Genetic susceptibility to chronic wasting disease in free-ranging white-tailed deer: Complement component C1q and Prnp polymorphisms

The genetic basis of susceptibility to chronic wasting disease (CWD) in free-ranging cervids is of great interest. Association studies of disease susceptibility in free-ranging populations, however, face considerable challenges including: the need for large sample sizes when disease is rare, animals of unknown pedigree create a risk of spurious results due to population admixture, and the inability to control disease exposure or dose. We used an innovative matched case–control design and conditional logistic regression to evaluate associations between polymorphisms of complement C1q and prion protein (Prnp) genes and CWD infection in white-tailed deer from the CWD endemic area in southcentral Wisconsin. To reduce problems due to admixture or disease-risk confounding, we used neutral genetic (microsatellite) data to identify closely related CWD-positive (n = 68) and CWD-negative (n = 91) female deer to serve as matched cases and controls. Cases and controls were also matched on factors (sex, location, age) previously demonstrated to affect CWD infection risk. For Prnp, deer with at least one Serine (S) at amino acid 96 were significantly less likely to be CWD-positive relative to deer homozygous for Glycine (G). This is the first characterization of genes associated with the complement system in white-tailed deer. No tests for association between any C1q polymorphism and CWD infection were significant at p \u3c 0.05. After controlling for Prnp, we found weak support for an elevated risk of CWD infection in deer with at least one Glycine (G) at amino acid 56 of the C1qC gene. While we documented numerous amino acid polymorphisms in C1q genes none appear to be strongly associated with CWD susceptibility

It's not too Late for the Harpy Eagle (Harpia harpyja): High Levels Of Genetic Diversity and Differentiation Can Fuel Conservation Programs

Article on the harpy eagle (Harpia harpyja) and how high levels of genetic diversity and differentiation can fuel conservation programs

Worst-case and smoothed analysis of k-means clustering with Bregman divergences

The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice despite its exponential worst-case running-time. To narrow the gap between theory and practice, k-means has been studied in the semi-random input model of smoothed analysis, which often leads to more realistic conclusions than mere worst-case analysis. For the case that n data points in R d are perturbed by Gaussian noise with standard deviation σ, it has been shown that the expected running-time is bounded by a polynomial in n and 1/σ. This result assumes that squared Euclidean distances are used as distance measure. In many applications, however, data is to be clustered with respect to Bregman divergences rather than squared Euclidean distances. A prominent example is the Kullback-Leibler divergence (a.k.a. relative entropy) that is commonly used to cluster web pages. To broaden the knowledge about this important class of distance measures, we analyze the running-time of the k-means method for Bregman divergences. We first give a smoothed analysis of k-means with (almost) arbitrary Bregman divergences, and we show bounds of poly(n √ k, 1/σ) and k kd ·poly(n, 1/σ). The latter yields a polynomial bound if k and d are small compared to n. On the other hand, we show that the exponential lower bound carries over to a huge class of Bregman divergences