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GenoDrawing: An autoencoder framework for image prediction from SNP markers by Federico Jurado-Ruiz; David Rousseau; Juan A. Botía; Maria José Aranzana is a scholarly article available to read on EtoBox.

What is GenoDrawing: An autoencoder framework for image prediction from SNP markers about?

Abstract Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN arc

Author

Federico Jurado-Ruiz; David Rousseau; Juan A. Botía; Maria José Aranzana

Publisher

Cold Spring Harbor Laboratory

Published

2023

Language

EN