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Updated: Apr 19, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning
Giulietta Minozzi1, Andrea Pedretti2, Stefano Biffani3
1Parco Tecnologico Padano Srl, Via Einstein 26900 Lodi, Italy ; Department of Veterinary Science and Public Health, University of Milan, Via Celoria 10, 20133 Milan, Italy.
Background:
Genome wide association studies are now widely used in the livestock sector to estimate the association among single nucleotide polymorphisms (SNPs) distributed across the whole genome and one or more trait. As computational power increases, the use of machine learning techniques to analyze large genome wide datasets becomes possible.
Methods:
The objective of this study was to identify SNPs associated with the three traits simulated in the 16th MAS-QTL workshop dataset using the Random Forest (RF) approach. The approach was applied to single and multiple trait estimated breeding values, and on yield deviations and to compare them with the results of the GRAMMAR-CG method.
Results:
The two QTL mapping methods used, GRAMMAR-CG and RF, were successful in identifying the main QTLs for trait 1 on chromosomes 1 and 4, for trait 2 on chromosomes 1, 4 and 5 and for trait 3 on chromosomes 1, 2 and 3.
Conclusions:
The results of the RF approach were confirmed by the GRAMMAR-CG method and validated by the effective QTL position, even if their approach to unravel cryptic genetic structure is different. Furthermore, both methods showed complementary findings. However, when the variance explained by the QTL is low, they both failed to detect significant associations.
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