Parameters and definitions for the model and its description.

<p>Parameters and definitions for the model and its description.</p

Ghanian sample summary.

<p>The frequency of inferred number of strains per sample (left) and and the panmixia coefficient by number of strains (right). MAP estimates used.</p

Example samples.

Four representative samples with WSAF for each SNP plotted against the PLAF, showing an absence of mixture (a), a partially panmixed sample (b), a simple mixture (c), and a complex mixture (d).</p

Model diagram.

The structure of the model can be understood in terms of four related states connecting the WSAF to the PLAF: no mixture (upper left); simple mixture (lower left); panmixture (upper right); and complex mixture (lower right).α is exaggerated for explanation; realistic values are less than 0.05.</p

Table of simulated parameter values. <i>C</i> is the number of read counts while <i>M</i>, <i>K</i> and <i>α</i> are as in Table 1.

<p>Table of simulated parameter values. <i>C</i> is the number of read counts while <i>M</i>, <i>K</i> and <i>α</i> are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004824#pcbi.1004824.t001" target="_blank">Table 1</a>.</p

Component inference.

<p><i>Maximum a posteriori</i> (MAP) inferred number of components by number of read counts across 10 simulations, with dotted line at the true number of components.</p

Performance for parameter inference.

<p>Upper row: mean squared deviation for strain frequencies by number of read counts (left) and by number of SNPs (right). Lower row: absolute normalized deviation for panmixia coefficient by number of read counts (left) and by number of SNPs.</p

Examples of real and model-simulated data.

<p>For three samples (rows), we present the observed data WSAF plotted against the PLAF (first column), a diagram of the inferred model indicating the bands, proportions, and panmixia coefficient (second column), and data simulated under the inferred model. Panmixia coefficient and strain proportions are the MAP values. In the second column, the model’s PLAF-varying mixture densities are shown in grey scale, with black equal to one.</p

Number of strains by F-statistic.

<p>Boxplot of the inbreeding coefficient (1 − <i>F</i>_<i>is</i>) for each sample grouped by the MAP number of inferred strains.</p

Identification of Novel Pre-Erythrocytic Malaria Antigen Candidates for Combination Vaccines with Circumsporozoite Protein

<div><p>Malaria vaccine development has been hampered by the limited availability of antigens identified through conventional discovery approaches, and improvements are needed to enhance the efficacy of the leading vaccine candidate RTS,S that targets the circumsporozoite protein (CSP) of the infective sporozoite. Here we report a transcriptome-based approach to identify novel pre-erythrocytic vaccine antigens that could potentially be used in combination with CSP. We hypothesized that stage-specific upregulated genes would enrich for protective vaccine targets, and used tiling microarray to identify <i>P</i>. <i>falciparum</i> genes transcribed at higher levels during liver stage versus sporozoite or blood stages of development. We prepared DNA vaccines for 21 genes using the predicted orthologues in <i>P</i>. <i>yoelii</i> and <i>P</i>. <i>berghei</i> and tested their efficacy using different delivery methods against pre-erythrocytic malaria in rodent models. In our primary screen using <i>P</i>. <i>yoelii</i> in BALB/c mice, we found that 16 antigens significantly reduced liver stage parasite burden. In our confirmatory screen using <i>P</i>. <i>berghei</i> in C57Bl/6 mice, we confirmed 6 antigens that were protective in both models. Two antigens, when combined with CSP, provided significantly greater protection than CSP alone in both models. Based on the observations reported here, transcriptional patterns of <i>Plasmodium</i> genes can be useful in identifying novel pre-erythrocytic antigens that induce protective immunity alone or in combination with CSP.</p></div