Performing Department
(N/A)
Non Technical Summary
While zoysiagrasses are recognized for their low input requirements and seeded cultivars are preferred by the industry for their cost-effectiveness and ease of distribution, a lack of understanding on the genetic components underlying seed yield, dormancy and germination have hindered breeding efforts. Several public and private programs have been working on the development of seeded cultivars; however, to date only two seeded cultivars are commercially available. In this project, we aim to develop a diversity panel for zoysiagrass consisting of approximately 250 genotypes to 1) understand ranges of genetic variation for seed traits, 2) increaseour understanding on the genetic components of seed yield and germination, and 3) initiate development of high-throughput phenotyping tools. We will perform genome sequencing and SNP calling on this diversity panel. Additionally, we will perform extensive phenotyping on seed traits, including germination rates, under controlled and field conditions. Genotypic and phenotypic data will be combined in a Genome Wide Association Study to identify genetic components underlying seed yield as well as germination efficiency. Additionally, we will investigate the use of imaging and machine learning techniques for counting seedheads and seeds. Data generated in this studywill provide a foundational understanding of the components of yield that could be used by breeders for making informed and more targeted selection decisions in breeding for seeded zoysiagrass cultivars.
Animal Health Component
60%
Research Effort Categories
Basic
30%
Applied
60%
Developmental
10%
Goals / Objectives
In this project, we aim to develop a diversity panel for zoysiagrass consisting of 250 genotypes to 1) understand ranges of genetic variation for seed traits, 2) initiate development of high-throughput phenotyping tools, and 3) perform genome sequencing and SNP calling on this panel. Data will be combined for GWAS to increase our understanding on the genetic components of seed yield.Obj 1:Phenotypic evaluation of the zoysiagrass diversity panel for seed yield components under field conditions and germination rates under controlled conditionsObj 2:Preliminary assessment on the use of imaging and machine learning for high-throughput phenotyping of inflorescence and seedhead traits in zoysiagrassObj 3:Develop genomic resources for the zoysiagrass diversity panel
Project Methods
Obj 1:1) Field Evaluations: A germplasm panel of 250 diverse zoysiagrass genotypes including plant introductions, commercial cultivars and breeding lines from the zoysiagrass breeding programs at NCSU and TAMUS will be established in summer 2024 in Raleigh, NC and Dallas, TX. The trial will be arranged as partially replicated experimental designs (PREP) with 40% of the genotypes replicated. During the establishment year, the percent green cover will be evaluated using Unmanned Aerial Systems (UAS) to assess genotype persistence. In spring 2025, flowering time will be recorded and inflorescence abundance will be collected visually (1-9 scale, 1 = no inflorescences and 9 = 100% plot cover by inflorescences) and using digital imaging. Then, inflorescences will be harvested, dried, and imaged to measure inflorescence morphology including seed head length, width, area, and number of seeds per seed head. Seeds will be harvested and bulked by parent, and evaluated for final count, individual and average seed area, length, width, volume, surface area, and perimeter using the WinSEEDLE image analysis system (Regent Instruments, Inc.). 2) Germination Tests: Seed viability will be assessed by germination tests according to Qian et al. (2013). The number of replications and seeds/replication will be determined according to seed availability. All data will be analyzed using a mixed model approach, implemented in ASReml-R, in order to verify the significance of genetic effect and get genotype Best Linear Unbiased Estimators (BLUEs).Obj 2:Modern deep learning methods that utilize convolutional neural networks (CNNs) are quickly becoming a popular alternative to visual tasks that involve counting features or objects within digital images. To develop the model, images will be collected from a subset of genotypes under controlled lighting conditions within the greenhouse environment. Images will be collected weekly from the start of flowering to 100% seedhead maturity for all genotypes. Inflorescence and seedheads will be labeled using open-source AI-assisted labeling tools and used to train and validate the deep-learning model. Labels will differentiate seedheads from inflorescences and be used to track flowering time and seedhead development across genotypes. Seedhead morphology will be calculated from the polygon masks and validated against measurements from the WinSEEDLE image analysis system in Objective 1. To validate and compare models the datasets will be divided into training, validation, and testing, and performance assessed using appropriate evaluation metrics. Additionally, separate color photographs of field plots will be collected and used to train and test the ability to phenotype seedhead development and morphology at both the plot and field scale using a CNN approach.Obj 3:Genomic DNA will be extracted from genotypes in the diversity panel and sequenced via genotyping-by-sequencing (GBS) on Illumina NovaSeq 6000 SP 150bp PE lane at the NCSU Genomic Sciences Laboratory (GSL). Cleaned sequencing reads will be mapped to the Nagirizaki ZJN r1.1 reference genome using BWA. GATK and TASSEL pipelines will be used to discover SNP variation. Increased depth of coverage (>15) will be applied to avoid error rates during SNP calling in allotetraploid zoysiagrass, which has been successfully used in our previous GBS sequencing of zoysiagrass populations of similar size. Population structure and genetic diversity will be analyzed based on SNP data using PLINK. GWAS will be conducted using TASSEL 5 or other packages within R or Python, designed for mixed linear modeling (MLM), to identify associations for seed trait data collected in Obj 1.