Pulse Crop Breeding and Genetics:
Cultivar development is a crucial technological component of agriculture. Maintaining genetic gain means that farmers are able to access cultivars that can cope with disease, insect and environmental stresses which reduces dependency on pesticides and natural resources such as water. Cultivar development in turn provides consumers with a superior, healthier product that is produced sustainably. For example, in the case of common bean and its benefits, we study how micronutrient content and bioavailability can be improved in the seed.
High-throughput phenotyping (HTP) provides an avenue for the estimation of crop parameters without compromising sample size. Rather, due to the fast nature of HTP, we are capable to embark in developing larger breeding populations that can be phenotyped with equal or greater accuracy as destructive methods in the laboratory. Our objective is to develop a system for the detection, refinement and calibration of pulse crop parameters in the field. For example, we hypothesize that we can estimate pulse grain quality indicators such as protein, oil, specific gravity and seed structural stability by collecting hyperspectral images across environmental and genetic variance spaces. We often experience high amounts of data across multiple platforms, so we utilize deep learning and multilayer perceptron approaches to decompose hyperspectral image data into simpler models to predict the performance of the aforementioned traits.
Due to the large size of segregating populations, plant breeding programs are often limited by the ability to identify superior individuals. The advent of field-based sensors to measure traits coupled with artificial intelligence and machine learning is enabling breeders to select for conventional traits faster and identify novel traits that are otherwise not detectable by the human eye to develop and accelerate plant improvement. We focus on developing and implementing plant phenomics tools in plant breeding programs for agronomic traits as well as quality traits.
Genetics and Genomics of Complex Traits:
Breeding and achieving genetic gain for complex traits with low heritability is a difficult task given the intricate nature of plant genomes and the multiple interactions that occur among the large numbers of genes controlling complex traits as well as the dynamics of traits with low heritability within segregating populations. We study how complex traits and their effects can be predicted and modelled to establish the magnitude of achievable gain for cultivar development using statistical genetics and next generation sequencing. We conduct research in genomewide selection to enhance the rate of genetic gain in pulse crops.
Genetic Architecture of Plant Traits:
The elucidation of the genes involved in the control of complex traits allows for the development of tools that enable plant breeding programs to develop assays and routines to select for traits without environmentally induced error. We focus on using high-throughput next generation sequencing DNA marker arrays to target candidate quantitative trait loci (QTL) and identify new sources of genetic variation for breeding and selection.
Computational and statistical genomics:
A plant breeding program is a long-term, multi-dimensional and resource-intensive process which requires as much information as possible to prevent genetic gain progress to recede. In our lab, we work on developing tools for the computational assessment of the time, resources and genetic variance necessary to achieve the desired goals of a plant breeding program. Combining genotypic and phenotypic information of a particular species, we make use of genetic models and statistics to simulate the outcomes of hundreds of thousands of segregating populations and estimate genetic gain. This is coupled with seeking gains in selection accuracy with approaches such as genomewide selection.
Exploring genetic diversity:
Plant breeding relies heavily on genetic variance to make progress and solve specific limitations existing cultivars. The role of germplasm collections is critical to agriculture and the alleles contained within these is invaluable to breeding progress. However, germplasm collections contain large numbers of accessions and therefore require allele mining to identify the right genetic material to start the pre-breeding process. We use computational, field and genomic tools to survey collections and extract valuable information to guide introgression programs when genetic variation is limited among elite cultivars.