On the potential of Angiosperms353 for population genomics.
Slimp, M.; Williams, L. D.; Hale, H.; Johnson, M. G.
Show abstract
Targeted sequencing using Angiosperms353 has emerged as a low-cost tool for phylogenetics, with early results spanning scales from all flowering plants to within genera. The use of universal markers at narrower scales--within populations-- would eliminate the need for specific marker development while retaining the benefits of full-gene sequences. However, it is unclear whether the Angiosperms353 markers provide sufficient variation within species to calculate demographic parameters. Using herbarium specimens from a 50-year-old floristic survey of Guadalupe Mountains National Park, we sequenced 95 samples from 24 species using Angiosperms353. We adapted a data workflow to process targeted sequencing data that calls variants within each species and prepares data for population genetic analysis. We calculated genetic diversity using standard metrics (e.g. heterozygosity, Tajimas D). Angiosperms353 gene recovery was associated with genomic library concentration, with limited phylogenetic bias. We identified over 1000 segregating variants with zero missing data within 22 of 24 species. A subset of these variants, which were filtered to remove linked SNPs, revealed high heterozygosity in many species. Tajimas D calculated within each species indicated a moderate number of markers potentially under selection and identified evidence of population bottlenecks in some species. Despite sequencing few individuals per species, the Angiosperms353 markers contained sufficient variation calculate demographic parameters. Larger sampling within species will allow for estimating gene flow and population dynamics in any angiosperm. Our study will benefit conservation genetics, where Angiosperms353 provides universal repeatable markers, low missing data, and haplotype information.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Patterns of genetic variation in a prairie wildflower, Silphium integrifolium, suggest a non-prairie origin and locally adaptive variation. 97%
- Diversification, Spread, and Admixture of Octoploid Strawberry in the Western Hemisphere 96%
- Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis (Poaceae) using genetic and clonal diversity 96%
Similar papers in this journal
- HybPhaser: a workflow for the detection and phasing of hybrids in target capture datasets 97%
- TagSeq for gene expression in non-model plants: a pilot study at the Santa Rita Experimental Range NEON core site 95%
- Targeted sequence capture array for phylogenetics and population genomics in the Salicaceae 95%
Similar papers in this journal
- Towards Stewardship of Wild Species and Their Domesticated Counterparts: A Case Study in Northern Wild Rice (Zizania palustris L.) 96%
- Covering the bases: population genomic structure of Lemna minor and the cryptic species L. japonica in Switzerland 96%
- Examining the molecular mechanisms contributing to the success of an invasive species across different ecosystems 94%
Similar papers in this journal
- Testing for evolutionary change in restoration: a genomic comparison between ex situ, native and commercial seed sources of Helianthus maximiliani 95%
- Identifying and testing marker-trait associations for growth and phenology in three pine species: implications for genomic prediction 95%
- Estimation of contemporary effective population size in plant populations: limitations of genomic datasets 94%
Similar papers in this journal
- A haplotype-complete chromosome-level assembly of octoploid Urochloa humidicola cv. Tully reveals multiple genomic compositions and evolutionary histories in the species 95%
- High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers 94%
- Genomic diversity and evolution in the Hawaiian Islands endemic Kokia (Malvaceae) 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.