Recipient Organization
OREGON STATE UNIVERSITY
(N/A)
CORVALLIS,OR 97331
Performing Department
Coastal Oregon Marine Experiment Station
Non Technical Summary
The increasing contribution of aquaculture to human food production is a worldwide phenomenon. As aquaculture systems mature, breeding programs to improve seedstock and livestock lines are being established. Availability and affordability of genetic/genomic tools used in selective breeding programs have advanced extremely rapidly. In contrast, evaluation of performance benefits of selective breeding remains a bottleneck, leading to development of the "phenomics" concept. Phenomics seeks to improve the efficiency with which desirable traits, generally survival and growth characteristics, are detected. Emulating advancements in crop agriculture, this project will employ a novel implementation of rapid predictive phenotyping of oyster larvae to bolster breeding programs across the US. Open-access granular phenotype data will refine breeding decisions and improve how industry selects broodstock and farm sites for their offspring.
Animal Health Component
0%
Research Effort Categories
Basic
100%
Applied
0%
Developmental
0%
Goals / Objectives
Refine a low-cost and predictive tool to assure a viable blue economy aiding in selection of superior cross-bred oyster families, by means of their tolerance to current and pending challenges.(1) Use high-throughput mitochondrial bioenergetics and metabolite assays to investigate performance of Pacific oyster (Crassostrea gigas) larvae and spat from cross-bred families. (2) Identify Pacific oyster families with varied success after growth periods at industry farm sites. (3) Refine early phenotyping approaches to a single low-cost principal trait that predicts superior oyster families. (4) Implement high-throughput phenotyping approaches for East US oyster breeding programs of the Eastern oyster Crassostrea virginica.
Project Methods
A project evaluation plan will monitor the success of major project milestones and accomplishments as the following: (1) Detect metabolic signatures using high-throughput methods in live larvae and fixed spat of cross-bred Pacific oysters (Crassostrea gigas). (2) Identify Pacific oyster families with varied success after growth periods at industry farm sites. (3) Refine early phenotyping approaches to a single principal trait that predicts superior Pacific oyster families. (4) Test high-throughput phenomics in East US oyster breeding programs.This project will be conducted alongside existing breeding efforts of the Pacific oyster and Eastern oyster led by USDA and their partner institutions. In brief, adult oysters from pre-selected founder populations chosen based on genotyping will achieve a high level of genetic diversity in their progeny. Each cross-bred oyster family will be sampled during early development. Live larvae and fixed spat samples will be measured for metabolic traits using multiple low-cost and high-throughput methodologies. General performance traits of each oyster family (survival and growth) will be tracked at industry farm sites to test whether rapid screening of oyster progeny improves the ability to differentiate between high and low performers to increase return for growers. Importantly, this study will pioneer predictive mitochondrial phenomics in a non-model mollusk using an extracellular flux analyzer, a significant departure from usual methods typically reserved for human biomedical fields.Educational efforts include laboratory instruction and development of standard operating procedures for phenomics approaches using a non-model mollusk (as described above). Professional development and experiential learning opportunities will be provided to graduate and undergraduate students in agricultural sciences through instrumental training and workshops led by manufacturer and/or partner institution(s).Expected data types include laboratory and field work as digital (i.e. exported instrumental data, manual entries, assay photos) and non-digital media (lab notebooks, meeting proceedings, etc.). Raw digital data includes experiment metadata, exported instrumental records, and field collections. Metabolite assays will be recorded with a spectrophotometric plate reader which exports directly as .xlsx format compatible with open-source statistics software. Raw digital data for mitochondrial bioenergetics is exported as zipped files; unzipped raw .xlsx files are also compatible as described. Resazurin assays using a smartphone app to validate results from the plate reader will be stored as raw .jpeg images and .csv files compatible for data diagnostics and analysis as described. Statistical results will guide visualizations to effectively disseminate key findings for scientific journals and at conferences, and outreach events.