Search bioRxivSearch

Biology subjects

Jiggins, C.

Publications and source records attributed to Jiggins, C..

3 recordsLinked to original sources

Recombination rate variation shapes barriers to introgression across butterfly genomes

Hybridisation and introgression can dramatically alter the relationships among groups of species, leading to phylogenetic discordance across the genome and between populations. Introgression can also erode species differences over time, but selection against introgression at certain loci acts to maintain post-mating species barriers. Theory predicts that species barriers made up of many loci throughout the genome should lead to a broad correlation between introgression and recombination rate, which determines the extent to which selection on deleterious foreign alleles will affect neutral alleles at physically linked loci. Here we describe the variation in genealogical relationships across the genome among three species of Heliconius butterflies: H. melpomene, H. cydno and H. timareta, using whole genomes of 92 individuals, and ask whether this variation can be explained by heterogeneous barriers to introgression. We find that species relationships vary predictably at the chromosomal scale. By quantifying recombination rate and admixture proportions, we then show that rates of introgression are predicted by variation in recombination rate. This implies that species barriers are highly polygenic, with selection acting against introgressed alleles across most of the genome. In addition, long chromosomes, which have lower recombination rates, produce stronger barriers on average than short chromosomes. Finally, we find a consistent difference between two species pairs on either side of the Andes, which suggests differences in the architecture of the species barriers. Our findings illustrate how the combined effects of hybridisation, recombination and natural selection, acting at multitudes of loci over long periods, can dramatically sculpt the phylogenetic relationships among species.

evolutionary biology

Genetic dissection of assortative mating behavior

The evolution of new species is made easier when traits under divergent ecological selection are also mating cues. Such ecological mating cues are now considered more common than previously thought, but we still know little about the genetic changes underlying their evolution, or more generally about the genetic basis for assortative mating behaviors. The warning patterns of Heliconius melpomene and H. cydno are under disruptive selection due to increased predation of non-mimetic hybrids, and are used during mate recognition. We carried out a genome-wide quantitative trait locus (QTL) analysis of preference behaviors between these species and showed that divergent male preference has a simple genetic basis. Three QTLs each explain a large proportion of the differences in preference behavior observed between the parental species. Two of these QTLs are on chromosomes with major color pattern genes, including one that is tightly associated with the gene optix. Different loci influence different aspects of attraction, suggesting that behavioral isolation in Heliconius involves the evolution of independently segregating modules, similar to those for the corresponding wing pattern cues. Hybridization and subsequent sharing of wing pattern loci has played an important role during adaptation and speciation in Heliconius butterflies. The existence of large effect preference loci could similarly assist the evolution of novel behavioral phenotypes through recombination and introgression, and should facilitate rapid speciation.

evolutionary biology

Patternize: An R Package For Quantifying Color Pattern Variation

O_LIThe use of image data to quantify, study and compare variation in the colors and patterns of organisms requires the alignment of images to establish homology, followed by color-based segmentation of images. Here we describe an R package for image alignment and segmentation that has applications to quantify color patterns in a wide range of organisms.\nC_LIO_LIpatternize is an R package that quantifies variation in color patterns obtained from image data. patternize first defines homology between pattern positions across specimens either through manually placed homologous landmarks or automated image registration. Pattern identification is performed by categorizing the distribution of colors using an RGB threshold, k-means clustering or watershed transformation.\nC_LIO_LIWe demonstrate that patternize can be used for quantification of the color patterns in a variety of organisms by analyzing image data for butterflies, guppies, spiders and salamanders. Image data can be compared between sets of specimens, visualized as heatmaps and analyzed using principal component analysis (PCA).\nC_LIO_LIpatternize has potential applications for fine scale quantification of color pattern phenotypes in population comparisons, genetic association studies and investigating the basis of color pattern variation across a wide range of organisms.\nC_LI

bioinformatics