Balanced polymorphism in bottlenecked populations: The case of the CCR5 5 ` cis-regulatory region in Amazonian Amerindians

The 5` cis-regulatory region of the CCR5 gene exhibits a strong signature of balancing selection in several human populations. Here we analyze the polymorphism of this region in Amerindians from Amazonia, who have a complex demographic history, including recent bottlenecks that are known to reduce genetic variability. Amerindians show high nucleotide diversity (pi = 0.27%) and significantly positive Tajima`s D, and carry haplotypes associated with weak and strong gene expression. To evaluate whether these signatures of balancing selection could be explained by demography, we perform neutrality tests based on empiric and simulated data. The observed Tajima`s D was higher than that of other world populations: higher than that found for 18 noncoding regions of South Amerindians, and higher than 99.6% of simulated genealogies, which assume nonequilibrium conditions. Moreover, comparing Amerindians and Asians, the Fst for CCR5 cis-regulatory region was unusually low, in relation to neutral markers. These findings indicate that, despite their complex demographic history, South Amerindians carry a detectable signature of selection on the CCR5 cis-regulatory region. (C) 2010 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.