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A Ranking Approach to Genomic Selection.

Mathieu Blondel¹, Akio Onogi², Hiroyoshi Iwata²

¹NTT Communication Science Laboratories, Kyoto, Japan.

|June 13, 2015

Summary

This summary is machine-generated.

Genomic selection (GS) can be improved by reframing it as a ranking problem. New ranking methods and the normalized discounted cumulative gain (NDCG) metric show promise for more accurate selection of high-value individuals in breeding programs.

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Area of Science:

Agricultural Science
Genetics
Machine Learning

Background:

Genomic selection (GS) is a breeding method using whole-genome markers.
Previous GS studies treated it as a regression problem to predict breeding values.
Model accuracy was assessed using Pearson correlation.

Purpose of the Study:

To reformulate genomic selection (GS) as an individual ranking problem.
To explore machine learning ranking methods for GS.
To introduce normalized discounted cumulative gain (NDCG) as a ranking accuracy metric.

Main Methods:

Compared 10 regression and 3 ranking methods across 6 datasets (4 plant species, 25 traits).
Evaluated ranking accuracy using NDCG.
Assessed traditional regression methods (Bayesian lasso, wBSR, BayesC) and tree-based ensembles (McRank, Random Forests, Gradient Boosting).

Main Results:

Tree-based ensemble methods (McRank, Random Forests, Gradient Boosting) demonstrated excellent ranking accuracy.
RKHS regression and RankSVM with RBF kernel also showed good accuracy.
Traditional regression methods were less suitable for ranking; Pearson correlation poorly correlated with NDCG.

Conclusions:

Ranking methods represent a promising new direction for genomic selection.
NDCG is a valuable evaluation metric for assessing ranking performance in GS.
The study highlights the potential of machine learning for advancing selective breeding.