Dataset Information

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

ABSTRACT: Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

SUBMITTER: Lin F

PROVIDER: S-EPMC7341141 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

Lin Fan F Lazarus Elena Z EZ Rhee Seung Y SY

G3 (Bethesda, Md.) 20200707 7

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. He ...[more]

PMID: 32430305

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Project description:microRNAs (miRNAs) are a class of non-coding RNAs playing a myriad of important roles in regulating gene expression. Of note, recent work demonstrated a critical role of miRNAs in the genesis and progression of brain arteriovenous malformations (bAVMs). Accordingly, here we examine miRNA signatures related to bAVMs and associated gene expression. In so doing we expound on the potential prognostic, diagnostic, and therapeutic significance of miRNAs in the clinical management of bAVMs. A PRISMA-based literature review was performed using PubMed/Medline database with the following search terms: "brain arteriovenous malformations", "cerebral arteriovenous malformations", "microRNA", and "miRNA". All preclinical and clinical studies written in English, regardless of date, were selected. For our bioinformatic analyses, miRWalk and miRTarBase machine learning algorithms were employed; the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was quired for associated pathways/functions. four studies were ultimately included in the final analyses. Sequencing data consistently revealed the decreased expression of miR-18a in bAVM-endothelial cells, resulting in increased levels of vascular endodermal growth factor (VEGF), Id-1, matrix metalloproteinase, and growth signals. Our analyses also suggest that the downregulation of miR-137 and miR-195* within vascular smooth muscle cells (VSMCs) may foster the activation of inflammation, aberrant angiogenesis, and phenotypic switching. In the peripheral blood, the overexpression of miR-7-5p, miR-629-5p, miR-199a-5p, miR-200b-3p, and let-7b-5p may contribute to endothelial proliferation and nidus development. The machine learning algorithms employed confirmed associations between miRNA-related target networks, vascular rearrangement, and bAVM progression. miRNAs expression appears to be critical in managing bAVMs' post-transcriptional signals. Targets of microRNAs regulate canonical vascular proliferation and reshaping. Although additional scientific evidence is needed, the identification of bAVM miRNA signatures may facilitate the development of novel prognostic/diagnostic tools and molecular therapies for bAVMs.

Project description:ObjectiveTo assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).Data sourcesElectronic health records maintained by Kaiser Permanente Northern California (KPNC).Study designRetrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies in terms of the number of readmissions prevented.Data collection1 539 285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2018 at 21 KPNC hospitals.Principal findingsThere appears to be substantial heterogeneity in patients' responses to the intervention (omnibus test for heterogeneity p = 2.23 × 10-7 ), particularly across levels of predicted risk. Notably, predicted treatment effects become more positive as predicted risk increases; patients at somewhat lower risk appear to have the largest predicted effects. Moreover, these estimates appear to be well calibrated, yielding the same estimate of annual readmissions prevented in the actual treatment subgroup (1246, 95% confidence interval [CI] 1110-1381) as did a formal evaluation of the Transitions Program (1210, 95% CI 990-1430). Estimates of the impacts of alternative targeting strategies suggest that as many as 4458 (95% CI 3925-4990) readmissions could be prevented annually, while decreasing the number needed to treat from 33 to 23, by targeting patients with the largest predicted effects rather than those at highest risk.ConclusionsCausal machine learning can be used to identify preventable hospital readmissions, if the requisite interventional data are available. Moreover, our results suggest a mismatch between risk and treatment effects.

Dataset Information

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

Publications

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

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