Sweeps in Space: Leveraging Geographic Data to Identify Beneficial Alleles in Anopheles gambiae

Abstract

As organisms adapt to environmental changes, natural selection modifies the frequency of nonneutral alleles. For beneficial mutations, the outcome of this process may be a selective sweep, in which an allele rapidly increases in frequency and perhaps reaches fixation within a population. Selective sweeps have well-studied effects on patterns of local genetic variation in panmictic populations, but much less is known about the dynamics of sweeps in continuous space. In particular, because limited movement across a landscape leads to unique patterns of population structure, spatial dynamics may influence the trajectory of selected mutations. Here, we use forward-in-time, individual-based simulations in continuous space to study the impact of space on beneficial mutations as they sweep through a population. In particular, we show that selection changes the joint distribution of allele frequency and geographic range occupied by a focal allele and demonstrate that this signal can be used to identify selective sweeps. We then leverage this signal to identify in-progress selective sweeps within the malaria vector Anopheles gambiae, a species under strong selection pressure from vector control measures. By considering space, we identify multiple previously undescribed variants with potential phenotypic consequences, including mutations impacting known IR-associated genes and altering protein structure and properties. Our results demonstrate a novel signal for detecting selection in spatial population genetic data that may have implications for genomic surveillance and understanding geographic patterns of genetic variation.

Keywords: Anopheles; genomics; insecticide resistance; selection; spatial genetics; vector.

Fig. 1.

A conceptual model of how positive selection impacts an allele’s spread across a 1D landscape. a, b, and c) A time series of a beneficial allele (in green) and a neutral allele (in blue) arising at position 50 and spreading through space is shown. The frequency of each allele at a given spatial position is indicated along the y axis.

Fig. 2.

Selective sweeps in continuous space. a) Example of simulated allele with selection coefficient 0.1 sweeping through a population with a dispersal distance (σ) of $0.40$. Individuals carrying the sweeping allele are colored in green. b) Joint frequency-area trajectories of sweeping alleles at dispersal distances $0.40$, $1.25$, and $4.0$. Line color represents the strength of selection; allele area is measured as the area of the polygon encompassing allele carriers.

Fig. 3.

Power to detect selective sweeps using continuous space. a) Example genome-wide joint distribution between frequency and area of all variants 195 time steps after the introduction of an allele with a selection coefficient of $s=0.1$ in a population with a mean per-generation dispersal distance of $0.40$. The black line represents the tenth percentile cutoff for SF outliers; the sweeping allele is highlighted in green. b) Example genome-wide windowed analysis of the ratio of SF outliers to nonoutliers at the same time point in the same simulation. The dotted line represents the tenth percentile cutoff for WSF outliers; the green triangle highlights the locus of the sweeping allele. c) Probability of detecting the sweeping allele as a SF outlier as the strictness of the outlier cutoff increases along the x axis. Panels represent σ values $0.40$, $1.25$, and $4.0$; line color represents the strength of selection. The dashed gray line represents the proportion of the genome identified as SF outliers. d) Probability of detecting the sweeping allele as a WSF outlier in the same simulations; the dashed gray line represents the proportion of the genome identified as WSF outliers.

Fig. 4.

Joint distribution between frequency and area of all variants within the Anopheles gambiae genome. Each point represents a genomic variant; the point’s color indicates its annotation. Outlier variants highlighted in vignettes are colored and outlined. Black lines represent SF outlier cutoffs for each of the major inversions and the noninverted portion of the genome, respectively. INSET: Sampling locations of An. gambiae samples. The size of the points represents the number of samples at each location.

Fig. 5.

Genome-wide windowed analysis of the ratio of SF outliers to nonoutliers. Flat horizontal lines represent WSF cutoffs for each of the major inversions and the noninverted portion of the genome, respectively. Known loci associated with insecticide resistance and outlier variants highlighted in vignettes are labeled.

Fig. 6.

Impacts of outlier variants highlighted in vignettes. a) Folding impacts of cytochrome P450 variant CYP4H27:Y274H. The left and right panels show position 274 and local structure for the ancestral tyrosine and the derived histidine, respectively. Relevant amino acids (S324, S326, E327) and hydrogen bonds are shown and the new hydrogen bond is highlighted in yellow. b) Intronic outlier SNPs at the CUB domain-containing protein locus. Variants are shown at their genomic locations and colored by area; their y-axis position represents their frequency. Coding sequences and intronic segments for genes at the locus are annotated below; arrows represent the direction of transcription. c) Structural locations of nonsynonymous variants in gustatory receptors. GRs are oriented with their extracellular domain upward and intracellular domain downward; chains are colored from light green at the N terminus to dark green at the C terminus and nonsynonymous mutations are highlighted in yellow.