Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava

Authors: LONG, EVAN - Cornell University; ROMAY, MARIA CINTA - Cornell University; RAMSTEIN, GUILLAUME - Aarhuis University; Buckler, Edward - Ed; ROBBINS, KELLY - Cornell University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/6/2022
Publication Date: 1/9/2023
Citation: Long, E.K., Romay, M., Ramstein, G., Buckler IV, E.S., Robbins, K.R. 2023. Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava. Frontiers in Plant Science. 13:1041925. https://doi.org/10.3389/fpls.2022.1041925.
DOI: https://doi.org/10.3389/fpls.2022.1041925

Interpretive Summary: Cassava is a root crop that is a major source of calories for over 500 million people worldwide. Domestication and generations of clonal reproduction through stem cuttings have resulted in an accumulation of many deleterious mutations. Deleterious mutations are any mutations that occur in the genome that reduce overall plant fitness and are normally purged through sexual reproduction and selection. In this study, we aimed to understand these deleterious mutations and test possible methods that leverage this information for better breeding decisions.

Technical Abstract: Introduction: Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression. Methods: To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava.