Genetic variation segregates as linked sets of variants or haplotypes. Haplotypes and linkage are central to genetics and underpin virtually all genetic and selection analysis. Yet, genomic data often omit haplotype information due to constraints in sequencing technologies. Here, we present “haplotagging,” a simple, low-cost linked-read sequencing technique that allows sequencing of hundreds of individuals while retaining linkage information. We apply haplotagging to construct megabase-size haplotypes for over 600 individual butterflies (Heliconius erato and H. melpomene), which form overlapping hybrid zones across an elevational gradient in Ecuador. Haplotagging identifies loci controlling distinctive high- and lowland wing color patterns. Divergent haplotypes are found at the same major loci in both species, while chromosome rearrangements show no parallelism. Remarkably, in both species, the geographic clines for the major wing-pattern loci are displaced by 18 km, leading to the rise of a novel hybrid morph in the center of the hybrid zone. We propose that shared warning signaling (Müllerian mimicry) may couple the cline shifts seen in both species and facilitate the parallel coemergence of a novel hybrid morph in both comimetic species. Our results show the power of efficient haplotyping methods when combined with large-scale sequencing data from natural populations.
One-sentence summary:Haplotagging, a novel linked-read sequencing technique that enables whole genome haplotyping in large populations, reveals the formation of a novel hybrid race in parallel hybrid zones of two co-mimicking Heliconius butterfly species through strikingly parallel divergences in their genomes. Short title:Haplotagging reveals parallel formation of hybrid races Abstract Genetic variation segregates as linked sets of variants, or haplotypes. Haplotypes and linkage are central to genetics and underpin virtually all genetic and selection analysis. And yet, genomic data often lack haplotype information, due to constraints in sequencing technologies. Here we present "haplotagging", a simple, low-cost linked-read sequencing technique that allows sequencing of hundreds of individuals while retaining linkage information. We apply haplotagging to construct megabasesize haplotypes for over 600 individual butterflies (Heliconius erato and H. melpomene), which form overlapping hybrid zones across an elevational gradient in Ecuador. Haplotagging identifies loci controlling distinctive high-and lowland wing color patterns. Divergent haplotypes are found at the same major loci in both species, while chromosome rearrangements show no parallelism. Remarkably, in both species the geographic clines for the major wing pattern loci are displaced by 18 km, leading to the rise of a novel hybrid morph in the centre of the hybrid zone.We propose that shared warning signalling (Müllerian mimicry) may couple the cline shifts seen in both species, and facilitate the parallel co-emergence of a novel hybrid morph in both co-mimetic species. Our results show the power of efficient haplotyping methods when combined with large-scale sequencing data from natural populations.
Meiotic recombination rates vary across the genome, often involving localized crossover hotspots and coldspots. Studying the molecular basis and mechanisms underlying this variation has been challenging due to the high cost and effort required to construct individualized genome-wide maps of recombination crossovers. Here we introduce a new method, called ReMIX, to detect crossovers from gamete DNA of a single individual using Illumina sequencing of 10X Genomics linked-read libraries. ReMIX reconstructs haplotypes and identifies the valuable rare molecules spanning crossover breakpoints, allowing quantification of the genomic location and intensity of meiotic recombination. Using a single mouse and stickleback fish, we demonstrate how ReMIX faithfully recovers recombination hotspots and landscapes that have previously been built using hundreds of offspring. ReMIX provides a high-resolution, high-throughput, and low-cost approach to quantify recombination variation across the genome, providing an exciting opportunity to study recombination among multiple individuals in diverse organisms.
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