Prediction of Sorghum bicolor Genotype from In-situ Images Using Autoencoder-identified SNPs

Abstract

Extensive genetic and phenotypic research is necessary for any effective plant breeding program. Such studies, however, require an immense amount of time and resources. In order to expedite the breeding process, we provide a novel method for rapid genotype prediction using in-situ images of plants. In this method, significant single nucleotide polymorphisms (SNPs) are first identified using a novel autoencoder framework with the goal of being more robust to false positive associations than standard genome wide association studies (GWAS). On-field images of various plant varieties are then used to train Convolutional Neural Networks (CNNs) to predict candidate alleles and validate phenotypic relationships. This image-based system allows for easy use on new plant varieties to gain real-time genetic information for better harvest prediction. The feasibility of our method for rapid genotype prediction was demonstrated on 345 Sorghum bicolor varieties with corresponding uncontrolled images 60 days after seed planting. Our autoencoder identified 4 significant SNPs that had an average allele classification accuracy of 70.58% on 68 previously unseen plant varieties.