Detection of associations with rare and common SNPs for quantitative traits: a nonparametric Bayes-based approach

We propose a nonparametric Bayes-based clustering algorithm to detect associations with rare and common single-nucleotide polymorphisms (SNPs) for quantitative traits. Unlike current methods, our approach identifies associations with rare genetic variants at the variant level, not the gene level. In this method, we use a Dirichlet process prior for the distribution of SNP-specific regression coefficients, conduct hierarchical clustering with a distance measure derived from posterior pairwise probabilities of two SNPs having the same regression coefficient, and explore data-driven approaches to select the number of clusters. SNPs falling inside the largest cluster have relatively low or close to zero estimates of regression coefficients and are considered not associated with the trait. SNPs falling outside the largest cluster have relatively high estimates of regression coefficients and are considered potential risk variants. Using the data from the Genetic Analysis Workshop 17, we successfully detected associations with both rare and common SNPs for a quantitative trait. We conclude that our method provides a novel and broadly applicable strategy for obtaining association results with a reasonably low proportion of false discovery and that it can be routinely used in resequencing studies

Incubation Periods of Yellow Fever Virus

Yellow fever virus is a global health threat due to its endemicity in parts of Africa and South America where human infections occur in residents and travelers. To understand yellow fever dynamics, it is critical to characterize the incubation periods of the virus in vector mosquitoes and humans. Here, we compare four statistical models of the yellow fever incubation periods fitted with historical data. The extrinsic incubation period in the urban vector Aedes aegypti was best characterized with a temperature-dependent Weibull model with a median of 10 days at 25°C (middle 95% = 2.0–37 days). The intrinsic incubation period, fitted with a log-normal model, had a median of 4.3 days (middle 95% = 2.3–8.6 days). These estimates and their associated statistical models provide a quantitative basis to assist in exposure assessments, model potential outbreaks, and evaluate the effectiveness of public health interventions