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QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
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Applying gradient tree boosting to QTL mapping with Shapley additive explanations.

Tomohiro Ishibashi1, Akio Onogi1

  • 1Department of Life Sciences, Faculty of Agriculture, Ryukoku University, Otsu, Shiga 520-2194, Japan.

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Summary
This summary is machine-generated.

This study introduces SHAP-assisted XGBoost (SHAP-XGB) for quantitative trait loci (QTL) mapping. SHAP-XGB effectively identifies main and epistatic QTL effects, outperforming traditional methods in detecting interactions.

Keywords:
SHAPassociation mappingepistasisgene interactionvariable selection

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Area of Science:

  • Quantitative genetics
  • Machine learning in genomics

Background:

  • Quantitative trait loci (QTL) mapping is crucial in genetics.
  • Identifying QTL interactions (epistasis) is challenging with conventional methods.
  • Machine learning offers advanced capabilities for complex genetic relationship analysis.

Purpose of the Study:

  • To apply and evaluate XGBoost with Shapley additive explanations (SHAPs) for QTL mapping.
  • To compare the performance of SHAP-assisted XGBoost (SHAP-XGB) against traditional QTL mapping techniques.
  • To explore the utility of SHAP for visualizing and interpreting epistatic QTL interactions.

Main Methods:

  • Utilized XGBoost, a gradient tree boosting algorithm, for QTL mapping in biparental populations.
  • Employed Shapley additive explanations (SHAPs) to assess feature importance and interactions.
  • Compared SHAP-XGB with composite interval mapping (CIM), multiple interval mapping (MIM), inclusive CIM (ICIM), and BayesC using simulations and rice data.

Main Results:

  • SHAP-XGB demonstrated comparable performance to conventional methods for main QTL effects.
  • SHAP-XGB significantly outperformed MIM, ICIM, and BayesC in detecting QTL interaction effects.
  • SHAP values enabled visualization of marker interactions for individual plants/lines.

Conclusions:

  • SHAP-XGB is a powerful tool for detecting and interpreting epistatic QTLs.
  • The method complements traditional QTL mapping approaches by providing detailed interaction insights.
  • SHAP-XGB advances the field of quantitative genetics by enhancing epistasis analysis.