Westhues, C. | Search bioRxiv

bioRxiv2024

Experimental evolution in maize with replicated divergent selection identifies plant-height associated SNPs

Experimental evolution studies are common in agricultural research, where they are often deemed "long term selection". These are often used to perform selection mapping, which involves identifying markers which were putatively under selection based on finding signals of selection left in the genome. A challenge of previous selection mapping studies, especially in agricultural research, has been the specification of robust significance thresholds. This is in large part because long term selection studies in crops have rarely included replication. Usually, significance thresholds in long term selection experiments are based on outliers from an empirical distribution. This approach is prone to missing true positives or including false positives. Under laboratory conditions with model species, replicated selection has been shown to be a powerful tool, especially for the specification of significance thresholds. Another challenge is that commonly used single-marker based statistics may identify neutral linked loci which have hitchhiked along with regions that are actually under selection. In this study, we conducted divergent, replicated selection for short and tall plant-height in a random mating maize population under real field conditions. Selection of the 5% tallest and shortest plants was conducted for three generations. Significance thresholds were specified using the false discovery rate for selection (FDRfS) based on a window-based statistic applied on a statistic leveraging replicated selection (FSTSum). Overall, we found 3 significant regions putatively under selection. One region was located on chromosome 3 close to the plant-height genes Dwarf1 and iAA8. We applied a haplotype block analysis to further dissect the pattern of selection in significant regions of the genome. We observed patterns of strong selection in the subpopulations selected for short plant height on chromosome 3.

genetics↗

bioRxiv2023

Interpretable Machine Learning Decodes Soil Microbiome's Response to Drought Stress

BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil metagenome and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable Machine Learning holds immense potential for drought stress classification in the soil metagenome based on marker taxa. ResultsThis study demonstrates that the metagenomic approach of Differential Abundance Analysis methods and Machine Learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil metagenome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. ConclusionsIn the detection of drought stress in the soil metagenome, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

bioinformatics↗