Nucleotide variation, linkage disequilibrium and founder-facilitated speciation in wild populations of the zebra finch (Taeniopygia guttata)

Abstract

The zebra finch has long been an important model system for the study of vocal learning, vocal production, and behavior. With the imminent sequencing of its genome, the zebra finch is now poised to become a model system for population genetics. Using a panel of 30 noncoding loci, we characterized patterns of polymorphism and divergence among wild zebra finch populations. Continental Australian populations displayed little population structure, exceptionally high levels of nucleotide diversity (pi = 0.010), a rapid decay of linkage disequilibrium (LD), and a high population recombination rate (rho approximately 0.05), all of which suggest an open and fluid genomic background that could facilitate adaptive variation. By contrast, substantial divergence between the Australian and Lesser Sunda Island populations (K(ST) = 0.193), reduced genetic diversity (pi = 0.002), and higher levels of LD in the island population suggest a strong but relatively recent founder event, which may have contributed to speciation between these populations as envisioned under founder-effect speciation models. Consistent with this hypothesis, we find that under a simple quantitative genetic model both drift and selection could have contributed to the observed divergence in six quantitative traits. In both Australian and Lesser Sundas populations, diversity in Z-linked loci was significantly lower than in autosomal loci. Our analysis provides a quantitative framework for studying the role of selection and drift in shaping patterns of molecular evolution in the zebra finch genome.

Figure 1.—

Range map and sampling localities of two zebra finch subspecies.

Figure 2.—

Nucleotide diversity (π) in Australian and Lesser Sundas zebra finch subspecies across 21 anonymous nuclear loci, 4 nuclear introns, and 5 Z-linked introns. Sixteen of the 30 loci are monomorphic in the Timor zebra finch T. guttata gutatta.

Figure 3.—

Results from clustering analysis in Structure. (A and B) Probabilistic assignments of individual genotypes to either three or two populations, respectively. (C) The mean and standard error around likelihoods from three replicate runs testing models of one to six populations. The likelihood estimate clearly plateaus at K = 2, suggesting a two-population model best fits the data (despite a slightly higher likelihood for K = 3 and 4).

Figure 4.—

Posterior probability distributions for seven parameters estimated using IM. Depicted are results from replicate runs of 25 million post-burn-in iterations. Each run was conducted using the same priors and thus can be combined. Point estimates for each parameter for each run are given with 95% quantiles in parentheses.

Figure 5.—

Rapid decay of LD in Australian zebra finches. Point estimates represent empirical r-squared values from pairwise comparisons among sites. Curves represent predictions of the decay of LD based on equation 3 from Weir and Hill (1986). The top solid line is based on ρ estimated from humans (0.0004), the bottom line is the multilocus average estimated using PHASE (ρ = 0.051), and the middle line is based on the minimum point estimate of ρ estimated by PHASE (ρ = 0.006).

Figure 6.—

Enhanced LD measured as r² across 10-kb trios in Timor vs. Australian zebra finches. Solid squares indicate r² values of 1, or perfect linkage. Shaded squares indicate r² between zero and 1. Open squares indicate r² values of zero. Although Lesser Sundas populations show greater LD, most significant LD is restricted to intralocus comparisons. High LD, therefore, is rarely detected at scales of even 1 kb.