Natural selection shapes nucleotide polymorphism across the genome of the nematode Caenorhabditis briggsae

Abstract

The combined actions of natural selection, mutation, and recombination forge the landscape of genetic variation across genomes. One frequently observed manifestation of these processes is a positive association between neutral genetic variation and local recombination rates. Two selective mechanisms and/or recombination-associated mutation (RAM) could generate this pattern, and the relative importance of these alternative possibilities remains unresolved generally. Here we quantify nucleotide differences within populations, between populations, and between species to test for genome-wide effects of selection and RAM in the partially selfing nematode Caenorhabditis briggsae. We find that nearly half of genome-wide variation in nucleotide polymorphism is explained by differences in local recombination rates. By quantifying divergence between several reproductively isolated lineages, we demonstrate that ancestral polymorphism generates a spurious signal of RAM for closely related lineages, with implications for analyses of humans and primates; RAM is, at most, a minor factor in C. briggsae. We conclude that the positive relation between nucleotide polymorphism and the rate of crossover represents the footprint of natural selection across the C. briggsae genome and demonstrate that background selection against deleterious mutations is sufficient to explain this pattern. Hill-Robertson interference also leaves a signature of more effective purifying selection in high-recombination regions of the genome. Finally, we identify an emerging contrast between widespread adaptive hitchhiking effects in species with large outcrossing populations (e.g., Drosophila) versus pervasive background selection effects on the genomes of organisms with self-fertilizing lifestyles and/or small population sizes (e.g., Caenorhabditis elegans, C. briggsae, Arabidopsis thaliana, Lycopersicon, human). These results illustrate how recombination, mutation, selection, and population history interact in important ways to shape molecular heterogeneity within and between genomes.

Figure 1.

Relationships among populations of C. briggsae and species of Caenorhabditis.

Figure 2.

Crossover rate estimates for C. briggsae chromosome 1 with each point-estimate based on the slope of the least-squares regression line through five mapped loci. Other chromosomes have similar marker density and general recombination profiles (Supplemental Fig. 2). Finer- and coarser-scale estimation of crossover rates are qualitatively similar, as well (Supplemental Fig. 1B). Note that the low-recombination chromosome centers do not correspond to centromeres, as centromeres are not distinct features of Caenorhabditis chromosomes.

Figure 3.

Lineage-specific synonymous-site (A) and replacement-site divergence (B) for quartiles of recombination rate in C. briggsae, in genome comparisons with C. remanei and C. elegans as outgroups. ANOVA models that include recombination rate, codon bias, background nucleotide composition, chromosome of origin, divergence, and their first-order interactions provide statistical support for the positive d_S-recombination and d_N-recombination associations. Error bars indicate ±1 standard error.

Figure 4.

Silent-site polymorphism for Tropical strains of C. briggsae (A) and divergence between Tropical and Kerala populations (B) as a function of crossover rate (intermediate scale, five-loci estimates). Only the polymorphism-crossover rate association is statistically significant (see text).

Figure 5.

The correlation between crossover rate and interpopulation divergence is stronger for comparisons of more closely related populations and in analyses using finer-scale estimates of crossover rate. P-values are indicated above the bars.

Figure 6.

Bi-plot of summaries of the variant frequency spectrum as a function of crossover rate. The bimodal distribution of Tajima's D does not correlate significantly with crossover rate, whereas Schaeffer's D/D_min does (Spearman's ρ = 0.54, P = 0.03).

Figure 7.

Distribution of correlation coefficients between neutral polymorphism and the polymorphism-recombination rate ratio (R_p-p:r). The observed correlation (Spearman rank correlation = 0.930; vertical line) falls within the distribution simulated for background selection (A), but not for the genetic hitchhiking model (B,C). The hitchhiking model in B uses scaled output of neutral coalescent simulations, whereas C uses direct simulation of a recurrent sweep process (Jensen et al. 2008). Simulations for A assume π₀ = 0.0138 and for B assume π₀ = 0.0486 (similar distributions result when π₀ are identical), as well as c = 0.000158, a = 7.67 × 10⁻⁹, 800-bp loci, μ = 1.7 × 10⁻⁹ (see Supplemental Fig. 2), and the observed fine-scale crossover rates for 22 loci. Parameters from both A and B are based on least-squares fitting of analytical selection models. Explicit recurrent hitchhiking simulations for C are based on π₀ = 0.035, c = 0.000158, s = 0.001, λ = 1 × 10⁻⁶, and fine-scale crossover rates for 22 loci.

Figure 8.

Sequence differences (S) per site between AF16 and shotgun reads of Tropical strain VT847 (polymorphism, gray squares) and shotgun reads of Temperate strain HK104 (divergence, black diamonds) are greater in high-crossover regions of the genome, for both synonymous sites (A) and replacement sites (B). (C) The ratio of replacement to synonymous site differences within or between populations indicates an excess of replacement differences in regions experiencing little crossover. Black and gray horizontal lines indicate the null expectation for inter- and intrapopulation differences, respectively.