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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function.

Jingjing Zhai¹, Yunjia Tang¹, Hao Yuan¹

¹State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.

Frontiers in Plant Science

|December 27, 2016

Summary

This summary is machine-generated.

A new method, Rank Aggregation-based data fusion for gene Prioritization (RAP), improves plant gene discovery by integrating multiple data types. RAP enhances accuracy in identifying genes for functions like flowering time and stress response in plants.

Keywords:

biological network data fusion flowering time gene prioritization machine learning meta-analysis rank aggregation systems biology

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Area of Science:

Plant genomics and bioinformatics
Computational biology and systems biology

Background:

Identifying genes for specific plant biological functions is challenging.
Existing network-based gene prioritization methods in plants like Arabidopsis thaliana suffer from limited accuracy due to network coverage and connectivity issues.
Integrating diverse biological data can potentially improve gene prioritization accuracy.

Purpose of the Study:

To develop a novel gene prioritization method, RafSee, integrating sequence, evolutionary, and epigenetic features.
To propose an integrative approach, Rank Aggregation-based data fusion for gene Prioritization (RAP), combining network-based methods with RafSee.
To enhance the accuracy of prioritizing candidate genes involved in specific plant biological functions.

Main Methods:

Developed RafSee using a random forest algorithm with plant sequence, evolutionary, and epigenetic features.
Proposed RAP, an integrative approach using order statistics-based meta-analysis to aggregate gene ranks.
Validated RAP by prioritizing flowering-time genes and genes related to abiotic stresses in Arabidopsis.

Main Results:

RafSee complements existing network-based systems like AraNet v2.
RAP significantly improved the ranking of flowering-time genes, outperforming AraNet v2 in first quartile rank by 39.46%.
RAP demonstrated effectiveness in prioritizing genes associated with various abiotic stresses.

Conclusions:

The RAP method offers a robust and accurate approach for gene prioritization in plants.
RAP enhances the identification of functionally important genes, aiding plant research and breeding.
An R package for RAP is available, supporting its application in Arabidopsis and non-model plant species.