AUTHOR=Yannam Venkata Rami Reddy , Lopes Marta , Guzman Carlos , Soriano Jose Miguel 

TITLE=Uncovering the genetic basis for quality traits in the Mediterranean old wheat germplasm and phenotypic and genomic prediction assessment by cross-validation test

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1127357

DOI=10.3389/fpls.2023.1127357

ISSN=1664-462X

ABSTRACT=The release of new wheat varieties is based on two main characteristics, grain yield and quality, to 12 meet the consumer’s demand. Identifying the genetic architecture for yield and key quality traits has 13 wide attention for genetic improvement to meet the global requirement. In this sense, the use of 14 landraces represents an impressive source of natural allelic variation. In this study, a genome-wide 15 association analysis (GWAS) with PCA and kinship matrix was performed to detect QTLs in bread 16 wheat for fifteen quality and agronomic traits using 170 diverse landraces from 24 Mediterranean 17 countries in two years of field trials. A total of 53 QTL hotspots containing 165 significant marker-18 trait associations (MTAs) were located across the genome for quality and agronomical traits except 19 for chromosome 2D. The major specific QTL hotspots for quality traits were QTL_3B.3 (13 MTAs 20 with a mean PVE of 8.2%) and QTL_4A.3 (15 MTAs, mean PVE of 11.0%), and for yield-related 21 traits were QTL_2B.1 (8 MTAs, mean PVE of 7.4%) and QTL_4B.2 (5 MTAs, mean PVE of 22 10.0%). A search for candidate genes (CG) identified 807 gene models within the QTL hotspots. Ten 23 of these CGs were expressed specifically in grain supporting the role of identified QTLs in 24 Landraces, associated to bread wheat quality traits and grain formation. A cross-validation approach 25 within the collection was performed to calculate the accuracies of genomic prediction for quality and 26 agronomical traits, ranging from -0.03 to 0.64 for quality and 0.46 to 0.65 for agronomic traits. In 27 addition, five prediction equations using the phenotypic data were developed to predict bread loaf 28 volume in landraces. The prediction ability varied from 0.67 to 0.82 depending on the complexity of 29 the traits considered to predict loaf volume.