The effects of a SNP in pre-miR-1206 on expression of mature miR-1206. A.

<p>Allelic forms of pre-miR-1206 were cloned into miRNA expression vector pEGP-miR. Graphs show analysis of mature miR-1206 expression in 293T cells, in relation to rs2114358 GG and AA genotypes. <b>B.</b> In prostate cancer cell lines. <b>C.</b> In colon cancer cell lines. <b>D.</b> In breast cancer cell lines.</p

SNPs in miRNA genes in 8q24.2 and 11q13.3 genotyped in SNP500 and HapMap samples.

a<p>allele 1/<u>allele 2</u>; allele frequencies are for underlined alleles. SNP500 samples:</p>b<p>Caucasian (n = 31),</p>c<p>African American (n = 24),</p>d<p>Hispanic (n = 23),</p>e<p>Pacific Rim (n = 24); HapMap samples: Hap CEU (n = 90), Hap CHB+JPT (n = 90), Hap YRI (n = 90).</p>f<p>miR-939 novel SNP at chr position 145590173;</p>g<p>miR-1237 novel SNP at chr position 63892701 (based on hg 18).</p><p>N.A. = not assessed.</p

Analysis of surface expression of Insulin Receptor (INSR) in miR-612 transfected cancer cell lines.

<p>Cell surface insulin receptor (INSR) expression was determined by anti-INSR staining and flow cytometry24 hours post-transfection of the indicated miR-612 expression vectors. The results are representative of four independent experiments.</p

The effects of two SNPs in pre-miR-612 on expression of mature miR-612. A.

<p>Allelic forms of pre-miR-612 were cloned into miRNA expression vector pEGP-miR. Graphs show analysis of mature miR-612 expression in 293T cells, in relation to combination of rs550894 and rs12803915 genotypes: CC/GG (reference), AA/GG and CC/AA. <b>B.</b> In prostate cancer cell lines. <b>C.</b> In colon cancer cell lines. <b>D.</b> In breast cancer cell lines.</p

Schematic outline of experimental procedures. A.

<p>Co-transfection of allelic forms of miR-612 with a miR-1206 construct used as a normalization control. <b>B.</b> Co-transfection scheme for testing SNP effects on miR-612 processing. Three different combinations of allelic forms of miR-612 (#1∼#3) were co-transfected with control miR-1206 expression construct.</p

Selection on a Variant Associated with Improved Viral Clearance Drives Local, Adaptive Pseudogenization of Interferon Lambda 4 (<i>IFNL4</i>)

<div><p>Interferon lambda 4 gene (<i>IFNL4</i>) encodes IFN-λ4, a new member of the IFN-λ family with antiviral activity. In humans <i>IFNL4</i> open reading frame is truncated by a polymorphic frame-shift insertion that eliminates IFN-λ4 and turns <i>IFNL4</i> into a polymorphic pseudogene. Functional IFN-λ4 has antiviral activity but the elimination of IFN-λ4 through pseudogenization is strongly associated with improved clearance of hepatitis C virus (HCV) infection. We show that functional IFN-λ4 is conserved and evolutionarily constrained in mammals and thus functionally relevant. However, the pseudogene has reached moderately high frequency in Africa, America, and Europe, and near fixation in East Asia. In fact, the pseudogenizing variant is among the 0.8% most differentiated SNPs between Africa and East Asia genome-wide. Its raise in frequency is associated with additional evidence of positive selection, which is strongest in East Asia, where this variant falls in the 0.5% tail of SNPs with strongest signatures of recent positive selection genome-wide. Using a new Approximate Bayesian Computation (ABC) approach we infer that the pseudogenizing allele appeared just before the out-of-Africa migration and was immediately targeted by moderate positive selection; selection subsequently strengthened in European and Asian populations resulting in the high frequency observed today. This provides evidence for a changing adaptive process that, by favoring IFN-λ4 inactivation, has shaped present-day phenotypic diversity and susceptibility to disease.</p></div

Phylogenetic tree showing the dN/dS ratio of each lineage analyzed.

<p>Phylogenetic tree showing the dN/dS ratio of each lineage analyzed.</p

F_ST values and for F_ST, XP-EHH, iHS and Fay and Wu's H (FW) the empirical P-values are shown for rs368234815 in every population.

<p>F_ST values and for F_ST, XP-EHH, iHS and Fay and Wu's H (FW) the empirical P-values are shown for rs368234815 in every population.</p

(A) Graphical representation of the different models of selection tested in the ABC analysis (NTR - neutral, SDN - selection on a de novo mutation, and SSV - selection on standing variation).

<p>We simulated one ancestral population that splits at the out-of-Africa event (at 51,000 years ago) into the African (AFR) and the non-African (non-AFR) populations, which experience subsequent migration. The star indicates the appearance of the focal mutation. In the first case the neutral (black) mutation appeared and evolved under neutrality in both populations. In the SDN model the advantageous mutation (red) is immediately under positive selection with strength s_A, and time when selection started t_mut (the prior parameter space for t_mut is indicated by a green line); selection strength is allowed to change in the non-African population to s_NA. In the SSV model the neutral (black) mutation appeared and evolved under neutrality, becoming advantageous in the non-African population (red line) at time t_mut. Prior parameter spaces can be found in methods. (<b>B</b>) Posterior probabilities of the model choice for the different selection models under perfect additivity. (<b>C</b>) Posterior probabilities of the model choice for the different dominance models (and neutrality, NTR). For all models except NTR the posterior probability represent the sum for the SDN and SSV selection models. (<b>D</b>) Posterior probabilities of the model choice for the different selection models under the supra-additive model. In (<b>B</b>), (<b>C</b>), and (<b>D</b>), NTR has negligible posterior probability and is therefore not visible.</p

Allele frequency of rs368234815 - ΔG allele (blue) and TT allele (green) for each population from the 1000 Genomes dataset.

<p>American populations of European and African origin (CEU, ASW) are placed near the geographic area of origin. For full population names see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004681#s4" target="_blank">Methods</a>.</p