Comparison between the differential analysis and the marginal causal approach on chicken microarray data.

<p>Each point corresponds to a gene for which the differential and marginal causal analyses have been applied.</p

Illustration of upstream and downstream causality.

<p>Nodes <i>X</i>₀ and <i>X</i>₁ are both upstream causally related to knocked-out gene <i>G</i>, while nodes <i>X</i>₂ and <i>X</i>₃ are both downstream causally related to <i>G</i>.</p

Models given observational or interventional data.

<p>Graphical representation of the <i>M</i>₁ (downstream) and <i>M</i>₀ (upstream or correlated) models under observational and interventional data.</p

Bayes factor for the simulated graph structure.

<p>Results from 100 simulations based on the graph in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171142#pone.0171142.g004" target="_blank">Fig 4</a>. Nodes simulated under the upstream/correlation model (<i>M</i>₀) appear to the left in black, and those simulated under the downstream model (<i>M</i>₁) appear to the right in red.</p

Venn diagram for the top 50 genes ranked according to the differential analysis (p-value or log-fold change) and marginal causal approach (Bayes factor or total causal effects.

<p>Ranking was performed from lowest to highest for p-values and highest to lowest for absolute total effect, absolute log fold-change, and Bayes factor.</p

Relative proportions of annotated small RNA-seq reads.

<p>Libraries were constructed using total RNA isolated from chicken plasma.</p

Size and relative abundance of small RNAs isolated from different compartments of chicken blood.

<p>Red cells (blue), PBMCs (green) and plasma (red).</p

Box-plot of −ΔC_T values of miRNAs profiled by RT-qPCR in chicken liver samples.

<p>Asterisks represent p values: * between 0.1 and 0.05; ** between 0.05 and 0.01; *** <0.01. FD =  Feed Deprivation; RF =  Re-Feeding.</p

Visualization of five clusters of co-abundant miRNAs in chicken plasma.

<p>The analysis was carried out on the 148 miRNAs retained after filtering. FD =  Feed Deprivation; RF =  Re-Feeding.</p

Y-chromosomal diversity in Europe is clinal and influenced primarily by geography, rather than by language

Clinal patterns of autosomal genetic diversity within Europe have been interpreted in previous studies in terms of a Neolithic demic diffusion model for the spread of agriculture; in contrast, studies using mtDNA have traced many founding lineages to the Paleolithic and have not shown strongly clinal variation. We have used 11 human Ychromosomal biallelic polymorphisms, defining 10 haplogroups, to analyze a sample of 3,616 Y chromosomes belonging to 47 European and circum-European populations. Patterns of geographic differentiation are highly nonrandom, and, when they are assessed using spatial autocorrelation analysis, they show significant clines for five of six haplogroups analyzed. Clines for two haplogroups, representing 45% of the chromosomes, are continentwide and consistent with the demic diffusion hypothesis. Clines for three other haplogroups each have different foci and are more regionally restricted and are likely to reflect distinct population movements, including one from north of the Black Sea. Principal-components analysis suggests that populations are related primarily on the basis of geography, rather than on the basis of linguistic affinity. This is confirmed in Mantel tests, which show a strong and highly significant partial correlation between genetics and geography but a low, nonsignificant partial correlation between genetics and language. Genetic-barrier analysis also indicates the primacy of geography in the shaping of patterns of variation. These patterns retain a strong signal of expansion from the Near East but also suggest that the demographic history of Europe has been complex and influenced by other major population movements, as well as by linguistic and geographic heterogeneities and the effects of drift