TY  - THES
T1  - QTL mapping and genomic prediction of complex traits based on high-density genotyping in multiple crosses of maize (Zea mays L.)
A1  - Stange,Michael
Y1  - 2013/12/09
N2  - Most important agronomic traits like disease resistance or grain yield (GY) in maize show a quantitative trait variation and, therefore, are controlled by dozens to thousands of quantitative trait loci (QTL). Mapping of these QTL is well established in plant genetics to elucidate the genetic architecture of quantitative traits and to detect QTL for knowledge-based breeding. Nowadays, high-density genotyping is routinely applied in maize breeding and offers a huge number of SNP markers used in association mapping and genomic selection (GS). This enables also the construction of high-density linkage maps with marker densities of 1 cM or even higher. Nevertheless, QTL mapping studies were until recently mostly based on low-density maps. This raises the question if high-density maps are an overkill for QTL mapping, or in contrast, if important QTL mapping parameters would profit from them. High-density maps could also be beneficial for dissection of the complex trait GY into its components 100-kernel weight (HKW) and kernel number (KN). Analysis of these less complex traits may help to unravel the genetic architecture and improve the predictive ability for complex traits. However, an open question is whether consideration of component traits and epistatic interactions in QTL mapping models are beneficial for predicting the performance of untested genotypes for the complex trait GY. 
In this thesis, high-density linkage maps were constructed for biparental maize populations of doubled haploid (DH) lines and applied in different QTL linkage mapping approaches. In detail, the objectives of this study were to (1) investigate the effect of high-density versus low-density linkage maps in QTL mapping of important QTL mapping parameters and to analyze the resolution of closely linked QTL with experimental data and computer simulations, (2) map QTL for HKW, KN, and GY with high-density maps and to analyze epistatic interactions, (3) compare the prediction accuracy for GY with different QTL mapping models, and (4) answer the question how the composition of the test set (TS) influences the accuracy in genomic prediction of progenies from individual crosses. 
This thesis was based on five interconnected biparental populations with a total of 699 DH lines evaluated in field experiments for GER resistance related traits as well as for HKW, KN, and GY. All DH lines were genotyped with the Illumina MaizeSNP50 Bead Chip and high-density linkage maps were constructed separately for each population. 
For evaluation of high-density versus low-density maps on QTL mapping parameters, three linkage maps with marker densities of 1, 2, and 5 cM were constructed, starting from the full linkage map with 7,169 markers mapped in the largest population (N=204). QTL mapping was performed with all three marker densities in the experimental population for GER resistance related traits and for yield related traits, as well as in a simulation study with different population sizes. In the simulation study, independent QTL with additive effects explaining 0.14 to 7.70% of the expected phenotypic variance, as well as linked QTL with map distances of 5 and 10 cM, were simulated. Results showed that high-density maps had only minor effects on the QTL detection power and the proportion of genotypic variance explained. In contrast, support interval length decreased with increasing marker density, indicating an increasing precision of QTL localization. The precision of QTL effect estimates was measured as deviation between the reference additive effects and the estimated QTL effects. It gained from an increase in marker density, especially for small and medium effect QTL. Increasing the marker density from 5 to 1 cM was advantageous for separately detecting linked QTL in coupling phase with both linkage distances. In conclusion, this study showed that QTL mapping parameters relevant for knowledge-based breeding profited from an increase in marker density. 
For QTL mapping of the complex trait GY and the components HKW and KN, three QTL mapping models were applied to the four largest populations, of which two models were based on the component traits HKW and KN. All models included tests for epistatic interactions. The results showed that heritability was slightly higher for the component traits compared to the complex trait. The average length of support intervals of detected QTL was short with 12 cM, indicating high precision of QTL localization. Co-located QTL with same parental origin of favorable alleles were detected within populations for different traits and between populations for same traits, reflecting common QTL across populations. However, to finally confirm these common QTL, multi-population QTL mapping should be conducted. Based on the detected QTL, predictions for GY showed that epistatic models did not outperform the respective additive models. Nevertheless, component trait based models can be advantageous for identification of favorable allele combinations for multiplicative traits. 
For all five populations, the comparison of genetic similarities reflected the crossing scheme with full-sib families, half-sib families and unrelated families. The evaluation of prediction accuracies for different scenarios depended on the composition of the TS. Highest prediction accuracies were observed for DH lines within full-sib families, medium values if full-sib DH lines were replaced by half-sib DH lines, and lowest values if the TS comprised of DH lines from unrelated crosses. 

In conclusion, I found high-density linkage maps to be advantageous for linkage mapping in biparental DH populations by improving important QTL mapping parameters. Higher costs for high-density genotyping are by far compensated by these advantages. Dissecting the complex trait GY into its component traits HKW and KN by component trait based QTL mapping models revealed a complex genetic network of GY. Future research should focus on high-density consensus maps applied in multi-population QTL mapping to take advantage of the improved QTL detection power and to confirm common QTL across populations.
 

KW  - Mais
KW  - QTL
KW  - Kartierung
CY  - Hohenheim
PB  - Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim
AD  - Garbenstr. 15, 70593 Stuttgart
UR  - http://opus.uni-hohenheim.de/volltexte/2013/901
ER  -