Evaluation of high-throughput phenotyping and genotyping for genomic
selection in alfalfa
Abstract
High-throughput phenotyping and genotyping have provided a vast source
of information for evaluating the genetic merit of different breeding
materials, but their implementation has been limited in alfalfa due to
the complexity of the genome and the perennial nature of the crop.
Vegetative indices (VIs) collected from an unmanned aerial vehicle (UAV)
equipped with multi-spectral camera can be used to study forage growth
and development throughout each growth cycle. Random regression models
could be implemented to fit such longitudinal phenotypes like VIs
collected over time to estimate growth curves, to access genetic
variation in growth and the relations of VIs to end-use traits like
forage yield and quality. The main objectives of this project are (1) to
incorporate aerial high-throughput phenotyping to predict performance
and genetic merit of the breeding materials, (2) to fit longitudinal
random regression model to estimate genotype-specific growth curves, and
(3) to develop a genotyping approach to estimate genetic relationships
between alfalfa populations. The imaging of the alfalfa experimental
trials was done every ~ 4.3 days throughout the growing
season. The Vegetative indices (VIs) close to the harvest date were
extracted and used to fit multi-traits models to evaluate the genetic
correlations between VIs and forage biomass yield. The VIs considered
were Normalized Vegetative index (NDVI), Green NDVI, Red Edge NDVI,
simple ratio of Near Infrared to Red (NIR), and Digital Surface Map
(DSM). The preliminary results showed highest correlation of Green NDVI
and biomass yield (0.4053, 0.7875, and 0.6779), followed by Rededge NDVI
and biomass yield (0.417, 0.7898, and 0.6417) for the first, second and
third cuttings respectively for the experimental trial located at
Helfer, Ithaca. Heritability estimates ranging from 0.03 to 0.75 was
observed indicating the presence of genetic variation in these VIs.
Pairwise Fst values estimated from population-level genotyping approach
was found to be efficient estimates of genetic relatedness between
populations. Random regression models with a linear spline function and
legendre polynomials including other environmental trials are under
evaluation to see the potentiality of these models to fit VIs from
multiple time points.