence intervals. Given rabiess persistence, this consistently low and narrow range of estimates is surprising. We combined incidence data from historical outbreaks of canine rabies from around the world with in-depth contact-tracing data from Tanzania to investigate initial growth rates (r0), generation-interval distributions (G), and reproductive numbers ([R]0). We improved on earlier estimates by choosing outbreak windows algorithmically; fitting r0 using a more appropriate statistical method that accounts for decreases through time; and incorporating uncertainty from both r0 and G in our confidence intervals on[R] 0. Our[R] 0 estimates are larger than previous estimates, with wider confidence intervals. These revised[R] 0 estimates suggest that a greater level of vaccination effort will be required to eliminate rabies than previously thought, but that the level of coverage required remains feasible. Our hybrid approach for estimating[R] 0 and its uncertainty is applicable to other disease systems where researchers estimate[R] 0 by combining data-based estimates of r0 and G.

ecology↗

bioRxiv2022

Predicting the temperature-driven development of stage-structured insect populations with a Bayesian hierarchical model

The management of forest pests relies on an accurate understanding of the species phenology. Thermal performance curves (TPCs) have traditionally been used to model insect phenology; many such models have been proposed and fitted to data from both wild and laboratory-reared populations, most of which have used maximum likelihood estimation (MLE). Analyses typically present point estimates of parameters with confidence intervals, but estimates of the correlations among TPC parameters are rarely provided. Neglecting aspects of model uncertainty such as correlation among parameters may lead to incorrect confidence intervals of predictions. This paper implements a Bayesian hierarchical model of insect phenology incorporating individual variation, quadratic variation in development rates across insects larval stages, and non-parametric adjustment terms that allow for deviations from a parametric TPC. We use Hamiltonian Monte Carlo (HMC) for estimation; the model is fitted to a laboratory-reared spruce budworm population as a case study. We assessed the accuracy of the model using stratified, 10-fold cross-validation. Using the posterior samples, we found prediction intervals for spruce budworm development for a given year.

ecology↗

bioRxiv2017

Penetrance of polygenic obesity susceptibility loci across the body mass index distribution: an update on scaling effects.

A growing number of single nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effect of these obesity susceptibility loci is uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI/obesity-associated SNPs in 75,230 adults of European ancestry along BMI percentiles using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of 9 SNPs (24%) increased significantly across the sample BMI distribution including, FTO (rs1421085, p=8.69x10-15), PCSK1 (rs6235, p=7.11x10-06), TCF7L2 (rs7903146, p=9.60x10-06), MC4R (rs11873305, p=5.08x10-05), FANCL (rs12617233, p=5.30x10-05), GIPR (rs11672660, p=1.64x-04), MAP2K5 (rs997295, p=3.25x10-04), FTO (rs6499653, p=6.23x10-04) and NT5C2 (rs3824755, p=7.90x10-04). We showed that such increases stem from unadjusted gene interactions that enhanced the effects of SNPs in persons with high BMI. When 125 height-associated were analyzed for comparison, only one (<1%), IGF1 (rs6219, p=1.80x10-04), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-Height, respectively) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p=7.03x10-37, Height: p=0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method to detect such interactions using only the sample outcome distribution.

genetics↗