AUTHOR=Shariatipour Nikwan , Heidari Bahram , Tahmasebi Ahmad , Richards Christopher 

TITLE=Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.709817

DOI=10.3389/fpls.2021.709817

ISSN=1664-462X

ABSTRACT=Comparative genomics and meta-QTL (MQTL) analysis are important tools for identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield, grain quality traits and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for meta-analysis. The results showed that 449 QTLs were successfully projected on to the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes.  This consolidation of MQTLs resulted in a 3-fold reduction in the confidence interval (CI) compared with CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in non-telomeric regions of chromosomes. The majority of micronutrients MQTLs located on the A and D genome. The QTLs for thousand kernel weight were frequently associated with QTLs for grain yield and grain protein content with co-localization occurring at 55 % and 63%, respectively. The co-localization of QTLs for grain yield and grain Fe was 52% and for QTLs of grain Fe and  Zn was 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous (OrMQTLs) QTLs in wheat, rice and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CG’s (e.g. TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, TraesCS4D02G290900) had effects on micronutrients contents, yield and yield related traits. The mapping refinements leading to the identification of these CG’s provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.