ArticleG3 (Bethesda, Md.)2019
QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 46 citations in OpenAlex.
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Haplotype-based multi-locus genome-wide association study reveals genomic regions associated with reproductive stage high temperature stress tolerance in rice.Plant molecular biology · 2026Article
- Meta-QTLs and candidate genes for kernel protein content in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance toInternational journal of molecular sciences · 2024Article
- Review
- Identification of quantitative trait loci associated with leaf rust resistance in rye by precision mapping.BMC plant biology · 2024Article
- Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS.G3 (Bethesda, Md.) · 2022Article
- Smart breeding approaches in post-genomics era for developing climate-resilient food crops.Frontiers in plant science · 2022Review
- Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice.Scientific reports · 2021Article
- Machine learning in plant science and plant breeding.iScience · 2021Review
- A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.Communications biology · 2020Article
- QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.G3 (Bethesda, Md.) · 2020Article
- Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.