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Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone

Cite this dataset

Sung, Cheng-Jung; Bell, Katherine L.; Nice, Chris C.; Martin, Noland H. (2017). Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone [Dataset]. Dryad. https://doi.org/10.5061/dryad.qg1p0

Abstract

Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 Single Nucleotide Polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative, and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian Genomic Cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect-size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I. hexagona alleles tending to be favored over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.

Usage notes

Funding

National Science Foundation, Award: DEB-1638773 DEB-0949424

Location

United States