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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

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Published on: May 21, 2020

Transcriptome-based prediction for polygenic traits in rice using different gene subsets.

Ryokei Tanaka¹, Tsubasa Kawai², Taiji Kawakatsu³

¹Institute of Crop Sciences, National Agriculture & Food Research Organization, Tsukuba, Ibaraki, 305-8518, Japan. tanakar015@affrc.go.jp.

|October 1, 2024

Summary

Predicting complex plant traits using gene expression data is challenging. Researchers found that selecting specific gene subsets, based on tissue expression and function, can improve prediction accuracy for polygenic traits in rice.

Keywords:

Core collection RNA-seq, genomic prediction Rice Root phenotypes Transcriptome-based prediction

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mRNA Interactome Capture from Plant Protoplasts

mRNA Interactome Capture from Plant Protoplasts

Published on: July 28, 2017

Area of Science:

Plant genetics and genomics
Systems biology
Bioinformatics

Background:

Transcriptome-based prediction links genetic variation to complex phenotypes.
Predicting polygenic traits is difficult due to unknown causal genes.
This study explores selecting gene subsets for improved polygenic trait prediction.

Purpose of the Study:

To evaluate the effectiveness of selecting gene subsets for predicting polygenic traits using transcript abundance.
To determine if gene-level features can enhance predictive ability compared to using all genes.

Main Methods:

Utilized phenotypic and transcriptomic data from 57 rice accessions.
Developed prediction models using leaf and root transcripts.
Employed tissue specificity, ontology annotations, and co-expression modules for gene subset selection.

Main Results:

Leaf transcripts better predicted shoot traits (e.g., plant height); root transcripts better predicted root traits (e.g., crown root length).
Some selected gene subsets improved predictive ability for specific traits, like crown root diameter (over 10% increase).
Genes related to 'gibberellic acid sensitivity' enhanced prediction for root dry weight.

Conclusions:

Selecting appropriate gene subsets for polygenic trait prediction from transcript data is feasible but challenging.
Further integration of diverse biological information and improved gene characterization are needed for optimal gene set selection.