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Comparison of regression tree-based methods in genomic selection.

Sahar Ashoori-Banaei1, Farhad Ghafouri Kesbi, Ahmad Ahmadi

  • 1Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, 6517838695 Hamedan, Iran. f.ghafouri@basu.ac.ir.

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Summary
This summary is machine-generated.

Random Forest (RF) and Boosting (BT) outperform Regression Trees (RT) in genomic selection prediction accuracy. RF is recommended due to its superior performance and efficiency across various genetic architectures and heritability levels.

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Area of Science:

  • Quantitative Genetics
  • Bioinformatics
  • Machine Learning in Genomics

Background:

  • Genomic selection is crucial for predicting breeding values using high-density genetic markers.
  • Tree-based methods offer potential for complex genetic architectures but require performance evaluation.

Purpose of the Study:

  • To compare the predictive performance of Regression Tree (RT), Random Forest (RF), and Boosting (BT) algorithms in genomic selection.
  • To assess the impact of genetic architecture and heritability on prediction accuracy.
  • To evaluate computational efficiency (time and memory) of these methods.

Main Methods:

  • Simulated a genome with five chromosomes and 5000 evenly distributed single-nucleotide polymorphisms for 1000 individuals.
  • Evaluated RT, RF, and BT under varying quantitative trait loci (QTL) numbers, QTL effect distributions, and heritability levels (0.1, 0.3, 0.5).
  • Measured prediction accuracy, computing time, and memory usage for each method.

Main Results:

  • RT demonstrated the lowest prediction accuracy, significantly lower than RF and BT.
  • RF consistently showed higher prediction accuracy than BT at lower heritability levels (0.1, 0.3), with comparable accuracy at heritability 0.5.
  • RF exhibited superior computational efficiency (time and memory) compared to BT.

Conclusions:

  • Regression Tree is not recommended for genomic selection due to its low predictive accuracy.
  • Random Forest is recommended for genomic selection, offering a robust balance of prediction accuracy and computational efficiency.
  • Heritability significantly influences prediction accuracy, while QTL number and effect distribution have less impact.