One size fits all? Direct evidence for the heterogeneity of genetic drift throughout the genome

Abstract

Effective population size (Ne) is a central parameter in population and conservation genetics. It measures the magnitude of genetic drift, rates of accumulation of inbreeding in a population, and it conditions the efficacy of selection. It is often assumed that a single Ne can account for the evolution of genomes. However, recent work provides indirect evidence for heterogeneity in Ne throughout the genome. We study this by examining genome-wide diversity in the Danish Holstein cattle breed. Using the differences in allele frequencies over a single generation, we directly estimated Ne among autosomes and smaller windows within autosomes. We found statistically significant variation in Ne at both scales. However, no correlation was found between the detected regional variability in Ne, and proxies for the intensity of linked selection (local recombination rate, gene density), or the presence of either past strong selection or current artificial selection on traits of economic value. Our findings call for further caution regarding the wide applicability of the Ne concept for understanding quantitatively processes such as genetic drift and accumulation of consanguinity in both natural and managed populations.

Keywords: Holstein breed; effective population size; genetic drift; linked selection; quantitative trait loci.

Figure 1.

Heterogeneity of estimated N_e in genomic windows. (a) Distribution of N_e over one generation (1995–2000) in 447 windows. Histogram: empirical distribution of N_e estimates. Dashed line: distribution expected under homogeneous N_e and incorporating standard errors on estimated N_e (electronic supplementary material, figure S5). Solid line: expected distribution for the estimated N_e under a model accounting for standard errors as above and further assuming lognormally distributed parametric variation in N_e among windows (see the electronic supplementary material). (b) Example of within-chromosome heterogeneity in estimated N_e. Each dot represents the N_e estimated per window. Errors bars indicate 1 s.e. (estimated by bootstrapping).

Figure 2.

Boxplot of log (N_e) among windows with either no QTL or harbouring QTLs coding for 1, 2 or 3 traits under artificial selection.