Selection on a variant associated with improved viral clearance drives local, adaptive pseudogenization of interferon lambda 4 (IFNL4)

Abstract

Interferon lambda 4 gene (IFNL4) encodes IFN-λ4, a new member of the IFN-λ family with antiviral activity. In humans IFNL4 open reading frame is truncated by a polymorphic frame-shift insertion that eliminates IFN-λ4 and turns IFNL4 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.

Figure 1. Phylogenetic tree showing the dN/dS ratio of each lineage analyzed.

Figure 2. Allele frequency of rs368234815 - ΔG allele (blue) and TT allele (green) for each population from the 1000 Genomes dataset.

American populations of European and African origin (CEU, ASW) are placed near the geographic area of origin. For full population names see Methods.

Figure 3. Empirical P-values of the F_ST and XP-EHH analysis (depicted as dots or diamonds, respectively) in the 30 Kb genomic locus around IFNL4 (chr19:39724153–39754153) for (A) CHS, (B) CHB, and (C) JPT using YRI as background.

All XP-EHH values are connected by a fitting curve (yellow line). The 1% tail of the genomic empirical distribution is indicated by the horizontal, dashed line. (d) Haplotype structure in the same region as above, for an African (YRI), European (CEU), and East Asian (CHS) population. Columns represent SNPs with a derived allele frequency >5% in at least one population (n = 99 SNPs), with the ancestral allele in white, and the derived allele in black. Lines represent the haplotypes they fall in, as inferred with SHAPEIT by the 1000 Genomes consortium . Haplotypes were sorted based on rs368234815 (red arrow) and SNPs in perfect LD with it in CHS (black arrows); see also Table 2 and Figure 4. The bar on the left-hand side of each plot indicates the haplotypes that carry the TT allele (red) or the ΔG allele (blue).

Figure 4. Map of the IFNL locus with locations of relevant SNPs (from Table 2 ) and the inferred recombination hotspot based on recombination rates from .

Figure 5. (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).

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) Posterior probabilities of the model choice for the different selection models under perfect additivity. (C) 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. (D) Posterior probabilities of the model choice for the different selection models under the supra-additive model. In (B), (C), and (D), NTR has negligible posterior probability and is therefore not visible.