Genome-wide association studies of three-dimensional (3D) cassava root crowns and agronomic traits using partially inbred populations - Sunvittayakul, Pongsakorn; Wonnapinij, Passorn; Chanchay, Pornchanan; Wannitikul, Pitchaporn; Sathitnaitham, Sukhita; Phanthanong, Phongnapha; Changwitchukarn, Kanokpoo; Suttangkakul, Anongpat; Ceballos, Hernan; Gomez, Leonardo D; Kittipadakul, Piya; Vuttipongchaikij, Supachai (MDPI, 2024)
Cassava (Manihot esculenta Crantz) is a key industrial crop in Southeast Asia and a staple for food security in Africa, owing to its resilience and efficiency in starch production. This study aims to unravel the genetic determinants of specific cassava root crown traits, utilizing 3D modeling for yield-related attributes and root crown morphology. Phenotypic analysis of 130 partially inbred lines and their six parental lines from Thai commercial varieties revealed a range of root traits within populations showcasing robust correlations among various traits, particularly root size parameters and root weight. Genotyping-by-sequencing yielded a total of 29,361 SNP markers identified within the nuclear genome of cassava and shared across all genotypes. Genome-Wide Association Studies (GWAS) of these 136 genotypes identified 23 significant SNPs for six out of 11 root crown traits, including 3D root angle, 3D surface area, root number, 3D crown diameter, root weight, and 3D volume. We found one shared significant SNP between 3D crown diameter and root weight, and another shared SNP between root weight and 3D volume. Two closely linked SNPs were identified for 3D volume, root weight, and 3D surface area. Linkage disequilibrium (LD) analysis for each pair of SNP markers indicated the linkage decay point at approximately 60 kb. Based on LD decay and available transcriptome data, candidate gene identification highlighted 29 genes associated with five traits, providing an understanding of the genetic basis of cassava root crown traits. Our findings offer novel insights into cassava storage root traits as well as data for marker development and candidate gene identification.