Biology subjects

Sheta, L. M.

Publications and source records attributed to Sheta, L. M..

3 recordsLinked to original sources

bioRxiv2023

Environment predicts the maintenance of reproductive isolation in a mosaic hybrid zone of rubber rabbitbrush

Widely distributed plants of western North America experience divergent selection across environmental gradients, have complex histories shaped by biogeographic barriers and distributional shifts, and often illustrate continuums of reproductive isolation. Rubber rabbitbrush (Ericameria nauseosa) is a foundational shrub species that occurs across diverse environments of western North America. Its remarkable phenotypic diversity is currently ascribed to two subspecies - E. n. nauseosa and E. n. consimilis - and 22 named varieties. We analyzed how genetic variation is partitioned across subspecies, varieties, and environment using high throughput sequencing of reduced representation libraries. We found clear evidence for divergence between the two subspecies, despite largely sympatric distributions. Numerous locations exhibiting admixed ancestry were not geographically localized but were widely distributed across a mosaic hybrid zone. The occurrence of hybrid and subspecific ancestries was strongly predicted by environmental variables as well as the proximity to major ecotones between ecoregions. Although this repeatability illustrates the importance of environmental factors in shaping reproductive isolation, variability in the outcomes of hybridization also indicated these factors likely differ across ecological contexts. There was mixed evidence for the evolutionary cohesiveness of varieties, but several genetically distinct and narrow endemic varieties exhibited admixed subspecific ancestries, hinting at the possibility for transgressive hybridization to contribute to phenotypic novelty and the colonization of new environments in E. nauseosa.

evolutionary biology↗

bioRxiv2021

Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub

The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic-environment association (GEA) analyses using both redundancy analysis (RDA) and a machine-learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure, and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.

evolutionary biology↗

bioRxiv2019

The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretations of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications. Currently, the CIViC knowledge model consists of four main components: Genes, Variants, Evidence Items, and Assertions. Each component has an associated knowledge model and methods for curation. Gene and Variant data contextualize the genomic region(s) involved in the clinical statement. Evidence Items provide structured associations between variants and their clinically predictive/therapeutic, prognostic, diagnostic, predisposing, and functional implications. Finally, CIViC Assertions summarize collections of CIViC Evidence Items for a specific Disease, Variant, and Clinical Significance with incorporation of clinical and technical guidelines. Here we present the CIViC knowledge model, curation standard operating procedures, and detailed examples to support community-driven curation of cancer variants.

bioinformatics↗