Computational Methods for Phenotype Prediction to Assist Plant Breeding

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To meet the food demands of an increasing population, crop breeding efficiency needs to be substantially improved. The long-term goal of my research program is to develop a suite of Artificial Intelligence tools to assist plant breeding by utilizing the whole genome level information (genotype) as well as environmental factors. The short-term goals in the next five years are to predict crops' physical properties or traits (phenotype) using genotype data only. This will provide breeders with effective trait selections and accelerate their breeding programs. Phenotype prediction is challenging because the number of features in genotype data is significantly more than the number of samples. Existing methods usually require extraordinarily large computational resources or fail to find the linkage between genotype and phenotype. In this proposal, multiple strategies will be developed to reduce the number of features, increase the sample size, and improve the prediction accuracy. Objective 1 is to reduce the number of features in the genotype data using advanced sampling algorithms. We will modify existing algorithms to make them suitable for large imbalanced data (like the plant) without creating selection bias. The performance of the algorithms will be evaluated by Arabidopsis thaliana, lentil, and wheat data. The resulting features will serve as a feasible input to a prediction algorithm with modest computational resources required. Objective 2 is to increase the sample size by developing a synthetic data generator that can produce data with similar characteristics to plant data. A set of statistical criteria will be developed to measure the similarities between synthetic and real data. The synthetic data will be generated using a practical machine learning model. The data generator will provide sufficient training data (together with results from Objective 1) for the phenotype prediction model. It can also help reduce the need to establish extremely large collections of plant genotype/phenotype data. Objective 3 is to incorporate results from Objectives 1&2 and predict plant phenotypes using a Deep Learning (DL) model. The model developed from my previous work has proven to be effective on bacteria data (which has a small number of features) and will be modified to fit for the plant data. Further, an interpretation layer will be added to the DL model to explain the results. Domain experts can read into the interpretable information and validate the predictions. The impact will be in three aspects: 1. It will advance the development of data comparison standards, by providing a set of statistical criteria for similarity measurement. 2. It will speed up and enhance the selection in plant breeding, by suggesting genomic characteristics that are reliably associated with plant phenotypes. 3. It will contribute to solving the problems that have large-feature-small-sample data, by developing DL models that can be generalized to other fields.