QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice

Abstract Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene con...

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Bibliographic Details
Published in:G3 : genes - genomes - genetics 2019-10, Vol.9 (10), p.3129-3138
Main Authors: Lin, Fan, Fan, Jue, Rhee, Seung Y
Format: Article
Language:English
Subjects:
Algorithms
Arabidopsis
Arabidopsis - genetics
causal gene
Computational Biology - methods
Genes, Plant
Investigations
Machine Learning
Oryza - genetics
Quantitative Trait Loci
Quantitative Trait, Heritable
Reproducibility of Results
rice
ROC Curve
Software
Web Browser
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Summary:Abstract Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.
ISSN:2160-1836
2160-1836
DOI:10.1534/g3.119.400319