Jose Miguel Soriano

IRTA - Institute of Agrifood Research and Technology

Dr. Jose Miguel Soriano is a scientist at IRTA. He is an experienced researcher in molecular breeding and genetic mapping. His previous work has been focused on the development of molecular markers, linkage mapping, and QTL analysis of important traits for fruit and maize breeding. He has carried postdoctoral research in internationally recognized groups at Plant Research International (Wageningen UR) and the University of Bologna. Currently his research activities are focused on association mapping and QTL analysis of important traits for wheat improvement, mainly to enhance drought tolerance in Mediterranean environments. He is author of more than 40 SCI papers and a number of international contributions to conferences. He has supervised several PhD and master theses and leaded different research projects.


Sustainable Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), Lleida, Spain

Bread wheat (Triticum aestivum L.) is a major staple food crop, with over 700 million tonnes being harvested annually. Wheat grain is a basic ingredient of many foods worldwide, with milled flour being used for a variety of products such as leavened and unleavened breads, noodles, cookies, cakes, pastries, and many other foods that provide 18% of the calories and 20% of the protein in the human diet globally ( The exponential rise of the human population in the last decades predicts a population of more than 9 billion by 2050, which will entail a 60% increase in the global wheat demand that year. Covering this rising need will be a huge challenge, particularly considering the 6%–25% decrease in wheat productivity projected by climate change models depending on the region. The wheat-growing area represents 27% of the arable land within the Mediterranean Basin, and global climate change predictions suggest increases in the mean temperature of the region of 4 – 5 ◦C in the next few decades, with a decrease in precipitation of 25%–30%, which will probably cause yield reductions of 24% or more. Ensuring the food security in the coming decades will require a combination of improved varieties and agronomic practices warranting environmental sustainability.

Due to the green revolution at the end of the 1960s, breeding was focused on increasing grain yield, which originated in a reduction in the quality parameters of the new cultivars, particularly protein content, which is considered a significant limitation for the baking industry. To enhance the quality characteristics by increasing genetic diversity, the use of wild relatives and landraces is a valuable approach in pre-breeding activities. Landraces are considered a natural reservoir of genetic variation within the species and an invaluable source of new alleles to widen the genetic variability in breeding populations as they were selected during their migration process and are well adapted to their regions of origin.

Using a panel of 153 bread wheat Mediterranean landraces, this study analyses the relationship between the climate of the countries where wheat landraces are specifically adapted, and their agronomic and grain quality characteristics. Regions with similar climate within the Mediterranean Basin were identified based on long-term climatic data of the 23 countries origin of the landraces.

The panel was genotyped with 13177 SNP markers and was grown on field experiments for two years under rainfed conditions in north-east Spain and 14 agronomic and 11 grain-quality traits were assessed. Great phenotypic variability was found in the collection. The agronomic performance of the landraces varied according to the climate of the four climatic regions identified within the Mediterranean Basin (south-east, south-west, north coast, and north-Balkan), which gradually varies from warm and dry to wet and cold. Cycle length, grain-filling rate and yield increased, but grain-filling duration decreased from south-eastern landraces to north Balkan ones. Grain weight accounted for 25% of yield variations and was lowest in landraces from the south-eastern region. Grain quality showed no geographical pattern related to the climatic region, however landraces from Bosnia-Herzegovina, Bulgaria and Romania differed from the rest in their high gluten strength (W), loaf volume (LV), mixing time (MT) and grain hardness, while opposite attributes were found in accessions from Jordan, Lebanon, and Cyprus. Landraces from south-western Mediterranean countries had low MT, alveograph-peak, W and LV. Molecular analyses revealed that genetic structure was mostly influenced by high temperatures before anthesis and rainfall, solar radiation and sunshine after anthesis.

Identifying the genetic architecture for yield and key quality traits is of great interest for breeding in order to detect the genome regions involved in trait variation. In this sense, the use of landraces represents an important source of natural allelic variation for this purpose. Thus, a genome-wide association analysis (GWAS) following a MLM procedure was performed to detect QTLs in bread wheat for quality and agronomic traits. A total of 53 QTL hotspots containing 165 significant marker-trait associations (MTAs) were located across the genome for quality and agronomical traits. The major specific QTL hotspots for quality traits were QTL_3B.3 (13 MTAs with a mean PVE of 8.2%) and QTL_4A.3 (15 MTAs, mean PVE of 11.0%), and for yield-related traits were QTL_2B.1 (8 MTAs, mean PVE of 7.4%) and QTL_4B.2 (5 MTAs, mean PVE of 10.0%). A search for candidate genes (CG) identified 807 gene models within the QTL hotspots. Ten of these CGs were expressed specifically in grain supporting the role of identified QTLs in landraces, associated to bread wheat quality traits and grain formation.

To evaluate the accuracy of genomic selection (GS) in Mediterranean wheat landraces to improve quality and agronomic traits a cross-validation approach within the collection was performed.

Accuracies ranged from -0.03 to 0.64 for quality and 0.46 to 0.65 for agronomic traits. In addition, the complexity of the analyses of some quality traits is a bottleneck in screening large germplasm collections. Multiple linear regression equations were developed to predict a complex trait such as loaf volume using a maximum of three simpler traits. In this way, five prediction equations using the phenotypic data were developed to predict bread loaf volume in landraces. The prediction ability varied from 0.67 to 0.82 depending on the complexity of the traits considered to predict loaf volume.