Haplotype tagging reveals parallel formation of hybrid races in two butterfly species
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 “haplota...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 25; pp. 1 - 10 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
22.06.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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 linkedread 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 1J.I.M., P.A.S., and M.K. contributed equally to this work. Edited by Molly Przeworski, Columbia University, New York, NY, and approved May 5, 2021 (received for review July 20, 2020) Author contributions: J.I.M., P.A.S., M.K., C.R., W.O.M., C.D.J., and Y.F.C. designed research; J.I.M., P.A.S., M.K., O.B.P., N.J.N., J.R.B., C.R., W.O.M., C.D.J., and Y.F.C. performed research; J.I.M., P.A.S., M.K., R.W.D., A.D., I.A., O.B.P., N.J.N., J.R.B., C.R., W.O.M., C.D.J., and Y.F.C. contributed new reagents/analytic tools; J.I.M., P.A.S., M.K., R.W.D., N.H.B., C.D.J., and Y.F.C. analyzed data; and J.I.M., N.H.B., C.D.J., and Y.F.C. wrote the paper with contributions from all authors. 2C.D.J. and Y.F.C. contributed equally to this work. |
ISSN: | 0027-8424 1091-6490 1091-6490 |
DOI: | 10.1073/pnas.2015005118 |