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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

Mutation mapping and identification by whole-genome sequencing.

Ignaty Leshchiner¹, Kristen Alexa, Peter Kelsey

¹Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.

Genome Research

|May 5, 2012

Summary

This summary is machine-generated.

Researchers developed a fast, cost-effective genetic mapping method using next-generation sequencing. This approach identifies gene mutations and causal variants in model organisms, simplifying disease gene discovery.

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Area of Science:

Genetics
Genomics
Bioinformatics

Background:

Genetic mapping is crucial for identifying genes involved in biological processes and diseases.
Traditional methods require pre-characterized markers, limiting efficiency.
Next-generation sequencing (NGS) offers potential but requires refined mapping tools.

Purpose of the Study:

To develop a rapid and cost-effective genetic mapping method using NGS.
To integrate single nucleotide polymorphism (SNP) discovery and mutation localization.
To create user-friendly tools for automated analysis of sequencing data.

Main Methods:

Utilized next-generation sequencing for mutation mapping.
Developed a hidden Markov model (HMM) to infer recombination breakpoints and define mutation regions.
Created an interactive online software for automated analysis of sequencing data.

Main Results:

Successfully mapped mutations in zebrafish and mouse models.
The HMM accurately narrowed down mutation areas by identifying recombination breakpoints.
The online software facilitated efficient and automated analysis of NGS data.

Conclusions:

The novel NGS-based method provides a fast, cost-effective approach to genetic mapping.
This methodology simplifies the identification of genes contributing to diseases.
The developed tools enhance the applicability of NGS for mutation mapping across model systems.