Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population

Abstract

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.

Genome‐wide association study identified novel candidate loci affecting wood formation in Norway spruce

“…,m), and otherwise is equal to zero. The number of knots has to be properly defined to provide an accurate description of the data under investigation, while avoiding overadaptation to data (Li and Sillanp€ a€ a, 2015;Camargo et al, 2018). In our case, we found two knots most suitable to the time intervals investigated.…”

“…The general definition of a linear spline with multiple knots is as followswhich is continuous and where K i ( i = 1, … , m ; K 1 < K 2 … < K m ) are defined as knots, and ( t − K i ) + = ( t − K i ) if t > K i ( K i > 0; i = 1, … , m ), and otherwise is equal to zero. The number of knots has to be properly defined to provide an accurate description of the data under investigation, while avoiding overadaptation to data (Li and Sillanpää, ; Camargo et al ., ). In our case, we found two knots most suitable to the time intervals investigated.…”

“…Studies aimed at dissecting the genetic basis of such dynamics in wood properties can benefit from the application of mathematical functions that account for year‐to‐year variation across annual growth rings, cambial age and distance from pith (Li et al ., ). The development of mathematical methods for the analysis of these longitudinal traits has made it possible to map QTLs underlying the dynamics of developmental traits (Yang et al ., ; Li and Sillanpää, ; Camargo et al ., ), and to enhance our understanding of the genetic architecture of the growth trajectories of such dynamic traits (Ma et al ., ; Xing et al ., ). Such functional mapping analysis can be conducted using a multistage approach (Heuven and Janss, ).…”

Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

“…Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.2 of 16 advanced generation intercross (MAGIC) populations [7][8][9][10]. QTL can be exploited for gene cloning, marker-assisted breeding, and genomic selection or prediction.Cultivated flax (Linum usitatissimum L.) is a self-pollinating annual crop valued for its seed oil and stem fibre.…”

“…2 of 16 advanced generation intercross (MAGIC) populations [7][8][9][10]. QTL can be exploited for gene cloning, marker-assisted breeding, and genomic selection or prediction.…”