Genome-wide association mapping of agronomic traits in sugar beet

Abstract

Recent results indicate that association mapping in populations from applied plant breeding is a powerful tool to detect QTL which are of direct relevance for breeding. The focus of this study was to unravel the genetic architecture of six agronomic traits in sugar beet. To this end, we employed an association mapping approach, based on a very large population of 924 elite sugar beet lines from applied plant breeding, fingerprinted with 677 single nucleotide polymorphism (SNP) markers covering the entire genome. We show that in this population linkage disequilibrium decays within a short genetic distance and is sufficient for the detection of QTL with a large effect size. To increase the QTL detection power and the mapping resolution a much higher number of SNPs is required. We found that for QTL detection, the mixed model including only the kinship matrix performed best, even in the presence of a considerable population structure. In genome-wide scans, main effect QTL and epistatic QTL were detected for all six traits. Our full two-dimensional epistasis scan revealed that for complex traits there appear to be epistatic master regulators, loci which are involved in a large number of epistatic interactions throughout the genome.

A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits

“…On the other hand, the MGLAD-LASSO was able to detect putative QTLs including markers 27, 162, 184, 259, 349 and 411. Except the markers 27 and 162, all the others were also reported in Würschum (2011).…”

“…The MG-LASSO with either QTL decision rules detected markers 192, 259, 289, 349, 418 and 553 as putative QTLs. Most of the putative QTLs have also been found by a linear mixed model-based association mapping method in Würschum et al (2011), except the one at marker 289. On the other hand, the MGLAD-LASSO was able to detect putative QTLs including markers 27, 162, 184, 259, 349 and 411.…”

“…We consider (i) a simulated longitudinal QTLMAS2009 data set (Coster et al, 2010) and (ii) a real sugar beet data set (Würschum et al, 2011;Würschum and Kraft, 2013) for evaluating our proposed LAD-LASSO methods. The two data sets are available from their original publications: Coster et al (2010) and Würschum and Kraft (2013), respectively.…”

“…Originally, six quantitative traits were under evaluation including three yield-related traits: white sugar yield (t ha − 1 ), sugar content (%), root yield (tha −1 ); and three qualityrelated traits: potassium content (K, mM), sodium content (Na, mM) and α-amino nitrogen content (N, mM). Würschum et al (2011) performed a single-trait association mapping separately on each of the six traits. In our analysis, we performed the multiple-trait analysis simultaneously on the three yield-related traits including white sugar yield, sugar content and root yield.…”

“…Materials and methods describe the robust multivariate LAD-LASSO model, its computational algorithm and the relevant QTL decision rules. In Results, our method was illustrated with a QTLMAS2009 simulated data set (Coster et al, 2010) and a real sugar beet data set (Würschum et al, 2011). We focus on the comparison between our robust multivariate LAD-LASSO and the multivariate normal LASSO method.…”