Biology subjects

Sharo, A. G.

Publications and source records attributed to Sharo, A. G..

3 recordsLinked to original sources

bioRxiv2024

GWAC: A machine learning method to identify functional variants in data-constrained species

As environments change, the ability of species to adapt depends on the functional variation they harbor. Identifying these functional variants is an important challenge in conservation genetics. Due to the limited data available for most species of conservation interest, genome-wide selection scans that link specific genetic variants with a phenotype are not feasible. However, functional variants may still be identified by considering predicted consequence, evolutionary conservation, and other sequence-based features. We developed Genome-Wide vAriant Classification (GWAC), a supervised machine learning framework to prioritize genome-wide variants by functional impact. GWAC requires only features that can be generated from an annotated genome. We evaluate GWAC by first using a set of human data constrained to match what may be available for threatened species. We find that GWAC weights features more heavily that are known to be predictive of functional variation and prioritizes both single nucleotide variants and indels, consistent with mutational constraint found in population genetics studies. GWAC performs nearly as well as CADD, a leading genome-wide predictor in humans that uses substantially more features and data that are typically available only for model organisms. While it is not possible to empirically evaluate GWAC on a species for which no functional variants are known, we find that a version of GWAC generated for the greater prairie chicken (Tympanuchus cupido pinnatus) weights features similarly to our human version. We compare the results of using a species-specific variant impact predictor against lifting-over variants from a closely related model organism and find that the species-specific approach retains functional variants that are lost during lift-over. We anticipate GWAC could be used to estimate conservation metrics such as genetic load and adaptive capacity, while also enabling researchers to identify individual variants responsible for adaptive phenotypes.

genomics↗

bioRxiv2024

Recent adaptation in an imperiled salmonid revealed by museum genomics

Steelhead/rainbow trout (Oncorhynchus mykiss) is an imperiled salmonid with two main life history strategies: migrate to the ocean or remain in freshwater. Domesticated hatchery forms of this species have been stocked into almost all California waterbodies, possibly resulting in introgression into natural populations and altered population structure. We compared whole-genome sequence data from contemporary populations against a set of museum population samples of steelhead from the same locations that were collected prior to most hatchery stocking. We observed minimal introgression and few steelhead-hatchery trout hybrids despite a century of extensive stocking. Our historical data show signals of introgression with a sister species and indications of an early hatchery facility. Finally, we found that migration-associated haplotypes have become less frequent over time, a likely adaptation to decreased opportunities for migration. Since contemporary migration-associated haplotype frequencies have been used to guide species management, we consider this to be a rare example of shifting baseline syndrome that has been validated with historical data. We suggest cautious optimism that a century of hatchery stocking has had minimal impact on California steelhead population genetic structure, but we note that continued shifts in life history may lead to further declines in the ocean-going form of the species.

genomics↗

bioRxiv2020

StrVCTVRE: A supervised learning method to predict the pathogenicity of human structural variants.

BackgroundWhole genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and structural variant detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. ResultsTo address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. ConclusionsWe anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in patients where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is available at https://compbio.berkeley.edu/proj/strvctvre/.

genomics↗