Revisiting classic clines in Drosophila melanogaster in the age of genomics

Abstract

Adaptation to spatially varying environments has been studied for decades, but advances in sequencing technology are now enabling researchers to investigate the landscape of genetic variation underlying this adaptation genome wide. In this review we highlight some of the decades-long research on local adaptation in Drosophila melanogaster from well-studied clines in North America and Australia. We explore the evidence for parallel adaptation and identify commonalities in the genes responding to clinal selection across continents as well as discussing instances where patterns differ among clines. We also investigate recent studies utilizing whole-genome data to identify clines in D. melanogaster and several other systems. Although connecting segregating genomic variation to variation in phenotypes and fitness remains challenging, clinal genomics is poised to increase our understanding of local adaptation and the selective pressures that drive the extensive phenotypic diversity observed in nature.

Keywords: latitudinal cline; local adaptation; spatially varying selection.

Figure I

A simplified fitness landscape contrasting discrete- and continuous-environment clines, as well as their expected shapes. A) In discrete-environment clines, the fitness landscape (top; pattern shown for the orange population, blue population would be a mirror image) is often represented by a step function with two fitness optima, where alleles from one species are selected against as they introgress away from their home population. Consequently, the slope of the resulting trait/allele frequency cline (bottom) is relatively shallow in the tails and transitions sharply through the tension zone, though the exact shape is dependent on the strength of selection and dispersal distance. B) The fitness landscape of a continuous-environment cline (top; pattern shown for leftmost population) represents a shifting fitness optimum along a continuous environmental gradient. The resulting trait/allele frequency cline (bottom, black line) may be less steep than a discrete-environment cline and should closely track the environmental selection pressure (green line).

Figure 1

Phenotypes and alleles often respond to clinal selection in predictable ways. For example, both the allele frequency of the SNP corresponding to amino acid residue 356 in cpo sampled from North America (circles; modified from Schmidt et al., 2008), and the wing area of flies sampled from eastern Australia (diamonds; modified from James et al., 1995) increase with latitude. Selection on these traits could be mediated by multiple environmental factors that correlate with latitude.

Figure 2

Genome-wide SNP data from the endpoints of the Australian (top) and North American (bottom) cline (modified from Reinhardt et al., 2014). Patterns of differentiation along chromosome arm 3R highlight exceptionally differentiated SNPs—potential targets of clinal selection. The dark blue line represents median estimates of F_ST, the density at a position is indicated by the intensity of the blue cloud, and black dots represent outliers.

Figure 3

Parallel clines in two distantly related mosquitoes along a latitudinal transect in Cameroon. A) Pies show the frequency of the standard (2L+^a, white) and inverted (2La, black) arrangement of inversion 2La in A. gambiae. The inset displays genomic differentiation surrounding the 2La inversion (shaded area) as F_ST —plotted over 200-kb windows (red) and 20-kb windows (blue)—between high and low latitude populations (modified from Cheng et al., 2012). B) The karyotype frequency of the standard homozygote (white), heterozygote (blue), and inverted homozygote (black) arrangement of inversion 3Ra in A. funestus (reproduced with permission from Ayala, Guerrero, & Kirkpatrick, 2013).