Abstract
Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops. When both wild and interspecific hybrid materials are available, a common approach has been to study two types of materials separately and simply compare the quantitative trait locus (QTL) regions. However, combining the two types of materials can potentially create a more efficient method of finding predictive QTLs. In this simulation study, we focused on scenarios involving causal marker expression suppressed by trans-regulatory mechanisms, where the otherwise easily lost associated signals benefit the most from combining the two types of data. A probabilistic sampling approach was used to prioritize consistent genotypic phenotypic patterns across both types of data sets. We chose random forest and gradient boosting to apply the prioritization scheme and found that both facilitated the investigation of predictive causal markers in most of the biological scenarios simulated.
Highlights
In agriculture, one of the most prominent hurdles to overcome has been the development of climate-resilient plants (Muñoz-Amatriaín et al, 2017; Narayana and von Wettberg, 2020; Sokolkova et al, 2020; von Wettberg et al, 2020)
The results show that weighted gradient boosting models performed better than unweighted gradient boosting models on all data sets and that weighted random forest performed better than unweighted random forest models in Bari chickpea in largeeffect-size scenarios with specific parameter settings
The results suggest that combining information from hybrid and wildtype materials generally performs better in detecting transdownregulated signals in hybrid materials than investigating hybrid material alone
Summary
One of the most prominent hurdles to overcome has been the development of climate-resilient plants (Muñoz-Amatriaín et al, 2017; Narayana and von Wettberg, 2020; Sokolkova et al, 2020; von Wettberg et al, 2020). The advent of modern sequencing technology has made it possible to investigate genomes on a finer scale than before (Stich and Melchinger, 2010; Narayana and von Wettberg, 2020). Modern sequence technologies can gain synergetic efficacy when combined with modern breeding systems used to fine-map quantitative trait locus (QTL) regions. MAGIC and Nested Association Mapping (NAM) are breeding systems that aim to find QTL regions with much finer scale by using multiple parental lines to increase genomic variations (Cavanagh et al, 2008; Kump et al, 2011; Tian et al, 2011; Song et al, 2017; Narayana and von Wettberg, 2020). In addition to advanced sequencing technologies and breeding systems, interspecific hybrid approaches have been crucial to agricultural advancements
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