A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r(2) of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r(2) of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62863/1/nature06258.pd

Dysregulation of IL-15-mediated T-cell homeostasis in TGF-β dominant-negative receptor transgenic mice

T-cell subpopulations, defined by their expression of CD4, CD8, naive, and memory cell-surface markers, occupy distinct homeostatic compartments that are regulated primarily by cytokines. CD8+ memory T cells, as defined by CD44hi surface expression, are dependent on IL-15 as a positive regulator of their homeostatic maintenance. Manipulation of IL-15 signaling through gene aberration, overexpression, or receptor alterations has been shown to dramatically affect T-cell homeostasis, with overexpression leading to fatal leukemia. Here we show that TGF-β is the critical negative regulator of murine CD8+ memory T-cell homeostasis with direct opposition to the positive effects of IL-15. This negative regulation is mediated, at least in part, by the ability of TGF-β to modulate expression of the β-chain of the IL-15 receptor, thus establishing a central axis between these 2 cytokines for homeostatic control of CD8+ memory T-cell populations. These data establish TGF-β as a critical and dominant tumor-suppressor pathway opposing IL-15-mediated CD8+ T-cell expansion and potential malignant transformation

Using Knowledge Fusion to Analyze Avian Influenza H5N1 in East and Southeast Asia

<div><p>Highly pathogenic avian influenza (HPAI) H5N1, a disease associated with high rates of mortality in infected human populations, poses a serious threat to public health in many parts of the world. This article reports findings from a study aimed at improving our understanding of the spatial pattern of the highly pathogenic avian influenza, H5N1, risk in East-Southeast Asia where the disease is both persistent and devastating. Though many disciplines have made important contributions to our understanding of H5N1, it remains a challenge to integrate knowledge from different disciplines. This study applies genetic analysis that identifies the evolution of the H5N1 virus in space and time, epidemiological analysis that determines socio-ecological factors associated with H5N1 occurrence, and statistical analysis that identifies outbreak clusters, and then applies a methodology to formally integrate the findings of the three sets of methodologies. The present study is novel in two respects. First it makes the initiative attempt to use genetic sequences and space-time data to create a space-time phylogenetic tree to estimate and map the virus' ability to spread. Second, by integrating the results we are able to generate insights into the space-time occurrence and spread of H5N1 that we believe have a higher level of corroboration than is possible when analysis is based on only one methodology. Our research identifies links between the occurrence of H5N1 by area and a set of socio-ecological factors including altitude, population density, poultry density, and the shortest path distances to inland water, coastlines, migrating routes, railways, and roads. This study seeks to lay a solid foundation for the interdisciplinary study of this and other influenza outbreaks. It will provide substantive information for containing H5N1 outbreaks.</p> </div

Summary results of the logistic regression model for the avian influenza H5N1 epidemics in East-Southeast Asia, Indonesia, and China, 1996–2009.

<p>These values are the average of 1000 bootstrap replicates of the logistic regression model. The meaning of the abbreviation shows as follow: Alt = average altitude; PopDen = population density; PolDen = poultry density, D2Water = minimal distance to inland water bodies; D2coast = minimal distance to coastline; D2Flyway = minimal distance to migratory bird pathways; D2Rail = minimal distance to railways; D2Road = minimal distance to roads. Con is the constant of the logistic regression models.</p

Probability maps predicting the occurrence of avian influenza (H5N1) in Thailand and Vietnam.

<p>(a) and (c) show the probabilities derived from the phylogenetic trees analyses (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029617#pone.0029617.s003" target="_blank">Figure S3</a>(b)); (b) and (d) show the results of the modified local <i>K</i> function analysis, depicting the spatial distribution of outbreak clusters. The experimental data covers the H5N1 outbreaks from late 2003 to 2009.</p

The spatial pattern of H5N1 in Indonesia, China, and East-Southeast Asia.

<p>(a), (c), (e) show the distribution of observed H5N1 outbreaks; (b), (d), and (f) show the probability maps integrating the findings of the phylogenetic analysis ( <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029617#pone.0029617.s002" target="_blank">Figures S2</a>(a), (d), and (g)), the modified local <i>K</i> function analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029617#pone.0029617.s002" target="_blank">Figures S2</a>(b), (e), and (h)), and the logistic regression analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029617#pone.0029617.s002" target="_blank">Figures S2</a>(c), (f), and (i)). The closer the probability is to 1, the greater is the probability of an H5N1 outbreaks.</p