Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom.
St. John's College, University of Cambridge, Cambridge CB2 1TP, United Kingdom.
Proc Natl Acad Sci U S A. 2021 Jun 22;118(25). doi: 10.1073/pnas.2015005118.
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 ( and ), 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.
遗传变异作为相关联的变体或单倍型进行分离。单倍型和连锁是遗传学的核心,几乎支撑着所有的遗传和选择分析。然而,由于测序技术的限制,基因组数据通常会忽略单倍型信息。在这里,我们提出了“haplotagging”,这是一种简单、低成本的连锁读取测序技术,允许对数百个人进行测序,同时保留连锁信息。我们应用 haplotagging 来构建超过 600 只蝴蝶( 和 )的兆碱基大小的单倍型,这些蝴蝶在厄瓜多尔的海拔梯度上形成重叠的杂交区。haplotagging 确定了控制独特的高海拔和低海拔翅膀颜色模式的基因座。在这两个物种中,相同的主要基因座都存在分歧的单倍型,而染色体重排则没有平行性。值得注意的是,在这两个物种中,主要翅膀图案基因座的地理渐变都被转移了 18 公里,导致在杂交区的中心出现了一种新的杂种形态。我们提出,共同的警告信号(缪勒拟态)可能会将这两个物种的渐变联系起来,并促进两个拟态物种中新的杂种形态的平行出现。我们的研究结果表明,当与来自自然种群的大规模测序数据相结合时,高效的单倍型分析方法具有强大的功能。