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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Credibility analysis of putative disease-causing genes using bioinformatics.

Olubunmi Abel¹, John F Powell, Peter M Andersen

¹King's Health Partners Centre for Neurodegeneration Research, King's College London, Department of Clinical Neuroscience, London, United Kingdom.

|June 12, 2013

Summary

This summary is machine-generated.

This study developed an automated method to score gene credibility for complex diseases like amyotrophic lateral sclerosis (ALS). The approach objectively ranks genes, aiding research where genetic evidence is uncertain.

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Area of Science:

Genetics
Neuroscience
Bioinformatics

Background:

Genetic studies for complex diseases face challenges due to diagnostic uncertainty and low prevalence.
Varying evidence levels for disease-causing genes can lead to ambiguity, necessitating objective ranking methods.
Amyotrophic lateral sclerosis (ALS) serves as a model for developing a gene credibility scoring system.

Purpose of the Study:

To develop an automated method for generating a credibility score for putative disease-causing genes.
To utilize publicly available data and bioinformatics tools for gene assessment.
To establish an objective ranking system for genes implicated in complex diseases.

Main Methods:

Collated genes associated with adult-onset familial ALS from literature.
Employed SQL for data extraction and integrated bioinformatics pathogenicity analysis.
Generated a credibility score to rank genes, validated against expert rankings.

Main Results:

The automated method produced a gene ranking: TARDBP, FUS, ANG, SPG11, NEFH, OPTN, ALS2, SETX, FIG4, VAPB, DCTN1, TAF15, VCP, DAO.
This objective ranking showed strong agreement with expert-ranked genes (Spearman's Rho = 0.69, P = 0.009).

Conclusions:

An automated method for scoring gene evidence in disease causation has been developed.
The method, validated using ALS, is applicable to other diseases with genetic uncertainties.
This tool aids in objectively assessing the credibility of genes linked to complex disorders.