Browsing by Subject "GWAS"

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Assessment of phenotypic, genomic and novel approaches for soybean breeding in Central Europe
(2022) Zhu, Xintian; Würschum, Tobias
Soybean is the economically most important leguminous crop worldwide and serves as a main source of plant protein for human nutrition and animal feed. Europe is dependent on plant protein imports and the EU protein self-sufficiency, which is an issue that has been on the political agenda for several decades, has recently received renewed interest. The protein imports are mainly in the form of soybean meal, and soybean therefore appears well-suited to mitigate the protein deficit in Europe. This, however, requires an improvement of soybean production as well as an expansion of soybean cultivation and thus breeding of new cultivars that combine agronomic performance with adaptation to the climatic conditions in Central Europe. The objective of this thesis was to characterize, evaluate and devise approaches that can improve the efficiency of soybean breeding. Breeding is essentially the generation of new genetic variation and the subsequent selection of superior genotypes as candidates for new cultivars. The process of selection can be supported by marker-assisted or genomic selection, which are both based on molecular markers. A first step towards the utilization of these approaches in breeding is the characterization of the genetic architecture underlying the target traits. In this study, we therefore performed QTL mapping for six target traits in a large population of 944 recombinant inbred lines from eight biparental families. The results showed that some major-effect QTL are present that could be utilized in marker-assisted selection, but in general the target traits are quantitatively inherited. For such traits controlled by numerous small-effect QTL, genomic selection has proven as a powerful tool to assist selection in breeding programs. We therefore also evaluated the genomic prediction accuracy and found this to be high and promising for the six traits of interest. In conclusion, these results illustrated the potential of genomic selection for soybean breeding programs, but a potential limitation of this approach are the costs required for genotyping with molecular markers. Phenomic selection is an alternative approach that uses near-infrared or other spectral data for prediction instead of the marker data used for its genomic counterpart. Here, we evaluated the phenomic predictive ability in soybean as well as in triticale and maize. Phenomic prediction based on near-infrared spectroscopy (NIRS) of seeds showed a comparable or even slightly higher predictive ability than genomic prediction. Collectively, our results illustrate the potential of phenomic selection for breeding of complex traits in soybean and other crops. The advantage of this approach is that NIRS data are often available anyhow and can be generated with much lower costs than the molecular marker data, also in high-throughput required to screen the large numbers of selection candidates in breeding programs. Soybean is a short-day plant originating from temperate China, and thus adaptation to the climatic conditions of Central Europe is a major breeding goal. In this study, we established a large diversity panel of 1,503 early-maturing soybeans, comprising of European breeding material and accessions from genebanks. This panel was evaluated in six environments, which revealed valuable genetic variation that can be introgressed into our breeding programs. In addition, we deciphered the genetic architecture of the adaptation traits flowering time and maturity. Taken together, the findings of this study show the potential of several phenotypic, genomic and novel approaches that can be integrated to improve the efficiency of soybean breeding and thus hold great promise to assist the expansion of soybean cultivation in Central Europe through breeding of adapted and agronomically improved cultivars.
Genome-wide association mapping of molecular and physiological component traits in maize
(2013) Riedelsheimer, Christian; Melchinger, Albrecht E.
Genome-wide association (GWA) mapping emerged as a powerful tool to dissect complex traits in maize. Yet, most agronomic traits were found to be highly polygenic and the detected associations explained together only a small portion of the total genetic variance. Hence, the majority of genetic factors underlying many agronomically important traits are still unknown. New approaches are needed for unravelling the chain from the genes to the phenotype which is still largely unresolved for most quantitative traits in maize. Instead of further enlarging the mapping population to increase the power to detect even smaller QTL, this thesis research aims to present an alternative route by mapping not the polygenic trait of primary interest itself, but genetically correlated molecular and physiological component traits. As such components represent biological sub-processes underlying the trait of interest, they are supposed to be genetically less complex and thus, more suitable for genetic mapping. Using large diversity panels of maize inbred lines, this approach is demonstrated with (i) biomass yield by using metabolites and lipids as molecular component traits and with (ii) chilling sensitivity by using physiological component traits such as photosynthesis parameters derived from chlorophyll fluorescence measurements. In a first step, we developed a sampling and randomization scheme which allowed us to obtain metabolic and lipid profiles from large-scale field trials. Both profiles were found to be inten- sively structured reflecting their functional grouping. They also showed repeatabilities higher than in comparable profiles obtained in previous studies with the model plant Arabidopsis under controlled conditions. By applying GWAS with 56,110 SNPs to metabolites and lipids, large-scale genetic associations explaining more than 30 % of the genetic variance were detected. Confounding with structure was found to be a problem of less extent for molecular components than for agronomic traits like flowering time. The lipidome was also found to show a multilevel control architecture similar as employed in controlling complex mechanical systems. In several instances, direct links between candidate genes underlying the detected associations and agronomic traits could be established. An example is cinnamoyl-CoA reductase, a key enzyme in the lingin biosynthesis pathway. It was found to be a candidate gene underlying a major QTL found for several intermediates in the lignin biosynthesis pathways. These intemediates were in turn found to be correlated with plant height, lignin content, and dry matter yield at the end of the vegetation period. The different signs of these correlations indicated that the relationships between pathway intermediates and the final product is not simple. Directly modeling complex traits with individual component traits may therefore require consideration of feedback loops and other interdependencies. Such connections were however found difficult to be established with physiological components underlying chilling sensitivity. The main reasons for this were the weak correlations between physiological components under controlled conditions and chilling sensitivity in the field as well as high levels of genotype × environment interactions caused by the complex and environment- dependent responses of maize after perception of chilling temperatures. The approach explored in this thesis research uses component traits to gain biological insights about the genetic control of biomass yield and chilling sensitivity evaluated in diverse populations of still manageable sizes. We showed that GWAS with 56k SNPs can identify large additive effects for component traits correlated with these traits. For mapping epistatic interactions and rare variants, classical linkage mapping with biparental populations will be a reasonable complementary approach. However, controlling and modeling genotype × environment interactions remains an important issue for understanding the genetic basis of especially chilling sensitivity. If the goal is merely to predict the phenotypic value in a given set of en- vironments, black-box genomic selection methods with either SNPs, molecular profiles, or a combination of both, are very promising strategies to achieve this goal.