Abstract
Global climate changes, combined with population growth and shifting dietary habits, introduce a huge demand for sustainable plant-based protein. Legumes have the potential to play a key role in meeting these demands. In addition to providing high levels of protein for both animal feed and human consumption, legumes fix atmospheric nitrogen, enrich soil fertility, and mitigate emissions of greenhouse gases. Despite their advantages, the production of legumes is still greatly surpassed by that of cereals, especially in Northern Europe, where much soybean is consequently imported to meet the demands on plant-based protein. To make legumes more attractive to farmers, breeding programs must focus on generating legumes that can compete in terms of yield, yield stability, and other key agronomic traits.
The main aim of this thesis is to enhance the understanding of key agronomic traits and genetic diversity in legumes and thereby lay the foundation for their improvement through genomics-based breeding. This was done by studying three different legume species: Lotus japonicus, white clover (Trifolium repens), and faba bean (Vicia faba).
In Lotus japonicus, we studied local adaptation in a population of 136 accessions sampled throughout Japan. Through a combination of genome-wide association studies (GWAS) and FST scans, we identified genomic regions associated with overwintering and flowering traits that showed strong signatures of adaption between northern and southern subpopulations.
In white clover, we investigated the factors that contribute to biomass yield in different growth phases, using data from a greenhouse experiment with 2392 individual plants representing 145 genotypes, each inoculated with one or a mix of 169 different rhizobium strains. We found that the represented rhizobium variation did not contribute significantly to the yield in any growth phase. Additionally, we could apply genomic prediction to white clover and eventually get the most precise prediction by training a model on the top 25 most significant GWAS markers associated with the initial plant size.
In faba bean, we studied the factors contributing to yield and yield stability in data from 17 commercial cultivars grown in Finnish and Danish environments over 2-3 years. We found that yield was greatly affected by environmental conditions, but that some cultivars still outperformed others both in terms of yield and yield stability. Additionally, we performed GWAS in a faba bean mapping population bred from seven parents and found genomic regions associated with 12 key agronomic traits. Many of the signals were stable across multiple environments. We also studied genetic differentiation and adaptation in a global diversity panel of 685 faba bean accessions. Here, we found that, based on their genotypes, the accessions could be divided into three geographically distinct subpopulations, between which we detected several genomic regions under selection.
The presented results contribute to an increased understanding of the genetic architecture of complex traits and provide promising results for genomics-based breeding, including accurate models for genomic prediction and candidate genes for integration in marker-assisted selection.
The main aim of this thesis is to enhance the understanding of key agronomic traits and genetic diversity in legumes and thereby lay the foundation for their improvement through genomics-based breeding. This was done by studying three different legume species: Lotus japonicus, white clover (Trifolium repens), and faba bean (Vicia faba).
In Lotus japonicus, we studied local adaptation in a population of 136 accessions sampled throughout Japan. Through a combination of genome-wide association studies (GWAS) and FST scans, we identified genomic regions associated with overwintering and flowering traits that showed strong signatures of adaption between northern and southern subpopulations.
In white clover, we investigated the factors that contribute to biomass yield in different growth phases, using data from a greenhouse experiment with 2392 individual plants representing 145 genotypes, each inoculated with one or a mix of 169 different rhizobium strains. We found that the represented rhizobium variation did not contribute significantly to the yield in any growth phase. Additionally, we could apply genomic prediction to white clover and eventually get the most precise prediction by training a model on the top 25 most significant GWAS markers associated with the initial plant size.
In faba bean, we studied the factors contributing to yield and yield stability in data from 17 commercial cultivars grown in Finnish and Danish environments over 2-3 years. We found that yield was greatly affected by environmental conditions, but that some cultivars still outperformed others both in terms of yield and yield stability. Additionally, we performed GWAS in a faba bean mapping population bred from seven parents and found genomic regions associated with 12 key agronomic traits. Many of the signals were stable across multiple environments. We also studied genetic differentiation and adaptation in a global diversity panel of 685 faba bean accessions. Here, we found that, based on their genotypes, the accessions could be divided into three geographically distinct subpopulations, between which we detected several genomic regions under selection.
The presented results contribute to an increased understanding of the genetic architecture of complex traits and provide promising results for genomics-based breeding, including accurate models for genomic prediction and candidate genes for integration in marker-assisted selection.
| Originalsprog | Engelsk |
|---|
| Forlag | Århus Universitet |
|---|---|
| Antal sider | 272 |
| Status | Udgivet - sep. 2022 |