Dataset Information

Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models.

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.

SUBMITTER: Shin MG

PROVIDER: S-EPMC8450460 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models.

Shin Min-Gyoung MG Nuzhdin Sergey V SV

Frontiers in genetics 20210906

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 pred ...[more]

PMID: 34552613

Similar Datasets

Project description:Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.

Dataset Information

Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models.

Publications

Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets